Evaluation support apparatus, method, and program

Abstract

To effectively support evaluation of an accessory by a user.SOLUTION: The evaluation assistance apparatus includes a receiver configured to receive a candidate image of an accessory that a user is considering carrying, an acquirer configured to acquire, using the candidate image, an evaluation result including first evaluation information indicating an evaluation of the accessory based on a first evaluation index relating to the user and second evaluation information indicating an evaluation of the accessory based on a second evaluation index relating to the accessory according to an evaluation scale for a predetermined group, and an outputter configured to output the evaluation result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an evaluation support device, method, and program. [Background technology]

[0002] Patent Document 1 discloses a technology related to a management device for managing evaluations of products. The management device determines evaluation items to be used to derive an evaluation value for the product from among multiple evaluation items related to the product, based on information about viewers who view the evaluations of the product. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-051301 Summary of the Invention [Problem to be solved by the invention]

[0004] When a user considers whether to own a new accessory, the user may have difficulty determining whether the accessory is suitable for the user, etc. The reason for this is that the evaluation index for making a decision may be insufficient.

[0005] In view of the above-mentioned problems, an object of the present disclosure is to provide an evaluation support device, method, and program for effectively supporting a user in evaluating an attachment. [Means for solving the problem]

[0006] The evaluation support device according to the present disclosure includes: A receiving means for receiving candidate images of accessories that the user is considering owning; An acquisition means for acquiring an evaluation result including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group, using the candidate image; an output means for outputting the evaluation result; Equipped with.

[0007] The evaluation support method according to the present disclosure includes: The computer Accept candidate images of accessories that the user is considering owning, Using the candidate image, obtain an evaluation result including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group; The evaluation results are output.

[0008] The evaluation support program according to the present disclosure is A reception process for receiving candidate images of accessories that the user is considering owning; an acquisition process for acquiring evaluation results including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group, using the candidate image; an output process for outputting the evaluation result; to be executed by the computer. [Effects of the Invention]

[0009] The present disclosure can effectively assist users in evaluating wearable items. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration of an evaluation support device according to the present disclosure. [Figure 2]1 is a flowchart showing the flow of an evaluation support method according to the present disclosure. [Figure 3] 1 is a block diagram showing the overall configuration of an evaluation support system according to the present disclosure. [Figure 4] FIG. 2 is a block diagram illustrating a configuration of a user terminal according to the present disclosure. [Figure 5] FIG. 2 is a block diagram showing the configuration of an evaluation support server according to the present disclosure. [Figure 6] 10 is a sequence chart showing the flow of an evaluation support process according to the present disclosure. [Figure 7] 10 is a flowchart showing the flow of evaluation information generation processing in the evaluation support server according to the present disclosure. [Figure 8] FIG. 10 is a diagram showing an example of displaying evaluation results according to the present disclosure. [Figure 9] FIG. 2 is a block diagram showing the configuration of an evaluation support server according to the present disclosure. [Figure 10] 10 is a flowchart showing the flow of evaluation information generation processing in the evaluation support server according to the present disclosure. [Figure 11] FIG. 10 is a diagram showing an example of displaying evaluation results according to the present disclosure. [Figure 12] 1 is a block diagram showing a configuration of an evaluation support system according to the present disclosure. [Figure 13] FIG. 2 is a block diagram showing the configuration of an evaluation support server according to the present disclosure. [Figure 14] 10 is a sequence chart showing the flow of an evaluation support process according to the present disclosure. [Figure 15] FIG. 2 is a block diagram illustrating a configuration of a user terminal according to the present disclosure. [Figure 16] 10 is a sequence chart showing the flow of an evaluation support process according to the present disclosure. [Figure 17] FIG. 2 is a block diagram showing a hardware configuration of a user terminal according to the present disclosure. [Figure 18] FIG. 2 is a block diagram showing the hardware configuration of an evaluation support server according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are designated by the same reference numerals, and for clarity of explanation, duplicate explanations will be omitted as necessary.

[0012] (Embodiment 1) 1 is a block diagram showing the configuration of an evaluation support device 1. The evaluation support device 1 is an information processing device for supporting a user in evaluating a new accessory when the user is considering whether or not to own the accessory. The evaluation support device 1 includes a receiving unit 11, an acquiring unit 12, and an output unit 13.

[0013] The reception unit 11 receives candidate images of accessories that the user is considering owning. Here, "accessories" refers to articles or the like that the user wears on his or her body. "Possession" may also include the user having the accessory in some way, such as by purchasing, transferring, or renting for a fee or free of charge. "Candidate images" include images capturing the appearance of the accessory. Furthermore, "candidate images" may also include images capturing attribute information related to the accessory.

[0014] The acquisition unit 12 acquires an evaluation result including first evaluation information and second evaluation information obtained by evaluating the wearable item using the candidate image. Here, the "first evaluation information" is information obtained by evaluating the wearable item based on a first evaluation index using the candidate image. The "first evaluation index" is an evaluation index related to the user. Furthermore, the "second evaluation information" is information obtained by evaluating the wearable item based on a second evaluation index using the candidate image. The "second evaluation index" is an evaluation index related to the wearable item based on an evaluation scale in a predetermined group. The "evaluation scale in a predetermined group" is an evaluation scale that is a perspective different from the user's subjectivity and has a population of multiple other people.

[0015] The output unit 13 outputs the evaluation result.

[0016] 2 is a flowchart showing the flow of the evaluation support method. First, the receiving unit 11 receives candidate images of wearable items that the user is considering owning (S1). Next, the acquiring unit 12 uses the candidate images to acquire evaluation results including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale for a predetermined group (S2). Then, the output unit 13 outputs the evaluation results acquired in step S2 (S3).

[0017] As described above, in this embodiment, a first evaluation index related to the user and a second evaluation index related to the wearable item based on an evaluation scale for a predetermined group are used as evaluation indexes for determining the appropriateness of the user owning a new wearable item. In other words, the first evaluation index is an index for evaluating the relationship between the user and the wearable item. On the other hand, the second evaluation index is an index for evaluating the wearable item based on an evaluation scale for a predetermined group, which is independent of the user's own perspective. In this way, the evaluation support device 1 can present to the user evaluation results in which the target wearable item is evaluated using evaluation indexes from different perspectives. Therefore, when considering whether to own a new wearable item, the user can refer to the evaluation results in which the target wearable item is evaluated using sufficient evaluation indexes. Therefore, the user can appropriately determine the appropriateness of owning the wearable item, such as whether the wearable item under consideration is suitable for the user. This effectively supports the user's evaluation of the wearable item.

[0018] The evaluation support device 1 includes a processor, a memory, and a storage device (not shown). The storage device stores a computer program that implements the processing of the evaluation support method shown in Fig. 2, for example. The processor then loads the computer program from the storage device into the memory and executes the computer program. The processor thereby realizes the functions of a reception unit 11, an acquisition unit 12, and an output unit 13.

[0019] Alternatively, each component of the evaluation support device 1 may be realized by dedicated hardware. Furthermore, some or all of the components of each device may be realized by general-purpose or dedicated circuits, processors, etc., or a combination of these. These may be configured by a single chip, or by multiple chips connected via a bus. Some or all of the components of each device may be realized by a combination of the above-mentioned circuits, etc., and programs. Furthermore, a CPU (Central Processing Unit), GPU (Graphics Processing Unit), FPGA (Field-Programmable Gate Array), quantum processor (quantum computer control chip), etc., may be used as the processor.

[0020] Furthermore, when some or all of the components of the evaluation support device 1 are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or decentralized. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, or the like, connected to each other via a communication network. Furthermore, the functions of the evaluation support device 1 may be provided in the form of SaaS (Software as a Service).

[0021] (Embodiment 2) Here, the problem that the technology disclosed herein aims to solve will be described in more detail. For example, when a user is shopping alone, they may have difficulty deciding whether or not to purchase a product they are considering (e.g., clothing). This may be due to factors related to the user, such as whether the product looks good on them when worn, whether the size is appropriate, or whether it suits their preferences. Another reason may be due to evaluation criteria outside of the user, such as whether the product is in line with trends or is highly fashionable. Still another reason may be whether the price of the product is appropriate for its quality. For example, it may be difficult for the user to determine whether the product is worth the price. In order to solve this problem, the technology disclosed herein will be described below.

[0022] FIG. 3 is a block diagram showing the overall configuration of the evaluation support system 1000. The evaluation support system 1000 is an information system that supports a user U in evaluating (determining whether to own) an accessory A by presenting evaluation results in which the accessory A is evaluated based on multiple evaluation indexes when the user U is considering owning the accessory A. The accessory A includes anything that the user U can wear, such as clothing (clothing, hats, or shoes), glasses, bags, or ornaments (accessories, nails (false nails), small items). The following description will explain a case in which the evaluation support system 1000 is used when the user U considers purchasing clothing as the accessory A in a physical store. However, the evaluation support system 1000 is not limited to application to physical stores and can also be applied to products sold on e-commerce sites.

[0023] The evaluation support system 1000 includes a user terminal 100 and an evaluation support server 200. The user terminal 100 and the evaluation support server 200 are communicably connected to each other via a communication network N. Here, the communication network N is a wired or wireless communication line including a wireless communication line.

[0024] The user terminal 100 is an information processing terminal capable of wireless communication operated by a user U. The user terminal 100 may be, for example, a mobile terminal such as a smartphone, a tablet terminal, or a notebook PC (Personal Computer), or may be a desktop PC. The user terminal 100 can be said to be an example of the evaluation support device 1 described above. FIG. 4 is a block diagram showing the configuration of the user terminal 100. The user terminal 100 includes a storage unit 110, a reception unit 121, an evaluation request unit 122, an acquisition unit 123, an output unit 124, a communication unit 131, an image capture unit 132, and an input / output unit 133.

[0025] The storage unit 110 includes, for example, a non-volatile storage device such as a hard disk or flash memory, and a memory such as a RAM (Random Access Memory), that is, a volatile storage device. The storage unit 110 stores user information 111. The user information 111 is information about the user U. The user information 111 includes at least identification information of the user U (e.g., a user ID). Furthermore, the user information 111 may include preference information indicating the size of each part of the user U's body, clothing preferences, etc.

[0026] The reception unit 121 is an example of the above-mentioned reception unit 11. The reception unit 121 receives, as a candidate image, an image of the wearable item A captured by the image capture unit 132. The candidate image is an image of the wearable item A that the user U is considering possessing (for example, purchasing), that is, a candidate for possession.

[0027] The evaluation request unit 122 transmits an evaluation request including the candidate image received by the receiving unit 121 and the user information 111 to the evaluation support server 200. The evaluation request is transmission data for requesting an evaluation result based on a plurality of evaluation indexes, which will be described later, for the accessory A included in the candidate image, using the user information 111.

[0028] The acquisition unit 123 is an example of the above-mentioned acquisition unit 12. The acquisition unit 123 acquires the evaluation result of the attachment A in response to the evaluation request from the evaluation support server 200. The evaluation result will be described in detail later.

[0029] The output unit 124 is an example of the above-mentioned output unit 13. The output unit 124 outputs the evaluation result acquired by the acquisition unit 123 to the input / output unit 133, thereby displaying it.

[0030] The communication unit 131 communicates between the user terminal 100 and the outside. For example, the communication unit 131 transmits an evaluation request from the evaluation request unit 122 to the evaluation support server 200 via the communication network N. The communication unit 131 also receives the evaluation result from the evaluation support server 200 via the communication network N and outputs it to the acquisition unit 123.

[0031] The photographing unit 132 is a photographing device such as a camera. The photographing unit 132 takes a photograph in response to an operation by the user U, and outputs the photographed image to the receiving unit 121.

[0032] The input / output unit 133 is an input / output device such as a touch panel. For example, the input / output unit 133 displays on a screen the evaluation results output by the output unit 124. The input / output unit 133 may also receive an operation by the user U on the screen and output the operation content to the reception unit 121, the evaluation request unit 122, or the like.

[0033] The evaluation support server 200 is an information processing device that executes the evaluation support process described below. The evaluation support device 1 may be realized as a computer system in which functions are distributed or made redundant by a plurality of computer devices.

[0034] Specifically, the evaluation support server 200 is an information processing device that generates an evaluation result based on the first evaluation index and the second evaluation index for the article A identified from the candidate image included in the evaluation request received from the user terminal 100, and transmits the evaluation result to the user terminal 100. Therefore, the evaluation support server 200 can be said to be an example of the evaluation support device 1 described above.

[0035] Here, the first evaluation index includes an evaluation index for at least one of the size match between the user and the wearable item or the user's preference. The evaluation index for the size match between the user and the wearable item is an index for evaluating whether the size of the wearable item is suitable for the user's physique, etc. An evaluation index for the size match between the user and the wearable item is, for example, a size evaluation index, which will be described later. The user's preference is information indicating the user's tendency for preferred colors, shapes, etc. of the wearable item and related items. Therefore, the evaluation index for the user's preference is an index for evaluating whether the color, shape, etc. of the wearable item matches the user's preference. An evaluation index for the user's preference is, for example, a preference evaluation index, which will be described later.

[0036] The second evaluation index is an evaluation index using an evaluation scale derived from information about the field of wearable products from a predetermined group. Examples of predetermined groups include industry associations in the field of the target wearable product, user groups that share information on SNS (social networking services) and post, view, and comment on articles, groups including writers and subscribers of online articles, and groups of fashion information providers and consumers. However, predetermined groups are not limited to these. The evaluation scale is a standard derived comprehensively from information such as the views of a predetermined group about the field of the target wearable product. Therefore, the second evaluation index can be said to be an index for evaluating the target wearable product using the evaluation scale.

[0037] Specifically, the second evaluation index may include an evaluation index for at least one of popularity and fashion in the field of wearable items. The evaluation index for popularity in the field of wearable items is, for example, a popularity evaluation index described below. Also, the evaluation index for fashion in the field of wearable items is, for example, a fashion evaluation index described below.

[0038] Furthermore, the evaluation support server 200 may generate evaluation information for the wearable item A identified from the candidate image, which is evaluated based on at least one of the first evaluation index or the second evaluation index. For example, the evaluation support server 200 may generate first evaluation information for evaluating the wearable item A based on the first evaluation index, and acquire second evaluation information generated by an external system or the like evaluating the wearable item A based on the second evaluation index. Alternatively, the evaluation support server 200 may acquire first evaluation information for evaluating the wearable item A based on the first evaluation index, and generate second evaluation information for evaluating the wearable item A based on the second evaluation index. Therefore, it can be said that the evaluation support server 200 acquires the generated first evaluation information and second evaluation information.

[0039] Furthermore, the evaluation support server 200 may generate third evaluation information for the wearable item A identified from the candidate image, in which the appropriateness of the price of the wearable item A is evaluated based on the third evaluation index. Therefore, it can be said that the evaluation support server 200 acquires the generated third evaluation information. The third evaluation index is an evaluation index related to the quality of the wearable item. The third evaluation index is, for example, a quality evaluation index described below.

[0040] 5 is a block diagram showing the configuration of the evaluation support server 200. The evaluation support server 200 includes a storage unit 210, a receiving unit 221, an evaluation unit 222, and a transmitting unit 223. The receiving unit 221 can be considered an example of the above-mentioned accepting unit 11. The evaluation unit 222 can be considered an example of the above-mentioned acquiring unit 12. The transmitting unit 223 can be considered an example of the above-mentioned output unit 13.

[0041] The storage unit 210 includes, for example, a non-volatile storage device such as a hard disk or flash memory, and a memory such as RAM, i.e., a volatile storage device. The storage unit 210 stores a user information DB (DataBase) 211, an accessory information DB 212, a size evaluation index 213, a preference evaluation index 214, a trend evaluation index 215, a fashionability evaluation index 216, and a quality evaluation index 217.

[0042] The user information DB 211 is a database that manages user information for one or more users. The user information DB 211 contains information that associates, for each user, the user's identification information with the sizes of each body part and preference information regarding clothing, etc. Note that the user's identification information is information that is uniquely identified by the identification information included in the above-described user information 111. The user information DB 211 also includes at least the identification information of the user U. The sizes of each body part may be, for example, information registered by the user U regarding his or her physique or size itself using the user terminal 100. The sizes of each body part may also be information derived, for example, by analyzing image data of the user U's body. The preference information is, for example, information indicating the corresponding user's preferred clothing, color tones and color combinations, clothing styles, etc. The preference information may also be information derived, for example, by analyzing the history of articles that the user U has viewed on the web, etc. using the user terminal 100, or the user U's purchase history of clothing, etc. The user information may also include information on accessories owned by the user U (a list of personal items, which corresponds to the accessory information described later).

[0043] The accessory information DB212 is a database that manages information about one or more types of accessory. The accessory information DB212 includes information about at least accessory A. The accessory information DB212 is information in which an image of the appearance, type, material (raw material), manufacturing location (production area), shape, price, etc. are associated with each accessory. Each of the user information DB211 and the accessory information DB212 can be considered to correspond to a storage area managed by database management software.

[0044] The size evaluation index 213 is information that defines an index for evaluating whether the user's body size fits the target accessory, whether the accessory is balanced, etc. The size evaluation index 213 may be information that defines evaluation items related to size.

[0045] The preference evaluation index 214 is information that defines an index for evaluating the degree to which the color, shape, etc. of the wearable item matches the user's preference. The preference evaluation index 214 may be information that defines evaluation items related to the user's preference.

[0046] The trend evaluation index 215 is information defining an index for evaluating the trendiness of a target wearable item in the wearable item field. Trendiness (evaluation) by a specific group can vary over time, even for the same item (color, shape, etc.). In other words, the trendiness evaluation scale can vary over time. The evaluation scale for the trend evaluation index 215 may be derived, for example, by collecting and analyzing social media images or posts and web articles published within a recent certain period in the wearable item field, along with evaluation comments on these. The evaluation scale for the trend evaluation index 215 may also be derived based on information on trendy colors from color information organizations in member countries. In this case, the specific group regarding trendiness can be the color information organizations in member countries. Therefore, the trend evaluation index 215 can be said to be information defining an index for evaluating a target wearable item based on the trendiness evaluation scale in the wearable item field. The trend evaluation index 215 may also be information defining evaluation items related to trendiness.

[0047] The fashionability evaluation index 216 is information defining an index for evaluating the fashionability of the target wearable item in the wearable item's field. Fashionability is not affected by time and can be evaluated based on an evaluation scale established for the type of occupation, use (genre), region, etc. Furthermore, fashionability is more universal than trendiness and can be evaluated based on an evaluation scale for design. In other words, a predetermined group related to fashionability can be a group of people involved in the type of occupation or use in the wearable item's field, or a group belonging to a region, etc. The fashionability evaluation scale may be derived, for example, by collecting and analyzing social media images or posts or web articles about the fashionability of the wearable item, as well as evaluation comments on these. The fashionability evaluation scale may also be derived by collecting and analyzing information such as photographs from books and magazines about fashion, expert opinions, and comments from their supporters and critics. The fashionability may also be evaluated based on an evaluation scale based on the color and shape of the target wearable item, its combination with other belongings, etc. The fashionability evaluation index 216 may be information that defines evaluation items related to fashionability.

[0048] The quality evaluation index 217 is information that defines an index for evaluating the degree of quality and price appropriateness of the target wearable item. In other words, the quality evaluation index 217 is information that defines an index for evaluating the degree of balance between the quality and price of the target wearable item. Quality includes the type, material (substance), manufacturing location (place of production), shape, etc. of the target wearable item. The quality evaluation index 217 may be defined by collecting and analyzing images or posts on social media and articles on the web related to the quality and price of the wearable item, as well as evaluation comments on these. The quality evaluation index 217 may also be information that defines evaluation items related to quality, evaluation items related to the relationship between quality and price, etc.

[0049] The receiving unit 221 receives an evaluation request including a candidate image and user information from the user terminal 100 .

[0050] The evaluation unit 222 refers to the wearable item information DB 212 and analyzes the candidate image to identify wearable item information including the type of the wearable item to be evaluated and the price of the wearable item. The evaluation unit 222 also identifies the size and preferences of the user corresponding to the received user information from the user information DB 211. Note that if the user information included in the evaluation request includes size and preferences, the evaluation unit 222 may identify the size and preferences of the user based on the evaluation request.

[0051] Then, the evaluation unit 222 evaluates the degree of compatibility between the identified size of the user and the identified size of the accessory information based on the size evaluation index 213. Specifically, the evaluation unit 222 generates size evaluation information based on the size evaluation index 213.

[0052] Furthermore, the evaluation unit 222 evaluates the degree of conformity between the identified user preferences and the identified wearable item information, based on the preference evaluation index 214. Specifically, the evaluation unit 222 generates preference evaluation information based on the preference evaluation index 214.

[0053] Furthermore, the evaluation unit 222 evaluates the popularity of the identified accessory information based on the popularity evaluation index 215. Specifically, the evaluation unit 222 generates popularity evaluation information based on the popularity evaluation index 215.

[0054] Furthermore, the evaluation unit 222 evaluates the fashionability of the identified accessory information based on the fashionability evaluation index 216. Specifically, the evaluation unit 222 generates fashionability evaluation information based on the fashionability evaluation index 216.

[0055] Furthermore, the evaluation unit 222 evaluates the quality of the identified accessory information and the appropriateness of the price of the identified accessory based on the quality evaluation index 217. Specifically, the evaluation unit 222 generates quality-price appropriateness evaluation information based on the quality evaluation index 217.

[0056] The evaluation unit 222 then generates an evaluation result by integrating the generated size evaluation information, preference evaluation information, trend evaluation information, fashionability evaluation information, and quality-price fairness evaluation information. In other words, the evaluation unit 222 can be said to be an acquisition unit that acquires the generated evaluation information as an evaluation result.

[0057] The evaluation unit 222 may derive evaluation scales for trends and fashionability by collecting and analyzing images or posts on social media and web articles in various fields of wearable items, as well as evaluation comments on these. The evaluation unit 222 may also derive evaluation scales for trends based on information on trendy colors from color information organizations in member countries. The evaluation unit 222 may also derive evaluation scales for fashionability by collecting and analyzing information such as photographs in books and magazines about fashionability, expert opinions, and comments from their supporters and critics. The evaluation unit 222 may then update the trends evaluation index 215, the fashionability evaluation index 216, and the fashionability evaluation index 216 using the collected information and the derived evaluation scales.

[0058] The transmission unit 223 transmits the evaluation result generated by the evaluation unit 222 to the user terminal 100 that is the source of the evaluation request.

[0059] The communication unit 231 communicates with the evaluation support server 200 and the outside. For example, the communication unit 231 receives an evaluation request from the user terminal 100 via the communication network N and outputs the evaluation request to the receiving unit 221. In addition, the communication unit 231 transmits the evaluation result from the transmitting unit 223 to the user terminal 100 via the communication network N.

[0060] 6 is a sequence chart showing the flow of the evaluation support process. First, the user U takes a photo of the wearable item A that the user U is considering purchasing using the user terminal 100. In response to this, the user terminal 100 accepts the photographed image of the wearable item A as a candidate image for purchase (possession) (S101). Then, in response to, for example, an operation by the user U, the user terminal 100 transmits an evaluation request including the candidate image and the user information 111 of the user U (including at least the user's identification information) to the evaluation support server 200 via the communication network N (S102). In response to this, the evaluation support server 200 performs an evaluation information generation process (S103).

[0061] 7 is a flowchart showing the flow of evaluation information generation processing in the evaluation support server 200. First, the receiving unit 221 receives an evaluation request including a candidate image and user information from the user terminal 100 via the communication network N (S111).

[0062] Next, the evaluation unit 222 refers to the accessory information DB 212 and identifies accessory information through image analysis (S112). Specifically, the evaluation unit 222 performs image analysis on the candidate image included in the evaluation request received in step S111 to identify the size and price of the accessory. For example, if the accessory is clothing, the evaluation unit 222 may analyze the product tag included in the candidate image to read the size and price of the target clothing to identify it. Alternatively, if the candidate image includes an object that serves as a length standard (such as a ruler), the evaluation unit 222 may recognize the shape and length standard of the accessory included in the candidate image through image analysis and identify the size of the clothing based on the recognition result. The evaluation unit 222 may also identify features of the clothing through image analysis of the candidate image. Examples of clothing features include, but are not limited to, type, material (material), manufacturing location (place of production), color, and shape.

[0063] The evaluation unit 222 also identifies the user's size and preferences from the user information (S113). Specifically, the evaluation unit 222 searches the user information DB 211 for user information corresponding to the user's identification information included in the evaluation request received in step S111, and identifies the size and preferences included in the searched user information. Alternatively, the evaluation unit 222 may identify the size of the wearable item corresponding to the type of wearable item identified in step S112 from a list of belongings included in the searched user information. The evaluation unit 222 may also identify the characteristics and tendencies of the user's preferences by image analysis of images of the wearable items included in the list of belongings, purchase history, article browsing history, etc. included in the searched user information.

[0064] The processing order of steps S112 and S113 is not limited to this. For example, the evaluation unit 222 may execute step S113 after step S112.

[0065] After steps S112 and S113, the evaluation unit 222 executes steps S114 to S118, which will be described later. Note that the processing order of steps S114 to S118 may be parallel, may be a predetermined order, or may be partly parallel.

[0066] The evaluation unit 222 generates size evaluation information based on the size evaluation index 213 from the identified user size and the wearable item information (S114). Specifically, the evaluation unit 222 evaluates to what extent the size of the wearable item matches the user's size based on the size evaluation index 213. For example, the evaluation unit 222 may calculate a numerical value indicating the degree of match of the wearable item size to the user's size, and generate size evaluation information including the calculated numerical value. The degree of size match may be, for example, larger the numerical value, the smaller the difference in size between the user and the wearable item. Alternatively, the degree of size match may be larger the numerical value, the higher the degree of match of the feature amounts between the user's body shape information and the shape information of the wearable item.

[0067] The evaluation unit 222 also generates preference evaluation information based on the preference evaluation index 214 from the identified user preferences and the wearable item information (S115). Specifically, the evaluation unit 222 evaluates the degree to which the color, shape, etc. of the wearable item information identified in step S112 matches the user preferences based on the preference evaluation index 214. For example, similar to step S114, the evaluation unit 222 may calculate a numerical value indicating the degree of match between the user preferences and the color, shape, etc. of the wearable item, and generate preference evaluation information including the calculated numerical value. The degree of preference match may be, for example, larger as the difference in color, shape, etc. between the user preferences and the wearable item becomes smaller. Alternatively, the degree of preference match may be larger as the degree of match between the user preferences and the wearable item in terms of feature amounts such as color, shape, etc. becomes higher.

[0068] The evaluation unit 222 also generates trend evaluation information that evaluates the wearable item based on the trend evaluation index 215 of the wearable item (S116). Specifically, the evaluation unit 222 evaluates the degree to which the color, shape, etc. of the wearable item information identified in step S112 match current trends based on the trend evaluation index 215. For example, the evaluation unit 222 may calculate a numerical value indicating the degree of trend matching for the color, shape, etc. of the wearable item, similar to step S114 above, and generate trend evaluation information including the calculated numerical value. The degree of trend matching may be, for example, a larger numerical value as the difference in color, shape, etc. between the trend evaluation index 215 and the wearable item becomes smaller. Alternatively, the degree of trend matching may be a larger numerical value as the degree of matching of feature quantities such as color, shape, etc. between the trend evaluation index 215 and the wearable item becomes higher.

[0069] Furthermore, the evaluation unit 222 generates fashionability evaluation information that evaluates the worn item based on the fashionability evaluation index 216 of the worn item (S117). Specifically, the evaluation unit 222 evaluates the fashionability of the color, shape, etc. of the worn item information identified in step S112 based on the fashionability evaluation index 216. For example, the evaluation unit 222 may generate fashionability evaluation information by evaluating the higher the fashionability as a larger numerical value.

[0070] The evaluation unit 222 also generates quality-price fairness evaluation information that evaluates the fairness of the quality and price of the wearable item based on the quality evaluation index 217 of the wearable item (S118). Specifically, the evaluation unit 222 evaluates the fairness of the quality and price of the wearable item information identified in step S112 based on the quality evaluation index 217. For example, the evaluation unit 222 may calculate a numerical value of fairness indicating the fairness of the quality and price of the wearable item based on the quality evaluation index 217, and generate the quality-price fairness evaluation information including the calculated numerical value. The fairness of the quality and price may be a larger numerical value when the degree of matching between the information (combination) that associates the quality of various characteristics (shape, color, etc.) in the field of the wearable item with a fair price defined by the quality evaluation index 217 and the relationship (combination) between the quality and price of the wearable item in question is higher. For example, the fairness of the quality and price may be a smaller numerical value when the wearable item is relatively low quality based on the quality evaluation index 217 but is expensive. In other words, the appropriateness of quality and price can also be called the degree of balance between quality and price.

[0071] After steps S114 to S118, the evaluation unit 222 integrates each piece of evaluation information to generate an evaluation result (S119). Specifically, the evaluation unit 222 integrates the size evaluation information, preference evaluation information, trendiness evaluation information, fashionability evaluation information, and quality-price fairness evaluation information to generate the evaluation result. That is, the evaluation unit 222 acquires an evaluation result including the size evaluation information, preference evaluation information, trendiness evaluation information, fashionability evaluation information, and quality-price fairness evaluation information.

[0072] Thereafter, the transmitting unit 223 transmits the generated evaluation result to the user terminal 100 via the communication network N (S120). In FIG. 6, the evaluation support server 200 transmits the evaluation result (group of evaluation information) to the user terminal 100 (S104).

[0073] In response, the user terminal 100 displays the evaluation results received from the evaluation support server 200 via the communication network N (S105). FIG. 8 illustrates an example of the display of the evaluation results. Here, the user terminal 100 displays an evaluation result display screen 4 on the input / output unit 133. The evaluation result display screen 4 includes a prospective purchase item image 41 and a radar chart 42. The prospective purchase item image 41 may be a standalone image of the wearable item extracted from an image captured by the user U using the user terminal 100, or an external appearance image corresponding to the target wearable item stored in the wearable item information DB 212. The radar chart 42 is an example in which evaluation results 426 for each of the size evaluation index 421 (size), preference evaluation index 422 (likeability), fashionability evaluation index 423 (trendiness), fashionability evaluation index 424 (fashionability), and quality evaluation index 425 (quality) are displayed as pentagons. As described above, the radar chart 42 is an example in which adjacent indices are connected by lines to calculate the numerical values ​​of each evaluation index. The display form of the evaluation result 426 is not limited to this.

[0074] This allows the user U to visually check the evaluation results evaluated using multiple evaluation indexes via the radar chart 42 on the evaluation result display screen 4. Therefore, the user U can comprehensively judge whether or not they should own the wearable item A. That is, the user U can make a judgment taking into consideration the individual evaluations of the size evaluation index 421, preference evaluation index 422, trendiness evaluation index 423, fashionability evaluation index 424, and quality evaluation index 425 of the wearable item A, as well as the balance of the evaluations among the indexes. Therefore, the evaluation support system 1000 can effectively support the user U in evaluating a product that the user is considering purchasing. Therefore, the evaluation support system 1000 can effectively support the user U in determining whether or not they should purchase the product that the user is considering purchasing.

[0075] (Embodiment 3) Here, for example, the user may be unsure whether to purchase the clothing, etc., because the clothing the user is considering purchasing matches the clothing the user currently owns at home, etc. One reason for this is that the user is unable to compare the clothing in combination with the clothing the user currently owns at home, etc.

[0076] Therefore, in this third embodiment, an example will be described in which the degree of matching between a combination of an accessory that a user U is considering owning and a list of belongings of the user U is evaluated. Note that the evaluation support system in the third embodiment is the same as that in FIG. 3 of the second embodiment, and therefore is not shown in the figure. Below, a description of the same functions as in the second embodiment will be omitted, and the differences from the second embodiment will be mainly described.

[0077] FIG. 9 is a block diagram showing the configuration of the evaluation support server 200a. The evaluation support server 200a differs from the evaluation support server 200 shown in FIG. 5 in that the user information DB 211a and the evaluation unit 222a are modified, and a combination evaluation index 218 and an image generation unit 224 are added. The user information DB 211a requires that each user's information in the user information DB 211a includes a list of belongings. Furthermore, the user information DB 211a includes a full-body image of each user. The combination evaluation index 218 is an index of the degree of compatibility of color, shape, etc., for a combination of multiple types of accessories. The combination evaluation index 218 is an evaluation index for a combination of accessories based on an evaluation scale in a predetermined group. The evaluation scale for the combination evaluation index 218 may be derived by collecting and analyzing images or posts on social media and web articles related to combinations of accessories, as well as evaluation comments on these. Additionally, the combination evaluation index 218 may be information in which an evaluation scale is derived and evaluation items are defined, similar to the trend evaluation index 215 and the fashionability evaluation index 216. The combination evaluation index 218 is an example of a fourth evaluation index.

[0078] The evaluation unit 222a generates fourth evaluation information in which the wearable item A is evaluated based on a fourth evaluation index related to the combination of the user U's belongings and the wearable item A under consideration. Then, the evaluation unit 222a acquires the evaluation result including the fourth evaluation information. The fourth evaluation information includes a matching degree indicating the degree of compatibility between the combination of the user U's belongings and the wearable item A under consideration. The matching degree may be, for example, information that expresses the compatibility as a percentage. Note that the matching degree may also be other index values ​​such as a numerical value or a level.

[0079] The image generating unit 224 generates an image of the user U wearing the accessory A under consideration. Specifically, the image generating unit 224 generates an image in which the image of the accessory A is attached to a whole-body image of the user U. Then, the transmitting unit 223 outputs the image generated by the image generating unit 224.

[0080] FIG. 10 is a flowchart showing the flow of evaluation information generation processing in the evaluation support server 200a. First, the evaluation support server 200a executes steps S111 to S113 of FIG. 7, and then executes steps S114 to S118. After steps S111 to S113, the evaluation unit 222a selects other wearable items that can be combined with the identified wearable item information (wearable item A) from the user U's belongings list (S121). For example, if the type of the identified wearable item A is a jacket, examples of other wearable items that can be combined with the jacket include pants (trousers) and shoes. In this case, the evaluation unit 222a selects images of pants, shoes, etc. from the user U's belongings list. Note that these combinations are merely examples.

[0081] Then, the evaluation unit 222a calculates the degree of matching when the wearable item A is combined with the selected other wearable items (S122). Specifically, the evaluation unit 222a calculates the degree of matching for the combination of the wearable item A and the selected other wearable items based on the combination evaluation index 218. The matching degree may be a larger numerical value as the evaluation of the combination based on the combination evaluation index 218 is relatively higher.

[0082] After steps S114 to S118 and S122, the evaluation unit 222a integrates the evaluation information and generates an evaluation result including the matching degree (S123). Specifically, the evaluation unit 222a integrates the size evaluation information, preference evaluation information, trend evaluation information, fashionability evaluation information, and quality-price fairness evaluation information with the matching degree to generate the evaluation result. In other words, the evaluation unit 222a acquires an evaluation result including the size evaluation information, preference evaluation information, trendiness evaluation information, fashionability evaluation information, quality-price fairness evaluation information, and matching degree.

[0083] After step S121, the image generation unit 224 generates a wearing image in which the user U wears the accessory A in combination with the selected other accessory (S124). After steps S123 and S124, the transmission unit 223 transmits the evaluation result and the wearing image to the user terminal 100 via the communication network N (S125).

[0084] In response, the user terminal 100 displays the evaluation results and the wearing image received from the evaluation support server 200a via the communication network N. FIG. 11 is a diagram showing an example of the display of the evaluation results. Here, it is assumed that the user terminal 100 displays an evaluation result display screen 4a on the input / output unit 133. The evaluation result display screen 4a includes a radar chart 42, a wearing image 43, and a matching degree 44. The wearing image 43 is an example of an image generated by the image generation unit 224. The wearing image 43 shows an example in which a prospective purchase image 431, a belongings image 432, and a belongings image 433 are worn on the entire body of the user U. In other words, the prospective purchase image 431 is an example of an image of the wearing item A that the user U is considering owning. The belongings image 432 and the belongings image 433 are examples of images of other wearing items that can be combined with the wearing item A selected in step S121. The matching degree 44 is also part of the evaluation result. In this example, when user U possesses accessory A, by wearing it in combination with accessory image 432 and accessory image 433 in the current accessory list, the matching degree 44 is evaluated as "70%."

[0085] This allows the user U to visually view the wearing image 43 and the matching degree 44 along with the radar chart 42 on the evaluation result display screen 4a. Therefore, if the user U possesses the wearing item A, it becomes easier for the user U to imagine how it can be combined with the items currently possessed. In other words, the user U can obtain information on realistic combinations from the list of items possessed that he / she registered in advance, without relying on a vague memory of the contents of his / her closet. Furthermore, the user U can easily understand the evaluation level of the combination. Therefore, the evaluation support system 1000 can effectively support the user U in evaluating a product that he / she is considering purchasing. Therefore, the evaluation support system 1000 can effectively support the user U in determining whether or not to purchase the product that he / she is considering purchasing.

[0086] (Embodiment 4) In this embodiment 4, an example will be described in which the evaluation process that was performed inside the evaluation support servers 200 and 200a is executed by an external system, and the evaluation support server acquires evaluation information or evaluation results. In the following, a description of the same functions as those in the second or third embodiment will be omitted, and the differences from the second or third embodiment will be mainly described.

[0087] 12 is a block diagram showing the configuration of an evaluation support system 1000b. The evaluation support system 1000b includes a user terminal 100, an evaluation support server 200b, and evaluation model servers 301, 302, ... 30n (n is a natural number equal to or greater than 1). Note that at least one evaluation model server 301 is required. The user terminal 100, the evaluation support server 200b, and the evaluation model servers 301 to 30n are each connected to each other via a communication network N so as to be able to communicate with each other.

[0088] The evaluation model server 301 or the like performs evaluation processing for a specific field of wearable items. For example, the evaluation model server 301 or the like may be provided with an AI (Artificial Intelligence) model that inputs candidate images and user information and outputs evaluation information evaluated based on specific evaluation indices. Therefore, when the evaluation model server 301 or the like receives an evaluation request from an external device via the communication network N, it returns the evaluation information or evaluation result output by the AI ​​model to the requestor. For example, the AI ​​model may be different for each of the above-mentioned size evaluation index 213, preference evaluation index 214, trendiness evaluation index 215, fashionability evaluation index 216, quality evaluation index 217, and combination evaluation index 218. Alternatively, the AI ​​model may be a combination of some of the above multiple evaluation indices. For example, a certain AI model may perform evaluation based on the size evaluation index 213 and preference evaluation index 214, i.e., the first evaluation index. In this case, the AI ​​model may input candidate images and user information and output size evaluation information and preference evaluation information. Furthermore, the other model may perform evaluation based on the trend evaluation index 215 and the fashionability evaluation index 216, i.e., the second evaluation index. In this case, the AI ​​model may input a candidate image and output trend evaluation information and fashionability evaluation information. Alternatively, the AI ​​model may perform evaluation using various combinations of multiple evaluation indexes. Furthermore, the AI ​​model may generate evaluation information based on all of the above evaluation indexes and output an evaluation result that integrates the evaluation information. Each of the evaluation model servers 301, etc., operates either a different AI model for each of the above evaluation indexes, an AI model for a combination of multiple evaluation indexes, or an AI model for all combinations. Furthermore, multiple evaluation model servers may operate a common AI model.

[0089] Fig. 13 is a block diagram showing the configuration of the evaluation support server 200b. The evaluation support server 200b differs from the evaluation support server 200 of Fig. 5 in that the storage unit 210 does not store the accessory information DB 212, the size evaluation index 213, the preference evaluation index 214, the trendiness evaluation index 215, the fashionability evaluation index 216, the quality evaluation index 217, and the combination evaluation index 218. The evaluation support server 200b also differs from the evaluation support server 200 of Fig. 5 in that the evaluation unit 222b is changed and an evaluation request unit 225 and an acquisition unit 226 are added.

[0090] The evaluation unit 222b identifies the size and preference of the user corresponding to the received user information, similar to the evaluation unit 222 in Fig. 5. The evaluation request unit 225 transmits an evaluation request including the received candidate image and the identified user information to each of the evaluation model servers 301, etc. Furthermore, when the evaluation model server operates AI models corresponding to the second evaluation index and the third evaluation index, the evaluation request unit 225 transmits an evaluation request including the received candidate image to the evaluation model server.

[0091] The acquisition unit 226 acquires evaluation information in response to the evaluation request from each of the evaluation model servers 301, etc. The evaluation unit 222b, like the evaluation unit 222 in FIG. 5, integrates the evaluation information to generate an evaluation result. In this case, the acquisition unit 226 can be said to acquire the evaluation result. Alternatively, if one evaluation model server is running AI models for all combinations, the acquisition unit 226 may acquire the evaluation result in response to the evaluation request from that evaluation model server.

[0092] Fig. 14 is a sequence chart showing the flow of the evaluation support process. First, as in Fig. 6, the user terminal 100 executes steps S101 and S102. Then, as in step S113 of Fig. 7, the evaluation unit 222b of the evaluation support server 200b identifies the size and preferences of the user from the user information included in the received evaluation request (S131).

[0093] Thereafter, the evaluation request unit 225 transmits an evaluation request including the received candidate image and the specified user information to the evaluation model server 301 (S211). Then, the acquisition unit 226 acquires evaluation information from the evaluation model server 301 (S212). Similarly, the evaluation request unit 225 transmits an evaluation request including the received candidate image and the specified user information to the evaluation model server 30n (S2n1). Then, the acquisition unit 226 acquires evaluation information from the evaluation model server 30n (S2n2).

[0094] For example, the evaluation model server 301 operates an AI model corresponding to the size evaluation index 213. The AI ​​model corresponding to the size evaluation index 213 is a trained model that has been trained in advance using predetermined training data. The training data is a set of images of wearable items that a user has purchased or has at home, and the sizes of users who own each wearable item, as input data, and size evaluation information based on the size of each wearable item in the input data as output data. Therefore, the evaluation model server 301 inputs the candidate image and the size of the user information included in the received evaluation request into the AI ​​model, and transmits the size evaluation information output from the AI ​​model to the evaluation support server 200.

[0095] Furthermore, the evaluation model server 302 operates an AI model corresponding to the preference evaluation index 214. The AI ​​model corresponding to the preference evaluation index 214 is a trained model trained in advance using predetermined training data. The training data is input data consisting of images of wearable items that users have purchased or have at home, and a set of preferences of users who own each wearable item, and output data consisting of preference evaluation information based on the characteristics (color, shape, etc.) of each wearable item in the input data.

[0096] The evaluation model server 303 (not shown) operates an AI model corresponding to the trend evaluation index 215. The AI ​​model corresponding to the trend evaluation index 215 is a trained model that has been trained in advance using predetermined training data. The training data is input data that is an image of an accessory on SNS or the like, and output data that is trend evaluation information based on trend color information from color information organizations of member countries and information indicating the trend in the field of accessories on SNS or the like.

[0097] The evaluation model server 304 (not shown) operates an AI model corresponding to the fashionability evaluation index 216. The AI ​​model corresponding to the fashionability evaluation index 216 is a trained model that has been trained in advance using predetermined training data. The training data is input data that is an image of an accessory on SNS or the like, and output data that is fashionability evaluation information based on information on SNS, books, etc. related to fashionability.

[0098] The evaluation model server 305 (not shown) operates an AI model corresponding to the quality evaluation index 217. The AI ​​model corresponding to the quality evaluation index 217 is a trained model that has been trained in advance using predetermined training data. The training data has images of the wearable items as input data and quality-price fairness evaluation information based on the combination of quality and price of each wearable item as output data.

[0099] Therefore, the evaluation request unit 225 transmits an evaluation request including the received candidate image and the specified user information to the evaluation model server 302. Then, the acquisition unit 226 acquires preference evaluation information from the evaluation model server 302. Furthermore, the evaluation request unit 225 transmits the evaluation request including the received candidate image to the evaluation model server 303. Then, the acquisition unit 226 acquires trend evaluation information from the evaluation model server 303. Furthermore, the evaluation request unit 225 transmits the evaluation request including the received candidate image to the evaluation model server 304. Then, the acquisition unit 226 acquires fashionability evaluation information from the evaluation model server 304. Furthermore, the evaluation request unit 225 transmits the evaluation request including the received candidate image to the evaluation model server 305. Then, the acquisition unit 226 acquires quality-price fairness evaluation information from the evaluation model server 305.

[0100] Thereafter, the evaluation unit 222b integrates each piece of evaluation information to generate an evaluation result (S132). Then, similar to step S104 in Fig. 6 above, the transmission unit 223 transmits the evaluation result (group of evaluation information) to the user terminal 100. Then, the user terminal 100 displays the evaluation result received from the evaluation support server 200b via the communication network N (S105).

[0101] In this way, in the fourth embodiment, the evaluation process itself is executed by an AI model of an external system, and the evaluation support server 200b transmits an evaluation request to the AI ​​model and acquires evaluation information or evaluation results. Note that the evaluation support server 200b may execute the evaluation process for some of the evaluation indexes. In this case, the storage unit 210 is assumed to store the evaluation indexes and the attachment information DB 212 required for the evaluation process.

[0102] Therefore, the evaluation support server 200b according to the fourth embodiment can reduce processing costs by distributing the evaluation process to an external system, compared to the evaluation support server 200 or 200a according to the second or third embodiment. Furthermore, since the evaluation process can be executed in parallel in each AI model, the processing speed may be improved.

[0103] (Embodiment 5) The user terminal 100c according to the fifth embodiment incorporates the configuration and functions of the above-mentioned authentication support server 200. Therefore, the evaluation support system according to the fifth embodiment is the same as that shown in FIG. 3 except that the evaluation support server 200 is removed and the user terminal 100 is replaced with 100c.

[0104] 15 is a block diagram showing the configuration of a user terminal 100c. The user terminal 100c includes a storage unit 110, a reception unit 121, an acquisition unit 123, an output unit 124, an evaluation unit 125, a communication unit 131, a photographing unit 132, and an input / output unit 133. The storage unit 110 stores user information 111, an accessory information DB 112, a size evaluation index 113, a preference evaluation index 114, a trend evaluation index 115, a fashionability evaluation index 116, and a quality evaluation index 117. The user information 111 is equivalent to the user information about user U in the user information DB 211 of FIG. 5. In other words, the user information 111 includes the user's size, preferences, etc. In addition, the wearable item information DB112, size evaluation index 113, preference evaluation index 114, trendiness evaluation index 115, fashionability evaluation index 116, and quality evaluation index 117 are similar to the wearable item information DB212, size evaluation index 213, preference evaluation index 214, trendiness evaluation index 215, fashionability evaluation index 216, and quality evaluation index 217 in Figure 6 above, respectively.

[0105] The evaluation unit 125 has the same function as the evaluation unit 222 in Fig. 5. That is, the evaluation unit 125 performs the processes of steps S112 to S119 in Fig. 7. Then, the acquisition unit 123 acquires the evaluation result generated by the evaluation unit 125. Then, the output unit 124 displays the evaluation result.

[0106] In this way, the fifth embodiment can also achieve the same effects as those of the second embodiment. Furthermore, the user terminal 100c may store the combination evaluation index 218 of Fig. 9 in the storage unit 110, and the evaluation unit 125 may have the same function as the evaluation unit 222a. As a result, the fifth embodiment can also achieve the same effects as those of the third embodiment.

[0107] (Embodiment 6) The user terminal 100 according to the sixth embodiment has the configuration and functions of the above-mentioned authentication support server 200b built in. Therefore, the evaluation support system according to the sixth embodiment is the same as that shown in Fig. 12 except that the evaluation support server 200b is removed. Also, the evaluation request unit 122 of the user terminal 100 transmits evaluation requests to each evaluation model server 301, etc., similar to the evaluation request unit 225 shown in Fig. 13.

[0108] FIG. 16 is a sequence chart showing the flow of the evaluation support process. The user terminal 100 accepts a photographed image of the accessory A as a candidate image for purchase (possession) (S101). The user terminal 100 then identifies the user's size and preferences from the user information 111 (S141). The evaluation request unit 122 then transmits an evaluation request including the candidate image and the identified user information (size and preferences) to the evaluation model server 301 via the communication network N, for example, in response to an operation by the user U (S311). The acquisition unit 123 then acquires the evaluation information from the evaluation model server 301 (S312). Similarly, the evaluation request unit 122 then transmits an evaluation request including the candidate image and the identified user information to the evaluation model server 30n (S3n1). The acquisition unit 226 then acquires the evaluation information from the evaluation model server 30n (S3n2).

[0109] Thereafter, the user terminal 100 integrates the evaluation information to generate an evaluation result (S142). Then, the output unit 124 displays the generated evaluation result (S105). In this way, the sixth embodiment can also achieve the same effects as the fourth embodiment.

[0110] (Other embodiments) 17 is a block diagram showing the hardware configuration of the user terminal 100 or 100c. The user terminal 100 or the like includes a memory 101, a processor 102, a network interface 103, a touch panel 104, and a camera 105.

[0111] The memory 101 is configured by a combination of volatile memory and non-volatile memory. The volatile memory is, for example, a volatile storage device such as RAM, and is a storage area for temporarily holding information while the processor 102 is operating. The non-volatile memory is, for example, a non-volatile storage device such as flash memory. The memory 101 stores at least a computer program that implements at least part of the processing of the evaluation support method for the user terminal 100, etc., according to the present disclosure.

[0112] The processor 102 is a control device that controls each component of the user terminal 100, etc. The processor 102 reads and executes software (computer programs) from the memory 101. As a result, the processor 102 realizes the functions of the reception unit 121, the evaluation request unit 122, the acquisition unit 123, the output unit 124, and the evaluation unit 125. That is, the processor 102 performs at least a part of the processing of the evaluation support method for the user terminal 100, etc., according to the present disclosure. The processor 102 may be, for example, a microprocessor, an MPU (Multi Processing Unit), or a CPU (Central Processing Unit). The processor 102 may also include multiple processors.

[0113] The network interface 103 may be used to communicate with a network node. The network interface 103 may include, for example, a network interface card (NIC) conforming to the IEEE 802.3 series. IEEE stands for Institute of Electrical and Electronics Engineers. The network interface 103 may also include a wireless local area network (LAN), a wired LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), etc. The network interface 103 corresponds to the communication unit 131 described above.

[0114] The touch panel 104 is a display device that displays information instructed by the processor 102 and is an input device that accepts operations from a user. The touch panel 104 is, for example, a screen such as a liquid crystal display or an organic electroluminescence (EL) display. The touch panel 104 corresponds to the input / output unit 133 described above.

[0115] The camera 105 photographs the attachment A etc. in response to an instruction from the processor 102, and outputs the photographed image to the processor 102. The camera 105 is, for example, a CCD (Charge Coupled Device) image sensor or a CMOS (Complementary Metal Oxide Semiconductor) sensor. The camera 105 corresponds to the photographing unit 132 described above.

[0116] 18 is a block diagram showing the hardware configuration of the evaluation support servers 200, 200a, and 200b. The evaluation support server 200 etc. includes a memory 201, a processor 202, and a network interface 203.

[0117] The memory 201 stores at least a computer program that implements at least a part of the processing of an evaluation support method, including evaluation information generation processing of the evaluation support server 200, etc., according to the present disclosure. Furthermore, the memory 201 may store a user information DB 211(a), an accessory information DB 212, a size evaluation index 213, a preference evaluation index 214, a trendiness evaluation index 215, a fashionability evaluation index 216, and a quality evaluation index 217 (and a combination evaluation index 218). Other configurations of the memory 201 are the same as those of the memory 101. The processor 202 is a control device that controls each component of the evaluation support server 200, etc. The processor 202 reads and executes software (computer programs) from the memory 201. As a result, the processor 202 realizes the functions of a receiving unit 221, an evaluation unit 222(a, b), a transmitting unit 223, an image generating unit 224, an evaluation request unit 225, and an acquiring unit 226. That is, the processor 202 performs at least a part of the processing of the evaluation support method in the evaluation support server 200 etc. according to the present disclosure. Other configurations of the processor 202 are the same as those of the above-mentioned processor 102. The network interface 203 has the same configuration as that of the above-mentioned network interface 103. The network interface 203 corresponds to the above-mentioned communication unit 231.

[0118] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0119] Each drawing is merely an example for describing one or more embodiments. Each drawing may relate not only to one particular embodiment, but also to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create, for example, an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessary to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate.

[0120] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes. (Appendix A1) A receiving means for receiving candidate images of accessories that the user is considering owning; An acquisition means for acquiring an evaluation result including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group, using the candidate image; an output means for outputting the evaluation result; An evaluation support device comprising: (Appendix A2) The second evaluation index is an evaluation index using the evaluation scale derived from information about the field of the wearable item in the group. 10. The evaluation support device according to claim A1. (Appendix A3) The second evaluation index includes an evaluation index of at least one of popularity and fashionability in the field of the wearable item. An evaluation support device according to appendix A1 or A2. (Appendix A4) The acquiring means acquires the evaluation result further including third evaluation information in which the appropriateness of the price of the accessory is evaluated based on a third evaluation index related to the quality of the accessory. An evaluation support device according to any one of appendices A1 to A3. (Appendix A5) The acquisition means acquires the evaluation result further including fourth evaluation information in which the wearable item is evaluated based on a fourth evaluation index related to a combination of the user's belongings and the wearable item. An evaluation support device according to any one of appendices A1 to A4. (Appendix A6) The output means outputs an image generated so that the user looks as if they are wearing the accessory. An evaluation support device according to any one of appendices A1 to A5. (Appendix A7) Further, an evaluation means is provided for generating evaluation information that evaluates the wearable item identified from the candidate image based on at least one of the first evaluation index or the second evaluation index, The acquiring means acquires the evaluation result including the evaluation information generated by the evaluating means. An evaluation support device according to any one of appendices A1 to A6. (Appendix A8) The first evaluation index includes at least one of an evaluation index of a match between the size of the user and the wearable item or a preference of the user. An evaluation support device according to any one of appendices A1 to A7. (Appendix B1) The computer Accept candidate images of accessories that the user is considering owning, Using the candidate image, obtain an evaluation result including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group; outputting the evaluation results; Evaluation support methods. (Appendix C1) A reception process for receiving candidate images of accessories that the user is considering owning; an acquisition process for acquiring evaluation results including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group, using the candidate image; an output process for outputting the evaluation result; An evaluation support program that causes a computer to execute the above.

[0121] Some or all of the elements (e.g., configurations and functions) described in Appendix A2 to Appendix A8 that are dependent on Appendix A1 {e.g., device} may also be dependent on Appendix B1 {e.g., method} and Appendix C1 {e.g., program} in the same dependency relationship as Appendix A2 to Appendix A8. Some or all of the elements described in any appendix may be applied to various hardware, software, recording means for recording software, systems, and methods. [Explanation of symbols]

[0122] 1 evaluation support device, 11 reception unit, 12 acquisition unit, 13 output unit, 1000 evaluation support system, 1000b evaluation support system, A wearing item, U user, N communication network, 100 user terminal, 100c user terminal, 110 memory unit, 111 user information, 112 wearing item information DB, 113 size evaluation index, 114 preference evaluation index, 115 trendiness evaluation index, 116 fashionability evaluation index, 117 quality evaluation index, 118 combination evaluation index, 121 reception unit, 122 evaluation request unit, 123 acquisition unit, 124 output unit, 125 evaluation unit, 131 communication unit, 132 photography unit, 133 input / output unit, 200 evaluation support server, 200a evaluation support server, 200b evaluation support server, 210 memory unit, 211 User information DB, 211a User information DB, 212 Wearing item information DB, 213 Size evaluation index, 214 Preference evaluation index, 215 Trend evaluation index, 216 Fashion evaluation index, 217 Quality evaluation index, 218 Combination evaluation index, 221 Receiving unit, 222 Evaluation unit, 222a Evaluation unit, 222b Evaluation unit, 223 Transmitting unit, 224 Image generation unit, 225 Evaluation request unit, 226 Acquisition unit, 231 Communication unit, 301 Evaluation model server, 302 Evaluation model server, 30n Evaluation model server, 4 Evaluation result display screen, 4a Evaluation result display screen, 41 Purchased item image, 42 Radar chart, 421 Size evaluation index, 422 Preference evaluation index, 423 Trend evaluation index, 424 Fashion evaluation index, 425 Quality evaluation index, 426 Evaluation result, 43 Wearing image, 431 Purchasing item image, 432 Possession item image, 433 Possession item image, 44 Matching degree, 101 Memory, 102 Processor, 103 Network interface, 104 Touch panel, 105 Camera, 201 Memory, 202 Processor, 203 Network interface

Claims

1. A receiving means for receiving candidate images of accessories that the user is considering owning; an acquisition means for acquiring an evaluation result including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group, using the candidate image; an output means for outputting the evaluation result; An evaluation support device comprising:

2. The second evaluation index is an evaluation index using the evaluation scale derived from information about the field of the wearable item in the group. The evaluation support device according to claim 1 .

3. The second evaluation index includes an evaluation index of at least one of popularity and fashionability in the field of the wearable item. The evaluation support device according to claim 1 or 2.

4. The acquiring means acquires the evaluation result further including third evaluation information in which the appropriateness of the price of the accessory is evaluated based on a third evaluation index related to the quality of the accessory. The evaluation support device according to claim 1 or 2.

5. The acquisition means acquires the evaluation result further including fourth evaluation information in which the wearable item is evaluated based on a fourth evaluation index related to a combination of the user's belongings and the wearable item. The evaluation support device according to claim 1 or 2.

6. The output means outputs an image generated so that the user looks as if they are wearing the accessory. The evaluation support device according to claim 1 or 2.

7. The apparatus further includes an evaluation unit that generates evaluation information for the attachment identified from the candidate image based on at least one of the first evaluation index and the second evaluation index, The acquiring means acquires the evaluation result including the evaluation information generated by the evaluating means. The evaluation support device according to claim 1 or 2.

8. The first evaluation index includes at least one of an evaluation index of a match between the size of the user and the wearable item or a preference of the user. The evaluation support device according to claim 1 or 2.

9. The computer Accept candidate images of accessories that the user is considering owning, Using the candidate image, obtain an evaluation result including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group; outputting the evaluation results; Evaluation support methods.

10. A reception process for receiving candidate images of accessories that the user is considering owning; an acquisition process for acquiring evaluation results including first evaluation information in which the wearable item is evaluated based on a first evaluation index related to the user, and second evaluation information in which the wearable item is evaluated based on a second evaluation index related to the wearable item using an evaluation scale in a predetermined group, using the candidate image; an output process for outputting the evaluation result; An evaluation support program that causes a computer to execute the above.

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