Note: Descriptions are shown in the official language in which they were submitted.
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
SYSTEMS AND METHODS FOR OBTAINING PRODUCT
INFORMATION IN REAL-TIME
TECHNICAL FIELD
[0001] The present disclosure relates to data processing systems and, more
particularly, to
systems and methods for obtaining client-specific product information and
automatically
performing comparisons of related products in real-time.
BACKGROUND
[0002] Documents containing product details/specification are often lengthy
and may be overly
complicated for a typical customer to understand. A product's specification
may include
technical jargon and/or industry-specific principles with which customers may
not be familiar.
Product details that are of particular interest for customers may sometimes be
obscured by non-
essential information. The process of manually reviewing product specification
documents and
distilling relevant information can be onerous for customers and prospective
buyers. As a
consequence, customers may fail to adequately review such documents.
[0003] This difficulty in processing product specification documents is
especially pronounced
for customers who wish to compare similar products based, at least in part, on
their respective
specifications. For example, policy documents, such as insurance policy
documents, can be
complex, making it difficult for ordinary customers to understand and
appreciate details of the
policy. Given limited knowledge of a product, the prospects of a customer
being able to (1)
identify similar products (e.g. products having similar specification) in an
expedient manner, and
(2) compare the products comprehensively in order to determine which of the
products they
prefer, is likely to be poor.
[0004] Accordingly, it would be desirable to provide a system that facilitates
real-time
acquisition of product data and presentation of the data to consumers, in a
manner that can
reduce the likelihood of human error or oversight in processing product
information.
1
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
BRIEF DESCRIPTION OF DRAWINGS
[0005] Reference will now be made, by way of example, to the accompanying
drawings which
show example embodiments of the present application and in which:
[0006] FIG. 1 is a schematic diagram illustrating an operating environment of
an example
embodiment;
[0007] FIG. 2 is a high-level operation diagram of an example computing system
for
implementing example embodiments of a product valuation system;
[0008] FIG. 3A depicts a simplified organization of software components stored
in an exemplary
memory of the example computing system of FIG. 2.
[0009] FIG. 3B depicts a simplified organization of components in an exemplary
processor of
the example computing system of FIG. 2;
[0010] FIG. 4 shows, in flowchart form, an example method for obtaining an
estimate of a value
of a product;
[0011] FIG. 5 shows, in flowchart form, an example method for analyzing text
to identify
product information;
[0012] FIG. 6 shows, in flowchart form, an example method for obtaining an
estimate of a value
of a product;
[0013] FIG. 7 shows, in flowchart form, an example method for automatically
requesting
cancellation of a product; and
[0014] FIG. 8 shows, in flowchart form, an example method for obtaining a
quote for an
insurance product.
[0015] Like reference numerals are used in the drawings to denote like
elements and features.
2
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0016] In one aspect, the present disclosure describes a computing system. The
computing
system includes a memory, a communications module that is communicable with an
external
network, and a processor coupled to the memory and the communications module.
The processor
is configured to: receive, from a first client device, a signal representing
image data depicting at
least one first document containing a product specification for a first
product; perform text
recognition on the image data to identify text in the at least one first
document; obtain a first
value representing the product specification for the first product based on
the recognized text;
select a second product having a product specification represented by the
first value; obtain a
second value associated with the selected second product; generate first
display data including a
graphical representation of the second value; and transmit, to the first
client device via the
communications module, a signal representing the first display data.
[0017] In some implementations, the processor may be further configured to
receive, from the
first client device, contextual data that defines a current status of the
first client device. The first
value representing the product specification for the first product may be
obtained based on the
recognized text and the contextual data.
[0018] In some implementations, the contextual data may comprise at least one
of: geolocation
data; temperature data; motion data; altitude data; and proximity data
indicating proximity to one
or more known devices.
[0019] In some implementations, performing text recognition on the image data
may include:
comparing the image data depicting the at least one first document to one or
more document
templates from a first database, the one or more document templates containing
product
specifications for a first set of products; and determining that there is a
match between the at
least one first document and a first one of the document templates from the
first database.
[0020] In some implementations, performing text recognition on the image data
may include
applying optical character recognition (OCR) on the image data.
[0021] In some implementations, the processor may be further configured to:
determine that the
recognized text does not contain text associated with at least one first
predetermined data field;
generate second display data for prompting a user of the first client device
to provide information
3
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
relating to the at least one first predetermined data field; and transmit, to
the first client device,
the second display data.
[0022] In some implementations, the second display data may include a
graphical user interface
including a fillable input form containing the at least one first
predetermined field. In some
implementations, the second display data may include a graphical user
interface including an
application form having the at least one first predetermined field
highlighted.
[0023] In some implementations, selecting the second product may include
determining that
product specification for the second product satisfies a predefined condition
associated with a
predetermined data field.
[0024] In another aspect, the present disclosure describes a method for
obtaining an estimate of a
value of a product. The method may include: receiving, from a first client
device, a signal
representing image data depicting at least one first document containing a
product specification
for a first product; performing text recognition on the image data to identify
text in the at least
one first document; obtaining a first value representing the product
specification for the first
product based on the recognized text; selecting a second product having a
product specification
represented by the first value; obtaining a second value associated with the
selected second
product; generating first display data including a graphical representation of
the second value;
and transmitting, to the first client device via the communications module, a
signal representing
the first display data.
[0025] In yet another aspect, the present disclosure may describe an
electronic device. The
electronic device may include a camera, a display interface, a communications
module
communicable with an external network, a memory, and a processor that is
coupled to the
camera, the display interface, the communications module, and the memory. The
processor may
be configured to: obtain image data depicting at least one first document
containing a product
specification for a first product; perform text recognition on the image data;
generate first display
data including a graphical user interface for requesting cancellation of the
first product, the
graphical user interface being pre-populated with data based on the recognized
text; and present
the first display data on the display interface.
4
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0026] Other example embodiments of the present disclosure will be apparent to
those of
ordinary skill in the art from a review of the following detailed descriptions
in conjunction with
the drawings.
[0027] In the present application, the term "and/or" is intended to cover all
possible
combinations and sub-combinations of the listed elements, including any one of
the listed
elements alone, any sub-combination, or all of the elements, and without
necessarily excluding
additional elements.
[0028] In the present application, the phrase "at least one of ...or..." is
intended to cover any
one or more of the listed elements, including any one of the listed elements
alone, any sub-
combination, or all of the elements, without necessarily excluding any
additional elements, and
without necessarily requiring all of the elements.
[0029] In the present application, the term "product specification" may
interchangeably refer to
descriptive information relating to a product and documents that provide
descriptions of the
product. For example, product specification for a policy product may refer to
policy details and
requirements and/or copies (e.g. digital, physical) of documents that contain
details of the policy.
[0030] For the ordinary consumer, comparing multiple products to determine a
preferred product
can often be a frustrating process. This is especially true of situations
where the products being
compared are relatively complex in character (e.g. insurance policies). In
particular, it may be
difficult for a customer to identify products that have the same or similar
product specifications,
such that a suitable comparison can be made. Generally, a customer will gather
product
information for a first product and query one or more databases containing
product data for
various products, in order to identify suitably related products. Obtaining
product information
can be a complicated and tedious exercise. Customers may be required to review
product
specifications in great detail and identify relevant data fields that can be
used in product database
queries. Errors and inaccuracies in the obtained product information for a
product may adversely
affect the identification of related products and, consequently, distort the
product comparisons
process.
[0031] In an aspect, the present application discloses a system for acquiring
product information
for a first product and identifying products that are suitably related to the
first product for a
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
particular client or customer. More specifically, a system for obtaining
product data from a client
device and providing recommendations of related products to the client is
disclosed. The system
of the present disclosure is designed to enable real-time acquisition of
product data, retrieval of
product details for related products for a particular client, and comparison
of product details for
two or more different products. In accordance with embodiments of the present
disclosure, a
computing system receives, from a client device, acquired product data for a
specific product.
The computing system processes the received data to obtain a first value
representing the product
specification for the product. A second, related product is automatically
selected for the client,
based on the product specification. The computing system may then perform
comparisons of the
related products according to one or more criteria. The comparison data may be
provided in
various forms to users associated with the client device.
[0032] In a further aspect, the present disclosure describes techniques of
displaying product
comparison data on an electronic device. The comparison data is based on a
real-time
comparison of two or more related products for a client. The product
comparison is preceded by
identification of product details for a first product associated with a
client, and selection of a
related product having the same or similar product specification as the first
product. The product
details for the first product may be collected from one or more different data
sources. In
particular, product data may be received from a client device associated with
the client. For
example, the product data may include image data depicting a product
specification associated
with the first product. Auxiliary data (e.g. contextual data) received from
the client device may
inform the identification and, subsequently, comparison of related products.
The product
comparison data may then be provided in a suitable form to a client associated
with the client
device. For example, a user interface which graphically represents the product
comparison data
may be transmitted to the client device for display thereon.
[0033] In a further aspect, the present disclosure describes systems and
methods for a client
device to automatically request cancellation of a product. The product may,
for example, be a
product that the client had purchased, subscribed to, etc. Product data
acquired by the client
device is used to automatically generate a request to cancel the product (e.g.
end a subscription,
notify of termination, return a digital product, request a refund, etc.). The
request may then be
transmitted to one or more computing systems associated with administering the
product.
6
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0034] Reference is first made to FIG. 1, which shows an exemplary operating
environment 100
in accordance with embodiments of the present disclosure. FIG. 1 illustrates
an exemplary
product valuation system (PVS) 150. The PVS 150 may implement processes for,
among others,
obtaining product-related data depicting a product's specification,
identifying text in the product
specification, selecting one or more related products having a particular
product specification,
obtaining values associated with the identified related products, generating
display data
representing comparison of said values for the products, and distributing the
display data to one
or more client devices.
[0035] The environment 100 includes a plurality of electronic devices 102.
Each electronic
device 102 is a computer system. An electronic device 102 may be associated
with an entity that
obtains product-related data for a first product and requests comparison of
the first product with
a suitably related second product. Such entity may be a consumer, a business,
a system
owner/administrator, or other parties desirous of requesting product
comparisons data for related
products.
[0036] In some embodiments, the electronic device 102 may be a portable
electronic device. For
example, the electronic device 102 may, as illustrated, be a smartphone. The
electronic device
102 may be a computing system of another type such as a personal computer, a
laptop computer,
a tablet computer, a notebook computer, a hand-held computer, a personal
digital assistant, a
portable navigation device, a mobile phone, a smart phone, a wearable
computing system (e.g., a
smart watch, a wearable activity monitor, wearable smart jewelry, and glasses
and other optical
devices that include optical head-mounted displays), an embedded computing
system (e.g., in
communication with a smart textile or electronic fabric), and any other type
of computing system
that may be configured to store data and software instructions, and execute
software instructions
to perform operations consistent with disclosed embodiments. In some
embodiments, the
electronic device 102 may include a smart card, chip card, integrated circuit
card (ICC), and/or
other card having an embedded integrated circuit.
[0037] The electronic device 102 is configured to execute software, such as a
products
comparison application (not shown). A products comparison application may, for
example, be a
web application (e.g. single-page application, or SPA), a mobile application,
or a desktop
application. In some embodiments, the products comparison application may be
implemented as
7
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
a component or feature of another application, such as a mobile banking or
payment app. The
products comparison application may be an application that can be used by a
client to request to
receive product information for various products and product comparison data
for two or more
different products. For example, the products comparison application may be
used to retrieve
information for products that are related to a user-selected product. As
another example, a user
may obtain product comparison data for two or more different but related
products upon making
a request using the products comparison application. In some embodiments, the
products
comparison application may comprise a Web browser application that is
configured to run and
display a Web form interface for clients to use when requesting product
comparison data.
[0038] The network 120 is a computer network. The network 120 allows computer
systems in
communication therewith to communicate. For example, as illustrated, the
network 120 may
allow the electronic devices 102 to communicate with the PVS 150 and a product
specification
templates database 180.
[0039] The product valuation system (PVS) 150 is implemented as part of a
computer system.
The PVS 150 may be implemented by one or more computing systems such as, for
example,
database servers, computer servers, and the like. For example, the PVS 150 may
be implemented
by servers that are connected to or integrated with resources accessible by a
plurality of
computing systems. As another example, the PVS 150 may be implemented by
servers
associated with a financial institution (e.g. bank, credit union, etc.)
interfacing with devices
associated with current and/or prospective customers of the financial
institution. The computing
systems may be in communication with each other using the network 120.
Alternatively, the
computing systems may communicate using another network such as, for example,
a local-area
network (LAN). In some embodiments, the PVS 150 may be implemented by multiple
computing systems organized in a tiered arrangement (e.g. middle-tier and back-
end computing
systems). In some embodiments, the PVS 150 may be provided by a cluster formed
of a plurality
of interoperating computing systems.
[0040] The PVS 150 may, in association with one or more different computer
systems, handle
various services relating to, among others, product information and comparison
data request
processing, image data analysis, product data retrieval and processing,
customer accounts data
management, and control of user interfaces for requesting and accessing
products information
8
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
and product comparison data. FIG. 1 illustrates a product value acquisition
module 152 which
may be included as part of a computer system implementing the PVS 150. The
product value
acquisition module 152 may be integrated into the PVS 150 or it may be
provided by a remote
computing unit that is communicably coupled to the computer system
administering the PVS 150.
[00411 The product value acquisition module 152 processes input data relating
to products and
generates values associated with the products. A "value" associated with a
product may be any
property of a product that can be quantitatively or qualitatively described
including, for example,
monetary value, type, function, appearance, term/duration, etc. The product
value acquisition
module 152 may acquire product-related data for a product and apply the data
as inputs to a
mechanism for deriving a value associated with the product. For example, the
product value
acquisition module 152 may implement one or more suitable algorithms for a
product, using the
acquired product data as inputs, to generate a value for the product.
[0042] The product specification templates (PST) database 180 may contain one
or more
templates of product specifications for various different products. In at
least some embodiments,
the PST database 180 may store templates of specifications for products that
are offered by
different providers. The templates in the PST database 180 may be categorized
according to one
or more criteria, such as product type, product line, and
manufacturer/provider, etc. The PST
database 180 may be updated (for example, by a database administrator, product
manufacturer,
etc.) by adding new or revised specifications or by deleting void or outdated
specifications. The
PST database 180 may be accessible by at least the PVS 150. Specifically, the
PST database 180
may be integrated into the PVS 150 or communicably connected to the PVS 150
via, for example,
the network 120.
[0043] FIG. 2 is a high-level operation diagram of an example computing system
200 that may
be configured to implement a PVS 150. The computing system 200 of FIG. 2
includes a variety
of modules. For example, as illustrated, the computing system 200 may include
a processor 202,
a memory 210, an input interface module 220, an output interface module 230,
and a
communications module 240. As illustrated, the foregoing example modules of
the computing
system 200 are in communication over a bus 250.
9
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0044] The processor 202 is a hardware processor. Processor 202 may, for
example, be one or
more ARM, Intel x86, PowerPC processors or the like.
[0045] The memory 210 allows data to be stored and retrieved. The memory 210
may include,
for example, random access memory, read-only memory, and persistent storage.
Persistent
storage may be, for example, flash memory, a solid-state drive or the like.
Read-only memory
and persistent storage are a computer-readable medium. A computer-readable
medium may be
organized using a file system such as may be administered by an operating
system governing
overall operation of the electronic device 102.
[0046] The input interface module 220 allows the computing system 200 to
receive input signals.
Input signals may, for example, correspond to input received from a user. The
input interface
module 220 may serve to interconnect the computing system 200 with one or more
input devices.
Input signals may be received from input devices by the input interface module
220. Input
devices may, for example, include one or more of a touchscreen input,
keyboard, trackball or the
like. In some embodiments, all or a portion of the input interface module 220
may be integrated
with an input device. For example, the input interface module 220 may be
integrated with one of
the aforementioned example input devices.
[0047] The output interface module 230 allows the computing system 200 to
provide output
signals. Some output signals may, for example allow provision of output to a
user. The output
interface module 230 may serve to interconnect the computing system 200 with
one or more
output devices. Output signals may be sent to output devices by output
interface module 230.
Output devices may include, for example, a display screen such as, for
example, a liquid crystal
display (LCD), a touchscreen display. Additionally or alternatively, output
devices may include
devices other than screens such as, for example, a speaker, indicator lamps
(such as for, example,
light-emitting diodes (LEDs)), and printers. In some embodiments, all or a
portion of the output
interface module 230 may be integrated with an output device. For example, the
output interface
module 230 may be integrated with one of the aforementioned example output
devices.
[0048] The communications module 240 allows the computing system 200 to
communicate with
other electronic devices and/or various communications networks. For example,
the
communications module 240 may allow the computing system 200 to send or
receive
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
communications signals. Communications signals may be sent or received
according to one or
more protocols or according to one or more standards. For example, the
communications module
240 may allow the computing system 200 to communicate via a cellular data
network, such as
for example, according to one or more standards such as, for example, Global
System for Mobile
Communications (GSM), Code Division Multiple Access (CDMA), Evolution Data
Optimized
(EVDO), Long-term Evolution (LTE) or the like. Additionally or alternatively,
the
communications module 240 may allow the computing system 200 to communicate
using near-
field communication (NFC), via Wi-Fi (TM), using Bluetooth (TM) or via some
combination of
one or more networks or protocols. Contactless payments may be made using NFC.
In some
embodiments, all or a portion of the communications module 240 may be
integrated into a
component of the computing system 200. For example, the communications module
may be
integrated into a communications chipset.
[0049] Software comprising instructions is executed by the processor 202 from
a computer-
readable medium. For example, software may be loaded into random-access memory
from
persistent storage of memory 210. Additionally or alternatively, instructions
may be executed by
the processor 202 directly from read-only memory of memory 210.
[0050] FIG. 3A depicts a simplified organization of software components stored
in memory 210
of the computing system 200. As illustrated, these software components include
an operating
system 300 and PVS 150.
[0051] The operating system 300 is software. The operating system 300 allows
the PVS 150 to
access the processor 202, the memory 210, the input interface module 220, the
output interface
module 230 and the communications module 240. The operating system 300 may be,
for
example, Apple iOS (TM), Google (TM) Android (TM), Linux (TM), Microsoft (TM)
Windows
(TM), or the like.
[0052] FIG. 3B depicts a simplified organization of components exemplary of an
embodiment of
the processor 202. As illustrated, the processor 202 includes an image
processing module 170
and a user interface (UI) manager module 320. The UI manager 320 manages
information that
may be displayed on a client device when a user makes a manual request to
obtain product
information for a user-selected product or products comparison data for two or
more products.
11
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
When a user requests to access product information or products comparison
data, it is desirable
to display information about the request that is specifically tailored for
that user. For example, a
requester may wish to view, via the user interface, information regarding the
products being
compared, such as product type, comparable features and properties, product
data and statistics,
manufacturer/provider, relative pricing, etc. The UI manager 320 may generate
a user interface
which can be displayed on the device of a requester, such that the requester
can make a manual
request (for product information and/or products comparison data) to a product
assessment
platform such as PVS 150, using the user interface. In particular, the UI
manager 320 may
determine display data that should be rendered for a requesting user's device.
[0053] The image processing module 170 includes, at least, a parsing module
172, which
contains a field recognition engine 173 and an OCR engine 174. Each of these
components and
their respective functions will be discussed in turn with reference to the
method 400 in FIG. 4.
[0054] Reference is made to FIG. 4, which shows, in flowchart form, an example
method 400
for obtaining an estimate of a value associated with a product. Additionally
or alternatively, the
method 400 may be implemented as a subroutine of a process for automatically
identifying
products that are related to a user-selected product and comparing the
identified products. The
method 400 may be performed by a computing system, such as PVS 150, that is
configured to
obtain, evaluate, and present product information for one or more products.
The product may, for
example, be a policy, such as an insurance policy. The computing system may be
communicably
coupled to a plurality of client devices corresponding to product information
requesting entities.
[0055] In operation 402, the computing system receives, from a client device,
image data
depicting at least one first document containing a product specification for a
first product. For
example, the computing system may receive, via the communications module, a
signal that
includes image data captured by the client device. The first document may be a
policy document
and/or a legal document. The first document may, for example, be an insurance
policy document
which identifies one or more terms, clauses, or conditions associated with an
insurance policy.
For example, the first document may reflect terms and conditions of a proposed
policy, such as
terms of a new automobile, health, life or housing insurance policy.
12
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0056] In some embodiments, the computing system may be configured to
automatically
recognize if the received image data depicts a certain type of document. An
image processing
module (such as image processing module 170 of FIG. 3B) of the computing
system may
implement one or more techniques for document image classification, in
operation 402. The
imaged document may be classified as a first type (e.g. insurance policy
declaration) based on,
for example, (local) image features, physical layout, logical structure,
and/or textual features. For
example, the received image may be processed by the image processing module
using, for
example, pattern recognition (e.g. template matching), document layout
analysis, and/or other
algorithms, to determine that the document that is captured in the image is of
a first type.
[0057] The image data may, in some embodiments, be processed to facilitate
further operations
of the method 400. For example, since the image data of any one image may not
reflect a
complete document, the computing system may perform stitching based on
multiple images in
order to determine a more complete representation of the first document. In
some instances, the
computing system may parse the image data into a plurality of segments by
automatically
evaluating a geometric structure of the first document represented in the
image data. For
example, the computing system may identify whitespace having predefined
characteristics in
order to separate the image into sections or segments. Whitespace that is
determined to represent
carriage returns or indentations may be used to identify paragraphs of the
document, and the
image data may be separated into paragraphs.
[0058] In operation 404, text recognition is performed based on the received
image data. For
example, the image data or a portion thereof (such as a section or segment)
may be analyzed to
identify text contained therein. The image data may be processed by a parsing
module (such as
parsing module 172 of FIG. 3B) of the computing system to extract one or more
text entry items
from the imaged document. In some embodiments, when performing text
recognition on the
image data, the computing system may compare the image to one or more document
templates
from a templates database (such as PST database 180). The document templates
may, for
example, contain product specifications for various products. The computing
system may
determine whether there is a match between the imaged (first) document and one
(or more) of the
document templates from the templates database.
13
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0059] An example implementation of operation 404 is described. The received
image data may
first be passed to a field recognition engine, which determines regions and
boundaries of the
received image that correspond to the various data fields of an identified
document type. The
field recognition engine may, for example, perform a layout analysis by
segmenting the
document image into regions having homogeneous content and assigning a logical
meaning (e.g.
association with a data field) to each of the regions. Additionally or
alternatively, the field
recognition engine may employ a template matching technique to identify
feature matches
between the received image and document templates. Specifically, template
matching can be
used to identify regions of the received image that match data field labels
and their neighboring
regions in one or more document templates. By way of example, in some
embodiments, the
received image may be compared to one or more document templates, in order to
identify
matches of data fields. A data field in the received image of the document may
be identified by
detecting a match with a data field in one of the templates based on, for
example,
dimensions/shape of the data field, text or graphics label associated with the
data field, and/or
relative location of the data field on the imaged document.
[0060] Once the data field boundaries (and, accordingly, the corresponding
data field regions) on
the received image are identified, the image may be further processed by the
optical character
recognition (OCR) engine. The OCR engine is capable of converting images of
typed,
handwritten, or printed text into digital format, such as machine-encoded
text. The OCR engine
detects an image representation of a text entry item in a particular data
field region and converts
the image representation into text format. In this way, the text associated
with the text entry
items represented in the received image of the document can be extracted.
[0061] In some embodiments, the OCR engine may be used in identifying data
fields on the
received image of the document. In particular, the text content of a data
entry item on the imaged
document that is detected by the OCR engine may indicate or suggest the
corresponding data
field. For example, if the detected text reads "One Hundred and Fifty
Dollars", the corresponding
data field may be presumed to be an "Amount" field. In some embodiments, the
OCR engine
may even be configured to detect and convert data field labels into text,
which would facilitate
identifying the data fields. For example, the OCR engine may read "Insured
Product:" and assign
the associated data field as a "Coverage Type" field; similarly, the OCR
engine may detect the
14
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
phrase "In the Amount of:" in the received image and determine that the
associated data field
(e.g. a blank space immediately adjacent to the data label) corresponds to the
"Amount" field.
These techniques of "dynamically" identifying data fields by the OCR engine
may be combined
with static template matching methods in some instances. For example, the
information obtained
by the OCR engine may be used to verify, or weight a level of confidence, that
the identification
of data fields by means of static template matching/comparison is correct.
[0062] In some embodiments, the text obtained at operation 404 may be further
processed. For
example, in some instances, the stitching described above may not be performed
on the image
and may, instead, be performed on the text itself. That is, text obtained from
multiple images
may be analyzed in order to prepare a more fulsome version of the text of the
first document.
Additionally or alternatively, segmentation operations may be performed based
on the analyzed
text. By way of further example, where the document is a policy document such
as a legal
document, paragraph or clause numbers may be used to separate the document
into sections or
segments to facilitate the analysis operations described below.
[0063] The text that is recognized from the image data in operation 404 may be
deficient. In
some embodiments, the computing system may be configured to determine that the
recognized
text does not contain text associated with at least one data field that is
known to be included in
the first document. In response to determining that there is a deficiency in
the recognized text,
the computing system may generate display data for prompting a user of the
client device to
provide information relating to the missing one or more data fields. For
example, the display data
may be a graphical user interface including a fillable input form containing
the at least one data
field. Alternatively, the display data may be a graphical user interface
including an application
form having the missing data fields highlighted. The computing system may then
transmit the
display data to the client device in order to solicit additional information
from the user(s)
associated with the client device.
[0064] Control flow next proceeds to operation 406, where a first value
representing the product
specification for the first product is obtained based on the recognized text.
That is, the text
extracted from the image data is analyzed to identify the first value
associated with the first
product. The analyzed text may be the text identified in operation 404 or a
further-processed
version of such text, such as a document section or segment.
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0065] The first value may, for example, comprise one or more data fields and
associated data
entry items. By way of example, the first value may be a set of numerical
values representing
one or both of a deductible and a coverage limit. The analysis may be
performed, for example,
using template-based analysis techniques, machine-learning techniques, or
both. For example, in
some embodiments, the computing system may be configured to recognize a
limited set of
documents. That is, the computing system may only recognize known types of
documents. By
way of example, where the first document represents an insurance policy, the
computing system
may be configured to recognize only a limited set of insurance policy
documents. The set may
be limited, for example, since insurance policy documents often include
standard language and
each insurer may use only a limited set of insurance policy documents.
[0066] In some embodiments, a template-based analysis may be a multi-stage
analysis. For
example, the computing system may first analyze the extracted text or the
image data to
determine a type associated with the first document. The type may be
determined, for example,
based on word combinations, phrases, sentences, logos, etc. that are known to
be contained in a
certain type of document and that are not included in other known types of
documents. Once
identified, the type may be used to identify a template that is to be used to
analyze the document,
and the first value may be determined using the text and the identified
template. For example,
the computing system may recognize that only one known template includes the
sentence
"However, note that a decrease in your deductible, if available, will result
in an increase in
premiums." Upon identifying this sentence in the text, the computing system
may select a
template that is associated with the document type that includes the
identified sentence and may
use this template to identify the first value. For example, the template may
indicate that the first
value is located adjacent to the text "Your deductible will be".
[0067] The computing system may have access to classification data, which may
be stored
locally or remotely, which defines a plurality of templates. For example, each
template may
specify one or more words, word combinations, phrases, or sentences that are
located near a
parameter that is to be identified. For example, a template may specify a word
combination that
immediately precedes a parameter for a certain known type of document. For
example, the
template may specify that the parameter is located adjacent to the phrase
"Your deductible will
16
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
be". The computing system may then identify the first parameter by identifying
the word
combination.
[0068] A machine learning module may be used by the computing system in
operation 406 to
obtain the first value. The machine learning module may have been trained to
identify the first
value. For example, the machine learning module may have been trained with a
set of
documents and known parameters specified in such documents. The training set
may have
included a plurality of documents and a deductible (or other first value)
contained in the text of
such documents. Accordingly, in some implementations of operation 406, the
computing system
may pass at least a portion of the text through a machine learning module that
has been trained to
identify one or more parameters including the first value. The machine
learning module may
output the first value based on the text.
[0069] FIG. 5 shows, in flowchart form, an example method 500 that may be
performed by a
computing system as a subroutine of operation 406 of FIG. 4.
[0070] In operation 510, the computing system determines whether the first
document
corresponds to a known type. This determination may be performed as described
above. For
example, word combinations, phrases, sentences, etc. in the text may be
analyzed to determine
whether they are associated with a known type of document.
[0071] If it is determined that the first document does not correspond to a
known type, machine
learning may be performed at operation 530 in the manner described above to
identify the first
parameter. In some instances, a template may also be created based on the text
of the imaged
document and stored (in operation 540) so that future instances of documents
having a type that
is the same as the first document can be analyzed using template-based
techniques. For example,
the template may be defined to specify a technique by which the first value
may be located; for
example, by defining nearby words or word combinations. Unique identification
data associated
with the document type may also be stored so that the computing system may
subsequently
identify other documents of the same type. For example, unique word
combinations, phrases,
sentences, etc. may be stored.
17
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0072] If, however, it is determined that the first document corresponds to a
known type,
template-based analysis techniques of the type described above may be
performed at operation
520 to identify the first value.
[0073] Returning again to FIG. 4, after the first value is identified, control
flow may proceed to
operation 408. In operation 408, a second product having a product
specification represented by
the first value is selected (for example, from a products database). More
specifically, the
computing system may identify a second product, different from the first
product, which has a
particular specification. In some embodiments, the second product (e.g. a
product offered by a
competitor to the provider of the first product) may be selected based on
determining that
product specification for the second product satisfies a predetermined
condition associated with a
predetermined data field. In particular, the second product may be selected so
that it has the same
or similar properties/values with respect to one or more specific data fields.
For example, if the
first product has a certain property (e.g. coverage limit is $500), a product
from a products
database may be selected as the second product if it has the same (i.e.
coverage limit is $500)
property or falls within a predefined range (i.e. coverage limit is $480;
coverage limit is within
$50 of that for the first product).
[0074] The selection of the second product may be performed in real-time, in
operation 408. The
second product may be selected by, for example, querying a database of
products to obtain, in
real-time, a product having a specification that is represented by the first
value. By selecting the
second product and acquiring product data for the second product in real-time,
the computing
system may obtain product information that is relevant for making a purchase
decision and
present the information to customers in a timely manner. A customer will
usually make a
purchase decision, including a comparison of similar or related products,
within a short span of
time. A system that dynamically obtains product data in real-time can inform a
purchase decision
during the time span in which the customer's attention is focused on a
specific product being
considered for purchase. In particular, such a system may be capable of
presenting product
comparison data for products that are related or similar to a specific product
that is considered by
a customer for purchase.
[0075] In operation 410, the computing system obtains a second value
associated with the
selected second product. The second value may, for example, be a numerical
value associated
18
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
with the second product, such as price, term length, etc. In at least some
embodiments, the
second value corresponds to a data field or property for which the first
product also has a value.
That is, the second value may be selected only if a corresponding first value
also exists. The
computing system may be configured to retrieve the second value from a
products database
containing information pertaining to the second product. For example, the
computing system
may itself maintain, or have access to, one or more databases that contain
product-related data
for the second product. The computing system may query the databases to
retrieve the second
value associated with the second product. In some embodiments, rather than
retrieving a
predefined value for the second value, the computing system may compute the
second value,
using one or more parameters represented by the first value as inputs. For
example, upon
determining the provider (e.g. manufacturer, company) that offers the second
product, the
computing system may be configured to apply a suitable algorithm associated
with said provider
to compute the second value for the second product.
[0076] In operation 412, the computing system generates display data that
includes a graphical
representation of the second value. The display data may then be provided to
the first client
device. That is, a signal representing the display data may be transmitted,
via the
communications module of the computing system, to the first client device.
[0077] In at least some embodiments, the display data generated in operation
412 may include a
representation of a comparison of the first product and the second product.
More specifically, the
second value associated with the second product (from operation 410) may be
compared to a
corresponding value for the first product. For example, where the second value
is a deductible
associated with a second insurance policy, a corresponding value (i.e.
deductible) for a different
first insurance policy product may be compared to the second value. The first
and second
products may be different versions/iterations of the same product, or products
that are offered by
different providers (e.g. competing insurance companies). For example, the
first document may
be a current policy document for a client and the second product may be a
policy product that is
offered by a competing provider.
[0078] The display data generated in operation 412 may indicate a difference
in the
corresponding values for the first and second products. For example, the
computing system may
19
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
be configured to identify differences between the corresponding values for the
two products and
to provide annotation data to highlight the differences.
[0079] In operation 414, a signal representing the display data is transmitted
to the first client
device. The display data may be transmitted to the first client device in real-
time, such that
relevant product information for facilitating a customer's purchase decision
can be presented to
the customer in a timely fashion. For example, the second value may be
included as part of a
graphical representation of product comparison data for the first and second
products that is
presented to the customer. The display data may be presented immediately after
the customer
captures image data depicting the first document containing a product
specification for the first
product.
[0080] More generally, the method 400 may be extended to provide product
comparison data for
three or more different products, based on capturing image data of a document
containing a
product specification for a first product. The image can be processed to
obtain product
specification for the first product, and a plurality of products having the
same or similar
production specification as the first product may be selected. The computing
system may then
generate product comparison data for the first and selected products, and
transmit the data to a
client device, for example, for presentation to a customer.
[0081] Reference is now made to FIG. 6, which shows another example method 600
for
obtaining an estimate of a value associated with a product. The method 600
may, in some
instances, be implemented as a subroutine of a process for automatically
identifying products
that are related to a user-selected product and comparing the identified
products. The method 600
may be performed by a computing system, such as PVS 150, that is configured to
obtain,
evaluate, and present product information for one or more products.
[0082] Operation 602, 604 and 610 of method 600 correspond to operations 402,
404 and 410 of
method 400, respectively. In operation 602, the computing system receives,
from a client device,
image data depicting at least one first document containing a product
specification for a first
product. Text recognition is performed on the received image data to extract
text from the at least
one first document, in operation 604. According to method 600, the computing
system may be
configured to additionally receive contextual data from the client device, in
operation 606. The
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
contextual data may define a current status of the client device, and may
comprise at least one of:
geolocation data, temperature data, motion data, altitude data, and proximity
data indicating
proximity to one or more known devices. The contextual data received from the
client device can
be useful in verifying product specification for the first product or as a
supplement to the text
recognition process in operation 604. For example, where the image data is
insufficient to enable
accurate text recognition, contextual data from the client device may be used
to supplement any
text extracted from the imaged document, in order to obtain a comprehensive
product
specification for the first product.
[0083] In operation 608, the computing system selects a second product related
to the first
product, based on both the text extracted from the imaged document and the
contextual data from
the client device. A second value associated with the selected second product
is then obtained, in
operation 610. This second value may be transmitted to the client device,
either in stand-alone
format or as part of a graphical representation of a comparison between the
first product and the
second product.
[0084] By way of example, the computing system may receive, from a client
device, geolocation
data associated with the device. The geolocation data may allow for inferring,
for example, an
address of a home, office, etc. when the device is assumed or confirmed to be
at those locations.
This information may lead to further inference, such as neighbourhood,
proximity to known
landmarks, etc., about the user/device's specific location. In the context of
an insurance policy
product for a specific property, the textual data obtained from analyzing
images of the policy
may be supplemented (and/or confirmed) by information about the property that
is directly
derived or inferred from contextual data received from the client device.
[0085] In some embodiments, the computing system may receive historical
contextual data from
a client device. The client device may, for example, maintain a log of
contextual data obtained at
the client device (e.g. via sensors associated with the device). As an
example, the client device
may log the speed of an automobile when the device is located inside the
automobile. This
historical data may be used to supplement or confirm information about an
automobile or
product specifications for products relating to the automobile, such as an
automobile insurance
policy.
21
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0086] Reference is now made to FIG. 7, which shows, in flowchart form, an
example method
700 for automatically requesting cancellation of a product. The method 700 may
be implemented
by an electronic device having an image-capture functionality. More
specifically, an electronic
device having a camera and a processor coupled to the camera may perform the
method 700.
[0087] In operation 702, the electronic device obtains image data depicting at
least one first
document containing a product specification for a first product. The first
product may, for
example, be a policy (e.g. insurance policy) to which a user of the electronic
device is subscribed
to and/or has purchased. The electronic device performs text recognition on
the image data, in
operation 704, to extract text from the imaged first document.
[0088] Upon extracting the text, the electronic device may generate, in
operation 706, display
data including a graphical user interface for requesting cancellation of the
first product. The
graphical user interface is pre-populated with data based on the recognized
text. In at least some
embodiments, the extracted text may be analyzed to identify text in one or
more predetermined
data fields which may be required for generating a suitably formatted
cancellation request. For
example, the computing system may determine, for the first product, those data
fields which are
at least minimally required for a properly formatted cancellation request to
the provider of the
first product. Such fields may include name of provider, contact information
for said provider,
and identifying information for the customer. The identification of data
fields, and subsequent
extraction of data from the data fields, may be based on techniques such as
template matching,
machine learning, etc.
[0089] In operation 708, the display data is presented on a display interface
of the electronic
device. The electronic device may be configured to receive, via an input
interface associated with
the electronic device, a user input indicating confirmation of a cancellation
request. In response
to receiving the user input, the electronic device may transmit the request
for cancellation to a
first remote server (e.g. administrator server for a product provider). The
server to which the
cancellation request is transmitted may be identified based, for example, on
text extracted from
the imaged document and querying a database of known product providers and
their
requirements for properly formatted cancellation requests.
22
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0090] FIG. 8 shows, in flowchart form, an example method 800 for obtaining a
quote for an
insurance policy. The method 800 may be performed by a computing system
implementing a
product valuation system, such as PVS 150 of FIG. I. For example, a server
that is connected to
one or more client devices and that has access to data relating to various
different insurance
policies may be configured to implement the method 800.
[0091] In operation 802, the computing system receives, from a first client
device, a signal
representing image data that depicts one or more of a client's insurance
declaration pages
associated with a first insurance product. In some embodiments, the image data
may only depict
certain parts of an insurance declaration page. For example, the image data
may depict only
those portions of documents that contain text. The text may, for example,
correspond to
predetermined data labels/fields and associated input data. The image data
may, in some cases,
depict portions that have specific locations on the documents. For example,
the image data may
capture portions corresponding to header and/or footer sections of the
documents.
[0092] In operation 804, text recognition may be performed on the image data
to identify text in
the one or more insurance declaration pages. The computing system may itself
perform text
recognition/extraction from the image data, or transmit the image data to a
text recognition
service. In at least some embodiments, the images are converted to machine-
encoded text (e.g.
by OCR), and the text is extracted for further processing. The extracted text
may include text that
is associated with specific data fields for the first insurance product. For
example, the text that is
extracted may include the name of a policy provider, a deductible amount,
coverage limits, asset
value, and total premium.
[0093] In operation 806, the computing system selects a second insurance
product based on the
identified text. In some embodiments, the second insurance product that is
selected may have
similar (or same) coverage (e.g. deductible, coverage amount) as the first
insurance product. That
is, the computing system may use the textual data extracted from the received
images to identify
a different insurance product that has a similar (or same) coverage. The first
and second
insurance products may, for example, be offered by different providers. In
particular, the second
insurance product may be a product that is offered by a provider different
from the provider for
the first insurance product.
23
CA 3018338 2018-09-21
IDF Reference: 18043-INS-CA-PAT
Our File: 337-0040CAP1
[0094] In operation 808, a second value associated with the selected second
insurance product is
obtained. This second value may, for example, be a premium associated with the
selected
insurance policy. Once the second value is obtained, it may be transmitted to
the first client
device for display on the device. For example, the second value may be
included as part of
comparison data that shows premiums associated with a plurality of insurance
policies. The
comparison data may include data relating to, at least, the first and second
insurance products,
and identify names of providers of the respective products. The display data
that is transmitted to
the first client device may include a graphical representation of the
comparison data.
[0095] The various embodiments presented above are merely examples and are in
no way meant
to limit the scope of this application. Variations of the innovations
described herein will be
apparent to persons of ordinary skill in the art, such variations being within
the intended scope of
the present application. In particular, features from one or more of the above-
described example
embodiments may be selected to create alternative example embodiments
including a sub-
combination of features which may not be explicitly described above. In
addition, features from
one or more of the above-described example embodiments may be selected and
combined to
create alternative example embodiments including a combination of features
which may not be
explicitly described above. Features suitable for such combinations and sub-
combinations would
be readily apparent to persons skilled in the art upon review of the present
application as a whole.
The subject matter described herein and in the recited claims intends to cover
and embrace all
suitable changes in technology.
24
CA 3018338 2018-09-21
