Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEM AND METHOD FOR ANALYZING AND TRANSMITTING SOCIAL
COMMUNICATION DATA
CROSS-REFERENCE TO RELATED APPLICATIONS:
[0001] This application claims priority to United States Provisional Patent
Application No.
61/880,027 filed on September 19, 2013, and titled "System and Method for
Continuous
Social Communication", the entire contents of which is incorporated herein by
reference.
TECHNICAL FIELD
[0002] The following generally relates to communication of social data
and particularly,
transmitting social communication data based upon feedback of earlier
communications.
BACKGROUND
[0003] In recent years social media has become a popular way for
individuals and
consumers to interact online (e.g. on the Internet). Social media also affects
the way
businesses aim to interact with their customers, fans, and potential customers
online.
[0004] Typically a person or persons create social media by writing
messages (e.g.
articles, online posts, blogs, comments, etc.), creating a video, or creating
an audio track.
This process can be difficult and time consuming.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] Embodiments will now be described by way of example only with
reference to
the appended drawings wherein:
[0006] FIG. 1 is a block diagram of a social communication system
interacting with the
Internet or a cloud computing environment, or both.
[00071 FIG. 2 is a block diagram of an example embodiment of a computing
system for
social communication, including example components of the computing system.
[0008] FIG. 3 is a block diagram of an example embodiment of multiple
computing
devices interacting with each other over a network to form the social
communication system.
[0009] FIG. 4 is a schematic diagram showing the interaction and flow of
data between
an active receiver module, an active composer module, an active transmitter
module and a
social analytic synthesizer module.
[0010] FIG. 5 is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for composing new social data and
transmitting the
same.
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[0011] FIG. 6 is a block diagram of an active receiver module showing
example
components thereof.
[0012] FIG. 7 is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for receiving social data.
[0013] FIG. 8 is a block diagram of an active composer module showing
example
components thereof.
[0014] FIG. 9A is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for composing new social data.
[0015] FIG. 9B is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for combining social data according to an
operation
described in FIG. 9A.
[0016] FIG. 9C is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for extracting social data according to an
operation
described in FIG. 9A.
[0017] FIG. 9D is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for creating social data according to an
operation
described in FIG. 9A.
[0018] FIG. 10 is a block diagram of an active transmitter module showing
example
components thereof.
[0019] FIG. 10A is a block diagram of an active transmitter module showing
example
components thereof in accordance with yet another embodiment.
[0020] FIG. 10B is a block diagram of example communication of a composed
social
media data with embedded trackers
[0021] FIG. 10C is a block diagram of exemplary components of a tracker
for use in
embedding in social media data messages.
[0022] FIG. 11 is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for transmitting the new social data.
[0023] FIG. 12 is a block diagram of a social analytic synthesizer module
showing
example components thereof.
[0024] FIG. 13 is a flow diagram of an example embodiment of computer
executable or
processor implemented instructions for determining adjustments to be made for
any of the
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processes implemented by the active receiver module, the active composer
module, and the
active transmitter module.
[0025] FIG. 14 is a flow diagram showing an example for determining an
inflection point.
DETAILED DESCRIPTION OF THE DRAWINGS
[0026] It will be appreciated that for simplicity and clarity of
illustration, where considered
appropriate, reference numerals may be repeated among the figures to indicate
corresponding or analogous elements. In addition, numerous specific details
are set forth in
order to provide a thorough understanding of the example embodiments described
herein.
However, it will be understood by those of ordinary skill in the art that the
example
embodiments described herein may be practiced without these specific details.
In other
instances, well-known methods, procedures and components have not been
described in
detail so as not to obscure the example embodiments described herein. Also,
the
description is not to be considered as limiting the scope of the example
embodiments
described herein.
[0027] Social data herein refers to content able to be viewed or heard, or
both, by
people over a data communication network, such as the Internet. Social data
includes, for
example, text, video, picture, photographs, graphics, and audio data, or
combinations
thereof. Examples of text include blogs, emails, messages, posts, articles,
comments, etc.
For example, text can appear on websites such as Facebook, Twitter, LinkedIn,
Pinterest,
other social networking websites, magazine websites, newspaper websites,
company
websites, blogs, etc. Text may also be in the form of comments on websites,
text provided
in an RSS feed, etc. Examples of video can appear on Facebook, YouTube, news
websites,
personal websites, blogs (also called vlogs), company websites, etc. Graphical
data, such
as pictures, can also be provided through the above mentioned outlets. Audio
data can be
provided through various websites, such as those mentioned above, audio-casts,
"Pod
casts", online radio stations, etc. It is appreciated that social data can
vary in form.
[0028] A social data object herein refers to a unit of social data, such
as a text article, a
video, a comment, a message, an audio track, a graphic, or a mixed-media
social piece that
includes different types of data. A stream of social data includes multiple
social data objects.
For example, in a string of comments from people, each comment is a social
data object. In
another example, in a group of text articles, each article is a social data
object. In another
example, in a group of videos, each video file is a social data object. Social
data includes at
least one social data object.
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[0029] It is recognized that effective social communication, from a
business perspective,
is a significant challenge. The expansive reach of digital social sites, such
as Twitter,
Facebook, YouTube, etc., the real time nature of communication, the different
languages
used, and the different communication modes (e.g. text, audio, video, etc.)
make it
challenging for businesses to effectively listen to and communicate with their
customers.
The increasing number of websites, channels, and communication modes can
overwhelm
businesses with too much real time data and little appropriate and relevant
information. It is
also recognized that people in decision making roles in business are often
left wondering
who is saying what, what communication channels are being used, and which
people are
important to listen to.
[0030] It is recognized that typically a person or persons generate
social data. For
example, a person generates social data by writing a message, an article, a
comment, etc.,
or by generating other social data (e.g. pictures, video, and audio data).
This generation
process, although sometimes partially aided by a computer, is time consuming
and uses
effort by the person or persons. For example, a person typically types in a
text message,
and inputs a number of computing commands to attach a graphic or a video, or
both. After a
person creates the social data, the person will need to distribute the social
data to a website,
a social network, or another communication channel. This is also a time
consuming process
that requires input from a person.
[0031] It is also recognized that when a person generates social data,
before the social
data is distributed, the person does not have a way to estimate how well the
social data will
be received by other people. After the social data has been distributed, a
person may also
not have a way to evaluate how well the content has been received by other
people.
Furthermore, many software and computing technologies require a person to view
a website
or view a report to interpret feedback from other people.
[0032] It is also recognized that generating social data that is
interesting to people, and
identifying which people would find the social data interesting is a difficult
process for a
person, and much more so for a computing device. Computing technologies
typically require
input from a person to identify topics of interest, as well as identify people
who may be
interested in a topic. It also recognized that generating large amounts of
social data
covering many different topics is a difficult and time-consuming process.
Furthermore, it is
difficult achieve such a task on a large data scale within a short time frame.
[0033] The proposed systems and methods described herein address one or
more of
these above issues. The proposed systems and methods use one or more computing
devices to receive social data, identify relationships between the social
data, compose new
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social data based on the identified relationships and the received social
data, and transmit
the new social data. In a preferred example embodiment, these systems and
methods are
automated and require no input from a person for continuous operation. In
another example
embodiment, some input from a person is used to customize operation of these
systems and
methods.
[0034] The proposed systems and methods are able to obtain feedback
during this
process to improve computations related to any of the operations described
above. For
example, feedback is obtained about the newly composed social data, and this
feedback can
be used to adjust parameters related to where and when the newly composed
social data is
transmitted. This feedback is also used to adjust parameters used in composing
new social
data and to adjust parameters used in identifying relationships. Further
details and example
embodiments regarding the proposed systems and methods are described below.
[0035] The proposed systems and methods may be used for real time
listening,
analysis, content composition, and targeted broadcasting. The systems, for
example,
capture global data streams of data in real time. The stream data is analyzed
and used to
intelligently determine content composition and intelligently determine who,
what, when, and
how the composed messages are to be sent.
[0036] Turning to FIG. 1, the proposed system 102 includes an active
receiver module
103, an active composer module 104, an active transmitter module 105, and a
social analytic
synthesizer module 106. The system 102 is in communication with the Internet
or a cloud
computing environment, or both 101. The cloud computing environment may be
public or
may be private. In an example embodiment, these modules function together to
receive
social data, identify relationships between the social data, compose new
social data based
on the identified relationships and the received social data, and transmit the
new social data.
[0037] The active receiver module 103 receives social data from the
Internet or the
cloud computing environment, or both. The receiver module 103 is able to
simultaneously
receive social data from many data streams. The receiver module 103 also
analyses the
received social data to identify relationships amongst the social data. Units
of ideas, people,
location, groups, companies, words, number, or values are herein referred to
as concepts.
The active receiver module 103 identifies at least two concepts and identifies
a relationship
between the at least two concepts. For example, the active receiver module
identifies
relationships amongst originators of the social data, the consumers of the
social data, and
the content of the social data. The receiver module 103 outputs the identified
relationships.
[0038] The active composer module 104 uses the relationships and social
data to
compose new social data. For example, the composer module 104 modifies,
extracts,
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combines, or synthesizes social data, or combinations of these techniques, to
compose new
social data. The active composer module 104 outputs the newly composed social
data.
Composed social data refers to social data composed by the system 102.
[0039] The active transmitter module 105 determines appropriate
communication
channels and social networks over which to send the newly composed social
data. The
active transmitter module 105 is also configured receive feedback about the
newly
composed social data using trackers associated with the newly composed social
data.
[0040] The social analytic synthesizer module 106 obtains data, including
but not limited
to social data, from each of the other modules 103, 104, 105 and analyses the
data. The
social analytic synthesizer module 106 uses the analytic results to generate
adjustments for
one or more various operations related to any of the modules 103, 104, 105 and
106.
[0041] In an example embodiment, there are multiple instances of each
module. For
example, multiple active receiver modules 103 are located in different
geographic locations.
One active receiver module is located in North America, another active
receiver module is
located in South America, another active receiver module is located in Europe,
and another
active receiver module is located in Asia. Similarly, there may be multiple
active composer
modules, multiple active transmitter modules and multiple social analytic
synthesizer
modules. These modules will be able to communicate with each other and send
information
between each other. The multiple modules allows for distributed and parallel
processing of
data. Furthermore, the multiple modules positioned in each geographic region
may be able
to obtain social data that is specific to the geographic region and transmit
social data to
computing devices (e.g. computers, laptops, mobile devices, tablets, smart
phones,
wearable computers, etc.) belonging to users in the specific geographic
region. In an
example embodiment, social data in South America is obtained within that
region and is
used to compose social data that is transmitted to computing devices within
South America.
In another example embodiment, social data is obtained in Europe and is
obtained in South
America, and the social data from the two regions are combined and used to
compose social
data that is transmitted to computing devices in North America.
[0042] Turning to FIG. 2, an example embodiment of a system 102a is
shown. For ease
of understanding, the suffix "a" or "b", etc. is used to denote a different
embodiment of a
previously described element. The system 102a is a computing device or a
server system
and it includes a processor device 201, a communication device 202 and memory
203. The
communication device is configured to communicate over wired or wireless
networks, or
both. The active receiver module 103a, the active composer module 104a, the
active
transmitter module 105a, and the social analytic synthesizer module 106a are
implemented
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by software and reside within the same computing device or server system 102a.
In other
words, the modules may share computing resources, such as for processing,
communication
and memory.
[0043] Turning to FIG. 3, another example embodiment of a system 102b is
shown. The
system 102b includes different modules 103b, 104b, 105b, 106b that are
separate
computing devices or server systems configured to communicate with each other
over a
network 313. In particular, the active receiver module 103b includes a
processor device
301, a communication device 302, and memory 303. The active composer module
104b
includes a processor device 304, a communication device 305, and memory 306.
The active
transmitter module 105b includes a processor device 307, a communication
device 308, and
memory 309. The social analytic synthesizer module 106b comprises a processor
device
310, a communication device 311, and memory 312.
[0044] Although only a single active receiver module 103b, a single
active composer
module 104b, a single active transmitter module 105b and a single social
analytic
synthesizer module 106b are shown in FIG. 3, it can be appreciated that there
may be
multiple instances of each module 103b, 104b, 105b and/or 106b that are able
to
communicate with each other using the network 313. As described above with
respect to
FIG. 1, there may be multiple instances of each module and these modules may
be located
in different geographic locations.
[0045] It can be appreciated that there may be other example embodiments
for
implementing the computing structure of the system 102.
[0046] It is appreciated that currently known and future known
technologies for the
processor device, the communication device and the memory can be used with the
principles described herein. Currently known technologies for processors
include multi-core
processors. Currently known technologies for communication devices include
both wired
and wireless communication devices. Currently known technologies for memory
include disk
drives and solid state drives. Examples of the computing device or server
systems include
dedicated rack mounted servers, desktop computers, laptop computers, set top
boxes, and
integrated devices combining various features. A computing device or a server
uses, for
example, an operating system such as Windows Server, Mac OS, Unix, Linux,
FreeBSD,
Ubuntu, etc.
[0047] It will be appreciated that any module or component exemplified
herein that
executes instructions may include or otherwise have access to computer
readable media
such as storage media, computer storage media, or data storage devices
(removable and/or
non-removable) such as, for example, magnetic disks, optical disks, or tape.
Computer
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storage media may include volatile and non-volatile, removable and non-
removable media
implemented in any method or technology for storage of information, such as
computer
readable instructions, data structures, program modules, or other data.
Examples of
computer storage media include RAM, ROM, EEPROM, flash memory or other memory
technology, CD-ROM, digital versatile disks (DVD) or other optical storage,
magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic storage
devices, or any
other medium which can be used to store the desired information and which can
be
accessed by an application, module, or both. Any such computer storage media
may be
part of the system 102, or any or each of the modules 103, 104, 105, 106, or
accessible or
connectable thereto. Any application or module herein described may be
implemented using
computer readable/executable instructions that may be stored or otherwise held
by such
computer readable media.
[0048] Turning to FIG. 4, the interactions between the modules are shown.
The system
102 is configured to listen to data streams, compose automated and intelligent
messages,
launch automated content, and listen to what people are saying about the
launched content.
[0049] In particular, the active receiver module 103 receives social data
401 from one or
more data streams. The data streams can be received simultaneously and in real-
time. The
data streams may originate from various sources, such as Twitter, Facebook,
YouTube,
LinkedIn, Pintrest, blog websites, news websites, company websites, forums,
ASS feeds,
emails, social networking sites, etc. The active receiver module 103 analyzes
the social
data, determines or identifies relationships between the social data, and
outputs these
relationships 402.
[0050] In a particular example, the active receiver module 103 obtains
social data about
a particular car brand and social data about a particular sports team from
different social
media sources. The active receiver 103 uses analytics to determine there is a
relationship
between the car brand and the sports team. For example, the relationship may
be that
buyers or owners of the car brand are fans of the sports team. In another
example, the
relationship may be that there is a high correlation between people who view
advertisements
of the car brand and people who attend events of the sports team. The one or
more
relationships are outputted.
[0051] The active composer module 104 obtains these relationships 402 and
obtains
social data corresponding to these relationships. The active composer module
104 uses
these relationships and corresponding data to compose new social data 403. The
active
composer module 104 is also configured to automatically create entire messages
or
derivative messages, or both. The active composer module 104 can subsequently
apply
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analytics to recommend an appropriate, or optimal, message that is machine-
created using
various social data geared towards a given target audience.
[0052] Continuing with the particular example, the active composer module
104
composes a new text article by combining an existing text article about the
car brand and an
existing text article about the sports team. In another example, the active
composer module
composes a new article about the car brand by summarizing different existing
articles of the
car brand, and includes advertisement about the sports team in the new
article. In another
example, the active composer module identifies people who have generated
social data
content about both the sports team and the car brand, although the social data
for each topic
may be published at different times and from different sources, and combines
this social
content together into a new social data message. In another example
embodiment, the
active composer module may combine video data and/or audio data related to the
car brand
with video data and/or audio data related to the sports team to compose new
video data
and/or audio data. Other combinations of data types can be used.
[0053] The active transmitter module 105 obtains the newly composed social
data 403
and determines a number of factors or parameters related to the transmission
of the newly
composed social data. The active transmitter module 105 also inserts or adds
markers to
track people's responses to the newly composed social data. Based on the
transmission
factors, the active transmitter module transmits the composed social data with
the markers
404. The active transmitter module is also configured to receive feedback
regarding the
composed social data 405, in which collection of the feedback includes use of
the markers.
The newly composed social data and any associated feedback 406 are sent to the
active
receiver module 103.
[0054] Continuing with the particular example regarding the car brand and
the sports
team, the active transmitter module 105 determines trajectory or transmission
parameters.
For example, social networks, forums, mailing lists, websites, etc. that are
known to be read
by people who are interested in the car brand and the sports team are
identified as
transmission targets. Also, special events, such as a competition event, like
a game or a
match, for the sports team are identified to determine the scheduling or
timing for when the
composed data should be transmitted. Location of targeted readers will also be
used to
determine the language of the composed social data and the local time at which
the
composed social data should be transmitted. Markers, such as number of clicks
(e.g. click
through rate), number of forwards, time trackers to determine length of time
the composed
social data is viewed, etc., are used to gather information about people's
reaction to the
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composed social data. The composed social data related to the car brand and
the sports
team and associated feedback are sent to the active receiver module 103.
[0055] Continuing with FIG. 4, the active receiver module 103 receives
the composed
social data and associated feedback 406. The active receiver module 103
analyses this
data to determine if there are any relationships or correlations. For example,
the feedback
can be used to determine or affirm that the relationship used to generate the
newly
composed social data is correct, or is incorrect.
[0056] Continuing with the particular example regarding the car brand and
the sports
team, the active receiver module 103 receives the composed social data and the
associated
feedback. If the feedback shows that people are providing positive comments
and positive
feedback about the composed social data, then the active receiver module
determines that
the relationship between the car brand and the sports team is correct. The
active receiver
module may increase a rating value associated with that particular
relationship between the
car brand and the sports team. The active receiver module may mine or extract
even more
social data related to the car brand and the sports team because of the
positive feedback. If
the feedback is negative, the active receiver module corrects or discards the
relationship
between the car brand and the sports team. A rating regarding the relationship
may
decrease. In an example embodiment, the active receiver may reduce or limit
searching for
social data particular to the car brand and the sports team.
[0057] Periodically, or continuously, the social analytic synthesizer
module 106 obtains
data from the other modules 103, 104, 105. The social analytic synthesizer
module 106
analyses the data to determine what adjustments can be made to the operations
performed
by each module, including module 106. It can be appreciated that by obtaining
data from
each of modules 103, 104 and 105, the social analytic synthesizer has greater
contextual
information compared to each of the modules 103, 104, 105 individually.
[0058] The proposed systems and methods described herein relate to
receiving and
analyzing social data from one or more associated modules (e.g. 103, 104,
105), the
modules for receiving, composing and/or transmitting social data and
communicating with
external targets of the social data regarding same. The social data can be
used in, for
example, but is not limited to, the context of continuous social
communication. In other
words, the system architecture and operations related to the social analytic
synthesizer
module, described below, may be used with the continuous social communication
system
described herein, may be used in isolation, or may be used with other systems
not described
here.
Active Transmitter Module 105
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[0059] One measure of positive feedback is for example: the number of
times that a
particular social media data was re-transmitted or forwarded (e.g. re-tweeted
or shared on
social media sites). Another measure of positive feedback is the new
destination of the
messages being forwarded. For example, a social media data message intentioned
for one
geographical country (e.g. Brazil) may be forwarded by users to other
geographical South
American countries. Thus, the social analytic synthesizer modules 106 is
configured for
receiving feedback regarding the final destination or final destinations of
messages
generated by the system 102 and detecting the rerouting of the messages. In
response, the
synthesizer module 106 is configured for altering one or more subsequent
social media data
to the detected final destination of prior similar messages.
[0060] In yet another aspect, the one or more modules 103, 104 and 105
are configured
to provide their respective social media data and/or feedback received
relating to the data
based on defined timing.
[0061] As discussed earlier, the social data object herein refers to a
unit of social data,
such as a text article, a video, an image, a picture, a photo, a comment, a
message, an
audio track, a graphic, or a mixed-media social piece that includes different
types of data.
As can be envisaged, the social data object can include any combination of the
above or a
plurality of each category, such as video(s), image(s), comment(s)...
[0062] One of the aforementioned social data object content (e.g.
representing an
advertisement or campaign content) could comprise two different versions of
the content
(e.g. a first content that is initially longer in duration and
transmitted/displayed for a duration
of n days and another abbreviated version that is subsequently transmitted or
displayed). As
an example, this is common for tv advertisers when first introducing a new
campaign that
lasts 30 seconds and then is subsequently shortened to 15 seconds as a follow
up to
provide reminders about the company and product.
Social Analytic Synthesizer Module 106 ¨ Adjusting Operations of System 102
[0063] In response, the social media data and/or feedback is forwarded to
the social
analytic synthesizer module 106 for further altering the operation of the
modules 103, 104,
and/or 105. For example, subsequent social media data may be tailored to
include one or
more of: format, content, geographical destination, language, particular
target destinations,
provided as exemplary adjustments. In one example, the synthesizer module 106
may
receive positive feedback regarding social media data transmitted during
certain times or
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dates. Accordingly, the synthesizer module 106 is configured to alter
subsequent similar
messages to be scheduled according to this knowledge.
[0064] In one embodiment, the social analytic synthesizer module 106 is
configured for
providing the suggested adjustments to the respective module 103, 104, and/or
105. In
another embodiment, the social analytic synthesizer module 106 is configured
to define the
adjusted social media data (e.g. new content, new language, new format, and
new target
destination) and to forward the new social data to the respective module for
transmission to
one or more targets.
[0065] Continuing with the particular example regarding the car brand and
the sports
team, the social analytic synthesizer module 106 obtains data that people are
responding
positively to the newly composed social data object in a second language
different than a
first language used in the newly composed social data object. Such information
can be
obtained from the active transmitter module 105 or from the active receiver
module 103, or
both. Therefore, the social analytic synthesizer module sends an adjustment
command to
the active composer module 104 to compose new social data about the car brand
and the
sports team using the second language.
[0066] In another example, the social analytic synthesizer module 106
obtains data that
positive feedback, about the newly composed social data object regarding the
car brand and
the sports team, is from particular geographical vicinity (e.g. a zip code, an
area code, a city,
a municipality, a state, a province, etc.). This data can be obtained by
analyzing data from
the active receiver module 103 or from the active transmitter module 105, or
both. The
social analytic synthesizer then generates and sends an adjustment command to
the active
receiver module 103 to obtain social data about that particular geographical
vicinity. Social
data about the particular geographical vicinity includes, for example, recent
local events,
local jargon and slang, local sayings, local prominent people, and local
gathering spots. The
social analytic synthesizer generates and sends an adjustment command to the
active
composer module 104 to compose new social data that combines social data about
the car
brand, the sports team and the geographical vicinity. The social analytic
synthesizer
generates and sends an adjustment command to the active transmitter module 105
to send
the newly composed social data to people located in the geographical vicinity,
and to send
the newly composed social data during time periods when people are likely to
read or
consume such social data (e.g. evenings, weekends, etc.).
[0067] Continuing with FIG. 4, each module is also configured to learn
from its own
gathered data and to improve its own processes and decision making algorithms.
Currently
known and future known machine learning and machine intelligence computations
can be
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used. For example, the active receiver module 103 has a feedback loop 407; the
active
composer module 104 has a feedback loop 408; the active transmitter module 105
has a
feedback loop 409; and the social analytic synthesizer module has a feedback
loop 410. In
this way, the process in each module can continuously improve individually,
and also
improve using the adjustments sent by the social analytic synthesizer module
106. This self-
learning on a module-basis and system-wide basis allows the system 102 to be
completely
automated without human intervention.
[0068] It can be appreciated that as more data is provided and as more
iterations are
performed by the system 102 for sending composed social data, then the system
102
becomes more effective and efficient.
[0069] Other example aspects of the system 102 are described below.
[0070] The system 102 is configured to capture social data in real time.
[0071] The system 102 is configured to analyze social data relevant to a
business or, a
particular person or party, in real time.
[0072] The system 102 is configured to create and compose social data that
is targeted
to certain people or a certain group, in real time.
[0073] The system 102 is configured to determine the best or appropriate
times to
transmit the newly composed social data.
[0074] The system 102 is configured to determine the best or appropriate
social
channels to reach the selected or targeted people or groups.
[0075] The system 102 is configured to determine what people are saying
about the new
social data sent by the system 102.
[0076] The system 102 is configured to apply metric analytics to
determine the
effectiveness of the social communication process.
[0077] The system 102 is configured to determine and recommend analysis
techniques
and parameters, social data content, transmission channels, target people, and
data
scraping and mining processes to facilitate continuous loop, end-to-end
communication.
[0078] The system 102 is configured to add N number of systems or
modules, for
example, using a master-slave arrangement.
[0079] It will be appreciated that the system 102 may perform other
operations.
[0080] In an example embodiment, computer or processor implemented
instructions,
which are implemented by the system 102, for providing social communication
includes
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obtaining social data. The system then composes a new social data object
derived from the
social data. It can be appreciated that the new social data object may have
exactly the
same content of the obtained social data, or a portion of the content of the
obtained social
data, or none of the content of the obtained social data. The system transmits
the new
social data object and obtains feedback associated with the new social data
object. The
system computes an adjustment command using the feedback, wherein executing
the
adjustment command adjusts a parameter used in the operations performed by the
system.
[0081] In an example embodiment, the system obtains a social data object
using the
active receiver module, and the active composer module passes the social data
object to the
active transmitter module for transmission. Computation and analysis is
performed to
determine if the social data object is suitable for transmission, and if so,
to which party and
at which time should the social data object be transmitted.
[0082] Another example embodiment of computer or processor implemented
instructions
is shown in FIG. 5 for providing social communication. The instructions are
implemented by
the system 102. At block 501, the system 102 receives social data. At block
502, the
system determines relationships and correlations between social data. At block
503, the
system composes new social data using the relationships and the correlations.
At block
504, the system transmits the composed social data. At block 505, the system
receives
feedback regarding the composed social data. At block 506, following block
505, the system
uses the feedback regarding the composed social data to adjust transmission
parameters of
the composed social data. In addition, or in the alternative, at block 507,
following block
505, the system uses the feedback regarding the composed social data to adjust
relationships and correlations between the received social data It can be
appreciated that
other adjustments can be made based on the feedback. As indicated by the
dotted lines, the
process loops back to block 501 and repeats.
Active Receiver Module
[0083] The active receiver module 103 automatically and dynamically
listens to N
number of global data streams and is connected to Internet sites or private
networks, or
both. The active receiver module may include analytic filters to eliminate
unwanted
information, machine learning to detect valuable information, and
recommendation engines
to quickly expose important conversations and social trends. Further, the
active receiver
module is able to integrate with other modules, such as the active composer
module 104,
the active transmitter module 105, and the social analytic synthesizer module
106.
[0084] Turning to FIG. 6, example components of the active receiver
module 103 are
shown. The example components include an initial sampler and marker module
601, an
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intermediate sampler and marker module 602, a post-data-storage sampler and
marker
module 603, an analytics module 604, and a relationships/correlations module
605.
[0085] To facilitate real-time and efficient analysis of the obtained
social data, different
levels of speed and granularity are used to process the obtained social data.
The module
601 is used first to initially sample and mark the obtained social data at a
faster speed and
lower sampling rate. This allows the active receiver module 103 to provide
some results in
real-time. The module 602 is used to sample and mark the obtained data at a
slower speed
and at a higher sampling rate relative to module 601. This allows the active
receiver module
103 to provide more detailed results derived from module 602, although with
some delay
compared to the results derived from module 601. The module 603 samples all
the social
data stored by the active receiver module at a relatively slower speed
compared to module
602, and with a much higher sampling rate compared to module 602. This allows
the active
receiver module 103 to provide even more detailed results which are derived
from module
603, compared to the results derived from module 602. It can thus be
appreciated, that the
different levels of analysis can occur in parallel with each other and can
provide initial results
very quickly, provide intermediate results with some delay, and provide post-
data-storage
results with further delay.
[0086] The sampler and marker modules 601, 602, 603 also identify and
extract other
data associated with the social data including, for example: the time or date,
or both, that the
social data was published or posted; hashtags; a tracking pixel; a web bug,
also called a
web beacon, tracking bug, tag, or page tag; a cookie; a digital signature; a
keyword; user
and/or company identity associated with the social data; an IP address
associated with the
social data; geographical data associated with the social data (e.g. geo
tags); entry paths of
users to the social data; certificates; users (e.g. followers) reading or
following the author of
the social data; users that have already consumed the social data; etc. This
data may be
used by the active receiver module 103 and/or the social analytic synthesizer
module 106 to
determine relationships amongst the social data.
[0087] The analytics module 604 can use a variety of approaches to
analyze the social
data and the associated other data. The analysis is performed to determine
relationships,
correlations, affinities, and inverse relationships. Non-limiting examples of
algorithms that
can be used include artificial neural networks, nearest neighbor, Bayesian
statistics, decision
trees, regression analysis, fuzzy logic, K-means algorithm, clustering, fuzzy
clustering, the
Monte Carlo method, learning automata, temporal difference learning, apriori
algorithms, the
ANOVA method, Bayesian networks, and hidden Markov models. More generally,
currently
known and future known analytical methods can be used to identify
relationships,
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correlations, affinities, and inverse relationships amongst the social data.
The analytics
module 604, for example, obtains the data from the modules 601, 602, and/or
603.
[0088] It will be appreciated that inverse relationships between two
concepts, for
example, is such that a liking or affinity to first concept is related to a
dislike or repelling to a
second concept.
[0089] The relationships/correlations module 605 uses the results from
the analytics
module to generate terms and values that characterize a relationship between
at least two
concepts. The concepts may include any combination of keywords, time,
location, people,
video data, audio data, graphics, etc.
[0090] The relationships module 605 can also identify keyword bursts. The
popularity of
a keyword, or multiple keywords, is plotted as a function of time. The
analytics module
identifies and marks interesting temporal regions as bursts in the keyword
popularity curve.
The analytics module identifies one or more correlated keywords associated
with the
keyword of interest (e.g. the keyword having a popularity burst). The
correlated keyword is
closely related to the keyword of interest at the same temporal region as the
burst. Such a
process is described in detail in U.S. Patent Application No. 12/501,324,
filed on July 10,
2009 and titled "Method and System for Information Discovery and Text
Analysis", the entire
contents of which are incorporated herein by reference.
[0091] In another example aspect, the relationships module 605 can also
identify
relationships between topics (e.g. keywords) and users that are interested in
the keyword.
The relationships module, for example, can identify a user who is considered
an expert in a
topic. If a given user regularly comments on a topic, and there many other
users who
"follow" the given user, then the given user is considered an expert. The
relationships
module can also identify in which other topics that an expert user has an
interest, although
the expert user may not be considered an expert of those other topics. The
relationships
module can obtain a number of ancillary users that a given user follows;
obtain the topics in
which the ancillary users are considered experts; and associate those topics
with the given
user. It can be appreciated that there are various ways to correlate topics
and users
together. Further details are described in U.S. Patent Application No.
61/837,933, filed on
June 21, 2013 and titled "System and Method for Analysing Social Network
Data", the entire
contents of which are incorporated herein by reference.
[0092] Turning to FIG. 7, example computer or processor implemented
instructions are
provided for receiving and analysing data according to the active receiver
module 103. At
block 701, the active receiver module receives social data from one or more
social data
streams. At block 702, the active receiver module initially samples the social
data using a
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fast and low definition sample rate (e.g. using module 601). At block 703, the
active receiver
module applies ETL (Extract, Transform, Load) processing. The first part of an
ETL process
involves extracting the data from the source systems. The transform stage
applies a series
of rules or functions to the extracted data from the source to derive the data
for loading into
the end target. The load phase loads the data into the end target, such as the
memory.
[0093] At block 704, the active receiver module samples the social data
using an
intermediate definition sample rate (e.g. using 601). At block 705, the active
receiver
module samples the social data using a high definition sample rate (e.g. using
module 603).
In an example embodiment, the initial sampling, the intermediate sampling and
the high
definition sampling are performed in parallel. In another example embodiment,
the
samplings occur in series.
[0094] Continuing with FIG. 7, after initially sampling the social data
(block 702), the
active receiver module inputs or identifies data markers (block 706). It
proceeds to analyze
the sampled data (block 707), determine relationships from the sampled data
(block 708),
and use the relationships to determine early or initial social trending
results (block 709).
[0095] Similarly, after block 704, the active receiver module inputs or
identifies data
markers in the sampled social data (block 710). It proceeds to analyze the
sampled data
(block 711), determine relationships from the sampled data (block 712), and
use the
relationships to determine intermediate social trending results (block 713).
[0096] The active receiver module also inputs or identifies data markers in
the sampled
social data (block 714) obtained from block 705. It proceeds to analyze the
sampled data
(block 715), determine relationships from the sampled data (block 716), and
use the
relationships to determine high definition social trending results (block
717).
[0097] In an example embodiment, the operations at block 706 to 709, the
operations at
block 710 to 713, and the operations at block 714 to 717 occur in parallel.
The relationships
and results from blocks 708 and 709, however, would be determined before the
relationships
and results from blocks 712, 713, 716 and 717.
[0098] It will be appreciated that the data markers described in blocks
706, 710 and 714
assist with the preliminary analysis and the sampled data and also help to
determine
relationships. Example embodiments of data markers include keywords, certain
images,
and certain sources of the data (e.g. author, organization, location, network
source, etc.).
The data markers may also be tags extracted from the sampled data.
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[0099] In an example embodiment, the data markers are identified by
conducting a
preliminary analysis of the sampled data, which is different'from the more
detailed analysis
in blocks 707, 711 and 715. The data markers can be used to identify trends
and sentiment.
[00100] In another example embodiment, data markers are inputted into the
sampled
data based on the detection of certain keywords, certain images, and certain
sources of
data. A certain organization can use this operation to input a data marker
into certain
sampled data. For example, a car branding organization inputs the data marker
"SUV" when
an image of an SUV is obtained from the sampling process, or when a text
message has at
least one of the words "SUV", "Jeep", "4X4", "CR-V", "Rav4", and "RDX". It can
be
appreciated that other rules for inputting data markers can be used. The
inputted data
markers can also be used during the analysis operations and the relationship
determining
operations to detect trends and sentiment.
[00101] Other example aspects of the active receiver module are provided
below.
[00102] The active receiver module 103 is configured to capture, in real time,
one or more
electronic data streams.
[00103] The active receiver module 103 is configured to analyse, in real time,
the social
data relevant to a business.
[00104] The active receiver module 103 is configured to translate text from
one language
to another language.
[00105] The active receiver module 103 is configured to interpret video, text,
audio and
pictures to create business information. A non-limiting example of business
information is
sentiment information.
[00106] The active receiver module 103 is configured to apply metadata to the
received
social data in order to provide further business enrichment. Non-limiting
examples of
metadata include geo data, temporal data, business driven characteristics,
analytic driven
characteristics, etc.
[00107] The active receiver module 103 is configured to interpret and predict
potential
outcomes and business scenarios using the received social data and the
computed
information.
[00108] The active receiver module 103 is configured to propose user segment
or target
groups based upon the social data and the metadata received.
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[00109] The active receiver module 103 is configured to proposed or recommend
social
data channels that are positively or negatively correlated to a user segment
or a target
group.
[00110] The active receiver module 103 is configured to correlate and
attribute groupings,
such as users, user segments, and social data channels. In an example
embodiment, the
active receiver module uses patterns, metadata, characteristics and
stereotypes to correlate
users, user segments and social data channels.
[00111] The active receiver module 103 is configured to operate with little or
no human
intervention.
[00112] The active receiver module 103 is configured to assign affinity data
and metadata
to the received social data and to any associated computed data. In an example
embodiment, affinity data is derived from affinity analysis, which is a data
mining technique
that discovers co-occurrence relationships among activities performed by (or
recorded
about) specific individuals, groups, companies, locations, concepts, brands,
devices, events,
and social networks.
Active Composer Module
[00113] The active composer module 104 is configured to analytically compose
and
create social data for communication to people. This module may use business
rules and
apply learned patterns to personalize content. The active composer module is
configured,
for example, to mimic human communication, idiosyncrasies, slang, and jargon.
This
module is configured to evaluate multiple social data pieces or objects
composed by itself
(i.e. module 104), and further configured to evaluate ranks and recommend an
optimal or an
appropriate response based on the analytics. Further, the active composer
module is able
to integrate with other modules, such as the active receiver module 103, the
active
transmitter module 105, and the social analytic synthesizer module 106. The
active
composer module can machine-create multiple versions of a personalized content
message
and recommend an appropriate, or optimal, solution for a target audience.
[00114] Turning to FIG. 8, example components of the active composer module
104 are
shown. Example components include a text composer module 801, a video composer
module 802, a graphics/picture composer module 803, an audio composer 804, and
an
analytics module 805. The composer modules 801, 802, 803 and 804 can operate
individually to compose new social data within their respective media types,
or can operate
together to compose new social data with mixed media types.
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[00115] The analytics module 805 is used to analyse the outputted social data,
identify
adjustments to the composing process, and generate commands to make
adjustments to the
composing process.
[00116] Turning to FIG. 9A, example computer or processor implemented
instructions are
provided for composing social data according the module 104. The active
composer module
obtains social data, for example from the active receiver module 103 (block
901). The active
composer module then composes a new social data object (e.g. text, video,
graphics,
picture, photo, audio) derived from the obtained social data (block 902).
[00117] Various approaches can be used to compose the new social data object,
or new
social data objects. For example, social data can be combined to create the
new social data
object (block 905), social data can be extracted to create the new social
object (block 906),
and new social data can be created to form the new social data object (block
907). The
operations from one or more of blocks 905, 906 and 907 can be applied to block
902.
Further details in this regard are described in FIGs. 9B, 9C and 9D.
[00118] Continuing with FIG. 9A, at block 903, the active composer module
outputs the
composed social data. The active composer module may also add identifiers or
trackers to
the composed social data, which are used to identify the sources of the
combined social
data and the relationship between the combined social data.
[00119] Turning to FIG. 9B, example computer or processor implemented
instructions are
provided for combining social data according to block 905. The active composer
module
obtains relationships and correlations between the social data (block 908).
The relationships
and correlations, for example, are obtained from the active receiver module.
The active
composer module also obtains the social data corresponding to the
relationships (block
909). The social data obtained in block 909 may be a subset of the social data
obtained by
the active receiver module, or may be obtained by third party sources, or
both. At block 910,
the active composer module composes new social data (e.g. a new social data
object) by
combining social data that is related to each other.
[00120] It can be appreciated that various composition processes can be used
when
implementing block 910. For example, a text summarizing algorithm can be used
(block
911). In another example, templates for combining text, video, graphics, etc.
can be used
(block 912). In an example embodiment, the templates may use natural language
processing to generate articles or essays. The template may include a first
section
regarding a position, a second section including a first argument supporting
the position, a
third section including a second argument supporting the position, a fourth
section including
a third argument supporting the position, and a fifth section including a
summary of the
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position. Other templates can be used for various types of text, including
news articles,
stories, press releases, etc.
[00121] Natural language processing catered to different languages can also be
used.
Natural language generation can also be used. It can be appreciated that
currently know and
future known composition algorithms that are applicable to the principles
described herein
can be used.
[00122] Natural language generation includes content determination, document
structuring, aggregation, lexical choice, referring expression generation, and
realisation.
Content determination includes deciding what information to mention in the
text. In this case
the information is extracted from the social data associated with an
identified relationship.
Document structuring is the overall organisation of the information to convey.
Aggregation is
the merging of similar sentences to improve readability and naturalness.
Lexical choice is
putting words to the concepts. Referring expression generation includes
creating referring
expressions that identify objects and regions. This task also includes making
decisions
about pronouns and other types of anaphora. Realisation includes creating the
actual text,
which should be correct according to the rules of syntax, morphology, and
orthography. For
example, using "will be" for the future tense of "to be".
[00123] Continuing with FIG. 9B, metadata obtained from the active receiver
module, or
obtained from third party sources, or metadata that has been generated by the
system 102,
may also be applied when composing the new social data object (block 913).
Furthermore,
a thesaurus database, containing words and phrases that are synonymous or
analogous to
keywords and key phrases, can also be used to compose the new social data
object (block
914). The thesaurus database may include slang and jargon.
[00124] Turning to FIG. 9C, example computer or processor implemented
instructions are
provided for extracting social data according to block 906. At block 915, the
active
composer module identifies characteristics related to the social data. These
characteristics
can be identified using metadata, tags, keywords, the source of the social
data, etc. At block
916, the active composer module searches for and extracts social data that is
related to the
identified characteristics.
[00125] For example, one of the identified characteristics is a social
network account
name of a person, an organization, or a place. The active composer module will
then
access the social network account to extract data from the social network
account. For
example, extracted data includes associated users, interests, favourite
places, favourite
foods, dislikes, attitudes, cultural preferences, etc. In an example
embodiment, the social
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network account is a LinkedIn account or a Facebook account. This operation
(block 918) is
an example embodiment of implementing block 916.
[00126] Another example embodiment of implementing block 916 is to obtain
relationships and use the relationships to extract social data. Relationships
can be obtained
in a number of ways, including but not limited to the methods described
herein. Another
example method to obtain a relationship is using Pearson's correlation.
Pearson's
correlation is a measure of the linear correlation (dependence) between two
variables X and Y, giving a value between +1 and -1 inclusive, where 1 is
total positive
correlation, 0 is no correlation, and -1 is negative correlation. For example,
if given data X,
and it is determined X and data Y are positively correlated, then data Y is
extracted.
[00127] Another example embodiment of implementing block 916 is to use
weighting to
extract social data (block 920). For example, certain keywords can be
statically or
dynamically weighted based on statistical analysis, voting, or other criteria.
Characteristics
that are more heavily weighted can be used to extract social data. In an
example
embodiment, the more heavily weighted a characteristic is, the wider and the
deeper the
search will be to extract social data related to the characteristic.
[00128] Other approaches for searching for and extracting social data can be
used.
[00129] At block 917, the extracted social data is used to form a new social
data object.
[00130] Turning to FIG. 9D, example computer or processor implemented
instructions are
provided for creating social data according to block 907. At block 921, the
active composer
module identifies stereotypes related to the social data. Stereotypes can be
derived from
the social data. For example, using clustering and decision tree classifiers,
stereotypes can
be computed.
[00131] In an example stereotype computation, a model is created. The model
represents a person, a place, an object, a company, an organization, or, more
generally, a
concept. As the system 102, including the composer module, gains experience
obtaining
data and feedback regarding the social communications being transmitted, the
active
composer module is able to modify the model. Features or stereotypes are
assigned to the
model based on clustering. In particular, clusters representing various
features related to
the model are processed using iterations of agglomerative clustering. If
certain of the
clusters meet a predetermined distance threshold, where the distance
represents similarity,
then the clusters are merged. For example, the Jaccard distance (based on the
Jaccard
index), a measure used for determining the similarity of sets, is used to
determine the
distance between two clusters. The cluster centroids that remain are
considered as the
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stereotypes associated with the model. For example, the model may be a
clothing brand
that has the following stereotypes: athletic, running, sports, swoosh, and
lust do it'.
[00132] In another example stereotype computation, affinity propagation is
used to
identify common features, thereby identifying a stereotype. Affinity
propagation is a
clustering algorithm that, given a set of similarities between pairs of data
points, exchanges
messages between data points so as to find a subset of exemplar points that
best describe
the data. Affinity propagation associates each data point with one exemplar,
resulting in a
partitioning of the whole data set into clusters. The goal of affinity
propagation is to minimize
the overall sum of similarities between data points and their exemplars.
Variations of the
affinity propagation computation can also be used. For example, a binary
variable model of
affinity propagation computation can be used. A non-limiting example of a
binary variable
model of affinity propagation is described in the document by Inmar E. Givoni
and Brendan
J. Frey, titled "A Binary Variable Model of Affinity Propagation", Neural
Computation 21,
1589-1600 (2009), the entire contents of which are hereby incorporated by
reference.
[00133] Another example stereotype computation is Market Basket Analysis
(Association
Analysis), which is an example of affinity analysis. Market Basket Analysis is
a
mathematical modeling technique based upon the theory that if you buy a
certain group of
products, you are likely to buy another group of products. It is typically
used to analyze
customer purchasing behavior and helps in increasing the sales and maintain
inventory by
focusing on the point of sale transaction data. Given a dataset, an apriori
algorithm trains
and identifies product baskets and product association rules. However, the
same approach
is used herein to identify characteristics of a person (e.g. stereotypes)
instead of products.
Furthermore, in this case, users' consumption of social data (e.g. what they
read, watch,
listen to, comment on, etc.) is analyzed. The apriori algorithm trains and
identifies
characteristic (e.g. stereotype) baskets and characteristic association rules.
[00134] Other methods for determining stereotypes can be used.
[00135] Continuing with FIG. 9D, the stereotypes are used as metadata (block
922). In
an example embodiment, the metadata is the new social data object (block 923),
or the
metadata can be used to derive or compose a new social data object (block
924).
[00136] It can be appreciated that the methods described with respect to
blocks 905, 906
and 907 to compose a new social data object can be combined in various way,
though not
specifically described herein. Other ways of composing a new social data
object can also be
applied.
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[00137] In an example embodiment of composing a social data object, the social
data
includes the name "Chris Farley". To compose a new social data object, social
data is
created using stereotypes. For example, the stereotypes 'comedian', 'fat',
`ninja', and
'blonde' are created and associated with Chris Farley. The stereotypes are
then used to
automatically create a caricature (e.g. a cartoon-like image of Chris Farley).
The image of
the person is automatically modified to include a funny smile and raised eye
brows to
correspond with the 'comedian' stereotype. The image of the person is
automatically
modified to have a wide waist to correspond with the 'fat' stereotype. The
image of the
person is automatically modified to include ninja clothing and weaponry (e.g.
a sword, a
staff, etc.) to correspond with the `ninja' stereotype. The image of the
person is
automatically modified to include blonde hair to correspond with the 'blonde'
stereotype. In
this way, a new social data object comprising the caricature image of Chris
Farley is
automatically created. Various graphic generation methods, derived from text,
can be used.
For example, a mapping database contains words that are mapped to graphical
attributes,
and those graphical attributes in turn can be applied to a template image.
Such a mapping
database could be used to generate the caricature image.
[00138] In another example embodiment, the stereotypes are used to create a
text
description of Chris Farley, and to identify in the text description other
people that match the
same stereotypes. The text description is the composed social data object. For
example,
the stereotypes of Chris Farley could also be used to identify the actor "John
Belushi" who
also fits the stereotypes of 'comedian' and `ninja'. Although the above
examples pertain to a
person, the same principles of using stereotypes to compose social data also
apply to
places, cultures, fashion trends, brands, companies, objects, etc.
[00139] The active composer module 104 is configured to operate with little or
no human
intervention.
Active Transmitter Module
[00140] The active transmitter module 105 analytically assesses preferred or
appropriate
social data channels to communicate the newly composed social data to certain
users and
target groups. In one aspect, the active transmitter module 105 also assesses
the preferred
time to send or transmit the newly composed social data.
[00141] Turning to FIG. 10, example components of the active transmitter
module 105 are
shown. Example components include a telemetry module 1001, a scheduling module
1002,
a tracking and analytics module 1003, and a data store for transmission 1004.
The
telemetry module 1001 is configured to determine or identify over which social
data channels
a certain social data object should be sent or broadcasted. A social data
object may be a
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text article, a message, a video, a comment, an audio track, a picture, a
photo, a graphic, or
a mixed-media social piece. For example, a social data object about a certain
car brand
should be sent to websites, RSS feeds, video or audio channels, blogs, or
groups that are
viewed or followed by potential car buyers, current owners of the car brand
and past owners
of the car brand. The scheduling module 1002 determines a preferred time range
or date
range, or both, for sending a composed social data object. For example, if a
newly
composed social data object is about stocks or business news, the composed
social data
object will be scheduled to be sent during working hours of a work day. The
tracking and
analytics module 1003 inserts data trackers or markers into a composed social
data object to
facilitate collection of feedback from people. Data trackers or markers
include, for example,
tags, feedback (e.g. like, dislike, ratings, thumb up, thumb down, etc.),
number of views on a
web page, etc.
[00142] The data store for transmission 1004 stores a social data object that
has the
associated data tracker or marker. The social data object may be packaged as a
"cart".
Multiple carts, having the same social data object or different social data
objects, are stored
in the data store 1004. The carts are launched or transmitted according to
associated
telemetry and scheduling parameters. The same cart can be launched multiple
times. One
or more carts may be organized under a campaign to broadcast composed social
data. The
data trackers or markers are used to analyse the success of a campaign, or of
each cart.
Exemplary Components of Active Transmitter Module
[00143] Referring to FIG. 10A, shown is a further exemplary components of the
active
transmitter module 105 depicting further components for processing the social
data.
Referring to Figure 10A, the active transmitter module 105 further comprises a
destination
locator module 1007 for determining target destination(s) of social data
messages, a
scheduling module 1002 for determining scheduling of social data messages
being
transmitted; an embed tracking module 1006 for embedding trackers (also
referred to as
markers herein) for tracking how well a message was received; and a feedback
analysis
module 1005 for analyzing feedback 1016 and/or tracker responses 1018 received
from one
or more destination targets or other active transmitter modules in
communication with an
instance of the active transmitter module 105. The embed tracking module 1006
is
configured to embed one or more types of trackers within the social data to
transmit as
composed social data with tracker 1014 to a single channel transmission or to
multiple
channels as shown in FIG. 10A.
[00144] In one aspect, the active transmitter module 105, could further
incorporate third
party pixels, emitters, trackers to use within the system and modify to define
that a message
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was seen or clicked upon by an end user (e.g. a customer). Upon receiving the
feedback,
the active transmitter module 105 and/or the synthesizer module 106 would be
configured to
use the third party feedback to further bias or adjust the operation of the
active transmitter
module 105 and adjust subsequent transmission of social data messages (e.g.
adjust where,
who, when, ...) receive the transmitted messages based on the third party
telemetry in
addition to the systems described herein for utilizing the feedback to
optimize future
transmission behaviour as defined by the active transmitter module 105 (e.g.
as defining
location, time of transmission, duration, end user(s), length of viewing time,
allowability to
retransmit the social data message to other parties...). The allowability can
define for
example, permissions for re-transmitting the social data from one target to
another target
(e.g. retweeting the message or sharing the message). As discussed herein, the
feedback
from the pixels, trackers and/or emitters is used, in one embodiment, to
generate new social
data content that is calculated by the synthesizer module 106 and/or the
transmitter module
105 to be relevant to the end user based on prior success and feedback. In
another aspect,
the subsequent social data content generated by the system is adjusted and
redirected
according to new transmission parameters (e.g. location, destination,
duration...) based on
patterns and correlations from the received feedback.
[00145] In yet a further aspect, the active transmitter module 105 and the
synthesizer
module 106 could, singularly or in combination, transmit user tracker
information to other
Internet companies and sites, including ad exchanges, which in turn, can track
the user's
prior Internet journey and interest, and then subsequently provide relevant
messages and/or
ads, for example for a pre-defined duration of time thereafter.
[00146] In one aspect, the content of the social data 1014 may be composed by
the
active composer module 104 and sent to a specific channel. Furthermore, the
social data
1014 may define that within each channel, the social data 1014 is to be
transmitted to
selective sub segment of users (e.g. as defined within transmission parameters
of the social
data 1014).
[00147] In another aspect, the social data 1014 may be provided to multiple
simultaneous
channels (e.g. social networking sites, forums, blogs...). In another aspect,
the active
transmitter module 105 may be configured to communicate with the synthesizer
module 106
for optimizing the transmission of the social data 1014.
[00148] For example, if a response is received from one or more users (e.g.
tracker
response 1016) that indicates that the social data content resonated with a
particular group
of people then subsequent social data of similar content would be optimized
for transmitting
to the same particular group of people. Alternatively, the feedback response
1018 and/or
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trackers 1016 can indicate that more positive feedback was received at one
social channel
versus another (e.g. FaceBook vs. Twitter) and thus the active transmitter
module 105 (e.g.
via the feedback analysis module 1005) and/or the synthesizer module 106 is
configured to
reroute subsequent message to the channel associated with the positive
feedback.
[00149] As defined earlier, the active transmitter module 105 can also
incorporate third
party pixels, emitters, trackers etc. to provide the third party verification
that a message was
seen or clicked upon by a customer. The synthesizer module 106 could,
alternatively
incorporate the feedback from third party tracking, and use this third party
feedback to bias
and/or adjust the active transmitter module 105 (and corresponding
transmission parameters
as described herein), and ultimately adjust where, when, who, etc. see a
transmitted
message based using the third party transmission telemetry.
[00150] Trackers
[00151] In one embodiment illustrated, the types of trackers comprise
emitters 1008,
cookies 1009, pixels 1010, and web bugs 1012.
[00152] In one aspect, the different types of trackers can be combined
together. The
trackers can provide information on for example, how many people visited a
particular
website associated with the social data, how many people read the social data,
and how
many people clicked through or forwarded the social data. Specific components
of trackers
are provided with respect to FIG. 10C.
[00153] Preferably, the trackers are seamlessly integrated by the embed
tracking module
1006 within the social data (e.g. text, video, pictures or photos, graphics,
and/or audio data,
or combinations thereof) such as to allow users to receive a tracker response
1016 that
tracks the activity and popularity of the social data tracker such as to
provide metrics that are
useful in modifying and improving the behaviour of the active transmitter
module 105 for
transmitting subsequent messages.
[00154] Emitters 1008
[00155] In one aspect, emitters 1008 are simply referred to digital code
embedded within
the composed social data message 1014 (e.g. text, video, pictures, photos,
graphics, and
audio data, or combinations thereof) that provide an emitter response to the
active
transmitter module 105 for each destination or hop which the social data
travels.
[00156] Cookies 1009
[00157] A cookie is a digital software code that is used to track an Internet
users' web
browsing activities. For example, if a user selects an advertisement on a
website (e.g.
generated by the social data 1014) the active transmitter module 105 will be
provided in the
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form of the tracker response 1016, the browsing history of the user across all
sites that are
associated with the social data 1014. In a further aspect, the tracker
response 1016 can
include the browsing history of the user with respect to all sites associated
with the source of
the social data (e.g. advertiser). The cookies 1009 as provided in the tracker
response 1016
can also provide information on the web pages the user has visited associated
with the
social data 1014, in what sequence and for how long. In one aspect, the
tracker further
utilizes finger printing such that the user's identity endures even if the
cookies 1009 are
deleted.
[00158] Tracking Pixels 1010
[00159] Tracking pixels 1010 are typically a small (e.g. 1X1 pixel size),
invisible to the eye
pixel, preferably inserted within a social data having and image or a video
segment that
allows tracking website visits, email tracking, and other types of
communication activity on
the Internet. As will be understood to a person skilled in the art, an
invisible to the eye pixel
refers to a pixel that is camouflaged or hidden within an image or video of
the social data
such as to not distort the image or social data carrying the tracking pixel
1010. Similarly a
tracking pixel could be hidden within a text message or an email message such
as to remain
hidden. In one aspect, the tracking pixel 1010 once embedded within a social
data message
remains hidden and the sending of the pixel back to its originator (e.g. the
active transmitter
module 106) is a process that is automatically performed without user
involvement. The
pixel can be sent back in the form of the tracker response 1016.
[00160] Tracking pixels 1010 can be defined as software code contained in
typically a
single clear/invisible pixel (e.g. a .gif format) that tracks the social data
messages 1014 as it
goes anywhere online.
[00161] Web Bugs 1012
[00162] A web bug is a digital object embedded within a web page or a mailing
list, or a
forum, or an email associated with the social data (e.g. social network site)
and it is usually
invisible to the user but allows checking that a user has viewed the page or
email. The
social data displayed on the website or mailing list or forum or email can be
in the form of
text, video, pictures, photos, graphics, and/or audio data, or combinations
thereof. The web
bug can be used for example for email tracking and page tagging for web
analytics. As will
be understood by a person skilled in the art, alternative names such as a web
beacon,
tracking bug, tag, or page tag are also used in the art to refer to the web
bugs 1012. The
web bug 1012 when provided by the tracker response 1016 can reveal for
example, who is
reading a web page (e.g. social network site), or email, or forum containing
the social data
message (e.g. posted on a social networking site). In accordance with one
embodiment, the
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web bugs 1012 can also be used to determine whether a social data message was
read or
forwarded elsewhere, or reposted. The web bug 1012 in one aspect, tracks the
IP address
of the computer receiving and/or reading the social data message, the time the
content was
received and/or reviewed, the type of user that made the request for viewing
the social data
1014. The active transmitter module 105 can then store this information as
received by
1016 and associate it with a unique tracking token attached to the originated
message (e.g.
social data 1014).
[00163] In accordance with yet another embodiment, the tracker 1014 can
contain a
trigger that causes end users receiving the social data message to collect and
provide
feedback from the end users (e.g. target recipients) regarding the social data
message 1014.
The feedback response 1018 can include real time engagement metrics (e.g.
click through
rates, frequency) that are fed back to the active transmitter module 105
and/or synthesizer
(SAS) module 106. The feedback response can include information regarding
velocity and
frequency of these engagement metrics (real time, near real time) for
subsequent use to
alter the telemetry (location, time of day, frequency, content) of the
delivered content (e.g.
social data 1014) via the feedback analysis module 1005.
[00164] Referring again to FIG. 10A, the feedback analysis module 1005 is
configured to
receive tracker response 1016 and feedback response 1018 from end users, other
servers in
communication with the active transmitter module 105 and/or other active
transmitter
modules that communicate with the recipients. The feedback analysis module
1005 thus
receives data relating to the social data 1014 including but not limited to:
identification of
users receiving the message (e.g. IP address), identification of initial
recipients and
subsequent recipients (e.g. forwards, re-tweets), engagement metrics, timing
of receipt of
message, duration read or viewed, number of times read or viewed, click-
through rate and
frequency, identification of locations (e.g. geographical locations) and
languages associated
with the social data (e.g. in which the social data was read/viewed or
forwarded in or
feedback language). As discussed earlier, based on the tracker response 1016
and/or
feedback response 1018, the feedback analysis module communicated with the
processor
307 for instructing the active transmitter module 105 to modify parameters of
subsequent
social data 1014 transmission to improve feedback and receptiveness of social
data. For
example, if the tracker response and/or feedback response 1016, 1018 reveal
that social
data message having a particular type or content is received more positively
(e.g. click
through rate or duration read or viewed or frequency of forwards) on a
particular day and
within a certain time of day, then the feedback analysis module 1005 deciphers
this
information and causes the active transmitter module 105 to transmit
subsequent social data
having a similar type or content within the certain time of day and on said
particular day (e.g.
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via scheduling module 1002). Additionally, the feedback analysis module 1005
is configured
to communicate with the synthesizer module 106 for receiving feedback from
other modules
(e.g. active composer module 104, and/or active receiver module 103) and
affect the
parameters (e.g. destination, timing, duration, language) for transmitting the
social data
1014.
[00165] The feedback analysis module 1005 can be configured to utilize pattern
learning
algorithms for analyzing the feedback and/or tracker responses and determining
optimization
patterns. Non-limiting examples of algorithms for implementing the analysis on
the tracker
and/or feedback responses by module 1005 can include artificial neural
networks, nearest
neighbor, Bayesian statistics, decision trees, regression analysis, fuzzy
logic, K-means
algorithm, clustering, fuzzy clustering, the Monte Carlo method, learning
automata, temporal
difference learning, apriori algorithms, the ANOVA method, Bayesian networks,
and hidden
Markov models. More generally, currently known and future known analytical
methods can
be used to identify relationships, correlations, affinities, and inverse
relationships amongst
the feedback and tracker responses for the social data 1014.
[00166] Exemplary Communication Flow of Information from the Active
Transmitter
Module
[00167] Referring to FIG.10B, shown in a schematic illustrating exemplary
communication
between an active transmitter module 105 and external recipients of the social
data 1014. In
one example, the social data 1014 is transmitter to User A 1020, the combined
message and
tracker 1407 is displayed (e.g. text, video and/or audio) to User A 1020. User
A 1020 then
forwards this social data message 1407 to User B 1022. The information
identifying User A,
time and duration of receipt/viewing of message, forwarding of message to User
B 1022 is
sent from User A 1020 as the tracker response 1016 to the active transmitter
module 105.
Additionally, a second tracker response 1016 is sent from User B 1022
identifying User B
1022 information (e.g. IP address, user name) and whether the message was
received
positively (e.g. click through rate, when viewed or read). The tracker
response 1016 can be
implemented with cookies, emitters, pixels, web bugs or other mechanisms
described
herein. In this aspect, User A 1020 and User B 1022 may be configured to
provide updates
along the way while the social data message is distributed. In this
configuration, the active
transmitter module 105 is configured to track the message from central server
to distribution
servers to each of the different customers (e.g. tracking each of the
intermediary steps) such
as to obtain full information on where the message has been and the estimated
time for
arrival at the destination.
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[00168] Although two users have been shown in FIG. 10B as user A and user B,
as can
be envisaged, this communication can be expanded to N users. Furthermore,
although a
specific flow of information is shown in Fig. 10B, this is not limiting and
other flows of
information could be envisaged for communicating and sharing the social data
message
across multiple communication channels and destinations. For example, the
message could
stop at user A. Alternatively, user A could communicate to user B and the
message could
stop there. Further alternatively, user A could communicate to a number of
users in addition
to user B. In another aspect, the users which A communicates with could in
turn
communicate/retransmit/repost the message (e.g. retweet) the message to one or
more
other users. Accordingly, the schematic in FIG. 10B is exemplary and not
limiting.
[00169] Referring again to FIG. 10B, in another example, the composed social
data with
embedded tracker 1014 is sent to a repeater module or another active
transmitter module
1030. In this scenario, the message is then broadcast to multiple users (User
D 1024, User
E 1026, and User F 1028). The trackers in each of the messages received by
User D, User
E and User F may be configured to communicate with their local repeater/ATM
Module 1030
which then consolidates the tracker responses 1016 and any feedback responses
1018
received from multiple associated users and send them to the active
transmitter module 105
to modify the transmission parameters (e.g. transmission targets or
scheduling) for
improving the feedback and visibility of subsequent social data messages 1014.
[00170] Generally, a repeater module as referred to herein is configured
similar to the
active transmitter module 105 but to repeat and retransmit a message intended
for a first
user to other users based upon feedback received. In the example shown in FIG.
10B, User
B 1022 can provide feedback via tracker response 1016 that the message is well
received
within a certain social networking site. The repeater module 1030 may then be
configured to
repeat the social data to multiple users within that certain social networking
site.
[00171] Referring to FIG. 10C, shown are exemplary components of the tracker
response
1016. The tracker response 1016 comprises a message received identifier 1040,
a
message read/unread identification 1042, a destination path identification
1044 (e.g. path
travelled and number of hops taken), end user identification 1046 (e.g. for
each user that has
viewed, read or forwarded the social data 1014), active/passive identification
1048 (whether
the message was actively or passively viewed), read or viewed parameters 1050
(timing/duration/frequency identification). Passive transmission can indicate
that a social
data object was received at the intended recipient target. Active transmission
can indicate
that the transmission was received and further exposed to a number of
additional users that
were not original recipients of the message. Referring to end user
identification module
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1046, this could include for example, a social user (e.g. a member of a social
data network
or channel). In one example, the social user has other social identity names
or handles on
the Internet associated with them (e.g. associated with different social data
websites). For
example, these can include, useraliasOtwittercom for Twitter; user name for
Facebook,
etc... Accordingly, in one aspect, the active transmitter module is further
configured to
comprise a matching algorithm module for associating various alias names and
user
identification handles with one another such as to help derive social names
and/or other
related social names. In a further aspect, the active transmitter module is
further configured
to store the various alias names and identities in a database (e.g. a social
customer master
record database) to associate further feedback with the same user.
[00172] Turning to FIG. 11, example computer or processor implemented
instructions are
provided for transmitting composed social data according the active
transmitter module 105.
At block 1101, the active transmitter module obtains the composed social data.
At block
1102, the active transmitter module determines the telemetry of the composed
social data.
At block 1103, the active transmitter module determines the scheduling for the
transmission
of the composed social data. Trackers, which are used to obtain feedback, are
added to the
composed social data (block 1104), and the social data including the trackers
are stored in
association with the scheduling and telemetry parameters (block 1105). At the
time
determined by the scheduling parameters, the active transmitter module sends
the
composed social data to the identified social data channels, as per the
telemetry parameters
(block 1106).
[00173] Continuing with FIG. 11, the active transmitter module receives
feedback using
the trackers (block 1107) and uses the feedback to adjust telemetry or
scheduling
parameters, or both (block 1108).
[00174] Other example aspects of the active transmitter module 105 are
provided below.
[00175] The active transmitter module 105 is configured to transmits messages
and,
generally, social data with little or no human intervention
[00176] The active transmitter module 105 is configured to use machine
learning and
analytic algorithms to select one or more data communication channels to
communicate a
composed social data object to an audience or user(s). The data communication
channels
include, but are not limited to, Internet companies such as FaceBook, Twitter,
and
Bloomberg. Channel may also include traditional TV, radio, and newspaper
publication
channels.
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[00177] The active transmitter module 105 is configured to automatically
broaden or
narrow the target communication channel(s) to reach a certain target audience
or user(s).
[00178] The active transmitter module 105 is configured to integrate data and
metadata
from third party companies or organizations to help enhance channel targeting
and user
targeting, thereby improving the effectiveness of the social data
transmission. As described
earlier, the third party data can include third party pixels, emitters,
trackers, etc. for providing
verification that a message was seen or clicked upon by an end user (e.g.
target
destination). As described earlier, the synthesizer module 106 uses the third
party feedback
to bias and/or adjust the transmission of social data messages based on the
third party
transmission telemetry and further analysis/correlation from the received
feedback data.
[00179] The active transmitter module 105 is configured to apply and transmit
unique
markers to track composed social data. The markers track the effectiveness of
the
composed social data, the data communication channel's effectiveness, and ROI
(return on
investment) effectiveness, among other key performance indicators.
[00180] The active transmitter module 105 is configured to automatically
recommend the
best time or an appropriate time to send/transmit the composed social data.
[00181] The active transmitter module 105 is configured to listen and
interpret whether
the composed social data was successfully received by the data communication
channel(s),
or viewed/consumer by the user(s), or both.
[00182] The active transmitter module 105 is configured to analyse the user
response of
the composed social data and automatically make changes to the target
channel(s) or
user(s), or both. In an example, the decision to make changes is based on
successful or
unsuccessful transmission (receipt by user).
[00183] The active transmitter module 105 is configured to filter out certain
data
communication channel(s) and user(s) for future or subsequent composed social
data
transmissions.
[00184] The active transmitter module 105 is configured to repeat the
transmission of
previously sent composed social data for N number of times depending upon
analytic
responses received by the active transmitter module. The value of N in this
scenario may be
analytically determined.
[00185] The active transmitter module 105 is configured to analytically
determine duration
of time between each transmission campaign.
[00186] The active transmitter module 105 is configured to apply metadata from
the
active composer module 104 to the transmission of the composed social data, in
order to
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provide further business information enrichment. The metadata includes, but is
not limited
to, geo data, temporal data, business driven characteristics, unique campaign
IDs,
keywords, hash tags or equivalents, analytic driven characteristics, etc.
[00187] The active transmitter module 105 is configured to scale in size, for
example, by
using multiple active transmitter modules 105. In other words, although one
module 105 is
shown in the figures, there may be multiple instances of the same module to
accommodate
large scale transmission of data.
[00188] Active Transmitter Module and Prediction
[00189] In one embodiment, the active transmitter module 105 is configured to
predict the
success of a social data message as transmitted to particular data
communication
channel(s) and/or users. That is, the active transmitter module 105 can be
configured to
store feedback on prior success (e.g. based on user feedback, re-posts, re-
tweets, or
resending of messages) and use machine learning techniques (e.g. Monte Carlo
simulations) to predict the likelihood of success of a message. The active
transmitter
module 105 may be provided with pre-defined thresholds or rules (e.g. stored
in a memory
309) defining success of a message (e.g. amount of time in which a message is
read or
viewed, number of forwards of a message, etc.). In one example, the active
transmitter
module 105 predict the outcome of social data message by predicting whether
the social
data message is likely to spread to additional data communication channel(s)
or to additional
users or geographical regions. Accordingly, the active transmitter module 105
is configured
to process the computed predictions (e.g. processor 307) and determine further
amendments or modifications to the social media data (e.g. content, timing of
delivery,
frequency of message delivery, message destination, communication channels,
languages,
and/or local jargon) to improve the outcome (e.g. likelihood of successful
feedback) of the
social data message. The active transmitter module 105 can be configured to
communicate
with the other modules (e.g. 103, 104 and 106) for reconfiguring the social
media data
according to the parameters depicted by the prediction operation of the active
transmitter
module 105. As described earlier, the social data object is any one of text, a
video, a picture
or a photo, a graphic, audio data, or a combination thereof. As defined
earlier, the active
transmitter module 105 can also incorporate third party pixels, emitters,
trackers etc. to
provide the third party verification that a message was seen or clicked upon
by a customer.
The synthesizer module 106 could, alternatively incorporate the feedback from
third party
tracking, and use this third party feedback to bias and/or adjust the active
transmitter module
105 (and corresponding transmission parameters as described herein), and
ultimately adjust
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where, when, who, etc. see a transmitted message based using the third party
transmission
telemetry.
[00190] The third party data can be used singularly or in conjunction with the
predictive
modules described herein to help predict the user transmission targeting
and/or destination.
Social Analytic Synthesizer Module
[00191] The social analytic synthesizer module 106 is configured to perform
machine
learning, analytics, and to make decisions according to business driven rules.
The results
and recommendations determined by the social analytic synthesizer module 106
are
intelligently integrated with any one or more of the active receiver module
103, the active
composer module 104, and the active transmitter module 105, or any other
module that can
be integrated with the system 102. This module 106 may be placed or located in
a number
of geo locations, facilitating real time communication amongst the other
modules. This
arrangement or other arrangements can be used for providing low latency
listening, social
content creation and content transmission on a big data scale.
[00192] The social analytic synthesizer module 106 is also configured to
identify unique
holistic patterns, correlations, and insights. In an example embodiment, the
module 106 is
able to identify patterns or insights by analysing all the data from at least
two other modules
(e.g. any two or more of modules 103, 104 and 105), and these patterns or
insights would
not have otherwise been determined by individually analysing the data from
each of the
modules 104, 104 and 105. The feedback or an adjustment command is provided by
the
social analytic synthesizer module 106, in an example embodiment, in real time
to the other
modules. Over time and over a number of iterations, each of the modules 103,
104, 105 and
106 become more effective and efficient at continuous social communication and
at their
own respective operations.
[00193] Turning to FIG. 12, example components of the social analytic
synthesizer
module 106 are shown. Example components include a copy of data from the
active
receiver module 1201, a copy of data from the active composer module 1202, and
a copy of
data from the active transmitter module 1203. These copies of data include the
inputted
data obtained by each module, the intermediary data, the outputted data of
each module, the
algorithms and computations used by each module, the parameters used by each
module,
etc. Preferably, although not necessarily, these data stores 1201, 1202 and
1203 are
updated frequently. In an example embodiment, the data from the other modules
103, 104,
105 are obtained by the social analytic synthesizer module 106 in real time as
new data from
these other modules become available.
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[00194] Continuing with FIG. 12, example components also include a data store
from a
third party system 1204, an analytics module 1205, a machine learning module
1206 and an
adjustment module 1207. The analytics module 1205 and the machine learning
module
1206 process the data 1201, 1202, 1203, 1204 using currently known and future
known
computing algorithms to make decisions and improve processes amongst all
modules (103,
104, 105, and 106).
[00195] The analytics module 1205 can communicate with the machine learning
module
1206 and use a variety of approaches to analyze the social data and the
associated other
data as received from modules 103, 104, and 105. The analysis is performed to
determine
relationships, correlations, affinities, and inverse relationships within the
data provided
independently from each module and to cross-correlate the data from each one
of the
modules 103, 104 and 105 with the remaining other ones of the modules 103, 104
and 105.
Non-limiting examples of algorithms that can be used to determine the
relationships amongst
the data include artificial neural networks, nearest neighbor, Bayesian
statistics, decision
trees, regression analysis, fuzzy logic, K-means algorithm, clustering, fuzzy
clustering, the
Monte Carlo method, learning automata, temporal difference learning, apriori
algorithms, the
ANOVA method, Bayesian networks, and hidden Markov models. More generally,
currently
known and future.known analytical methods can be used to identify
relationships,
correlations, affinities, and inverse relationships amongst the social data
obtained from the
modules 103, 104 and/or 105 (as well as previous data from the synthesizer
module 106).
As defined earlier, the active transmitter module 105 can also incorporate
third party pixels,
emitters, trackers etc. to provide the third party verification that a message
was seen or
clicked upon by a customer. The synthesizer module 106 could, alternatively
incorporate the
feedback from third party tracking, and use this third party feedback to bias
and/or adjust the
active transmitter module 105 (and corresponding transmission parameters as
described
herein), and ultimately adjust where, when, who, etc. see a transmitted
message based
using the third party transmission telemetry.
[00196] The third party data can be used singularly or in conjunction with the
predictive
modules described herein to help predict the user transmission targeting
and/or destination.
[00197] The adjustment module 1207 generates adjustment commands based on the
results from the analytics module and the machine learning module. The
adjustment
commands are then sent to the respective modules (e.g. any one or more of
modules 103,
104, 105, and 106).
[00198] In an example embodiment, data from a third party system 1204 can be
from
another social network, such as LinkedIn, Facebook, Twittter, etc.
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[00199] Other example aspects of the social analytic synthesizer module 106
are below.
[00200] The social analytic synthesizer module 106 is configured to integrate
data in real
time from one or more sub systems and modules, included but not limited to the
active
receiver module 103, the active composer module 104, and the active
transmitter module
105. External or third party systems can be integrated with the module 106.
[00201] The social analytic synthesizer module 106 is configured to apply
machine
learning and analytics to the obtained data to search for "holistic" data
patterns, correlations
and insights.
[00202] The social analytic synthesizer module 106 is configured to feed back,
in real
time, patterns, correlations and insights that were determined by the
analytics and machine
learning processes (e.g. analytics module 1205 and/or machine learning module
1206). The
feedback is directed to the modules 103, 104, 105, and 106 and this integrated
feedback
loop improves the intelligence of each module and the overall system 102
overtime. In yet
another aspect, the synthesizer module 106 is configured to directly alter
subsequent social
media data generated by the system 102 prior to transmission to end users
based on the
criteria that the subsequent social media data is similar to prior social
media data from which
patterns, correlations and/or insights were determined.
[00203] The social analytic synthesizer module 106 is configured to scale the
number of
such modules. In other words, although the figures show one module 106, there
may be
multiple instances of such a module 106 to improve the effectiveness and
response time of
the feedback.
[00204] The social analytic synthesizer module 106 is configured to operate
automatically
(without any user input), and/or semi-automatically (user input for defining
business rules
and/or criteria for triggering retrieval of social data and/or triggering
adjustment of operations
of system 102).
[00205] Turning to FIG. 13, example computer or processor implemented
instructions are
provided for analysing data and providing adjustment commands based on the
analysis,
according to module 106. At block 1301, the social analytic synthesizer module
obtains and
stores data from the active receiver module, the active composer module and
the active
transmitter module. Analytics and machine learning are applied to the data
(block 1302).
The social analytic synthesizer determines adjustments to make in the
algorithms or
processes used in any of the active receiver module, active composer module,
and the
active transmitter module (block 1303). The adjustments, or adjustment
commands, are
then sent to the corresponding module or corresponding modules (block 1304).
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Determining Transmission Destination of New Messages
[00206] Although the embodiments above discuss the active transmitter module
105
changing the transmission parameters for subsequent social data. In one
aspect, the
feedback response 1018 and/or tracker response 1016 is forwarded to the
synthesizer
module 106 for defining adjustments for new social media data messages based
on the
feedback response 1018 and/or tracker response 1016. Additionally, the
synthesizer
module 106 is configured to utilize prior knowledge, prior learned patterns
and pre-defined
rules (e.g. as stored on memory 312 or memory 309). For example, the
determined patterns
may reveal one or more influencers for a particular topic. Accordingly, the
synthesizer
module 106 is configured to define adjustments to the operations of the source
modules 103,
104 and 105 to tailor subsequent social media data of the same topic according
to formatting
preferences (e.g. language), content and/or destination (e.g. via the active
transmitter
module 105) of the revealed influencers.
[00207] General example embodiments of the systems and methods are described
below.
[00208] In general, a method performed by a computing device for communicating
social
data, includes: obtaining social data; deriving at least two concepts from the
social data;
determining a relationship between the at least two concepts; composing a new
social data
object using the relationship; transmitting the new social data object;
obtaining user
feedback associated with new social data object; and computing an adjustment
command
using the user feedback, wherein executing the adjustment command adjusts a
parameter
used in the method.
[00209] In an aspect of the method, an active receiver module is configured to
at least
obtain the social data, derive the least two concepts from the social data,
and determine the
relationship between the at least two concepts; an active composer module is
configured to
at least compose the new social data object using the relationship; an active
transmitter
module is configured to at least transmit the new social data object; and
wherein the active
receiver module, the active composer module and the active transmitter module
are in
communication with each other.
[00210] In an aspect of the method, each of the active receiver module, the
active
composer module and the active transmitter module are in communication with a
social
analytic synthesizer module, and the method further includes the social
analytic synthesizer
module sending the adjustment command to at least one of the active receiver
module, the
active composer module and the active transmitter module.
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[00211] In an aspect of the method, the method further includes executing the
adjustment
command and repeating the method.
[00212] In an aspect of the method, obtaining the social data includes the
computing
device communicating with multiple social data streams in real time.
[00213] In an aspect of the method, determining the relationship includes
using a
machine learning algorithm or a pattern recognition algorithm, or both.
[00214] In an aspect of the method, composing the new social data object
includes using
natural language generation.
[00215] In an aspect of the method, the method further includes
determining a social
communication channel over which to transmit the new social data object, and
transmitting
the new social data object over the social communication channel, wherein the
social
communication channel is determined using at least one of the at least two
concepts.
[00216] In an aspect of the method, the method further includes determining a
time at
which to transmit the new social data object, and transmitting the new social
data object at
the time, wherein the time is determined using at least one of the at least
two concepts.
[00217] In an aspect of the method, the method further includes adding a data
tracker to
the new social data object before transmitting the new social data object,
wherein the data
tracker facilitates collection of the user feedback.
[00218] In an aspect of the method, the new social data object is any one of
text, a video,
a picture, a graphic, audio data, or a combination thereof.
[00219] In general, there is provided a method performed by a computing device
for
communicating social data, comprising: obtaining the social data from one or
more sources;
composing a new social data object derived from the social data; transmitting
the new social
data object; obtaining at least one feedback associated with the new social
data object;
computing an adjustment command using said feedback, wherein executing the
adjustment
command adjusts at least one of steps of obtaining, composing, and
transmitting for
subsequent social data objects in dependence upon said feedback.
[00220] In one aspect, an active receiver module is configured to at least
obtain the social
data; an active composer module is configured to at least compose the new
social data
object; an active transmitter module is configured to at least transmit the
new social data
object; and wherein the active receiver module, the active composer module and
the active
transmitter module are in communication with a social analytic synthesizer
module for
computing the adjustment.
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[00221] In another aspect, each feedback is weighted according to predefined
rules and a
higher weighting being associated with a higher degree of adjustment.
[00222] In another aspect, computing an adjustment further comprises
determining
patterns based on feedback associated with data from each of the active
receiver module,
the active composer module and the active transmitter module, the patterns for
use in
subsequently generating the adjustment to the respective at least one steps of
obtaining,
composing and transmitting subsequent social data objects.
[00223] In another aspect, computing an adjustment for the step of obtaining
said at least
one feedback further comprises using said patterns for deriving at least two
concepts from
the social data; determining a relationship between the at least two concepts;
and
composing the new social data object using the relationship.
[00224] In another aspect, the social data comprises a social data object and
the new
social data object comprises the social data object.
[00225] In another aspect, the method further comprises the social analytic
synthesizer
module sending the adjustment command to at least one of the active receiver
module, the
active composer module and the active transmitter module.
[00226] In another aspect, the method further comprises executing the
adjustment
command and repeating the method.
[00227] In another aspect, obtaining the social data comprises the computing
device
communicating with multiple social data streams in real time.
[00228] In another aspect, determining patterns comprises using at least one
of: a
machine learning algorithm and a pattern recognition algorithm based on prior
positive
feedback associated with the social data.
[00229] In another aspect, the adjustment based on said patterns further
adjusts the
social communication channel over which to transmit the new social data
object, and the
method comprises transmitting the new social data object over the social
communication
channel.
[00230] In another aspect, determining a time at which to transmit the new
social data
object, and transmitting the new social data object at the time, wherein the
time is
determined using detected patterns from said feedback.
[00231] In another aspect, wherein the social communication channel is
determined
based upon determining an inflection point of prior communication of the new
social data
based upon said feedback, the inflection point indicating a user that multiply
broadcasts the
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new social data, the adjustment comprising causing subsequent transmission of
social data
to be transmitter to the inflection point.
[00232] In another aspect, the method further comprises transmitting the new
social data
object to at least one destination, wherein said at least one feedback
indicates a
transmission path of said new social data, the transmission path indicating re-
transmission of
said new social data to an alternate destination than said at least one
destination and
computing said adjustment comprises adjusting subsequent destination of
subsequent social
data objects in dependence upon said alternate destination.
[00233] In another aspect, the adjustment further comprises re-composing
subsequent
social data objects in dependence upon said alternate destination.
[00234] It will be appreciated that different features of the example
embodiments of the
system and methods, as described herein, may be combined with each other in
different
ways. In other words, different modules, operations and components may be used
together
according to other example embodiments, although not specifically stated.
[00235] The steps or operations in the flow diagrams described herein are just
for
example. There may be many variations to these steps or operations without
departing from
the spirit of the invention or inventions. For instance, the steps may be
performed in a
differing order, or steps may be added, deleted, or modified.
[00236] Although the above has been described with reference to certain
specific
embodiments, various modifications thereof will be apparent to those skilled
in the art
without departing from the scope of the claims appended hereto.
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