Information processing apparatus, information processing method, and information processing program

Abstract

To improve the convenience in using an information service.SOLUTION: An information processing apparatus includes an acquisition unit and a determination unit. The acquisition unit acquires, when a user shows a predetermined action regarding the use of a predetermined information service, attribute information of the user including at least physical information of the user and action information indicating the action of the user in the predetermined information service, or the attribute information of the user including at least physical information of the user and purchase information indicating a purchase of the user in the predetermined information service, as user information of the user. The determination unit determines recommendation information, based on the user information and management information managed by the predetermined information service.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Conventionally, methods have been proposed for outputting items to a user that match the user's physical characteristics. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2022-145944 Summary of the Invention [Problem to be solved by the invention]

[0004] For example, when a user is trying to purchase a product using an information service such as an e-commerce site, there is a need to comprehensively determine the most suitable recommended products based on physical information and various behavioral histories, and to use the results of this determination to support the user's purchasing behavior and the sales staff's customer service behavior.

[0005] The above-mentioned conventional technologies merely recommend items to users that match their physical characteristics, so it is difficult to say that they can meet the needs of behavioral support, and they cannot be said to be highly convenient when using information services.

[0006] The present invention has been made in view of the above, and proposes an information processing device, an information processing method, and an information processing program that can improve convenience when using information services. [Means for solving the problem]

[0007] In order to solve the above problem, one form of information processing device according to the present invention comprises an acquisition unit that, when a user exhibits a predetermined behavior related to the use of a predetermined information service, acquires, as user information of the user, attribute information of the user including at least physical information of the user and behavioral information indicating the user's behavior in the predetermined information service, or attribute information of the user including at least physical information of the user and purchase information indicating the user's purchases in the predetermined information service, and a determination unit that determines recommendation information based on the user information and management information managed by the predetermined information service. [Effects of the Invention]

[0008] According to the present invention, it is possible to improve convenience when using information services. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an overview of information processing according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 3] FIG. 3 is a diagram (1) showing an example of functions realized in accordance with information cooperation between a business device and an information processing device. [Figure 4] FIG. 4 is a diagram (2) showing an example of functions realized in accordance with information cooperation between a business device and an information processing device. [Figure 5] FIG. 5 is a diagram (3) showing an example of functions realized in accordance with information cooperation between a business device and an information processing device. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of an apparatus according to an embodiment. [Figure 7] FIG. 7 is a diagram showing a scene in which the user U is guided to a personal diagnosis. [Figure 8] FIG. 8 is a flowchart showing the procedure of information processing according to the embodiment. [Figure 9]FIG. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0011] One or more embodiments (including examples, modifications, and application examples) described below can be implemented independently. However, at least a portion of the embodiments described below may be implemented in appropriate combination with at least a portion of another embodiment. These embodiments may include novel features that are different from each other. Therefore, these embodiments may contribute to solving different purposes or problems and may produce different effects from each other.

[0012] (Embodiment) 1. Introduction For example, apparel and beauty companies often operate not only brick-and-mortar stores but also online stores, i.e., electronic commerce (EC) sites. Therefore, there is a need to utilize customer information collected through the use of EC sites and information on the company's sales staff (e.g., salespeople who serve customers in brick-and-mortar stores) to appropriately support both user purchasing behavior and the customer service activities of sales staff.

[0013] Therefore, the inventors of the present invention focused on aggregating the information necessary for "looking good" and providing digital support for selecting "looking good" items, and arrived at the proposed technology of the present invention.

[0014] According to the proposed technology of the present invention, when a user (e.g., a person who intends to shop at an EC site) exhibits a predetermined behavior regarding the use of a predetermined information service (e.g., an EC site), the user's attribute information including at least the user's physical information and behavioral information indicating the user's behavior in the predetermined information service, or the user's attribute information including at least the user's physical information and purchase information indicating the user's purchases in the predetermined information service, are acquired as user information. Then, recommendation information is determined based on the acquired user information and management information managed by the predetermined information service (e.g., customer information accumulated as the EC site is used by various customers, and salesperson information of the sales staff belonging to the EC site).

[0015] As a result, the proposed technology of the present invention can support the purchasing behavior of users by presenting recommended information to them, and can also support the customer service activities of sales staff by presenting recommended information to them. Therefore, the proposed technology of the present invention can increase the convenience of using information services.

[0016] In the following embodiments, use cases are shown in which the proposed technology of the present invention is applied to various recommendations in the fashion field (mainly recommendations for clothing and clothing styling). However, the situations in which the proposed technology of the present invention can be used are not limited to recommendations in the fashion field. For example, the proposed technology of the present invention can be applied to any object in which the concept of appropriateness / inappropriateness is created based on user attribute information, etc., and is judged in a dialogue with a specific person. As an example, the proposed technology of the present invention can also be applied to recommendations for household items such as home appliances.

[0017] 2. Overview of the embodiment Fig. 1 is a diagram illustrating an overview of information processing according to an embodiment. Fig. 1 illustrates a scene in which recommendation information is determined based on customer information accumulated through use of an e-commerce site Wx operated by a business operator Bn, such as an apparel company or a beauty company, and user information of a user U who has recently accessed the e-commerce site Wx, and the determined recommendation information is used to support the user U and a sales staff member F of the business operator Bn.

[0018] The business operator Bn may operate not only the e-commerce site Wx but also a physical store SP, and the sales staff F referred to here may be a salesperson who serves customers at the physical store SP. In this regard, the sales staff F can be said to belong to the e-commerce site Wx. The sales staff F may also upload recommended items and styling examples using the recommended items to the e-commerce site Wx or a predetermined posting site as salesperson information. Therefore, in the information processing according to the embodiment, this salesperson information is also used in determining the recommendation information.

[0019] 1, the information processing according to the embodiment is realized by an information processing device 100. The information processing device 100 may be implemented as a so-called cloud server device. Recommendation information determined by the information processing device 100 is distributed to a user terminal 10 used by a user U and a store terminal 20 used by a sales staff member F at a store SP.

[0020] In addition, the information processing device 100 stores customer information accumulated as a result of use of the EC site Wx, such as attribute information including physical information of the customer CS, behavioral information (behavioral history) of the customer CS at the EC site Wx, and purchase information (purchase history) indicating purchases made by the customer CS at the EC site Wx.

[0021] The physical information of the customer CS may be information on the results of a diagnosis based on a personal diagnosis of the customer CS. For example, if the customer CS has undergone a bone structure diagnosis, the physical information may include information on the bone structure type of the customer CS. There are three bone structure types, for example, straight, wavy, and natural.

[0022] If the customer CS has undergone a personal color diagnosis, the physical information includes information about the customer CS's personal color. For example, there are four types of personal color: warm-based spring, cool-based summer, warm-based autumn, and cool-based winter.

[0023] If the customer CS has undergone a face type diagnosis, the physical information includes information about the face type of the customer CS. There are eight face types: adult cool face, adult elegant face, adult soft elegant face, adult feminine face, child cool casual face, child fresh face, child cute face, and child active cute face.

[0024] In addition to physical information, customer CS attribute information may include the size of clothes usually worn, foot shape, favorite clothing brand, favorite clothing taste, hobbies, height, clothing-related concerns, favorite colors, favorite celebrities, favorite outfits, etc.

[0025] The behavioral information may include search history on the EC site Wx, browsing history on the EC site Wx, favorite history on the EC site Wx (e.g., favorite products and favorite staff), try-on history showing the results of trying on products at a physical store SP, word-of-mouth history on the EC site Wx, review history on the EC site Wx, etc. The behavioral information may also include information indicating the customer service received by the customer CS. The information indicating the customer service may be entered as an evaluation result of the customer CS regarding the sales staff F, or may be entered by the sales staff F himself as a customer service record.

[0026] The purchase information may include information about the products purchased by the customer CS (for example, brand name, item name, item color, and item size).

[0027] Here, the information processing device 100 may generate recommendation information determination data, which is information for determining recommendation information, based on the customer information.

[0028] For example, the information processing device 100 may generate rule data (an example of data for determining recommendation information) for determining recommendation information by checking against the user information of the user U (i.e., based on a rule). For example, the information processing device 100 may generate rule data in which a set of attribute information and behavioral information included in the customer information is associated with the recommendation information according to a specific rule. The information processing device 100 may also generate rule data in which a set of attribute information and purchasing information included in the customer information is associated with the recommendation information according to a specific rule. The information processing device 100 may also generate rule data in which a set of attribute information, behavioral information, and purchasing information included in the customer information is associated with the recommendation information according to a specific rule.

[0029] As another example, the information processing device 100 may generate a machine learning model (an example of data for determining recommendation information) trained using an algorithm that analyzes what kind of recommendation information is optimally tailored to each individual user U. For example, the information processing device 100 may generate a machine learning model that outputs recommendation information corresponding to input user information based on trends analyzed from a set of attribute information and behavioral information included in the customer information. Furthermore, the information processing device 100 may generate a machine learning model that outputs recommendation information corresponding to input user information based on trends analyzed from a set of attribute information and purchase information included in the customer information. The information processing device 100 may generate a machine learning model that outputs recommendation information corresponding to input user information based on trends analyzed from a set of attribute information, behavioral information, and purchase information included in the customer information.

[0030] In this state, when the user U exhibits a predetermined behavior (for example, browsing or purchasing a product) on the EC site Wx, the information processing device 100 acquires user information of the user U. For example, the information processing device 100 acquires, as the user information of the user U, attribute information of the user U including at least physical information of the user, behavioral information (behavioral history) of the user U on the EC site Wx, and purchase information (purchase history) indicating purchases made by the user U on the EC site Wx.

[0031] The physical information of the user U may be information on the results of a diagnosis based on a personal diagnosis of the user U. For example, if the user U has undergone a bone structure diagnosis, the physical information includes information on the bone structure type of the user U. There are three bone structure types, for example, straight, wavy, and natural.

[0032] If the user U has undergone a personal color diagnosis, the physical information includes information about the personal color of the user U. There are, for example, four types of personal colors: warm-toned spring type, cool-toned summer type, warm-toned autumn type, and cool-toned winter type.

[0033] If the user U has undergone a face type diagnosis, the physical information includes information about the face type of the user U. There are eight face types: adult cool face, adult elegant face, adult soft elegant face, adult feminine face, child cool casual face, child fresh face, child cute face, and child active cute face.

[0034] In addition to physical information, the attribute information of user U may include the size of clothes he or she usually wears, foot shape, favorite clothing brand, favorite clothing taste, hobbies, height, clothing-related concerns, favorite colors, favorite celebrities, favorite outfits, etc.

[0035] The attribute information may be registered in advance by the user U. The information processing device 100 may also have a function of personal diagnosis. For example, when the user U accesses the EC site Wx, the information processing device 100 may guide the user U to a site where a personal diagnosis can be taken, and when the user U inputs information necessary for the personal diagnosis, the information processing device 100 may perform the personal diagnosis based on the input information.

[0036] Furthermore, if the predetermined behavior indicated by the user U on the EC site Wx is a "predetermined behavior using the EC site," the information processing device 100 may acquire the usage history indicating this usage as behavior information of the user U. Furthermore, the "predetermined behavior using the EC site Wx" referred to here may be, for example, searching on the EC site Wx, browsing the EC site Wx, adding items to favorites on the EC site Wx, reserving a try-on session at a physical store SP using the EC site Wx, and trying items on at the physical store SP.

[0037] If the predetermined behavior indicated by the user U on the EC site Wx is "purchasing behavior on the EC site," the information processing device 100 may acquire a purchase history indicating this purchase as purchase information of the user U. For example, the information processing device 100 acquires purchase information including information on the product purchased by the user U (e.g., brand name, item name, item color, item size).

[0038] When the information processing device 100 acquires the above-described user information, it determines recommendation information based on the acquired user information and customer information accumulated as various customers use the EC site Wx. Specifically, the information processing device 100 determines recommendation information based on information (rule data or a machine learning model) for determining recommendation information generated based on the customer information and the user information. For example, when the information processing device 100 uses rule data as information for determining recommendation information, it compares the user information with the rule data to determine recommendation information. When the information processing device 100 uses a machine learning model as information for determining recommendation information, it determines recommendation information from output information resulting from inputting user information into the machine learning model.

[0039] The information processing device 100 can determine various pieces of recommendation information by combining the results of the personal diagnosis of the user U, i.e., attribute information including type information of the user U, with behavioral information or purchase information of the user U. An example is shown below.

[0040] (a): Type information x purchase history or browsing history x age = Items based on the purchasing or browsing tendencies of customer CS who has similar attributes (for example, skeletal type) to user U are determined as recommendation information. (b): Type information x favorite clothing taste or hobbies = Information about sales staff member F who matches the style, hobbies, and preferences of user U is determined as recommendation information. (c): Type information x clothing-related concerns x word-of-mouth or review history = recommendation information is determined for products, coordination, etc. that are best suited to resolving user U's concerns. (d): Type information x favorite coordination = Recommended advice on how to wear (use) the item according to the user U’s type is determined as recommendation information. (e): Type information x purchase history = Coordination using items owned by user U is determined as advice information. (f): Type information (e.g., bone structure type) x type information (e.g., personal color) x height or size = items that match the type and attributes of user U, or information about sales staff F that is compatible with the type and attributes of user U, is determined as recommendation information. (g): User U's favorite clothing brand and favorite clothing taste x user U's preferences analyzed from fitting history and purchase history x type information = items that suit user U's preferences and type, or information about sales staff F who is compatible with user U's preferences and type, is determined as recommendation information.

[0041] Returning to the explanation of FIG. 1, the information processing device 100 presents the determined recommendation information to the user U and the sales staff F. For example, the information processing device 100 displays a purchase support screen including the recommendation information on the user terminal 10. The purchase support screen may be a screen within the EC site Wx accessed by the user this time. The information processing device 100 also displays a customer service support screen including the recommendation information on the store terminal 20.

[0042] So far, an overview of the information processing according to the embodiment executed by the information processing device 100 has been explained using Fig. 1. According to the information processing according to the embodiment, a user U can obtain apparel items suited to his / her specific situation, information on the sales staff F, or advice information, and thus can receive appropriate support for purchasing products.

[0043] Furthermore, according to the information processing of the embodiment, the sales staff F can obtain apparel items or advice information that are suitable for the specific situation of the user U, and can therefore receive appropriate support when serving the user U, for example, in a store.

[0044] As described above, the information processing device 100 can improve the overall convenience when using the EC site Wx.

[0045] [3. System Configuration] Fig. 2 is a diagram showing an example of the configuration of an information processing system according to an embodiment. Fig. 2 shows an information processing system 1 as an example of the information processing system according to an embodiment. Information processing according to an embodiment (i.e., the proposed technology of the present invention) is realized in the information processing system 1.

[0046] 2, the information processing system 1 may be classified into businesses Bn (e.g., apparel businesses) that provide services (e.g., apparel services). In FIG. 2, a first business B1 that provides a first service SV1 is shown as an example of the nth business Bn that provides the nth service SVn.

[0047] Furthermore, the n-th service SVn provided by the n-th business operator Bn may be the above-mentioned EC site Wx. Therefore, for example, in the example of Fig. 2, the first business operator B1 provides the EC site W1 as the first service SV1. Although not shown, the second business operator B2 provides the EC site W2 as the second service SV2, and the third business operator B3 provides the EC site W3 as the third service SV3.

[0048] The nth business operator Bn operates not only an e-commerce site Wx but also a physical store SP. Therefore, each nth business operator Bn is associated with a store terminal 20-n used by sales staff F and the like at the physical store SP. Also, as shown in FIG. 2, the nth service SVn, which is an information service provided by the e-commerce site Wx, is actually provided by a business operator device 30-n belonging to the nth business operator Bn. In FIG. 1, a store terminal 20-1 and the like belonging to the first business operator B1 are shown as an example of a store terminal 20-n belonging to the nth business operator Bn, and a business operator device 30-1 and the like belonging to the first business operator B1 are shown as an example of a business operator device 30-n belonging to the nth business operator Bn. In the following embodiments, when it is not necessary to distinguish between the store terminal 20-n and the business operator device 30-n for each nth business operator Bn, they will simply be referred to as the store terminal 20 and the business operator device 30.

[0049] 1, an information processing system 1 according to the embodiment includes a user terminal 10, a store terminal 20, a business operator device 30, and an information processing device 100. The user terminal 10, the store terminal 20, the business operator device 30, and the information processing device 100 are connected via a network N (for example, the Internet or an intranet).

[0050] The user terminal 10 is an example of an information processing terminal used by a user U. The user terminal 10 may be a smartphone, a wearable device, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), etc. For example, the user terminal 10 accesses the business operator device 30 and displays the EC site Wx provided by the business operator device 30 and the purchase support screen described in FIG. 1 on the display screen of the user terminal 10.

[0051] The store terminal 20 is an example of an information processing terminal used by a sales staff member F who is a salesperson who serves customers at a physical store SP. The store terminal 20 may be a smartphone, a wearable device, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, or the like. For example, the store terminal 20 accesses the business operator's device 30 and displays the customer service support screen described in FIG. 1 on the display screen of the store terminal 20.

[0052] The business operator device 30 controls the EC site Wx and may be implemented as a so-called cloud server device. The business operator device 30 transmits the EC site Wx to the user terminal 10 in response to access from the user terminal 10. The business operator device 30 also transmits a purchase support screen including the recommendation information determined by the information processing device 100 to the user terminal 10, and transmits a customer service support screen including the recommendation information determined by the information processing device 100 to the store terminal 20.

[0053] As shown in FIG. 1, the business operator device 30 stores, as customer information accumulated through use of the EC site Wx, attribute information including physical information of the customer CS, behavioral information (behavioral history) of the customer CS on the EC site Wx, and purchase information (purchase history) indicating purchases made by the customer CS on the EC site Wx. Although not shown, the business operator device 30 may also store salesperson information of the sales staff F. The salesperson information includes recommended items uploaded by the sales staff F to the EC site Wx or a predetermined posting site, as well as styling examples using the recommended items. In this way, the customer information and salesperson information are managed by the EC site Wx and can therefore be considered examples of management information.

[0054] The information processing device 100 is a central device that performs information processing according to the embodiment and plays a role in determining recommendation information. The information processing device 100 may also be implemented as a cloud server device. For example, when a user terminal 10 accesses an EC site Wx in response to an operation by a user U and exhibits a predetermined behavior (e.g., browsing or purchasing a product) on the EC site Wx, the information processing device 100 acquires user information of the user U. Then, the information processing device 100 determines recommendation information based on the user information and management information.

[0055] The information processing device 100 may return recommendation information to the EC site Wx (i.e., the business operator device 30) accessed by the user terminal 10, so that the user U can view a purchase support screen within the EC site Wx. The information processing device 100 may also return recommendation information to the EC site Wx (i.e., the business operator device 30) accessed by the store terminal 20, so that the sales staff member F can view a customer service support screen within the EC site Wx. The customer service support screen may be controlled so that it can only be viewed with dedicated login information that only the sales staff member F has.

[0056] The administrator who manages the information processing device 100 is different from any of the nth businesses Bn, and is in a position to provide the nth business Bn with an information linking platform, which will be described later, as a business service.

[0057] [4. Relationship between business equipment and information processing equipment] The information processing according to the embodiment may be realized by cooperation between the business operator device 30 and the information processing device 100. Specifically, a configuration may be adopted in which the business operator device 30 and the information processing device 100 are information-linked, so that the information processing device 100 functions as a platform that provides recommendation information to the business operator device 30, thereby introducing a function for providing recommendation information into the EC site Wx. As a result, the information processing device 100 can detect that a user U has exhibited a predetermined behavior (e.g., browsing or purchasing a product) on the EC site Wx, and can acquire user information in response to the detection of the predetermined behavior and determine recommendation information based on the user information.

[0058] The information processing device 100 acquires user information from the business device 30, but may also store the acquired user information in its own device. For example, the information processing device 100 may store user information in its own device in association with a user ID. In this way, the information processing device 100 acquires and stores user information in response to a predetermined behavior (e.g., browsing or product purchase) exhibited by a user U on the EC site Wx. For example, when a sales staff member F accesses the EC site Wx to serve the user and specifies the account of the user U to be served, the information processing device 100 can obtain recommendation information determined based on the user information of the user U.

[0059] Furthermore, information linkage between the business operator device 30 and the information processing device 100 may be performed using the above-mentioned user ID, but it is preferable that the user ID here is not uniquely set on the business operator device 30 side, but is a user ID for linkage that is common between the business operator device 30 and the information processing device 100. For example, the user U may access the information processing device 100 via the business operator device 30 and set a user ID for linkage in the information processing device 100.

[0060] Here, an example of information processing that can be realized by information collaboration between the information processing device 100 and business operator devices 30 corresponding to different n-th business operators Bn will be described with reference to Fig. 3. Figs. 3 to 5 are diagrams showing an example of functions realized in response to information collaboration between the business operator devices 30 and the information processing device 100.

[0061] First, Fig. 3 will be described. Fig. 3 is a diagram (1) showing an example of a function realized in accordance with information linkage between the business operator device 30 and the information processing device 100. Fig. 3 shows a scene in which information processing is performed between the nth business operator Bn corresponding to the nth service SVn accessed by the user U and the information processing device 100, using only management information managed in the nth service SVn accessed by the user U. Specifically, Fig. 3 shows an example of information processing in a case in which the user U accesses the first service SV1 (EC site W1) provided by the first business operator B1 and performs a predetermined action.

[0062] 3, the information processing device 100 has already linked with the business operator device 30-1 corresponding to the first business operator B1, and therefore references the customer information held by the business operator device 30-1 to generate rule data or a machine learning model that is valid only for the business operator device 30-1. Note that the information processing device 100 may also generate rule data or a machine learning model by further combining the salesperson information of sales staff member F who belongs to the first business operator B1.

[0063] In this state, if the user U accesses the first service SV1 and performs a predetermined action, the information processing device 100 acquires user information associated with the user ID of the user U from the business operator device 30-1, and determines recommendation information based on the acquired user information and rule data (or machine learning model). According to this information processing, the recommendation information determined in Fig. 3 is information about products handled by the first service SV1 or about sales staff F belonging to the first business operator B1.

[0064] Next, Fig. 4 will be described. Fig. 4 is a diagram (2) showing an example of a function realized in accordance with information linkage between the business operator device 30 and the information processing device 100. Fig. 4 shows a scene in which information processing is performed between the information processing device 100 and an nth business operator Bn corresponding to the other nth service SVn, using management information managed in the other nth service SVn that is different from the nth service SVn accessed by the user U. Specifically, Fig. 4 shows an example in which the user U accesses a first service SV1 provided by a first business operator B1 and performs a predetermined action, and management information managed in a second service SV2 (EC site W2) provided by a second business operator B2 that is different from the first business operator B1 is used for the information processing.

[0065] According to the example of Figure 4, the information processing device 100 is already connected to the business operator device 30-1 corresponding to the first business operator B1, and therefore refers to the customer information held by the business operator device 30-1 and generates rule data or a machine learning model that is valid only with the business operator device 30-1.

[0066] 4, the information processing device 100 has also been linked with the business operator device 30-2 corresponding to the second business operator B2, and therefore references the customer information held by the business operator device 30-2 to generate rule data or a machine learning model that is valid only for the business operator device 30-2. Note that the information processing device 100 may also generate rule data or a machine learning model by further combining the salesperson information of sales staff member F who belongs to the second business operator B2.

[0067] In this state, if user U accesses the first service SV1 and performs a predetermined action, the information processing device 100 acquires user information associated with the user ID of user U from the business operator device 30-1. Meanwhile, the information processing device 100 determines recommendation information based on rule data (or a machine learning model) valid only between the business operator device 30-2 and the user information acquired from the business operator device 30-1. According to this information processing, the recommendation information determined in FIG. 4 is information about products handled by the second service SV2 or information about sales staff F belonging to the second business operator B2.

[0068] Next, Fig. 5 will be described. Fig. 5 is a diagram (3) showing an example of a function realized in accordance with information linkage between the business operator device 30 and the information processing device 100. Fig. 5 shows a scene in which information processing is performed using management information managed by the first service SV1 to the n-th service SVn (i.e., each of all n-th services SVn). Specifically, Fig. 5 shows an example of information processing in which it is assumed that a user U accesses the first service SV1 provided by the first operator B1 and performs a predetermined action, and management information managed by each of the first service SV1 provided by the first operator B1 to the n-th service SVn provided by the n-th operator Bn is collectively used.

[0069] 5, the information processing device 100 has already linked with each of the business operator devices 30-n corresponding to the n-th business operator Bn, and therefore references the customer information held by each of the business operator devices 30-n to generate rule data or machine learning models that are valid for use with all of the business operator devices 30-n. Note that the information processing device 100 may also generate rule data or machine learning models by further combining the salesperson information of the sales staff F belonging to each of the n-th business operators Bn.

[0070] In this state, if user U accesses the first service SV1 and performs a predetermined action, the information processing device 100 acquires user information associated with the user ID of user U from the business device 30-1. Meanwhile, the information processing device 100 determines recommendation information based on rule data (or machine learning model) valid for all business device 30-n and the user information acquired from the business device 30-1. According to this information processing, the recommendation information determined in FIG. 5 is information about a product handled by any of the first service SV1 to the n-th service SVn, or information about a sales staff member F belonging to any of the first service SV1 to the n-th service SVn.

[0071] [5. Functional Configuration] An example of the configuration of the business operator device 30 and the information processing device 100 according to the embodiment will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of the device configuration according to the embodiment.

[0072] [Business equipment 30] As shown in FIG. 6, the business operator device 30 according to the embodiment includes a communication unit 31, a storage unit 32, and a control unit 33.

[0073] (Communications Department 31) The communication unit 31 is realized by, for example, a network interface card (NIC) etc. The communication unit 31 is connected to the network N by wire or wirelessly, and transmits and receives information to and from, for example, the user terminal 10 and the information processing device 100.

[0074] (Storage unit 32) The storage unit 32 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in Fig. 6, the storage unit 32 may include a behavior information storage unit 32a, a purchase information storage unit 32b, and a salesperson information storage unit 32c.

[0075] (Control unit 33) The control unit 33 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like, executing various programs stored in a storage device inside the business operator device 30 using RAM as a work area. The control unit 33 is also realized by an integrated circuit, such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0076] As shown in Fig. 6, the control unit 33 has a reception unit 33a, a transmission unit 33b, a reception unit 33c, and a presentation unit 33d, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the control unit 33 is not limited to the configuration shown in Fig. 6, and may be other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between the processing units included in the control unit 33 is not limited to the connection relationship shown in Fig. 6, and may be other connection relationships.

[0077] (Reception unit 33a) The reception unit 33a receives access from an information processing terminal. For example, the reception unit 33a receives access from the user terminal 10. For example, when a predetermined action (e.g., browsing or purchasing a product) using the EC site Wx is performed using the user terminal 10, the reception unit 33a receives information indicating the predetermined action. The reception unit 33a also receives access from the store terminal 20.

[0078] (Transmitter 33b) The transmitter 33b transmits information to the information processing device 100. For example, the transmitter 33b transmits information indicating a predetermined behavior using the EC site Wx to the information processing device 100. The transmitter 33b may also transmit management information in response to access from the information processing device 100. For example, the transmitter 33b transmits to the information processing device 100 behavior information stored in the behavior information storage unit 32a, purchase information stored in the purchase information storage unit 32b, salesperson information stored in the salesperson information storage unit 32c, and the like.

[0079] (receiving unit 33c) The receiving unit 33c receives information from the information processing device 100. For example, the receiving unit 33c receives recommendation information determined by the information processing device 100.

[0080] (Presentation part 33d) The presentation unit 33d presents the recommendation information received by the reception unit 33c. For example, the presentation unit 33d presents the recommendation information to the user U by displaying a purchase support screen including the recommendation information on the user terminal 10. The presentation unit 33d also presents the recommendation information to the sales staff F by displaying a customer service support screen including the recommendation information on the store terminal 20.

[0081] [Information processing device 100] As shown in FIG. 6, the information processing device 100 according to the embodiment includes a communication unit 110, a storage unit 120, and a control unit 130.

[0082] (Communication unit 110) The communication unit 110 is realized by, for example, a NIC etc. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the user terminal 10 and the store terminal 20, for example.

[0083] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in Fig. 6, the storage unit 120 may have a diagnosis result storage unit 121 and a recommendation information determination data storage unit 122. The diagnosis result storage unit 121 stores diagnosis results such as a bone structure diagnosis, a personal color diagnosis, and a face type diagnosis. The recommendation information determination data storage unit 122 stores rule data and machine learning models.

[0084] (control unit 130) The control unit 130 is realized by a CPU, an MPU, or the like executing various programs stored in a storage device inside the information processing device 100 using RAM as a work area. The control unit 130 is also realized by an integrated circuit such as an ASIC or an FPGA.

[0085] As shown in Fig. 6, the control unit 130 has a generation unit 131, a diagnosis unit 132, a determination unit 133, an acquisition unit 134, a determination unit 135, and a presentation control unit 136, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Fig. 6, and may have other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship shown in Fig. 6, and may be other connection relationships.

[0086] (Generation unit 131) The generation unit 131 generates data for determining recommendation information. As described in FIG. 1, the generation unit 131 generates rule data for determining recommendation information in accordance with the user information of the user U (i.e., on a rule basis). The generation unit 131 may also generate a machine learning model trained using an algorithm that analyzes what is the optimal recommendation information tailored to each individual user U.

[0087] The generation unit 131 may generate rule data or a machine learning model using management information such as, for example, behavioral information stored in the behavioral information storage unit 32a, purchasing information stored in the purchasing information storage unit 32b, and salesperson information stored in the salesperson information storage unit 32c.

[0088] (Diagnosis unit 132) The diagnosis unit 132 determines the type of the user U through a personal diagnosis. The diagnosis unit 132 determines the skeletal type of the user U through a bone structure diagnosis. The diagnosis unit 132 may also determine the personal color of the user U through a personal color diagnosis, or may determine the face type of the user U through a face type diagnosis.

[0089] Here, with reference to Fig. 7, a flow in which the information processing device 100 obtains the diagnosis result of the personal diagnosis by guiding the user U to the personal diagnosis will be described. Fig. 7 is a diagram showing a scene in which the user U is guided to the personal diagnosis. Fig. 7 shows a scene in which the user U who has accessed the EC site W1 is guided to the bone structure diagnosis. Note that Fig. 7 illustrates an example of guidance to the bone structure diagnosis, but guidance to the personal color diagnosis or face type diagnosis can also be achieved with a similar configuration.

[0090] 7, the EC site W1 has a banner BN1 provided as guidance information to a body type diagnosis. When the user U presses the banner BN1, the user U is redirected from the current business operator device 30-1 to the information processing device 100. In other words, the user U is redirected to a body type diagnosis site C1 provided by the information processing device 100. The body type diagnosis site C1 may be composed of pages P11 to P15.

[0091] In this example, the user U is first moved to page P11, which is the top page, and then can move to page P12, which explains the details of the bone structure diagnosis. From page P12, the user U can move to page P13, which allows uploading of information necessary for the bone structure diagnosis. A bone structure diagnosis requires a frontal photo of the whole body, a side photo of the whole body, and a back photo of the whole body, and page P13 has a function for accepting uploads of these photos.

[0092] When the upload is complete, the diagnosis unit 132 performs a bone structure diagnosis based on the uploaded photo. While the bone structure diagnosis is being performed, page P14 is displayed as shown in FIG. 7, and when the bone structure diagnosis is complete, the user U is transitioned to page P15 of the diagnosis result. The diagnosis unit 132 may register the diagnosis result in the diagnosis result storage unit 121.

[0093] 7, a decision button BT15 is provided on page P15, and when the decision button BT15 is pressed, a process of deciding recommended information is performed by the decision unit 135. When the recommended information is decided, the user U is transitioned to the original business operator device 30-1, as shown in FIG. 7. In other words, the user U is returned to the EC site W1 provided by the business operator device 30-1. At this time, the recommended information may be displayed on the EC site W1.

[0094] (Judgment unit 133) 6, the determination unit 133 determines whether or not the user U has exhibited a predetermined behavior related to the use of the EC site Wx. The determination unit 133 may also perform other determinations necessary for the information processing according to the embodiment.

[0095] (Acquisition part 134) The acquisition unit 134 acquires user information of the user U. For example, the acquisition unit 134 may acquire the user information when the user U exhibits a predetermined behavior related to the use of the EC site Wx.

[0096] For example, when a user U accesses an EC site Wx and performs a predetermined action of registering user information as member information in the business device 30, the acquisition unit 134 may acquire the user information registered as member information.

[0097] The user information may include attribute information of user U, behavioral information of user U, and purchasing information of user U, and the acquiring unit 134 may acquire a set of attribute information and behavioral information that includes at least physical information of user U. The acquiring unit 134 may also acquire a set of attribute information and purchasing information that includes at least physical information of user U. The acquiring unit 134 may also acquire a set of attribute information, behavioral information, and purchasing information that includes at least physical information of user U.

[0098] For example, when a user U uses the EC site Wx, the acquisition unit 134 acquires behavioral information indicating the user U's usage history of the EC site Wx, and when the user U purchases a product offered on the EC site Wx, the acquisition unit 134 acquires purchase information indicating the user U's product purchase history. As an example, when the user U accesses the EC site Wx and browses any page included in the EC site Wx, the acquisition unit 134 acquires behavioral information indicating the browsing history. Furthermore, when the user U purchases a product on the EC site Wx, the acquisition unit 134 acquires purchase information indicating the browsing history. Note that the acquisition unit 134 may also acquire user information when a predetermined behavior, such as using an SNS related to the EC site Wx, is performed.

[0099] The acquisition unit 134 may also acquire user information when a predetermined behavior, such as using an SNS related to the EC site Wx, is performed. The acquisition unit 134 acquires the attribute information, behavioral information, and purchase information, excluding physical information, of the user information from the business operator device 30 (storage unit 12) corresponding to the EC site Wx accessed by the user U, but may acquire the physical information (type information) from its own device (storage unit 120).

[0100] (Decision unit 135) The determination unit 135 determines recommendation information based on the user information acquired by the acquisition unit 134 and management information managed by the EC site Wx (for example, customer information accumulated as the EC site Wx is used by various customers CS, and salesperson information of sales staff F belonging to the EC site Wx).

[0101] As described above, the customer information includes, for example, attribute information including physical information of the customer CS, behavioral information (behavioral history) of the customer CS on the EC site Wx, and purchase information (purchase history) indicating purchases made by the customer CS on the EC site Wx. Therefore, the customer information is stored as management information in the behavioral information storage unit 32a and the purchase information storage unit 32b. Therefore, the determination of recommendation information based on the management information by the determination unit 135 means a determination using the rule data or machine learning model generated by the generation unit 131.

[0102] For example, the determination unit 135 determines recommendation information based on the tendency of the customer CS corresponding to the user information among the customers CS who have used the EC site Wx, which tendency is generated based on the customer information.

[0103] For example, assume that the acquisition unit 134 acquires user information X1 indicating that a user U with attributes of "straight body type, cool winter color, casual taste preference, height 160 cm" has "viewed the EC site W1." In this case, the determination unit 135 determines recommendation information (e.g., recommendation information) based on the tendency of customers CS who have a history of "viewing the EC site W1" among customers corresponding to the user information X1, i.e., customers CS who match or are similar to the attributes of "straight body type, cool winter color, casual taste preference, height 160 cm" and who have a history of "viewing the EC site W1," which is generated based on the customer information (e.g., customer CS who match or are similar to the attributes of "straight body type, cool winter color, casual taste preference, height 160 cm" and who have a history of "viewing the EC site W1" tend to "purchase product AA on the EC site W1").

[0104] In addition, if there is no customer CS who matches or is similar to the attributes of "straight bone structure, cool winter, casual style preference, height 160 cm" and has a history of "browsing EC site W1", the determination unit 135 may expand the range to include customer CS who have a history of "browsing other EC site Wx", and determine recommendation information based on the trends when the range is expanded.

[0105] As another example, assume that the acquisition unit 134 acquires user information X2 indicating that a user U with an attribute of "straight body type" "purchased product BB on EC site W2." In this case, the determination unit 135 determines recommendation information (e.g., recommendation information) based on the tendency of customers CS who correspond to the user information X2, i.e., customers CS who match or are similar to the attribute of "straight body type" and have a history of "purchasing product BB on EC site W2," which is generated based on the customer information (e.g., customer CS who match or are similar to the attribute of "straight body type" and have a history of "purchasing product BB on EC site W2" "tends to purchase product CC on EC site W2").

[0106] Similarly, if there is no customer CS who matches or is similar to the attribute "straight body structure" and has a history of "purchasing product BB on EC site W2," the determination unit 135 may broaden the range to include customers CS who match or are similar to the attribute "straight body structure" and have a history of "viewing product BB on EC site W2," and determine recommendation information based on the trends when the range is broadened.

[0107] Here, the determination unit 133 may determine whether the recommendation information contains a product that corresponds to either a brand preferred by the user U or a price range specified by the user U. If the determination unit 135 determines that the recommendation information contains a product that corresponds to either a brand preferred by the user U or a price range specified by the user U, the determination unit 135 may preferentially determine that product as a recommended product. As a result, the determination unit 135 can effectively guide the user to purchase the recommended product.

[0108] Furthermore, the determination unit 133 may determine whether or not the recommendation information contains a product that corresponds to the optimal coordination put together based on the user information. If it is determined that the recommendation information contains a product that corresponds to the optimal coordination put together based on the user information, the determination unit 135 may prioritize the product as recommendation information.

[0109] For example, if a coordination is created in which a long coat T2 is best paired with blouse T1 based on the fact that user U previously purchased blouse T1 using EC site W1 (i.e., user U owns blouse T1), the determination unit 135 may preferentially determine long coat T2 as the recommended product if the long coat T2 is included in the recommendation information. Note that, for example, if the long coat T2 is not available on EC site W1, the determination unit 133 may determine whether the long coat T2 is available on another EC site Wx different from EC site W1. In this case, the determination unit 133 may, for example, narrow down the EC site Wx to those that correspond to user U's preferences (e.g., brands preferred by user U) and determine whether the long coat T2 is available.

[0110] 1, it has been explained that the information processing device 100 can determine, as advice information, a coordination that utilizes items owned by the user U, based on the type information and purchase history of the user U. Thus, the determination unit 135 can determine, as recommendation information, advice information that suggests a coordination, based on the user information and the recommendation information determination data generated from the management information.

[0111] Furthermore, when the determination unit 135 determines a recommended product as recommendation information, it may further determine optimal styling (how to use) information for the determined recommended product. In FIG. 1, it has been explained that the information processing device 100 can determine, as advice information, a styling (how to use) appropriate for the type of user U, based on the type information and favorite coordination of user U. Thus, the determination unit 135 can determine, as recommendation information, advice information that suggests how to wear the recommended product, based on the user information and the recommendation information determination data generated from the management information.

[0112] Furthermore, the determination unit 135 can determine not only the product but also information about the sales staff F as the recommendation information. As explained above, the management information also includes salesperson information, which is information about the sales staff F belonging to the EC site Wx. Therefore, the determination unit 135 identifies the sales staff F corresponding to the user information from among the sales staff F belonging to the EC site Wx based on the salesperson information, and determines the information about the identified sales staff F as the recommendation information.

[0113] For example, assume that the acquisition unit 134 acquires user information X1 indicating that a user U with attributes of "straight body type, cool winter color, casual style preference, height 160 cm" has "viewed the EC site W1." In this case, the determination unit 135 identifies the sales staff member F corresponding to the user information X1, that is, the sales staff member F who matches or is similar to the attributes of "straight body type, cool winter color, casual style preference, height 160 cm," from among the sales staff members F belonging to the EC site W1.

[0114] As another example, assume that the acquisition unit 134 acquires user information X2 indicating that a user U with an attribute of "wave bone structure" "purchased product BB on EC site W2." In this case, the determination unit 135 identifies, from among the sales staff F belonging to EC site W2, the sales staff F corresponding to the user information X2, i.e., the sales staff F whose attribute matches or is similar to the attribute of "wave bone structure."

[0115] The determination unit 135 may then determine, as recommendation information, information suggesting the identified sales staff member F as a person to consult with about purchases, or information on styling examples using recommended items by the identified sales staff member F. As a result, the user U can consider items to purchase by referring to the sales staff member F who has similar attributes and behavior to the user U, and can efficiently obtain items that suit the user U.

[0116] In addition, if there is no sales staff member F corresponding to the attributes of "straight bone structure, cool winter, preference for casual style, height 160 cm", the determination unit 135 may expand the range of one of the attributes (for example, preference or height) and determine information about sales staff member F included in the expanded range as recommendation information.

[0117] In addition, if there is no sales staff member F corresponding to the attribute "wave bone structure," the determination unit 135 may widen the range of bone structure types (for example, to straight and natural) and determine information about sales staff member F included in the widened range as recommendation information.

[0118] (Presentation control unit 136) The presentation control unit 136 controls the presentation of the recommendation information determined by the determination unit 135 to the user U and the sales staff F. For example, the presentation control unit 136 generates a purchase support screen including the recommendation information. Then, the presentation control unit 136 transmits the purchase support screen to the business operator device 30 corresponding to the EC site Wx so that the purchase support screen is displayed as a page within the EC site Wx accessed by the user U. In this case, the business operator device 30 displays the purchase support screen on the user terminal 10.

[0119] The presentation control unit 136 also generates a customer service assistance screen including the recommendation information. The presentation control unit 136 then transmits the customer service assistance screen to the business operator device 30 corresponding to the EC site Wx so that the customer service assistance screen is displayed as a page within the EC site Wx accessed by the sales staff member F. In this case, the business operator device 30 displays the customer service assistance screen on the store terminal 20.

[0120] [6. Example of operation of information processing device] An example of the operation of the information processing device 100 according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing the procedure of information processing according to the embodiment. Fig. 8 shows a scene in which information processing is performed between the business operator device 30 corresponding to the EC site Wx accessed by the user U and the information processing device 100, using only management information managed in the EC site Wx. In the example of Fig. 8, it is assumed that the information processing device 100 has already generated recommendation information determination data based on the management information.

[0121] First, the determination unit 133 determines whether the user U has accessed any of the EC sites Wx and exhibited a predetermined behavior (step S801). If the user U has not accessed any of the EC sites Wx and exhibited a predetermined behavior (step S801; No), the determination unit 133 waits until the user U accesses any of the EC sites Wx and exhibits a predetermined behavior.

[0122] On the other hand, if the acquisition unit 134 determines that the user U has accessed one of the EC sites Wx and exhibited a predetermined behavior (step S801; Yes), it acquires the user information of the user U, which corresponds to the EC site Wx (hereinafter referred to as "EC site W1") that the user U accessed this time to perform the predetermined behavior (step S802).

[0123] The acquisition unit 134 also acquires recommendation information determination data generated based on management information managed by the EC site W1 (step S803).

[0124] Based on the user information of user U and the data for determining recommendation information, the determination unit 135 identifies customer CSs of the EC site W1 that correspond to (match or are similar to) the attributes or behavior of user U (step S804).

[0125] The determining unit 135 determines whether or not the customer CS corresponding to the attribute or behavior of the user U has been identified (step S805).

[0126] If the determination unit 135 can identify a customer CS corresponding to the attributes or behavior of user U (i.e., if a customer CS corresponding to the attributes or behavior of user U exists) (step S805: Yes), it calculates the tendency of the customer CS identified in step S805 based on the user information of user U and the data for determining recommendation information (step S806).

[0127] Then, the determination unit 135 determines recommendation information based on the calculated tendency (step S807).

[0128] The presentation control unit 136 controls the presentation so that the recommendation information is presented to the user U or the sales staff F belonging to the EC site Wx (step S808).

[0129] On the other hand, if the determination unit 135 is unable to identify a customer CS corresponding to the attributes or behavior of user U (i.e., if there is no customer CS corresponding to the attributes or behavior of user U) (step S805: No), it controls to expand the range of attributes or behavior of user U (step S809).

[0130] Then, the determining unit 135 executes the processes from step S804 onward again using the attribute or behavior after the expanded range.

[0131] 7. Other Embodiments In the above embodiment, the information processing device 100 functions as a platform that provides recommendation information to the business operator device 30, thereby introducing a function for providing recommendation information into the EC site Wx.

[0132] On the other hand, the information processing device 100 may be configured to provide an electronic mall in which various businesses Bn open stores, and to perform information processing according to the embodiment for the virtual stores of each business Bn within the electronic mall.

[0133] [8. Hardware Configuration] The information processing device 100 according to the embodiment may be realized, for example, by a computer 1000 configured as shown in Fig. 9. Fig. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the device according to the embodiment. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0134] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0135] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, etc. The communication interface 1500 receives data from other devices via a predetermined communication network and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined communication network.

[0136] The CPU 1100 controls an output device such as a display and an input device such as a keyboard via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. The CPU 1100 also outputs generated data to the output device via the input / output interface 1600.

[0137] Media interface 1700 reads a program or data stored in recording medium 1800 and provides it to CPU 1100 via RAM 1200. CPU 1100 loads the program or data from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded program. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0138] For example, when the computer 1000 functions as the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined communication network.

[0139] [9. Other] Furthermore, among the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0140] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0141] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0142] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that include the aspects described in the "present invention" section and that have been modified and improved in various ways based on the knowledge of those skilled in the art. [Explanation of symbols]

[0143] 1. Information Processing Systems 10 User terminal 20 Store terminals 30 Operator equipment 32a Behavior information storage unit 32b Purchasing information storage section 32c Salesperson information storage section 100 Information processing device 110 Communications Department 120 Storage section 121 Diagnostic result storage unit 122 Recommendation information determination data storage unit 130 Control Unit 131 Generation part 132 Diagnostic Department 133 Judgment section 134 Acquisition Department 135 Decision Section 136 Presentation control unit

Claims

1. an acquisition unit that acquires, when a user exhibits a predetermined behavior related to the use of a predetermined information service, user information of the user, such as attribute information of the user including at least physical information of the user and behavior information indicating the user's behavior in the predetermined information service, or attribute information of the user including at least physical information of the user and purchase information indicating the user's purchase in the predetermined information service; a determination unit that determines recommendation information based on the user information and management information managed by a predetermined information service; An information processing device comprising:

2. The acquisition unit When the user uses the predetermined information service, the behavior information indicating the user's usage history of the predetermined information service is acquired; When the user purchases a product provided by the predetermined information service, the purchase information indicating the user's product purchase history is acquired. The information processing device according to claim 1 .

3. The management information includes customer information accumulated as a result of using the predetermined information service, The determination unit determines the recommendation information based on a tendency of a customer corresponding to the user information among the customers who have used the predetermined information service, the tendency being generated based on the customer information. The information processing device according to claim 1 .

4. The determination unit determines, in the recommendation information, information on a product that corresponds to either a brand preferred by the user or a price range designated by the user, with priority as a recommended product. The information processing device according to claim 3 .

5. The determination unit further determines, as the recommendation information, information on an optimal way to wear the recommended product based on the user information. The information processing device according to claim 4 .

6. The management information includes salesperson information, which is information about salespersons who belong to the predetermined information service. The information processing device according to claim 1 .

7. The determination unit identifies a salesperson corresponding to the user information from among salespersons belonging to the predetermined information service based on the salesperson information, and determines information about the identified salesperson as the recommendation information. The information processing device according to claim 6 .

8. each of the predetermined information services and the information processing device are linked to each other by identification information that identifies the user; when the user exhibits the predetermined behavior in a first information service provided by a first business operator among the predetermined information services, the acquisition unit acquires, as user information of the user, attribute information of the user including at least physical information of the user and behavior information indicating the user's behavior in the first information service, or attribute information of the user including at least physical information of the user and purchase information indicating the user's purchase in the first information service; The determination unit determines recommendation information corresponding to a second information service, based on management information managed by a second information service provided by a second business operator different from the first business operator, among the predetermined information services, and the user information. The information processing device according to claim 1 .

9. An information processing method executed by an information processing device, an acquisition step of acquiring, when a user exhibits a predetermined behavior related to the use of a predetermined information service, user information of the user, such as attribute information of the user including at least physical information of the user and behavior information indicating the user's behavior in the predetermined information service, or attribute information of the user including at least physical information of the user and purchase information indicating the user's purchase in the predetermined information service; a determination step of determining recommendation information based on the user information and management information managed by a predetermined information service; An information processing method including:

10. an acquisition step of acquiring, when a user exhibits a predetermined behavior related to the use of a predetermined information service, user information of the user, such as attribute information of the user including at least physical information of the user and behavior information indicating the user's behavior in the predetermined information service, or attribute information of the user including at least physical information of the user and purchase information indicating the user's purchase in the predetermined information service; a determination procedure for determining recommendation information based on the user information and management information managed by a predetermined information service; An information processing program that causes a computer to execute the above.

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