System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a filter condition based on a budget and an education policy set by a parent; means for generating a recommended product list for proposing a toy based on a preference of a child; means for purchasing a toy selected by the parent with reference to the recommended product list in an online shop; and means for generating and transmitting a request for requesting a present to a relative or a grandparent living in a distant place.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Many parents are troubled by the difficulty of selecting gifts for their children, often resulting in purchases that are beyond their budget or are not educationally appropriate. Furthermore, an increasing number of families feel that they want to avoid the time-consuming and labor-intensive process of visiting toy stores. These issues make it difficult to select appropriate gifts, and at the same time, relatives and grandparents who live far away have limited options for sending gifts. Effective and convenient solutions to these challenges are needed. [Means for solving the problem]

[0005] This invention provides a system that allows parents to input filter conditions based on their budget and educational policy and uses a generation AI to suggest toys based on their child's preferences. Specifically, the system includes a means for inputting the filter conditions set by the parent, a means for using the generation AI to generate a list of recommended products that meet the filter conditions and also match the child's preferences, a means for referring to the recommended product list and purchasing the selected toy at an online shop, and a means for generating and sending a request to ask relatives or grandparents who live far away to purchase a gift.

[0006] This allows parents to easily find the perfect gift that fits their budget and educational goals, and also makes it easy to request gifts from distant relatives or grandparents.

[0007] "Parent" refers to a child's guardian, the person in charge of selecting and purchasing gifts for the child.

[0008] A "budget" refers to the upper limit of the amount that parents set for purchasing gifts.

[0009] "Educational policy" refers to the values ​​and standards that parents place importance on in their children's education and development, and includes the types and characteristics of toys that should be selected based on those values ​​and standards.

[0010] "Filter conditions" refers to the selection criteria set by parents when choosing a gift, including budget, category, exclusion conditions, etc.

[0011] "Generative AI" refers to a system that uses artificial intelligence technology to generate information and make recommendations or selections based on specific conditions.

[0012] A "recommended product list" refers to a list of multiple toys that the generation AI selects based on filter conditions and suggests to parents and children.

[0013] An "online shop" refers to a virtual store that sells products over the Internet, where users can select products and complete the purchase process.

[0014] A "request" refers to a request message generated to ask a relative or grandparent who lives far away to send a gift.

[0015] "System" refers to an integrated platform that includes the means for filter input, product recommendations using generative AI, online checkout, and request generation. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a system that inputs filter conditions based on the budget and educational policy set by parents and uses generative AI to suggest toys based on the child's preferences.

[0038] Specifically, the system is implemented as follows.

[0039] The parent (user) launches the application using their device (smartphone, tablet, PC, etc.). Through the interface displayed on the device screen, they input their budget, product category (e.g., educational products), and exclusion criteria (e.g., specific characters or inappropriate content). The device formats these filter criteria and sends them to the server.

[0040] When the server receives the filter conditions, it searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server generates a list of recommended products based on the search results and sends it back to the device.

[0041] The device displays the received recommended product list to the parent (user). The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with the selected product information.

[0042] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[0043] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0044] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "Request Button" to request an educational board game as a gift from the grandparents. The server then sends a notification to the grandparents via email, completing the process.

[0045] In this way, the present invention allows parents to easily select and purchase the most suitable present that suits their budget and educational policy, and also makes it easy to request presents from relatives or grandparents who live far away.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user starts up the device and opens the application, and the device displays a filter condition input screen to the user.

[0049] Step 2:

[0050] The user enters filter criteria such as budget, category (e.g., educational products), exclusion criteria (e.g., specific characters, inappropriate content), etc. The device checks the entered information and converts it into the required format.

[0051] Step 3:

[0052] The terminal generates a request to transmit the converted filter condition to the server. The terminal transmits the request including the filter condition to the server.

[0053] Step 4:

[0054] The server parses the incoming request and extracts the filter criteria. The server connects to the database and performs a search based on the filter criteria. It extracts the relevant products, taking into account budget, category, and exclusion criteria.

[0055] Step 5:

[0056] The server uses generative AI to select the best candidates from the extracted product list based on a model that has learned the child's past selection history and preferences. The server creates a list of the best candidate products.

[0057] Step 6:

[0058] The server sends the generated recommended product list back to the terminal, which then displays the received recommended product list to the user, who then selects the desired product from the displayed list.

[0059] Step 7:

[0060] The user presses the purchase button. The terminal stores the selected product information. The terminal generates an order request and sends it to the server.

[0061] Step 8:

[0062] The server analyzes the received order request and connects to the online shop's API, which then uses it to check product availability and process the order.

[0063] Step 9:

[0064] If the order is successful, the server generates a confirmation notification and sends it to the terminal, which displays a notification of order completion to the user.

[0065] Step 10:

[0066] The user presses the "begging button." The device generates a begging request and sends it to the server.

[0067] Step 11:

[0068] The server receives the begging request and generates an email or message to send a notification to the specified relative or grandparent. The server sends the generated notification to the relative or grandparent.

[0069] By following the above steps, the present invention allows parents to easily select the most suitable gift that suits their budget and educational policy, and also makes it easy to request gifts from relatives or grandparents who live far away.

[0070] Example 1

[0071] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0072] With conventional online shopping systems, parents had difficulty choosing toys that suited their children's tastes and fit within their budget. The process of manually setting filter criteria based on educational guidelines to find the right product was also tedious. Furthermore, when requesting gifts from distant relatives or grandparents, the process was complicated and time-consuming.

[0073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0074] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for formatting and transmitting the filter conditions, means for searching a database based on the filter conditions, means for generating a recommended product list based on the past selection history and the child's preferences using a generative AI model, means for returning the recommended product list to the user terminal, means for purchasing the toys selected by the parents at an online shop, and means for generating and transmitting a request to request a gift from relatives or grandparents living far away. This enables parents to efficiently select toys that fit their educational policy within their budget and easily purchase the optimal product based on their child's preferences, as well as to quickly request gifts from relatives or grandparents living far away.

[0075] A "parent" is a guardian or caregiver of a child who uses the system to select and purchase toys for the child.

[0076] "Budget" refers to the upper limit of the amount of money to be spent on purchasing toys, and is a numerical value set by the parent as a filter condition.

[0077] "Educational policy" refers to the educational direction and goals that parents set for their children, and product categories and exclusion conditions are set based on this policy.

[0078] "Filter conditions" are data that include selection criteria such as budget, product category, and exclusion conditions set by the parent.

[0079] A "device" is an electronic device, such as a smartphone, tablet, or computer, that parents use to operate the system.

[0080] A "server" is a computing system capable of receiving filter criteria, searching a database, and using a generative AI model to generate and return a list of recommended products.

[0081] A "database" is an information storage device that stores toy information and allows the server to search based on filter conditions.

[0082] A "generative AI model" is an artificial intelligence algorithm that learns past selection history and children's preferences to generate an optimal list of recommended products.

[0083] A "recommended product list" is a list of products generated by a generative AI model and selected based on filter conditions and past history.

[0084] "Online Shop" refers to an e-commerce platform where toys can be purchased via the Internet.

[0085] The "begging button" is part of a user interface that allows parents to request gifts from relatives or grandparents who live far away.

[0086] A "request" is data that a user sends to a server to perform a specific operation, and includes a request for a gift, etc.

[0087] This invention is a system that allows parents to input filter criteria based on their budget and educational goals, and uses a generative AI model to suggest toys based on the child's preferences.

[0088] The system's main hardware includes the user's smartphone, tablet, or PC, and a server that hosts the database and generative AI model. The database also stores toy information.

[0089] The software includes an application that provides a user interface (UI), a database management system (DBMS), and the algorithms for the generative AI model. Users launch the application using a terminal and enter filter conditions through an intuitive user interface.

[0090] The device formats the information entered by the user, converts it into a data format, and sends it to the server. The server uses the received filter criteria to search for toy information in its database. The search results are input into a generative AI model, which generates a list of recommended products based on past selection history and the child's preferences. This list of recommended products is then sent back to the device and displayed to the user.

[0091] The user selects the desired product from the recommended product list and completes the purchase procedure at the online shop. The terminal sends a purchase request to the server, which then completes the order by connecting with the online shop's API.

[0092] Furthermore, by using the "Request Button" on the user interface, users can request gifts from relatives or grandparents who live far away. The device generates this request and sends it to the server. The server analyzes the request and sends a notification to the relatives or grandparents.

[0093] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters. The server extracts products that meet these conditions from a database, and the generation AI creates a list of 10 recommended products by referring to past selection history. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "beg button" to request an educational board game as a gift from her grandparents. The server notifies the grandparents by email, completing the process.

[0094] An example of a prompt might be, "Generate a list of recommended educational toys priced under 5,000 yen, excluding specific characters. Also, generate a prompt to request an educational board game as a gift for grandparents."

[0095] In this way, the present invention is a system that not only allows parents to easily select and purchase the most suitable present that matches their budget and educational policy, but also makes it easy to request presents from relatives and grandparents who live far away.

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1:

[0098] The user launches an application.

[0099] Input: None

[0100] Output: Application launch

[0101] The user launches the dedicated application on a device such as a smartphone, tablet, or PC, which displays the initial screen of the application.

[0102] Step 2:

[0103] The user enters the budget, product category, and exclusion criteria.

[0104] Input: Budget, Product Category, Exclusion Conditions

[0105] Output: The formatted filter condition

[0106] Through the application interface, users input their budget (e.g., 5,000 yen), product category (e.g., educational products), and exclusion criteria (e.g., specific characters). The interface is designed to be intuitive.

[0107] Step 3:

[0108] The device formats the input and sends it to the server.

[0109] Input: Filter conditions before formatting

[0110] Output: The formatted filter conditions sent to the server

[0111] The terminal formats the filter conditions entered by the user and converts them into a data format (e.g., JSON format). The converted data is sent to the server.

[0112] Step 4:

[0113] The server searches the database based on the filter criteria.

[0114] Input: Filter conditions after formatting

[0115] Output: A list of toys that meet the filter criteria

[0116] The server uses the received filter conditions to search for toy information in the database, extracting records that match the conditions using SQL queries, etc.

[0117] Step 5:

[0118] The server uses the generative AI model to generate a list of recommended products.

[0119] Input: A list of toys that meet the filter criteria

[0120] Output: Recommended product list

[0121] The server inputs the extracted toy information into a generative AI model to generate a list of recommended products based on past selection history and the child's preferences. This list prioritizes products that best match the criteria.

[0122] Step 6:

[0123] The server returns a list of recommended products to the terminal.

[0124] Input: Recommended product list

[0125] Output: Returned recommended product list

[0126] The server converts the generated recommended product list into a data format such as JSON and returns it to the user's device. The communication is encrypted.

[0127] Step 7:

[0128] The terminal displays a list of recommended products to the user.

[0129] Input: Returned recommended product list

[0130] Output: Recommended product list displayed on the screen

[0131] The device displays the received list of recommended products on the screen, allowing the user to intuitively check the list and view detailed information about each product.

[0132] Step 8:

[0133] The user selects the desired product and completes the purchase procedure.

[0134] Input: User product selection and purchase information

[0135] Output: Purchase request

[0136] The user selects the desired product from the displayed list of recommended products, then enters the necessary information on the application's purchase page and proceeds with the purchase process.

[0137] Step 9:

[0138] The terminal sends a purchase request to the server.

[0139] Input: Purchase information

[0140] Output: Purchase request sent to the server

[0141] The terminal formats the purchase information entered by the user and sends it to the server as an online order request.

[0142] Step 10:

[0143] The server connects with the online shop API to complete the purchase process.

[0144] Input: Purchase Request

[0145] Output: Purchase completion notification

[0146] The server sends the received purchase request to the online shop's API and proceeds with the order process. Once the order is complete, the server sends a confirmation to the user.

[0147] Step 11:

[0148] The user uses the "Request button" to request a gift.

[0149] Input: Begging request information

[0150] Output: Formatted begging request

[0151] Users can use the application's "Request Button" to request a gift from a distant relative or grandparent. When the button is pressed, a new request screen appears and the user can enter the necessary information.

[0152] Step 12:

[0153] The server analyzes the begging request and notifies the specified relatives or grandparents.

[0154] Input: Begging request information

[0155] Output: Notifications sent to relatives and grandparents

[0156] The server receives the request, analyzes it, and then sends an email or in-app notification to the designated relative or grandparent, containing the requested product information and a link to purchase it.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] Nowadays, it is becoming increasingly difficult for parents to choose the perfect gift for their children. Finding a toy that suits the child's tastes and fits their budget and educational goals from the wide variety of products on offer can be particularly time-consuming. Asking relatives or grandparents who live far away to buy a gift can also be a complicated process. This can take time and effort for parents, making it difficult to make an efficient purchase.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes means for inputting filter conditions based on a budget and educational policy set by a parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for purchasing the toy selected by the parent at an online shop by referring to the recommended product list, means for generating and transmitting a request to request a gift from a relative or grandparent living far away, communication means for formatting the filter conditions and transmitting them to the server, display means for displaying the received recommendation results on the parent's terminal, and data processing means for generating a recommended product list based on the child's preferences using a generation AI. This allows parents to easily receive suggestions for optimal toys and efficiently purchase them at an online shop or request gifts from a relative or grandparent living far away.

[0162] "Filter conditions" are conditions that are applied when selecting products based on the budget and educational policy set by the parent.

[0163] The "recommended product list" is a list of toys recommended to parents, proposed by the generative AI based on the child's preferences.

[0164] "Online Shop" means a website or mobile application used to purchase goods over the Internet.

[0165] A "request" is a request to ask relatives or grandparents who live far away to send a present.

[0166] The "communication means" is a means for formatting the filter conditions and transmitting them to the server.

[0167] The "display means" is a means for displaying the received recommendation results on the parent's terminal.

[0168] "Data processing means" means means for using a generating AI to generate a recommended product list based on a child's preferences.

[0169] "Generative AI" is an artificial intelligence technology that learns past selection history and children's preferences to generate an optimal list of recommended products.

[0170] This invention is a system that allows parents to input filter conditions based on their budget and educational policy, and suggests toys that are optimal for their child's preferences. This system works by allowing parents to input detailed filter conditions using a device such as a smartphone.

[0171] First, the parent (user) launches the application using a device such as a smartphone. An interface is displayed on the device screen, and the parent enters filter conditions such as budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content). The filter conditions are then formatted and sent to the server.

[0172] The server searches a large amount of toy information in its database based on the received filter conditions. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. This process uses data processing libraries such as Python's requests library, pandas, and scikit-learn. The search results are generated as a list of recommended products and sent back to the device.

[0173] Once the recommendation list is sent to the device, the parent can select the desired product based on the list. The selected product is then ordered through the online shop's API, allowing the parent (user) to easily complete the purchase. The app also includes a "begging button" function that allows users to request gifts from distant relatives or grandparents. Using this function, a request is generated and a notification is sent to the specified relative or grandparent.

[0174] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server searches the database based on the criteria and uses generative AI to create a list of 10 recommendations that take into account the child's past preferences. The mother selects an educational block set from this list, orders it from the online shop, and completes the purchase. She also uses the "beg" button to request an educational board game from her grandparents as a gift. The server then notifies the grandparents by email, completing the process.

[0175] Example prompt sentence:

[0176] "A parent is looking to purchase educational products within a budget of ¥5,000. Please exclude certain cartoon characters. The child likes blocks and puzzles. Please recommend the best toys."

[0177] In this way, parents can easily choose toys that suit their children's tastes and can also easily ask relatives or grandparents for gifts, saving parents a lot of time and effort.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] The user launches the application using a terminal. Through the interface displayed on the screen, the user inputs filter conditions such as budget, product category, and exclusion conditions. The input data includes the specific budget amount and the names of characters to be excluded. The terminal then generates filter conditions based on the input data.

[0181] Step 2:

[0182] The terminal formats the generated filter conditions and sends them to the server. Specifically, the terminal creates formatted JSON data and sends it to the server using an HTTP POST request. The input is the formatted filter conditions, and the output is the request sent to the server.

[0183] Step 3:

[0184] The server analyzes the received filter conditions and searches for toy information in a database. The database stores information such as product name, price, category, target age, and character name. The server uses an SQL query to extract data that matches these conditions. The input is the filter conditions, and the output is candidate search results.

[0185] Step 4:

[0186] The server uses a generative AI to select the best candidate products from the search results based on the child's preferences. The generative AI model learns past selection history and the child's preferences, and applies a ranking algorithm to evaluate the candidates. The input is the candidate search results data, and the output is a list of the best recommended products.

[0187] Step 5:

[0188] The server generates a list of recommended products and returns it to the terminal. The generated list is sent to the terminal in JSON format using an HTTP response. The input is the list of recommended products, and the output is the response sent to the terminal.

[0189] Step 6:

[0190] The device displays the received recommended product list to the parent. The screen displays a list of recommended product names, prices, images, etc. The input is the recommended product list, and the output is the display on the screen.

[0191] Step 7:

[0192] The user refers to the recommended product list and selects the product they wish to purchase. The selected product information is formatted as data for the order process in conjunction with the online shop's API. The input is the selected data from the recommended product list, and the output is the request data to the online shop.

[0193] Step 8:

[0194] The terminal calls the online shop's API and places an order for the selected product. The API request is sent including the product ID and payment information, and a response confirming the order is received. The input is the order data, and the output is the response confirming the order.

[0195] Step 9:

[0196] The user uses the "beg button" to generate a gift request from relatives or grandparents. The request includes product information and the recipient's email address. The input is the gift request data, and the output is the request sent to the server.

[0197] Step 10:

[0198] The server receives gift requests and sends emails and notifications to the specified relatives and grandparents. The emails contain product information and a purchase link. The input is the gift request data, and the output is the notification email.

[0199] This series of processes allows users to easily select toys that suit their child's preferences, and also enables them to efficiently purchase toys online and request gifts to be sent to distant locations.

[0200] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0201] This invention combines a system that uses generative AI to suggest toys based on a child's preferences by inputting filter conditions based on the parent's budget and educational policy, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into consideration.

[0202] An embodiment of the system is as follows.

[0203] The parent (user) launches the application using their own device (smartphone, tablet, PC, etc.). The application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content), the emotion engine recognizes the user's emotions and provides simpler options and guidance if the user is confused, or emphasizes their happiness if they are pleased.

[0204] The device formats the filter conditions entered by the user and the emotional data analyzed by the emotion engine, and sends them to the server. Upon receiving this information, the server searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server then adjusts the list of recommended products generated, taking into account the data from the emotion engine, and sends it back to the device.

[0205] The device then displays the received list of recommended products to the parent (user). If the emotion engine detects positive emotions such as joy or excitement in the user, it highlights those products. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with information about the selected product.

[0206] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[0207] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0208] As a concrete example, when a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if the mother seems confused. The server extracts relevant products from a database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "Request Button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[0209] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[0210] The processing flow will be explained below.

[0211] Step 1:

[0212] The user starts up the device and opens the application. The device displays a filter condition input screen to the user. At the same time, the device uses the camera and microphone to capture the user's facial expressions and voice, and inputs them into the emotion engine.

[0213] Step 2:

[0214] The emotion engine recognizes the user's emotions in real time through facial expression recognition and voice analysis. The device collects the recognized user's emotional data and adjusts the screen display and options as needed.

[0215] Step 3:

[0216] The user enters filter conditions such as budget, category (e.g., educational products), and exclusion conditions (e.g., specific characters, inappropriate content). The device checks the entered information and formats it.

[0217] Step 4:

[0218] The terminal generates a request to send the filter conditions together with the user's emotion data to the server. The terminal sends the request to the server.

[0219] Step 5:

[0220] The server analyzes the received request, extracts filter conditions, and performs filtering according to the user's emotions, taking into account the data from the emotion engine.

[0221] Step 6:

[0222] The server connects to the database and searches for the best toy based on the filter criteria and emotion data. Generative AI is used to select the best candidate product, taking into account past selection history and the child's preferences.

[0223] Step 7:

[0224] The server adjusts the generated recommended product list and creates a list that emphasizes products that the user is likely to enjoy based on the emotional data.The server then sends the recommended product list to the terminal.

[0225] Step 8:

[0226] The device displays the recommended product list received from the server to the user. If the emotion engine detects the user's joy or excitement, the product is highlighted. The user selects the desired product from the list.

[0227] Step 9:

[0228] The user presses the purchase button. The terminal stores the selected product information, generates an online order request, and sends it to the server.

[0229] Step 10:

[0230] The server analyzes the received order request and connects to the online shop's API. The server checks the product inventory and processes the order. When the order is complete, the server sends a confirmation to the terminal.

[0231] Step 11:

[0232] The terminal displays a notification to the user that the order has been completed. The user can then use the "Request Button" to request a gift from a distant relative or grandparent.

[0233] Step 12:

[0234] The device generates a begging request and sends it to the server. The server receives the request and generates an email or message to send a notification to the specified relatives or grandparents. The server then sends the generated notification to the relatives or grandparents.

[0235] As a result, the present invention makes it possible to easily select and purchase the most suitable present that takes into consideration the parents' budget, educational policy, and even the user's feelings, and to easily request presents from distant relatives or grandparents.

[0236] Example 2

[0237] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0238] With conventional systems, parents had difficulty finding the right toy based on their budget and educational goals, which required time and effort. Furthermore, recommendations often failed to take into account the child's preferences or the parents' feelings, resulting in an inability to make the best choice. Furthermore, the process of requesting gifts from distant relatives or grandparents was cumbersome.

[0239] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0240] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for collecting and analyzing user emotion data using an emotion engine for analyzing the user's emotions, means for formatting the filter conditions and emotion data and transmitting it to the server, means for the server to use a generative AI model based on the filter conditions and emotion data to generate an optimal recommended product list, means for purchasing the toys selected by the parents from an online shop by referring to the recommended product list, and means for generating and transmitting a request to request a gift from a relative or grandparent living far away. This enables a smooth toy selection and purchase process that takes into account the parents' budget, educational policy, and emotions, and makes it easy to request a gift from a relative or grandparent living far away.

[0241] "Parents" refers to guardians who select and purchase toys for their children.

[0242] A "budget" refers to the maximum amount of money parents set that can be spent on purchasing toys.

[0243] "Educational policy" refers to the policies and objectives that parents have for their children to have educational values ​​and learning opportunities.

[0244] "Filter conditions" refer to restrictions and requirements such as budget, product category, and exclusion conditions, and include setting information entered by the parent.

[0245] An "emotion engine" refers to software or hardware that analyzes a user's facial expressions and voice to determine the user's emotions.

[0246] "Emotion data" refers to data based on the user's facial expressions and voice that is collected and analyzed by the emotion engine.

[0247] "Recommended product list" refers to a list of optimal toys generated by the server based on the filter conditions and emotional data set by the parent.

[0248] "Generative AI model" refers to an algorithm or system that uses an AI model that has learned about a child's past selection history and preferences to generate an optimal list of recommended products.

[0249] "Online Shop" refers to a website or platform that sells and purchases products over the Internet.

[0250] A "request" refers to an electronic message or notification to ask a distant relative or grandparent to send a gift.

[0251] "Server" refers to a computer system that processes various data, generates a list of recommended products, and sends requests.

[0252] "Terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to launch and operate applications.

[0253] This invention combines a system that uses a generative AI model to suggest toys based on a child's preferences, based on parental input filter criteria such as budget and educational goals, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into account.

[0254] An embodiment of the system is as follows.

[0255] The parent (user) launches the application on their device (smartphone, tablet, PC, etc.). This application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone.

[0256] As users enter their filter criteria (budget, product category, exclusions), the emotion engine recognizes their emotions, providing simple options and guidance if they are confused, and emphasizing their joy if they are happy.

[0257] The device formats the filter conditions entered by the user and the emotion data analyzed by the emotion engine, and sends it to the server as a single data package, which includes compiling the data in JSON format.

[0258] The server receives the data package and analyzes the filter criteria and emotion data. The server searches through a database of numerous toy products that match the criteria. The server then uses a generative AI model to generate an optimal list of recommended products based on the model's learning of past selection history and the child's preferences.

[0259] The generated recommended product list is further refined based on the emotional data. For example, products that the emotional engine detects as pleasing to the user are placed at the top of the list. The final recommended product list is then sent back to the user's device.

[0260] The device then displays the received list of recommended products to the user. If the emotion engine detects the user's positive emotions (joy or excitement), it highlights the product in question. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the selected product information is sent from the device to the server.

[0261] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the purchase of the toy. Once the order is completed, the server receives a commission from the online shop.

[0262] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0263] As a concrete example, when a mother uses her device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if she seems confused. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "beg button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[0264] Examples of prompts include:

[0265] 1. "I'm looking for educational products for a 5-year-old child under 5,000 yen. I'd like to exclude certain characters."

[0266] 2. "Provide a simple guide for confused users."

[0267] 3. "Products that detect joy in the mother's face should be at the top of the list."

[0268] 4. "Select your educational block set and proceed to purchase it from our online shop."

[0269] 5. "Please write and send an email to my grandparents requesting a gift."

[0270] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1:

[0273] The user launches the application

[0274] Input: Use the terminal to launch the application and access the filter criteria input screen.

[0275] Data processing: A UI is displayed for entering filter items such as budget, product category, and exclusion conditions.

[0276] Output: The filter criteria entered by the user.

[0277] Specific operation: A user opens the application on a device such as a smartphone, tablet, or PC and begins entering filter criteria.

[0278] Step 2:

[0279] Device collects and analyzes emotional data

[0280] Input: Uses the device's camera and microphone to collect the user's facial expressions and voice in real time.

[0281] Data processing: The emotion engine analyzes facial and voice data to detect the user's emotional state (e.g., joy, confusion).

[0282] Output: Detected emotion data.

[0283] How it works: The device uses a camera to capture a picture of the user's face and a microphone to record their voice, and the emotion engine then analyzes this data to determine the user's emotions.

[0284] Step 3:

[0285] The device formats the data and sends it to the server

[0286] Input: User filter criteria and collected emotion data.

[0287] Data processing: Format the filter conditions and emotion data into a single data package (e.g., JSON format).

[0288] Output: A formatted data package.

[0289] Specific operation: The device compiles the filter conditions and emotion data in JSON format or similar and sends them to the server.

[0290] Step 4:

[0291] The server receives the data and starts processing

[0292] Input: Formatted data package sent from the terminal.

[0293] Data processing: Analyze and interpret the filter conditions and sentiment data, and generate search queries to the database according to the filter conditions.

[0294] Output: The query for the database search.

[0295] What happens next: The server receives the data package, interprets the filter conditions, and creates a query to query the database.

[0296] Step 5:

[0297] The server searches the database

[0298] Input: A search query based on filter criteria.

[0299] Data processing: Search and extract toy information that matches the filter criteria from the information stored in the database.

[0300] Output: Toys that match the filter criteria.

[0301] Specific operation: The server issues a search query to the database and extracts toy information that matches the criteria.

[0302] Step 6:

[0303] The server generates the recommendation list using generative AI models

[0304] Input: Toy information that matches the filter criteria, past selection history, and child preferences.

[0305] Data processing: Generative AI models are used to generate optimal product recommendations based on this information.

[0306] Output: A list of recommended products.

[0307] How it works: The server uses a generative AI model to create a list of recommended products based on toy information that matches the filter criteria, past selection history, and the child's preferences.

[0308] Step 7:

[0309] The server adjusts the list with emotion data and returns it to the device.

[0310] Input: Recommended product list and user sentiment data.

[0311] Data processing: Adjust the recommended product list by incorporating emotional data. For example, products that are detected as pleasing to the user will be placed at the top of the list.

[0312] Output: A tailored list of recommended products.

[0313] Specific operation: The products in the list are adjusted based on the emotional data, and the server returns the final list to the device.

[0314] Step 8:

[0315] The device displays a list of recommendations to the user

[0316] Input: The recommended product list returned by the server.

[0317] Data processing: Converting the recommended product list into a format that can be displayed in the user interface.

[0318] Output: A visual list of recommended products for the user.

[0319] What it does: The device displays a list of recommended products on the screen, highlighting products with particularly positive sentiment detected by the sentiment engine.

[0320] Step 9:

[0321] The user selects a product and begins the purchase process

[0322] Input: The product you want from the recommended products list.

[0323] Data processing: Based on the user's selection, detailed information about the selected product is obtained and the purchase checkout screen is displayed.

[0324] Output: Selected product information and checkout screen.

[0325] Specific behavior: The user selects an item from the list and presses the purchase button.

[0326] Step 10:

[0327] The server connects to the online shop API and completes the order.

[0328] Input: User selected product information and purchase request.

[0329] Data processing: Link with the online shop API to proceed with the purchase. Notify the customer that the order has been processed.

[0330] Output: Purchase completion notification and order confirmation information.

[0331] Specific operation: The server calls the online shop API and processes the order. After the process is complete, it sends a purchase completion notification to the terminal.

[0332] Step 11:

[0333] Users can request gifts using the begging button

[0334] Input: Your begging request and the information of any specified relatives or grandparents.

[0335] Data processing: Formatting the request and generating a message to ask distant relatives or grandparents to send a gift.

[0336] Output: Gift request message.

[0337] Specific operation: The user presses the request button and enters information to request a gift.

[0338] Step 12:

[0339] The server analyzes the request and notifies relatives and grandparents

[0340] Input: A formatted gift request message.

[0341] Data processing: Send your request to your relatives or grandparents via email or notification.

[0342] Output: Notifications sent to relatives and grandparents.

[0343] Specific operation: The server analyzes the request and sends a gift request notification to the specified relatives or grandparents.

[0344] This system allows parents to efficiently select and purchase toys, and also makes it easy to request gifts from relatives or grandparents who live far away.

[0345] (Application example 2)

[0346] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0347] With conventional online shopping systems, parents had difficulty finding appropriate options when selecting gifts based on their children's preferences and educational goals. Furthermore, simple filtering and search results were provided without considering the user's feelings, potentially resulting in a poor user experience. Furthermore, when requesting gifts from distant relatives or grandparents, complicated procedures were often required, making the process difficult.

[0348] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for providing a response according to the user's emotions using an emotion engine that recognizes the user's emotions, and means for generating and sending a request to request a gift from relatives or grandparents living far away. This allows parents to easily set filter conditions based on the budget and educational policy and efficiently select toys that suit their child's preferences. In addition, the emotion engine can analyze the user's emotions and emphasize positive emotions, improving the user experience. Furthermore, it becomes possible to easily request gifts from distant relatives or grandparents.

[0349] A "budget" is the upper limit of the amount that parents set when purchasing gifts.

[0350] "Educational policy" refers to parents' philosophy and goals regarding their children's education and development, and is a criterion to be taken into consideration when choosing a gift.

[0351] "Filter conditions" are conditions for limiting product selection based on specific criteria such as budget and educational objectives.

[0352] A "recommended product list" is a list of toys suitable for children's preferences, suggested by a generative AI model based on filter conditions.

[0353] A "generative AI model" is an artificial intelligence model that learns past selection history and children's preferences to suggest the most suitable products.

[0354] An "emotion engine" is a software or hardware mechanism that analyzes a user's emotions from their facial expressions, voice, etc., and provides a response accordingly.

[0355] A "request" is request information for requesting the purchase of a present for a relative or grandparent who lives far away.

[0356] An "online shop" is a website or service that sells products over the Internet.

[0357] "User" refers to a parent or guardian who uses the system to purchase or request a gift.

[0358] The system for implementing this invention includes a mechanism for inputting filter conditions based on parents' budgets and educational policies, and then using a generative AI model to recommend products based on those filter conditions. The system also includes an emotion engine that recognizes the user's emotions and adjusts responses to improve the user experience. It also includes a function for requesting gifts from distant relatives or grandparents.

[0359] The server includes the following means:

[0360] 1. A way for parents to enter filter criteria based on their budget and educational preferences.

[0361] 2. A means for generating a list of recommended products to suggest toys based on a child's preferences, the list of recommended products being generated based on a model that learns past selection history and child preferences using a generative AI model.

[0362] 3. A way to use an emotion engine to analyze user sentiment and provide simple guidance when the user is confused, or highlight products when positive sentiment is detected.

[0363] 4. A way to generate and send requests for gifts to distant relatives and grandparents.

[0364] The device is primarily a smartphone and operates in the following specific manner.

[0365] The emotion engine analyzes the facial expressions and voice of the user (parent) using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion criteria (e.g., excluding specific characters), the emotion engine recognizes the user's emotions and adjusts the response. The device sends the filter criteria and emotion data to the server, which generates a list of recommended products and adjusts the list based on the emotion data and sends it back.

[0366] Here's a specific example: A mother uses her device to search for educational products within a budget of 5,000 yen and sets filter conditions to exclude specific characters. At this time, the emotion engine analyzes the mother's facial expressions and provides simple guidance if she appears confused. The generative AI model considers past selection history and the child's preferences to select the most suitable products and generate a list of recommended products. Products that the emotion engine detects bring joy to the mother are particularly highlighted. The mother selects an educational block set from this list and completes the order on the online shop.

[0367] Furthermore, a "begging button" can be used to ask grandparents who live far away for a gift. The emotion engine highlights the mother's wishes, analyzes the request, and sends emails and notifications to the grandparents.

[0368] An example of a prompt for the generative AI model is as follows:

[0369] "Based on the user's criteria, please recommend toys that meet the following criteria:

[0370] Budget: Under 5,000 yen

[0371] Category: Educational Products

[0372] Exclusion criteria: Does not include certain anime characters

[0373] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0374] Step 1:

[0375] The user starts the smartphone application and inputs filter conditions such as budget, educational policy, product category, exclusion conditions, etc. The input filter conditions are temporarily saved in the device's internal memory.

[0376] Step 2:

[0377] The device's camera and microphone are used to capture the user's facial expressions and voice. This emotional data is sent to the device's emotion engine, which analyzes the user's emotions. The analysis results are output as emotional labels, such as positive, negative, or confused.

[0378] Step 3:

[0379] The filter conditions and emotion data are sent from the device to the server. The device securely transfers the data to the server using the HTTPS protocol. In this step, the filter conditions and emotion analysis results are received by the server as input data.

[0380] Step 4:

[0381] The server searches the database for matching product information based on the filter conditions. It then passes a prompt to the generative AI model, which then generates a list of recommended products. The prompt is entered in a form that includes budget, exclusion conditions, and product categories. An example of this prompt is as follows:

[0382] "Based on the user's criteria, please recommend toys that meet the following criteria:

[0383] Budget: Under 5,000 yen

[0384] Category: Educational Products

[0385] Exclusion criteria: Does not include certain anime characters

[0386] Step 5:

[0387] The output of the generative AI model is a list of recommended products, which is then sent from the server to the device. Sentiment data is also taken into account, and the list is tailored to highlight products for which positive sentiment is detected.

[0388] Step 6:

[0389] The device displays the received recommended product list to the user, highlighting products that the user has indicated are particularly pleasing to the user, and the user can refer to these options and select a product to purchase.

[0390] Step 7:

[0391] The procedure for purchasing the product selected by the user from the online shop is sent from the terminal to the server, and the server sends the order information via the online shop's API to complete the purchase procedure.

[0392] Step 8:

[0393] After completing the purchase process, if the user wants to request a gift from a distant relative or grandparent, they can press the "Request Button." The device generates a request and sends it to the server. This request includes product information and sentiment analysis results.

[0394] Step 9:

[0395] The server analyzes the request and sends emails and notifications to the designated relatives and grandparents, ensuring the gift request goes smoothly.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0397] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0399] [Second embodiment]

[0400] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0401] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0402] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0404] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0406] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0407] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0408] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0411] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0412] The present invention relates to a system that inputs filter conditions based on the budget and educational policy set by parents and uses generative AI to suggest toys based on the child's preferences.

[0413] Specifically, the system is implemented as follows.

[0414] The parent (user) launches the application using their device (smartphone, tablet, PC, etc.). Through the interface displayed on the device screen, they input their budget, product category (e.g., educational products), and exclusion criteria (e.g., specific characters or inappropriate content). The device formats these filter criteria and sends them to the server.

[0415] When the server receives the filter conditions, it searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server generates a list of recommended products based on the search results and sends it back to the device.

[0416] The device displays the received recommended product list to the parent (user). The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with the selected product information.

[0417] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[0418] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0419] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "Request Button" to request an educational board game as a gift from the grandparents. The server then sends a notification to the grandparents via email, completing the process.

[0420] In this way, the present invention allows parents to easily select and purchase the most suitable present that suits their budget and educational policy, and also makes it easy to request presents from relatives or grandparents who live far away.

[0421] The processing flow will be explained below.

[0422] Step 1:

[0423] The user starts up the device and opens the application, and the device displays a filter condition input screen to the user.

[0424] Step 2:

[0425] The user enters filter criteria such as budget, category (e.g., educational products), exclusion criteria (e.g., specific characters, inappropriate content), etc. The device checks the entered information and converts it into the required format.

[0426] Step 3:

[0427] The terminal generates a request to transmit the converted filter condition to the server. The terminal transmits the request including the filter condition to the server.

[0428] Step 4:

[0429] The server parses the incoming request and extracts the filter criteria. The server connects to the database and performs a search based on the filter criteria. It extracts the relevant products, taking into account budget, category, and exclusion criteria.

[0430] Step 5:

[0431] The server uses generative AI to select the best candidates from the extracted product list based on a model that has learned the child's past selection history and preferences. The server creates a list of the best candidate products.

[0432] Step 6:

[0433] The server sends the generated recommended product list back to the terminal, which then displays the received recommended product list to the user, who then selects the desired product from the displayed list.

[0434] Step 7:

[0435] The user presses the purchase button. The terminal stores the selected product information. The terminal generates an order request and sends it to the server.

[0436] Step 8:

[0437] The server analyzes the received order request and connects to the online shop's API, which then uses it to check product availability and process the order.

[0438] Step 9:

[0439] If the order is successful, the server generates a confirmation notification and sends it to the terminal, which displays a notification of order completion to the user.

[0440] Step 10:

[0441] The user presses the "begging button." The device generates a begging request and sends it to the server.

[0442] Step 11:

[0443] The server receives the begging request and generates an email or message to send a notification to the specified relative or grandparent. The server sends the generated notification to the relative or grandparent.

[0444] By following the above steps, the present invention allows parents to easily select the most suitable gift that suits their budget and educational policy, and also makes it easy to request gifts from relatives or grandparents who live far away.

[0445] Example 1

[0446] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0447] With conventional online shopping systems, parents had difficulty choosing toys that suited their children's tastes and fit within their budget. The process of manually setting filter criteria based on educational guidelines to find the right product was also tedious. Furthermore, when requesting gifts from distant relatives or grandparents, the process was complicated and time-consuming.

[0448] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0449] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for formatting and transmitting the filter conditions, means for searching a database based on the filter conditions, means for generating a recommended product list based on the past selection history and the child's preferences using a generative AI model, means for returning the recommended product list to the user terminal, means for purchasing the toys selected by the parents at an online shop, and means for generating and transmitting a request to request a gift from relatives or grandparents living far away. This enables parents to efficiently select toys that fit their educational policy within their budget and easily purchase the optimal product based on their child's preferences, as well as to quickly request gifts from relatives or grandparents living far away.

[0450] A "parent" is a guardian or caregiver of a child who uses the system to select and purchase toys for the child.

[0451] "Budget" refers to the upper limit of the amount of money to be spent on purchasing toys, and is a numerical value set by the parent as a filter condition.

[0452] "Educational policy" refers to the educational direction and goals that parents set for their children, and product categories and exclusion conditions are set based on this policy.

[0453] "Filter conditions" are data that include selection criteria such as budget, product category, and exclusion conditions set by the parent.

[0454] A "device" is an electronic device, such as a smartphone, tablet, or computer, that parents use to operate the system.

[0455] A "server" is a computing system capable of receiving filter criteria, searching a database, and using a generative AI model to generate and return a list of recommended products.

[0456] A "database" is an information storage device that stores toy information and allows the server to search based on filter conditions.

[0457] A "generative AI model" is an artificial intelligence algorithm that learns past selection history and children's preferences to generate an optimal list of recommended products.

[0458] A "recommended product list" is a list of products generated by a generative AI model and selected based on filter conditions and past history.

[0459] "Online Shop" refers to an e-commerce platform where toys can be purchased via the Internet.

[0460] The "begging button" is part of a user interface that allows parents to request gifts from relatives or grandparents who live far away.

[0461] A "request" is data that a user sends to a server to perform a specific operation, and includes a request for a gift, etc.

[0462] This invention is a system that allows parents to input filter criteria based on their budget and educational goals, and uses a generative AI model to suggest toys based on the child's preferences.

[0463] The system's main hardware includes the user's smartphone, tablet, or PC, and a server that hosts the database and generative AI model. The database also stores toy information.

[0464] The software includes an application that provides a user interface (UI), a database management system (DBMS), and the algorithms for the generative AI model. Users launch the application using a terminal and enter filter conditions through an intuitive user interface.

[0465] The device formats the information entered by the user, converts it into a data format, and sends it to the server. The server uses the received filter criteria to search for toy information in its database. The search results are input into a generative AI model, which generates a list of recommended products based on past selection history and the child's preferences. This list of recommended products is then sent back to the device and displayed to the user.

[0466] The user selects the desired product from the recommended product list and completes the purchase procedure at the online shop. The terminal sends a purchase request to the server, which then completes the order by connecting with the online shop's API.

[0467] Furthermore, by using the "Request Button" on the user interface, users can request gifts from relatives or grandparents who live far away. The device generates this request and sends it to the server. The server analyzes the request and sends a notification to the relatives or grandparents.

[0468] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters. The server extracts products that meet these conditions from a database, and the generation AI creates a list of 10 recommended products by referring to past selection history. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "beg button" to request an educational board game as a gift from her grandparents. The server notifies the grandparents by email, completing the process.

[0469] An example of a prompt might be, "Generate a list of recommended educational toys priced under 5,000 yen, excluding specific characters. Also, generate a prompt to request an educational board game as a gift for grandparents."

[0470] In this way, the present invention is a system that not only allows parents to easily select and purchase the most suitable present that matches their budget and educational policy, but also makes it easy to request presents from relatives and grandparents who live far away.

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Step 1:

[0473] The user launches an application.

[0474] Input: None

[0475] Output: Application launch

[0476] The user launches the dedicated application on a device such as a smartphone, tablet, or PC, which displays the initial screen of the application.

[0477] Step 2:

[0478] The user enters the budget, product category, and exclusion criteria.

[0479] Input: Budget, Product Category, Exclusion Conditions

[0480] Output: The formatted filter condition

[0481] Through the application interface, users input their budget (e.g., 5,000 yen), product category (e.g., educational products), and exclusion criteria (e.g., specific characters). The interface is designed to be intuitive.

[0482] Step 3:

[0483] The device formats the input and sends it to the server.

[0484] Input: Filter conditions before formatting

[0485] Output: The formatted filter conditions sent to the server

[0486] The terminal formats the filter conditions entered by the user and converts them into a data format (e.g., JSON format). The converted data is sent to the server.

[0487] Step 4:

[0488] The server searches the database based on the filter criteria.

[0489] Input: Filter conditions after formatting

[0490] Output: A list of toys that meet the filter criteria

[0491] The server uses the received filter conditions to search for toy information in the database, extracting records that match the conditions using SQL queries, etc.

[0492] Step 5:

[0493] The server uses the generative AI model to generate a list of recommended products.

[0494] Input: A list of toys that meet the filter criteria

[0495] Output: Recommended product list

[0496] The server inputs the extracted toy information into a generative AI model to generate a list of recommended products based on past selection history and the child's preferences. This list prioritizes products that best match the criteria.

[0497] Step 6:

[0498] The server returns a list of recommended products to the terminal.

[0499] Input: Recommended product list

[0500] Output: Returned recommended product list

[0501] The server converts the generated recommended product list into a data format such as JSON and returns it to the user's device. The communication is encrypted.

[0502] Step 7:

[0503] The terminal displays a list of recommended products to the user.

[0504] Input: Returned recommended product list

[0505] Output: Recommended product list displayed on the screen

[0506] The device displays the received list of recommended products on the screen, allowing the user to intuitively check the list and view detailed information about each product.

[0507] Step 8:

[0508] The user selects the desired product and completes the purchase procedure.

[0509] Input: User product selection and purchase information

[0510] Output: Purchase request

[0511] The user selects the desired product from the displayed list of recommended products, then enters the necessary information on the application's purchase page and proceeds with the purchase process.

[0512] Step 9:

[0513] The terminal sends a purchase request to the server.

[0514] Input: Purchase information

[0515] Output: Purchase request sent to the server

[0516] The terminal formats the purchase information entered by the user and sends it to the server as an online order request.

[0517] Step 10:

[0518] The server connects with the online shop API to complete the purchase process.

[0519] Input: Purchase Request

[0520] Output: Purchase completion notification

[0521] The server sends the received purchase request to the online shop's API and proceeds with the order process. Once the order is complete, the server sends a confirmation to the user.

[0522] Step 11:

[0523] The user uses the "Request button" to request a gift.

[0524] Input: Begging request information

[0525] Output: Formatted begging request

[0526] Users can use the application's "Request Button" to request a gift from a distant relative or grandparent. When the button is pressed, a new request screen appears and the user can enter the necessary information.

[0527] Step 12:

[0528] The server analyzes the begging request and notifies the specified relatives or grandparents.

[0529] Input: Begging request information

[0530] Output: Notifications sent to relatives and grandparents

[0531] The server receives the request, analyzes it, and then sends an email or in-app notification to the designated relative or grandparent, containing the requested product information and a link to purchase it.

[0532] (Application example 1)

[0533] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0534] Nowadays, it is becoming increasingly difficult for parents to choose the perfect gift for their children. Finding a toy that suits the child's tastes and fits their budget and educational goals from the wide variety of products on offer can be particularly time-consuming. Asking relatives or grandparents who live far away to buy a gift can also be a complicated process. This can take time and effort for parents, making it difficult to make an efficient purchase.

[0535] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0536] In this invention, the server includes means for inputting filter conditions based on a budget and educational policy set by a parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for purchasing the toy selected by the parent at an online shop by referring to the recommended product list, means for generating and transmitting a request to request a gift from a relative or grandparent living far away, communication means for formatting the filter conditions and transmitting them to the server, display means for displaying the received recommendation results on the parent's terminal, and data processing means for generating a recommended product list based on the child's preferences using a generation AI. This allows parents to easily receive suggestions for optimal toys and efficiently purchase them at an online shop or request gifts from a relative or grandparent living far away.

[0537] "Filter conditions" are conditions that are applied when selecting products based on the budget and educational policy set by the parent.

[0538] The "recommended product list" is a list of toys recommended to parents, proposed by the generative AI based on the child's preferences.

[0539] "Online Shop" means a website or mobile application used to purchase goods over the Internet.

[0540] A "request" is a request to ask relatives or grandparents who live far away to send a present.

[0541] The "communication means" is a means for formatting the filter conditions and transmitting them to the server.

[0542] The "display means" is a means for displaying the received recommendation results on the parent's terminal.

[0543] "Data processing means" means means for using a generating AI to generate a recommended product list based on a child's preferences.

[0544] "Generative AI" is an artificial intelligence technology that learns past selection history and children's preferences to generate an optimal list of recommended products.

[0545] This invention is a system that allows parents to input filter conditions based on their budget and educational policy, and suggests toys that are optimal for their child's preferences. This system works by allowing parents to input detailed filter conditions using a device such as a smartphone.

[0546] First, the parent (user) launches the application using a device such as a smartphone. An interface is displayed on the device screen, and the parent enters filter conditions such as budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content). The filter conditions are then formatted and sent to the server.

[0547] The server searches a large amount of toy information in its database based on the received filter conditions. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. This process uses data processing libraries such as Python's requests library, pandas, and scikit-learn. The search results are generated as a list of recommended products and sent back to the device.

[0548] Once the recommendation list is sent to the device, the parent can select the desired product based on the list. The selected product is then ordered through the online shop's API, allowing the parent (user) to easily complete the purchase. The app also includes a "begging button" function that allows users to request gifts from distant relatives or grandparents. Using this function, a request is generated and a notification is sent to the specified relative or grandparent.

[0549] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server searches the database based on the criteria and uses generative AI to create a list of 10 recommendations that take into account the child's past preferences. The mother selects an educational block set from this list, orders it from the online shop, and completes the purchase. She also uses the "beg" button to request an educational board game from her grandparents as a gift. The server then notifies the grandparents by email, completing the process.

[0550] Example prompt sentence:

[0551] "A parent is looking to purchase educational products within a budget of ¥5,000. Please exclude certain cartoon characters. The child likes blocks and puzzles. Please recommend the best toys."

[0552] In this way, parents can easily choose toys that suit their children's tastes and can also easily ask relatives or grandparents for gifts, saving parents a lot of time and effort.

[0553] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0554] Step 1:

[0555] The user launches the application using a terminal. Through the interface displayed on the screen, the user inputs filter conditions such as budget, product category, and exclusion conditions. The input data includes the specific budget amount and the names of characters to be excluded. The terminal then generates filter conditions based on the input data.

[0556] Step 2:

[0557] The terminal formats the generated filter conditions and sends them to the server. Specifically, the terminal creates formatted JSON data and sends it to the server using an HTTP POST request. The input is the formatted filter conditions, and the output is the request sent to the server.

[0558] Step 3:

[0559] The server analyzes the received filter conditions and searches for toy information in a database. The database stores information such as product name, price, category, target age, and character name. The server uses an SQL query to extract data that matches these conditions. The input is the filter conditions, and the output is candidate search results.

[0560] Step 4:

[0561] The server uses a generative AI to select the best candidate products from the search results based on the child's preferences. The generative AI model learns past selection history and the child's preferences, and applies a ranking algorithm to evaluate the candidates. The input is the candidate search results data, and the output is a list of the best recommended products.

[0562] Step 5:

[0563] The server generates a list of recommended products and returns it to the terminal. The generated list is sent to the terminal in JSON format using an HTTP response. The input is the list of recommended products, and the output is the response sent to the terminal.

[0564] Step 6:

[0565] The device displays the received recommended product list to the parent. The screen displays a list of recommended product names, prices, images, etc. The input is the recommended product list, and the output is the display on the screen.

[0566] Step 7:

[0567] The user refers to the recommended product list and selects the product they wish to purchase. The selected product information is formatted as data for the order process in conjunction with the online shop's API. The input is the selected data from the recommended product list, and the output is the request data to the online shop.

[0568] Step 8:

[0569] The terminal calls the online shop's API and places an order for the selected product. The API request is sent including the product ID and payment information, and a response confirming the order is received. The input is the order data, and the output is the response confirming the order.

[0570] Step 9:

[0571] The user uses the "beg button" to generate a gift request from relatives or grandparents. The request includes product information and the recipient's email address. The input is the gift request data, and the output is the request sent to the server.

[0572] Step 10:

[0573] The server receives gift requests and sends emails and notifications to the specified relatives and grandparents. The emails contain product information and a purchase link. The input is the gift request data, and the output is the notification email.

[0574] This series of processes allows users to easily select toys that suit their child's preferences, and also enables them to efficiently purchase toys online and request gifts to be sent to distant locations.

[0575] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0576] This invention combines a system that uses generative AI to suggest toys based on a child's preferences by inputting filter conditions based on the parent's budget and educational policy, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into consideration.

[0577] An embodiment of the system is as follows.

[0578] The parent (user) launches the application using their own device (smartphone, tablet, PC, etc.). The application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content), the emotion engine recognizes the user's emotions and provides simpler options and guidance if the user is confused, or emphasizes their happiness if they are pleased.

[0579] The device formats the filter conditions entered by the user and the emotional data analyzed by the emotion engine, and sends them to the server. Upon receiving this information, the server searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server then adjusts the list of recommended products generated, taking into account the data from the emotion engine, and sends it back to the device.

[0580] The device then displays the received list of recommended products to the parent (user). If the emotion engine detects positive emotions such as joy or excitement in the user, it highlights those products. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with information about the selected product.

[0581] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[0582] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0583] As a concrete example, when a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if the mother seems confused. The server extracts relevant products from a database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "Request Button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[0584] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[0585] The processing flow will be explained below.

[0586] Step 1:

[0587] The user starts up the device and opens the application. The device displays a filter condition input screen to the user. At the same time, the device uses the camera and microphone to capture the user's facial expressions and voice, and inputs them into the emotion engine.

[0588] Step 2:

[0589] The emotion engine recognizes the user's emotions in real time through facial expression recognition and voice analysis. The device collects the recognized user's emotional data and adjusts the screen display and options as needed.

[0590] Step 3:

[0591] The user enters filter conditions such as budget, category (e.g., educational products), and exclusion conditions (e.g., specific characters, inappropriate content). The device checks the entered information and formats it.

[0592] Step 4:

[0593] The terminal generates a request to send the filter conditions together with the user's emotion data to the server. The terminal sends the request to the server.

[0594] Step 5:

[0595] The server analyzes the received request, extracts filter conditions, and performs filtering according to the user's emotions, taking into account the data from the emotion engine.

[0596] Step 6:

[0597] The server connects to the database and searches for the best toy based on the filter criteria and emotion data. Generative AI is used to select the best candidate product, taking into account past selection history and the child's preferences.

[0598] Step 7:

[0599] The server adjusts the generated recommended product list and creates a list that emphasizes products that the user is likely to enjoy based on the emotional data.The server then sends the recommended product list to the terminal.

[0600] Step 8:

[0601] The device displays the recommended product list received from the server to the user. If the emotion engine detects the user's joy or excitement, the product is highlighted. The user selects the desired product from the list.

[0602] Step 9:

[0603] The user presses the purchase button. The terminal stores the selected product information, generates an online order request, and sends it to the server.

[0604] Step 10:

[0605] The server analyzes the received order request and connects to the online shop's API. The server checks the product inventory and processes the order. When the order is complete, the server sends a confirmation to the terminal.

[0606] Step 11:

[0607] The terminal displays a notification to the user that the order has been completed. The user can then use the "Request Button" to request a gift from a distant relative or grandparent.

[0608] Step 12:

[0609] The device generates a begging request and sends it to the server. The server receives the request and generates an email or message to send a notification to the specified relatives or grandparents. The server then sends the generated notification to the relatives or grandparents.

[0610] As a result, the present invention makes it possible to easily select and purchase the most suitable present that takes into consideration the parents' budget, educational policy, and even the user's feelings, and to easily request presents from distant relatives or grandparents.

[0611] Example 2

[0612] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0613] With conventional systems, parents had difficulty finding the right toy based on their budget and educational goals, which required time and effort. Furthermore, recommendations often failed to take into account the child's preferences or the parents' feelings, resulting in an inability to make the best choice. Furthermore, the process of requesting gifts from distant relatives or grandparents was cumbersome.

[0614] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0615] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for collecting and analyzing user emotion data using an emotion engine for analyzing the user's emotions, means for formatting the filter conditions and emotion data and transmitting it to the server, means for the server to use a generative AI model based on the filter conditions and emotion data to generate an optimal recommended product list, means for purchasing the toys selected by the parents from an online shop by referring to the recommended product list, and means for generating and transmitting a request to request a gift from a relative or grandparent living far away. This enables a smooth toy selection and purchase process that takes into account the parents' budget, educational policy, and emotions, and makes it easy to request a gift from a relative or grandparent living far away.

[0616] "Parents" refers to guardians who select and purchase toys for their children.

[0617] A "budget" refers to the maximum amount of money parents set that can be spent on purchasing toys.

[0618] "Educational policy" refers to the policies and objectives that parents have for their children to have educational values ​​and learning opportunities.

[0619] "Filter conditions" refer to restrictions and requirements such as budget, product category, and exclusion conditions, and include setting information entered by the parent.

[0620] An "emotion engine" refers to software or hardware that analyzes a user's facial expressions and voice to determine the user's emotions.

[0621] "Emotion data" refers to data based on the user's facial expressions and voice that is collected and analyzed by the emotion engine.

[0622] "Recommended product list" refers to a list of optimal toys generated by the server based on the filter conditions and emotional data set by the parent.

[0623] "Generative AI model" refers to an algorithm or system that uses an AI model that has learned about a child's past selection history and preferences to generate an optimal list of recommended products.

[0624] "Online Shop" refers to a website or platform that sells and purchases products over the Internet.

[0625] A "request" refers to an electronic message or notification to ask a distant relative or grandparent to send a gift.

[0626] "Server" refers to a computer system that processes various data, generates a list of recommended products, and sends requests.

[0627] "Terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to launch and operate applications.

[0628] This invention combines a system that uses a generative AI model to suggest toys based on a child's preferences, based on parental input filter criteria such as budget and educational goals, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into account.

[0629] An embodiment of the system is as follows.

[0630] The parent (user) launches the application on their device (smartphone, tablet, PC, etc.). This application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone.

[0631] As users enter their filter criteria (budget, product category, exclusions), the emotion engine recognizes their emotions, providing simple options and guidance if they are confused, and emphasizing their joy if they are happy.

[0632] The device formats the filter conditions entered by the user and the emotion data analyzed by the emotion engine, and sends it to the server as a single data package, which includes compiling the data in JSON format.

[0633] The server receives the data package and analyzes the filter criteria and emotion data. The server searches through a database of numerous toy products that match the criteria. The server then uses a generative AI model to generate an optimal list of recommended products based on the model's learning of past selection history and the child's preferences.

[0634] The generated recommended product list is further refined based on the emotional data. For example, products that the emotional engine detects as pleasing to the user are placed at the top of the list. The final recommended product list is then sent back to the user's device.

[0635] The device then displays the received list of recommended products to the user. If the emotion engine detects the user's positive emotions (joy or excitement), it highlights the product in question. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the selected product information is sent from the device to the server.

[0636] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the purchase of the toy. Once the order is completed, the server receives a commission from the online shop.

[0637] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0638] As a concrete example, when a mother uses her device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if she seems confused. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "beg button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[0639] Examples of prompts include:

[0640] 1. "I'm looking for educational products for a 5-year-old child under 5,000 yen. I'd like to exclude certain characters."

[0641] 2. "Provide a simple guide for confused users."

[0642] 3. "Products that detect joy in the mother's face should be at the top of the list."

[0643] 4. "Select your educational block set and proceed to purchase it from our online shop."

[0644] 5. "Please write and send an email to my grandparents requesting a gift."

[0645] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[0646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0647] Step 1:

[0648] The user launches the application

[0649] Input: Use the terminal to launch the application and access the filter criteria input screen.

[0650] Data processing: A UI is displayed for entering filter items such as budget, product category, and exclusion conditions.

[0651] Output: The filter criteria entered by the user.

[0652] Specific operation: A user opens the application on a device such as a smartphone, tablet, or PC and begins entering filter criteria.

[0653] Step 2:

[0654] Device collects and analyzes emotional data

[0655] Input: Uses the device's camera and microphone to collect the user's facial expressions and voice in real time.

[0656] Data processing: The emotion engine analyzes facial and voice data to detect the user's emotional state (e.g., joy, confusion).

[0657] Output: Detected emotion data.

[0658] How it works: The device uses a camera to capture a picture of the user's face and a microphone to record their voice, and the emotion engine then analyzes this data to determine the user's emotions.

[0659] Step 3:

[0660] The device formats the data and sends it to the server

[0661] Input: User filter criteria and collected emotion data.

[0662] Data processing: Format the filter conditions and emotion data into a single data package (e.g., JSON format).

[0663] Output: A formatted data package.

[0664] Specific operation: The device compiles the filter conditions and emotion data in JSON format or similar and sends them to the server.

[0665] Step 4:

[0666] The server receives the data and starts processing

[0667] Input: Formatted data package sent from the terminal.

[0668] Data processing: Analyze and interpret the filter conditions and sentiment data, and generate search queries to the database according to the filter conditions.

[0669] Output: The query for the database search.

[0670] What happens next: The server receives the data package, interprets the filter conditions, and creates a query to query the database.

[0671] Step 5:

[0672] The server searches the database

[0673] Input: A search query based on filter criteria.

[0674] Data processing: Search and extract toy information that matches the filter criteria from the information stored in the database.

[0675] Output: Toys that match the filter criteria.

[0676] Specific operation: The server issues a search query to the database and extracts toy information that matches the criteria.

[0677] Step 6:

[0678] The server generates the recommendation list using generative AI models

[0679] Input: Toy information that matches the filter criteria, past selection history, and child preferences.

[0680] Data processing: Generative AI models are used to generate optimal product recommendations based on this information.

[0681] Output: A list of recommended products.

[0682] How it works: The server uses a generative AI model to create a list of recommended products based on toy information that matches the filter criteria, past selection history, and the child's preferences.

[0683] Step 7:

[0684] The server adjusts the list with emotion data and returns it to the device.

[0685] Input: Recommended product list and user sentiment data.

[0686] Data processing: Adjust the recommended product list by incorporating emotional data. For example, products that are detected as pleasing to the user will be placed at the top of the list.

[0687] Output: A tailored list of recommended products.

[0688] Specific operation: The products in the list are adjusted based on the emotional data, and the server returns the final list to the device.

[0689] Step 8:

[0690] The device displays a list of recommendations to the user

[0691] Input: The recommended product list returned by the server.

[0692] Data processing: Converting the recommended product list into a format that can be displayed in the user interface.

[0693] Output: A visual list of recommended products for the user.

[0694] What it does: The device displays a list of recommended products on the screen, highlighting products with particularly positive sentiment detected by the sentiment engine.

[0695] Step 9:

[0696] The user selects a product and begins the purchase process

[0697] Input: The product you want from the recommended products list.

[0698] Data processing: Based on the user's selection, detailed information about the selected product is obtained and the purchase checkout screen is displayed.

[0699] Output: Selected product information and checkout screen.

[0700] Specific behavior: The user selects an item from the list and presses the purchase button.

[0701] Step 10:

[0702] The server connects to the online shop API and completes the order.

[0703] Input: User selected product information and purchase request.

[0704] Data processing: Link with the online shop API to proceed with the purchase. Notify the customer that the order has been processed.

[0705] Output: Purchase completion notification and order confirmation information.

[0706] Specific operation: The server calls the online shop API and processes the order. After the process is complete, it sends a purchase completion notification to the terminal.

[0707] Step 11:

[0708] Users can request gifts using the begging button

[0709] Input: Your begging request and the information of any specified relatives or grandparents.

[0710] Data processing: Formatting the request and generating a message to ask distant relatives or grandparents to send a gift.

[0711] Output: Gift request message.

[0712] Specific operation: The user presses the request button and enters information to request a gift.

[0713] Step 12:

[0714] The server analyzes the request and notifies relatives and grandparents

[0715] Input: A formatted gift request message.

[0716] Data processing: Send your request to your relatives or grandparents via email or notification.

[0717] Output: Notifications sent to relatives and grandparents.

[0718] Specific operation: The server analyzes the request and sends a gift request notification to the specified relatives or grandparents.

[0719] This system allows parents to efficiently select and purchase toys, and also makes it easy to request gifts from relatives or grandparents who live far away.

[0720] (Application example 2)

[0721] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0722] With conventional online shopping systems, parents had difficulty finding appropriate options when selecting gifts based on their children's preferences and educational goals. Furthermore, simple filtering and search results were provided without considering the user's feelings, potentially resulting in a poor user experience. Furthermore, when requesting gifts from distant relatives or grandparents, complicated procedures were often required, making the process difficult.

[0723] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for providing a response according to the user's emotions using an emotion engine that recognizes the user's emotions, and means for generating and sending a request to request a gift from relatives or grandparents living far away. This allows parents to easily set filter conditions based on the budget and educational policy and efficiently select toys that suit their child's preferences. In addition, the emotion engine can analyze the user's emotions and emphasize positive emotions, improving the user experience. Furthermore, it becomes possible to easily request gifts from distant relatives or grandparents.

[0724] A "budget" is the upper limit of the amount that parents set when purchasing gifts.

[0725] "Educational policy" refers to parents' philosophy and goals regarding their children's education and development, and is a criterion to be taken into consideration when choosing a gift.

[0726] "Filter conditions" are conditions for limiting product selection based on specific criteria such as budget and educational objectives.

[0727] A "recommended product list" is a list of toys suitable for children's preferences, suggested by a generative AI model based on filter conditions.

[0728] A "generative AI model" is an artificial intelligence model that learns past selection history and children's preferences to suggest the most suitable products.

[0729] An "emotion engine" is a software or hardware mechanism that analyzes a user's emotions from their facial expressions, voice, etc., and provides a response accordingly.

[0730] A "request" is request information for requesting the purchase of a present for a relative or grandparent who lives far away.

[0731] An "online shop" is a website or service that sells products over the Internet.

[0732] "User" refers to a parent or guardian who uses the system to purchase or request a gift.

[0733] The system for implementing this invention includes a mechanism for inputting filter conditions based on parents' budgets and educational policies, and then using a generative AI model to recommend products based on those filter conditions. The system also includes an emotion engine that recognizes the user's emotions and adjusts responses to improve the user experience. It also includes a function for requesting gifts from distant relatives or grandparents.

[0734] The server includes the following means:

[0735] 1. A way for parents to enter filter criteria based on their budget and educational preferences.

[0736] 2. A means for generating a list of recommended products to suggest toys based on a child's preferences, the list of recommended products being generated based on a model that learns past selection history and child preferences using a generative AI model.

[0737] 3. A way to use an emotion engine to analyze user sentiment and provide simple guidance when the user is confused, or highlight products when positive sentiment is detected.

[0738] 4. A way to generate and send requests for gifts to distant relatives and grandparents.

[0739] The device is primarily a smartphone and operates in the following specific manner.

[0740] The emotion engine analyzes the facial expressions and voice of the user (parent) using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion criteria (e.g., excluding specific characters), the emotion engine recognizes the user's emotions and adjusts the response. The device sends the filter criteria and emotion data to the server, which generates a list of recommended products and adjusts the list based on the emotion data and sends it back.

[0741] Here's a specific example: A mother uses her device to search for educational products within a budget of 5,000 yen and sets filter conditions to exclude specific characters. At this time, the emotion engine analyzes the mother's facial expressions and provides simple guidance if she appears confused. The generative AI model considers past selection history and the child's preferences to select the most suitable products and generate a list of recommended products. Products that the emotion engine detects bring joy to the mother are particularly highlighted. The mother selects an educational block set from this list and completes the order on the online shop.

[0742] Furthermore, a "begging button" can be used to ask grandparents who live far away for a gift. The emotion engine highlights the mother's wishes, analyzes the request, and sends emails and notifications to the grandparents.

[0743] An example of a prompt for the generative AI model is as follows:

[0744] "Based on the user's criteria, please recommend toys that meet the following criteria:

[0745] Budget: Under 5,000 yen

[0746] Category: Educational Products

[0747] Exclusion criteria: Does not include certain anime characters

[0748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0749] Step 1:

[0750] The user starts the smartphone application and inputs filter conditions such as budget, educational policy, product category, exclusion conditions, etc. The input filter conditions are temporarily saved in the device's internal memory.

[0751] Step 2:

[0752] The device's camera and microphone are used to capture the user's facial expressions and voice. This emotional data is sent to the device's emotion engine, which analyzes the user's emotions. The analysis results are output as emotional labels, such as positive, negative, or confused.

[0753] Step 3:

[0754] The filter conditions and emotion data are sent from the device to the server. The device securely transfers the data to the server using the HTTPS protocol. In this step, the filter conditions and emotion analysis results are received by the server as input data.

[0755] Step 4:

[0756] The server searches the database for matching product information based on the filter conditions. It then passes a prompt to the generative AI model, which then generates a list of recommended products. The prompt is entered in a form that includes budget, exclusion conditions, and product categories. An example of this prompt is as follows:

[0757] "Based on the user's criteria, please recommend toys that meet the following criteria:

[0758] Budget: Under 5,000 yen

[0759] Category: Educational Products

[0760] Exclusion criteria: Does not include certain anime characters

[0761] Step 5:

[0762] The output of the generative AI model is a list of recommended products, which is then sent from the server to the device. Emotional data is also taken into account, and the list is tailored to highlight products for which positive sentiment is detected.

[0763] Step 6:

[0764] The device displays the received recommended product list to the user, highlighting products that the user has indicated are particularly pleasing to the user, and the user can refer to these options and select a product to purchase.

[0765] Step 7:

[0766] The procedure for purchasing the product selected by the user at the online shop is sent from the terminal to the server, and the server sends the order information via the online shop's API to complete the purchase procedure.

[0767] Step 8:

[0768] After completing the purchase process, if the user wants to request a gift from a distant relative or grandparent, they can press the "Request Button." The device generates a request and sends it to the server. This request includes product information and sentiment analysis results.

[0769] Step 9:

[0770] The server analyzes the request and sends emails and notifications to the designated relatives and grandparents, ensuring the gift request goes smoothly.

[0771] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0772] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0774] [Third embodiment]

[0775] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0776] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0777] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0778] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0779] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0780] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0781] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0782] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0783] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0784] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0785] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0786] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0787] The present invention relates to a system that inputs filter conditions based on the budget and educational policy set by parents and uses generative AI to suggest toys based on the child's preferences.

[0788] Specifically, the system is implemented as follows.

[0789] The parent (user) launches the application using their device (smartphone, tablet, PC, etc.). Through the interface displayed on the device screen, they input their budget, product category (e.g., educational products), and exclusion criteria (e.g., specific characters or inappropriate content). The device formats these filter criteria and sends them to the server.

[0790] When the server receives the filter conditions, it searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server generates a list of recommended products based on the search results and sends it back to the device.

[0791] The device displays the received recommended product list to the parent (user). The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with the selected product information.

[0792] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[0793] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0794] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "Request Button" to request an educational board game as a gift from the grandparents. The server then sends a notification to the grandparents via email, completing the process.

[0795] In this way, the present invention allows parents to easily select and purchase the most suitable present that suits their budget and educational policy, and also makes it easy to request presents from relatives or grandparents who live far away.

[0796] The processing flow will be explained below.

[0797] Step 1:

[0798] The user starts up the device and opens the application, and the device displays a filter condition input screen to the user.

[0799] Step 2:

[0800] The user enters filter criteria such as budget, category (e.g., educational products), exclusion criteria (e.g., specific characters, inappropriate content), etc. The device checks the entered information and converts it into the required format.

[0801] Step 3:

[0802] The terminal generates a request to transmit the converted filter condition to the server. The terminal transmits the request including the filter condition to the server.

[0803] Step 4:

[0804] The server parses the incoming request and extracts the filter criteria. The server connects to the database and performs a search based on the filter criteria. It extracts the relevant products, taking into account budget, category, and exclusion criteria.

[0805] Step 5:

[0806] The server uses generative AI to select the best candidates from the extracted product list based on a model that has learned the child's past selection history and preferences. The server creates a list of the best candidate products.

[0807] Step 6:

[0808] The server sends the generated recommended product list back to the terminal, which then displays the received recommended product list to the user, who then selects the desired product from the displayed list.

[0809] Step 7:

[0810] The user presses the purchase button. The terminal stores the selected product information. The terminal generates an order request and sends it to the server.

[0811] Step 8:

[0812] The server analyzes the received order request and connects to the online shop's API, which then uses it to check product availability and process the order.

[0813] Step 9:

[0814] If the order is successful, the server generates a confirmation notification and sends it to the terminal, which displays a notification of order completion to the user.

[0815] Step 10:

[0816] The user presses the "begging button." The device generates a begging request and sends it to the server.

[0817] Step 11:

[0818] The server receives the begging request and generates an email or message to send a notification to the specified relative or grandparent. The server sends the generated notification to the relative or grandparent.

[0819] By following the above steps, the present invention allows parents to easily select the most suitable gift that suits their budget and educational policy, and also makes it easy to request gifts from relatives or grandparents who live far away.

[0820] Example 1

[0821] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0822] With conventional online shopping systems, parents had difficulty choosing toys that suited their children's tastes and fit within their budget. The process of manually setting filter criteria based on educational guidelines to find the right product was also tedious. Furthermore, when requesting gifts from distant relatives or grandparents, the process was complicated and time-consuming.

[0823] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0824] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for formatting and transmitting the filter conditions, means for searching a database based on the filter conditions, means for generating a recommended product list based on the past selection history and the child's preferences using a generative AI model, means for returning the recommended product list to the user terminal, means for purchasing the toys selected by the parents at an online shop, and means for generating and transmitting a request to request a gift from relatives or grandparents living far away. This enables parents to efficiently select toys that fit their educational policy within their budget and easily purchase the optimal product based on their child's preferences, as well as to quickly request gifts from relatives or grandparents living far away.

[0825] A "parent" is a guardian or caregiver of a child who uses the system to select and purchase toys for the child.

[0826] "Budget" refers to the upper limit of the amount of money to be spent on purchasing toys, and is a numerical value set by the parent as a filter condition.

[0827] "Educational policy" refers to the educational direction and goals that parents set for their children, and product categories and exclusion conditions are set based on this policy.

[0828] "Filter conditions" are data that include selection criteria such as budget, product category, and exclusion conditions set by the parent.

[0829] A "device" is an electronic device, such as a smartphone, tablet, or computer, that parents use to operate the system.

[0830] A "server" is a computing system capable of receiving filter criteria, searching a database, and using a generative AI model to generate and return a list of recommended products.

[0831] A "database" is an information storage device that stores toy information and allows the server to search based on filter conditions.

[0832] A "generative AI model" is an artificial intelligence algorithm that learns past selection history and children's preferences to generate an optimal list of recommended products.

[0833] A "recommended product list" is a list of products generated by a generative AI model and selected based on filter conditions and past history.

[0834] "Online Shop" refers to an e-commerce platform where toys can be purchased via the Internet.

[0835] The "begging button" is part of a user interface that allows parents to request gifts from relatives or grandparents who live far away.

[0836] A "request" is data that a user sends to a server to perform a specific operation, and includes a request for a gift, etc.

[0837] This invention is a system that allows parents to input filter criteria based on their budget and educational goals, and uses a generative AI model to suggest toys based on the child's preferences.

[0838] The system's main hardware includes the user's smartphone, tablet, or PC, and a server that hosts the database and generative AI model. The database also stores toy information.

[0839] The software includes an application that provides a user interface (UI), a database management system (DBMS), and the algorithms for the generative AI model. Users launch the application using a terminal and enter filter conditions through an intuitive user interface.

[0840] The device formats the information entered by the user, converts it into a data format, and sends it to the server. The server uses the received filter criteria to search for toy information in its database. The search results are input into a generative AI model, which generates a list of recommended products based on past selection history and the child's preferences. This list of recommended products is then sent back to the device and displayed to the user.

[0841] The user selects the desired product from the recommended product list and completes the purchase procedure at the online shop. The terminal sends a purchase request to the server, which then completes the order by connecting with the online shop's API.

[0842] Furthermore, by using the "Request Button" on the user interface, users can request gifts from relatives or grandparents who live far away. The device generates this request and sends it to the server. The server analyzes the request and sends a notification to the relatives or grandparents.

[0843] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters. The server extracts products that meet these conditions from a database, and the generation AI creates a list of 10 recommended products by referring to past selection history. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "beg button" to request an educational board game as a gift from her grandparents. The server notifies the grandparents by email, completing the process.

[0844] An example of a prompt might be, "Generate a list of recommended educational toys priced under 5,000 yen, excluding specific characters. Also, generate a prompt to request an educational board game as a gift for grandparents."

[0845] In this way, the present invention is a system that not only allows parents to easily select and purchase the most suitable present that matches their budget and educational policy, but also makes it easy to request presents from relatives and grandparents who live far away.

[0846] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0847] Step 1:

[0848] The user launches an application.

[0849] Input: None

[0850] Output: Application launch

[0851] The user launches the dedicated application on a device such as a smartphone, tablet, or PC, which displays the initial screen of the application.

[0852] Step 2:

[0853] The user enters the budget, product category, and exclusion criteria.

[0854] Input: Budget, Product Category, Exclusion Conditions

[0855] Output: The formatted filter condition

[0856] Through the application interface, users input their budget (e.g., 5,000 yen), product category (e.g., educational products), and exclusion criteria (e.g., specific characters). The interface is designed to be intuitive.

[0857] Step 3:

[0858] The device formats the input and sends it to the server.

[0859] Input: Filter conditions before formatting

[0860] Output: The formatted filter conditions sent to the server

[0861] The terminal formats the filter conditions entered by the user and converts them into a data format (e.g., JSON format). The converted data is sent to the server.

[0862] Step 4:

[0863] The server searches the database based on the filter criteria.

[0864] Input: Filter conditions after formatting

[0865] Output: A list of toys that meet the filter criteria

[0866] The server uses the received filter conditions to search for toy information in the database, extracting records that match the conditions using SQL queries, etc.

[0867] Step 5:

[0868] The server uses the generative AI model to generate a list of recommended products.

[0869] Input: A list of toys that meet the filter criteria

[0870] Output: Recommended product list

[0871] The server inputs the extracted toy information into a generative AI model to generate a list of recommended products based on past selection history and the child's preferences. This list prioritizes products that best match the criteria.

[0872] Step 6:

[0873] The server returns a list of recommended products to the terminal.

[0874] Input: Recommended product list

[0875] Output: Returned recommended product list

[0876] The server converts the generated recommended product list into a data format such as JSON and returns it to the user's device. The communication is encrypted.

[0877] Step 7:

[0878] The terminal displays a list of recommended products to the user.

[0879] Input: Returned recommended product list

[0880] Output: Recommended product list displayed on the screen

[0881] The device displays the received list of recommended products on the screen, allowing the user to intuitively check the list and view detailed information about each product.

[0882] Step 8:

[0883] The user selects the desired product and completes the purchase procedure.

[0884] Input: User product selection and purchase information

[0885] Output: Purchase request

[0886] The user selects the desired product from the displayed list of recommended products, then enters the necessary information on the application's purchase page and proceeds with the purchase process.

[0887] Step 9:

[0888] The terminal sends a purchase request to the server.

[0889] Input: Purchase information

[0890] Output: Purchase request sent to the server

[0891] The terminal formats the purchase information entered by the user and sends it to the server as an online order request.

[0892] Step 10:

[0893] The server connects with the online shop API to complete the purchase process.

[0894] Input: Purchase Request

[0895] Output: Purchase completion notification

[0896] The server sends the received purchase request to the online shop's API and proceeds with the order process. Once the order is complete, the server sends a confirmation to the user.

[0897] Step 11:

[0898] The user uses the "Request button" to request a gift.

[0899] Input: Begging request information

[0900] Output: Formatted begging request

[0901] Users can use the application's "Request Button" to request a gift from a distant relative or grandparent. When the button is pressed, a new request screen appears and the user can enter the necessary information.

[0902] Step 12:

[0903] The server analyzes the begging request and notifies the specified relatives or grandparents.

[0904] Input: Begging request information

[0905] Output: Notifications sent to relatives and grandparents

[0906] The server receives the request, analyzes it, and then sends an email or in-app notification to the designated relative or grandparent, containing the requested product information and a link to purchase it.

[0907] (Application example 1)

[0908] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0909] Nowadays, it is becoming increasingly difficult for parents to choose the perfect gift for their children. Finding a toy that suits the child's tastes and fits their budget and educational goals from the wide variety of products on offer can be particularly time-consuming. Asking relatives or grandparents who live far away to buy a gift can also be a complicated process. This can take time and effort for parents, making it difficult to make an efficient purchase.

[0910] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0911] In this invention, the server includes means for inputting filter conditions based on a budget and educational policy set by a parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for purchasing the toy selected by the parent at an online shop by referring to the recommended product list, means for generating and transmitting a request to request a gift from a relative or grandparent living far away, communication means for formatting the filter conditions and transmitting them to the server, display means for displaying the received recommendation results on the parent's terminal, and data processing means for generating a recommended product list based on the child's preferences using a generation AI. This allows parents to easily receive suggestions for optimal toys and efficiently purchase them at an online shop or request gifts from a relative or grandparent living far away.

[0912] "Filter conditions" are conditions that are applied when selecting products based on the budget and educational policy set by the parent.

[0913] The "recommended product list" is a list of toys recommended to parents, proposed by the generative AI based on the child's preferences.

[0914] "Online Shop" means a website or mobile application used to purchase goods over the Internet.

[0915] A "request" is a request to ask relatives or grandparents who live far away to send a present.

[0916] The "communication means" is a means for formatting the filter conditions and transmitting them to the server.

[0917] The "display means" is a means for displaying the received recommendation results on the parent's terminal.

[0918] "Data processing means" means means for using a generating AI to generate a recommended product list based on a child's preferences.

[0919] "Generative AI" is an artificial intelligence technology that learns past selection history and children's preferences to generate an optimal list of recommended products.

[0920] This invention is a system that allows parents to input filter conditions based on their budget and educational policy, and suggests toys that are optimal for their child's preferences. This system works by allowing parents to input detailed filter conditions using a device such as a smartphone.

[0921] First, the parent (user) launches the application using a device such as a smartphone. An interface is displayed on the device screen, and the parent enters filter conditions such as budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content). The filter conditions are then formatted and sent to the server.

[0922] The server searches a large amount of toy information in its database based on the received filter conditions. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. This process uses data processing libraries such as Python's requests library, pandas, and scikit-learn. The search results are generated as a list of recommended products and sent back to the device.

[0923] Once the recommendation list is sent to the device, the parent can select the desired product based on the list. The selected product is then ordered through the online shop's API, allowing the parent (user) to easily complete the purchase. The app also includes a "begging button" function that allows users to request gifts from distant relatives or grandparents. Using this function, a request is generated and a notification is sent to the specified relative or grandparent.

[0924] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server searches the database based on the criteria and uses generative AI to create a list of 10 recommendations that take into account the child's past preferences. The mother selects an educational block set from this list, orders it from the online shop, and completes the purchase. She also uses the "beg" button to request an educational board game from her grandparents as a gift. The server then notifies the grandparents by email, completing the process.

[0925] Example prompt sentence:

[0926] "A parent is looking to purchase educational products within a budget of ¥5,000. Please exclude certain cartoon characters. The child likes blocks and puzzles. Please recommend the best toys."

[0927] In this way, parents can easily choose toys that suit their children's tastes and can also easily ask relatives or grandparents for gifts, saving parents a lot of time and effort.

[0928] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0929] Step 1:

[0930] The user launches the application using a terminal. Through the interface displayed on the screen, the user inputs filter conditions such as budget, product category, and exclusion conditions. The input data includes the specific budget amount and the names of characters to be excluded. The terminal then generates filter conditions based on the input data.

[0931] Step 2:

[0932] The terminal formats the generated filter conditions and sends them to the server. Specifically, the terminal creates formatted JSON data and sends it to the server using an HTTP POST request. The input is the formatted filter conditions, and the output is the request sent to the server.

[0933] Step 3:

[0934] The server analyzes the received filter conditions and searches for toy information in a database. The database stores information such as product name, price, category, target age, and character name. The server uses an SQL query to extract data that matches these conditions. The input is the filter conditions, and the output is candidate search results.

[0935] Step 4:

[0936] The server uses a generative AI to select the best candidate products from the search results based on the child's preferences. The generative AI model learns past selection history and the child's preferences, and applies a ranking algorithm to evaluate the candidates. The input is the candidate search results data, and the output is a list of the best recommended products.

[0937] Step 5:

[0938] The server generates a list of recommended products and returns it to the terminal. The generated list is sent to the terminal in JSON format using an HTTP response. The input is the list of recommended products, and the output is the response sent to the terminal.

[0939] Step 6:

[0940] The device displays the received recommended product list to the parent. The screen displays a list of recommended product names, prices, images, etc. The input is the recommended product list, and the output is the display on the screen.

[0941] Step 7:

[0942] The user refers to the recommended product list and selects the product they wish to purchase. The selected product information is formatted as data for the order process in conjunction with the online shop's API. The input is the selected data from the recommended product list, and the output is the request data to the online shop.

[0943] Step 8:

[0944] The terminal calls the online shop's API and places an order for the selected product. The API request is sent including the product ID and payment information, and a response confirming the order is received. The input is the order data, and the output is the response confirming the order.

[0945] Step 9:

[0946] The user uses the "beg button" to generate a gift request from relatives or grandparents. The request includes product information and the recipient's email address. The input is the gift request data, and the output is the request sent to the server.

[0947] Step 10:

[0948] The server receives gift requests and sends emails and notifications to the specified relatives and grandparents. The emails contain product information and a purchase link. The input is the gift request data, and the output is the notification email.

[0949] This series of processes allows users to easily select toys that suit their child's preferences, and also enables them to efficiently purchase toys online and request gifts to be sent to distant locations.

[0950] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0951] This invention combines a system that uses generative AI to suggest toys based on a child's preferences by inputting filter conditions based on the parent's budget and educational policy, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into consideration.

[0952] An embodiment of the system is as follows.

[0953] The parent (user) launches the application using their own device (smartphone, tablet, PC, etc.). The application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content), the emotion engine recognizes the user's emotions and provides simpler options and guidance if the user is confused, or emphasizes their happiness if they are pleased.

[0954] The device formats the filter conditions entered by the user and the emotional data analyzed by the emotion engine, and sends them to the server. Upon receiving this information, the server searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server then adjusts the list of recommended products generated, taking into account the data from the emotion engine, and sends it back to the device.

[0955] The device then displays the received list of recommended products to the parent (user). If the emotion engine detects positive emotions such as joy or excitement in the user, it highlights those products. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with information about the selected product.

[0956] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[0957] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[0958] As a concrete example, when a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if the mother seems confused. The server extracts relevant products from a database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "Request Button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[0959] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[0960] The processing flow will be explained below.

[0961] Step 1:

[0962] The user starts up the device and opens the application. The device displays a filter condition input screen to the user. At the same time, the device uses the camera and microphone to capture the user's facial expressions and voice, and inputs them into the emotion engine.

[0963] Step 2:

[0964] The emotion engine recognizes the user's emotions in real time through facial expression recognition and voice analysis. The device collects the recognized user's emotional data and adjusts the screen display and options as needed.

[0965] Step 3:

[0966] The user enters filter conditions such as budget, category (e.g., educational products), and exclusion conditions (e.g., specific characters, inappropriate content). The device checks the entered information and formats it.

[0967] Step 4:

[0968] The terminal generates a request to send the filter conditions together with the user's emotion data to the server. The terminal sends the request to the server.

[0969] Step 5:

[0970] The server analyzes the received request, extracts filter conditions, and performs filtering according to the user's emotions, taking into account the data from the emotion engine.

[0971] Step 6:

[0972] The server connects to the database and searches for the best toy based on the filter criteria and emotion data. Generative AI is used to select the best candidate product, taking into account past selection history and the child's preferences.

[0973] Step 7:

[0974] The server adjusts the generated recommended product list and creates a list that emphasizes products that the user is likely to enjoy based on the emotional data.The server then sends the recommended product list to the terminal.

[0975] Step 8:

[0976] The device displays the recommended product list received from the server to the user. If the emotion engine detects the user's joy or excitement, the product is highlighted. The user selects the desired product from the list.

[0977] Step 9:

[0978] The user presses the purchase button. The terminal stores the selected product information, generates an online order request, and sends it to the server.

[0979] Step 10:

[0980] The server analyzes the received order request and connects to the online shop's API. The server checks the product inventory and processes the order. When the order is complete, the server sends a confirmation to the terminal.

[0981] Step 11:

[0982] The terminal displays a notification to the user that the order has been completed. The user can then use the "Request Button" to request a gift from a distant relative or grandparent.

[0983] Step 12:

[0984] The device generates a begging request and sends it to the server. The server receives the request and generates an email or message to send a notification to the specified relatives or grandparents. The server then sends the generated notification to the relatives or grandparents.

[0985] As a result, the present invention makes it possible to easily select and purchase the most suitable present that takes into consideration the parents' budget, educational policy, and even the user's feelings, and to easily request presents from distant relatives or grandparents.

[0986] Example 2

[0987] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0988] With conventional systems, parents had difficulty finding the right toy based on their budget and educational goals, which required time and effort. Furthermore, recommendations often failed to take into account the child's preferences or the parents' feelings, resulting in an inability to make the best choice. Furthermore, the process of requesting gifts from distant relatives or grandparents was cumbersome.

[0989] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0990] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for collecting and analyzing user emotion data using an emotion engine for analyzing the user's emotions, means for formatting the filter conditions and emotion data and transmitting it to the server, means for the server to use a generative AI model based on the filter conditions and emotion data to generate an optimal recommended product list, means for purchasing the toys selected by the parents from an online shop by referring to the recommended product list, and means for generating and transmitting a request to request a gift from a relative or grandparent living far away. This enables a smooth toy selection and purchase process that takes into account the parents' budget, educational policy, and emotions, and makes it easy to request a gift from a relative or grandparent living far away.

[0991] "Parents" refers to guardians who select and purchase toys for their children.

[0992] A "budget" refers to the maximum amount of money parents set that can be spent on purchasing toys.

[0993] "Educational policy" refers to the policies and objectives that parents have for their children to have educational values ​​and learning opportunities.

[0994] "Filter conditions" refer to restrictions and requirements such as budget, product category, and exclusion conditions, and include setting information entered by the parent.

[0995] An "emotion engine" refers to software or hardware that analyzes a user's facial expressions and voice to determine the user's emotions.

[0996] "Emotion data" refers to data based on the user's facial expressions and voice that is collected and analyzed by the emotion engine.

[0997] "Recommended product list" refers to a list of optimal toys generated by the server based on the filter conditions and emotional data set by the parent.

[0998] "Generative AI model" refers to an algorithm or system that uses an AI model that has learned about a child's past selection history and preferences to generate an optimal list of recommended products.

[0999] "Online Shop" refers to a website or platform that sells and purchases products over the Internet.

[1000] A "request" refers to an electronic message or notification to ask a distant relative or grandparent to send a gift.

[1001] "Server" refers to a computer system that processes various data, generates a list of recommended products, and sends requests.

[1002] "Terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to launch and operate applications.

[1003] This invention combines a system that uses a generative AI model to suggest toys based on a child's preferences, based on parental input filter criteria such as budget and educational goals, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into account.

[1004] An embodiment of the system is as follows.

[1005] The parent (user) launches the application on their device (smartphone, tablet, PC, etc.). This application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone.

[1006] As users enter their filter criteria (budget, product category, exclusions), the emotion engine recognizes their emotions, providing simple options and guidance if they are confused, and emphasizing their joy if they are happy.

[1007] The device formats the filter conditions entered by the user and the emotion data analyzed by the emotion engine, and sends it to the server as a single data package, which includes compiling the data in JSON format.

[1008] The server receives the data package and analyzes the filter criteria and emotion data. The server searches through a database of numerous toy products that match the criteria. The server then uses a generative AI model to generate an optimal list of recommended products based on the model's learning of past selection history and the child's preferences.

[1009] The generated recommended product list is further refined based on the emotional data. For example, products that the emotional engine detects as pleasing to the user are placed at the top of the list. The final recommended product list is then sent back to the user's device.

[1010] The device then displays the received list of recommended products to the user. If the emotion engine detects the user's positive emotions (joy or excitement), it highlights the product in question. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the selected product information is sent from the device to the server.

[1011] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the purchase of the toy. Once the order is completed, the server receives a commission from the online shop.

[1012] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[1013] As a concrete example, when a mother uses her device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if she seems confused. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "beg button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[1014] Examples of prompts include:

[1015] 1. "I'm looking for educational products for a 5-year-old child under 5,000 yen. I'd like to exclude certain characters."

[1016] 2. "Provide a simple guide for confused users."

[1017] 3. "Products that detect joy in the mother's face should be at the top of the list."

[1018] 4. "Select your educational block set and proceed to purchase it from our online shop."

[1019] 5. "Please write and send an email to my grandparents requesting a gift."

[1020] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[1021] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1022] Step 1:

[1023] The user launches the application

[1024] Input: Use the terminal to launch the application and access the filter criteria input screen.

[1025] Data processing: A UI is displayed for entering filter items such as budget, product category, and exclusion conditions.

[1026] Output: The filter criteria entered by the user.

[1027] Specific operation: A user opens the application on a device such as a smartphone, tablet, or PC and begins entering filter criteria.

[1028] Step 2:

[1029] Device collects and analyzes emotional data

[1030] Input: Uses the device's camera and microphone to collect the user's facial expressions and voice in real time.

[1031] Data processing: The emotion engine analyzes facial and voice data to detect the user's emotional state (e.g., joy, confusion).

[1032] Output: Detected emotion data.

[1033] How it works: The device uses a camera to capture a picture of the user's face and a microphone to record their voice, and the emotion engine then analyzes this data to determine the user's emotions.

[1034] Step 3:

[1035] The device formats the data and sends it to the server

[1036] Input: User filter criteria and collected emotion data.

[1037] Data processing: Format the filter conditions and emotion data into a single data package (e.g., JSON format).

[1038] Output: A formatted data package.

[1039] Specific operation: The device compiles the filter conditions and emotion data in JSON format or similar and sends them to the server.

[1040] Step 4:

[1041] The server receives the data and starts processing

[1042] Input: Formatted data package sent from the terminal.

[1043] Data processing: Analyze and interpret the filter conditions and sentiment data, and generate search queries to the database according to the filter conditions.

[1044] Output: The query for the database search.

[1045] What happens next: The server receives the data package, interprets the filter conditions, and creates a query to query the database.

[1046] Step 5:

[1047] The server searches the database

[1048] Input: A search query based on filter criteria.

[1049] Data processing: Search and extract toy information that matches the filter criteria from the information stored in the database.

[1050] Output: Toys that match the filter criteria.

[1051] Specific operation: The server issues a search query to the database and extracts toy information that matches the criteria.

[1052] Step 6:

[1053] The server generates the recommendation list using generative AI models

[1054] Input: Toy information that matches the filter criteria, past selection history, and child preferences.

[1055] Data processing: Generative AI models are used to generate optimal product recommendations based on this information.

[1056] Output: A list of recommended products.

[1057] How it works: The server uses a generative AI model to create a list of recommended products based on toy information that matches the filter criteria, past selection history, and the child's preferences.

[1058] Step 7:

[1059] The server adjusts the list with emotion data and returns it to the device.

[1060] Input: Recommended product list and user sentiment data.

[1061] Data processing: Adjust the recommended product list by incorporating emotional data. For example, products that are detected as pleasing to the user will be placed at the top of the list.

[1062] Output: A tailored list of recommended products.

[1063] Specific operation: The products in the list are adjusted based on the emotional data, and the server returns the final list to the device.

[1064] Step 8:

[1065] The device displays a list of recommendations to the user

[1066] Input: The recommended product list returned by the server.

[1067] Data processing: Converting the recommended product list into a format that can be displayed in the user interface.

[1068] Output: A visual list of recommended products for the user.

[1069] What it does: The device displays a list of recommended products on the screen, highlighting products with particularly positive sentiment detected by the sentiment engine.

[1070] Step 9:

[1071] The user selects a product and begins the purchase process

[1072] Input: The product you want from the recommended products list.

[1073] Data processing: Based on the user's selection, detailed information about the selected product is obtained and the purchase checkout screen is displayed.

[1074] Output: Selected product information and checkout screen.

[1075] Specific behavior: The user selects an item from the list and presses the purchase button.

[1076] Step 10:

[1077] The server connects to the online shop API and completes the order.

[1078] Input: User selected product information and purchase request.

[1079] Data processing: Link with the online shop API to proceed with the purchase. Notify the customer that the order has been processed.

[1080] Output: Purchase completion notification and order confirmation information.

[1081] Specific operation: The server calls the online shop API and processes the order. After the process is complete, it sends a purchase completion notification to the terminal.

[1082] Step 11:

[1083] Users can request gifts using the begging button

[1084] Input: Your begging request and the information of any specified relatives or grandparents.

[1085] Data processing: Formatting the request and generating a message to ask distant relatives or grandparents to send a gift.

[1086] Output: Gift request message.

[1087] Specific operation: The user presses the request button and enters information to request a gift.

[1088] Step 12:

[1089] The server analyzes the request and notifies relatives and grandparents

[1090] Input: A formatted gift request message.

[1091] Data processing: Send your request to your relatives or grandparents via email or notification.

[1092] Output: Notifications sent to relatives and grandparents.

[1093] Specific operation: The server analyzes the request and sends a gift request notification to the specified relatives or grandparents.

[1094] This system allows parents to efficiently select and purchase toys, and also makes it easy to request gifts from relatives or grandparents who live far away.

[1095] (Application example 2)

[1096] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1097] With conventional online shopping systems, parents had difficulty finding appropriate options when selecting gifts based on their children's preferences and educational goals. Furthermore, simple filtering and search results were provided without considering the user's feelings, potentially resulting in a poor user experience. Furthermore, when requesting gifts from distant relatives or grandparents, complicated procedures were often required, making the process difficult.

[1098] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for providing a response according to the user's emotions using an emotion engine that recognizes the user's emotions, and means for generating and sending a request to request a gift from relatives or grandparents living far away. This allows parents to easily set filter conditions based on the budget and educational policy and efficiently select toys that suit their child's preferences. In addition, the emotion engine can analyze the user's emotions and emphasize positive emotions, improving the user experience. Furthermore, it becomes possible to easily request gifts from distant relatives or grandparents.

[1099] A "budget" is the upper limit of the amount that parents set when purchasing gifts.

[1100] "Educational policy" refers to parents' philosophy and goals regarding their children's education and development, and is a criterion to be taken into consideration when choosing a gift.

[1101] "Filter conditions" are conditions for limiting product selection based on specific criteria such as budget and educational objectives.

[1102] A "recommended product list" is a list of toys suitable for children's preferences, suggested by a generative AI model based on filter conditions.

[1103] A "generative AI model" is an artificial intelligence model that learns past selection history and children's preferences to suggest the most suitable products.

[1104] An "emotion engine" is a software or hardware mechanism that analyzes a user's emotions from their facial expressions, voice, etc., and provides a response accordingly.

[1105] A "request" is request information for requesting the purchase of a present for a relative or grandparent who lives far away.

[1106] An "online shop" is a website or service that sells products over the Internet.

[1107] "User" refers to a parent or guardian who uses the system to purchase or request a gift.

[1108] The system for implementing this invention includes a mechanism for inputting filter conditions based on parents' budgets and educational policies, and then using a generative AI model to recommend products based on those filter conditions. The system also includes an emotion engine that recognizes the user's emotions and adjusts responses to improve the user experience. It also includes a function for requesting gifts from distant relatives or grandparents.

[1109] The server includes the following means:

[1110] 1. A way for parents to enter filter criteria based on their budget and educational preferences.

[1111] 2. A means for generating a list of recommended products to suggest toys based on a child's preferences, the list of recommended products being generated based on a model that learns past selection history and child preferences using a generative AI model.

[1112] 3. A way to use an emotion engine to analyze user sentiment and provide simple guidance when the user is confused, or highlight products when positive sentiment is detected.

[1113] 4. A way to generate and send requests for gifts to distant relatives and grandparents.

[1114] The device is primarily a smartphone and operates in the following specific manner.

[1115] The emotion engine analyzes the facial expressions and voice of the user (parent) using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion criteria (e.g., excluding specific characters), the emotion engine recognizes the user's emotions and adjusts the response. The device sends the filter criteria and emotion data to the server, which generates a list of recommended products and adjusts the list based on the emotion data and sends it back.

[1116] Here's a specific example: A mother uses her device to search for educational products within a budget of 5,000 yen and sets filter conditions to exclude specific characters. At this time, the emotion engine analyzes the mother's facial expressions and provides simple guidance if she appears confused. The generative AI model considers past selection history and the child's preferences to select the most suitable products and generate a list of recommended products. Products that the emotion engine detects bring joy to the mother are particularly highlighted. The mother selects an educational block set from this list and completes the order on the online shop.

[1117] Furthermore, a "begging button" can be used to ask grandparents who live far away for a gift. The emotion engine highlights the mother's wishes, analyzes the request, and sends emails and notifications to the grandparents.

[1118] An example of a prompt for the generative AI model is as follows:

[1119] "Based on the user's criteria, please recommend toys that meet the following criteria:

[1120] Budget: Under 5,000 yen

[1121] Category: Educational Products

[1122] Exclusion criteria: Does not include certain anime characters

[1123] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1124] Step 1:

[1125] The user starts the smartphone application and inputs filter conditions such as budget, educational policy, product category, exclusion conditions, etc. The input filter conditions are temporarily saved in the device's internal memory.

[1126] Step 2:

[1127] The device's camera and microphone are used to capture the user's facial expressions and voice. This emotional data is sent to the device's emotion engine, which analyzes the user's emotions. The analysis results are output as emotional labels, such as positive, negative, or confused.

[1128] Step 3:

[1129] The filter conditions and emotion data are sent from the device to the server. The device securely transfers the data to the server using the HTTPS protocol. In this step, the filter conditions and emotion analysis results are received by the server as input data.

[1130] Step 4:

[1131] The server searches the database for matching product information based on the filter conditions. It then passes a prompt to the generative AI model, which then generates a list of recommended products. The prompt is entered in a form that includes budget, exclusion conditions, and product categories. An example of this prompt is as follows:

[1132] "Based on the user's criteria, please recommend toys that meet the following criteria:

[1133] Budget: Under 5,000 yen

[1134] Category: Educational Products

[1135] Exclusion criteria: Does not include certain anime characters

[1136] Step 5:

[1137] The output of the generative AI model is a list of recommended products, which is then sent from the server to the device. Sentiment data is also taken into account, and the list is tailored to highlight products for which positive sentiment is detected.

[1138] Step 6:

[1139] The device displays the received recommended product list to the user, highlighting products that the user has indicated are particularly pleasing to the user, and the user can refer to these options and select a product to purchase.

[1140] Step 7:

[1141] The procedure for purchasing the product selected by the user from the online shop is sent from the terminal to the server, and the server sends the order information via the online shop's API to complete the purchase procedure.

[1142] Step 8:

[1143] After completing the purchase process, if the user wants to request a gift from a distant relative or grandparent, they can press the "Request Button." The device generates a request and sends it to the server. This request includes product information and sentiment analysis results.

[1144] Step 9:

[1145] The server analyzes the request and sends emails and notifications to the designated relatives and grandparents, ensuring the gift request goes smoothly.

[1146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1147] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1148] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1149] [Fourth embodiment]

[1150] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1154] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1159] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1161] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1162] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1163] The present invention relates to a system that inputs filter conditions based on the budget and educational policy set by parents and uses generative AI to suggest toys based on the child's preferences.

[1164] Specifically, the system is implemented as follows.

[1165] The parent (user) launches the application using their device (smartphone, tablet, PC, etc.). Through the interface displayed on the device screen, they input their budget, product category (e.g., educational products), and exclusion criteria (e.g., specific characters or inappropriate content). The device formats these filter criteria and sends them to the server.

[1166] When the server receives the filter conditions, it searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server generates a list of recommended products based on the search results and sends it back to the device.

[1167] The device displays the received recommended product list to the parent (user). The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with the selected product information.

[1168] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[1169] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[1170] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "Request Button" to request an educational board game as a gift from the grandparents. The server then sends a notification to the grandparents via email, completing the process.

[1171] In this way, the present invention allows parents to easily select and purchase the most suitable present that suits their budget and educational policy, and also makes it easy to request presents from relatives or grandparents who live far away.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] The user starts up the device and opens the application, and the device displays a filter condition input screen to the user.

[1175] Step 2:

[1176] The user enters filter criteria such as budget, category (e.g., educational products), exclusion criteria (e.g., specific characters, inappropriate content), etc. The device checks the entered information and converts it into the required format.

[1177] Step 3:

[1178] The terminal generates a request to transmit the converted filter condition to the server. The terminal transmits the request including the filter condition to the server.

[1179] Step 4:

[1180] The server parses the incoming request and extracts the filter criteria. The server connects to the database and performs a search based on the filter criteria. It extracts the relevant products, taking into account budget, category, and exclusion criteria.

[1181] Step 5:

[1182] The server uses generative AI to select the best candidates from the extracted product list based on a model that has learned the child's past selection history and preferences. The server creates a list of the best candidate products.

[1183] Step 6:

[1184] The server sends the generated recommended product list back to the terminal, which then displays the received recommended product list to the user, who then selects the desired product from the displayed list.

[1185] Step 7:

[1186] The user presses the purchase button. The terminal stores the selected product information. The terminal generates an order request and sends it to the server.

[1187] Step 8:

[1188] The server analyzes the received order request and connects to the online shop's API, which then uses it to check product availability and process the order.

[1189] Step 9:

[1190] If the order is successful, the server generates a confirmation notification and sends it to the terminal, which displays a notification of order completion to the user.

[1191] Step 10:

[1192] The user presses the "begging button." The device generates a begging request and sends it to the server.

[1193] Step 11:

[1194] The server receives the begging request and generates an email or message to send a notification to the specified relative or grandparent. The server sends the generated notification to the relative or grandparent.

[1195] By following the above steps, the present invention allows parents to easily select the most suitable gift that suits their budget and educational policy, and also makes it easy to request gifts from relatives or grandparents who live far away.

[1196] Example 1

[1197] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1198] With conventional online shopping systems, parents had difficulty choosing toys that suited their children's tastes and fit within their budget. The process of manually setting filter criteria based on educational guidelines to find the right product was also tedious. Furthermore, when requesting gifts from distant relatives or grandparents, the process was complicated and time-consuming.

[1199] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1200] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for formatting and transmitting the filter conditions, means for searching a database based on the filter conditions, means for generating a recommended product list based on the past selection history and the child's preferences using a generative AI model, means for returning the recommended product list to the user terminal, means for purchasing the toys selected by the parents at an online shop, and means for generating and transmitting a request to request a gift from relatives or grandparents living far away. This enables parents to efficiently select toys that fit their educational policy within their budget and easily purchase the optimal product based on their child's preferences, as well as to quickly request gifts from relatives or grandparents living far away.

[1201] A "parent" is a guardian or caregiver of a child who uses the system to select and purchase toys for the child.

[1202] "Budget" refers to the upper limit of the amount of money to be spent on purchasing toys, and is a numerical value set by the parent as a filter condition.

[1203] "Educational policy" refers to the educational direction and goals that parents set for their children, and product categories and exclusion conditions are set based on this policy.

[1204] "Filter conditions" are data that include selection criteria such as budget, product category, and exclusion conditions set by the parent.

[1205] A "device" is an electronic device, such as a smartphone, tablet, or computer, that parents use to operate the system.

[1206] A "server" is a computing system capable of receiving filter criteria, searching a database, and using a generative AI model to generate and return a list of recommended products.

[1207] A "database" is an information storage device that stores toy information and allows the server to search based on filter conditions.

[1208] A "generative AI model" is an artificial intelligence algorithm that learns past selection history and children's preferences to generate an optimal list of recommended products.

[1209] A "recommended product list" is a list of products generated by a generative AI model and selected based on filter conditions and past history.

[1210] "Online Shop" refers to an e-commerce platform where toys can be purchased via the Internet.

[1211] The "begging button" is part of a user interface that allows parents to request gifts from relatives or grandparents who live far away.

[1212] A "request" is data that a user sends to a server to perform a specific operation, and includes a request for a gift, etc.

[1213] This invention is a system that allows parents to input filter criteria based on their budget and educational goals, and uses a generative AI model to suggest toys based on the child's preferences.

[1214] The system's main hardware includes the user's smartphone, tablet, or PC, and a server that hosts the database and generative AI model. The database also stores toy information.

[1215] The software includes an application that provides a user interface (UI), a database management system (DBMS), and the algorithms for the generative AI model. Users launch the application using a terminal and enter filter conditions through an intuitive user interface.

[1216] The device formats the information entered by the user, converts it into a data format, and sends it to the server. The server uses the received filter criteria to search for toy information in its database. The search results are input into a generative AI model, which generates a list of recommended products based on past selection history and the child's preferences. This list of recommended products is then sent back to the device and displayed to the user.

[1217] The user selects the desired product from the recommended product list and completes the purchase procedure at the online shop. The terminal sends a purchase request to the server, which then completes the order by connecting with the online shop's API.

[1218] Furthermore, by using the "Request Button" on the user interface, users can request gifts from relatives or grandparents who live far away. The device generates this request and sends it to the server. The server analyzes the request and sends a notification to the relatives or grandparents.

[1219] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters. The server extracts products that meet these conditions from a database, and the generation AI creates a list of 10 recommended products by referring to past selection history. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. The mother also uses the "beg button" to request an educational board game as a gift from her grandparents. The server notifies the grandparents by email, completing the process.

[1220] An example of a prompt might be, "Generate a list of recommended educational toys priced under 5,000 yen, excluding specific characters. Also, generate a prompt to request an educational board game as a gift for grandparents."

[1221] In this way, the present invention is a system that not only allows parents to easily select and purchase the most suitable present that matches their budget and educational policy, but also makes it easy to request presents from relatives and grandparents who live far away.

[1222] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1223] Step 1:

[1224] The user launches an application.

[1225] Input: None

[1226] Output: Application launch

[1227] The user launches the dedicated application on a device such as a smartphone, tablet, or PC, which displays the initial screen of the application.

[1228] Step 2:

[1229] The user enters the budget, product category, and exclusion criteria.

[1230] Input: Budget, Product Category, Exclusion Conditions

[1231] Output: The formatted filter condition

[1232] Through the application interface, users input their budget (e.g., 5,000 yen), product category (e.g., educational products), and exclusion criteria (e.g., specific characters). The interface is designed to be intuitive.

[1233] Step 3:

[1234] The device formats the input and sends it to the server.

[1235] Input: Filter conditions before formatting

[1236] Output: The formatted filter conditions sent to the server

[1237] The terminal formats the filter conditions entered by the user and converts them into a data format (e.g., JSON format). The converted data is sent to the server.

[1238] Step 4:

[1239] The server searches the database based on the filter criteria.

[1240] Input: Filter conditions after formatting

[1241] Output: A list of toys that meet the filter criteria

[1242] The server uses the received filter conditions to search for toy information in the database, extracting records that match the conditions using SQL queries, etc.

[1243] Step 5:

[1244] The server uses the generative AI model to generate a list of recommended products.

[1245] Input: A list of toys that meet the filter criteria

[1246] Output: Recommended product list

[1247] The server inputs the extracted toy information into a generative AI model to generate a list of recommended products based on past selection history and the child's preferences. This list prioritizes products that best match the criteria.

[1248] Step 6:

[1249] The server returns a list of recommended products to the terminal.

[1250] Input: Recommended product list

[1251] Output: Returned recommended product list

[1252] The server converts the generated recommended product list into a data format such as JSON and returns it to the user's device. The communication is encrypted.

[1253] Step 7:

[1254] The terminal displays a list of recommended products to the user.

[1255] Input: Returned recommended product list

[1256] Output: Recommended product list displayed on the screen

[1257] The device displays the received list of recommended products on the screen, allowing the user to intuitively check the list and view detailed information about each product.

[1258] Step 8:

[1259] The user selects the desired product and completes the purchase procedure.

[1260] Input: User product selection and purchase information

[1261] Output: Purchase request

[1262] The user selects the desired product from the displayed list of recommended products, then enters the necessary information on the application's purchase page and proceeds with the purchase process.

[1263] Step 9:

[1264] The terminal sends a purchase request to the server.

[1265] Input: Purchase information

[1266] Output: Purchase request sent to the server

[1267] The terminal formats the purchase information entered by the user and sends it to the server as an online order request.

[1268] Step 10:

[1269] The server connects with the online shop API to complete the purchase process.

[1270] Input: Purchase Request

[1271] Output: Purchase completion notification

[1272] The server sends the received purchase request to the online shop's API and proceeds with the order process. Once the order is complete, the server sends a confirmation to the user.

[1273] Step 11:

[1274] The user uses the "Request button" to request a gift.

[1275] Input: Begging request information

[1276] Output: Formatted begging request

[1277] Users can use the application's "Request Button" to request a gift from a distant relative or grandparent. When the button is pressed, a new request screen appears and the user can enter the necessary information.

[1278] Step 12:

[1279] The server analyzes the begging request and notifies the specified relatives or grandparents.

[1280] Input: Begging request information

[1281] Output: Notifications sent to relatives and grandparents

[1282] The server receives the request, analyzes it, and then sends an email or in-app notification to the designated relative or grandparent, containing the requested product information and a link to purchase it.

[1283] (Application example 1)

[1284] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1285] Nowadays, it is becoming increasingly difficult for parents to choose the perfect gift for their children. Finding a toy that suits the child's tastes and fits their budget and educational goals from the wide variety of products on offer can be particularly time-consuming. Asking relatives or grandparents who live far away to buy a gift can also be a complicated process. This can take time and effort for parents, making it difficult to make an efficient purchase.

[1286] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1287] In this invention, the server includes means for inputting filter conditions based on a budget and educational policy set by a parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for purchasing the toy selected by the parent at an online shop by referring to the recommended product list, means for generating and transmitting a request to request a gift from a relative or grandparent living far away, communication means for formatting the filter conditions and transmitting them to the server, display means for displaying the received recommendation results on the parent's terminal, and data processing means for generating a recommended product list based on the child's preferences using a generation AI. This allows parents to easily receive suggestions for optimal toys and efficiently purchase them at an online shop or request gifts from a relative or grandparent living far away.

[1288] "Filter conditions" are conditions that are applied when selecting products based on the budget and educational policy set by the parent.

[1289] The "recommended product list" is a list of toys recommended to parents, proposed by the generative AI based on the child's preferences.

[1290] "Online Shop" means a website or mobile application used to purchase goods over the Internet.

[1291] A "request" is a request to ask relatives or grandparents who live far away to send a present.

[1292] The "communication means" is a means for formatting the filter conditions and transmitting them to the server.

[1293] The "display means" is a means for displaying the received recommendation results on the parent's terminal.

[1294] "Data processing means" means means for using a generating AI to generate a recommended product list based on a child's preferences.

[1295] "Generative AI" is an artificial intelligence technology that learns past selection history and children's preferences to generate an optimal list of recommended products.

[1296] This invention is a system that allows parents to input filter conditions based on their budget and educational policy, and suggests toys that are optimal for their child's preferences. This system works by allowing parents to input detailed filter conditions using a device such as a smartphone.

[1297] First, the parent (user) launches the application using a device such as a smartphone. An interface is displayed on the device screen, and the parent enters filter conditions such as budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content). The filter conditions are then formatted and sent to the server.

[1298] The server searches a large amount of toy information in its database based on the received filter conditions. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. This process uses data processing libraries such as Python's requests library, pandas, and scikit-learn. The search results are generated as a list of recommended products and sent back to the device.

[1299] Once the recommendation list is sent to the device, the parent can select the desired product based on the list. The selected product is then ordered through the online shop's API, allowing the parent (user) to easily complete the purchase. The app also includes a "begging button" function that allows users to request gifts from distant relatives or grandparents. Using this function, a request is generated and a notification is sent to the specified relative or grandparent.

[1300] As a concrete example, a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter criteria to exclude specific anime characters. The server searches the database based on the criteria and uses generative AI to create a list of 10 recommendations that take into account the child's past preferences. The mother selects an educational block set from this list, orders it from the online shop, and completes the purchase. She also uses the "beg" button to request an educational board game from her grandparents as a gift. The server then notifies the grandparents by email, completing the process.

[1301] Example prompt sentence:

[1302] "A parent is looking to purchase educational products within a budget of ¥5,000. Please exclude certain cartoon characters. The child likes blocks and puzzles. Please recommend the best toys."

[1303] In this way, parents can easily choose toys that suit their children's tastes and can also easily ask relatives or grandparents for gifts, saving parents a lot of time and effort.

[1304] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1305] Step 1:

[1306] The user launches the application using a terminal. Through the interface displayed on the screen, the user inputs filter conditions such as budget, product category, and exclusion conditions. The input data includes the specific budget amount and the names of characters to be excluded. The terminal then generates filter conditions based on the input data.

[1307] Step 2:

[1308] The terminal formats the generated filter conditions and sends them to the server. Specifically, the terminal creates formatted JSON data and sends it to the server using an HTTP POST request. The input is the formatted filter conditions, and the output is the request sent to the server.

[1309] Step 3:

[1310] The server analyzes the received filter conditions and searches for toy information in a database. The database stores information such as product name, price, category, target age, and character name. The server uses an SQL query to extract data that matches these conditions. The input is the filter conditions, and the output is candidate search results.

[1311] Step 4:

[1312] The server uses a generative AI to select the best candidate products from the search results based on the child's preferences. The generative AI model learns past selection history and the child's preferences, and applies a ranking algorithm to evaluate the candidates. The input is the candidate search results data, and the output is a list of the best recommended products.

[1313] Step 5:

[1314] The server generates a list of recommended products and returns it to the terminal. The generated list is sent to the terminal in JSON format using an HTTP response. The input is the list of recommended products, and the output is the response sent to the terminal.

[1315] Step 6:

[1316] The device displays the received recommended product list to the parent. The screen displays a list of recommended product names, prices, images, etc. The input is the recommended product list, and the output is the display on the screen.

[1317] Step 7:

[1318] The user refers to the recommended product list and selects the product they wish to purchase. The selected product information is formatted as data for the order process in conjunction with the online shop's API. The input is the selected data from the recommended product list, and the output is the request data to the online shop.

[1319] Step 8:

[1320] The terminal calls the online shop's API and places an order for the selected product. The API request is sent including the product ID and payment information, and a response confirming the order is received. The input is the order data, and the output is the response confirming the order.

[1321] Step 9:

[1322] The user uses the "beg button" to generate a gift request from relatives or grandparents. The request includes product information and the recipient's email address. The input is the gift request data, and the output is the request sent to the server.

[1323] Step 10:

[1324] The server receives gift requests and sends emails and notifications to the specified relatives and grandparents. The emails contain product information and a purchase link. The input is the gift request data, and the output is the notification email.

[1325] This series of processes allows users to easily select toys that suit their child's preferences, and also enables them to efficiently purchase toys online and request gifts to be sent to distant locations.

[1326] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1327] This invention combines a system that uses generative AI to suggest toys based on a child's preferences by inputting filter conditions based on the parent's budget and educational policy, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into consideration.

[1328] An embodiment of the system is as follows.

[1329] The parent (user) launches the application using their own device (smartphone, tablet, PC, etc.). The application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion conditions (e.g., specific characters or inappropriate content), the emotion engine recognizes the user's emotions and provides simpler options and guidance if the user is confused, or emphasizes their happiness if they are pleased.

[1330] The device formats the filter conditions entered by the user and the emotional data analyzed by the emotion engine, and sends them to the server. Upon receiving this information, the server searches for products that match the filter conditions from the large amount of toy information stored in the database. It then uses generative AI to select the most appropriate candidate products based on a model that has learned past selection history and children's preferences. The server then adjusts the list of recommended products generated, taking into account the data from the emotion engine, and sends it back to the device.

[1331] The device then displays the received list of recommended products to the parent (user). If the emotion engine detects positive emotions such as joy or excitement in the user, it highlights those products. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the device sends an online order request to the server along with information about the selected product.

[1332] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the toy purchase. Once the order is completed, the server receives a commission from the online shop.

[1333] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a begging request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[1334] As a concrete example, when a mother uses a device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if the mother seems confused. The server extracts relevant products from a database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "Request Button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[1335] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[1336] The processing flow will be explained below.

[1337] Step 1:

[1338] The user starts up the device and opens the application. The device displays a filter condition input screen to the user. At the same time, the device uses the camera and microphone to capture the user's facial expressions and voice, and inputs them into the emotion engine.

[1339] Step 2:

[1340] The emotion engine recognizes the user's emotions in real time through facial expression recognition and voice analysis. The device collects the recognized user's emotional data and adjusts the screen display and options as needed.

[1341] Step 3:

[1342] The user enters filter conditions such as budget, category (e.g., educational products), and exclusion conditions (e.g., specific characters, inappropriate content). The device checks the entered information and formats it.

[1343] Step 4:

[1344] The terminal generates a request to send the filter conditions together with the user's emotion data to the server. The terminal sends the request to the server.

[1345] Step 5:

[1346] The server analyzes the received request, extracts filter conditions, and performs filtering according to the user's emotions, taking into account the data from the emotion engine.

[1347] Step 6:

[1348] The server connects to the database and searches for the best toy based on the filter criteria and emotion data. Generative AI is used to select the best candidate product, taking into account past selection history and the child's preferences.

[1349] Step 7:

[1350] The server adjusts the generated recommended product list and creates a list that emphasizes products that the user is likely to enjoy based on the emotional data.The server then sends the recommended product list to the terminal.

[1351] Step 8:

[1352] The device displays the recommended product list received from the server to the user. If the emotion engine detects the user's joy or excitement, the product is highlighted. The user selects the desired product from the list.

[1353] Step 9:

[1354] The user presses the purchase button. The terminal stores the selected product information, generates an online order request, and sends it to the server.

[1355] Step 10:

[1356] The server analyzes the received order request and connects to the online shop's API. The server checks the product inventory and processes the order. When the order is complete, the server sends a confirmation to the terminal.

[1357] Step 11:

[1358] The terminal displays a notification to the user that the order has been completed. The user can then use the "Request Button" to request a gift from a distant relative or grandparent.

[1359] Step 12:

[1360] The device generates a begging request and sends it to the server. The server receives the request and generates an email or message to send a notification to the specified relatives or grandparents. The server then sends the generated notification to the relatives or grandparents.

[1361] As a result, the present invention makes it possible to easily select and purchase the most suitable present that takes into consideration the parents' budget, educational policy, and even the user's feelings, and to easily request presents from distant relatives or grandparents.

[1362] Example 2

[1363] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1364] With conventional systems, parents had difficulty finding the right toy based on their budget and educational goals, which required time and effort. Furthermore, recommendations often failed to take into account the child's preferences or the parents' feelings, resulting in an inability to make the best choice. Furthermore, the process of requesting gifts from distant relatives or grandparents was cumbersome.

[1365] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1366] In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parents, means for collecting and analyzing user emotion data using an emotion engine for analyzing the user's emotions, means for formatting the filter conditions and emotion data and transmitting it to the server, means for the server to use a generative AI model based on the filter conditions and emotion data to generate an optimal recommended product list, means for purchasing the toys selected by the parents from an online shop by referring to the recommended product list, and means for generating and transmitting a request to request a gift from a relative or grandparent living far away. This enables a smooth toy selection and purchase process that takes into account the parents' budget, educational policy, and emotions, and makes it easy to request a gift from a relative or grandparent living far away.

[1367] "Parents" refers to guardians who select and purchase toys for their children.

[1368] A "budget" refers to the maximum amount of money parents set that can be spent on purchasing toys.

[1369] "Educational policy" refers to the policies and objectives that parents have for their children to have educational values ​​and learning opportunities.

[1370] "Filter conditions" refer to restrictions and requirements such as budget, product category, and exclusion conditions, and include setting information entered by the parent.

[1371] An "emotion engine" refers to software or hardware that analyzes a user's facial expressions and voice to determine the user's emotions.

[1372] "Emotion data" refers to data based on the user's facial expressions and voice that is collected and analyzed by the emotion engine.

[1373] "Recommended product list" refers to a list of optimal toys generated by the server based on the filter conditions and emotional data set by the parent.

[1374] "Generative AI model" refers to an algorithm or system that uses an AI model that has learned about a child's past selection history and preferences to generate an optimal list of recommended products.

[1375] "Online Shop" refers to a website or platform that sells and purchases products over the Internet.

[1376] A "request" refers to an electronic message or notification to ask a distant relative or grandparent to send a gift.

[1377] "Server" refers to a computer system that processes various data, generates a list of recommended products, and sends requests.

[1378] "Terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to launch and operate applications.

[1379] This invention combines a system that uses a generative AI model to suggest toys based on a child's preferences, based on parental input filter criteria such as budget and educational goals, with an emotion engine that recognizes the user's emotions. This system can recommend the best gift while taking the user's emotions into account.

[1380] An embodiment of the system is as follows.

[1381] The parent (user) launches the application on their device (smartphone, tablet, PC, etc.). This application provides a filter condition input screen. The emotion engine analyzes the user's facial expressions and voice using the device's camera and microphone.

[1382] As users enter their filter criteria (budget, product category, exclusions), the emotion engine recognizes their emotions, providing simple options and guidance if they are confused, and emphasizing their joy if they are happy.

[1383] The device formats the filter conditions entered by the user and the emotion data analyzed by the emotion engine, and sends it to the server as a single data package, which includes compiling the data in JSON format.

[1384] The server receives the data package and analyzes the filter criteria and emotion data. The server searches through a database of numerous toy products that match the criteria. The server then uses a generative AI model to generate an optimal list of recommended products based on the model's learning of past selection history and the child's preferences.

[1385] The generated recommended product list is further refined based on the emotional data. For example, products that the emotional engine detects as pleasing to the user are placed at the top of the list. The final recommended product list is then sent back to the user's device.

[1386] The device then displays the received list of recommended products to the user. If the emotion engine detects the user's positive emotions (joy or excitement), it highlights the product in question. The user selects the desired product from the list and begins the purchase process at the online shop. When the user presses the purchase button, the selected product information is sent from the device to the server.

[1387] The server receives the order request and processes it in conjunction with the online shop's API, allowing the user to complete the purchase of the toy. Once the order is completed, the server receives a commission from the online shop.

[1388] Users can also use the "begging button" to request gifts from distant relatives or grandparents. The device generates a request and sends it to the server. The server analyzes the request and sends emails or notifications to the specified relatives or grandparents.

[1389] As a concrete example, when a mother uses her device to search for educational products within a budget of 5,000 yen and enters filter conditions to exclude specific anime characters, the emotion engine analyzes the mother's facial expressions and voice and provides simple guidance if she seems confused. The server extracts relevant products from the database and creates a list of 10 recommendations, taking into account the child's past preferences. This list highlights products that the emotion engine detects would please the mother. The mother selects an educational block set from the list, orders it from the online shop, and completes the purchase. She then uses the "beg button" to request an educational board game from her grandparents as a gift, and the emotion engine highlights the mother's wishes, allowing the request to proceed smoothly. The server then sends a notification to the grandparents via email, completing the process.

[1390] Examples of prompts include:

[1391] 1. "I'm looking for educational products for a 5-year-old child under 5,000 yen. I'd like to exclude certain characters."

[1392] 2. "Provide a simple guide for confused users."

[1393] 3. "Products that detect joy in the mother's face should be at the top of the list."

[1394] 4. "Select your educational block set and proceed to purchase it from our online shop."

[1395] 5. "Please write and send an email to my grandparents requesting a gift."

[1396] In this way, the present invention allows parents to easily select and purchase the most suitable present based on their budget, educational policy, and feelings, and also makes it possible to easily request presents from relatives and grandparents who live far away.

[1397] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1398] Step 1:

[1399] The user launches the application

[1400] Input: Use the terminal to launch the application and access the filter criteria input screen.

[1401] Data processing: A UI is displayed for entering filter items such as budget, product category, and exclusion conditions.

[1402] Output: The filter criteria entered by the user.

[1403] Specific operation: A user opens the application on a device such as a smartphone, tablet, or PC and begins entering filter criteria.

[1404] Step 2:

[1405] Device collects and analyzes emotional data

[1406] Input: Uses the device's camera and microphone to collect the user's facial expressions and voice in real time.

[1407] Data processing: The emotion engine analyzes facial and voice data to detect the user's emotional state (e.g., joy, confusion).

[1408] Output: Detected emotion data.

[1409] How it works: The device uses a camera to capture a picture of the user's face and a microphone to record their voice, and the emotion engine then analyzes this data to determine the user's emotions.

[1410] Step 3:

[1411] The device formats the data and sends it to the server

[1412] Input: User filter criteria and collected emotion data.

[1413] Data processing: Format the filter conditions and emotion data into a single data package (e.g., JSON format).

[1414] Output: A formatted data package.

[1415] Specific operation: The device compiles the filter conditions and emotion data in JSON format or similar and sends them to the server.

[1416] Step 4:

[1417] The server receives the data and starts processing

[1418] Input: Formatted data package sent from the terminal.

[1419] Data processing: Analyze and interpret the filter conditions and sentiment data, and generate search queries to the database according to the filter conditions.

[1420] Output: The query for the database search.

[1421] What happens next: The server receives the data package, interprets the filter conditions, and creates a query to query the database.

[1422] Step 5:

[1423] The server searches the database

[1424] Input: A search query based on filter criteria.

[1425] Data processing: Search and extract toy information that matches the filter criteria from the information stored in the database.

[1426] Output: Toys that match the filter criteria.

[1427] Specific operation: The server issues a search query to the database and extracts toy information that matches the criteria.

[1428] Step 6:

[1429] The server generates the recommendation list using generative AI models

[1430] Input: Toy information that matches the filter criteria, past selection history, and child preferences.

[1431] Data processing: Generative AI models are used to generate optimal product recommendations based on this information.

[1432] Output: A list of recommended products.

[1433] How it works: The server uses a generative AI model to create a list of recommended products based on toy information that matches the filter criteria, past selection history, and the child's preferences.

[1434] Step 7:

[1435] The server adjusts the list with emotion data and returns it to the device.

[1436] Input: Recommended product list and user sentiment data.

[1437] Data processing: Adjust the recommended product list by incorporating emotional data. For example, products that are detected as pleasing to the user will be placed at the top of the list.

[1438] Output: A tailored list of recommended products.

[1439] Specific operation: The products in the list are adjusted based on the emotional data, and the server returns the final list to the device.

[1440] Step 8:

[1441] The device displays a list of recommendations to the user

[1442] Input: The recommended product list returned by the server.

[1443] Data processing: Converting the recommended product list into a format that can be displayed in the user interface.

[1444] Output: A visual list of recommended products for the user.

[1445] What it does: The device displays a list of recommended products on the screen, highlighting products with particularly positive sentiment detected by the sentiment engine.

[1446] Step 9:

[1447] The user selects a product and begins the purchase process

[1448] Input: The product you want from the recommended products list.

[1449] Data processing: Based on the user's selection, detailed information about the selected product is obtained and the purchase checkout screen is displayed.

[1450] Output: Selected product information and checkout screen.

[1451] Specific behavior: The user selects an item from the list and presses the purchase button.

[1452] Step 10:

[1453] The server connects to the online shop API and completes the order.

[1454] Input: User selected product information and purchase request.

[1455] Data processing: Link with the online shop API to proceed with the purchase. Notify the customer that the order has been processed.

[1456] Output: Purchase completion notification and order confirmation information.

[1457] Specific operation: The server calls the online shop API and processes the order. After the process is complete, it sends a purchase completion notification to the terminal.

[1458] Step 11:

[1459] Users can request gifts using the begging button

[1460] Input: Your begging request and the information of any specified relatives or grandparents.

[1461] Data processing: Formatting the request and generating a message to ask distant relatives or grandparents to send a gift.

[1462] Output: Gift request message.

[1463] Specific operation: The user presses the request button and enters information to request a gift.

[1464] Step 12:

[1465] The server analyzes the request and notifies relatives and grandparents

[1466] Input: A formatted gift request message.

[1467] Data processing: Send your request to your relatives or grandparents via email or notification.

[1468] Output: Notifications sent to relatives and grandparents.

[1469] Specific operation: The server analyzes the request and sends a gift request notification to the specified relatives or grandparents.

[1470] This system allows parents to efficiently select and purchase toys, and also makes it easy to request gifts from relatives or grandparents who live far away.

[1471] (Application example 2)

[1472] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1473] With conventional online shopping systems, parents had difficulty finding appropriate options when selecting gifts based on their children's preferences and educational goals. Furthermore, simple filtering and search results were provided without considering the user's feelings, potentially resulting in a poor user experience. Furthermore, when requesting gifts from distant relatives or grandparents, complicated procedures were often required, making the process difficult.

[1474] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting filter conditions based on the budget and educational policy set by the parent, means for generating a recommended product list to suggest toys based on the child's preferences, means for providing a response according to the user's emotions using an emotion engine that recognizes the user's emotions, and means for generating and sending a request to request a gift from relatives or grandparents living far away. This allows parents to easily set filter conditions based on the budget and educational policy and efficiently select toys that suit their child's preferences. In addition, the emotion engine can analyze the user's emotions and emphasize positive emotions, improving the user experience. Furthermore, it becomes possible to easily request gifts from distant relatives or grandparents.

[1475] A "budget" is the upper limit of the amount that parents set when purchasing gifts.

[1476] "Educational policy" refers to parents' philosophy and goals regarding their children's education and development, and is a criterion to be taken into consideration when choosing a gift.

[1477] "Filter conditions" are conditions for limiting product selection based on specific criteria such as budget and educational objectives.

[1478] A "recommended product list" is a list of toys suitable for children's preferences, suggested by a generative AI model based on filter conditions.

[1479] A "generative AI model" is an artificial intelligence model that learns past selection history and children's preferences to suggest the most suitable products.

[1480] An "emotion engine" is a software or hardware mechanism that analyzes a user's emotions from their facial expressions, voice, etc., and provides a response accordingly.

[1481] A "request" is request information for requesting the purchase of a present for a relative or grandparent who lives far away.

[1482] An "online shop" is a website or service that sells products over the Internet.

[1483] "User" refers to a parent or guardian who uses the system to purchase or request a gift.

[1484] The system for implementing this invention includes a mechanism for inputting filter conditions based on parents' budgets and educational policies, and then using a generative AI model to recommend products based on those filter conditions. The system also includes an emotion engine that recognizes the user's emotions and adjusts responses to improve the user experience. It also includes a function for requesting gifts from distant relatives or grandparents.

[1485] The server includes the following means:

[1486] 1. A way for parents to enter filter criteria based on their budget and educational preferences.

[1487] 2. A means for generating a list of recommended products to suggest toys based on a child's preferences, the list of recommended products being generated based on a model that learns past selection history and child preferences using a generative AI model.

[1488] 3. A way to use an emotion engine to analyze user sentiment and provide simple guidance when the user is confused, or highlight products when positive sentiment is detected.

[1489] 4. A way to generate and send requests for gifts to distant relatives and grandparents.

[1490] The device is primarily a smartphone and operates in the following specific manner.

[1491] The emotion engine analyzes the facial expressions and voice of the user (parent) using the device's camera and microphone. When the user inputs their budget, product category (e.g., educational products), and exclusion criteria (e.g., excluding specific characters), the emotion engine recognizes the user's emotions and adjusts the response. The device sends the filter criteria and emotion data to the server, which generates a list of recommended products and adjusts the list based on the emotion data and sends it back.

[1492] Here's a specific example: A mother uses her device to search for educational products within a budget of 5,000 yen and sets filter conditions to exclude specific characters. At this time, the emotion engine analyzes the mother's facial expressions and provides simple guidance if she appears confused. The generative AI model considers past selection history and the child's preferences to select the most suitable products and generate a list of recommended products. Products that the emotion engine detects bring joy to the mother are particularly highlighted. The mother selects an educational block set from this list and completes the order on the online shop.

[1493] Furthermore, a "begging button" can be used to ask grandparents who live far away for a gift. The emotion engine highlights the mother's wishes, analyzes the request, and sends emails and notifications to the grandparents.

[1494] An example of a prompt for the generative AI model is as follows:

[1495] "Based on the user's criteria, please recommend toys that meet the following criteria:

[1496] Budget: Under 5,000 yen

[1497] Category: Educational Products

[1498] Exclusion criteria: Does not include certain anime characters

[1499] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1500] Step 1:

[1501] The user starts the smartphone application and inputs filter conditions such as budget, educational policy, product category, exclusion conditions, etc. The input filter conditions are temporarily saved in the device's internal memory.

[1502] Step 2:

[1503] The device's camera and microphone are used to capture the user's facial expressions and voice. This emotional data is sent to the device's emotion engine, which analyzes the user's emotions. The analysis results are output as emotional labels, such as positive, negative, or confused.

[1504] Step 3:

[1505] The filter conditions and emotion data are sent from the device to the server. The device securely transfers the data to the server using the HTTPS protocol. In this step, the filter conditions and emotion analysis results are received by the server as input data.

[1506] Step 4:

[1507] The server searches the database for matching product information based on the filter conditions. It then passes a prompt to the generative AI model, which then generates a list of recommended products. The prompt is entered in a form that includes budget, exclusion conditions, and product categories. An example of this prompt is as follows:

[1508] "Based on the user's criteria, please recommend toys that meet the following criteria:

[1509] Budget: Under 5,000 yen

[1510] Category: Educational Products

[1511] Exclusion criteria: Does not include certain anime characters

[1512] Step 5:

[1513] The output of the generative AI model is a list of recommended products, which is then sent from the server to the device. Sentiment data is also taken into account, and the list is tailored to highlight products for which positive sentiment is detected.

[1514] Step 6:

[1515] The device displays the received recommended product list to the user, highlighting products that the user has indicated are particularly pleasing to the user, and the user can refer to these options and select a product to purchase.

[1516] Step 7:

[1517] The procedure for purchasing the product selected by the user from the online shop is sent from the terminal to the server, and the server sends the order information via the online shop's API to complete the purchase procedure.

[1518] Step 8:

[1519] After completing the purchase process, if the user wants to request a gift from a distant relative or grandparent, they can press the "Request Button." The device generates a request and sends it to the server. This request includes product information and sentiment analysis results.

[1520] Step 9:

[1521] The server analyzes the request and sends emails and notifications to the designated relatives and grandparents, ensuring the gift request goes smoothly.

[1522] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1523] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1524] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1525] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1526] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1527] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1528] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1529] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1530] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1531] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1532] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1533] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1534] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1535] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1536] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1537] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1538] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1539] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1540] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1541] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1542] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1543] The following is further disclosed regarding the above embodiment.

[1544] (Claim 1)

[1545] a means for inputting filter criteria based on parental budgets and educational objectives;

[1546] means for generating a recommended product list for suggesting toys based on the child's preferences;

[1547] a means for purchasing the toy selected by the parent through the online shop by referring to the recommended product list;

[1548] A means for generating and sending requests for gifts to distant relatives or grandparents;

[1549] A system including:

[1550] (Claim 2)

[1551] 10. The system of claim 1, wherein the filter conditions include a filter condition limited to an educational product category.

[1552] (Claim 3)

[1553] The system of claim 1, wherein the recommended product list is generated using a generative AI based on a model that learns past selection history and children's preferences.

[1554] "Example 1"

[1555] (Claim 1)

[1556] a means for inputting filter criteria based on parental budgets and educational objectives;

[1557] means for formatting the filter conditions and sending them to the server;

[1558] means for searching the database based on the filter criteria to extract relevant products;

[1559] means for generating a list of recommended products based on the child's past selection history and preferences using a generative AI model;

[1560] means for returning the recommended product list to the user terminal;

[1561] a means for purchasing the toy selected by the parent through the online shop by referring to the recommended product list;

[1562] A means for generating and sending requests for gifts to distant relatives or grandparents;

[1563] A system including:

[1564] (Claim 2)

[1565] 10. The system of claim 1, wherein the filter conditions include a filter condition limited to an educational product category.

[1566] (Claim 3)

[1567] The system of claim 1, wherein the recommended product list is generated using a generative AI model based on a model that learns past selection history and child preferences.

[1568] "Application Example 1"

[1569] (Claim 1)

[1570] a means for inputting filter criteria based on parental budgets and educational objectives;

[1571] means for generating a recommended product list for suggesting toys based on the child's preferences;

[1572] a means for purchasing the toy selected by the parent through the online shop by referring to the recommended product list;

[1573] A means for generating and sending requests for gifts to distant relatives or grandparents;

[1574] a communication means for formatting the filter conditions and transmitting them to the server;

[1575] a display means for displaying the received recommendation results on the parent's terminal;

[1576] data processing means for generating a recommended product list based on the child's preferences using a generative AI;

[1577] A system including:

[1578] (Claim 2)

[1579] 10. The system of claim 1, wherein the filter conditions include a filter condition limited to an educational product category.

[1580] (Claim 3)

[1581] The system of claim 1, wherein the recommended product list is generated using a generative AI based on a model that learns past selection history and children's preferences.

[1582] "Example 2: Combining Emotion Engines"

[1583] (Claim 1)

[1584] a means for inputting filter criteria based on parental budgets and educational objectives;

[1585] means for collecting and analyzing user emotion data using an emotion engine for analyzing user emotions;

[1586] means for formatting the filter conditions and emotion data and transmitting them to a server;

[1587] A means for the server to use a generative AI model based on the filter conditions and emotion data to generate an optimal recommended product list;

[1588] a means for purchasing the toy selected by the parent through the online shop by referring to the recommended product list;

[1589] A means for generating and sending requests for gifts to distant relatives or grandparents;

[1590] A system including:

[1591] (Claim 2)

[1592] 10. The system of claim 1, wherein the filter criteria are limited to educational product categories and includes means for providing a simple guide if the user is confused.

[1593] (Claim 3)

[1594] The system of claim 1, wherein the recommended product list is generated using a generative AI model based on a model that learns past selection history and children's preferences, and the list is adjusted taking into account the user's emotions.

[1595] "Application example 2 when combining emotion engines"

[1596] (Claim 1)

[1597] a means for inputting filter criteria based on parental budgets and educational objectives;

[1598] means for generating a recommended product list for suggesting toys based on the child's preferences;

[1599] a means for purchasing the toy selected by the parent through the online shop by referring to the recommended product list;

[1600] means for providing a response according to the user's emotion using an emotion engine that recognizes the user's emotion;

[1601] A means for generating and sending requests for gifts to distant relatives or grandparents;

[1602] A system including:

[1603] (Claim 2)

[1604] 10. The system of claim 1, wherein the filter conditions include a filter condition limited to an educational product category.

[1605] (Claim 3)

[1606] The system of claim 1, wherein the recommended product list is generated using a generative AI model based on a model that learns past selection history and child preferences.

[1607] (Claim 4)

[1608] 10. The system of claim 1, wherein the emotion engine includes means for analyzing user emotions and highlighting products that detect positive emotions. [Explanation of symbols]

[1609] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting filter criteria based on parental budgets and educational objectives; means for generating a recommended product list for suggesting toys based on the child's preferences; a means for purchasing the toy selected by the parent through the online shop by referring to the recommended product list; A means for generating and sending requests for gifts to distant relatives or grandparents; A system including:

2. The system of claim 1 , wherein the filter conditions include a filter condition limited to an educational product category.

3. The system of claim 1 , wherein the recommended product list is generated using a generation AI based on a model that learns past selection history and the child's preferences.

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