Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device capable of properly proposing a user to purchase a product, an information processing method, and an information processing program.SOLUTION: An information processing device comprises an acquisition unit, a determination unit, a prediction unit, and a proposal unit. The acquisition unit acquires information of a product purchase history of a user. The determination unit determines a product purchase interval of the user on the basis of the information of the product purchase history acquired by the acquisition unit. The prediction unit predicts a next product purchase timing by the user on the basis of the purchase interval determined by the determination unit. The proposal unit proposes product purchase to the user at the next purchase timing predicted by the prediction unit.SELECTED DRAWING: Figure 3

Description

Technical Field

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

Background Art

[0002] Conventionally, a technique for proposing products to a user based on information on the user's product purchase history is known. For example, in Patent Document 1, based on information on the user's product purchase history, a purchase interval of a product is detected, and from the purchase interval of the product and the purchase date when the product was last purchased, a product exceeding the purchase interval is detected, and a technique for notifying information regarding the detected product is proposed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above conventional technique, since a product exceeding the purchase interval is notified, there is a high possibility that the user is purchasing the product at another store, and there is a possibility that the product purchase cannot be appropriately proposed to the user.

[0005] The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program capable of appropriately proposing product purchases to a user.

Means for Solving the Problems

[0006] The information processing device according to the present invention comprises an acquisition unit, a determination unit, a prediction unit, and a proposal unit. The acquisition unit acquires information on the user's product purchase history. The determination unit determines the interval between the user's product purchases based on the product purchase history information acquired by the acquisition unit. The prediction unit predicts when the user will purchase the product next based on the purchase interval determined by the determination unit. The proposal unit proposes to the user that they purchase the product at the next purchase time predicted by the prediction unit. [Effects of the Invention]

[0007] According to one embodiment, the system has the effect of appropriately suggesting product purchases to users. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of information processing according to the embodiment. [Figure 2] Figure 2 shows an example of the configuration of an information processing system according to the embodiment. [Figure 3] Figure 3 shows an example of the configuration of an information processing device according to the embodiment. [Figure 4] Figure 4 shows an example of a user information table stored in the user information storage unit according to the embodiment. [Figure 5] Figure 5 is a flowchart showing an example of information processing by the processing unit of the information processing device according to the embodiment. [Figure 6] Figure 6 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing device according to the embodiment. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, the embodiments for implementing the information processing apparatus, information processing method, and information processing program according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the information processing apparatus, information processing method, and information processing program according to the present application. Furthermore, each embodiment can be appropriately combined as long as the processing content is not inconsistent. Also, the same parts are denoted by the same reference numerals in each of the following embodiments, and redundant descriptions are omitted.

[0010] [1. An example of information processing] First, an example of information processing according to the embodiment will be described using Figure 1. Figure 1 is a diagram showing an example of information processing according to the embodiment, which is executed by the information processing device 1.

[0011] The information processing device 1 shown in Figure 1 is an information processing device that works in conjunction with the user U's terminal device 2 to provide various types of information to the user U online. It can be implemented, for example, by one or more servers or a cloud system. In the example shown in Figure 1, one terminal device 2 is shown, but the information processing device 1 works in conjunction with each of multiple terminal devices 2 to provide various services online to the user U of each terminal device 2.

[0012] The services provided to user U by the information processing device 1 are, for example, e-commerce services (e.g., online shopping malls, flea markets, auctions, etc.), but are not limited to such examples. For example, the services provided by the information processing device 1 may also include services such as web search and content distribution.

[0013] The information processing device 1 obtains product purchase history information, which is information about each user U's product purchase history, from, for example, an internal storage unit or an external database (step S1).

[0014] The merchandise purchase history information includes a plurality of purchase information each containing information about the merchandise purchased by user U in the past. The purchase information includes, for example, as information about the merchandise, information for specifying the merchandise, information indicating the purchase date and time, information indicating the purchase destination of the merchandise by user U, and the like.

[0015] The information for specifying the merchandise is information indicating the name of the merchandise or information about the identification code of the merchandise. The identification code of the merchandise is a JAN (Japanese Article Number) code, but may also be an identification code common among stores or a code indicating the model number of the merchandise.

[0016] The information indicating the purchase destination of the merchandise by user U is, in the case of an e - commerce street including a plurality of stores (virtual stores), information for specifying the store where user U purchased the merchandise, and in the case of a flea market or an auction, information for specifying the seller from whom user U purchased the merchandise.

[0017] Subsequently, the information processing apparatus 1 determines, for each merchandise, the purchase interval of the merchandise for each user U based on the merchandise purchase history information of each user U acquired in step S1 (step S2).

[0018] The information processing apparatus 1 specifies, for each user U, the merchandise that user U purchased multiple times during a predetermined period TA (for example, the past one year) as the target merchandise. When there are a plurality of merchandise that user U purchased multiple times, the information processing apparatus 1 specifies each of these plurality of merchandise as the target merchandise. Note that user U may purchase a plurality of merchandise in a single purchase.

[0019] The information processing apparatus 1 determines, for example, as the purchase interval, the value obtained by multiplying the interval TB between the oldest purchase date and time and the latest purchase date and time among the multiple purchase date and times during the predetermined period TA by the number of purchases N1 during the predetermined period TA. For example, the information processing apparatus 1 determines that the purchase interval of the target merchandise with an interval TB of 3 months and a purchase number N1 of 9 is 1 month. Thus, the information processing apparatus 1 determines the purchase interval of the target merchandise per unit quantity.

[0020] For products that can be identified by an identification code, the information processing device 1 treats multiple products with the same identification code, even if they are purchased from different stores, as the same target product. Also, for products that cannot be identified by an identification code, the information processing device 1 does not treat multiple products purchased from different stores as the same target product, even if they are the same product.

[0021] In addition, the information processing device 1 can also determine the purchase interval of the target products of the target user, who is the user U to be determined, by using the information on the product purchase history of the target user and the information on the product purchase history of other users U other than the target user.

[0022] For example, the information processing device 1 can also use a product purchased only once by the user U as the target product. In this case, the information processing device 1 determines the purchase interval of the target products of other users U other than the target user based on the information on the product purchase history of other users U other than the target user, and determines such a purchase interval as the purchase interval of the target products of the target user.

[0023] The information on the product purchase history of other users U used to determine the purchase interval of other users U is the information on the product purchase history of other users U whose attributes are similar to those of the target user. That the attributes are similar to those of the target user means, for example, that one or more specific attributes (such as a specific age group, a specific gender, a specific family composition, a specific income bracket, and a specific hobby, etc.) are common. Note that the information on the product purchase history of other users U used to determine the purchase interval of other users U may be the information on the product purchase history of all other users U other than the target user.

[0024] In this way, the information processing device 1 can change the method for determining the purchase interval of the target products according to the number of purchases of the target products by the target user in a predetermined period TA.

[0025] The information on other users' product purchase history used to determine the second purchase interval is the product purchase history of other users U whose attributes are similar to the target user, but it may also be the product purchase history of all other users U other than the target user. Similar attributes to the target user means, for example, that they share one or more of the following attributes: a specific age group, a specific gender, a specific family structure, a specific income bracket, and specific preferences.

[0026] Next, the information processing device 1 predicts the next purchase time for each target product by user U based on the purchase interval determined in step S2 (step S3). The next purchase time for a target product may be a period of more than one day, for example, a one-day period indicated by year, month, and day (for example, April 1, 2023).

[0027] For example, the information processing device 1 calculates a corrected purchase interval by multiplying the purchase interval determined in step S2 by the number of purchases made by the target user at the most recent purchase date and time. Then, the information processing device 1 predicts when the target user will make their next purchase of the target product by adding the most recent purchase date and time by the target user to the corrected purchase interval.

[0028] For example, if the purchase interval determined in step S2 is 1 month and the number of purchases at the most recent purchase date and time is 2, the information processing device 1 will determine that the corrected purchase interval is 2 months. In this case, if the most recent purchase date and time by user U is February 1, 2023, the information processing device 1 will predict that the next purchase date of the target product by user U will be April 1, 2023, which is 2 months after February 1, 2023.

[0029] The information processing device 1 determines whether the purchase interval determined in step S2 falls within a predetermined interval range TR if the number of times the target user has purchased the target product is less than or equal to a threshold (for example, 1), and can exclude the next purchase timing of products whose purchase interval determined in step S2 does not fall within the predetermined interval range TR from the prediction target.

[0030] The range TR is, for example, from 7 days to 90 days, but is not limited to this example. The range TR may be set to different ranges for each category, or to different ranges depending on the attributes of the target user. Furthermore, the information processing device 1 can make the range TR larger for target products that are purchased more frequently, and can also make the range TR larger for target products that the target user owns.

[0031] Next, the information processing device 1 proposes the purchase of the target product at the next purchase time predicted in step S3 (step S4). For example, the information processing device 1 proposes the purchase of the target product to the target user by sending a push notification to the target user's terminal device 2 suggesting the target product at the next purchase time predicted in step S3.

[0032] For example, if the information processing device 1 predicts in step S3 that the next purchase time for the target product is product A, it can push notification information containing the string "It's the perfect time to repurchase product A" to the target user's terminal device 2, thereby suggesting that the target user purchase the target product.

[0033] Furthermore, the information processing device 1 can also suggest the purchase of the target product to the target user by displaying the information as new information on the shopping screen displayed on the terminal device 2 via the shopping application installed on the terminal device 2.

[0034] The suggested information includes information necessary to display the purchase page for the target product in the shopping application (hereinafter referred to as the shopping app) installed on terminal device 2. The target user can select the suggested information displayed on terminal device 2 by tapping or clicking, thereby displaying the purchase page for the target product in the shopping app on terminal device 2.

[0035] Furthermore, the information processing device 1 can receive opt-out settings from user U regarding the proposed purchase of the aforementioned target product. For example, when the information processing device 1 receives an opt-out setting request from user U's terminal device 2, it accepts the opt-out setting.

[0036] When the information processing device 1 receives an opt-out setting, it excludes user U, who has made the opt-out setting, from the processing target of steps S2 to S4 as an opt-out user. Instead of proposing the purchase of the target product, the information processing device 1 can propose to user U products that are frequently suggested to opt-out users, based on the opt-out user's product purchase history information, with products that have been purchased many times by opt-out users being suggested to opt-out users more frequently.

[0037] In this way, the information processing device 1 determines the interval between user U's product purchases based on user U's product purchase history, and predicts when user U will purchase the product next based on the determined purchase interval. Then, the information processing device 1 proposes that user U purchase the product at the predicted next purchase time. In this way, the information processing device 1 can appropriately propose product purchases to user U.

[0038] The configuration of the information processing system, including the information processing device 1 and terminal device 2 that perform such processing, will be described in detail below.

[0039] [2. Configuration of the Information Processing System] Figure 2 shows an example of the configuration of an information processing system according to the embodiment. As shown in Figure 2, the information processing system 100 according to the embodiment includes an information processing device 1 and a plurality of terminal devices 2.

[0040] Multiple terminal devices 2 are used by different users U. Terminal devices 2 include, for example, notebook PCs (personal computers), desktop PCs, smartphones, tablet PCs, and wearable devices. Wearable devices include, but are not limited to, smart glasses or smartwatches. Users U are those who utilize services provided by information processing devices 1, etc.

[0041] Each of the information processing device 1 and the terminal device 2 is connected to each other via a network N, either by wire or wireless, enabling communication between them. Note that the information processing system 100 shown in Figure 2 may include multiple information processing devices 1, etc.

[0042] Network N includes, for example, WANs (Wide Area Networks) such as the Internet, and mobile communication networks such as LTE (Long Term Evolution), 4G (4th Generation), and 5G (5th Generation: 5th Generation Mobile Communication System).

[0043] Terminal device 2 can connect to network N via short-range wireless communication such as a mobile communication network, Bluetooth®, or Wi-Fi (Local Area Network), and communicate with information processing device 1.

[0044] [3. Configuration of Information Processing Device 1] Figure 3 shows an example of the configuration of an information processing device 1 according to an embodiment. As shown in Figure 3, the information processing device 1 includes a communication unit 10, a storage unit 11, and a processing unit 12.

[0045] [3.1. Communications Section 10] The communication unit 10 is implemented, for example, by a communication module or a NIC (Network Interface Card). The communication unit 10 is connected to the network N by wire or wireless connection and transmits and receives information with various other devices. For example, the communication unit 10 transmits and receives information with the terminal device 2 via the network N.

[0046] [3.2. Storage section 11] The storage unit 11 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as hard disks and optical discs. The storage unit 11 has a user information storage unit 20 and a store information storage unit 21.

[0047] [3.2.1. User Information Storage Unit 20] The user information storage unit 20 stores various information about user U. Figure 4 is a diagram showing an example of a user information table stored in the user information storage unit 20 according to this embodiment.

[0048] In the example shown in Figure 4, the user information table stored in the user information storage unit 20 includes information on items such as "User ID (Identifier)", "Attribute Information", "History Information", and "Setting Information". The "User ID" is an identifier that identifies user U, and is assigned to each user U.

[0049] "Attribute information" refers to attribute information that indicates the attributes of user U associated with the "User ID". User U's attributes include, for example, demographic attributes and psychographic attributes. Demographic attributes are demographic attributes and include multiple attribute items such as age, gender, occupation, place of residence, annual income, and family structure.

[0050] Psychographic attributes are psychological attributes that include multiple attribute items related to lifestyle, values, interests, etc. For example, each of the multiple attribute items in a psychographic attribute is an object of interest to user U, such as cars, clothes, travel, games, camping, motorcycles, trains, home appliances, or personal computers.

[0051] "History information" includes information about user U's purchase history, search history, and browsing history.

[0052] Product purchase history information includes information about products purchased by user U in an online shopping mall that includes multiple stores (virtual stores), information about the purchase cost, information about the date and time of purchase, and information about where the product was purchased. Product information includes information such as the product name, product description, and product image. In addition, product information may also include information such as the product identification code.

[0053] Furthermore, the product purchase history information may include information on user U's product purchase history on online sites other than the e-commerce mall, and may also include information on user U's product purchase history at physical stores.

[0054] Search history information includes, for example, information about web content searches on search engines and search history on various websites. For instance, search history information includes information that identifies the content searched by user U, the date and time of the search, and the user ID of user U.

[0055] Browsing history information is information about user U's web content browsing history, and includes information that identifies the content user U viewed, the date and time of viewing, and the user ID of user U who viewed the content.

[0056] "Configuration information" refers to the configuration information of user U associated with the "User ID". The configuration information may include, for example, information indicating the user's opt-out setting for the repeat purchase suggestion service. The repeat purchase suggestion service is a service that suggests the purchase of the aforementioned target products to user U. Note that the information stored in the user information storage unit 20 is not limited to the information described above and may include various other information about user U.

[0057] [3.2.2. Store Information Storage Unit 21] The store information storage unit 21 stores information for each store. For example, the store information storage unit 21 stores store information such as the store ID, store name, store content (e.g., the store's shopping page), and information on each product sold in the store (including the product's shopping page). Product information includes information such as the product name, product description, and product image. Product information may also include information such as the product identification code.

[0058] [3.3. Processing Unit 12] The processing unit 12 is a controller, and is realized, for example, by a CPU (Central Processing Unit) or MPU (Micro Processing Unit) executing various programs stored in the memory device inside the terminal device 2 using RAM as the working area.

[0059] The processing unit 12 may be partially or entirely implemented by an integrated circuit, such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0060] As shown in Figure 3, the processing unit 12 includes an acquisition unit 30, a reception unit 31, a determination unit 32, a prediction unit 33, a proposal unit 34, and a provision unit 35, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the processing unit 12 is not limited to the configuration shown in Figure 3, and other configurations are also acceptable as long as they perform the information processing described later.

[0061] [3.3.1. Acquisition part 30] The acquisition unit 30 acquires various information from external information processing devices and terminal devices 2 via the communication unit 10, and stores the acquired information in the storage unit 11.

[0062] For example, the acquisition unit 30 acquires user information, which is information about user U, from an external information processing device or terminal device 2 via the communication unit 10, and adds the acquired user information to the store information table of the user information storage unit 20.

[0063] Furthermore, the acquisition unit 30 acquires various types of information from the storage unit 11. For example, the acquisition unit 30 acquires user information, which is information about user U, from the user information storage unit 20 or the like. The user information acquired by the acquisition unit 30 includes, for example, some or all of at least one of the attribute information, history information, and setting information mentioned above.

[0064] For example, the acquisition unit 30 acquires information on the target user's product purchase history. The acquisition unit 30 also acquires information on the product purchase history of other users U besides the target user. The product purchase history includes the history of purchases of products in an online shopping mall that includes multiple stores.

[0065] Furthermore, the acquisition unit 30 acquires store information, which is information about the store, from the store information storage unit 21 and the like. The store information acquired by the acquisition unit 30 includes at least one or more pieces or all of the following pieces of information: store name information, store content, and information about each product.

[0066] [3.3.2. Reception Department 31] The reception unit 31 receives various requests and information from external information processing devices and terminal devices 2 via the communication unit 10.

[0067] For example, the reception unit 31 receives an opt-out request transmitted from the terminal device 2. The opt-out request includes information to identify user U of the terminal device 2, and information indicating the setting to opt out of the repeat purchase suggestion service.

[0068] Furthermore, the reception unit 31 receives store information transmission requests, product information transmission requests, or search queries from the terminal device 2. A store information transmission request is a request to transmit store content (for example, the store's shopping page). A product information transmission request is a request to transmit product information (for example, the product's shopping page). A search query is a query for searching for stores or products in the online shopping mall, and may include, for example, search keywords.

[0069] [3.3.3. Judgment unit 32] The determination unit 32 determines the purchase interval of each user U based on the product purchase history information acquired by the acquisition unit 30.

[0070] The determination unit 32 identifies products that user U has purchased multiple times during a predetermined period as target products for each user U. If user U has purchased multiple products multiple times, the information processing device 1 identifies each of these products as a target product. Note that user U may purchase multiple products in a single transaction.

[0071] The determination unit 32 determines the purchase interval by multiplying the interval TB between the oldest and latest purchase dates and times among multiple purchase dates and times within a predetermined period TA by the number of purchases N1 within the predetermined period TA. For example, the determination unit 32 determines that the purchase interval for a target product is 1 month if the interval TB is 3 months and the number of purchases N1 is 9. In this way, the determination unit 32 determines the purchase interval for the target product per unit quantity.

[0072] The determination unit 32 treats multiple items with the same identification code as the same item, even if they were purchased from different stores, if the item can be identified by its identification code. However, the information processing device 1 does not treat multiple items purchased from different stores as the same item, even if they are the same product, if the item cannot be identified by its identification code.

[0073] Thus, if the product purchased by user U is a product for which a product code can be identified, the determination unit 32 determines the purchase interval based on user U's purchase history of the same product at multiple stores. If the product purchased by user U is a product for which a product code cannot be identified, the determination unit 32 determines the purchase interval based on user U's purchase history of the product from a single store.

[0074] Furthermore, even for products whose product codes cannot be identified, the determination unit 32 can determine whether they are identical based on, for example, the product image, and for products determined to be identical across multiple stores, it can determine the purchase interval based on the purchase history of user U for the same product across multiple stores.

[0075] Furthermore, the determination unit 32 can also determine the purchase interval of the target user U, which is the user being determined, using information from the target user's product purchase history and information from the product purchase history of other users U who are not the target user.

[0076] For example, the determination unit 32 can also select a product that user U has purchased only once as the target product. In this case, the determination unit 32 determines the purchase interval of the target product by other users U based on the purchase history information of other users U, and determines that purchase interval as the purchase interval of the target product for the target user. In this way, the determination unit 32 can determine the purchase interval of a product that user U has purchased only once within a predetermined period, based on the purchase history information of other users U.

[0077] The product purchase history information of other users U used to determine the purchase interval of other users U is information of other users U whose attributes are similar to those of the target user. Similar attributes to the target user means, for example, that they share one or more specific attributes (e.g., a specific age group, a specific gender, a specific family structure, a specific income bracket, and specific preferences). Note that the product purchase history information of other users U used to determine the purchase interval of other users U may also include the product purchase history information of all other users U other than the target user.

[0078] Furthermore, the determination unit 32 can also correct the purchase interval of the target user's products based on the purchase intervals of other users U. In this case, the determination unit 32 determines the purchase interval of the target user's products as the first purchase interval based on the target user's product purchase history information, and determines the purchase interval of other users U as the first purchase interval based on the product purchase history information of other users U.

[0079] The determination unit 32 can then determine the purchase interval of the target product for the target user by adding the first purchase interval and the second purchase interval with weights. For example, the determination unit 32 reduces the weight of the first purchase interval and increases the weight of the second purchase interval as the number of times the target user purchases the target product decreases. In this way, the determination unit 32 can change the method for determining the purchase interval of the target product according to the number of times the target user purchases the target product during a predetermined period TA.

[0080] Furthermore, the determination unit 32 can, for example, determine the first purchase interval described above as the purchase interval for the target user of the target product if the number of times the target user has purchased the target product during a predetermined period of TA is equal to or greater than a threshold, and if not, determine the value obtained by weighting and adding the first purchase interval and the second purchase interval described above as the purchase interval for the target user of the target product.

[0081] The product purchase history information of other users U used to determine the second purchase interval is information about the product purchase history of other users U that have similar attributes to the target user. Similar attributes to the target user means, for example, that they share one or more specific attributes (e.g., a specific age group, a specific gender, a specific family structure, a specific income bracket, and specific preferences). Note that the product purchase history information of other users U used to determine the second purchase interval may also include the product purchase history information of all other users U other than the target user.

[0082] In this way, the determination unit 32 can change the method for determining the purchase interval of the target product according to the number of times the target user has purchased the target product during a predetermined period of TA.

[0083] In the example above, the purchase interval for the target product per unit quantity is the purchase interval for one unit of the target product. However, the unit quantity is not limited to one unit; for example, it could be the average number of target products purchased per purchase by the target user. In this case, the average number of purchases may be a value including decimals, or it may be a value obtained by rounding to the decimal part or to the mth decimal place. m is an integer of 1 or greater.

[0084] [3.3.4. Prediction section 33] The prediction unit 33 predicts the next purchase time for each product by user U based on the purchase interval determined by the determination unit 32. The next purchase time for the target product, which is the product that the prediction unit 33 predicts, may be a period of more than one day, for example, instead of a one-day period indicated by year, month, and day.

[0085] For example, the prediction unit 33 calculates a corrected purchase interval by multiplying the purchase interval determined by the determination unit 32 by the number of purchases made by user U at the most recent purchase date and time. Then, the prediction unit 33 predicts when user U will make their next purchase of the target product by adding the most recent purchase date and time by user U to the corrected purchase interval.

[0086] For example, if the purchase interval determined by the determination unit 32 is 1 month and the number of purchases at the most recent purchase date and time is 2, the prediction unit 33 determines that the corrected purchase interval is 2 months. In this case, if the most recent purchase date and time by user U is February 1, 2023, the information processing device 1 predicts that the next purchase time for the target product by user U will be April 1, 2023, which is two months after February 1, 2023.

[0087] The prediction unit 33 determines whether the purchase interval determined by the determination unit 32 is within a predetermined interval range TR if the number of times the target user has purchased the target product is less than or equal to a threshold (for example, 1), and can exclude the next purchase timing of products for which the purchase interval determined by the determination unit 32 is not within the predetermined interval range TR from the prediction target.

[0088] The range TR is, for example, a range from 7 days to 90 days, but is not limited to this example. The range TR may be set to different ranges for each category, or to different ranges depending on the user U's attributes. Furthermore, the information processing device 1 can make the range TR larger for target products that are purchased more frequently, and can also make the range TR larger for target products that user U owns.

[0089] Furthermore, the prediction unit 33 can also predict when user U will purchase the target product next, by adding the latest purchase date and time by user U to the purchase interval determined by the determination unit 32, if the unit quantity is the average number of purchases of the target product.

[0090] [3.3.5. Proposal Department 34] The proposal unit 34 proposes that the target user U purchase the target product at the next purchase time predicted by the prediction unit 33.

[0091] The timing of suggesting the purchase of the target product to the target user is, but is not limited to, the timing when the next purchase period predicted by the prediction unit 33 begins, or the timing when the shopping application is launched on the terminal device 2 during the next purchase period predicted by the prediction unit 33.

[0092] For example, the suggestion unit 34 proposes the purchase of a target product to the target user by sending a push notification to the target user's terminal device 2 with suggestion information that the target product is due for purchase at the next time predicted by the prediction unit 33.

[0093] For example, if the next purchase time for the target product predicted by the prediction unit 33 is product A, the suggestion unit 34 can suggest the purchase of the target product to the target user by sending a push notification to the target user's terminal device 2 containing suggestion information, such as the string "It's the perfect time to repurchase product A."

[0094] Furthermore, the suggestion unit 34 can also suggest the purchase of the target product to the target user by displaying the information provided as new information on the shopping screen displayed on the terminal device 2 by the shopping application installed on the terminal device 2.

[0095] The suggested information includes information that allows the user to display the purchase page for the target product in the shopping app installed on terminal device 2. The target user can select the suggested information displayed on terminal device 2 by tapping or clicking, thereby displaying the purchase page for the target product in the shopping app on terminal device 2.

[0096] The purchase page for the target product is, for example, a page where user U can purchase the target product based on the number of purchases made at the most recent purchase date and time. User U can purchase the target product based on the number of purchases made at the most recent purchase date and time simply by selecting the purchase button on such a purchase page, without having to select the quantity.

[0097] Furthermore, after the proposal unit 34 has made a proposal to the target user to purchase the target product, if the target user does not purchase the target product in response to the proposal during the period of purchase timing predicted by the prediction unit 33, the proposal unit 34 provides the target user with an incentive to purchase the target product.

[0098] Incentives for purchasing the target product include, but are not limited to, the provision of discount coupons for the target product, the awarding of bonus points when the target user purchases the target product, the awarding of electronic money when the target user purchases the target product, or the provision of the target product for free when the target user purchases the target product.

[0099] If the target user does not purchase the target product in response to a purchase proposal during the purchase period of the target product predicted by the prediction unit 33, the proposal unit 34 can propose the purchase of the target product to the target user by pushing incentive information, which is information indicating an incentive to purchase the target product, to the terminal device 2 after the purchase period of the target product predicted by the prediction unit 33 has elapsed.

[0100] Furthermore, if the target user does not purchase the target product in response to a purchase proposal during the purchase period predicted by the prediction unit 33, the proposal unit 34 can propose the purchase of the target product to the target user by notifying the terminal device 2 of incentive information, which is information indicating an incentive to purchase the target product, as new information after the purchase period predicted by the prediction unit 33 has elapsed.

[0101] In this case, the proposal unit 34 can offer a higher incentive to user U the longer the period between the predicted purchase time of the target product (as predicted by the prediction unit 33) and the time the shopping app is launched again. The higher the incentive, the greater the amount when converted to points or cash.

[0102] When the opt-out setting is received by the reception unit 31, the suggestion unit 34, instead of suggesting the purchase of the target product to user U who has set up the opt-out setting as an opt-out user, can suggest products to user U based on the opt-out user's product purchase history information, with products that have been purchased more frequently by opt-out users being suggested to opt-out users more frequently.

[0103] The timing for suggesting products with high purchase volumes is, for example, when the shopping app is launched on terminal device 2. The suggestion unit 34 selects products according to the aforementioned suggestion frequency in response to a new product request from the shopping app as suggested products, and transmits information about these suggested products to terminal device 2 as new product information. As a result, information about the suggested products is displayed on terminal device 2 as new product information, and information about products that the target user purchases in large quantities is provided to the target user as suggested product information with priority.

[0104] Furthermore, the suggestion unit 34 can suggest to the opt-out user products related to the target product whose next purchase time is predicted by the prediction unit 33 (hereinafter sometimes referred to as related products), and products that are purchased in large quantities are suggested to the opt-out user more frequently, thus increasing the frequency of suggestions to the opt-out user.

[0105] In this case, the timing for suggesting the purchase of related products to the opt-out user is the timing when the shopping app is launched on terminal device 2, but it may also be the timing when the next purchase period predicted by the prediction unit 33 begins.

[0106] Furthermore, related products include, but are not limited to, products whose probability of being purchased simultaneously with the target product is above a certain threshold, or products sold as a set with the target product.

[0107] [3.3.6.Providing Department 35] When the receiving unit 31 receives a request from the terminal device 2 to transmit store information, the providing unit 35 transmits the store content corresponding to the store information transmission request to the terminal device 2, thereby providing the store content corresponding to the store information transmission request to user U.

[0108] Furthermore, when the receiving unit 31 receives a product information transmission request from the terminal device 2, the providing unit 35 transmits product information corresponding to the product information transmission request (for example, the product's shopping page) to the terminal device 2, thereby providing the user U with product information corresponding to the product information transmission request.

[0109] Furthermore, if the suggested information displayed on the terminal device 2 is selected by the target user by tapping or clicking, the provision unit 35 can send the purchase page for the target product to the terminal device 2 and display the purchase page for the target product on the terminal device 2.

[0110] [4. Processing Procedure] Next, the procedure for information processing by the processing unit 12 of the information processing device 1 according to the embodiment will be described. Figure 5 is a flowchart showing an example of information processing by the processing unit 12 of the information processing device 1 according to the embodiment. The processing in Figure 5 is performed, for example, for each user U.

[0111] As shown in Figure 5, the processing unit 12 of the information processing device 1 determines whether or not the purchase timing prediction timing has arrived (step S10). The purchase timing prediction timing is, for example, the timing when the number of new purchases or purchases by user U exceeds a threshold, or the timing of sultamination that occurs at a predetermined period.

[0112] If the processing unit 12 determines that it is time to predict the purchase timing (step S10: Yes), it obtains product purchase history information from the storage unit 11 (step S11). Based on the product purchase history information obtained in step S11, the processing unit 12 determines the purchase cycle of the product (step S12). Then, based on the purchase cycle of the product determined in step S12, the processing unit 12 predicts the next purchase timing of the product (step S13).

[0113] When the processing in step S13 is completed, the processing unit 12 determines whether or not it is time to suggest a product (step S14). The timing for suggesting a product is, for example, when the next purchase period for the product predicted in step S13 begins.

[0114] If the processing unit 12 determines that it is time to suggest a product (step S14: Yes), it determines whether or not there is an opt-out setting (step S15). If the processing unit 12 determines that there is an opt-out setting (step S15: Yes), it suggests to user U that they purchase the product that is due for purchase next (step S16). If the processing unit 12 determines that there is no opt-out setting (step S15: No), it suggests to user U that they purchase a product other than the product that is due for purchase next (step S17).

[0115] The processing unit 12 determines whether it has reached the end of operation timing (step S18) if the processing in step S16 is completed, if the processing in step S17 is completed, if it determines that it is not the purchase timing prediction timing (step S10: No), or if it determines that it is not the product proposal timing (step S14: No). The processing unit 12 determines that it has reached the end of operation timing, for example, when the power to the information processing device 1 is turned off.

[0116] If the processing unit 12 determines that it is not yet time to terminate the operation (step S18: No), it proceeds to step S10. If it determines that it is time to terminate the operation (step S18: Yes), it terminates the process shown in Figure 5.

[0117] [5. Transformation] In the example described above, the suggestion unit 34 sends a push notification of suggestion information to user U when the next purchase period predicted by the prediction unit 33 begins. However, it can also send a push notification of suggestion information to user U at a time when the target product is likely to be purchased.

[0118] For example, the determination unit 32 can perform a suggested time slot extraction process for each product or product category, calculating the ratio of the number of users U who purchased the products indicated in the suggested information to the total number of users U who provided the suggested information, and can determine the time slots when products are most likely to be purchased. The determination unit 32 can also perform the suggested time slot extraction process for each attribute of user U, and can determine the time slots when products are most likely to be purchased, for example, for each attribute of user U.

[0119] Furthermore, the determination unit 32 can also determine, based on user U's schedule information, that time slots when no schedule is set are time slots when products are more likely to be purchased. In this case, the acquisition unit 30 acquires user U's schedule information from an external information processing device or terminal device 2, etc.

[0120] Furthermore, if the target product is one whose demand changes seasonally (for example, water), the determination unit 32 can also determine the purchase interval of the target user by correcting the purchase interval using seasonal (period-specific) correction values. For example, the determination unit 32 corrects the purchase interval with a correction value that shortens the purchase interval during periods of high demand, and corrects the purchase interval with a correction value that lengthens the purchase interval during periods of low demand.

[0121] Furthermore, if, for example, the prediction unit 34 does not purchase the target product in response to a purchase suggestion for the target product during the period of purchase timing predicted by the prediction unit 33, it may also suggest an alternative product to the target product after the purchase timing predicted by the prediction unit 33 has elapsed.

[0122] Substitute products include, for example, products of the same type as the target product (e.g., mineral water, diapers, shampoo, etc.) but from a different brand; products of the same type as the target product but cheaper; and products of the same type as the target product but more popular.

[0123] [6. Hardware Configuration] The information processing device 1 according to the above embodiment is implemented by a computer 80 having a configuration such as that shown in Figure 6. Figure 6 is a hardware configuration diagram showing an example of a computer 80 that implements the functions of the information processing device 1 according to the embodiment. The computer 80 has a CPU 81, RAM 82, ROM (Read Only Memory) 83, HDD (Hard Disk Drive) 84, communication interface (I / F) 85, input / output interface (I / F) 86, and media interface (I / F) 87.

[0124] The CPU 81 operates based on programs stored in the ROM 83 or HDD 84, and controls various parts. The ROM 83 stores the boot program executed by the CPU 81 when the computer 80 starts up, as well as programs that depend on the computer 80's hardware.

[0125] HDD84 stores programs executed by CPU81 and data used by such programs. The communication interface85 receives data from other devices via network N (see Figure 2) and sends it to CPU81, and transmits the data generated by CPU81 to other devices via network N.

[0126] The CPU 81 controls output devices such as displays and printers, and input devices such as keyboards or mice, via the input / output interface 86. The CPU 81 acquires data from input devices via the input / output interface 86. The CPU 81 also outputs data it has generated to output devices via the input / output interface 86.

[0127] The media interface 87 reads a program or data stored in the recording medium 88 and provides it to the CPU 81 via the RAM 82. The CPU 81 loads the program from the recording medium 88 onto the RAM 82 via the media interface 87 and executes the loaded program. The recording medium 88 can be, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0128] For example, when the computer 80 functions as an information processing device 1 according to the embodiment, the CPU 81 of the computer 80 realizes the functions of the processing unit 12 by executing a program loaded on the RAM 82. The HDD 84 stores data from the storage unit 11. The CPU 81 of the computer 80 reads and executes these programs from the recording medium 88, but as another example, these programs may be obtained from other devices via a network N.

[0129] [7. Other] Furthermore, among the processes described in 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 by known methods. In addition, the processing procedures, specific names, and various data and parameters shown in the above document and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0130] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0131] For example, the information processing device 1 described above may be implemented using a terminal device and a server computer, or using multiple server computers. Furthermore, depending on the function, it may be implemented by calling external platforms via APIs (Application Programming Interfaces) or network computing, allowing for flexible configuration changes.

[0132] Furthermore, the embodiments and modifications described above can be combined as appropriate, provided that the processing content is not inconsistent.

[0133] [8. Effects] As described above, the information processing device 1 according to the embodiment comprises an acquisition unit 30, a determination unit 32, a prediction unit 33, and a proposal unit 34. The acquisition unit 30 acquires information on the user U's product purchase history. The determination unit 32 determines the interval between user U's product purchases based on the product purchase history information acquired by the acquisition unit 30. The prediction unit 33 predicts when user U will purchase the product next based on the purchase interval determined by the determination unit 32. The proposal unit 34 proposes that user U purchase the product at the next purchase time predicted by the prediction unit 33. As a result, the information processing device 1 can appropriately propose the purchase of a product to user U.

[0134] Furthermore, the acquisition unit 30 acquires information on the product purchase history of other users U besides user U, and the determination unit 32 determines the purchase interval based on the product purchase history information of user U and the product purchase history information of other users U. As a result, the information processing device 1 can appropriately suggest product purchases to user U.

[0135] Furthermore, if the number of times user U has purchased a product is below a threshold, the prediction unit 33 determines whether the purchase interval falls within a predetermined range, and excludes products whose purchase interval falls outside the predetermined range from the prediction of the next purchase timing. This allows the information processing device 1 to appropriately suggest product purchases to user U.

[0136] Furthermore, the product purchase history includes the purchase history of products in an online shopping mall that includes multiple stores. The determination unit 32 determines the purchase interval based on the purchase history of the same product A from multiple stores if the product purchased by user U is a product for which a product code can be identified. If the product purchased by user U is a product for which a product code cannot be identified, the determination unit 32 determines the purchase interval based on the purchase history of the product from a single store. This allows the information processing device 1 to appropriately suggest product purchases to user U.

[0137] Furthermore, the determination unit 32 determines the purchase interval for a product that user U has purchased only once within a predetermined period, based on information from other users' product purchase history. This allows the information processing device 1 to appropriately suggest product purchases to user U.

[0138] Furthermore, the determination unit 32 changes the method for determining the interval between product purchases according to the number of times user U has purchased the product. This allows the information processing device 1 to appropriately suggest product purchases to user U.

[0139] Furthermore, the information processing device 1 includes a reception unit 31 that receives opt-out settings for product purchase suggestions made by the suggestion unit 34. When an opt-out setting is received by the reception unit 31, the suggestion unit 34, based on the user U's product purchase history, suggests products to user U that are frequently suggested to user U, with the products being those that user U has purchased the most times N1. This allows the information processing device 1 to appropriately suggest product purchases to user U.

[0140] Furthermore, if user U does not purchase the product after receiving a purchase offer from the suggestion unit 34, the suggestion unit 34 provides user U with an incentive to purchase the product. This allows the information processing device 1 to effectively suggest the purchase of the product to user U.

[0141] Although embodiments of the present application have been described in detail based on the drawings, these are illustrative examples, and the present invention can be implemented in various other forms, including those described in the disclosure section of the invention, based on the knowledge of those skilled in the art.

[0142] Furthermore, the terms "section, module, unit" mentioned above can be replaced with "means" or "circuit," etc. For example, the acquisition unit can be replaced with acquisition means or acquisition circuit. [Explanation of Symbols]

[0143] 1. Information Processing Device 2 Terminal devices 10 Communications Department 11 Storage section 12 Processing Units 20 User information storage unit 21 Store Information Storage Unit 30 Acquisition Department 31 Reception Department 32 Judgment section 33 Prediction Section 34 Proposal Department 35 Providing Department 100 Information Processing Systems N Network

Claims

1. An acquisition unit that acquires information on the user's product purchase history, A determination unit determines the interval between the user's purchases of products based on the product purchase history information acquired by the acquisition unit, A prediction unit predicts when the user will purchase the product next, based on the purchase interval determined by the determination unit. The system includes a proposal unit that proposes to the user the purchase of the product at the next purchase time predicted by the prediction unit, The aforementioned product purchase history is, This includes purchase history of products in an online shopping mall that includes multiple stores, The determination unit, If the product purchased by the user is a product for which a product code can be identified, the purchase interval is determined based on the user's purchase history of the same product from multiple stores. If the product purchased by the user is a product for which a product code cannot be identified, the purchase interval is determined based on the user's purchase history of the product from a single store. An information processing device characterized by the following:

2. An acquisition unit that acquires information on the user's product purchase history, A determination unit determines the interval between the user's purchases of products based on the product purchase history information acquired by the acquisition unit, A prediction unit predicts when the user will purchase the product next, based on the purchase interval determined by the determination unit. A proposal unit that proposes to the user the purchase of the product at the next purchase time predicted by the prediction unit, The system includes a receiving unit that accepts opt-out settings for the proposal to purchase the product made by the proposal unit, The aforementioned proposal section is, When the opt-out setting is received by the reception unit, based on the user's purchase history, products that the user has purchased more frequently will be suggested to the user as products that are suggested to the user more frequently. An information processing device characterized by the following:

3. The acquisition unit is, Obtain information on the purchase history of other users besides the aforementioned user, The determination unit, Based on the purchase history information of the aforementioned user and the purchase history information of the aforementioned other user, the purchase interval is determined. The information processing apparatus according to claim 1 or 2.

4. The prediction unit, If the number of times the user has purchased the product is below a threshold, it is determined whether the purchase interval falls within a predetermined range, and purchase times for products whose purchase interval falls outside the predetermined range are excluded from the prediction of the next purchase time. The information processing apparatus according to claim 1 or 2.

5. The determination unit, The purchase interval for a product that the user has purchased only once within a predetermined period is determined based on the purchase history information of other users. The information processing apparatus according to claim 3.

6. The determination unit, The method for determining the purchase interval of the product is changed according to the number of times the user has purchased the product. The information processing apparatus according to claim 1 or 2.

7. The aforementioned proposal section is, If the user does not purchase the product despite the offer to purchase it, an incentive to purchase the product will be provided to the user. The information processing apparatus according to claim 1 or 2.

8. A method of information processing performed by a computer, The acquisition process involves obtaining information about the user's product purchase history, A determination step is performed to determine the interval between the user's purchases of products based on the product purchase history information obtained in the acquisition step, A prediction step predicts when the user will make their next purchase of the product based on the purchase interval determined by the determination step, The process includes a proposal step which proposes to the user the purchase of the product at the next purchase time predicted by the prediction step, The aforementioned product purchase history is, This includes purchase history of products in an online shopping mall that includes multiple stores, The aforementioned determination step is, If the product purchased by the user is a product for which a product code can be identified, the purchase interval is determined based on the user's purchase history of the same product from multiple stores. If the product purchased by the user is a product for which a product code cannot be identified, the purchase interval is determined based on the user's purchase history of the product from a single store. An information processing method characterized by the following:

9. A method of information processing performed by a computer, The acquisition process involves obtaining information about the user's product purchase history, A determination step is performed to determine the interval between the user's purchases of products based on the product purchase history information obtained in the acquisition step, A prediction step predicts when the user will make their next purchase of the product based on the purchase interval determined by the determination step, A proposal step in which the user is proposed to purchase the product at the next purchase time predicted by the prediction step, The process includes an acceptance process for accepting opt-out settings for the proposal to purchase the product through the aforementioned proposal process, The proposed process is as follows: If the opt-out setting is accepted through the aforementioned acceptance process, based on the user's product purchase history, products that the user has purchased more frequently will be suggested to the user as products that are suggested to the user more frequently. An information processing method characterized by the following:

10. Procedure for obtaining information on a user's product purchase history, A determination procedure for determining the interval between product purchases by the user based on the product purchase history information obtained by the acquisition procedure, A prediction procedure for predicting when the user will next purchase the product, based on the purchase interval determined by the determination procedure, The computer is instructed to execute a suggestion procedure that proposes to the user the purchase of the product at the next purchase time predicted by the prediction procedure, The aforementioned product purchase history is, This includes purchase history of products in an online shopping mall that includes multiple stores, The aforementioned determination procedure is: If the product purchased by the user is a product for which a product code can be identified, the purchase interval is determined based on the user's purchase history of the same product from multiple stores. If the product purchased by the user is a product for which a product code cannot be identified, the purchase interval is determined based on the user's purchase history of the product from a single store. An information processing program characterized by the following features.

11. Procedure for obtaining information on a user's product purchase history, A determination procedure for determining the interval between product purchases by the user based on the product purchase history information obtained by the acquisition procedure, A prediction procedure for predicting when the user will next purchase the product, based on the purchase interval determined by the determination procedure, A proposal procedure that proposes to the user the purchase of the product at the next purchase time predicted by the prediction procedure, The computer is instructed to execute a reception procedure for accepting opt-out settings for the proposed purchase of the product according to the aforementioned proposed procedure. The proposed procedure is, If the opt-out setting is accepted through the aforementioned acceptance procedure, based on the user's purchase history, products that the user has purchased more frequently will be suggested to the user as products that are suggested to the user more frequently. An information processing program characterized by the following features.

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