Note: Descriptions are shown in the official language in which they were submitted.
CA 02886794 2015-03-31
WO 2014/055395 PCMJS2013/062570
- 1 -
SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR
INTERFACING SOFTWARE ENGINES
BACKGROUND
[001] Internet searching is a popular way for intemet users to collect
information about products that they are considering for purchase. Popular web
search engines, such as GOOGLEO, YAHOO! 0 and LIVE SEARCH (formerly
MSN SEARCH ), rely on user-inputted, keyword-based search queries in order to
provide links to relevant web pages and web documents arranged in relevancy-
ranked lists. Accordingly, users can input search queries in an effort to find
web
pages and web documents that focus on the category of products or specific
products
they wish to learn about.
[002] Search engines provide users with the ability to educate themselves
about the products of interest to the extent such information is deemed
relevant to
the user-inputted search queries and is available. Thus, search engines often
aid
users in making informed decisions regarding purchasing products of interest.
Nevertheless, the actual comparison of the products and decision-making
processes
are left to the users as this is beyond the intended purposes of a search
engine.
[003] Popular search engine firms rely on search advertising as a major
source of income. The fact that intemet users often use search engines with an
eye
towards purchasing products makes them particularly appealing to advertisers
who
are attempting to reach their target consumer audience. Since popular intemet
search
engines rely on keywords in providing results, it follows that search
advertising is
also sold and delivered on the basis of keywords.
[004] Popular search engine firms conduct running auctions to sell
advertising space according to the bids received for keywords. Higher demand
keywords command higher bid prices. Typically, advertisers are charged based
on
click-throughs and not merely the display of their advertisements in response
to the
keywords. Popular search engines thus typically position advertisements on the
search result pages based, at least in part, on click-through rates ("CTRs").
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 2 -
SUMMARY
[005] In one aspect, a computer-implemented method of providing vendor-
related data in response to decision-related data is provided. The method can
include
providing a decision engine on a user-accessible network; interfacing a
marketing
engine with the decision engine on the network; receiving a plurality of user
inputs
with the decision engine; processing decision-related data with the decision
engine
in accordance with the plurality of user inputs; sharing the decision-related
data with
the marketing engine; and transmitting vendor-related data via the network.
[006] In another aspect, a computer program product having a computer
storage medium and a computer program mechanism embedded in the computer
storage medium for causing a computer to interface a decision engine and a
marketing engine is provided. The computer program mechanism can include a
first
computer code device configured to interface with the decision engine; a
second
computer code device configured to interface with the marketing engine; and a
third
computer code device configured to facilitate data sharing between the
decision
engine and the marketing engine.
[007] In yet another aspect, a system for providing vendor-related data in
response to decision-related data is provided. The system can include one or
more
servers on a network; a decision engine provided on the one or more servers,
the
decision engine connected to one or more storage devices for storing, at least
in part,
decision-related data; a marketing engine provided on the one or more servers,
the
marketing engine connected to the one or more storage devices for storing, at
least
in part, vendor-related data; an interfacing application tying the marketing
engine to
the decision engine, the interfacing application on the one or more servers,
wherein
the interfacing application is capable of facilitating data sharing between
the
decision engine and the marketing engine; and wherein the decision engine is
capable of serving a plurality of client computing devices on a user-
accessible
portion of the network.
[008] An embodiment of the present disclosure is a computer-implemented
method for providing vendor-related data. The method comprises receiving
modified decision-related data from a decision engine. The modified decision-
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 3 -
related data is generated by the decision engine by applying at least one user
input to
decision-related data, wherein the decision-related data is at least one
factor used as
a criteria in selecting at least one decision option. The at least one
decision option
is an output of the decision engine in response to the application of the at
least one
user input to the decision-related data.
[009] In addition, the embodiment retrieves vendor-related data from a
marketing database based on one or more properties of the modified decision-
related
data. The retrieving includes matching the at least one factor to at least one
vendor
factor associated with the vendor-related data and obtaining vendor-related
data
associated with at least a subset of the at least one vendor factor. The at
least one
vendor factor is a criteria used in selecting the vendor-related data.
[010] Also, the embodiment processes the modified decision-related data to
modify the at least one vendor factor and selects a subset of the vendor-
related data
based on the modified at least one vendor factor.
[011] The embodiment may provide the selected subset of vendor-related
data to the user. The embodiment may also trigger a marketing engine
associated
with the marketing database to provide the selected subset of vendor-related
data to
the user. Additionally, the embodiment may provide the selected subset of
vendor-
related data to a marketing engine associated with the marketing database as a
recommendation of vendor-related data to provide to the user. The marketing
engine utilizes the recommendation to provide vendor-related data to the user.
[012] The modified decision-related data may include at least one factor
having a weighted score, where the weighted score of the at least one factor
is
determined by at least an importance assigned to the at least one factor by
the user.
[013] The embodiment may retrieve the vendor-related data by selecting
the marketing database corresponding to a vendor and retrieving the vendor-
related
data from the selected at least one marketing database.
[014] The embodiment may select the marketing database corresponding to
the vendor by selecting the vendor based on whether or not goods and/or
services
provided by the vendor correspond to the decision-related data.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 4 -
[015] Also, the embodiment may process the modified decision-related data
by ranking the vendor-related data by applying weighted scores associated with
the
decision-related data to the vendor factors and selecting a subset of the
ranked
vendor-related data. Further, the embodiment may select the subset of ranked
vendor-related data by selecting the subset based on an advertisement
subscription
of a vendor associated with the vendor-related data. In addition, the
embodiment
may provide the vendor-related data to the user by presenting the vendor-
related
data along with at least one decision option. The at least one decision option
is
presented to the user by the decision engine utilizing the modified decision-
related
data. The at least one decision option may be product and/or service related
data.
[016] Another embodiment of the present disclosure is a system for
providing vendor-related data. The system comprises one or more
processors
on a network. The system also comprises an interface, provided on the one or
more
processors, configured to receive modified decision-related data from a
decision
engine. The modified decision-related data is generated by the decision engine
by
applying at least one user input to decision-related data. The decision-
related data is
at least one factor used as a criteria in selecting at least one decision
option. The at
least one decision option is an output of the decision engine in response to
the
application of the at least one user input to the decision-related data.
[017] The system also comprises a retrieving module, provided on the one
or more processors, configured to retrieve vendor-related data from a
marketing
database based on one or more properties of the modified decision-related
data. The
retrieving module is further configured to match the at least one factor to at
least one
vendor factor associated with the vendor-related data and obtain vendor-
related data
associated with at least a subset of the at least one vendor factor. The at
least one
vendor factor is a criteria used in selecting the vendor-related data.
[018] In addition, the system comprises a selection module, provided on the
one or more processors, configured to process the modified decision-related
data to
modify the at least one vendor factor. The selection module is further
configured to
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 5 -
select a subset of the vendor-related data based on the modified at least one
vendor
factor.
[019] The interface may be further configured to provide the selected
subset of vendor-related data to the user. The interface may also be further
configured to trigger a marketing engine associated with the marketing
database to
provide the selected subset of vendor-related data to the user. Also, the
interface
may be further configured to provide the selected subset of vendor-related
data to a
marketing engine associated with the marketing database as a recommendation of
vendor-related data to provide to the user, the marketing engine utilizing the
recommendation to provide vendor-related data to the user.
[020] The modified decision-related data includes at least one factor
having
a weighted score, the weighted score of the at least one factor determined by
at least
an importance assigned to the at least one factor by the user.
[021] The retrieving module may be further configured to select the
marketing database corresponding to a vendor and retrieve the vendor-related
data
from the selected at least one marketing database. The retrieving module may
also
be further configured to select the vendor based on whether or not goods
and/or
services provided by the vendor correspond to the decision-related data.
[022] The selecting module may include a ranking module configured to
rank the vendor-related data by applying weighted scores associated with the
decision-related data to the vendor factors. The selecting module may be
further
configured to select a subset of the ranked vendor-related data. Also, the
selecting
module may be further configured to select the subset of the ranked vendor-
related
data based on an advertisement subscription of a vendor associated with the
vendor-
related data.
[023] The system may further comprise a presenting module configured to
provide the vendor-related data along with at least one decision option. The
at least
one decision option is presented to the user by the decision engine utilizing
the
modified decision-related data. The at least one decision option may be
product
and/or service related data.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 6 -
[024] Yet another example embodiment is a computer-implemented
method for providing vendor-related data. The method comprises receiving
modified decision-related data from a decision engine. The modified decision-
related data is generated by the decision engine by applying at least one user
input to
decision related data. The method also comprises retrieving vendor-related
data
from a marketing database based on one or more properties of the modified
decision-related data. Also, the method comprises processing the modified
decision-
related data to modify at least one vendor factor associated with the
retrieved
vendor-related data and selecting a subset of the retrieved vendor-related
data based
on the modified at least one vendor factor.
BRIEF DESCRIPTION OF THE DRAWINGS
[025] The foregoing and other objects, features and advantages will be
apparent from the following more particular description of the embodiments, as
illustrated in the accompanying drawings in which like reference characters
refer to
the same parts throughout the different views. The drawings are not
necessarily to
scale, emphasis instead being placed upon illustrating the principles of the
embodiments.
[026] FIG. 1 is a schematic illustration of a decision making and response
system.
[027] FIG. 2 is an enlarged schematic illustration of the system shown in
FIG. 1.
[028] FIG. 3 is an illustration of a factors data entry page that may be
used
with the system shown in FIG. 1.
[029] FIG. 4 is an illustration of a graph factors page that may be used
with
the system shown in FIG. 1.
[030] FIG. 5 is an illustration of a decision options page that may be used
with the system shown in FIG. 1.
[031] FIG. 6 is an illustration of a raw data page that may be used with
the
system shown in FIG. 1.
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 7 -
[032] FIG. 7 is an illustration of a score results page that may be used
with
the system shown in FIG. 1.
[033] FIG. 8 is an illustration of a graph results page that may be used
with
the system shown in FIG. 1.
[034] FIG. 9 is an illustration of a page including a pop-up summary
window that may be used with the system shown in FIG. 1.
[035] FIG. 10 is a flowchart of a method of comparing different options
using the system shown in FIG. 1.
[036] FIG. 11 is an illustration of a marketing profile data entry page
that
may be used with the system shown in FIG. 1.
[037] FIG. 12 is an illustration of another marketing profile data entry
page
that may be used with the system shown in FIG. 1.
[038] FIG. 13 is a flowchart of a method of providing vendor-related data
in response to decision-related data using the system shown in FIG. 1.
[039] FIG. 14 is a block diagram of decision and response engines that may
be provided to the system shown in FIG. 1.
[040] FIG. 15 is an exemplary diagram of a unique set of factors associated
with a user.
[041] FIG. 16 is another exemplary diagram of a unique set of factors
associated with a user.
[042] FIG. 17 is an exemplary diagram of a base set of factors of a user.
[043] FIG. 18 is an exemplary diagram of a score based upon factors of a
user.
[044] FIG. 19 is another exemplary diagram of a score based upon factors
of a user.
[045] FIG. 20 is another exemplary diagram of a score based upon factors
of a user.
[046] FIG. 21 is another exemplary diagram of a score based upon factors
of a user.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 8 -
[047] FIG. 22 is network diagram illustrating a connector engine used to
connect a decision engine to a marketing engine in accordance with an example
embodiment of the present disclosure.
[048] FIG. 23 is a block diagram of a connector engine in accordance with
an example embodiment of the present disclosure.
[049] FIGS. 24A-D are tables illustrating decision logic utilized by a
connector engine in accordance with an example embodiment of the present
disclosure.
[050] FIG. 25 is a flow diagram of an example method for providing
vendor-related data.
DETAILED DESCRIPTION
[051] One skilled in the art will realize the invention may be embodied in
other specific forms without departing from the spirit or essential
characteristics
thereof. The foregoing embodiments are therefore to be considered in all
respects
illustrative rather than limiting of the invention described herein. Scope of
the
invention is thus indicated by the appended claims, rather than by the
foregoing
description, and all changes that come within the meaning and range of
equivalency
of the claims are therefore intended to be embraced therein.
[052] Aspects of the present invention are disclosed in the following
description and related figures directed to specific embodiments of the
invention.
Those skilled in the art will recognize that alternate embodiments may be
devised
without departing from the spirit or the scope of the claims. Additionally,
well-
known elements of exemplary embodiments of the invention will not be described
in
detail or will be omitted so as not to obscure the relevant details of the
invention.
[053] As used herein, the word "exemplary" means "serving as an example,
instance or illustration." The embodiments described herein are not limiting,
but
rather are exemplary only. It should be understood that the described
embodiments
are not necessarily to be construed as preferred or advantageous over other
embodiments. Moreover, the terms "embodiments of the invention", "embodiments"
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 9 -
or "invention" do not require that all embodiments of the invention include
the
discussed feature, advantage or mode of operation.
[054] Further, many of the embodiments described herein are described in
terms of sequences of actions to be performed by, for example, elements of a
computing device. It should be recognized by those skilled in the art that the
various
sequence of actions described herein can be performed by specific circuits
(e.g.,
application specific integrated circuits (ASICs)) and/or by program
instructions
executed by at least one processor. Additionally, the sequence of actions
described
herein can be embodied entirely within any form of computer-readable storage
medium such that execution of the sequence of actions enables the processor to
perform the functionality described herein. Thus, the various aspects of the
present
invention may be embodied in a number of different forms, all of which have
been
contemplated to be within the scope of the claimed subject matter. In
addition, for
each of the embodiments described herein, the corresponding form of any such
embodiments may be described herein as, for example, "a computer configured
to"
perform the described action.
[055] FIG. 1 is a schematic illustration of a decision making and response
system 100. FIG. 2 is an enlarged schematic illustration of system 100. In the
exemplary embodiment, system 100 may include a web portal 102, at least one
first
user 104 and at least one second user 106. In the exemplary embodiment, each
first
user 104 and each second user 106 may be coupled in communication to web
portal
102 using a network 110. In the exemplary embodiment, network 110 may be the
Internet. In an alternative embodiment, network 110 may be a local area
network
(LAN), a wireless LAN, a wide area network (WAN) and/or any other type of
connection that enables system 100 to function as described herein. System
100, in
the exemplary embodiment, may also include at least one server 112 and at
least one
database 114. In the exemplary embodiment, server 112 may be coupled in
communication to database 114 using a network connection 115 that is coupled
to a
local network 116 such as, but not limited to, a LAN, a wireless LAN, a WAN
and/or any other connection that enables system 100 to function as described
herein.
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 10 -
[056] In the exemplary embodiment, first users 104 may be any entity that
desires to make a decision on a plurality of decision options using at least
one factor.
Each factor represents a specific consideration that a user may take into
account
when selecting the decision option. System 100 may be used with decision
analyses
including, but not limited to, the purchase of any type of products or
services, the
purchase of any type of real estate, determining which school to attend,
determining
which career path to pursue or any other decision. In one embodiment, first
users
104 may be the general public. Second users 106 may be any entity that is
interested
in the decision analysis conducted by first users 104. In one embodiment,
second
users 106 may be any entity that desires to advertise decision options to
first users
104. In another embodiment, second users 106 may include, but not limited to,
advertising agencies, advertisers and specific product entities.
[057] In the exemplary embodiment, server 112 may include at least one
engine 118 programmed therein. Alternatively, a plurality of servers 112 may
be
used, wherein each server 112 may include at least one engine 118 programmed
therein. As used herein, the term "engine" may refer to a collection of logic
and/or
code that may be executed on server 112 or any other type of device or
processor
that is capable of producing a response. In the exemplary embodiment, first
engine
118 may be a decision engine. In one embodiment, server 112 may include a
second
engine 120 that may be a connector engine and a third engine 122 that may be a
response engine, such as an advertising/marketing engine. In another
embodiment,
server 112 may include any type of engines or any number of engines that
enable
system 100 to function as described herein.
[058] In the exemplary embodiment, the term "server" is not limited to just
those integrated circuits referred to in the art as a computer, but broadly
refers to a
processor, a microcontroller, a microcomputer, a programmable logic
controller, an
application specific integrated circuit and other programmable circuits. These
aforementioned terms may be used interchangeably herein. In the exemplary
embodiment, server 112 may include a bus 130 or other communication mechanism
for communicating information, and a processor 132 coupled with bus 130 for
processing the information. In one embodiment, a plurality of processors 132
may
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 11 -
be arranged in a multi-processor arrangement to facilitate faster processing
as
compared to a single processor arrangement. In the exemplary embodiment,
system
100 may also include a main memory 134, such as a random access memory (RAM)
or other dynamic storage device (e.g., dynamic RAM (DRAM), static RAM
(SRAM) and synchronous DRAM (SDRAM)) coupled to bus 130 for storing
information and instructions to be executed by processor 132. In addition,
main
memory 134 may be used for storing temporary variables or other intermediate
information during the execution of instructions by processor 132. System 100
may
further include a read only memory (ROM) 136 or other static storage device
(e.g.,
programmable ROM (PROM), erasable PROM (EPROM) and electrically erasable
PROM (EEPROM)) coupled to bus 130 for storing static information and
instructions for processor 132.
[059] System 100 may also include a disk controller 138 coupled to bus
130 to control one or more storage devices for storing information and
instructions.
In the exemplary embodiment, storage devices may include, but not limited to,
a
magnetic hard disk 140 and a removable media drive 142 (e.g., floppy disk
drive,
read-only compact disc drive, read/write compact disc drive, compact disc
jukebox,
tape drive and removable magneto-optical drive). The storage devices may be
coupled to system 100 using any appropriate device interface known to one
having
ordinary skill in the art (e.g., small computer system interface (SCSI),
integrated
device electronics (IDE), enhanced-IDE (E-IDE), direct memory access (DMA), or
ultra-DMA). System 100 may also include special purpose logic devices (e.g.,
application specific integrated circuits (ASICs)) or configurable logic
devices (e.g.,
simple programmable logic devices (SPLDs), complex programmable logic devices
(CPLDs), and field programmable gate arrays (FPGAs)).
[060] In the exemplary embodiment, main memory 134, hard disk 140 and
removable media drive 142 are examples of computer-readable mediums that
facilitate holding instructions programmed according to the teachings of the
invention, data structures, tables, records and/or other data described
herein. The
term "computer-readable medium" or "computer-readable media" as used herein
refers to any medium that facilitates storing and/or providing instructions to
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 12 -
processor 132 for the execution thereof. The computer-readable media may
include,
but not limited to, non-volatile media, volatile media and transmission media.
Non-
volatile media may include, but not limited to, hard disks, floppy disks,
tape,
magneto-optical disks, PROMs (EPROM, EEPROM, flash EPROM), DRAM,
SRAM, SDRAM or any other magnetic medium. Moreover, non-volatile media may
also include compact discs (e.g., CD-ROM) or any other optical medium.
Further,
non-volatile media may include punch cards, paper tape or other physical
medium
with patterns of holes. Volatile media may include dynamic memory, such as
main
memory 134. Transmission media may include coaxial cables, copper wire and
fiber
optics, including the wires that make up bus 130. Transmission media may also
include carrier waves such as acoustic or light waves that may be generated
using
radio waves and infrared data communications.
[061] In the exemplary embodiment, the computer-readable media may
include software that facilitates controlling system 100. Such software may
include,
but is not limited to, device drivers, operating systems, development tools
and
applications software. Such computer-readable media further includes the
computer
program product of the present invention for performing all or a portion (if
processing is distributed) of the processing performed in implementing the
invention.
[062] The computer code devices of the present invention may be any
interpretable or executable code mechanism, including but not limited to
scripts,
interpretable programs, dynamic link libraries (DLLs), Java classes, and
complete
executable programs. Moreover, parts of the processing of the present
invention may
be distributed for better performance, reliability, and/or cost.
[063] In the exemplary embodiment, system 100 may further include a
display controller 144 coupled to bus 130 to control a display 146, such as a
cathode
ray tube (CRT), liquid crystal display (LCD) or any other type of display to
facilitate
displaying information to a computer user. System 100 may include a plurality
of
input devices, such as a keyboard 148 and a pointing device 150, to facilitate
interacting with the computer user and providing information to processor 132.
Alternatively, a touch screen may be used in conjunction with display 146. In
one
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 13 -
embodiment, pointing device 150 may be a mouse, a trackball or a pointing
stick for
communicating direction information and command selections to processor 132
and
for controlling cursor movement on display 146. In addition, a printer (not
shown)
may be coupled to system 100 to facilitate printing data stored and/or
generated by
system 100.
[064] System 100 also includes a communication interface 152 coupled to
bus 130, wherein communication interface 152 may be coupled in communication
to
LAN 116 or network 110 using network connection 115. In one embodiment,
communication interface 152 may be a network interface card that is coupled in
communication to any packet switched LAN. In another embodiment,
communication interface 152 may be an asymmetrical digital subscriber line
(ADSL) card, an integrated services digital network (ISDN) card or a modem to
facilitate providing a data communication connection to network connection
115. In
yet another embodiment, wireless connections may be used to couple
communication interface 152 to LAN 116 and/or network 110. In the exemplary
embodiment, communication interface 152 sends and receives electrical,
electromagnetic or optical signals that carry digital data to and from system
100,
which are exemplary forms of carrier waves that facilitate transporting
information.
Network connection 115 facilitates providing data communication between web
portal 102 and data devices using networks 116 and 130. Specifically, network
connection 115 may couple first users 104 and/or second users 106 to web
portal
102 using at least one of local network 116 and network 110. System 100 may
also
transmit and receive data, including program code, through networks 116 and
110
using network connection 115 and communication interface 152. Moreover,
network
connection 115 may couple server 112 in communication to a mobile device 132
such as a personal digital assistant (PDA), a laptop computer, a cellular
telephone, a
smart phone, an ultra-compact mobile device or any other mobile device that
enables system 100 to function as described herein.
[065] During operation, system 100 may perform a portion or all of the
processing steps of the invention in response to processor 132 executing one
or more
sequences of one or more instructions contained within main memory 134 and/or
- 1 4 -
other forms of computer-readable media. In one embodiment, processor 132 may
execute the instructions contained within the computer-readable media. In
another
embodiment, hard-wired circuitry may be used in place of or in combination
with
the instructions. Thus, the exemplary embodiments described herein are not
limited
to any specific combination of hardware circuitry and software. For example,
the
instructions may initially be carried on a magnetic disk of a remote computer.
The
remote computer can load the instructions for implementing all or a portion of
the
present invention remotely into a dynamic memory and send the instructions
over a
telephone line using a modem. A modem local to system 100 may receive the data
on the telephone line and use an infrared transmitter to convert the data to
an
infrared signal. An infrared detector coupled to bus 130 can receive the data
carried
in the infrared signal and place the data on bus 130. Bus 130 carries the data
to main
memory 134, from which processor 132 retrieves and executes the instructions.
The
instructions received by main memory 134 may optionally be stored on hard disk
140 or removable media drive 142 either before or after execution by processor
132.
[066] Other aspects of the invention may include data transmission and
Internet-related activities. See Gralla, Preston, How the Internet Works, Ziff-
Davis
Press (1996). Still other aspects of the invention may utilize wireless data
transmission, such as those described in U.S. Pat. Nos. 6,456,645, 5,818,328
and/or
6,208,445.
[067] FIG. 3 is an illustration of a factors data entry interface page, or
factors page 200 that may be used with engine 118. It should be understood
that the
following description is a non-limiting example pertaining to an exemplary
decision
engine. In the exemplary embodiment, factors page 200 includes a navigation
area
202 and a factor data area 204. Navigation area 202 includes a plurality of
radio
buttons such as, but not limited to, a factors button 206, an options button
208, a
data button 210, a results button 212, a graph results button 214, a graph
factors
button 216, a clear button 218 and a summary button 220. Each radio button
facilitates directing a user to the respective interface page. In the
exemplary
embodiment, clear button 218 facilitates hiding a score summary pop-up window
CA 2886794 2020-01-09
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 15 -
294 (shown in FIG. 9) and summary button 220 facilitates displaying score
summary pop-up window 294 on the page. Moreover, navigation area 202 may
include a save/load button 222 and a new decisions button 224. In the
exemplary
embodiment, save/load button 222 enables the user to either save their
decision
analysis progress or load a previously saved decision analysis. Additional
information may be included when the user saves their results, such as the
ability to
choose a destination to save a file to, the ability to name the file to be
saved and the
ability to include a description of the results. Additionally, engine 118 may
allow the
user to load saved decision analyses as well as delete prior saved results.
Moreover,
new decisions button 224 enables the user to begin a new decision analysis.
[068] Factor data area 204 may include a plurality of data columns
226 and
at least one data row 228. The plurality of data columns 226 may include, but
not
limited to, a factors column 232, a first data column 234 and a second data
column
236, wherein each column 232, 234 and 236 includes at least one data entry
field
238. Each data row 228 may include an ON/OFF toggle button 244, an importance
indicator 246 and at least one data entry field 238 associated with columns
232, 234
and 236. In the exemplary embodiment, each ON/OFF button 244 facilitates
including or excluding the associated data row 228 from the decision analysis
performed by engine 118. Moreover, each importance indicator 246 facilitates
indicating the importance of the associated factor that is assigned by the
user. In the
exemplary embodiment, an aspect of each factor may be indicated in at least
one of
first and second data columns 234 and 236. Specifically, in one embodiment, an
aspect of each factor may be indicated with a range of values. In another
embodiment, an aspect of each factor may be indicated as a subjective
assessment.
In yet another embodiment, an aspect of each factor may be indicated as at
least one
of a yes/no answer, a true/false answer, a multiple-choice answer, numerical
data
and any other type of entry known by one having ordinary skill in the art. For
example, in the event that each factor may be quantified, a most desired value
may
be entered into first data column 234 and a least desired value may be entered
into
second data column 236. In the event that each factor may be represented as a
subjective assessment, the user may enter the word "opinion" in at least one
of first
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 16 -
and second columns 234 and 236. In the event that each factor may be
represented
as yes/no or true/false entry, the user may indicate the most desired entry in
first data
column 234 and the least desired entry in second data column 236.
[069] FIG. 4 is an illustration of a graph factors page 250. In the
exemplary
embodiment, the user may navigate to graph factors page 250 by clicking graph
factors radio button 216 in navigation area 202 included in graph factors page
250.
Graph factors page 250 may also include a display area 252 that may include a
chart
254 that graphically represents the importance of each factor assigned by the
user
using importance indicator 246. Specifically, chart 254 includes a y-axis 256
that
includes each factor used in the decision process and an x-axis 258 that
includes the
relative importance of each factor. At least one graph bar 260 is associated
with each
factor such that graph bar 260 may graphically represent the importance of
each
factor. Moreover, the relative importance of each factor is displayed by
importance
percentage basis. In an alternative embodiment, display area 252 may display
the
factor importance as a pie chart, a list or any other method of displaying
information
known to one having ordinary skill in the art to enable engine 118 to function
as
described herein.
[070] FIG. 5 is an illustration of a decision options page 252. In the
exemplary embodiment, the user may navigate to decision options page 252 by
clicking options radio button 208 in navigation area 202 included in decision
options
page 252. Decision options page 252 may also include a decision options area
254
that may include at least one data row 256 that may include a decision option
258
and at least one ON/OFF toggle button 244 associated therewith. In the
exemplary
embodiment, and described in more detail below, engine 118 may determine at
least
one decision option 258 based on the factors entered by the user in factors
page 200.
In one embodiment, the user may indicate a broad category of the desired
decision
options 258 to which each factor may apply. In another embodiment, engine 118
may automatically determine the decision option 258 category that applies
based on
the factors entered by the user in factors page 200. In yet another
embodiment, the
user may enter a plurality of decision options 258 and at least one factor
into system
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 17 -
100 to enable engine 118 to decide on an appropriate decision option 258 based
on
the entered decision options 258 and factors.
[071] FIG. 6 is an illustration of a raw data page 260. In the exemplary
embodiment, the user may navigate to data page 260 by clicking data radio
button
210 in navigation area 202 included within data page 260. Data page 260 may
also
include a data display area 262 that may include at least one data matrix 264.
Specifically, data matrix 264 may include at least one data column 266
associated
with each decision option 258 determined by engine 118. Moreover, data matrix
264
may include at least one data row 268 associated with each factor entered by
the
user. In the exemplary embodiment, as described in more detail below, engine
118
determines the objective score or rating for each factor for each decision
option 258
and displays the values in matrix form. In the exemplary embodiment, each
factor
score is non-weighted. As a result, data page 260 displays the raw data to the
user.
For example, as described in more detail below, in the event the factor score
may be
quantified, a numeric value is displayed. In the event the factor value is an
opinion,
the value may be displayed as a subjective assessment using a percent scale.
For
example, a subjective assessment may be displayed as 8 stars out of 10 stars,
as
described in more detail below. Further, in the event the factor value may be
displayed as a yes/no answer or true/false answer, such an answer is
displayed.
Moreover, in the event the factor score is a multiple choice answer, a single
answer
choice that applies to the decision option is displayed. For example, if the
factor is a
color, then the factor score may be indicated as the specific color of the
specific
decision option.
[072] FIG. 7 is an illustration of a score results page 270. In the
exemplary
embodiment, the user may navigate to results page 270 by clicking results
radio
button 212 in navigation area 202 included within results page 270. Results
page
270 may also include a data display area 272 that may include at least one
data
matrix 274. Specifically, data matrix 274 may include at least one data column
276
associated with each decision option 258 determined by engine 118. Moreover,
data
matrix 264 may also include at least one data row 278 associated with each
factor
entered by the user. In the exemplary embodiment, as described in more detail
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 18 -
below, engine 118 determines the weighted score of each factor using the non-
weighted factor score and the importance assigned to the factor by the user.
Each
weighted factor score for each decision option 258 is displayed in matrix
form. In
the exemplary embodiment, each weighted factor score is a numeric number that
is
used to determine a total score for each decision option 258.
[073] FIG. 8 is an illustration of a graph results page 280. In the
exemplary
embodiment, the user may navigate to graph results page 280 by clicking graph
results radio button 214 in navigation area 202 included in graph results page
280.
Graph results page 280 may also include a display area 282 that may include a
chart
284 that graphically represents the score of each decision option 258
determined by
engine 118. Specifically, chart 284 includes a y-axis 286 that includes at
least one
decision option 258 compared by engine 118 and an x-axis 288 that includes the
total scores of each decision option 258. A graph bar 290 is associated with
each
decision option 258 such that graph bar 290 may graphically represent the
total
score each decision option 258. In an alternative embodiment, display area 282
may
display the total score as a pie chart, a list or any other method of
displaying
information known to one having ordinary skill in the art to enable system 100
to
function as described herein.
[074] In the exemplary embodiment, once the score for each decision
option 258 is determined by engine 118, as shown in FIG. 9, the user may click
on
summary button 220 to display score summary pop-up window 294 on any page.
Score summary pop-up window 294 may include a list of the compared decision
options 258 and the scores associated with each decision option 258.
[075] FIG. 10 is a flow chart of exemplary method 300 of comparing
different options using system 100. During operation, a user desiring to
decide on a
decision option may utilize system 100, and more specifically engine 118, to
facilitate determining which decision option to choose. In one embodiment,
system
100 may be utilized or implemented on network 110, such as, but not limited
to, the
Internet. Engine 118 may be used with decision analyses including, but not
limited
to, the purchase of any type of products or services, the purchase of any type
of real
estate, deciding which school to attend, deciding which career to pursue or
any other
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 19 -
type of decision analysis. In the exemplary embodiment, the user may begin a
new
decision analysis by clicking new decisions button 224 in any of the pages
shown in
FIGS. 3-9. In method step 302, the user may then navigate to factors page 200,
as
shown in FIG. 3, by clicking factors radio button 206, wherein the user may
enter a
plurality of factors, corresponding to various aspects of the desired decision
option,
into factors column 232. Moreover, the user may enter specific data for each
factor
into first data column 234 and/or second data column 236. The factor data may
be
quantified by engine 118 according to an importance level assigned to each
factor by
the user using importance indicator 246. In method step 304, once the user has
entered the plurality of factors, the factor data and assigned the importance
level of
each factor, engine 118 may determine a plurality of decision options 258 to
be
analyzed and compared in the decision analysis, as shown in FIG. 4.
[076] Although engine 118 may be used with any decision analysis, a non-
limiting example pertaining to deciding which automobile the user should
purchase
is used to describe the operation of engine 118. In this non-limiting example,
engine
118 may analyze and compare various aspects of automobiles to enable the user
to
make a decision on which automobile to purchase. Specifically, the plurality
of
factors may represent various aspects of automobiles that the user may use to
analyze and compare potential automobiles. For example, factors 1-10 shown in
FIG. 3 may represent, miles per gallon (MPG), cost, miles per tank, interior
noise
level, appearance, quarter mile time, 0-60 miles per hour (MPH) time, turning
radius, anti-lock braking system (ABS) and exterior color, respectively. Once
the
user enters the plurality of automobile factors into factors column 232 in
factors
page 200, the user may enter unique factor data for each factor in at least
one of first
and second columns 234 and 236.
[077] In the non-limiting vehicle purchasing example, the unique factor
data may represent aspects of a potentially acceptable automobile that the
user
would purchase. In the exemplary embodiment, at least one factor may be
represented as a range. For example, the user may be interested in an
automobile
that can travel between a range of about 10 to about 35 miles per gallon and
cost
between a range of about $15,000 to about $25,000. Moreover, at least one
other
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 20 -
factor may be represented as a subjective assessment, a yes/no answer, a
true/false
answer or a multiple-choice answer. In one embodiment, an automobile factor
that
may be a subjective assessment may include interior noise level or appearance.
In
such an embodiment, the user may enter "opinion" in at least one of first and
second
data columns 234 and 236 for the interior noise level factor. In another
embodiment,
a factor that may include a yes/no or true/false answer may be whether the
automobile has anti-lock brakes installed. In such an embodiment, in the event
the
user desires an automobile that includes anti-lock brakes, the user may enter
"yes" as
the best answer in first data column 234 and "no" as the worst answer in
second data
column 236. In yet another embodiment, a factor that may include a multiple-
choice
answer may be the color of the automobile. In such an embodiment, the user may
enter "red, green and blue" as the most desired colors in first data column
234 and
"white, black and yellow" as the least desired colors in second data column
236.
Lastly, once the user has entered the unique factor data for each factor, the
user may
assign the importance level to each factor using importance indicator 246.
After the
importance level of each factor have been assigned, the user may view a
graphical
representation of the importance levels of each factor by navigating to graph
factors
page 250 by clicking the graph factors radio button 216 on any page shown in
FIGS.
3-9.
[078] Once the
user has entered the plurality of factors, entered the factor
data and assigned the importance level to each factor in factors page 200,
engine 118
may determine a plurality of decision options and output those options in
decision
options page 252, for example as shown in FIG. 5. In one embodiment,
information
for a plurality of decision options, such as but not limited to products, may
be
located on the Internet. In one embodiment, engine 118 may search the Internet
or
any other network or database for decision option information, which has been
tagged and therefore searchable using an Internet search engine or any other
tool. In
the exemplary embodiment, ten decision options 258 may be returned and
displayed
in decision options page 252. Alternatively, any number of decision options
258
may be returned. In the non-limiting vehicle purchasing decision example,
decision
options A-J may represent different makes and/or models of vehicles that
engine 118
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 21 -
determined were relevant to the decision analysis. In the exemplary
embodiment,
each determined decision option 258 is displayed on a corresponding data row
256
that includes ON/OFF button 244 associated therewith. The user may include the
determined decision option 258 in the decision analysis by switching ON/OFF
244
button to "ON" or exclude the determined decision option 258 by switching
ON/OFF 244 button to "OFF". In the exemplary embodiment, all ON/OFF buttons
244 are switched to "ON" which means that all of decision options A-J will be
analyzed and compared in the decision analysis. Alternatively, the user may
manually provide the plurality of decision options 258 to engine 118.
[079] Once the plurality of decision options 258 have been determined by
engine 118, the factor data for each decision option 258 may be displayed in
data
page 260, as shown in FIG. 6. In the non-limiting vehicle purchasing decision
example, the aspects of each automobile are displayed in data page 260. For
example, decision option A, or automobile A, gets 42 MPG, costs $24,777, gets
600
miles per tank, received 7 out of 8 stars for interior noise level and has an
exterior
color of red.
[080] Referring back to FIG. 10, in method step 306, system 100, and more
specifically engine 118 may calculate a numeric value, or raw value, that
represents
the overall desirability of each factor. Specifically, engine 118 may
calculate a
numeric value of the data entered in first data column 234 that represents a
best or
most desirable value. Further, engine 118 may calculate a numeric value of the
factor data entered in second data column 236 that represents a worst or least
desirable value. For example, engine 118 may calculate a numeric value of 10,
or
any other value that represents the best or most desirable value, for factors
that
include data that is equal to or greater than the data entered in first data
column 234.
Similarly, engine 118 may calculate a numeric value of 0, or any other value
that
represents the worst or least desirable value, for factors that include data
that is
equal to or less than the data entered in second data column 236. Moreover,
engine
118 may calculate a corresponding numeric value for factors that include data
that is
between the best and worst values entered in first and second data columns 234
and
236 using an algorithm such as linear computation. In the non-limiting vehicle
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 22 -
purchasing decision example, for factor 1, or MPG, the user entered a most
desirable
MPG of 35 and a least desirable MPG of 10. In such an example, engine 118 may
calculate a numeric value of 5 for a decision option having an MPG of 22.5, a
numeric value of 2.5 for a decision option having an MPG of 16.25, a numeric
value
of 7.5 for a decision option having an MPG of 28.75 and so forth and so on.
Alternatively, factor data that falls between the best and worst desired
values entered
in first and second data columns 234 and 236 may have numeric values assigned
by
engine 118 using polynomial computation, logarithmic computation, power
computation, exponential computation, moving average computation or any other
computation method that enables engine 118 to function as described herein.
[081] In the non-limiting vehicle purchasing decision example, for factor
4,
or interior noise level, the user entered "opinion" in at least one of first
and second
data column 234 and 236, shown in FIG. 3. In the event the decision option 258
includes a subjective assessment or review from a third party that assessment
may
be used to calculate a numeric value. For example, in one embodiment, the
third
party automobile review agency may determine that a particular vehicle has a
relatively low amount of interior noise and therefore award 7 stars to that
vehicle for
interior noise. In such an example, engine 118 may calculate the rating
percent and
convert that percent to the numeric number. For example, 7 out of 10 stars
would
equal a 70% interior noise rating. Engine 118 may then calculate the interior
noise
factor as 7. In the event the decision option 258 does not include a
subjective
assessment for a third party that may be used to calculate a numeric value,
engine
118 may inform the user that no numeric value was calculated for that
particular
factor.
[082] In the non-limiting vehicle purchasing decision example, for factor
9,
or ABS, the user entered "yes", or "true", in first data column 234 and "no",
or
"false", in second data column 236. In such an example, in the event a vehicle
includes ABS, the engine 118 may calculate a numeric value of 10 for factor 9.
In
the event a vehicle does not include ABS, then engine 118 may calculate a
numeric
value of 0 for factor 9.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 23 -
[083] In the non-limiting vehicle purchasing decision example, for factor
10, or exterior vehicle color, the user entered "red, green, blue" in first
data column
234 and "white, black, yellow" in second data column 236. In such an example,
in
the event a vehicle includes an exterior color of red, green or blue, engine
118 may
calculate a numeric value of 10 for factor 10. In the event the vehicle
includes an
exterior color of white, black or yellow, engine 118 may calculate a numeric
value
of 0 for factor 10. Further, in the event that the vehicle color is neither,
red, green,
blue, white, black nor yellow, engine 118 may alert the user of the vehicle's
color.
[084] In the event the user wishes to exclude a factor from the decision
analysis, the user may switch ON/OFF button 240 in the factors page 200 to
"OFF"
for the factor. As a result, engine 118 will not consider that factor in the
decision
analysis.
[085] In method step 308, once the numeric value of each factor of each
decision option 258 is calculated, system 100, and more specifically engine
118 may
calculate a weighted score for each factor using the importance assigned to
each
factor by the user. Scores of each factor may be weighted in any manner, for
example by applying a higher weight to scores for factors having a greater
importance to the user. Further, the weighted scores can be based on any
scaling
method and should not be limited to the numbers or scales shown herein. In the
non-
limiting vehicle purchasing decision example, the user ranks factor 1 and 2,
or MPG
and cost, as the most important factors in the decision analysis and therefore
assigns
an importance ranking of 100 to factors 1 and 2. Further, in the exemplary
embodiment, the user assigned an importance value of 50 to factor 3, or miles
per
tank. Moreover, the user assigned an importance value of 25 to factors 4-10.
As a
result, in this example, the user desires an affordable automobile that
achieves
substantially good gas mileage and may be driven a relatively far distance on
a
single tank of gas. However, other factors, such as interior noise level,
acceleration,
turning radius and ABS braking are other factors that, while not as important
as cost,
MPG and miles per tank, they may influence the decision of the user in one way
or
another.
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 24 -
[086] For example, factor 1, or MPG factor of decision option A, gets 42
MPG. The user indicated that the most desirable MPG for an automobile is 35
MPG.
The MPG factor for automobile A is greater than the most desired value and as
a
result, MPG factor receives a score of 10. Further, the user assigned an
importance
of 100 for the MPG factor. As a result, the MPG score is multiplied by the
importance level. As such, the exemplary weighted score for the MPG factor for
automobile A is 10×100=1000, as shown in FIG. 7. In the event the MPG
was
22.5, as described above, the MPG score would be 5 and the weighted score
would
be 5×100=500. Moreover, the interior noise level factor received 7 out
of 10
stars, therefore, receiving a score of 7. The user assigned an importance
level of 25
for this factor. As a result, the weighted score of the interior noise level
is
7×25=175. Further, the exterior color of automobile A is red, resulting
in a
score of 10. The user assigned an importance level of 25 to this factor. As
such, the
weighted score of the exterior color factor is 10×25=250. Similar
calculations
are performed for all desired decision options.
[087] Once the weighted score for each factor is calculated by engine 118,
the score for each factor and total score for each decision option 258 may be
displayed in results page 270, as shown in FIG. 7. In the exemplary
embodiment,
each automobile, or decision option 258, is displayed with the weighted score
of
each factor. Moreover, the total score of each automobile is calculated by
engine
118 and displayed for each decision option 258. In the non-limiting vehicle
purchasing decision example, decision option A, or automobile A, received a
total
score of 2553, automobile B received a total score of 1960, automobile C
received a
total score of 2359 and automobile D received a total score of 2118.
[088] In method step 310, as shown in FIG. 10, engine 118 ranks each
decision option based on the total score. In method step 312, engine 118
displays a
graphical representation of the total scores of each decision option, or
automobile,
may be displayed in graph results page 280, as shown in FIG. 8. In one
embodiment,
engine 118 may display the ranks of each decision option based on the total
score.
Once the total scores of each decision option or automobile are calculated by
engine
118, the user may display summary pop-up window 294 on any page by clicking
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 25 -
summary button 220, as shown in FIG. 9. Alternatively, summary pop-up window
may display any decision analysis information. As such, the user may interpret
from
FIG. 8 that automobiles options A and J include a majority of the user's
desired
factors. As a result, system 100 enables the user to choose a decision option
based
on at least one factor.
[089] Referring again to FIG. 1 as well as FIG. 14, system 100 may include
at least one server 112. Server 112 may include first engine 118, second
engine 120
and third engine 122. In at least one embodiment, first engine 118 may be a
decision
engine, second engine 120 may be a connector engine and third engine 122 may
be a
response engine.
[090] Connector engine 120 may be an interfacing application for
facilitating data sharing between decision engine 118 and response engine 122.
Response engine 122 may process the shared data and respond with any type of
response. For example, response engine 122 may process the presence or absence
of
factors, the presence or absence of groups of factors, the presence or absence
of
decision options, the presence or absence of raw data, the presence or absence
of
thresholds for factor importance ratings, the presence or absence of
thresholds for
factor weights, the presence or absence of thresholds for group factor
weights, the
presence or absence of thresholds for decision option ranks, the presence or
absence
of thresholds for decision option scores, the bounds of factor definitions,
the bounds
of normalized factor definitions, the bounds of weighted factor definitions
and the
like. Response engine 122 may respond with advertisements, alerts,
information,
grades, URLs, merchants, coupons, opportunities, video, audio, queries,
analyses
and any other response known to one having ordinary skill in the art. In at
least one
exemplary embodiment, response engine 122 may be a marketing/advertising
engine
for responding with types of vendor-related data.
[091] Decision engine 118 may be any embodiment described above or any
other decision engine known to one having ordinary skill in the art. Decision
engine
118 may be capable of serving a plurality of first users 104 operating client
computing devices on a user-accessible portion of network 110, such as the
Internet.
In web-based embodiments, users 104 may be provided access to decision engine
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 26 -
118 for comparing decision options, for example and referred to hereinafter,
but not
limited to, products over the Internet through the use of suitable web-
browsers
operating on the client computing devices. Decision engine 118 may receive a
plurality of user inputs and process decision-related data in accordance with
the
plurality of user inputs. Decision engine 118 may transmit a plurality of
product
results (e.g., in the form of decision options, relevant products, ranked
products,
etc.) via network 110 to the client computing devices for consideration by
users 104.
In at least one embodiment, the one or more product results may be scored, in
total
and/or by factor, and listed for display. The product results may be listed by
rank,
for example, with the highest scoring product appearing first in the list.
Also, in at
least one embodiment, the comparison of products may be based on decision
options
selected by user 104 from a predetermined set of products made available for
user-
selection or entry by decision engine 118.
[092] In at least one embodiment, decision engine 118 may provide one or
more product modules for comparing types and/or classes of products. As non-
limiting examples, products modules may be provided for automobiles, real
estate,
schools, employment or any other type of product, service or action which may
utilize a decision. Moreover, in a non-limiting manner, automobile product
modules
may be provided for economy vehicles, sport vehicles, sport utility vehicles,
luxury
vehicles and family vehicles.
[093] Decision engine 118 may provide a plurality of relevant factors for
selection by users 104 within each product module. As described above, all or
less
than all of the plurality of factors may be further definable by at least one
of a range
of numeric values (broadly read so as to also include a single numeric value),
Boolean designations (i.e. yes/no answers and true/false answers), multiple-
choice
values/answers and any other data for defining factors known to one having
ordinary
skill in the art. Also, numeric ranges defining factors may include best and
worst
values with intermediate values defined between the two using any method known
to one having ordinary skill in the art. Boolean designations and multiple-
choice
values may also be used to define best and worst values for the factors.
Subjective
factors may have pre-supplied definitions, such as those provided by third
party
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 27 -
entities. For instance, opinion definitions may be rated on a scale of 1 to 10
stars or
any other scale known to one having ordinary skill in the art.
[094] As described above, all or less than all of the plurality of factors
may
be assigned importance levels/ratings by user 104. The importance ratings may
be
used by decision engine 118 to weigh the factors. In at least one embodiment
and as
described above, user 104 may select, define and assign importance ratings to
factors through one or more navigation screens/windows presented by decision
engine 118 to user 104 via the graphical user interface and display of a
client
computing device.
[095] In processing the decision-related data, decision engine 118 may
define factors in accordance with the user inputs. For instance, a factor may
be
definable by numeric range. User 104 can input the numeric values (or numeric
value) setting the numeric range through data entry provided by decision
engine
118. Also, a factor may be definable by one or more Boolean
designations/conditions. User 104 may select one of the two answers for each
Boolean condition through data entry provided by decision engine 118.
Moreover, a
factor may be definable by one or more answers out of multiple available
values.
User 104 may select one or more answers for each set of multiple-choice values
through data entry provided by decision engine 118. In at least one
embodiment,
subjective factors may not be definable by user 104, but may be defined by
decision
engine 118 (through, for example, third party ratings) and user 104 may select
which
factors may be considered. All or less than all of the factors selected by
user 104 for
consideration may be normalized on a standardized numerical scale (e.g., 1 to
10).
Factors may then be weighed in accordance with user inputs for weighing such
factors, which may be predominantly or wholly based on the importance ratings
assigned to the factors by user 104.
[096] As such, the decision-related data may include the factors,
importance ratings for the factors and weights for the factors. Factor weights
may be
expressed as percentages, as one non-limiting example. The decision-related
data
may also include definitions for factors, whether, for example, by numeric
scale,
Boolean conditions, multiple-choice answers or subjective assessments. The
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 28 -
decision-related data may also include product results (e.g., decision
options), ranks
for product results and scores (whether weighted or unweighted) for factors
and
product results. The decision-related data may also include data about user
104
and/or the client computing device of user 104. The decision-related data may
additionally include metadata associated with any of the above or any other
needed
data, as will be readily recognized by one having ordinary skill in the art.
All or part
of the decision-related data may be stored on at least one database 114.
[097] Still referring to FIGS. 1 and 14, in at least one embodiment,
marketing engine 122 may be interfaced with decision engine 118. Marketing
engine
122 and decision engine 118 may be interfaced via interfacing application 120.
Interfacing application 120 may allow for decision-related data processed by
decision engine 118 to be shared with marketing engine 122. Marketing engine
122
may also process the decision-related data and process or provide any desired
sales,
advertisement, coupon or related data. Decision engine 118 and marketing
engine
122 may process the decision-related data in parallel or nearly in parallel.
Marketing
engine 122 may transmit vendor-related data, or any other desired sales,
advertisement, coupon or related data to the client computing devices of users
104 in
accordance with the decision-related data.
[098] The vendor-related data may include vendor advertisements, vendor
locations, vendor ratings, vendor website hypertext links, vendor coupons, any
combination thereof and like data known to one having ordinary skill in the
art. The
vendor-related data and any decision-related data may be transmitted together
so as
to be displayed to user 104 on a single web page or single set of web pages
(e.g.,
related navigation screens). For example, a web page displayed to user 104 may
show scored product results and may show vendor advertisements therewith. All
or
part of the vendor-related data can be stored on at least one database 114.
[099] Vendor-related data may also include vendor marketing profiles that
marketing engine 122 may process and match to the decision-related data that
is also
processed by marketing engine 122. The vendor marketing profiles may establish
criteria for transmitting the other vendor-related data, such as vendor
advertisements. The vendor marketing profiles may establish criteria for
sending
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 29 -
other vendor-related data (e.g., vendor advertisements) based on decision
factors,
importance ratings for the decision factors, importance ratings for the
factors (or for
groups of the factors), weights for factors (or for groups of the factors),
ranks for the
factors, definitions for the factors (and the underlying data defining the
factors),
product results (e.g., decision options), ranks for the products results,
scores for the
factor or product results, any combination thereof and the like.
[0100] Interfacing application 120 may tie marketing engine 122 to
decision
engine 118. Interfacing application 120 may be on at least one server 112. A
primary
purpose of interfacing application 120 may be to facilitate data between
decision-
engine 118 and marketing engine 122. In particular, interfacing application
120 may
facilitate sharing of all or part of the decision-related data with marketing
engine
122. As such, interfacing application 120 may provide the needed decision-
related
data to marketing engine 122.
[0101] Accordingly, an interfacing computer program product stored on
a
computer storage medium may include a computer program mechanism embedded
in the computer storage medium for causing a computer to interface decision
engine
118 and marketing engine 122. The computer code mechanism may include a
computer code device configured to interface with decision engine 118. The
computer code mechanism may also include another computer code device
configured to interface with marketing engine 122. The computer code mechanism
may further include yet another computer code device configured to facilitate
data
sharing between decision engine 118 and marketing engine 122.
[0102] FIGS. 11 and 12 illustrate marketing profile data entry page
1100 and
marketing profile data entry page 1200, respectively, that may be used with
system
100, particularly, with marketing engine 122. In web-based embodiments, data
entry
pages 1100, 1200 may be web page forms provided by marketing engine 122 to the
client computing devices of second users 106 via network 110. Second users 106
may be any entity or party involved in marketing or advertising, such as, but
not
limited to, advertising agencies, advertisers and specific product entities.
[0103] Marketing engine 122 may effectuate electronic advertising by
providing users 106 with marketing profiles to establish criteria for
conducting
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 30 -
electronic advertising campaigns. Accordingly, marketing engine 122 may
present
one or more user-fillable marketing profile forms to users 106 for data entry
of
inputs defining criteria for an electronic advertising campaign. The marketing
profiles and associated forms may be provided in numerous configurations and
designs, as will be readily recognized by one having ordinary skill in the
art. As
such, marketing profiles may make use of numerous data entry fields and GUI
widgets in providing fillable forms to users 106.
[0104] Referring particularly to FIG. 11, web page form 1100 for
establishing criteria for conducting an electronic advertising campaign is
illustrated
in accordance with at least one exemplary embodiment. Marketing profile form
1100 may include a plurality of data entry fields for accepting user inputs
corresponding to required or optional campaign information and criteria. Form
1100
may also provide directions that guide users 106 to fill out form 1100 by the
way of
data inputs for establishing or modifying an electronic advertising campaign.
[0105] Form 1100 may be directed to a running auction-style of
electronic
advertising campaigns, which may operate, at least in part, based on a bid-per-
click
system. Accordingly, the electronic advertising campaign may be priced based
on a
pay-per-click system or may be based and priced on any other system known to
one
having ordinary skill in the art. The running auction-style of electronic
advertising
campaigns and the pay-per-click pricing systems are well-known to one having
ordinary skill in the art and further discussion directed thereto will be
limited or
omitted herein.
[0106] Form 1100 may include numerous sections of which the following
are exemplary of. Form 1100 may include a title/heading section 1102. As shown
in
an illustrative and non-limiting manner, form 1100 may be entitled
"Advertising
Campaign Dates, Clicks and Dollars." Section 1104 of form 1100 may provide for
data entry regarding account identifiers, such as, but not limited to, account
name
and account number. In at least one embodiment, account identifiers may be
automatically provided by marketing engine 122 to user 106 who may be logged
into their user account. Alternatively, user 106 may input data for
identifying an
account.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
-31 -
[0107] Form 1100 may include present date and campaign start date
section
1106. The present date may be automatically provided to user 106 by marketing
engine 122 and the campaign start date may be selected by user 106, as one non-
limiting example. Form 1100 may also include section 1108 establishing
campaign
termination criteria. User 106 may input data establishing such termination
criteria.
As shown in an illustrative and non-limiting manner, termination criteria may
be
conditioned in the alternative with the first occurrence of any of the
termination
criteria resulting in the termination of the electronic advertising campaign.
For
instance, the alternative termination criteria may be a termination date, an
amount of
click-through or a campaign budget ceiling.
[0108] Form 1100 may further include bid-per-click section 1110. As
shown
in an illustrative and non-limiting manner, bid-per-click section 1110 may be
used to
establish a present bid-per-click and a maximum bid-per-click. User 106 may
input
such data and later be subjected to a running auction conducted by marketing
engine
122 based on bid-per-click data.
[0109] Moreover, form 1100 may include product module section 1112. In
at
least one embodiment, decision engine 118 may provide and be applied to
different
products separately through a plurality of product modules focused on
comparing
certain types and/or classes of products. User 106 may only advertise in a
product
module(s) that user 106 deems most relevant to the goods and/or services user
106 is
marketing. Accordingly, user 106 may select which product module(s) to apply
the
electronic advertising campaign to by inputting such into product module
section
1112. In at least one embodiment, user 106 may select more than one product
module to effectuate the electronic advertising campaign within.
[0110] Furthermore, form 1100 may include keyword section 1114. In at
least one embodiment, decision engine 118 may provide for keyword-based
searching and comparisons alone or in combination with factor-based
comparisons.
For example, keyword-based searching may be provided by decision-engine 118 in
order to allow first user 104 to identify one or more relevant product modules
in
which to perform a factor-based comparison. Second user 106 may select one or
more keywords to apply an electronic advertising campaign to.
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 32 -
[0111] Referring particularly to FIG. 12, web page form 1200 for
establishing criteria for conducting an electronic advertising campaign is
illustrated
in accordance with at least one exemplary embodiment. Form 1200 may function
as
a continuation or counterpart of form 1200 in establishing campaign criteria.
In
particular, form 1200 may be directed to establishing factor-based and results-
based
criteria for the electronic advertising campaign. Form 1200 may include a
plurality
of data entry fields for accepting user inputs corresponding to required or
optional
campaign information and criteria. Form 1200 may also provide directions that
guide users 106 to fill out form 1200 by the way of data inputs for
establishing or
modifying an electronic advertising campaign.
[0112] Factor-based and result-based form 1200 may include numerous
sections of which the following are exemplary of. For example, form 1200 may
include sections 1202, 1204, 1206, 1208, 1210, 1212 directed to establishing
criteria
based on factors of interest, importance ratings for the factors, weights
(e.g., by
percentage) for the factors, weights for a first group of factors, weights for
a second
group of factors, rank of vendor's product in results list, respectively, and
any other
suitable sections for establishing criteria, as will be readily recognized to
one having
ordinary skill in the art.
[0113] Factors of interest section 1202 may include one or more
factors of
interest 1214 subject to criteria being established in relation thereto. In at
least one
embodiment, factors of interest 1214 may be provided in section 1202 by
marketing
engine 122. Users 106 may establish criteria in relation to factors of
interest 1214
automatically provided by marketing engine 122.
[0114] Importance rating section 1204 may be provided to user 106 by
marketing engine 122 for inputting a minimum level of importance for any
factor
that would trigger the electronic advertising campaign. For example, by
placing an
importance rating value in an appropriate data entry field 1216 for a factor,
user 106
may set the importance rating floor for that factor.
[0115] Individual factor weight section 1206 may be provided to user
106 by
marketing engine 122 for inputting a percentage of the overall decision for
any
factor that would trigger the electronic advertising campaign. For example, by
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 33 -
placing a weight value in an appropriate data entry field 1218 for a factor,
user 106
may set a percentage floor for that factor.
[0116] Either or both of first group weight section 1208 and second
group
weight section 1210 may be provided to user 106 by marketing engine 122 for
inputting a percentage of the overall decision for any group of factors that
would
trigger the electronic advertising campaign. For example, by selecting more
than one
factor by placing indicia in the appropriate data entry fields 1224, 1226 and
setting a
percentage floor by placing a weight value in the appropriate date entry field
1220,
1222, user 106 may set a cumulative percentage floor for the group of selected
factors.
[0117] Product rank section 1212 may be provided to user 106 by
marketing
engine 122 for inputting a position for their product within a results list
that would
trigger the electronic advertising campaign. For example, by placing a rank
value in
data entry field 1228, user 106 may set a rank floor for triggering the
electronic
advertising campaign.
[0118] FIG. 13 is a flow chart of exemplary method 1300 of providing
vendor-related data to a client computing device in response to decision-
related.
Method 1300 may be performed by marketing engine 122 in accordance with the
vendor marketing profiles. In method step 1302, marketing engine 122 may
receive
decision-related data processed by decision engine 118. The decision-related
data
available from decision engine 118 may include, but is not limited to, factors
(including required and optional factors), factor importance ratings, factor
weights,
raw data (e.g., numerical, descriptive, perspective, subjective and
objective), factor
definitions, normalized factor definitions, weighted factor definitions,
factor scores,
product results, product ranks, product scores and the like.
[0119] In method step 1304, marketing engine 122 may process the
decision-related data in accordance with the vendor marketing profiles. For
example,
marketing engine 122 may match decision-related data to the vendor marketing
profiles in determining what, if any, vendor-related data should be sent to a
client
computing device of user 104.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 34 -
[0120] In accordance with the vendor marketing profiles for each
product
module, marketing engine 122 may process (and match) the presence or absence
of
factors, the presence or absence of groups of factors, the presence or absence
of
product results, the presence or absence of raw data, the presence or absence
of
thresholds for factor importance ratings, the presence or absence of
thresholds for
factor weights, the presence or absence of thresholds for group factor
weights, the
presence or absence of thresholds for product ranks, the presence or absence
of
thresholds for product scores, the bounds of factor definitions, the bounds of
normalized factor definitions, the bounds of weighted factor definitions and
the like.
[0121] Marketing engine 122 may also process the vendor marketing
profile
data to determine the status (e.g., active or inactive) of electronic
advertising
campaigns, bid-per-click data (e.g., present or maximum) and the like.
Similarly,
marketing engine 122 can process or provide sales-related data, facilitate
sales and
provide any other data relevant to sales or which may be desired to facilitate
sales,
such as advertisements or coupons. Marketing engine 122 may also apply rules
governing the running auction in determining which vendor-related data is to
be
transmitted and in what order of priority it will be displayed on a client
computing
device of user 104.
[0122] In method step 1306, marketing engine 122 may transmit the
vendor-
related data, as determined, to a client computing device of user 104 via
network
110. The vendor-related data may include any of vendor advertisements, vendor
locations, vendor ratings, vendor websites links, vendor coupons, text, audio,
video,
images, database query answers, any combination thereof and like data known to
one having ordinary skill in the art. In some embodiments, the vendor-related
data
may be primarily in the form of electronic advertisements providing hypertext
links
to the websites of users 106 or any other supply or information source, but is
not so
limited. Moreover, electronic advertisements and any other suitable vendor-
related
data may appear on any GUI screen (e.g., results pages, navigation windows,
etc.).
Furthermore, the electronic advertisements may be provided anywhere (above,
below, to the side of, etc.) in relation to a list of product results or any
other
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 35 -
decision-related data on a web page displayed on a client computing device of
user
104.
[0123] In still another exemplary embodiment, and referring to FIG.
15,
examples of decision factors and scoring may be shown. In this example, a
number
of factors may be used in a decision making process. Here, through interface
1500, a
number of exemplary factors 1502 may be shown. Additionally, a bar graph 1504
that may correlate to a percentage of overall decision, or weight, 1506 may be
displayed. Finally, a total value 1508 may be represented at a bottom portion
of the
interface 1500. The factors 1502 and their manner of being displayed in
interface
1500 may act as both numerical and graphical representations of a user's
importance
settings when he or she is engaged in a decision making process.
[0124] In the example shown with FIG. 15, a fingerprint, or set of
factors,
such as decision-related factors, that pertain to a desired user, may be
determined,
realized or utilized. The fingerprint may be used to compare options from
within a
predetermined or specific set of decision possibilities or outcomes. For
example,
because each user of a decision making engine, such as those described in
previous
examples, may be unique insofar as they may each generate a different mix of
factor
weights, these uniqueness of each user may result in different rankings of the
available options. Thus, through a comparison of the different factors 1502
and
weights 1506 of each user's decision making, various dynamics of the decision
may
be further understood and explored. Thus, any factors and weighting chosen by
a
user may be stored and compared in any desired manner. For example, the
factors
and weighting of any number of different users may be compared. Similarly, the
factors and weighting of any number of users may be compared with the factors
and
weighting of one or more experts in a desired field. Additionally, a
fingerprint of
any user may be saved automatically or may be saved as desired at any time.
Any
fingerprint data may then be stored in a database or multiple databases for
any
desired form of comparison.
[0125] As shown in the example with respect to FIG. 15, a user may
base a
purchasing decision on five factors 1502, for example price, gas mileage,
reliability,
navigational system and exterior appearance. The user may then apply a weight
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 36 -
1506 to each factor and the weight may be shown both in numerical form 1506 as
well as graphical form 1504. Additionally, although a bar graph is shown in
exemplary FIG. 15, any form of graph or any other representation of weight
1506
may be utilized. In the present example, a user has given a weight of 26.1% to
the
factor of price, 21.7% to the factor of gas mileage and equal weights of 17.4%
to the
factors of reliability, navigational system and exterior appearance. The total
1508 is
thus shown as 100%. This information, when viewed together, may be saved or
presented as a fingerprint of that particular user for the exemplary
purchasing
decision shown in FIG. 15.
[0126] Fingerprint data, or saved user data regarding a purchasing
decision,
may then be used in any of a variety of manners. For example, different users
may
view fingerprint data, perhaps anonymously, of other users who may have
engaged
in a similar purchasing decision. Also, different exemplary fingerprints may
be
shown to provide a user with the various options and alternatives that may be
displayed as the weighted factors are varied. Further, a database of
fingerprint data
from any number of users of a decision making engine may be collected and used
in
any further manner desired. In one exemplary, various demographics may be
viewed
and polled for their desired weighted factors in a purchasing decision.
[0127] One exemplary manner of viewing demographic data, as shown in
exemplary FIG. 16, could be querying a database to determine a list of average
weighted factors for purchasing an automobile for any desired characteristics,
such
as asking what predetermined sex of users of the decision making engine, who
live
in a predetermined geographical area, have a predetermined income and have any
other predetermined characteristics. As shown on interface 1600, the factors
1602 of
such users may be shown to apply weights 1606 (and as shown in graphs 1604)
may
represent the combined or averaged fingerprint data for any number of users
fulfilling any desired criteria.
[0128] In another exemplary embodiment, and as shown in exemplary FIG.
1700, a user may be presented with or a decision making engine may otherwise
employ a default set of fingerprint data or default set of weighted
characteristics. In
these exemplary embodiments, any default data may be based on a predetermined
or
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
-37 -
historical interpretation of data. Additionally, any number of factors may be
utilized
in the default data, for example the use of ten factors and their respective
default
weights. Further, the amount of default fingerprint data may be changed or
varied in
any desired manner. For example, if a user is presented with a list of default
data,
which could correspond to the data shown in interface 1700, the user may be
able to
add or remove any number of factors so as to refine the list of default
fingerprint
data to a greater or smaller number of factors. This can assist a user who may
or may
not know a predetermined amount to weight a factor in a decision or purchase
they
may be planning to make. In the example shown in FIG. 17, the user may decide
that a factor such as exterior appearance in the list of default factors 1702
is not
important to that user. This factor may then be removed, along with its
corresponding weight 1706. Thus, a user may be presented with a new or varied
list
of choices as based on the altered factors. Similarly, any new factor not
shown on
the list of factors 1702 may be added as desired.
[0129] In another exemplary embodiment, and as shown with respect to
FIGS. 18 and 19, after fingerprint or any other data is saved for a user, the
user may
input any desired product data or datasets into a decision engine. The
decision
engine may then provide the user with a score based upon the user's desired
factors
1802 and weights 1806 (or importance settings). In the examples of FIGS. 18
and
19, an automobile purchase decision may be contemplated by the user. The
factors
1802 and weights 1806 may then be inputted per the user's fingerprint data and
corresponding graphs 1804 may be shown. A decision engine may then process
this
data and, based upon any available automobile, provide the user with scores
and a
best option.
[0130] As shown in exemplary interface 1900 of FIG. 19, a series of
scores
and a best choice may be shown. Based upon the information shown amongst the
fingerprint data of exemplary FIG. 18, two or more choices, in this case
automobiles, may be returned to the user with their respective scores. Similar
to the
comparison methodology discussed previously, a first automobile may show
scores
1902 based upon an equal weighting of all available factors. The first
automobile
may then show scores 1904 based upon the weighted factors from the user's
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 38 -
fingerprint. Similarly, weighted and unweighted scores 1906 and 1908,
respectively,
may be shown for a second automobile choice. These scores may then be totaled
in a
bottom portion 1910 of interface 1900. At a top portion of interface 1900, the
two
weighted scores using the user's fingerprint of data may then be shown 1912.
The
automobile, or any other product or decision choice otherwise, with the
highest
score with respect to the fingerprint data weighted values of the user may
then be
highlighted in any desired manner, for example by displaying the word "WINNER"
proximate to the highest score.
[0131] In a further example of the use of fingerprint data, as shown
in
exemplary FIGS. 20 and 21, different factors 2002 and weights 2006 (as further
shown with graph 2004) may be saved or otherwise known as the fingerprint data
of
a user. The decision making engine may then process the data and provide the
user
with the output scores 2102 through 2108, the totals 2110 and the display
scores
2112 at a top portion of interface 2100 so as to clearly show the product
choice with
the highest score.
[0132] In still another exemplary embodiment, any of the fingerprint
data
that may be stored for a user in the examples related to FIGS. 16-21 may be
utilized
on a mobile device, such as a mobile computing device. In this example, a user
may
be at a location, such as a store, that sells products that a user may be
interested in
purchasing. There may, however, be a variety of products from which the user
may
desire to choose. Thus, a user may input data regarding the physical product
in any
desired manner into their mobile device. Exemplary manners of inputting data
may
include typing a name, typing a serial number, photographing a product,
photographing a serial number, scanning a barcode, photographing a barcode or
any
other manner of inputting data desired. The user's mobile device may then be
connected with a decision making engine similar to any of the exemplary
embodiments described herein. Additionally, the mobile device may be connected
to
the decision making engine in any desired fashion. Using fingerprint or any
other
user-related input data, the decision making engine may process the data
inputted by
the user into the mobile device. The processing of the data may allow for the
user to
receive a number of scores, for example weighted scores as described herein,
on
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 39 -
their mobile device. The user may then be able to purchase an item based upon
the
scores provided to them.
[0133] FIG. 22 is a block diagram of a communications network 2200 for
implementing an embodiment of the present disclosure. The communications
network 2200 includes a collection of communication links interconnecting a
plurality of nodes, such as communication units 2215a-b, access points 2220a-
b,
intermediate nodes 2230a-n, connector engine 2205, marketing database/engine
2235, and decision engine 2210. These interconnected nodes communicate with
each other by exchanging data packets according to a pre-defined set of
network
protocols, such as the Transmission Control Protocol/Internet Protocol
(TCP/IP) and
the Session Initiation Protocol (SIP). A network protocol as used herein is a
formal
set of rules that define how data is exchanged between nodes in a
communications
network.
[0134] As described in more detail herein, the connector engine 2205
may be
employed to improve, customize, or otherwise modify a marketing campaign by
connecting the decision engine 2210 to the marketing database/engine 2235 to
provide a user of communication devices 2215a-b with data associated with a
marketing campaign (e.g., vendor-related data). Before describing the
connector
engine 2205 in more detail, a description of the communications network 2200
is
provided. It should be understood that the connector engine 2205 may be
employed
in other network topologies or applications, such as single processor
machines.
[0135] Communication units 2215a-b may be conventional communication
units, such as laptop computers, desktop computers, wireless transmit/receive
units
(WTRUs) (e.g., wireless telephones, personal digital assistants (PDAs), and
smart
phones), IP telephones, and the like, that convert information (e.g., data)
into signals
that are transmitted to the access points 2220a-b via wireless links.
[0136] The access point 2220a contains logic that enable the
communication
units 2215a-b to transmit the information (e.g., data) to the decision engine
2210 via
the network 2250. For example, the access point 2220a may include circuitry
configured to receive signals (e.g., radio frequency (RF) signals), from the
communication units 2215a-b, that carry the information via wireless links.
Once
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 40 -
the signals are received, the access point 2220a converts the signals into
data packets
according to the pre-defined set of network protocols. The access point 2220a
then
transmits the data packets to the network 2250 via gateway 2222a. The access
point
2220b transmit data/information is a similar manner as the access point 2220b.
In
addition, the access points 2220a-b convert data packets received from the
network
2250 into signals and transmit the signals to the communication units 2215a-b
and
the decision engine 2210 via wireless links.
[0137] Examples of access points 2220a-b that may be used with the
present
invention include certain Institute of Electrical and Electronic Engineers
(IEEE)
802.11 compliant access points as well as certain cellular telephone wireless
systems
that support the transmission of traffic (e.g., data traffic). Other forms of
access
points now known or hereafter developed are contemplated to be operable with
embodiments of the present disclosure.
[0138] The network 2250 receives the data packets from the gateways
2222a-b and transmits the data packets to a destination via the intermediate
nodes
2230a-n. The destination can be defined by a destination address. In an
example,
the destination address is located in a header of the data packets. The
intermediate
nodes 2230a-n are typically conventional intermediate nodes, such as routers,
that
are configured to implement a communications protocol such as an Internet
Protocol
(IP) or Voice Over Internet Protocol (VoIP) network 2250.
[0139] It should be noted that embodiments of the present disclosure
may be
adopted to work with fixed as well as mobile devices that are able to
communicate
with a communication network. Examples of fixed devices include telephone
units,
personal computers, and the like that are wired to a network.
[0140] As illustrated, the connector engine 2205 may be located at an
intermediate point of the network 2250 between the communication units 2215a-b
and decision engine 2210. Optionally, the connector engine 2205 may be
logically
or physically coupled directly to the communication units 2215a-b and/or
decision
engine 2210.
[0141] The connector engine 2205 employs an interface (as illustrated
in
FIG. 23) which enables vendors (associated with marketing engines 2235) to
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 41 -
provide marketing data (e.g., vendor-related data such as advertisements,
coupons,
and discounts) to users of the communication units 2215a-b in communication
with
the decision engine 2210. In some examples and as described above in reference
to
FIGS. 6-10, the users of the communication units 2215a-b access the decision
engine 2210 in order to compare decision options, for example,
products/services
over the Internet. For instance, the user may wish to purchase a camera (i.e.,
a
product) and may utilize the decision engine 2210 to assist with identifying
cameras
and ultimately deciding which specific type of camera to purchase, such as a
Canon camera or a Nikon camera (i.e., decision options). In such a scenario,
vendors, whose products are not associated with the decision engine 2210, may
wish
to provide marketing data to the users in response to at least one of the
following:
the user interaction with the decision engine 2210 or results of the decision
engine
2210.
[0142] For instance, the user may utilize the decision engine 2210 to
make a
decision to purchase a camera by utilizing data entry interface page 200 (as
illustrated in FIG. 3). In this example, the decision engine provides the user
with
factors on the page 200 that users generally consider when purchasing a camera
(e.g., price, lens type, and zoom capabilities). Based on a user's input with
respect
to each of the factors 232 (see FIG. 3) (e.g., input to the data fields 234,
236 (see
FIG. 3) and application of importance values 245 (see FIG. 3) to each factor),
the
decision engine 2210 provides a selection of cameras (e.g., decision options)
to the
user to consider in making a decision to purchase a camera (see FIG. 8-9).
[0143] In some scenarios, the decision engine 2210 may not present a
camera associated with a vendor as a decision option (i.e., a result of the
decision
engine). In this case, the vendor may wish to provide the user with marketing
data
associated with a camera sold by the vendor. In addition, the vendor may wish
provide the user with marketing data associated with a camera that best fits
the
user's input with respect to the factors 232 (see FIG. 3) (i.e., the user
interaction
with the decision engine 2210).
[0144] In an embodiment of the present disclosure, the connector
engine
2205 monitors the network 2250 for communications between a user of one of the
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 42 -
communication devices 2215a-b and decision engine 2210 over network 2250. The
connector engine 2205 may monitor SIP messages (or other type of messages)
that
indicate whether the user is attempting initiate a communications session with
a
server known to employ a decision engine 2210. For instance, the connector
engine
2205 may compare destination addresses associated with the messages to a
lookup
table that includes servers known to be hosting decision engines. If the
destination
address matches an address associated with such a server, the connector engine
2205
determines that the user is initiating a communication session between the
user and a
decision engine.
[0145] Once the connector engine 2205 determines that a communication
session is occurring between a user and a decision engine, the connector
engine
monitors and retrieves communication data between the user's communication
device 2215a-b and the decision engine 2210. As stated above, a user may be
utilizing the decision engine 2210 because the user wants the decision engine
to
provide decision options associated with the user's desire to purchase a
good/service
(e.g., a camera). The decision engine 2210, as stated above, may provide a
user
interface (as illustrated in FIGS. 3-9) for the user by which the user may
interact
with in order to facilitate the decision engine 2210 to provide decision
options to the
user (e.g., a set of specific cameras for which the user to consider to
purchase). In
an example and as stated herein, the decision engine 2210 may provide a user
with a
plurality of relevant factors associated with a product/service of interest.
In this
example, the user may be interested in cameras. Thus, the decision engine may
provide a list of factors 232 (sec FIG. 3) (e.g., lens, price, weight, digital
information, and zoom) to the user as described above in reference to FIGS 3-
4. At
the end of the user's interaction with the decision engine 2210, the decision
engine
2210 outputs at least one decision option (e.g., a suggested camera to
purchase) to
the user.
[0146] The connector engine 2205 receives data associated with the
user's
interaction with the decision engine 2210. For example, the connector engine
2210
receives the user inputs with respect to each of the factors 232 (see FIG.
3)In
addition, the connector engine 2210 receives the importance values provided by
the
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 43 -
user with respect to each factor 232 (see FIG. 3) and the results output by
the
decision engine 2210 (e.g., decision options (see FIGS. 6-9).
[0147] In response to receiving the data associated with the user's
interaction
with the decision engine 2210, the connector engine 2210 retrieves marketing
data
(e.g., vendor-related data) from the marketing database/engine 2235. In an
example,
the connector engine 2210 may retrieve the marketing data by submitting a
request
to the marketing database/engine 2235 for marketing data that corresponds to
the
user's interaction with the decision engine 2210. In response to the request,
the
marketing database/engine 2235 returns relevant marketing data to the
connector
engine 2210. The marketing database/engine 2235 may be a central server
including
a plurality of marketing databases/engines corresponding to a plurality of
vendors.
Alternatively, the connector engine 2210 may be coupled to a plurality of
marketing
database/engines, each database/engine corresponding to a single vendor. In
this
example, the vendors may be a manufacturer of cameras such as Nikon, Canon,
and
Kodak . The vendors may also be retailers that sell said cameras such as Best
Buy,
Wal-Mart, and Sears. Alternatively, the vendors may be any entity that would
like
to provide marketing data in response to the user's desire to purchase a
camera (e.g.,
airlines and travel booking entities).
[0148] In order to determine which vendors offer products or services
that
are responsive to a user's desires, and, thus, the marketing databases/engines
from
which to retrieve marketing data (e.g., vendor-related data), the connector
engine
2205 matches at least one factor of the decision-related data with vendor
factors
associated with the marketing data. The connector engine 2205 then obtains the
marketing data associated with at least a subset of the matched vendor
factors. In
response to selecting the marketing data, the connector 2205 processes the
received
data associated with the user's interaction with the decision engine 2210
(e.g.,
modified decision-related data (factors)) and modifies the vendor factors
associated
with the selected marketing data. The connector engine 2205 then selects a
subset
of the marketing data (e.g., vendor-related data) based on the modified vendor
factors.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 44 -
[0149] FIG. 23 is a block diagram of an example embodiment of a
connector engine 2305 that includes an interface 2301, presenting module 2303,
selection module 2305, ranking module 2311, and retrieving module 2307.
[0150] The interface 2301, which is provided on one or more
processors, is
configured to receive data associated with a user's interaction via
communication
devices 2215a-b with the decision engine 2210. The interface 2301 is also
configured to pass the data to one of the aforementioned modules based on the
type
of data received. For example, the data received may be user inputs to the
decision
engine, modified decision-related data (e.g., factors as modified by the user
inputs),
and decision options (e.g., output of the decision engine 2210). In response
to
receiving the data, the interface 2301 passes the received data to one of the
aforementioned modules as described in more detail herein.
[0151] The interface 2301 provides the received data (e.g., user
inputs and
modified decision-related data) to the retrieving module 2307, which is also
provided on the one or more processors. The retrieving module 2307 is
configured
to retrieve marketing data (e.g., vendor-related data) from a marketing
database/engine based on one or more properties of the modified decision-
related
data (e.g., factors, values associated with the factors based on user inputs,
and
importance values assigned to the factors based on user inputs).
[0152] In an example, the retrieving module 2307 matches at least one
factor
232 (see FIGS. 3-4) to at least one vendor factor associated with vendor-
related data
(e.g., marketing data). A vendor associates vendor factors with the vendor-
related
data such that an engine (e.g., the connector engine 2305) can utilize the
vendor
factors as criteria for selecting the vendor-related data. The retrieving
module 2307
then obtains the marketing data (e.g., vendor-related data) associated with at
least a
subset of the matched vendor factors from a marketing database. The matched
vendor factors are the same as the factors used by the decision engine as
criteria for
selecting a decision option (e.g., a product/service of interest to a user).
However, in
this instance the vendor factors, as stated above, are criteria for selecting
the vendor-
related data rather than criteria for selecting a decision option.
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 45 -
[0153] The retrieving module may select a marketing database that
corresponds to a vendor based on whether or not goods and/or services provided
by
the vendor correspond to the factors used by the decision engine as criteria
in
selecting a decision option. The retrieving module 2307 then passes the
retrieved
vendor-related data, modified decision-related data, and the matched vendor
factors
to the selection module 2309.
[0154] The selection module 2309 is provided on the one or more
processors. The selection module 2309 is configured to process the modified
decision-related data to modify the matched vendor factors. The matched vendor
factors are used as criteria by the selection module 2309 for selecting
marketing
data. For instance, the selection module 2309 applies the user inputs to the
matched
vendor factors. The selection module 2309 then applies the user inputs to the
matched vendor factors. Once the selection module 2309 modifies the matched
vendor factors, the selection module 2309 selects a subset of the marketing
data
(e.g., vendor-related data) based on the matched vendor factors as modified by
the
user inputs.
[0155] The selection module 2309 includes a ranking module 2311 that
is
configured to rank the selected marketing data by applying weighted factor
scores
associated with the decision-related data to the matched vendor factors. Based
on
the ranks, the selection module 2309 selects a subset of the marketing data.
In
addition, the selection module 2309 may further refine the selection of the
subset of
the marketing data based on an advertisement subscription of a vendor
associated
with the marketing data. The selection module 2309 may then provide the
selected
subset of marketing data to the presenting module 2303.
[0156] The presenting module 2309 is configured to provide the
marketing
data along with the output of the decision engine in a format (e.g., size,
corresponding graphics, layout, etc) based on a type of communication device
the
user is utilizing to communicate over the network 2250. For instance, a
laptop/desktop computer may have more resources (e.g., larger displays,
memory,
processing power) for displaying the decision options and marketing data than,
for
example, a mobile phone. In an example, the presenting module 2309 determines
a
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 46 -
type of communication device the user is utilizing based on communication
device
information received from the interface 2301.
[0157] Such communication device information may include information
that indicates the resources available to a communications device. For
example, the
operating system or browser that the communication device is employing to
communicate over the network 2250 can provide an indication to the presenting
module 2309 regarding the resources available to the user for displaying the
marketing data along with the output of the decision engine (e.g., decision
options).
The presenting module 2303 may extract the information from communication
messages (provided by the interface 2301) sent by the user over the network
2250.
Alternatively, the presenting module may determine such information based on a
format of the decision options output by the decision engine. Based on such
communication device information, the presenting module 2309 provides the
vendor-related data and decision options in a format that is optimal for
displaying
such data on the user's communication device.
[0158] The interface 2301 then receives the format information from
the
presenting module 2303 and selected marketing data from the selection module
2309 and i) provides the selected marketing data (in a format determined by
the
presenting module 2303) to the user, ii) provides the marketing data to a
marketing
engine as a recommendation of marketing data to provide to the user, and/or
iii)
triggers the marketing engine to provide the selected marketing data to the
user. For
example, the interface 2301 may trigger the marketing engine to provide the
selected
marketing data.
[0159] FIGS. 24A-C are graphs illustrating decision logic utilized by
a
connector engine. As stated above, the connector engine receives modified
decision-related data from a decision engine (see FIG. 3). In an example, the
modified decision-related data may be factors 232 (see FIG. 3)used as criteria
in
selecting decision options by the decision engine. The factors 232 (see FIG.
3)may
be modified (i.e., modified decision-related data) with importance values 246
(see
FIG. 3)(e.g., weights) corresponding to each of the factors 232 (see FIG. 3).
As
illustrated, the factors 232 (see FIG. 3) are associated with a user's desire
to
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 47 -
purchase a product (e.g., a camera). The decision engine may use the factors
232
(see FIG. 3) to return at least one decision option (see FIG. 7-9) to a user.
. It should
be known that many other factors/combination of factors may also be utilized
by the
decision engine.
[0160] The connector engine then retrieves marketing data (e.g.,
vendor-
related data) 2410 from a marketing database/engine. In the example
illustrated in
FIG. 24A, the marketing database/engine stores a table 2400a of vendor factors
2426a and vendor-related data 2410a. For each of the vendor factors 2426, the
table
2400a includes un-weighted vendor factor scores 2434 corresponding to each of
the
vendor Options A-D 2410a (e.g., vendor-related data). The un-weighted factor
scores 2434 may be subjective scores assigned by each vendor based on a
marketing
campaign initiated by the vendor.
[0161] In order to retrieve the marketing data, the connector engine
matches
the factors 232 (see FIG. 3) with vendor factors 2426 associated with vendor-
related
data 2410a. Based on the results of the matching, the connector engine obtains
vendor-related data 2410b (see FIG. 24B). For instance, the factors 232 (see
FIG. 3)
may only match with a subset of the vendor factors 2426a. The connector engine
then retrieves, as illustrated in FIG. 24B, the matched vendor factors 2426b,
the
vendor-related data 2410b associated with at least a subset of the matched
vendor
factors 2426b, and the corresponding un-weighted vendor factor scores 2434b.
[0162] FIG. 24B illustrates an example table 2400b associated with
Nikon
Advertisements generated by the connector engine in response to retrieving the
aforementioned data and receiving the modified decision-related data (see FIG.
3).
In addition, the connector engine applies the user inputs (i.e., importance
values 246
(see FIG. 3)) to the matched vendor factors 2426b to generate weighted vendor
factors 2425. The weighted vendor factors 2425 have associated importance
values
2427. As illustrated, the vendor-related data 2410b and associated un-weighted
vendor factor scores 2434 are associated with a single vendor (e.g., Nikon).
It
should be noted that the connector engine may be performing a similar analysis
using a subset of the matched vendor factors 2426b with respect to at least
one other
vendor (e.g., Canon) of a plurality of vendors. In order to select vendor-
related data
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 48 -
best suited for the user among the plurality of vendors, the connector must
normalize
the un-weighted factor scores 2434b. The connector may utilize any known
method
of normalization.
[0163] Once the un-weighted vendor factor scores 2434b are normalized,
the
connector engine applies weights to each of the normalized un-weighted vendor
factor scores. In the current example, the connector engine multiplies the
importance values 2427 associated with the weighted vendor factors 2425 by the
normalized un-weighted factor scores to obtain weighted factor scores 2435 as
illustrated in FIG. 24C. In response to calculating the weighted scores 2435,
now
referring to FIG. 24C, the connector engine calculates a total score 2430
corresponding to each item of the vendor-related data 2410b. For instance, the
connector engine calculates the total score 2430 for each item, in this
illustrated
example, by summing the weighted factor scores for each item of the vendor-
related
data 2410b. Subsequently, the connector engine provides a ranking 2415 to each
item of the vendor-related data 2410 based the calculated total scores 2430.
In this
example, the connector engine assigns the item of the vendor-related data 2410
that
has the highest total score with the highest rank.
[0164] The connector engine may then select a subset of the vendor-
related
data 2410b based on respective rankings. In addition, referring to FIG. 24D,
the
connector engine may compare the total scores for all items of vendor-related
data
2410 for one vendor with respect the total scores for all items of vendor-
related data
with other vendors. Based on the comparison, the connector engine computes a
global ranking score 2416c for all vendor-related data associated with a
subset of the
matched vendor factors 2426b. For example, the table 2401a is associated with
the
vendor-related data 2411a corresponding to Vendor A. Using the total scores
2431a,
the connector engine calculates rankings 2416a with respect to the vendor-
related
data 2411a. In addition, the table 240 lb is associated with the vendor-
related data
2411b corresponding to Vendor B. Using the total scores 243 lb, the connector
engine calculates rankings 2416b with respect to the vendor-related data
2411a. In
one example, the connector engine may create a global ranking table 2402 by
comparing the total score 2431a from the table 2401a to the total score 243 lb
from
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
- 49 -
the table 240 lb. Based on the comparison, the connector engine creates a
global
rank 2416c of the vendor-related data 2411a-b.
[0165] In another embodiment, the connector engine may create a global
ranking table using information associated with an advertisement/marketing
subscription service subscribed to by each of the vendors. For example, the
connector engine may add a subscription based tier of ranking to the global
ranking
table 2402. For instance, the connector engine may select a threshold number
(e.g.,
top ten) of the highest ranked ads from the global ranking table 2402. The
connector
engine may then re-rank the selected ads based on an amount the vendor is
willing
to pay in response a user's interaction with the vendor's ads (e.g., pay-per-
click). In
another example, the connector may rank and select the ads associated with a
vendor
based on an amount the vendor is willing to pay in response a user's
interaction with
the vendor's ads (e.g., pay-per-click).
[0166] In an embodiment, a vendor may provide the connector engine
with
rules. The connector engine uses the rules to select a subset of the vendor-
related
data. In an example, a vendor may only wish the connector to select vendor-
related
data associated with the vendor of the data received a score above a certain
threshold
or a global ranking above a certain threshold.
[0167] FIG. 25 is a flow diagram of an example method 2500 for
providing
vendor-related data. At 2505, the method 2500 begins. At 2510, a connector
engine
(e.g., connector engine 2205 of FIG. 22) receives modified decision-related
data
from a decision engine (e.g., decision engine 2210 of FIG. 22). The received
data is
associated with a user's interaction with the decision engine during a
communications session between the user and the decision engine. The modified
decision-related data is generated by a decision engine by applying at least
one user
input to decision-related data. In particular, the decision-related data is at
least one
factor used as a criterion in selecting at least one decision option. The at
least one
decision option is an output of the decision engine in response to the
application of
the at least one user input to the decision-related data. In an example, the
at least
one decision option is product and/or service related data. In another
example, the
modified decision-related data includes at least one factor having a weighted
score,
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 50 -
where the weighted score of the at least one factor is determined by at least
an
importance assigned to the at least one factor by the user.
[0168] At 2515, the connector engine retrieves vendor-related data
from a
marketing database (e.g., marketing database 2235 of FIG. 22) based on one or
more
properties of the modified decision-related data. In particular, the connector
engine
matches the at least one factor to at least one vendor factor associated with
the
vendor-related data and obtains vendor-related data associated with at least a
subset
of the at least one vendor factor. The at least one vendor factor is a
criterion used in
selecting the vendor-related data. In an example, the connector engine
retrieves the
vendor-related data by selecting the marketing database corresponding to a
vendor
and retrieving the vendor-related data from the selected at least one
marketing
database. In another example, the connector engine may select the marketing
database corresponding to the vendor by selecting the vendor based on whether
or
not goods and/or services provided by the vendor correspond to the decision-
related
data.
[0169] The connector engine, at 2520, processes the modified decision-
related data to modify the at least one vendor factor. In addition, the
connector
engine may rank the vendor-related data by applying weighted scores associated
with the decision-related data to the vendor factors. At 2525, the connector
engine
selects a subset of the vendor-related data based on the modified at least one
vendor
factor. In an example, the connector engine may select the vendor-related data
by
selecting a subset of the ranked vendor-related data. In another example, the
connector engine may select the subset of ranked vendor-related data by
selecting
the subset based on an advertisement subscription of a vendor associated with
the
vendor-related data. At 2426, the connector engine determines if a user's
communication session with the decision engine has terminated or is about to
terminate. If the communication session is still active and the connector
engine may
repeat the aforementioned actions.
[0170] At 2530, if the connector engine determines that the
communication
session has terminated or is about to terminate, the connector engine may end
processing by outputting the selected subset of the vendor-related data based
on the
CA 02886794 2015-03-31
WO 2014/055395 PCT/US2013/062570
-51 -
modified at least one vendor factor. For instance, the connector engine may i)
providing the selected subset of vendor-related data to the user, ii) trigger
a
marketing engine associated with the marketing database to provide the
selected
subset of vendor-related data to the user, and/or iii) provide the selected
subset of
vendor-related data to a marketing engine associated with the marketing
database as
a recommendation of vendor-related data to provide to the user, where the
marketing
engine utilizes the recommendation to provide vendor-related data to the user.
[0171] In another embodiment, the connector engine may also add an
additional tier of ranking that includes factors provided by the connector
engine
itself. In particular, the connector engine may apply a subscription service
factor
(e.g., an amount the vendor is willing to pay-per-click). For example, the
connector
engine may add rank each item of the vendor-related data based on a
subscription
service elected by the vendor for the specific items (e.g., an amount the
vendor is
willing to pay-per-click).
[0172] Further example embodiments of the present disclosure may be
configured using a computer program product; for example, controls may be
programmed in software for implementing example embodiments of the present
disclosure. Further example embodiments of the present disclosure may include
a
non-transitory computer readable medium containing instruction that may be
executed by a processor, and, when executed, cause the processor to complete
methods described herein. It should be understood that elements of the block
and
flow diagrams described herein may be implemented in software, hardware,
firmware, or other similar implementation determined in the future. In
addition, the
elements of the block and flow diagrams described herein may be combined or
divided in any manner in software, hardware, or firmware. If implemented in
software, the software may be written in any language that can support the
example
embodiments disclosed herein. The software may be stored in any form of
computer
readable medium, such as random access memory (RAM), read only memory
(ROM), compact disk read only memory (CD-ROM), and so forth. In operation, a
general purpose or application specific processor loads and executes software
in a
manner well understood in the art. It should be understood further that the
block and
CA 02886794 2015-03-31
WO 2014/055395
PCT/US2013/062570
- 52 -
flow diagrams may include more or fewer elements, be arranged or oriented
differently, or be represented differently. It should be understood that
implementation may dictate the block, flow, and/or network diagrams and the
number of block and flow diagrams illustrating the execution of embodiments of
the
disclosure.
[0173] The foregoing description and accompanying figures illustrate
the
principles, preferred embodiments and modes of operation of the invention.
However, the invention should not be construed as being limited to the
particular
embodiments discussed above. Additional variations of the embodiments
discussed
above will be appreciated by those skilled in the art.
[0174] Therefore, the above-described embodiments should be regarded
as
illustrative rather than restrictive. Accordingly, it should be appreciated
that
variations to those embodiments can be made by those skilled in the art
without
departing from the scope of the invention as defined by the following claims.
