U.S. patent application number 14/065670 was filed with the patent office on 2014-05-08 for server, analysis method and computer program product.
This patent application is currently assigned to KABUSHIKI KAISHA TOSHIBA. The applicant listed for this patent is KABUSHIKI KAISHA TOSHIBA. Invention is credited to Goh Itoh, Masashi Nishiyama, Hidetaka Ohira, Masahiro Sekine, Kaoru Sugita.
Application Number | 20140129329 14/065670 |
Document ID | / |
Family ID | 50623243 |
Filed Date | 2014-05-08 |
United States Patent
Application |
20140129329 |
Kind Code |
A1 |
Sekine; Masahiro ; et
al. |
May 8, 2014 |
SERVER, ANALYSIS METHOD AND COMPUTER PROGRAM PRODUCT
Abstract
According to an embodiment, a server includes a first acquiring
unit, a second acquiring unit, an analyzing unit, and an output
unit. The first acquiring unit is configured to acquire recognition
information includes a product identification information for
identifying the product. The second acquiring unit is configured to
acquire combination information including the product
identification information of the product to be combined with an
object image including an object. The analyzing unit is configured
to calculate product priorities for respective products by
analyzing the recognition information and the combination
information. The output unit is configured to output information
based on the product priorities.
Inventors: |
Sekine; Masahiro; (Tokyo,
JP) ; Nishiyama; Masashi; (Kanagawa, JP) ;
Sugita; Kaoru; (Saitama, JP) ; Ohira; Hidetaka;
(Kanagawa, JP) ; Itoh; Goh; (Tokyo, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KABUSHIKI KAISHA TOSHIBA |
Tokyo |
|
JP |
|
|
Assignee: |
KABUSHIKI KAISHA TOSHIBA
Tokyo
JP
|
Family ID: |
50623243 |
Appl. No.: |
14/065670 |
Filed: |
October 29, 2013 |
Current U.S.
Class: |
705/14.53 ;
705/26.5 |
Current CPC
Class: |
G06Q 30/0623 20130101;
G06Q 30/0621 20130101; G06Q 30/0251 20130101; G06Q 30/0255
20130101; G06Q 30/0631 20130101; G06Q 30/0643 20130101 |
Class at
Publication: |
705/14.53 ;
705/26.5 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 30/06 20060101 G06Q030/06 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 5, 2012 |
JP |
2012-243762 |
Claims
1. A server comprising: a first acquiring unit configured to
acquire a piece of recognition information including a piece of
product identification information for identifying a product
included in a product image; a recognition information storage unit
configured to store the piece of recognition information; a second
acquiring unit configured to acquire a piece of combination
information including the piece of product identification
information of the product to be combined with an object image
including an object; a combination information storage unit
configured to store the piece of combination information; an
analyzing unit configured to calculate product priorities for
respective products by analyzing a plurality of pieces of
recognition information stored in the recognition information
storage unit and a plurality of pieces of combination information
stored in the combination information storage unit; and an output
unit configured to output information based on the product
priorities.
2. The server according to claim 1, wherein the analyzing unit
calculates the product priorities on the basis of first product
priorities for the respective products and second product
priorities for the respective products, the analyzing unit
calculates the first product priorities by analyzing the pieces of
recognition information, and the analyzing unit calculates the
second product priorities by analyzing the pieces of combination
information.
3. The server according to claim 2, wherein each piece of
recognition information includes date and time of recognition, each
piece of combination information includes date and time of
combination, the analyzing unit sets, to a higher level, the first
product priority of the product represented by the piece of product
identification information associated with the date and time of
recognition closer to current date and time by analyzing the pieces
of recognition information, and the analyzing unit sets, to a
higher level, the second product priority of the product
represented by the piece of product identification information
associated with the date and time of combination closer to the
current date and time by analyzing the pieces of combination
information.
4. The server according to claim 2, wherein the analyzing unit
sets, to a higher level, the first product priority of the product
represented by the piece of product identification information
having a value whose number of occurrences is larger by analyzing
the pieces of recognition information, and the analyzing unit sets,
to a higher level, the second product priority of the product
represented by the piece of product identification information
having a value whose number of occurrences is larger by analyzing
the pieces of combination information.
5. The server according to claim 1, wherein each piece of
recognition information includes a plurality of kinds of related
information relating to the product, the analyzing unit further
analyzes whether there is a piece of combination information
including the piece of product identification information of the
product whose product priority satisfies a first predetermined
condition in the pieces of combination information, and generates
first recommendation information for recommending related
information on the basis of a result of the analysis among the
kinds of related information, and the output unit outputs
information based on the product priority and the first
recommendation information to the recognizing unit.
6. The server according to claim 1, wherein each piece of
combination information includes an image for combination, the
image for combination is provided for each category of product,
each of the pieces of combination information includes a category
of the corresponding product, if there is a plurality of categories
of a product whose product priority satisfies a second
predetermined condition, the analyzing unit analyzes the number of
occurrences of each of the categories in the pieces of combination
information and generates second recommendation information for
recommending the category with the largest number of occurrences,
and the output unit outputs information based on the product
priority and the second recommendation information to the combining
unit.
7. A server comprising: a first acquiring unit configured to
acquire a piece of recognition information including a piece of
product identification information for identifying a product
included in a product image; a recognition information storage unit
configured to store the piece of recognition information; a second
acquiring unit configured to acquire a piece of combination
information including the piece of product identification
information of the product to be combined with an object image
including an object; a combination information storage unit
configured to store the piece of combination information; a third
acquiring unit configured to acquire a piece of sales promotion
information relating to sales promotion of the product and a piece
of purchase information including at least the piece of product
identification information of the product; a sales information
storage unit configured to store the piece of sales promotion
information and the piece of purchase information; an analyzing
unit configured to perform at least one of first analysis and
second analysis, the first analysis calculating product priorities
for respective products by analyzing a plurality of pieces of
recognition information stored in the recognition information
storage unit, a plurality of pieces of combination information
stored in the combination information storage unit, and a plurality
of pieces of purchase information stored in the sales information
storage unit, the second analysis obtaining updated contents of the
pieces of sales promotion information by analyzing at least either
the pieces of recognition information or the pieces of combination
information in addition to the pieces of purchase information; and
an output unit configured to output at least one of information
based on the product priorities and the updated contents.
8. The server according to claim 7, wherein the analyzing unit
calculates the product priorities on the basis of first product
priorities for the respective products, second product priorities
for the respective products, and third product priorities for the
respective products, the analyzing unit calculates the first
product priorities by analyzing the pieces of recognition
information, the analyzing unit calculates the second product
priorities by analyzing the pieces of combination information, and
the analyzing unit calculates the third product priorities by
analyzing the pieces of purchase information.
9. The server according to claim 8, wherein the analyzing unit
sets, to a higher level, the first product priority of the product
represented by the piece of product identification information
having a value whose number of occurrences is larger by analyzing
the pieces of recognition information, the analyzing unit sets, to
a higher level, the second product priority of the product
represented by the piece of product identification information
having a value whose number of occurrences is larger by analyzing
the pieces of combination information, and the analyzing unit sets,
to a higher level, the third product priority of the product
represented by the piece of product identification information
having a value whose number of occurrences is larger by analyzing
the pieces of purchase information.
10. The server according to claim 8, wherein each piece of
recognition information includes date and time of recognition, each
piece of combination information includes date and time of
combination, each piece of purchase information includes date and
time of purchase, the analyzing unit sets, to a higher level, the
first product priority of the product represented by the piece of
product identification information associated with the date and
time of recognition closer to current date and time by analyzing
the pieces of recognition information, the analyzing unit sets, to
a higher level, the second product priority of the product
represented by the piece of product identification information
associated with the date and time of combination closer to the
current date and time by analyzing the pieces of combination
information, and the analyzing unit sets, to a higher level, the
third product priority of the product represented by the piece of
product identification information associated with the date and
time of purchase closer to the current date and time by analyzing
the pieces of purchase information.
11. The server according to claim 7, wherein each piece of
recognition information includes a plurality of kinds of related
information relating to the product, the analyzing unit further
analyzes whether there is a piece of combination information
including the piece of product identification information of the
product whose product priority satisfies a first predetermined
condition in the pieces of combination information, and generates
first recommendation information for recommending related
information on the basis of a result of the analysis among the
kinds of related information, and the output unit outputs
information based on the product priority and the first
recommendation information to the recognizing unit.
12. The server according to claim 7, wherein each piece of
combination information includes an image for combination, each of
the pieces of combination information includes a category of the
corresponding product, if there is a plurality of categories of a
product whose product priority satisfies a second predetermined
condition, the analyzing unit analyzes the number of occurrences of
each of the categories in the pieces of combination information and
generates second recommendation information for recommending the
category with the largest number of occurrences, and the output
unit outputs information based on the product priority and the
second recommendation information to the combining unit.
13. The server according to claim 7, wherein each piece of
recognition information includes a piece of product image
information of the product image, each piece of sales promotion
information includes a piece of first sales promotion information
relating to sales promotion using the product image, the analyzing
unit analyzes the pieces of purchase information and analyzes the
number of occurrences, in the pieces of recognition information, of
the piece of product image information associated with the piece of
product identification information whose number of occurrences in
the pieces of purchase information satisfies a third predetermined
condition, and the analyzing unit obtains, as the updated contents,
updated contents of the pieces of first sales promotion information
on the basis of the number of occurrences of the piece of product
image information.
14. The server according to claim 7, wherein each piece of
combination information includes an image for combination and a
piece of combination image information of the image for
combination, each piece of sales promotion information includes a
piece of second sales promotion information relating to sales
promotion using the image for combination, and the analyzing unit
analyzes the pieces of purchase information and analyzes the number
of occurrences, in the pieces of combination information, of the
piece of combination image information associated with the piece of
product identification information having a value whose number of
occurrences in the pieces of purchase information satisfies a
fourth predetermined condition, and the analyzing unit obtains, as
the updated contents, updated contents of the pieces of second
sales promotion information on the basis of the number of
occurrences of the piece of combination image information.
15. An analysis method comprising: acquiring a piece of recognition
information including a piece of product identification information
for identifying a product included in a product image; storing the
piece of recognition information in a recognition information
storage unit, acquiring a piece of combination information
including the piece of product identification information of the
product to be combined with an object image including an object;
storing the piece of combination information in a combination
information storage unit; calculating product priorities for
respective products by analyzing a plurality of pieces of
recognition information stored in the recognition information
storage unit and a plurality of pieces of combination information
stored in the combination information storage unit; and outputting
information based on the product priorities.
16. A computer program product comprising a computer-readable
medium containing a program executed by a computer, the program
causing the computer to execute: acquiring a piece of recognition
information including a piece of product identification information
for identifying a product included in a product image; storing the
piece of recognition information in a recognition information
storage unit, acquiring a piece of combination information
including the piece of product identification information of the
product to be combined with an object image including an object;
storing the piece of combination information in a combination
information storage unit; calculating product priorities for
respective products by analyzing a plurality of pieces of
recognition information stored in the recognition information
storage unit and a plurality of pieces of combination information
stored in the combination information storage unit; and outputting
information based on the product priorities.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is based upon and claims the benefit of
priority from Japanese Patent Application No. 2012-243762, filed on
Nov. 5, 2012; the entire contents of which are incorporated herein
by reference.
FIELD
[0002] Embodiments described herein relate generally to a server,
an analysis method, and a computer program product.
BACKGROUND
[0003] In retail industry for general consumers, there are recently
increasing attempts to differentiate shopping styles by creating
new user qualities, and O2O (Online to Offline), for example, is
attracting attention. The O2O means interaction of online and
offline buying behaviors and influence of online information on
buying behavior at brick-and-motor shops or the like, and services
such as finding stores using location-based services of portable
terminals and coupons available online and usable at
brick-and-motor shops have been expanding.
[0004] In the meantime, various technologies relating to O2O such
as technology for virtual fitting using product images are being
developed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 is a diagram illustrating an example of a system
according to a first embodiment;
[0006] FIG. 2 is a diagram illustrating an example of a first
terminal according to the first embodiment;
[0007] FIG. 3 is a diagram illustrating an example of a second
terminal according to the first embodiment;
[0008] FIG. 4 is a diagram illustrating an example of a server
according to the first embodiment;
[0009] FIG. 5 is a table illustrating examples of recognition
information according to the first embodiment;
[0010] FIG. 6 is a table illustrating examples of combination
information according to the first embodiment;
[0011] FIG. 7 is a diagram illustrating an example of a data
structure of product information according to the first
embodiment;
[0012] FIG. 8 is a flowchart illustrating an example of processing
according to the first embodiment;
[0013] FIG. 9 is a diagram illustrating an example of a system
according to a second embodiment;
[0014] FIG. 10 is a diagram illustrating an example of a third
terminal according to the second embodiment;
[0015] FIG. 11 is a diagram illustrating an example of a server
according to the second embodiment;
[0016] FIG. 12 is a table illustrating examples of purchase
information according to the second embodiment;
[0017] FIG. 13 is a table illustrating examples of first sales
promotion information before being updated according to the second
embodiment;
[0018] FIG. 14 is a table illustrating examples of first sales
promotion information after being updated according to the second
embodiment;
[0019] FIG. 15 is a table illustrating examples of store layout
information before being updated according to the second
embodiment;
[0020] FIG. 16 is a table illustrating examples of store layout
information after being updated according to the second
embodiment;
[0021] FIG. 17 is a flowchart illustrating an example of processing
according to the second embodiment;
[0022] FIG. 18 is a diagram illustrating an example of a system
according to a modification; and
[0023] FIG. 19 is a diagram illustrating an example of a hardware
configuration of the server according to the embodiments and
modifications.
DETAILED DESCRIPTION
[0024] According to an embodiment, a server includes a first
acquiring unit, a recognition information storage unit, a second
acquiring unit, a combination information storage unit, an
analyzing unit, and an output unit. The first acquiring unit is
configured to acquire a piece of recognition information including
a piece of product identification information for identifying a
product included in a product image. The recognition information
storage unit is configured to store the piece of recognition
information. The second acquiring unit is configured to acquire a
piece of combination information including the piece of product
identification information of the product to be combined with an
object image including an object. The combination information
storage unit is configured to store the piece of combination
information. The analyzing unit is configured to calculate product
priorities for respective products by analyzing a plurality of
pieces of recognition information stored in the recognition
information storage unit and a plurality of pieces of combination
information stored in the combination information storage unit. The
output unit is configured to output information based on the
product priorities.
[0025] Embodiments will be described in detail below with reference
to the accompanying drawings.
First Embodiment
[0026] FIG. 1 is a configuration diagram illustrating an example of
a system 1 according to a first embodiment. As illustrated in FIG.
1, the system 1 includes a first terminal 10, a second terminal 20,
and a server 30. The first terminal 10, the second terminal 20 and
the server 30 are connected via a network 2. The network 2 can be
realized by the Internet or a local area network (LAN), for
example.
[0027] In the first embodiment, an example in which the first
terminal 10 is an image recognition terminal that includes a
recognizing unit 11 and that acquires related information on a real
object of interest to the user by being held over the real object
will be described. The first terminal 10 can be realized by a
portable terminal, for example. In the following, to acquire
related information on a real object by focus the first terminal 10
over the real object may be referred to as "focus".
[0028] Similarly, in the first embodiment, an example in which the
second terminal 20 is an image combining terminal that includes a
combining unit 21 and that performs virtual fitting simulation,
virtual installation simulation and the like will be described. The
second terminal 20 is installed in a store selling products, for
example. In the following, to experience a product of interest to
the user through virtual fitting simulation, virtual installation
simulation or the like may be referred to as "try".
[0029] With the system 1, it is assumed that the user holds the
first terminal 10 over a real object of interest to acquire related
information on the product and that, starting from the acquired
related information, the user is encouraged to go to the store in
which the second terminal 20 is installed and to experience the
product through virtual fitting simulation or virtual installation
simulation, which is linked to purchase of the product.
[0030] FIG. 2 is a configuration diagram illustrating an example of
the first terminal 10 according to the first embodiment. As
illustrated in FIG. 2, the first terminal 10 includes a recognizing
unit 11, an imaging unit 12, a feedback information storage unit
13, a display unit 14, and an output unit 15.
[0031] The recognizing unit 11 may be implemented by making a
processor such as a central processing unit (CPU) execute a
program, that is, by software, may be implemented by hardware such
as an integrated circuit (IC), or may be implemented by combination
of software and hardware, for example. The imaging unit 12 can be
realized by an imager such as a digital camera, for example. The
feedback information storage unit 13 can be realized by a storage
device that can magnetically, optically or electrically store
information such as a hard disk drive (HDD), a solid state drive
(SSD), a memory card, an optical disk, or a random access memory
(RAM), for example. The display unit 14 can be realized by a
display device such as a liquid crystal display or a touch panel
display, for example. The output unit 15 can be realized by a
communication device such as a network interface card (NIC), for
example.
[0032] The imaging unit 12 images a real object of interest to the
user to generate a product image. Examples of the real object of
interest to the user include an advertisement of a product of
interest to the user, but the real object may be a product itself
of interest to the user.
[0033] The feedback information storage unit 13 stores feedback
information. Details of the feedback information will be described
later.
[0034] The recognizing unit 11 includes an image recognizing unit
16 and a feedback unit 17.
[0035] The image recognizing unit 16 recognizes a product image,
estimates a product included in the product image, and selects at
least any one of a plurality of kinds of related information on the
product. Specifically, the image recognizing unit 16 acquires
product information on the estimated product from the server 30 and
selects at least any one of a plurality of kinds of related
information contained in the acquired product information. The
product information acquired by the image recognizing unit 16
contains a product ID (an example of product identification
information) of the estimated product and a plurality of kinds of
related information. Examples of the kinds of related information
include attribute information and accompanying information of the
estimated product. Examples of the attribute information include
brand, price, color, and material, and examples of the accompanying
information include word of mouth, recommended coordinates and
store information (address, map, etc.).
[0036] When feedback information of the estimated product is stored
in the feedback information storage unit 13, the image recognizing
unit 16 selects related information according to the feedback
information.
[0037] The feedback unit 17 stores feedback information based on
information transmitted from the server 30 in the feedback
information storage unit 13.
[0038] The display unit 14 displays the related information
selected by the image recognizing unit 16. The display unit 14
displays word of mouth, recommended coordinates, store information
or the like of the product estimated by the image recognizing unit
16 as an image, for example.
[0039] The output unit 15 outputs the recognition information to
the server 30. The recognition information at least contains a
product ID of the product estimated by the image recognizing unit
16. The recognition information may contain product image
information and related information of the product image. The
product image information may be the product image itself or may be
an image matched with the product image in image recognition
performed by the image recognizing unit 16 or an image ID of the
image. The recognition information may contain the date and time of
recognition, the position of recognition, a user ID of the user,
and the like.
[0040] FIG. 3 is a configuration diagram illustrating an example of
the second terminal 20 according to the first embodiment. As
illustrated in FIG. 3, the second terminal 20 includes a combining
unit 21, an imaging unit 22, a feedback information storage unit
23, a display unit 24, and an output unit 25.
[0041] The combining unit 21 may be implemented by making a
processor such as a central processing unit (CPU) execute a
program, that is, by software, may be implemented by hardware such
as an IC, or may be implemented by combination of software and
hardware, for example. The imaging unit 22 can be realized by an
imager such as a digital camera, for example. The feedback
information storage unit 23 can be realized by a storage device
that can magnetically, optically or electrically store information
such as an HDD, an SSD, a memory card, an optical disk, or a RAM,
for example. The display unit 24 can be realized by a display
device such as a liquid crystal display or a touch panel display,
for example. The output unit 25 can be realized by a communication
device such as an NIC, for example.
[0042] The imaging unit 22 images an object to be combined to
generate an image to be combined. Examples of the object to be
combined include the user.
[0043] The feedback information storage unit 23 stores feedback
information. Details of the feedback information will be described
later.
[0044] The combining unit 21 includes an image combining unit 26
and a feedback unit 27.
[0045] The image combining unit 26 combines the image to be
combined generated by the imaging unit 22 and an image for
combination of a product (such as clothes). Specifically, the image
combining unit 26 acquires product information of a plurality of
products from the server 30, displays images for combination
contained in the acquired product information on the display unit
24, and combines an image for combination selected by the user with
the image to be combined generated by the imaging unit 22. The
product information acquired by the image combining unit 26
contains product IDs (an example of product identification
information) of the products and a group of images for combination.
Since the images for combination are present for each category of
products, the images for combination are in a form of groups. The
category may be the kind or the use of products or the state in
which products are tried on.
[0046] When feedback information is stored in the feedback
information storage unit 23, the image combining unit 26 displays
the images for combination on the display unit 24 in a manner that
the user can preferentially select an image for combination
indicated by the feedback information.
[0047] The feedback unit 27 stores feedback information based on
information transmitted from the server 30 in the feedback
information storage unit 23.
[0048] The display unit 24 displays images for combination to be
selected by the user and combined images obtained by combination by
the image combining unit 26.
[0049] The output unit 25 outputs combination information to the
server 30. The combination information at least contains a product
ID of the product estimated by the image combining unit 26. The
combination information may contain combined image information of
an image to be combined and combination image information of an
image for combination. The combined image information may be the
image to be combined itself or may contain depth information
obtained by sensing the image to be combined, skeleton information
indicating the outline of a person, and measurement information
such as height, weight, chest circumference, sitting height and the
like in addition to the image to be combined. The combination image
information may be the image for combination itself or may be an
image ID of the image for combination. The combination information
may contain the date and time of combination, the position of
combination, a user ID of the user, the category of the product and
the like.
[0050] FIG. 4 is a configuration diagram illustrating an example of
the server 30 according to the first embodiment. As illustrated in
FIG. 4, the server 30 includes a first acquiring unit 31, a
recognition information storage unit 32, a second acquiring unit
33, a combination information storage unit 34, an analyzing unit
35, an output unit 36, and a product information storage unit
37.
[0051] The first acquiring unit 31, the second acquiring unit 33
and the analyzing unit 35 may be implemented by making a processor
such as a central processing unit (CPU) execute a program, that is,
by software, may be implemented by hardware such as an integrated
circuit (IC), or may be implemented by combination of software and
hardware, for example. The recognition information storage unit 32,
the combination information storage unit 34, and the product
information storage unit 37 can be realized by a storage device
that can magnetically, optically or electrically store information
such as an HDD, an SSD, a memory card, an optical disk, or a RAM,
for example. The output unit 36 can be realized by a communication
device such as an NIC, for example.
[0052] The first acquiring unit 31 acquires recognition information
including at least the product ID of the product estimated by the
recognizing unit 11 from the recognizing unit 11 (the output unit
15), and stores the acquired recognition information in the
recognition information storage unit 32. The recognition
information may further contain information as mentioned in the
description of the output unit 15.
[0053] The recognition information storage unit 32 stores a
plurality of pieces of recognition information stored by the first
acquiring unit 31. FIG. 5 is a table illustrating examples of the
recognition information according to the first embodiment. In the
examples illustrated in FIG. 5, the recognition information is
information in which a number, the date and time of recognition,
the product image information, a product ID, and the related
information displayed at the first terminal 10 are associated, but
the recognition information is not limited thereto.
[0054] The second acquiring unit 33 acquires combination
information including at least the product ID of the product in the
image for combination combined with the image to be combined from
the combining unit 21 (the output unit 25), and stores the acquired
combination information in the combination information storage unit
34. The combination information may further contain information as
mentioned in the description of the output unit 25.
[0055] The combination information storage unit 34 stores a
plurality of pieces of combination information stored by the second
acquiring unit 33. FIG. 6 is a table illustrating examples of the
combination information according to the first embodiment. In the
examples illustrated in FIG. 6, the combination information is
information in which the number, the date and time of combination,
the combined image information, the product ID and the category are
associated, but the combination information is not limited
thereto.
[0056] The analyzing unit 35 analyzes a plurality of pieces of
recognition information stored in the recognition information
storage unit 32 and a plurality of pieces of combination
information stored in the combination information storage unit 34,
and calculates product priority of each product. Specifically, the
analyzing unit 35 analyzes the pieces of recognition information to
calculate first product priority of each product, analyzes the
pieces of combination information to calculate second product
priority of each product, and calculate the product priority of
each product on the basis of the first product priority and the
second product priority of each product.
[0057] For example, the analyzing unit 35 calculates the product
priority E of a certain product by calculating the first product
priority Er and the second product priority Es of the product and
calculating weighted addition of the calculated first product
priority Er and second product priority Es as expressed by Equation
(1). If the product ID of the product for which the first product
priority Er is calculated is not present in the pieces of
combination information, the second product priority Es of the
product is obviously 0, and if the product ID of the product for
which the second priority Es is calculated is not present in the
pieces of recognition information, the first product priority Er of
the product is obviously 0.
E=wr.times.Er+ws.times.Es (1)
[0058] In Equation (1), wr represents the weight of the priority Er
and ws represents the weight of the priority Es.
[0059] Note that the analyzing unit 35 analyzes the pieces of
recognition information and sets the first product priority Er of
the product represented by a product ID associated with recognition
date and time to be higher as the recognition date and time is
closer to the current date and time. That is, the analyzing unit 35
sets the first product priority Er to be higher for a product over
which a terminal was held on the date and time closer to the
current date and time.
[0060] The analyzing unit 35 also analyzes the pieces of
recognition information and sets the first product priority Er to
be higher for a product represented by a product ID having a value
whose number of occurrences is larger. That is, the analyzing unit
35 sets the first product priority Er to be higher for a product
over which a terminal was held a larger number of times.
[0061] Similarly, the analyzing unit 35 analyzes the pieces of
combination information and sets the second product priority Es of
a product represented by a product ID associated with combination
date and time contained in the combination information to be higher
as the combination date and time is closer to the current date and
time. That is, the analyzing unit 35 sets the second product
priority Es to be higher for a product that was tried on the date
and time closer to the current date and time.
[0062] The analyzing unit 35 also analyzes the pieces of
combination information and sets the second product priority Es to
be higher for a product represented by a product ID having a value
whose number of occurrences is larger. That is, the analyzing unit
35 sets the second product priority Es to be higher for a product
that was tried a larger number of times.
[0063] The analyzing unit 35 further analyzes whether or not
combination information including a product ID of a product whose
product priority E satisfies a first predetermined condition exists
in the pieces of combination information, and generates first
recommendation information recommending related information
according to the analysis result among a plurality of kinds of
related information. The first predetermined condition may be a
threshold or may be the product priorities E from the highest
priority to a certain predetermined rank of priority.
[0064] For example, if combination information including a product
ID of a product whose product priority E satisfies the first
predetermined condition does not exist in the pieces of combination
information, the analyzing unit 35 generates first recommendation
information recommending store information among a plurality of
kinds of related information. In this case, since "focus" is
performed but "try" is not performed, it is possible to encourage
the user to perform "try" by recommending the store information of
the store in which the second terminal 20 is installed, and as a
result, it may be possible to motivate the user to buy the
product.
[0065] If, for example, combination information including a product
ID of a product whose product priority E satisfies the first
predetermined condition exists in the pieces of combination
information, the analyzing unit 35 generates first recommendation
information recommending recommended coordinates among a plurality
of kinds of related information. In this case, since both "focus"
and "try" are performed, it may be possible to motivate the user to
buy other products recommended in the recommended coordinates by
recommending the recommended coordinates.
[0066] Furthermore, if there exists a plurality of categories of a
product whose product priority satisfies a second predetermined
condition, the analyzing unit 35 analyzes the number of occurrences
of each of the categories in a plurality of pieces of combination
information and generates second recommendation information
recommending a category with the largest number of occurrences. The
second predetermined condition may be a threshold or may be the
product priorities E from the highest priority to a certain
predetermined rank of priority.
[0067] For example, it is assumed that a product with the product
priority E satisfying the second predetermined condition is a bag
that can be carried in three ways: a handbag, a shoulder bag, and a
backpack. In this case, since the categories of the bag are
handbag, shoulder bag and backpack, the analyzing unit 35 analyzes
the number of occurrence of each of handbag, shoulder bag and
backpack in the pieces of combination information. Then, if the
number of occurrences of shoulder bag is the largest, the analyzing
unit 35 generates second recommendation information recommending
the shoulder bag. In this case, since it is popular among users to
perform "try" on the shoulder bag, it may be possible to motivate
the user to buy the product by recommending the shoulder bag. If,
however, "try" on the shoulder bag style of the bag is already
performed, second recommendation information recommending another
category such as handbag or backpack on which "try" has not been
performed may be generated.
[0068] The output unit 36 outputs information regarding a product
based on the product priority calculated by the analyzing unit 35
to at least one of the recognizing unit 11 and the combining unit
21. The information based on the product priority may be the
product priority itself or may be related information or an image
for combination of the product with the product priority. The
related information and the image for combination can be obtained
from the product information storage unit 37.
[0069] The output unit 36 also outputs information based on the
product priority calculated by the analyzing unit 35 and the first
recommendation information generated by the analyzing unit 35 to
the recognizing unit 11. The information based on the product
priority and the first recommendation information may be
information indicating the product priority and recommended related
information or may be recommended related information on the
product with the product priority.
[0070] The output unit 36 also outputs information based on the
product priority calculated by the analyzing unit 35 and the second
recommendation information generated by the analyzing unit 35 to
the combining unit 21. The information based on the product
priority and the second recommendation information may be the
product priority and an image ID of a recommended image for
combination or may be a recommended image for combination of the
product with the product priority.
[0071] The information output by the output unit 36 in this manner
is used as feedback information at the recognizing unit 11 and the
combining unit 21, so that information with higher probability of
motivating the user to buy a product is preferentially displayed at
the first terminal 10 and the second terminal 20.
[0072] When it is requested by the image recognizing unit 16 to
acquire product information, the output unit 36 acquires the
requested product information from the product information storage
unit 37 and outputs the acquired product information to the image
recognizing unit 16. Similarly, when it is requested by the image
combining unit 26 to acquire product information, the output unit
36 acquires the requested product information from the product
information storage unit 37 and outputs the acquired product
information to the image combining unit 26.
[0073] The product information storage unit 37 stores product
information of products. FIG. 7 is a diagram illustrating an
example of a data structure of the product information according to
the first embodiment. In the example illustrated in FIG. 7, the
product information is information in which a product ID, attribute
information (brand, price, color, material, etc.), accompanying
information (word of mouth, recommended coordinates, store
information (address, map, etc.), etc.) and a group of images for
combination are associated, but the product information is not
limited thereto.
[0074] FIG. 8 is a flowchart illustrating an example of a flow of
procedures of processing performed by the server 30 according to
the first embodiment.
[0075] First, the first acquiring unit 31 acquires recognition
information including at least a product ID of a product estimated
by the recognizing unit 11 from the recognizing unit 11 (the output
unit 15), and stores the acquired recognition information in the
recognition information storage unit 32 (step S101).
[0076] Subsequently, the second acquiring unit 33 acquires
combination information including at least a product ID of a
product in an image for combination combined with an image to be
combined from the combining unit 21 (the output unit 25), and
stores the acquired combination information in the combination
information storage unit 34 (step S103).
[0077] Subsequently, the analyzing unit 35 analyzes a plurality of
pieces of recognition information stored in the recognition
information storage unit 32 to calculate first product priority of
each product, analyzes a plurality of pieces of combination
information stored in the combination information storage unit 34
to calculate second product priority of each product, and
calculates product priority of each product on the basis of the
first product priority and the second product priority of each
product (step S105).
[0078] Subsequently, the analyzing unit 35 further analyzes whether
or not combination information including a product ID of a product
whose product priority satisfies the first predetermined condition
exists in the pieces of combination information, and generates
first recommendation information recommending related information
according to the analysis result among a plurality of kinds of
related information (step S107).
[0079] Subsequently, the output unit 36 outputs information based
on the product priority calculated by the analyzing unit 35 and the
first recommendation information generated by the analyzing unit 35
to the recognizing unit 11 (step S109).
[0080] Subsequently, if there exists a plurality of categories of a
product whose product priority satisfies the second predetermined
condition, the analyzing unit 35 analyzes the number of occurrences
of each of the categories in the pieces of combination information
and generates second recommendation information recommending a
category with the largest number of occurrences (step S111).
[0081] Subsequently, the output unit 36 outputs information based
on the product priority calculated by the analyzing unit 35 and the
second recommendation information generated by the analyzing unit
35 to the combining unit 21 (step S113).
[0082] As described above, according to the first embodiment, since
the product priority taking history of various O2O related
technologies into consideration can be calculated by analyzing the
history of the recognition information and the history of the
combination information to calculate the product priority, products
of greater interest to the user can be extracted. In addition,
according to the first embodiment, since information based on the
calculated product priority is output to the recognizing unit and
the combining unit, the recognizing unit and the combining unit can
preferentially present products of greater interest to the user by
using the information and it is thus possible to increase the
probability of motivating the user to buy a product.
[0083] In particular, according to the first embodiment, since not
only information on a product of higher interest to the user but
also information with high probability of motivating the user to
buy a product can be extracted from a plurality of kinds of related
information of the product, the recognizing unit can preferentially
present information of greater interest to the user by using the
information and it is thus possible to increase the probability of
motivating the user to buy a product.
[0084] Similarly, according to the first embodiment, since not only
information on a product of higher interest to the user but also
information with high probability of motivating the user to buy a
product can be extracted from the categories of the product, the
combining unit can preferentially present information of greater
interest to the user by using the information and it is thus
possible to increase the probability of motivating the user to buy
a product.
[0085] According to the first embodiment, since the recognizing
unit 11 (the output unit 15) can contain product image information
and related information in the recognition information, it is also
possible to figure out over what real objects the user held the
terminal and what products the user is interested in. For example,
it is possible to figure out whether the user got interested in a
product X by focus the terminal over an advertisement A or by focus
the terminal over an advertisement B, which allows the history
through which the user got interested in the product X to be used
in the analysis.
[0086] Similarly, according to the first embodiment, since the
combining unit 21 (the output unit 25) can contain combined image
information and combination image information in the combination
information, it is also possible to figure out what image for
combination is combined with what image to be combined. For
example, it is possible to figure out such a fact that people with
a body type A often try on clothes Y or such a fact that people
with a body type B often try on clothes Z, and it is thus possible
to obtain a tendency of "try" of each user by data analysis.
Second Embodiment
[0087] In the second embodiment, an example in which a third
terminal including a managing unit that manages sales information
on sales of products is further provided will be described. In the
following, the difference from the first embodiment will be mainly
described and components having similar functions as in the first
embodiment will be designated by the same names and reference
numerals as in the first embodiment, and the description thereof
will not be repeated.
[0088] FIG. 9 is a configuration diagram illustrating an example of
a system 101 according to the second embodiment. As illustrated in
FIG. 9, the system 101 is different from that in the first
embodiment in a server 130 and a third terminal 140 thereof.
[0089] In the second embodiment, an example in which the third
terminal 140 is a management terminal that includes a managing unit
141 and that manages sales information related to sales of products
will be described.
[0090] FIG. 10 is a configuration diagram illustrating an example
of the third terminal 140 according to the second embodiment. As
illustrated in FIG. 10, the third terminal 140 includes a managing
unit 141, a sales information storage unit 142, a display unit 143,
and an output unit 144.
[0091] The managing unit 141 may be implemented by making a
processor such as a CPU execute a program, that is, by software,
may be implemented by hardware such as an IC, or may be implemented
by combination of software and hardware, for example. The sales
information storage unit 142 can be realized by a storage device
that can magnetically, optically or electrically store information
such as an HDD, an SSD, a memory card, an optical disk, or a RAM,
for example. The display unit 143 can be realized by a display
device such as a liquid crystal display or a touch panel display,
for example. The output unit 144 can be realized by a communication
device such as an NIC, for example.
[0092] The sales information storage unit 142 stores sales
information related to sales of products. Examples of the sales
information include purchase information indicating details of
purchase of a product, sales promotion information relating to
sales promotion of a product, customer information, inventory
information, and training information relating to training of store
staff. The purchase information contains at least a product ID of a
product to be purchased. The purchase information may also contain
the date and time of purchase. The sales promotion information
contains first sales promotion information relating to sales
promotion using product images and second sales promotion
information relating to sales promotion using images for
combination. Examples of the sales promotion information include
information on advertising strategy, store layout, procurement
plan, product lineup, and methods for recommending products to
customers.
[0093] The managing unit 141 manages the sales information stored
in the sales information storage unit 142.
[0094] The display unit 143 displays the sales information managed
by the managing unit 141.
[0095] The output unit 144 outputs the sales information to the
server 130. For example, the output unit 144 outputs purchase
information and sales promotion information to the server 130.
[0096] FIG. 11 is a configuration diagram illustrating an example
of the server 130 according to the second embodiment. As
illustrated in FIG. 11, the server 130 is different from that in
the first embodiment in an analyzing unit 135, a third acquiring
unit 138, and a sales information storage unit 139.
[0097] The third acquiring unit 138 may be implemented by making a
processor such as a CPU execute a program, that is, by software,
may be implemented by hardware such as an IC, or may be implemented
by combination of software and hardware, for example. The sales
information storage unit 139 can be realized by a storage device
that can magnetically, optically or electrically store information
such as an HDD, an SSD, a memory card, an optical disk, or a RAM,
for example.
[0098] The third acquiring unit 138 acquires purchase information
and sales promotion information including at least a product ID of
a product to be purchased from the managing unit 141 (the output
unit 144), and stores the acquired purchase information and sales
promotion information in the sales information storage unit 139.
Note that the purchase information and the sales promotion
information may further contain information mentioned in the
description of the sales information storage unit 142.
[0099] The sales information storage unit 139 stores a plurality of
pieces of purchase information and sales promotion information
stored by the third acquiring unit 138. FIG. 12 is a table
illustrating examples of the purchase information according to the
second embodiment. In the examples illustrated in FIG. 12, the
purchase information is information in which a number, the date and
time of purchase, and a product ID are associated, but the purchase
information is not limited thereto.
[0100] The analyzing unit 135 performs at least one of first
analysis of analyzing a plurality of pieces of recognition
information stored in the recognition information storage unit 32,
a plurality of pieces of combination information stored in the
combination information storage unit 34, and a plurality of pieces
of purchase information stored in the sales information storage
unit 139 to calculate the product priority of each product and a
second analysis of analyzing at least either a plurality of pieces
of recognition information or a plurality of pieces of combination
information in addition to a plurality of pieces of purchase
information to obtain updated contents of sales information.
[0101] First, the first analysis will be described.
[0102] The analyzing unit 135 analyzes a plurality of pieces of
recognition information to calculate first product priority of each
product, analyzes a plurality of pieces of combination information
to calculate second product priority of each product, analyzes a
plurality of pieces of purchase information to calculate third
product priority of each product, calculate the product priority of
each product on the basis of the first product priority, the second
product priority and the third product priority of each
product.
[0103] For example, the analyzing unit 135 calculates the product
priority E of a certain product by calculating the first product
priority Er, the second product priority Es and the third product
priority Eb of the product and calculating weighted addition of the
calculated first product priority Er, second product priority Es
and third product priority Eb as expressed by Equation (2).
E=wr.times.Er+ws.times.Es+wb.times.Eb (2)
[0104] In Equation (2), wb represents the weight of the priority
Eb.
[0105] Note that the analyzing unit 135 analyzes the pieces of
recognition information and sets the first product priority Er of
the product represented by a product ID associated with recognition
date and time to be higher as the recognition date and time is
closer to the current date and time. That is, the analyzing unit
135 sets the first product priority Er to be higher for a product
over which a terminal was held on the date and time closer to the
current date and time.
[0106] The analyzing unit 135 also analyzes the pieces of
recognition information and sets the first product priority Er to
be higher for a product represented by a product ID having a value
whose number of occurrences is larger. That is, the analyzing unit
135 sets the first product priority Er to be higher for a product
over which a terminal was held a larger number of times.
[0107] Similarly, the analyzing unit 135 analyzes the pieces of
combination information and sets the second product priority Es of
a product represented by a product ID associated with combination
date and time contained in the combination information to be higher
as the combination date and time is closer to the current date and
time. That is, the analyzing unit 135 sets the second product
priority Es to be higher for a product that was tried on the date
and time closer to the current date and time.
[0108] The analyzing unit 135 also analyzes the pieces of
combination information and sets the second product priority Es to
be higher for a product represented by a product ID having a value
whose number of occurrences is larger. That is, the analyzing unit
135 sets the second product priority Es to be higher for a product
that was tried a larger number of times.
[0109] Similarly, the analyzing unit 135 analyzes the pieces of
purchase information and sets the third product priority Eb of a
product represented by a product ID associated with purchase date
and time contained in the purchase information to be higher as the
purchase date and time is closer to the current date and time. That
is, the analyzing unit 135 sets the third product priority Eb to be
higher for a product that was purchased on the date and time closer
to the current date and time.
[0110] The analyzing unit 135 also analyzes the pieces of purchase
information and sets the third product priority Eb to be higher for
a product represented by a product ID having a value whose number
of occurrences is larger. That is, the analyzing unit 135 sets the
third product priority Eb to be higher for a product that was
purchased a larger number of times.
[0111] Since the generation of the first recommendation information
and the second recommendation information is the same as that in
the first embodiment, the description thereof will not be
repeated.
[0112] Next, the second analysis will be described.
[0113] The analyzing unit 135 determines whether or not the
behavior of "focus" and the behavior of "try" of the user led to
purchase of a product by analyzing at least either a plurality of
pieces of recognition information or a plurality of pieces of
combination information in addition to a plurality of pieces of
purchase information and obtains updated contents of sales
promotion information.
[0114] Specifically, the analyzing unit 135 analyzes a plurality of
pieces of purchase information, analyzes the number of occurrences,
in the pieces of recognition information, of product image
information associated with a product ID having a value whose
number of occurrences in the purchase information satisfies a third
predetermined condition, and obtains updated contents of the first
sales promotion information according to the number of occurrences
of the product image information. The third predetermined condition
may be thresholds in multiple steps including an increase
determination threshold for determining whether or not to increase
a value and a decrease determination threshold for determining
whether or not to decrease a value, for example.
[0115] FIG. 13 is a table illustrating examples of the first sales
promotion information before being updated according to the second
embodiment, and FIG. 14 is a table illustrating examples of the
first sales promotion information after being updated according to
the second embodiment. In the examples illustrated in FIGS. 13 and
14, the first sales promotion information is information in which a
number, an advertisement ID, an image ID (product image
information) of a product image, and the number of advertisements
are associated, but the first sales promotion information is not
limited thereto. In the examples illustrated in FIG. 13, it is
assumed that the number of occurrences, in the pieces of purchase
information, of the value of product ID associated with each of
image IDs "IMAGE 10392" and "IMAGE 10192" satisfies the increase
determination threshold while the number of occurrences, in the
pieces of purchase information, of the value of product ID
associated with image ID "IMAGE 10291" satisfies the decrease
threshold. It is also assumed that the numbers of occurrences, in
the pieces of recognition information, of the image IDs "IMAGE
10392" and "IMAGE 10192" are larger than an average while the
number of occurrences of the image ID "IMAGE 10291" is much smaller
than the average.
[0116] That is, it is found that focus over an advertisement A and
an advertisement C led to purchase of products for the products
corresponding to the image IDs "IMAGE 10392" and "IMAGE 10192", the
effect of the advertisement A and the advertisement C is high, and
sales promotion using the advertisement A and the advertisement C
is therefore to be enhanced. On the other hand, it is found that
focus over an advertisement B had not led to purchase of the
product for the product corresponding to the image ID "IMAGE
10291", the effect of the advertisement B is low, and sales
promotion using the advertisement B is to be reduced.
[0117] In this case, the analyzing unit 135 obtains updated
contents in which the numbers of advertisement for the image IDs
"IMAGE 10392" and "IMAGE 10192" are increased by 10 while the
number of advertisements for the image ID "IMAGE 10291" is
decreased by 20, for example, as the updated contents of the first
sales promotion information. As a result, it is possible to update
the first sales promotion information illustrated in FIG. 13 with
that as illustrated in FIG. 14.
[0118] The analyzing unit 135 also analyzes the pieces of purchase
information, analyzes the number of occurrences, in the pieces of
combination information, of combination image information
associated with a product ID having a value whose number of
occurrences in the purchase information satisfies a fourth
predetermined condition, and obtains updated contents of the second
sales promotion information according to the number of occurrences
of the combination image information. The fourth predetermined
condition may be thresholds in multiple steps including an increase
determination threshold for determining whether or not to increase
a value and a decrease determination threshold for determining
whether or not to decrease a value, for example.
[0119] The analyzing unit 135 can also obtain updated contents of
store layout by analyzing a plurality of pieces of purchase
information and determining the sales rate of products sold
together. The sales rate of products sold together can be
calculated from purchase date and time or the like in the purchase
information.
[0120] FIG. 15 is a table illustrating examples of the store layout
information before being updated according to the second
embodiment, and FIG. 16 is a table illustrating examples of the
store layout information after being updated according to the
second embodiment. In the examples illustrated in FIGS. 15 and 16,
the store layout information is information in which a number, a
shelf ID, and a product ID are associated, but the store layout
information is not limited thereto. It is assumed that a shelf A
and a shelf B are adjacent to each other while a shelf C is
adjacent to neither of the shelf A and the shelf B. In the examples
illustrated in FIG. 15, it is assumed that the sales rates of
products sold together of products IDs "PRODUCT 20928" and "PRODUCT
20290" satisfy the increase determination threshold.
[0121] That is, since the rate of being sold together is high for
the product IDs "PRODUCT 20928" and "PRODUCT 20290", it is found
that the sales promotion is to be enhanced by placing these
products on adjacent shelves. In this case, the analyzing unit 135
obtains updated contents of arranging the product with the product
ID "PRODUCT 20290" on the shelf B and arranging the product with
the product ID "PRODUCT 20660" on the shelf C. As a result, it is
possible to update the store layout information illustrated in FIG.
15 with that as illustrated in FIG. 16.
[0122] The output unit 36 performs at least one of first output of
outputting information based on the product priority calculated by
the analyzing unit 135 to at least one of the recognizing unit 11
and the combining unit 21 and second output of outputting the
updated contents obtained by the analyzing unit 135 to the managing
unit 141.
[0123] Since the first output is the same as in the first
embodiment, the description thereof will not be repeated.
[0124] As for the second output, information output by the output
unit 36 in this manner is used for update of sales promotion
information at the managing unit 141, and sales promotion
information with higher probability of motivating the user to buy a
product will thus be managed at the third terminal 140.
[0125] FIG. 17 is a flowchart illustrating an example of a flow of
procedures of processing performed by the server 130 according to
the second embodiment.
[0126] First, the first acquiring unit 31 acquires recognition
information including at least a product ID of a product estimated
by the recognizing unit 11 from the recognizing unit 11 (the output
unit 15), and stores the acquired recognition information in the
recognition information storage unit 32 (step S401).
[0127] Subsequently, the second acquiring unit 33 acquires
combination information including at least a product ID of a
product in an image for combination combined with an image to be
combined from the combining unit 21 (the output unit 25), and
stores the acquired combination information in the combination
information storage unit 34 (step S403).
[0128] Subsequently, the third acquiring unit 138 acquires purchase
information and sales promotion information including at least a
product ID of a product to be purchased from the managing unit 141
(the output unit 144), and stores the acquired purchase information
and sales promotion information in the sales information storage
unit 139 (step S405).
[0129] Subsequently, the analyzing unit 135 analyzes a plurality of
pieces of recognition information stored in the recognition
information storage unit 32 to calculate the first product priority
of each product, analyzes a plurality of pieces of combination
information stored in the combination information storage unit 34
to calculate the second product priority of each product, analyzes
a plurality of pieces of purchase information stored in the sales
information storage unit 139 to calculate the third product
priority of each product, and calculates the product priority of
each product on the basis of the first product priority, the second
product priority and the third product priority of each product
(step S407).
[0130] Subsequently, the analyzing unit 135 further analyzes
whether or not combination information including a product ID of a
product whose product priority satisfies the first predetermined
condition exists in the pieces of combination information, and
generates first recommendation information recommending related
information according to the analysis result among a plurality of
kinds of related information (step S409).
[0131] Subsequently, the output unit 36 outputs information based
on the product priority calculated by the analyzing unit 135 and
the first recommendation information generated by the analyzing
unit 135 to the recognizing unit 11 (step S411).
[0132] Subsequently, if there exists a plurality of categories of a
product whose product priority satisfies the second predetermined
condition, the analyzing unit 135 analyzes the number of
occurrences of each of the categories in the pieces of combination
information and generates second recommendation information
recommending a category with the largest number of occurrences
(step S413).
[0133] Subsequently, the output unit 36 outputs information based
on the product priority calculated by the analyzing unit 135 and
the second recommendation information generated by the analyzing
unit 135 to the combining unit 21 (step S415).
[0134] Subsequently, the analyzing unit 135 analyzes at least
either of a plurality of pieces of recognition information or a
plurality of pieces of combination information in addition to a
plurality of pieces of purchase information to obtain updated
contents of the sales promotion information (step S417).
[0135] Subsequently, the output unit 36 outputs the updated
contents of the sales promotion information obtained by the
analyzing unit 135 to the managing unit 141 (step S419).
[0136] As described above, according to the second embodiment,
products of greater interest to the user can be extracted more
effectively by further analyzing the purchase information to
calculate the product priority. In addition, according to the
second embodiment, since information based on the calculated
product priority is output to the recognizing unit and the
combining unit, the recognizing unit and the combining unit can
more preferentially present products of greater interest to the
user by using the information and it is thus possible to further
increase the probability of motivating the user to buy a
product.
[0137] Furthermore, according to the second embodiment, more
effective sales management can be realized by analyzing at least
one of the history of the recognition information and the history
of the combination information in addition to the history of the
purchase information, which can lead to analysis and improvement of
advertising effectiveness, improvement in product lineup,
efficiency in product recommendation to customers (improvement in
methods for training store staff), improvement in procurement plan,
and improvement in store layouts.
Modifications
[0138] While examples in which histories of the image recognition
terminal that implements "focus", the image combining terminal that
implements "try" and the management terminal that manages sales
information are used have been described in the embodiments
described above, the embodiments are not limited thereto, and
histories of terminals using various O2O related technologies can
be used such as the history of a terminal implementing "search"
that is searching for related product information according to
attributes of a product over which "focus" is performed.
[0139] Furthermore, in the embodiments described above, the
analyzing unit 35 need not necessarily analyze all the histories.
That is, the analyzing unit 35 may set any of the weights to 0.
[0140] Furthermore, while examples in which the first terminal 10
including the recognizing unit 11 and the second terminal 20
including the combining unit 21 are different terminals have been
described in the embodiments described above, the recognizing unit
11 and the combining unit 21 may be included in one terminal 250 as
in a system 201 illustrated in FIG. 18.
Hardware Configuration
[0141] FIG. 19 is a diagram illustrating an example of a hardware
configuration of the server according to the embodiments and
modifications. The server according to the embodiments and
modifications described above includes a control device 901 such as
a CPU, a storage device 902 such as a ROM and a RAM, an external
storage device 903 such as a HDD, a display device 904 such as a
display, an input device 905 such as a keyboard and a mouse, and a
communication device 906 such as a communication interface (I/F),
which is a hardware configuration utilizing a common computer
system.
[0142] Programs to be executed by the server according to the
embodiments and modifications described above are recorded on a
computer readable recording medium such as a CD-ROM, a CD-R, a
memory card, a digital versatile disk (DVD) and a flexible disk
(FD) in a form of a file that can be installed or executed, and
provided therefrom as a computer program product.
[0143] Alternatively, the programs to be executed by the server
according to the embodiments and modifications may be stored on a
computer system connected to a network such as the Internet, and
provided by being downloaded via the network. Still alternatively,
the programs to be executed by the server according to the
embodiments and modifications may be provided or distributed
through a network such as the Internet. Still alternatively, the
programs to be executed by the server according to the embodiments
and modifications may be embedded in a ROM or the like in advance
and provided therefrom.
[0144] The programs to be executed by the server according to the
embodiments and modifications have modular structures for
implementing the units described above on a computer system. In an
actual hardware configuration, the CPU reads programs from the HDD
and executes the programs on the RAM, whereby the respective units
described above are implemented on a computer system.
[0145] For example, the order in which the steps in the flowcharts
in the embodiments described above are performed may be changed, a
plurality of steps may be performed at the same time or the order
in which the steps are performed may be changed each time the steps
are performed to the extent that the changes are not inconsistent
with the nature thereof.
[0146] As described above, according to the embodiments and
modifications described above, information with high probability of
motivating the user to buy a product can be extracted.
[0147] While certain embodiments have been described, these
embodiments have been presented by way of example only, and are not
intended to limit the scope of the inventions. Indeed, the novel
embodiments described herein may be embodied in a variety of other
forms; furthermore, various omissions, substitutions and changes in
the form of the embodiments described herein may be made without
departing from the spirit of the inventions. The accompanying
claims and their equivalents are intended to cover such forms or
modifications as would fall within the scope and spirit of the
inventions.
* * * * *