System

Abstract

To provide a system for enabling a consumer to quickly and effectively utilize merchandise of 100 yen.SOLUTION: Receiving a question input by a user; sending the question to a server; analyzing, by the server, the question using a generative AI model and suggesting a corresponding solution; determining, by the server, a commodity corresponding to the solution from a database; and obtaining, by the user, location information of the user; A system comprising: means for transmitting the information to a server; means for the server to identify a nearby store based on the location information; means for the server to check stock information of the identified store based on the item; means for the server to provide the stock information to a user; and means for the terminal to display the stock information.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] As rising prices increase the cost of living for consumers, 100-yen stores have become an important option for consumers. However, the wide variety of product lineups at these stores makes it difficult for consumers to efficiently find products that meet their needs. Furthermore, due to the time and effort required to visit a physical store, improved convenience for consumers is desired. The present invention aims to solve these problems and provide a system that allows consumers to quickly and effectively utilize 100-yen products. [Means for solving the problem]

[0005] The present invention is a system including means for accepting a question input by a user to a terminal, means for transmitting the question to a server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for the server to identify a product corresponding to the solution from a database, means for the terminal to acquire location information of the user and transmit the information to the server, means for the server to identify nearby stores based on the location information, means for the server to check inventory information of the identified store based on the product, means for the server to provide inventory information to the user, and means for the terminal to display inventory information.In particular, the system is configured to enable users to efficiently obtain optimal 100-yen items by including means for the generative AI model to analyze the question using natural language processing and means for the server to identify nearby stores by referring to a geographic information database.

[0006] A "user" is an individual or entity that uses the system to ask a question.

[0007] A "terminal" is a device through which a user inputs data and communicates with the system, such as a smartphone, tablet, or PC.

[0008] A "server" is a computer system that receives input data from users and performs analysis and database searches, and has functions including generative AI models and geographic information databases.

[0009] A "question" is text information that a user inputs into the system via a terminal regarding a problem or issue that the user wants to solve.

[0010] A "generative AI model" is a collection of algorithms and programs that use natural language processing technology to analyze questions from users and propose appropriate solutions.

[0011] "Solutions" are methods or product lists that can help solve the user's problem, suggested by the generative AI model after analyzing the question.

[0012] The "database" is an information system that stores product information based on solutions, including detailed data on 100-yen items.

[0013] "Location information" is information that indicates the geographical location of a user, and is obtained from GPS data, IP address, etc.

[0014] A "store" is a physical retail store that sells items priced at 100 yen, and is identified based on the user's location information.

[0015] "Inventory information" is data that indicates how much of a particular product is currently in stock at a particular store.

[0016] "Natural language processing" is a technology or method that allows computers to understand, analyze, and appropriately process human language.

[0017] A "geographic information database" is a database that stores and can search geographic location information, and is used to identify nearby stores. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] This invention is a system that allows users to efficiently solve everyday problems using 100-yen items. This system automatically performs a series of procedures: it accepts questions entered by users, analyzes them, proposes appropriate solutions, identifies the necessary products at 100-yen stores, and displays inventory information for nearby stores.

[0040] Program processing explanation

[0041] Questions about assignments

[0042] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[0043] Sending questions to the server

[0044] The terminal sends the question entered by the user to the server. This transmission is carried out via the Internet, and the question content reaches the server.

[0045] Analyzing questions and proposing solutions

[0046] The server passes the received question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as "knife shields, silicone kitchen mitts, and vegetable cutters."

[0047] Identifying products from the database

[0048] Based on the solution proposed by the generative AI model, the server identifies the corresponding product information from the database, for example, retrieving detailed information about knife shields, silicone kitchen mitts, and vegetable cutters from the database.

[0049] Obtaining user location information

[0050] The device then obtains the user's location, which is obtained using GPS data and / or IP address, and sends it to the server.

[0051] Identifying nearby stores

[0052] The server identifies nearby 100-yen stores based on the user's location information, and references a geographic information database to find the store closest to the user's location.

[0053] Check stock information

[0054] The server checks the inventory information of the identified store, retrieves the inventory status of the relevant product from the database, and lists the stores that have the product in stock.

[0055] Providing results to users

[0056] The server organizes and formats the inventory information and sends it to the terminal, which displays it to the user.

[0057] Specific examples

[0058] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server then retrieves detailed information about these products from the database and identifies 100-yen shops in Shinjuku Ward based on the user's location information. It then retrieves information about the stores that stock these products, and informs the user that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store, silicone kitchen mitts on the 1st floor of the Shinjuku Nishi-chome store, and vegetable cutters on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to quickly and efficiently find and purchase the products they need.

[0059] In this way, the present invention helps users quickly and efficiently locate materials and tools to solve life's problems.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user inputs a question into the terminal. For example, the user inputs, "Please tell me about some useful tools for cooking."

[0063] Step 2:

[0064] The device sends a question to the server, which receives the question via the Internet.

[0065] Step 3:

[0066] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and generates a corresponding solution. For example, it suggests "knife shield, silicone kitchen mitt, vegetable cutter."

[0067] Step 4:

[0068] Based on the generated solution, the server identifies the relevant product from the database and retrieves detailed information about "knife shield, silicone kitchen mitt, vegetable cutter" from the database.

[0069] Step 5:

[0070] The device obtains the user's location information using GPS data and IP address, and sends this information to the server.

[0071] Step 6:

[0072] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[0073] Step 7:

[0074] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[0075] Step 8:

[0076] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[0077] Step 9:

[0078] The terminal displays the received inventory information to the user. The display screen shows information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishichome store 1F, vegetable cutter: Shibuya store 3F."

[0079] This allows users to efficiently find the products they need and purchase them at the nearest store.

[0080] Example 1

[0081] 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."

[0082] In conventional systems, users have had to spend a lot of time and effort finding products and services that efficiently solve problems in their daily lives. Even when an appropriate solution is proposed, it is the user's responsibility to identify where to purchase the necessary products, which can be time-consuming. The present invention aims to solve these problems.

[0083] 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.

[0084] In this invention, the server includes means for accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for the server to identify a product corresponding to the solution from a database, means for the terminal to acquire location information of the user and transmit the information to the server, means for the server to identify nearby stores based on the location information, means for the server to check inventory information of the identified store based on the product, means for the server to provide the inventory information to the user, and means for the terminal to display the inventory information. This allows users to efficiently find solutions and quickly purchase the necessary products.

[0085] A "terminal" is an electronic device that is directly operated by a user to input questions and transmit location information.

[0086] A "question" is information that a user inputs into a terminal and sends to a server, asking about the details of a problem and a solution.

[0087] A "server" is a central computer system that receives and analyzes questions sent from terminals via the Internet, proposes solutions, identifies products, and checks inventory information.

[0088] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze questions and propose corresponding solutions.

[0089] The "database" is a system that stores and manages related data such as products that correspond to the solutions proposed by the generative AI model, information on nearby stores, and inventory information.

[0090] "Location information" is data that indicates the user's current location and is obtained using GPS data or an IP address.

[0091] The "nearby store" is the nearest commercial facility that sells the products necessary for the solution, identified based on the user's location information.

[0092] "Stock information" is data that indicates whether a particular product is in stock at a nearby store.

[0093] A "solution" is a specific means or item for solving a problem that the generative AI model proposes as a result of analyzing the question.

[0094] This invention is a system that allows users to efficiently solve problems in their daily lives, focusing in particular on solving problems using 100-yen items. This system automatically performs a series of processes: analyzing questions entered by users, proposing solutions, identifying needed products, and providing inventory information for nearby stores.

[0095] Hardware and Software

[0096] Hardware used

[0097] This system uses the following hardware:

[0098] Device (smartphone, tablet, computer, etc.): Used by users to enter questions and obtain location information.

[0099] Server: A central computer system that analyzes questions, proposes solutions, identifies products, and manages inventory information.

[0100] Software used

[0101] This system uses the following software:

[0102] Generative AI model: Uses natural language processing techniques to analyze questions and suggest solutions.

[0103] Database: Stores and manages product information, store information, inventory information, etc.

[0104] Geographic information database: Provides geographic data for identifying nearby stores based on the user's location information.

[0105] GPS and IP location services: Technologies for obtaining a user's location.

[0106] Processing flow explanation

[0107] The device accepts questions from users and sends them to a server. The server then passes the questions to a generative AI model for analysis. The generative AI model uses natural language processing technology to understand the questions and propose corresponding solutions. For example, it may propose a "knife shield," a "silicone kitchen mitt," or a "vegetable cutter."

[0108] The server then identifies the corresponding product information from the database based on the proposed solution. The device obtains the user's location information and sends this information to the server. The server then references the geographic information database to identify the nearest 100-yen store based on the user's location information. It also obtains the inventory information of the proposed product from the corresponding store.

[0109] Finally, the server sends the organized inventory information to the terminal, which displays it to the user, allowing the user to quickly and efficiently find the product they need.

[0110] Specific examples

[0111] For example, if a user types "Please tell me about some useful cooking tools" into their device, the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server retrieves detailed information about each product from a database and identifies nearby 100-yen stores based on the user's location. It also retrieves information about stores that stock each product.

[0112] Example prompt sentence:

[0113] "Please suggest some convenient 100 yen items to use in cooking."

[0114] By using this system, users can quickly and efficiently find and purchase the products they need. The system automates the entire process by linking the terminal and server, significantly reducing the amount of work required by the user.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] The user inputs a question into the device. The input question is in text format, such as "Please tell me about some useful cooking tools." The input question is acquired through the input interface in the device and temporarily stored in memory.

[0118] (Input) Questions entered by the user

[0119] (Output) Text data of the question

[0120] Specifically, the user enters text into the input form on the terminal and presses the enter key to confirm the question.

[0121] Step 2:

[0122] The device sends the entered question to the server. A POST request is sent to the server's API endpoint via the HTTP protocol. This request includes the text data of the entered question.

[0123] (Input) Text data of the question

[0124] (Output) POST request sent to the server

[0125] Specifically, the terminal creates a POST request to the URL, includes the text data of the question in the request body, and sends it to the server.

[0126] Step 3:

[0127] The server receives questions sent from the device. The received questions are stored in internal memory and then passed to the generative AI model for analysis. The generative AI model uses natural language processing techniques to analyze the questions and generate appropriate solutions. For example, it may suggest a "knife shield," "silicone kitchen mitt," or "vegetable cutter."

[0128] (Input) Text data of the question sent to the server

[0129] (Output) A list of solutions as analysis results

[0130] Specifically, the server passes the text data of the question to the API of the generative AI model and receives the analysis results.

[0131] Step 4:

[0132] Based on the solutions proposed by the generative AI model, the server identifies the corresponding product information from the database. This is the process of searching the database using the solution list as a query to obtain detailed information about knife shields, silicone kitchen mitts, and vegetable cutters.

[0133] (Input) Solution List

[0134] (Output) Product details

[0135] Specifically, the server creates an SQL query, queries the database, and retrieves detailed product information.

[0136] Step 5:

[0137] The device acquires the user's location information. This location information is accurately acquired using GPS data and IP address. The acquired location information is sent to the server.

[0138] (Input) None (user's current location)

[0139] (Output) Location data

[0140] Specifically, the device calls the location information service, obtains GPS data, and sends it to the server as a POST request.

[0141] Step 6:

[0142] The server identifies nearby 100-yen stores based on the user's location information, and in the process references a geographic information database to find the store closest to the user's location.

[0143] (Input) Location data

[0144] (Output) Nearby store information

[0145] Specifically, the server sends a query including location information to a geographic information database to obtain information about the nearest store.

[0146] Step 7:

[0147] The server checks the inventory information of nearby stores, queries the database for the availability of the suggested product, and generates a list of stores that have it in stock.

[0148] (Input) Nearby store information, detailed product information

[0149] (Output) Inventory information list

[0150] Specifically, the server sends a query to the inventory management system to obtain the product's inventory status.

[0151] Step 8:

[0152] The server organizes inventory information and sends it to the terminal. The terminal displays the received information to the user. For example, it displays information that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store.

[0153] (Input) Inventory information list

[0154] (Output) Inventory information displayed to the user

[0155] Specifically, the server formats the inventory information into JSON format and sends it to the device as an HTTP response. The device receives this and prepares it to be displayed on the user interface.

[0156] These are the specific processing steps of this system. By clarifying the specific operations and inputs / outputs performed at each step, it becomes easier to understand the operation of the entire system.

[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] Currently, there are insufficient ways for users to efficiently solve specific problems in their daily lives using products from 100-yen stores. For example, users cannot efficiently check which stores have a particular product in stock, forcing them to visit multiple stores. There is also a lack of an easy way for users to find out which products are suitable for their problem. This makes the user's shopping experience inconvenient and time-consuming.

[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 accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for identifying a product corresponding to the solution from the database, means for transmitting the information to the server, means for identifying a nearby sales outlet based on the location information, means for checking inventory information of the identified sales outlet based on the product, means for providing the inventory information to the user, means for displaying the inventory information, means for the user to check detailed information about the suggested product, and means for providing the generative AI model with a specific prompt sentence suggested based on the content of the user's question. This allows a user to quickly and efficiently find a solution to a specific problem and check information about products in stock at nearby stores.

[0162] A "terminal" is an electronic device that a user uses to input data and display information.

[0163] A "server" is a computer system that processes data sent by users and provides related information.

[0164] A "question" is text data that a user inputs into a terminal regarding a specific problem or piece of information.

[0165] A "generative AI model" is an artificial intelligence model that uses machine learning technology to analyze natural language and propose optimal solutions.

[0166] A "solution" is a product or method for solving a problem that the generative AI model suggests in response to a user's question.

[0167] "Products" is a general term for items sold at 100 yen stores.

[0168] "Location information" is data indicating the user's current location, and is obtained using GPS, IP address, etc.

[0169] A "sales point" is a physical location where products are sold, such as a 100 yen store.

[0170] "Inventory information" is data indicating the inventory status of a product at a specific sales point.

[0171] A "prompt" is text data that describes specific instructions or questions that are passed to a generative AI model.

[0172] The system for realizing this invention automates a series of processes: accepting and analyzing user inquiries, and providing appropriate product and inventory information. This system is built using a smartphone, a natural language processing library (e.g., spaCy), a generative AI model (e.g., OpenAI GPT), a location information acquisition API (e.g., Google Maps API), a database (e.g., MySQL), and a web server (e.g., Node.js).

[0173] First, the user inputs a question into their device (smartphone). For example, they input a question such as, "Please tell me about some useful cleaning products." This information is sent to the server via a form on the smartphone. The submitted question is received by the web server (Node.js) and analyzed using a generative AI model (OpenAI GPT).

[0174] The server uses a generative AI model and a natural language processing library (spaCy) to analyze the question and propose appropriate solutions. Solutions could include products such as a duster cloth, a roller cleaner, or a spray bottle. Detailed information about these suggested products is retrieved from a database (MySQL), and the information is sent to the user's device.

[0175] Next, the user's smartphone obtains their current location using a location information acquisition API (Google Maps API) and sends it to the server. The server then references the geographic information database based on this location information to identify nearby sales locations. After identifying the sales locations, it retrieves inventory information for the suggested products from the database and organizes and formats the information to provide to the user.

[0176] Finally, inventory information is displayed on the user's device, allowing them to check the availability of the suggested products at nearby sales locations. For example, information such as "Duster cloths are in stock on the 3rd floor of the Shinjuku store," "Duster cleaners are in stock on the 2nd floor of the Shinjuku West store," and "Spray bottles are in stock on the 1st floor of the Shinjuku South store" is provided. This allows users to quickly and efficiently find the products they need.

[0177] Examples of specific prompts include:

[0178] "I'd like to know what cleaning products are available at the 100 yen store. Would you recommend a duster cloth, a dustpan, a spray bottle, etc.? Please search for product information and availability information for these items."

[0179] Through this entire process, users can quickly and efficiently find the best solution to their problem and then get the product at the nearest point of sale, thereby improving the convenience of their lives.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] The user inputs a question into the terminal.

[0183] Input: The user enters a specific question in text format into the device's input form. For example, "Please tell me about some useful cleaning products."

[0184] Data processing: The user's question text is received and temporarily stored within the device.

[0185] Output: The question text data is ready to be sent to the server.

[0186] Step 2:

[0187] The terminal sends a question to the server.

[0188] Input: A submitting action by the user (such as pressing a button).

[0189] Data processing: The question text data is converted into HTTP request format and sent to the server.

[0190] Output: The server receives the query.

[0191] Step 3:

[0192] The server analyzes the question using a generative AI model.

[0193] Input: The query text data received by the server.

[0194] Data processing: The question text is passed to a generative AI model (e.g., OpenAI GPT) and analyzed using a natural language processing library (e.g., spaCy).

[0195] Output: A solution suggested by the generative AI model (e.g., a duster cloth, a dust roller, a spray bottle, etc.).

[0196] Step 4:

[0197] The server identifies a product corresponding to the solution from the database.

[0198] Input: A list of solutions proposed by the generative AI model.

[0199] Data processing: Query the MySQL database to obtain detailed information about the relevant product (such as name, category, image link, etc.).

[0200] Output: A list of details about the identified products.

[0201] Step 5:

[0202] The device acquires the user's location information and sends it to the server.

[0203] Input: A request to obtain the device's location.

[0204] Data processing: GPS data is obtained using the Google Maps API and sent to the server in the form of an HTTP request.

[0205] Output: The server receives the user's location.

[0206] Step 6:

[0207] The server identifies nearby sales locations based on the location information.

[0208] Input: The user's location data received by the server.

[0209] Data processing: Refer to a geographical information database to find the nearest sales point based on location information.

[0210] Output: A list of identified sales locations.

[0211] Step 7:

[0212] The server checks the inventory information of the identified sales base based on the product.

[0213] Input: A list of identified points of sale and a list of suggested products.

[0214] Data processing: Obtain inventory information for proposed products at each sales location from the MySQL database.

[0215] Output: A detailed list of inventory information.

[0216] Step 8:

[0217] The server provides inventory information to the user.

[0218] Input: A detailed list of retrieved inventory information.

[0219] Data processing: Convert the inventory information into a format that is easy for the user to understand and send it to the terminal in the form of an HTTP response.

[0220] Output: Stock information is provided to the terminal.

[0221] Step 9:

[0222] The device displays inventory information.

[0223] Input: Inventory information sent from the server.

[0224] Data processing: Converting inventory information into the data structure required for display in the user interface.

[0225] Output: The inventory information is displayed on the terminal display for the user to review.

[0226] Through these steps, users can quickly and efficiently find the products they need and purchase them at a nearby 100-yen store.

[0227] 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.

[0228] This invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support.

[0229] Program processing explanation

[0230] Questions about assignments

[0231] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[0232] Sending questions to the server

[0233] The terminal sends the question entered by the user to the server, which receives the question via the Internet.

[0234] Question analysis and emotion recognition

[0235] The server passes the question to the generative AI model for analysis, while the device recognizes emotions from the user's input text and generates user emotion data using an emotion engine.

[0236] Proposed solutions

[0237] The server generates solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that will help relieve stress.

[0238] Identifying products from the database

[0239] Based on the generated solution, the server identifies the relevant product from the database, for example, obtaining detailed information on "knife shield, silicone kitchen mitt, vegetable cutter."

[0240] Obtaining user location information

[0241] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[0242] Identifying nearby stores

[0243] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[0244] Check stock information

[0245] The server checks the inventory information of nearby stores based on the identified product, and then references each store's inventory database to create a list of stores that stock the suggested product.

[0246] Providing results to users

[0247] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[0248] Displaying results to the user

[0249] The terminal displays the received inventory information to the user, such as "Knife shield: Shinjuku store 2nd-3rd floor, Silicone kitchen mitt: Shinjuku Nishichome store 1st floor, Vegetable cutter: Shibuya store 3rd floor."

[0250] Specific examples

[0251] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes the emotion from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[0252] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need.

[0253] This invention allows users to get appropriate solutions depending on their emotional state, providing more personalized support.

[0254] The processing flow will be explained below.

[0255] Step 1:

[0256] The user inputs a question into the terminal. For example, "Please tell me about some useful tools for cooking."

[0257] Step 2:

[0258] The device sends a question to the server, which receives the question via the Internet.

[0259] Step 3:

[0260] The device activates an emotion engine to recognize emotions from the user's input text. The emotion engine analyzes the user's input text using natural language processing and generates emotion data such as "stress."

[0261] Step 4:

[0262] Emotion data is sent to the server, which receives it.

[0263] Step 5:

[0264] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as a knife shield, a silicone kitchen knife mitt, and a vegetable cutter.

[0265] Step 6:

[0266] The server combines emotional data with the generated solutions and adjusts the priority of solutions according to the emotion. For example, if it detects "stress," it prioritizes products that are easy to use and reduce stress.

[0267] Step 7:

[0268] The server identifies the products corresponding to the preferred solution from its database, for example, retrieving details for "knife shields, silicone kitchen mitts, vegetable cutters."

[0269] Step 8:

[0270] The device obtains the user's location information using GPS data and IP address and sends it to the server.

[0271] Step 9:

[0272] The server then uses the location information to refer to a geographical information database to identify nearby 100-yen shops. For example, it finds shops in Shinjuku Ward.

[0273] Step 10:

[0274] The server checks the inventory information for the identified products. It queries each store's inventory database and creates a list of stores that stock "knife shields, silicone kitchen mitts, and vegetable cutters."

[0275] Step 11:

[0276] The server organizes and formats the inventory information and sends a solution list that takes into account the user's emotional data to the terminal. For example, it generates a list including information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishi-chome store 1F, vegetable cutter: Shibuya store 3F."

[0277] Step 12:

[0278] The terminal displays the received inventory information and solution list to the user, allowing the user to efficiently find the product they need and purchase it at the nearest store.

[0279] Example 2

[0280] 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."

[0281] In conventional systems, when users receive suggestions to solve problems in their daily lives, the suggestions do not take into account the user's emotional state, making it difficult to provide optimal solutions. Furthermore, the process of identifying nearby stores based on the user's location information and checking inventory information is cumbersome, placing a heavy burden on the user. The purpose of this invention is to solve these problems and provide more personalized and efficient support.

[0282] 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.

[0283] In this invention, the server includes: means for accepting a question entered by a user into the terminal; means for transmitting the question to the server; means for the server to analyze the question using a generative AI model and propose a corresponding solution; means for the server to identify a product corresponding to the solution from a database; means for the terminal to recognize the user's emotions, generate emotion data and transmit it to the server; means for the server to tailor a solution based on the emotion data; means for the terminal to acquire the user's location information and transmit the information to the server; means for the server to identify nearby stores based on the location information; means for the server to check inventory information of the identified stores based on the product; means for the server to provide the inventory information to the user; and means for the terminal to display the inventory information. This allows for suggestions that take the user's emotional state into consideration and provides an optimal solution. Furthermore, combining location information and inventory information allows users to efficiently find products.

[0284] "User" refers to a person who uses the system to enter questions and receive suggestions.

[0285] "Terminal" refers to the device a user uses to enter questions and view results, such as a personal computer or smartphone.

[0286] "Server" refers to a computer system that plays a central role in analyzing user queries and providing appropriate solutions.

[0287] A "question" refers to an input that expresses a problem that a user wants the system to solve or something that the user wants to know.

[0288] "Generative AI model" refers to an artificial intelligence model used to analyze a question and generate an appropriate solution.

[0289] "Analysis" refers to the process of understanding the content of the question entered by the user and grasping its meaning.

[0290] "Solution" refers to a specific answer or suggestion provided to the analyzed question.

[0291] A "database" refers to a collection of systematically accumulated data such as product and store information that corresponds to a solution.

[0292] "Emotion" refers to the psychological state inferred from the user's input, and appropriate solutions are suggested based on this.

[0293] "Emotion data" refers to data that recognizes emotions from the user's input text and expresses that state in numerical values ​​or categories.

[0294] "Location information" refers to data that indicates a user's current physical location and is obtained from GPS data, IP address, etc.

[0295] "Nearby stores" refers to stores located within an available area identified based on the user's location information.

[0296] "Inventory information" refers to data regarding the availability and quantity of a product at a particular store.

[0297] "Display" refers to the visual presentation of information on the screen of a device.

[0298] The present invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support. The following describes an embodiment of the present invention in detail.

[0299] First, the user inputs a question into a terminal. The terminal used here can be a personal computer or smartphone. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the server via the terminal.

[0300] The question received by the server is sent to a generative AI model (e.g., ChatGPT (registered trademark)) for analysis. The generative AI model uses natural language processing to understand the content of the question and generate relevant solutions. Here, a device with emotion recognition capabilities generates emotion data from the user's input text and sends it to the server. An emotion engine (e.g., Microsoft (registered trademark) Azure (registered trademark) Emotion API) is used for this emotion recognition.

[0301] The server adjusts the appropriate solution based on the solutions provided by the generative AI model and the emotional data sent from the device. For example, if the user is feeling stressed, the server will prioritize suggesting products that help relieve stress. Possible solutions include a knife shield, a silicone kitchen mitt, and a vegetable cutter.

[0302] The server then retrieves detailed information about the suggested products from a database, which includes product names, prices, ratings, etc. Specific product information can improve the quality of the solutions provided to users.

[0303] The device then acquires the user's location information using GPS data and IP address and sends it to the server. The server then references the geographic information database based on the location information to identify 100-yen shops near the user. For example, it identifies "100-yen shops in Shinjuku Ward."

[0304] The server then checks the inventory information of each store for the identified items. The server then compiles the inventory information for each item and provides it to the user. This information includes details of which items are in stock at which stores.

[0305] Finally, the terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shield: Shinjuku store 2-3F, Silicone kitchen mitt: Shinjuku Nishichome store 1F, Vegetable cutter: Shibuya store 3F." This allows users to efficiently find the products they need.

[0306] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes emotions from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[0307] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need. This invention allows users to receive appropriate solutions based on their emotional state, providing more personalized support.

[0308] Keywords include generative AI model and prompt sentence.

[0309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0310] Step 1:

[0311] User question input

[0312] The user inputs a question into the terminal, for example, "Please tell me some useful tools for cooking." The input data is a question in the form of a string, and this information is used in the next processing step.

[0313] Step 2:

[0314] Submit a Question

[0315] The terminal transmits the input question to the server, using the Internet Protocol to transmit the user's input data, generating question data that arrives at the server as output.

[0316] Step 3:

[0317] Question Analysis

[0318] The server passes the received question to the generative AI model, which analyzes the question. The generative AI model uses natural language processing technology to understand the question and generate a solution prompt. For example, in response to the question "Please tell me about some useful cooking tools," it generates a command to search for "100 yen cooking tools." A list of candidate solutions is generated as the output.

[0319] Step 4:

[0320] emotion recognition

[0321] The device recognizes emotions from the user's input text. Using the emotion engine, it generates emotion data (e.g., stress, joy, etc.) from the user's input and sends it to the server. The input is the user's text data, and the output is the meta information of the emotion data.

[0322] Step 5:

[0323] Adjusting the solution

[0324] The server tailors solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that help relieve stress. As an output, it generates a prioritized list of solutions.

[0325] Step 6:

[0326] Obtaining detailed product information

[0327] The server retrieves product details from the database based on the adjusted solution list, such as the price, rating, description, etc. of "Knife Shield, Silicone Kitchen Mitt, Vegetable Cutter." The input is the solution list, and the output is a list of product details.

[0328] Step 7:

[0329] Obtaining user location information

[0330] The device acquires the user's location information using GPS data and IP address and sends it to the server. The input is the trigger for location acquisition, and the output is the location data.

[0331] Step 8:

[0332] Identifying nearby stores

[0333] The server references a geographic information database based on the user's location information to identify nearby 100-yen shops. For example, it generates a list of shops in Shinjuku Ward. The input is location data, and the output is a list of identified shops.

[0334] Step 9:

[0335] Check stock information

[0336] The server then references the inventory database of each store based on the identified product and checks stock information. For example, it obtains information such as "Knife shields are on the 2nd and 3rd floors of the Shinjuku store, and silicone kitchen mitts are on the 1st floor of the Shinjuku Nishi-chome store." The input is store and product data, and the output is the stock check result.

[0337] Step 10:

[0338] Providing results

[0339] The server organizes and formats the data to be provided to the user based on the acquired inventory information and sends it to the terminal. The input is inventory information, and the output is the information provided to the user.

[0340] Step 11:

[0341] Results display

[0342] The terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shields: Shinjuku store 2-3F, silicone kitchen mitts: Shinjuku Nishi-chome store 1F, vegetable cutters: Shibuya store 3F." The input is formatted inventory information, and the output is a visual display to the user.

[0343] (Application example 2)

[0344] 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."

[0345] Modern users face various problems in their daily lives and often find it difficult to find solutions quickly. In particular, in situations where it is necessary to find optimal solutions based on emotions and moods, the challenge is that conventional systems cannot provide personalized support by making suggestions that take into account the emotions of individual users.

[0346] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0347] In this invention, the server includes means for analyzing a question entered by a user using a generative AI model and proposing an optimal solution, means for recognizing the user's emotions and generating emotion data, and means for proposing products based on the user's emotions, thereby providing an appropriate solution corresponding to the user's emotions and enabling more personalized support.

[0348] A "terminal" is an electronic device that a user uses to input and display information.

[0349] A "question" is text information that a user inputs into a terminal in search of a solution or suggestion.

[0350] A "server" is a computer system that processes information received from users and provides appropriate solutions or information.

[0351] A "generative AI model" is an artificial intelligence model that runs on a server and uses natural language processing to analyze text data entered by a user.

[0352] A "database" is a collection of information that stores product information, inventory information, geographical information, etc. and is referenced by the server.

[0353] "Location information" is geographic data about a user's current location, obtained through GPS or IP address.

[0354] "Emotion data" is information that represents an emotional state identified from the user's input text.

[0355] "Products" are available items stored in the database that are the subject of suggestions.

[0356] A "store" is a place that sells products and holds inventory, and is identified based on the user's location.

[0357] "Inventory information" is information indicating the availability of a product at a particular store.

[0358] This invention is a support system for users to solve problems in daily life, especially in the food delivery field. This system realizes more personalized support by recognizing the user's emotions and providing appropriate solutions based on those emotions.

[0359] The system is configured as follows: A user inputs a question using a device (such as a smartphone). This input text information is sent from the device to a server. The server analyzes the question using a generative AI model and proposes an appropriate solution to the user's problem. The device also has an emotion recognition engine that recognizes emotions from the user's input text, generating user emotion data.

[0360] The server then suggests the optimal delivery menu based on the analysis results and emotional data. For example, if a user enters "I'm very tired today," this text is sent to the server, and the emotion recognition engine recognizes the emotion "tired." Based on this information, the server suggests dishes that will help relieve stress, such as "soup" or "warm bread."

[0361] The server then retrieves detailed information about the proposed menu from the database and identifies nearby restaurants based on the user's location. This process also references a geographical database. The server then checks the inventory information of the identified restaurants and lists restaurants that stock the proposed menu. This information is then sent back to the device, which displays the results to the user.

[0362] Specifically, the following food delivery assistant implementations are possible:

[0363] Program processing explanation

[0364] Hardware: Smartphone, server, GPS module

[0365] Software: Flask (web application framework), Transformers (natural language processing model), database management system

[0366] For example, if a user types "I'm very tired today," the text is sent to the server. The server uses an emotion recognition engine to analyze the input text and recognize the emotion "tired." Based on this emotion, the server generates a list of options such as "soup" and "warm bread." This allows the user to find appropriate delivery options based on their emotional state.

[0367] Prompt Sentence Examples

[0368] User input: "I'm very tired today"

[0369] AI Prompt: "Identify the sentiment from the above input and suggest dishes the user might like."

[0370] This system allows users to quickly find the best solution that takes their emotions into consideration, improving the experience of using delivery services.

[0371] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0372] Step 1:

[0373] The user inputs a question into the terminal. The question includes the specific content for which the user wants a solution and the user's current feelings. For example, the user might input "I'm very tired today."

[0374] Input: Question text

[0375] Output: Question text entered into the terminal

[0376] Step 2:

[0377] The device sends the entered question to the server, and the text data is sent to the server via the Internet.

[0378] Input: Question text typed into the terminal

[0379] Output: The question text sent to the server

[0380] Step 3:

[0381] The server analyzes the question using a generative AI model and creates a proposal to solve the problem. It uses an emotion recognition engine to generate emotion data from the user's text. It then makes a proposal based on the analysis results and emotion data.

[0382] Input: Question text

[0383] Output: Analysis results, emotion data, suggestions

[0384] Step 4:

[0385] The server identifies product information corresponding to the proposed solution from a database, including details of the proposed menu.

[0386] Input: Analysis results, emotion data

[0387] Output: Product information list

[0388] Step 5:

[0389] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[0390] Input: User location (GPS data or IP address)

[0391] Output: Location information sent to the server

[0392] Step 6:

[0393] The server identifies nearby stores based on the location information and references a geographic information database to find the store closest to the user's current location.

[0394] Input:Location

[0395] Output: List of nearby stores

[0396] Step 7:

[0397] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[0398] Input: Nearby store list, product information list

[0399] Output: Stock information list

[0400] Step 8:

[0401] The server compiles and formats inventory information and sends it to the device, providing data including a list of stores that stock the product and its location.

[0402] Input: Stock Information List

[0403] Output: Organized inventory information sent to the device

[0404] Step 9:

[0405] The terminal displays the inventory information received from the server to the user. The display screen shows a list of stores that have the product in stock and their locations within those stores.

[0406] Input: Organized inventory information

[0407] Output: Inventory information displayed to the user

[0408] 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.

[0409] 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 (registered trademark) (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.

[0410] 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.

[0411] [Second embodiment]

[0412] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] In the smart glasses 214, the 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.

[0423] 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."

[0424] This invention is a system that allows users to efficiently solve everyday problems using 100-yen items. This system automatically performs a series of procedures: it accepts questions entered by users, analyzes them, proposes appropriate solutions, identifies the necessary products at 100-yen stores, and displays inventory information for nearby stores.

[0425] Program processing explanation

[0426] Questions about assignments

[0427] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[0428] Sending questions to the server

[0429] The terminal sends the question entered by the user to the server. This transmission is carried out via the Internet, and the question content reaches the server.

[0430] Analyzing questions and proposing solutions

[0431] The server passes the received question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as "knife shields, silicone kitchen mitts, and vegetable cutters."

[0432] Identifying products from the database

[0433] Based on the solution proposed by the generative AI model, the server identifies the corresponding product information from the database, for example, retrieving detailed information about knife shields, silicone kitchen mitts, and vegetable cutters from the database.

[0434] Obtaining user location information

[0435] The device then obtains the user's location, which is obtained using GPS data and / or IP address, and sends it to the server.

[0436] Identifying nearby stores

[0437] The server identifies nearby 100-yen stores based on the user's location information, and references a geographic information database to find the store closest to the user's location.

[0438] Check stock information

[0439] The server checks the inventory information of the identified store, retrieves the inventory status of the relevant product from the database, and lists the stores that have the product in stock.

[0440] Providing results to users

[0441] The server organizes and formats the inventory information and sends it to the terminal, which displays it to the user.

[0442] Specific examples

[0443] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server then retrieves detailed information about these products from the database and identifies 100-yen shops in Shinjuku Ward based on the user's location information. It then retrieves information about the stores that stock these products, and informs the user that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store, silicone kitchen mitts on the 1st floor of the Shinjuku Nishi-chome store, and vegetable cutters on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to quickly and efficiently find and purchase the products they need.

[0444] In this way, the present invention helps users quickly and efficiently locate materials and tools to solve life's problems.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] The user inputs a question into the terminal. For example, the user inputs, "Please tell me about some useful tools for cooking."

[0448] Step 2:

[0449] The device sends a question to the server, which receives the question via the Internet.

[0450] Step 3:

[0451] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and generates a corresponding solution. For example, it suggests "knife shield, silicone kitchen mitt, vegetable cutter."

[0452] Step 4:

[0453] Based on the generated solution, the server identifies the relevant product from the database and retrieves detailed information about "knife shield, silicone kitchen mitt, vegetable cutter" from the database.

[0454] Step 5:

[0455] The device obtains the user's location information using GPS data and IP address, and sends this information to the server.

[0456] Step 6:

[0457] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[0458] Step 7:

[0459] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[0460] Step 8:

[0461] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[0462] Step 9:

[0463] The terminal displays the received inventory information to the user. The display screen shows information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishichome store 1F, vegetable cutter: Shibuya store 3F."

[0464] This allows users to efficiently find the products they need and purchase them at the nearest store.

[0465] Example 1

[0466] 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."

[0467] In conventional systems, users have had to spend a lot of time and effort finding products and services that efficiently solve problems in their daily lives. Even when an appropriate solution is proposed, it is the user's responsibility to identify where to purchase the necessary products, which can be time-consuming. The present invention aims to solve these problems.

[0468] 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.

[0469] In this invention, the server includes means for accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for the server to identify a product corresponding to the solution from a database, means for the terminal to acquire location information of the user and transmit the information to the server, means for the server to identify nearby stores based on the location information, means for the server to check inventory information of the identified store based on the product, means for the server to provide the inventory information to the user, and means for the terminal to display the inventory information. This allows users to efficiently find solutions and quickly purchase the necessary products.

[0470] A "terminal" is an electronic device that is directly operated by a user to input questions and transmit location information.

[0471] A "question" is information that a user inputs into a terminal and sends to a server, asking about the details of a problem and a solution.

[0472] A "server" is a central computer system that receives and analyzes questions sent from terminals via the Internet, proposes solutions, identifies products, and checks inventory information.

[0473] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze questions and propose corresponding solutions.

[0474] The "database" is a system that stores and manages related data such as products that correspond to the solutions proposed by the generative AI model, information on nearby stores, and inventory information.

[0475] "Location information" is data that indicates the user's current location and is obtained using GPS data or an IP address.

[0476] The "nearby store" is the nearest commercial facility that sells the products necessary for the solution, identified based on the user's location information.

[0477] "Stock information" is data that indicates whether a particular product is in stock at a nearby store.

[0478] A "solution" is a specific means or item for solving a problem that the generative AI model proposes as a result of analyzing the question.

[0479] This invention is a system that allows users to efficiently solve problems in their daily lives, focusing in particular on solving problems using 100-yen items. This system automatically performs a series of processes: analyzing questions entered by users, proposing solutions, identifying needed products, and providing inventory information for nearby stores.

[0480] Hardware and Software

[0481] Hardware used

[0482] This system uses the following hardware:

[0483] Device (smartphone, tablet, computer, etc.): Used by users to enter questions and obtain location information.

[0484] Server: A central computer system that analyzes questions, proposes solutions, identifies products, and manages inventory information.

[0485] Software used

[0486] This system uses the following software:

[0487] Generative AI model: Uses natural language processing techniques to analyze questions and suggest solutions.

[0488] Database: Stores and manages product information, store information, inventory information, etc.

[0489] Geographic information database: Provides geographic data for identifying nearby stores based on the user's location information.

[0490] GPS and IP location services: Technologies for obtaining a user's location.

[0491] Processing flow explanation

[0492] The device accepts questions from users and sends them to a server. The server then passes the questions to a generative AI model for analysis. The generative AI model uses natural language processing technology to understand the questions and propose corresponding solutions. For example, it may propose a "knife shield," a "silicone kitchen mitt," or a "vegetable cutter."

[0493] The server then identifies the corresponding product information from the database based on the proposed solution. The device obtains the user's location information and sends this information to the server. The server then references the geographic information database to identify the nearest 100-yen store based on the user's location information. It also obtains the inventory information of the proposed product from the corresponding store.

[0494] Finally, the server sends the organized inventory information to the terminal, which displays it to the user, allowing the user to quickly and efficiently find the product they need.

[0495] Specific examples

[0496] For example, if a user types "Please tell me about some useful cooking tools" into their device, the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server retrieves detailed information about each product from a database and identifies nearby 100-yen stores based on the user's location. It also retrieves information about stores that stock each product.

[0497] Example prompt sentence:

[0498] "Please suggest some convenient 100 yen items to use in cooking."

[0499] By using this system, users can quickly and efficiently find and purchase the products they need. The system automates the entire process by linking the terminal and server, significantly reducing the amount of work required by the user.

[0500] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0501] Step 1:

[0502] The user inputs a question into the device. The input question is in text format, such as "Please tell me about some useful cooking tools." The input question is acquired through the input interface in the device and temporarily stored in memory.

[0503] (Input) Questions entered by the user

[0504] (Output) Text data of the question

[0505] Specifically, the user enters text into the input form on the terminal and presses the enter key to confirm the question.

[0506] Step 2:

[0507] The device sends the entered question to the server. A POST request is sent to the server's API endpoint via the HTTP protocol. This request includes the text data of the entered question.

[0508] (Input) Text data of the question

[0509] (Output) POST request sent to the server

[0510] Specifically, the terminal creates a POST request to the URL, includes the text data of the question in the request body, and sends it to the server.

[0511] Step 3:

[0512] The server receives questions sent from the device. The received questions are stored in internal memory and then passed to the generative AI model for analysis. The generative AI model uses natural language processing techniques to analyze the questions and generate appropriate solutions. For example, it may suggest a "knife shield," "silicone kitchen mitt," or "vegetable cutter."

[0513] (Input) Text data of the question sent to the server

[0514] (Output) A list of solutions as analysis results

[0515] Specifically, the server passes the text data of the question to the API of the generative AI model and receives the analysis results.

[0516] Step 4:

[0517] Based on the solutions proposed by the generative AI model, the server identifies the corresponding product information from the database. This is the process of searching the database using the solution list as a query to obtain detailed information about knife shields, silicone kitchen mitts, and vegetable cutters.

[0518] (Input) Solution List

[0519] (Output) Product details

[0520] Specifically, the server creates an SQL query, queries the database, and retrieves detailed product information.

[0521] Step 5:

[0522] The device acquires the user's location information. This location information is accurately acquired using GPS data and IP address. The acquired location information is sent to the server.

[0523] (Input) None (user's current location)

[0524] (Output) Location data

[0525] Specifically, the device calls the location information service, obtains GPS data, and sends it to the server as a POST request.

[0526] Step 6:

[0527] The server identifies nearby 100-yen stores based on the user's location information, and in the process references a geographic information database to find the store closest to the user's location.

[0528] (Input) Location data

[0529] (Output) Nearby store information

[0530] Specifically, the server sends a query including location information to a geographic information database to obtain information about the nearest store.

[0531] Step 7:

[0532] The server checks the inventory information of nearby stores, queries the database for the availability of the suggested product, and generates a list of stores that have it in stock.

[0533] (Input) Nearby store information, detailed product information

[0534] (Output) Inventory information list

[0535] Specifically, the server sends a query to the inventory management system to obtain the product's inventory status.

[0536] Step 8:

[0537] The server organizes inventory information and sends it to the terminal. The terminal displays the received information to the user. For example, it displays information that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store.

[0538] (Input) Inventory information list

[0539] (Output) Inventory information displayed to the user

[0540] Specifically, the server formats the inventory information into JSON format and sends it to the device as an HTTP response. The device receives this and prepares it to be displayed on the user interface.

[0541] These are the specific processing steps of this system. By clarifying the specific operations and inputs / outputs performed at each step, it becomes easier to understand the operation of the entire system.

[0542] (Application example 1)

[0543] 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."

[0544] Currently, there are insufficient ways for users to efficiently solve specific problems in their daily lives using products from 100-yen stores. For example, users cannot efficiently check which stores have a particular product in stock, forcing them to visit multiple stores. There is also a lack of an easy way for users to find out which products are suitable for their problem. This makes the user's shopping experience inconvenient and time-consuming.

[0545] 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.

[0546] In this invention, the server includes means for accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for identifying a product corresponding to the solution from the database, means for transmitting the information to the server, means for identifying a nearby sales outlet based on the location information, means for checking inventory information of the identified sales outlet based on the product, means for providing the inventory information to the user, means for displaying the inventory information, means for the user to check detailed information about the suggested product, and means for providing the generative AI model with a specific prompt sentence suggested based on the content of the user's question. This allows a user to quickly and efficiently find a solution to a specific problem and check information about products in stock at nearby stores.

[0547] A "terminal" is an electronic device that a user uses to input data and display information.

[0548] A "server" is a computer system that processes data sent by users and provides related information.

[0549] A "question" is text data that a user inputs into a terminal regarding a specific problem or piece of information.

[0550] A "generative AI model" is an artificial intelligence model that uses machine learning technology to analyze natural language and propose optimal solutions.

[0551] A "solution" is a product or method for solving a problem that the generative AI model suggests in response to a user's question.

[0552] "Products" is a general term for items sold at 100 yen stores.

[0553] "Location information" is data indicating the user's current location, and is obtained using GPS, IP address, etc.

[0554] A "sales point" is a physical location where products are sold, such as a 100 yen store.

[0555] "Inventory information" is data indicating the inventory status of a product at a specific sales point.

[0556] A "prompt" is text data that describes specific instructions or questions that are passed to a generative AI model.

[0557] The system for realizing this invention automates the process of accepting and analyzing user inquiries and providing appropriate product and inventory information. This system is built using a smartphone, a natural language processing library (e.g., spaCy), a generative AI model (e.g., OpenAI GPT), a location information acquisition API (e.g., Google Maps API), a database (e.g., MySQL), and a web server (e.g., Node.js).

[0558] First, the user inputs a question into their device (smartphone). For example, they input a question such as, "Please tell me about some useful cleaning products." This information is sent to the server via a form on the smartphone. The submitted question is received by the web server (Node.js) and analyzed using a generative AI model (OpenAI GPT).

[0559] The server uses a generative AI model and a natural language processing library (spaCy) to analyze the question and propose appropriate solutions. Solutions could include products such as a duster cloth, a roller cleaner, or a spray bottle. Detailed information about these suggested products is retrieved from a database (MySQL), and the information is sent to the user's device.

[0560] Next, the user's smartphone obtains their current location using a location information acquisition API (Google Maps API) and sends it to the server. The server then references the geographic information database based on this location information to identify nearby sales locations. After identifying the sales locations, it retrieves inventory information for the suggested products from the database and organizes and formats the information to provide to the user.

[0561] Finally, inventory information is displayed on the user's device, allowing them to check the availability of the suggested products at nearby sales locations. For example, information such as "Duster cloths are in stock on the 3rd floor of the Shinjuku store," "Duster cleaners are in stock on the 2nd floor of the Shinjuku West store," and "Spray bottles are in stock on the 1st floor of the Shinjuku South store" is provided. This allows users to quickly and efficiently find the products they need.

[0562] Examples of specific prompts include:

[0563] "I'd like to know what cleaning products are available at the 100 yen store. Would you recommend a duster cloth, a dustpan, a spray bottle, etc.? Please search for product information and availability information for these items."

[0564] Through this entire process, users can quickly and efficiently find the best solution to their problem and then get the product at the nearest point of sale, thereby improving the convenience of their lives.

[0565] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0566] Step 1:

[0567] The user inputs a question into the terminal.

[0568] Input: The user enters a specific question in text format into the device's input form. For example, "Please tell me about some useful cleaning products."

[0569] Data processing: The user's question text is received and temporarily stored within the device.

[0570] Output: The question text data is ready to be sent to the server.

[0571] Step 2:

[0572] The terminal sends a question to the server.

[0573] Input: A submitting action by the user (such as pressing a button).

[0574] Data processing: The question text data is converted into HTTP request format and sent to the server.

[0575] Output: The server receives the query.

[0576] Step 3:

[0577] The server analyzes the question using a generative AI model.

[0578] Input: The query text data received by the server.

[0579] Data processing: The question text is passed to a generative AI model (e.g., OpenAI GPT) and analyzed using a natural language processing library (e.g., spaCy).

[0580] Output: A solution suggested by the generative AI model (e.g., a duster cloth, a dust roller, a spray bottle, etc.).

[0581] Step 4:

[0582] The server identifies a product corresponding to the solution from the database.

[0583] Input: A list of solutions proposed by the generative AI model.

[0584] Data processing: Query the MySQL database to obtain detailed information about the relevant product (such as name, category, image link, etc.).

[0585] Output: A list of details about the identified products.

[0586] Step 5:

[0587] The device acquires the user's location information and sends it to the server.

[0588] Input: A request to obtain the device's location.

[0589] Data processing: GPS data is obtained using the Google Maps API and sent to the server in the form of an HTTP request.

[0590] Output: The server receives the user's location.

[0591] Step 6:

[0592] The server identifies nearby sales locations based on the location information.

[0593] Input: The user's location data received by the server.

[0594] Data processing: Refer to a geographical information database to find the nearest sales point based on location information.

[0595] Output: A list of identified sales locations.

[0596] Step 7:

[0597] The server checks the inventory information of the identified sales base based on the product.

[0598] Input: A list of identified points of sale and a list of suggested products.

[0599] Data processing: Obtain inventory information for proposed products at each sales location from the MySQL database.

[0600] Output: A detailed list of inventory information.

[0601] Step 8:

[0602] The server provides inventory information to the user.

[0603] Input: A detailed list of retrieved inventory information.

[0604] Data processing: Convert the inventory information into a format that is easy for the user to understand and send it to the terminal in the form of an HTTP response.

[0605] Output: Stock information is provided to the terminal.

[0606] Step 9:

[0607] The device displays inventory information.

[0608] Input: Inventory information sent from the server.

[0609] Data processing: Converting inventory information into the data structure required for display in the user interface.

[0610] Output: The inventory information is displayed on the terminal display for the user to review.

[0611] Through these steps, users can quickly and efficiently find the products they need and purchase them at a nearby 100-yen store.

[0612] 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.

[0613] This invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support.

[0614] Program processing explanation

[0615] Questions about assignments

[0616] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[0617] Sending questions to the server

[0618] The terminal sends the question entered by the user to the server, which receives the question via the Internet.

[0619] Question analysis and emotion recognition

[0620] The server passes the question to the generative AI model for analysis, while the device recognizes emotions from the user's input text and generates user emotion data using an emotion engine.

[0621] Proposed solutions

[0622] The server generates solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that will help relieve stress.

[0623] Identifying products from the database

[0624] Based on the generated solution, the server identifies the relevant product from the database, for example, obtaining detailed information on "knife shield, silicone kitchen mitt, vegetable cutter."

[0625] Obtaining user location information

[0626] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[0627] Identifying nearby stores

[0628] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[0629] Check stock information

[0630] The server checks the inventory information of nearby stores based on the identified product, and then references each store's inventory database to create a list of stores that stock the suggested product.

[0631] Providing results to users

[0632] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[0633] Displaying results to the user

[0634] The terminal displays the received inventory information to the user, such as "Knife shield: Shinjuku store 2nd-3rd floor, Silicone kitchen mitt: Shinjuku Nishichome store 1st floor, Vegetable cutter: Shibuya store 3rd floor."

[0635] Specific examples

[0636] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes the emotion from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[0637] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need.

[0638] This invention allows users to get appropriate solutions depending on their emotional state, providing more personalized support.

[0639] The processing flow will be explained below.

[0640] Step 1:

[0641] The user inputs a question into the terminal. For example, "Please tell me about some useful tools for cooking."

[0642] Step 2:

[0643] The device sends a question to the server, which receives the question via the Internet.

[0644] Step 3:

[0645] The device activates an emotion engine to recognize emotions from the user's input text. The emotion engine analyzes the user's input text using natural language processing and generates emotion data such as "stress."

[0646] Step 4:

[0647] Emotion data is sent to the server, which receives it.

[0648] Step 5:

[0649] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as a knife shield, a silicone kitchen knife mitt, and a vegetable cutter.

[0650] Step 6:

[0651] The server combines emotional data with the generated solutions and adjusts the priority of solutions according to the emotion. For example, if it detects "stress," it prioritizes products that are easy to use and reduce stress.

[0652] Step 7:

[0653] The server identifies the products corresponding to the preferred solution from its database, for example, retrieving details for "knife shields, silicone kitchen mitts, vegetable cutters."

[0654] Step 8:

[0655] The device obtains the user's location information using GPS data and IP address and sends it to the server.

[0656] Step 9:

[0657] The server then uses the location information to refer to a geographical information database to identify nearby 100-yen shops. For example, it finds shops in Shinjuku Ward.

[0658] Step 10:

[0659] The server checks the inventory information for the identified products. It queries each store's inventory database and creates a list of stores that stock "knife shields, silicone kitchen mitts, and vegetable cutters."

[0660] Step 11:

[0661] The server organizes and formats the inventory information and sends a solution list that takes into account the user's emotional data to the terminal. For example, it generates a list including information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishi-chome store 1F, vegetable cutter: Shibuya store 3F."

[0662] Step 12:

[0663] The terminal displays the received inventory information and solution list to the user, allowing the user to efficiently find the product they need and purchase it at the nearest store.

[0664] Example 2

[0665] 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."

[0666] In conventional systems, when users receive suggestions to solve problems in their daily lives, the suggestions do not take into account the user's emotional state, making it difficult to provide optimal solutions. Furthermore, the process of identifying nearby stores based on the user's location information and checking inventory information is cumbersome, placing a heavy burden on the user. The purpose of this invention is to solve these problems and provide more personalized and efficient support.

[0667] 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.

[0668] In this invention, the server includes: means for accepting a question entered by a user into the terminal; means for transmitting the question to the server; means for the server to analyze the question using a generative AI model and propose a corresponding solution; means for the server to identify a product corresponding to the solution from a database; means for the terminal to recognize the user's emotions, generate emotion data and transmit it to the server; means for the server to tailor a solution based on the emotion data; means for the terminal to acquire the user's location information and transmit the information to the server; means for the server to identify nearby stores based on the location information; means for the server to check inventory information of the identified stores based on the product; means for the server to provide the inventory information to the user; and means for the terminal to display the inventory information. This allows for suggestions that take the user's emotional state into consideration and provides an optimal solution. Furthermore, combining location information and inventory information allows users to efficiently find products.

[0669] "User" refers to a person who uses the system to enter questions and receive suggestions.

[0670] "Terminal" refers to the device a user uses to enter questions and view results, such as a personal computer or smartphone.

[0671] "Server" refers to a computer system that plays a central role in analyzing user queries and providing appropriate solutions.

[0672] A "question" refers to an input that expresses a problem that a user wants the system to solve or something that the user wants to know.

[0673] "Generative AI model" refers to an artificial intelligence model used to analyze a question and generate an appropriate solution.

[0674] "Analysis" refers to the process of understanding the content of the question entered by the user and grasping its meaning.

[0675] "Solution" refers to a specific answer or suggestion provided to the analyzed question.

[0676] A "database" refers to a collection of systematically accumulated data such as product and store information that corresponds to a solution.

[0677] "Emotion" refers to the psychological state inferred from the user's input, and appropriate solutions are suggested based on this.

[0678] "Emotion data" refers to data that recognizes emotions from the user's input text and expresses that state in numerical values ​​or categories.

[0679] "Location information" refers to data that indicates a user's current physical location and is obtained from GPS data, IP address, etc.

[0680] "Nearby stores" refers to stores located within an available area identified based on the user's location information.

[0681] "Inventory information" refers to data regarding the availability and quantity of a product at a particular store.

[0682] "Display" refers to the visual presentation of information on the screen of a device.

[0683] The present invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support. The following describes an embodiment of the present invention in detail.

[0684] First, the user inputs a question into a terminal. The terminal used here can be a personal computer or smartphone. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the server via the terminal.

[0685] The question received by the server is sent to a generative AI model (e.g., ChatGPT) for analysis. The generative AI model uses natural language processing to understand the content of the question and generate a relevant solution. Here, a device with emotion recognition capabilities generates emotion data from the user's input text and sends it to the server. An emotion engine (e.g., Microsoft Azure Emotion API) is used for this emotion recognition.

[0686] The server adjusts the appropriate solution based on the solutions provided by the generative AI model and the emotional data sent from the device. For example, if the user is feeling stressed, the server will prioritize suggesting products that help relieve stress. Possible solutions include a knife shield, a silicone kitchen mitt, and a vegetable cutter.

[0687] The server then retrieves detailed information about the suggested products from a database, which includes product names, prices, ratings, etc. Specific product information can improve the quality of the solutions provided to users.

[0688] The device then acquires the user's location information using GPS data and IP address and sends it to the server. The server then references the geographic information database based on the location information to identify 100-yen shops near the user. For example, it identifies "100-yen shops in Shinjuku Ward."

[0689] The server then checks the inventory information of each store for the identified items. The server then compiles the inventory information for each item and provides it to the user. This information includes details of which items are in stock at which stores.

[0690] Finally, the terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shield: Shinjuku store 2-3F, Silicone kitchen mitt: Shinjuku Nishichome store 1F, Vegetable cutter: Shibuya store 3F." This allows users to efficiently find the products they need.

[0691] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes emotions from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[0692] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need. This invention allows users to receive appropriate solutions based on their emotional state, providing more personalized support.

[0693] Keywords include generative AI model and prompt sentence.

[0694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0695] Step 1:

[0696] User question input

[0697] The user inputs a question into the terminal, for example, "Please tell me some useful tools for cooking." The input data is a question in the form of a string, and this information is used in the next processing step.

[0698] Step 2:

[0699] Submit a Question

[0700] The terminal transmits the input question to the server, using the Internet Protocol to transmit the user's input data, generating question data that arrives at the server as output.

[0701] Step 3:

[0702] Question Analysis

[0703] The server passes the received question to the generative AI model, which analyzes the question. The generative AI model uses natural language processing technology to understand the question and generate a solution prompt. For example, in response to the question "Please tell me about some useful cooking tools," it generates a command to search for "100 yen cooking tools." A list of candidate solutions is generated as the output.

[0704] Step 4:

[0705] emotion recognition

[0706] The device recognizes emotions from the user's input text. Using the emotion engine, it generates emotion data (e.g., stress, joy, etc.) from the user's input and sends it to the server. The input is the user's text data, and the output is the meta information of the emotion data.

[0707] Step 5:

[0708] Adjusting the solution

[0709] The server tailors solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that help relieve stress. As an output, it generates a prioritized list of solutions.

[0710] Step 6:

[0711] Obtaining detailed product information

[0712] The server retrieves product details from the database based on the adjusted solution list, such as the price, rating, description, etc. of "Knife Shield, Silicone Kitchen Mitt, Vegetable Cutter." The input is the solution list, and the output is a list of product details.

[0713] Step 7:

[0714] Obtaining user location information

[0715] The device acquires the user's location information using GPS data and IP address and sends it to the server. The input is the trigger for location acquisition, and the output is the location data.

[0716] Step 8:

[0717] Identifying nearby stores

[0718] The server references a geographic information database based on the user's location information to identify nearby 100-yen shops. For example, it generates a list of shops in Shinjuku Ward. The input is location data, and the output is a list of identified shops.

[0719] Step 9:

[0720] Check stock information

[0721] The server then references the inventory database of each store based on the identified product and checks stock information. For example, it obtains information such as "Knife shields are on the 2nd and 3rd floors of the Shinjuku store, and silicone kitchen mitts are on the 1st floor of the Shinjuku Nishi-chome store." The input is store and product data, and the output is the stock check result.

[0722] Step 10:

[0723] Providing results

[0724] The server organizes and formats the data to be provided to the user based on the acquired inventory information and sends it to the terminal. The input is inventory information, and the output is the information provided to the user.

[0725] Step 11:

[0726] Results display

[0727] The terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shields: Shinjuku store 2-3F, silicone kitchen mitts: Shinjuku Nishi-chome store 1F, vegetable cutters: Shibuya store 3F." The input is formatted inventory information, and the output is a visual display to the user.

[0728] (Application example 2)

[0729] 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."

[0730] Modern users face various problems in their daily lives and often find it difficult to find solutions quickly. In particular, in situations where it is necessary to find optimal solutions based on emotions and moods, the challenge is that conventional systems cannot provide personalized support by making suggestions that take into account the emotions of individual users.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0732] In this invention, the server includes means for analyzing a question entered by a user using a generative AI model and proposing an optimal solution, means for recognizing the user's emotions and generating emotion data, and means for proposing products based on the user's emotions, thereby providing an appropriate solution corresponding to the user's emotions and enabling more personalized support.

[0733] A "terminal" is an electronic device that a user uses to input and display information.

[0734] A "question" is text information that a user inputs into a terminal in search of a solution or suggestion.

[0735] A "server" is a computer system that processes information received from users and provides appropriate solutions or information.

[0736] A "generative AI model" is an artificial intelligence model that runs on a server and uses natural language processing to analyze text data entered by a user.

[0737] A "database" is a collection of information that stores product information, inventory information, geographical information, etc. and is referenced by the server.

[0738] "Location information" is geographic data about a user's current location, obtained through GPS or IP address.

[0739] "Emotion data" is information that represents an emotional state identified from the user's input text.

[0740] "Products" are available items stored in the database that are the subject of suggestions.

[0741] A "store" is a place that sells products and holds inventory, and is identified based on the user's location.

[0742] "Inventory information" is information indicating the availability of a product at a particular store.

[0743] This invention is a support system for users to solve problems in daily life, especially in the food delivery field. This system realizes more personalized support by recognizing the user's emotions and providing appropriate solutions based on those emotions.

[0744] The system is configured as follows: A user inputs a question using a device (such as a smartphone). This input text information is sent from the device to a server. The server analyzes the question using a generative AI model and proposes an appropriate solution to the user's problem. The device also has an emotion recognition engine that recognizes emotions from the user's input text, generating user emotion data.

[0745] The server then suggests the optimal delivery menu based on the analysis results and emotional data. For example, if a user enters "I'm very tired today," this text is sent to the server, and the emotion recognition engine recognizes the emotion "tired." Based on this information, the server suggests dishes that will help relieve stress, such as "soup" or "warm bread."

[0746] The server then retrieves detailed information about the proposed menu from the database and identifies nearby restaurants based on the user's location. This process also references a geographical database. The server then checks the inventory information of the identified restaurants and lists restaurants that stock the proposed menu. This information is then sent back to the device, which displays the results to the user.

[0747] Specifically, the following food delivery assistant implementations are possible:

[0748] Program processing explanation

[0749] Hardware: Smartphone, server, GPS module

[0750] Software: Flask (web application framework), Transformers (natural language processing model), database management system

[0751] For example, if a user types "I'm very tired today," the text is sent to the server. The server uses an emotion recognition engine to analyze the input text and recognize the emotion "tired." Based on this emotion, the server generates a list of options such as "soup" and "warm bread." This allows the user to find appropriate delivery options based on their emotional state.

[0752] Prompt Sentence Examples

[0753] User input: "I'm very tired today"

[0754] AI Prompt: "Identify the sentiment from the above input and suggest dishes the user might like."

[0755] This system allows users to quickly find the best solution that takes their emotions into consideration, improving the experience of using delivery services.

[0756] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0757] Step 1:

[0758] The user inputs a question into the terminal. The question includes the specific content for which the user wants a solution and the user's current feelings. For example, the user might input "I'm very tired today."

[0759] Input: Question text

[0760] Output: Question text entered into the terminal

[0761] Step 2:

[0762] The device sends the entered question to the server, and the text data is sent to the server via the Internet.

[0763] Input: Question text typed into the terminal

[0764] Output: The question text sent to the server

[0765] Step 3:

[0766] The server analyzes the question using a generative AI model and creates a proposal to solve the problem. It uses an emotion recognition engine to generate emotion data from the user's text. It then makes a proposal based on the analysis results and emotion data.

[0767] Input: Question text

[0768] Output: Analysis results, emotion data, suggestions

[0769] Step 4:

[0770] The server identifies product information corresponding to the proposed solution from a database, including details of the proposed menu.

[0771] Input: Analysis results, emotion data

[0772] Output: Product information list

[0773] Step 5:

[0774] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[0775] Input: User location (GPS data or IP address)

[0776] Output: Location information sent to the server

[0777] Step 6:

[0778] The server identifies nearby stores based on the location information and references a geographic information database to find the store closest to the user's current location.

[0779] Input:Location

[0780] Output: List of nearby stores

[0781] Step 7:

[0782] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[0783] Input: Nearby store list, product information list

[0784] Output: Stock information list

[0785] Step 8:

[0786] The server compiles and formats inventory information and sends it to the device, providing data including a list of stores that stock the product and its location.

[0787] Input: Stock Information List

[0788] Output: Organized inventory information sent to the device

[0789] Step 9:

[0790] The terminal displays the inventory information received from the server to the user. The display screen shows a list of stores that have the product in stock and their locations within those stores.

[0791] Input: Organized inventory information

[0792] Output: Inventory information displayed to the user

[0793] 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.

[0794] 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.

[0795] 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.

[0796] [Third embodiment]

[0797] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0798] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0799] 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).

[0800] 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.

[0801] 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.

[0802] 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).

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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."

[0809] This invention is a system that allows users to efficiently solve everyday problems using 100-yen items. This system automatically performs a series of procedures: it accepts questions entered by users, analyzes them, proposes appropriate solutions, identifies the necessary products at 100-yen stores, and displays inventory information for nearby stores.

[0810] Program processing explanation

[0811] Questions about assignments

[0812] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[0813] Sending questions to the server

[0814] The terminal sends the question entered by the user to the server. This transmission is carried out via the Internet, and the question content reaches the server.

[0815] Analyzing questions and proposing solutions

[0816] The server passes the received question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as "knife shields, silicone kitchen mitts, and vegetable cutters."

[0817] Identifying products from the database

[0818] Based on the solution proposed by the generative AI model, the server identifies the corresponding product information from the database, for example, retrieving detailed information about knife shields, silicone kitchen mitts, and vegetable cutters from the database.

[0819] Obtaining user location information

[0820] The device then obtains the user's location, which is obtained using GPS data and / or IP address, and sends it to the server.

[0821] Identifying nearby stores

[0822] The server identifies nearby 100-yen stores based on the user's location information, and references a geographic information database to find the store closest to the user's location.

[0823] Check stock information

[0824] The server checks the inventory information of the identified store, retrieves the inventory status of the relevant product from the database, and lists the stores that have the product in stock.

[0825] Providing results to users

[0826] The server organizes and formats the inventory information and sends it to the terminal, which displays it to the user.

[0827] Specific examples

[0828] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server then retrieves detailed information about these products from the database and identifies 100-yen shops in Shinjuku Ward based on the user's location information. It then retrieves information about the stores that stock these products, and informs the user that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store, silicone kitchen mitts on the 1st floor of the Shinjuku Nishi-chome store, and vegetable cutters on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to quickly and efficiently find and purchase the products they need.

[0829] In this way, the present invention helps users quickly and efficiently locate materials and tools to solve life's problems.

[0830] The processing flow will be explained below.

[0831] Step 1:

[0832] The user inputs a question into the terminal. For example, the user inputs, "Please tell me about some useful tools for cooking."

[0833] Step 2:

[0834] The device sends a question to the server, which receives the question via the Internet.

[0835] Step 3:

[0836] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and generates a corresponding solution. For example, it suggests "knife shield, silicone kitchen mitt, vegetable cutter."

[0837] Step 4:

[0838] Based on the generated solution, the server identifies the relevant product from the database and retrieves detailed information about "knife shield, silicone kitchen mitt, vegetable cutter" from the database.

[0839] Step 5:

[0840] The device obtains the user's location information using GPS data and IP address, and sends this information to the server.

[0841] Step 6:

[0842] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[0843] Step 7:

[0844] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[0845] Step 8:

[0846] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[0847] Step 9:

[0848] The terminal displays the received inventory information to the user. The display screen shows information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishichome store 1F, vegetable cutter: Shibuya store 3F."

[0849] This allows users to efficiently find the products they need and purchase them at the nearest store.

[0850] Example 1

[0851] 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."

[0852] In conventional systems, users have had to spend a lot of time and effort finding products and services that efficiently solve problems in their daily lives. Even when an appropriate solution is proposed, it is the user's responsibility to identify where to purchase the necessary products, which can be time-consuming. The present invention aims to solve these problems.

[0853] 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.

[0854] In this invention, the server includes means for accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for the server to identify a product corresponding to the solution from a database, means for the terminal to acquire location information of the user and transmit the information to the server, means for the server to identify nearby stores based on the location information, means for the server to check inventory information of the identified store based on the product, means for the server to provide the inventory information to the user, and means for the terminal to display the inventory information. This allows users to efficiently find solutions and quickly purchase the necessary products.

[0855] A "terminal" is an electronic device that is directly operated by a user to input questions and transmit location information.

[0856] A "question" is information that a user inputs into a terminal and sends to a server, asking about the details of a problem and a solution.

[0857] A "server" is a central computer system that receives and analyzes questions sent from terminals via the Internet, proposes solutions, identifies products, and checks inventory information.

[0858] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze questions and propose corresponding solutions.

[0859] The "database" is a system that stores and manages related data such as products that correspond to the solutions proposed by the generative AI model, information on nearby stores, and inventory information.

[0860] "Location information" is data that indicates the user's current location and is obtained using GPS data or an IP address.

[0861] The "nearby store" is the nearest commercial facility that sells the products necessary for the solution, identified based on the user's location information.

[0862] "Stock information" is data that indicates whether a particular product is in stock at a nearby store.

[0863] A "solution" is a specific means or item for solving a problem that the generative AI model proposes as a result of analyzing the question.

[0864] This invention is a system that allows users to efficiently solve problems in their daily lives, focusing in particular on solving problems using 100-yen items. This system automatically performs a series of processes: analyzing questions entered by users, proposing solutions, identifying needed products, and providing inventory information for nearby stores.

[0865] Hardware and Software

[0866] Hardware used

[0867] This system uses the following hardware:

[0868] Device (smartphone, tablet, computer, etc.): Used by users to enter questions and obtain location information.

[0869] Server: A central computer system that analyzes questions, proposes solutions, identifies products, and manages inventory information.

[0870] Software used

[0871] This system uses the following software:

[0872] Generative AI model: Uses natural language processing techniques to analyze questions and suggest solutions.

[0873] Database: Stores and manages product information, store information, inventory information, etc.

[0874] Geographic information database: Provides geographic data for identifying nearby stores based on the user's location information.

[0875] GPS and IP location services: Technologies for obtaining a user's location.

[0876] Processing flow explanation

[0877] The device accepts questions from users and sends them to a server. The server then passes the questions to a generative AI model for analysis. The generative AI model uses natural language processing technology to understand the questions and propose corresponding solutions. For example, it may propose a "knife shield," a "silicone kitchen mitt," or a "vegetable cutter."

[0878] The server then identifies the corresponding product information from the database based on the proposed solution. The device obtains the user's location information and sends this information to the server. The server then references the geographic information database to identify the nearest 100-yen store based on the user's location information. It also obtains the inventory information of the proposed product from the corresponding store.

[0879] Finally, the server sends the organized inventory information to the terminal, which displays it to the user, allowing the user to quickly and efficiently find the product they need.

[0880] Specific examples

[0881] For example, if a user types "Please tell me about some useful cooking tools" into their device, the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server retrieves detailed information about each product from a database and identifies nearby 100-yen stores based on the user's location. It also retrieves information about stores that stock each product.

[0882] Example prompt sentence:

[0883] "Please suggest some convenient 100 yen items to use in cooking."

[0884] By using this system, users can quickly and efficiently find and purchase the products they need. The system automates the entire process by linking the terminal and server, significantly reducing the amount of work required by the user.

[0885] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0886] Step 1:

[0887] The user inputs a question into the device. The input question is in text format, such as "Please tell me about some useful cooking tools." The input question is acquired through the input interface in the device and temporarily stored in memory.

[0888] (Input) Questions entered by the user

[0889] (Output) Text data of the question

[0890] Specifically, the user enters text into the input form on the terminal and presses the enter key to confirm the question.

[0891] Step 2:

[0892] The device sends the entered question to the server. A POST request is sent to the server's API endpoint via the HTTP protocol. This request includes the text data of the entered question.

[0893] (Input) Text data of the question

[0894] (Output) POST request sent to the server

[0895] Specifically, the terminal creates a POST request to the URL, includes the text data of the question in the request body, and sends it to the server.

[0896] Step 3:

[0897] The server receives questions sent from the device. The received questions are stored in internal memory and then passed to the generative AI model for analysis. The generative AI model uses natural language processing techniques to analyze the questions and generate appropriate solutions. For example, it may suggest a "knife shield," "silicone kitchen mitt," or "vegetable cutter."

[0898] (Input) Text data of the question sent to the server

[0899] (Output) A list of solutions as analysis results

[0900] Specifically, the server passes the text data of the question to the API of the generative AI model and receives the analysis results.

[0901] Step 4:

[0902] Based on the solutions proposed by the generative AI model, the server identifies the corresponding product information from the database. This is the process of searching the database using the solution list as a query to obtain detailed information about knife shields, silicone kitchen mitts, and vegetable cutters.

[0903] (Input) Solution List

[0904] (Output) Product details

[0905] Specifically, the server creates an SQL query, queries the database, and retrieves detailed product information.

[0906] Step 5:

[0907] The device acquires the user's location information. This location information is accurately acquired using GPS data and IP address. The acquired location information is sent to the server.

[0908] (Input) None (user's current location)

[0909] (Output) Location data

[0910] Specifically, the device calls the location information service, obtains GPS data, and sends it to the server as a POST request.

[0911] Step 6:

[0912] The server identifies nearby 100-yen stores based on the user's location information, and in the process references a geographic information database to find the store closest to the user's location.

[0913] (Input) Location data

[0914] (Output) Nearby store information

[0915] Specifically, the server sends a query including location information to a geographic information database to obtain information about the nearest store.

[0916] Step 7:

[0917] The server checks the inventory information of nearby stores, queries the database for the availability of the suggested product, and generates a list of stores that have it in stock.

[0918] (Input) Nearby store information, detailed product information

[0919] (Output) Inventory information list

[0920] Specifically, the server sends a query to the inventory management system to obtain the product's inventory status.

[0921] Step 8:

[0922] The server organizes inventory information and sends it to the terminal. The terminal displays the received information to the user. For example, it displays information that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store.

[0923] (Input) Inventory information list

[0924] (Output) Inventory information displayed to the user

[0925] Specifically, the server formats the inventory information into JSON format and sends it to the device as an HTTP response. The device receives this and prepares it to be displayed on the user interface.

[0926] These are the specific processing steps of this system. By clarifying the specific operations and inputs / outputs performed at each step, it becomes easier to understand the operation of the entire system.

[0927] (Application example 1)

[0928] 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."

[0929] Currently, there are insufficient ways for users to efficiently solve specific problems in their daily lives using products from 100-yen stores. For example, users cannot efficiently check which stores have a particular product in stock, forcing them to visit multiple stores. There is also a lack of an easy way for users to find out which products are suitable for their problem. This makes the user's shopping experience inconvenient and time-consuming.

[0930] 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.

[0931] In this invention, the server includes means for accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for identifying a product corresponding to the solution from the database, means for transmitting the information to the server, means for identifying a nearby sales outlet based on the location information, means for checking inventory information of the identified sales outlet based on the product, means for providing the inventory information to the user, means for displaying the inventory information, means for the user to check detailed information about the suggested product, and means for providing the generative AI model with a specific prompt sentence suggested based on the content of the user's question. This allows a user to quickly and efficiently find a solution to a specific problem and check information about products in stock at nearby stores.

[0932] A "terminal" is an electronic device that a user uses to input data and display information.

[0933] A "server" is a computer system that processes data sent by users and provides related information.

[0934] A "question" is text data that a user inputs into a terminal regarding a specific problem or piece of information.

[0935] A "generative AI model" is an artificial intelligence model that uses machine learning technology to analyze natural language and propose optimal solutions.

[0936] A "solution" is a product or method for solving a problem that the generative AI model suggests in response to a user's question.

[0937] "Products" is a general term for items sold at 100 yen stores.

[0938] "Location information" is data indicating the user's current location, and is obtained using GPS, IP address, etc.

[0939] A "sales point" is a physical location where products are sold, such as a 100 yen store.

[0940] "Inventory information" is data indicating the inventory status of a product at a specific sales point.

[0941] A "prompt" is text data that describes specific instructions or questions that are passed to a generative AI model.

[0942] The system for realizing this invention automates the process of accepting and analyzing user inquiries and providing appropriate product and inventory information. This system is built using a smartphone, a natural language processing library (e.g., spaCy), a generative AI model (e.g., OpenAI GPT), a location information acquisition API (e.g., Google Maps API), a database (e.g., MySQL), and a web server (e.g., Node.js).

[0943] First, the user inputs a question into their device (smartphone). For example, they input a question such as, "Please tell me about some useful cleaning products." This information is sent to the server via a form on the smartphone. The submitted question is received by the web server (Node.js) and analyzed using a generative AI model (OpenAI GPT).

[0944] The server uses a generative AI model and a natural language processing library (spaCy) to analyze the question and propose appropriate solutions. Solutions could include products such as a duster cloth, a roller cleaner, or a spray bottle. Detailed information about these suggested products is retrieved from a database (MySQL), and the information is sent to the user's device.

[0945] Next, the user's smartphone obtains their current location using a location information acquisition API (Google Maps API) and sends it to the server. The server then references the geographic information database based on this location information to identify nearby sales locations. After identifying the sales locations, it retrieves inventory information for the suggested products from the database and organizes and formats the information to provide to the user.

[0946] Finally, inventory information is displayed on the user's device, allowing them to check the availability of the suggested products at nearby sales locations. For example, information such as "Duster cloths are in stock on the 3rd floor of the Shinjuku store," "Duster cleaners are in stock on the 2nd floor of the Shinjuku West store," and "Spray bottles are in stock on the 1st floor of the Shinjuku South store" is provided. This allows users to quickly and efficiently find the products they need.

[0947] Examples of specific prompts include:

[0948] "I'd like to know what cleaning products are available at the 100 yen store. Would you recommend a duster cloth, a dustpan, a spray bottle, etc.? Please search for product information and availability information for these items."

[0949] Through this entire process, users can quickly and efficiently find the best solution to their problem and then get the product at the nearest point of sale, thereby improving the convenience of their lives.

[0950] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0951] Step 1:

[0952] The user inputs a question into the terminal.

[0953] Input: The user enters a specific question in text format into the device's input form. For example, "Please tell me about some useful cleaning products."

[0954] Data processing: The user's question text is received and temporarily stored within the device.

[0955] Output: The question text data is ready to be sent to the server.

[0956] Step 2:

[0957] The terminal sends a question to the server.

[0958] Input: A submitting action by the user (such as pressing a button).

[0959] Data processing: The question text data is converted into HTTP request format and sent to the server.

[0960] Output: The server receives the query.

[0961] Step 3:

[0962] The server analyzes the question using a generative AI model.

[0963] Input: The query text data received by the server.

[0964] Data processing: The question text is passed to a generative AI model (e.g., OpenAI GPT) and analyzed using a natural language processing library (e.g., spaCy).

[0965] Output: A solution suggested by the generative AI model (e.g., a duster cloth, a dust roller, a spray bottle, etc.).

[0966] Step 4:

[0967] The server identifies a product corresponding to the solution from the database.

[0968] Input: A list of solutions proposed by the generative AI model.

[0969] Data processing: Query the MySQL database to obtain detailed information about the relevant product (such as name, category, image link, etc.).

[0970] Output: A list of details about the identified products.

[0971] Step 5:

[0972] The device acquires the user's location information and sends it to the server.

[0973] Input: A request to obtain the device's location.

[0974] Data processing: GPS data is obtained using the Google Maps API and sent to the server in the form of an HTTP request.

[0975] Output: The server receives the user's location.

[0976] Step 6:

[0977] The server identifies nearby sales locations based on the location information.

[0978] Input: The user's location data received by the server.

[0979] Data processing: Refer to a geographical information database to find the nearest sales point based on location information.

[0980] Output: A list of identified sales locations.

[0981] Step 7:

[0982] The server checks the inventory information of the identified sales base based on the product.

[0983] Input: A list of identified points of sale and a list of suggested products.

[0984] Data processing: Obtain inventory information for proposed products at each sales location from the MySQL database.

[0985] Output: A detailed list of inventory information.

[0986] Step 8:

[0987] The server provides inventory information to the user.

[0988] Input: A detailed list of retrieved inventory information.

[0989] Data processing: Convert the inventory information into a format that is easy for the user to understand and send it to the terminal in the form of an HTTP response.

[0990] Output: Stock information is provided to the terminal.

[0991] Step 9:

[0992] The device displays inventory information.

[0993] Input: Inventory information sent from the server.

[0994] Data processing: Converting inventory information into the data structure required for display in the user interface.

[0995] Output: The inventory information is displayed on the terminal display for the user to review.

[0996] Through these steps, users can quickly and efficiently find the products they need and purchase them at a nearby 100-yen store.

[0997] 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.

[0998] This invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support.

[0999] Program processing explanation

[1000] Questions about assignments

[1001] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[1002] Sending questions to the server

[1003] The terminal sends the question entered by the user to the server, which receives the question via the Internet.

[1004] Question analysis and emotion recognition

[1005] The server passes the question to the generative AI model for analysis, while the device recognizes emotions from the user's input text and generates user emotion data using an emotion engine.

[1006] Proposed solutions

[1007] The server generates solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that will help relieve stress.

[1008] Identifying products from the database

[1009] Based on the generated solution, the server identifies the relevant product from the database, for example, obtaining detailed information on "knife shield, silicone kitchen mitt, vegetable cutter."

[1010] Obtaining user location information

[1011] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[1012] Identifying nearby stores

[1013] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[1014] Check stock information

[1015] The server checks the inventory information of nearby stores based on the identified product, and then references each store's inventory database to create a list of stores that stock the suggested product.

[1016] Providing results to users

[1017] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[1018] Displaying results to the user

[1019] The terminal displays the received inventory information to the user, such as "Knife shield: Shinjuku store 2nd-3rd floor, Silicone kitchen mitt: Shinjuku Nishichome store 1st floor, Vegetable cutter: Shibuya store 3rd floor."

[1020] Specific examples

[1021] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes the emotion from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[1022] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need.

[1023] This invention allows users to get appropriate solutions depending on their emotional state, providing more personalized support.

[1024] The processing flow will be explained below.

[1025] Step 1:

[1026] The user inputs a question into the terminal. For example, "Please tell me about some useful tools for cooking."

[1027] Step 2:

[1028] The device sends a question to the server, which receives the question via the Internet.

[1029] Step 3:

[1030] The device activates an emotion engine to recognize emotions from the user's input text. The emotion engine analyzes the user's input text using natural language processing and generates emotion data such as "stress."

[1031] Step 4:

[1032] Emotion data is sent to the server, which receives it.

[1033] Step 5:

[1034] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as a knife shield, a silicone kitchen knife mitt, and a vegetable cutter.

[1035] Step 6:

[1036] The server combines emotional data with the generated solutions and adjusts the priority of solutions according to the emotion. For example, if it detects "stress," it prioritizes products that are easy to use and reduce stress.

[1037] Step 7:

[1038] The server identifies the products corresponding to the preferred solution from its database, for example, retrieving details for "knife shields, silicone kitchen mitts, vegetable cutters."

[1039] Step 8:

[1040] The device obtains the user's location information using GPS data and IP address and sends it to the server.

[1041] Step 9:

[1042] The server then uses the location information to refer to a geographical information database to identify nearby 100-yen shops. For example, it finds shops in Shinjuku Ward.

[1043] Step 10:

[1044] The server checks the inventory information for the identified products. It queries each store's inventory database and creates a list of stores that stock "knife shields, silicone kitchen mitts, and vegetable cutters."

[1045] Step 11:

[1046] The server organizes and formats the inventory information and sends a solution list that takes into account the user's emotional data to the terminal. For example, it generates a list including information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishi-chome store 1F, vegetable cutter: Shibuya store 3F."

[1047] Step 12:

[1048] The terminal displays the received inventory information and solution list to the user, allowing the user to efficiently find the product they need and purchase it at the nearest store.

[1049] Example 2

[1050] 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."

[1051] In conventional systems, when users receive suggestions to solve problems in their daily lives, the suggestions do not take into account the user's emotional state, making it difficult to provide optimal solutions. Furthermore, the process of identifying nearby stores based on the user's location information and checking inventory information is cumbersome, placing a heavy burden on the user. The purpose of this invention is to solve these problems and provide more personalized and efficient support.

[1052] 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.

[1053] In this invention, the server includes: means for accepting a question entered by a user into the terminal; means for transmitting the question to the server; means for the server to analyze the question using a generative AI model and propose a corresponding solution; means for the server to identify a product corresponding to the solution from a database; means for the terminal to recognize the user's emotions, generate emotion data and transmit it to the server; means for the server to tailor a solution based on the emotion data; means for the terminal to acquire the user's location information and transmit the information to the server; means for the server to identify nearby stores based on the location information; means for the server to check inventory information of the identified stores based on the product; means for the server to provide the inventory information to the user; and means for the terminal to display the inventory information. This allows for suggestions that take the user's emotional state into consideration and provides an optimal solution. Furthermore, combining location information and inventory information allows users to efficiently find products.

[1054] "User" refers to a person who uses the system to enter questions and receive suggestions.

[1055] "Terminal" refers to the device a user uses to enter questions and view results, such as a personal computer or smartphone.

[1056] "Server" refers to a computer system that plays a central role in analyzing user queries and providing appropriate solutions.

[1057] A "question" refers to an input that expresses a problem that a user wants the system to solve or something that the user wants to know.

[1058] "Generative AI model" refers to an artificial intelligence model used to analyze a question and generate an appropriate solution.

[1059] "Analysis" refers to the process of understanding the content of the question entered by the user and grasping its meaning.

[1060] "Solution" refers to a specific answer or suggestion provided to the analyzed question.

[1061] A "database" refers to a collection of systematically accumulated data such as product and store information that corresponds to a solution.

[1062] "Emotion" refers to the psychological state inferred from the user's input, and appropriate solutions are suggested based on this.

[1063] "Emotion data" refers to data that recognizes emotions from the user's input text and expresses that state in numerical values ​​or categories.

[1064] "Location information" refers to data that indicates a user's current physical location and is obtained from GPS data, IP address, etc.

[1065] "Nearby stores" refers to stores located within an available area identified based on the user's location information.

[1066] "Inventory information" refers to data regarding the availability and quantity of a product at a particular store.

[1067] "Display" refers to the visual presentation of information on the screen of a device.

[1068] The present invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support. The following describes an embodiment of the present invention in detail.

[1069] First, the user inputs a question into a terminal. The terminal used here can be a personal computer or smartphone. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the server via the terminal.

[1070] The question received by the server is sent to a generative AI model (e.g., ChatGPT) for analysis. The generative AI model uses natural language processing to understand the content of the question and generate a relevant solution. Here, a device with emotion recognition capabilities generates emotion data from the user's input text and sends it to the server. An emotion engine (e.g., Microsoft Azure Emotion API) is used for this emotion recognition.

[1071] The server adjusts the appropriate solution based on the solutions provided by the generative AI model and the emotional data sent from the device. For example, if the user is feeling stressed, the server will prioritize suggesting products that help relieve stress. Possible solutions include a knife shield, a silicone kitchen mitt, and a vegetable cutter.

[1072] The server then retrieves detailed information about the suggested products from a database, which includes product names, prices, ratings, etc. Specific product information can improve the quality of the solutions provided to users.

[1073] The device then acquires the user's location information using GPS data and IP address and sends it to the server. The server then references the geographic information database based on the location information to identify 100-yen shops near the user. For example, it identifies "100-yen shops in Shinjuku Ward."

[1074] The server then checks the inventory information of each store for the identified items. The server then compiles the inventory information for each item and provides it to the user. This information includes details of which items are in stock at which stores.

[1075] Finally, the terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shield: Shinjuku store 2-3F, Silicone kitchen mitt: Shinjuku Nishichome store 1F, Vegetable cutter: Shibuya store 3F." This allows users to efficiently find the products they need.

[1076] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes emotions from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[1077] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need. This invention allows users to receive appropriate solutions based on their emotional state, providing more personalized support.

[1078] Keywords include generative AI model and prompt sentence.

[1079] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1080] Step 1:

[1081] User question input

[1082] The user inputs a question into the terminal, for example, "Please tell me some useful tools for cooking." The input data is a question in the form of a string, and this information is used in the next processing step.

[1083] Step 2:

[1084] Submit a Question

[1085] The terminal transmits the input question to the server, using the Internet Protocol to transmit the user's input data, generating question data that arrives at the server as output.

[1086] Step 3:

[1087] Question Analysis

[1088] The server passes the received question to the generative AI model, which analyzes the question. The generative AI model uses natural language processing technology to understand the question and generate a solution prompt. For example, in response to the question "Please tell me about some useful cooking tools," it generates a command to search for "100 yen cooking tools." A list of candidate solutions is generated as the output.

[1089] Step 4:

[1090] emotion recognition

[1091] The device recognizes emotions from the user's input text. Using the emotion engine, it generates emotion data (e.g., stress, joy, etc.) from the user's input and sends it to the server. The input is the user's text data, and the output is the meta information of the emotion data.

[1092] Step 5:

[1093] Adjusting the solution

[1094] The server tailors solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that help relieve stress. As an output, it generates a prioritized list of solutions.

[1095] Step 6:

[1096] Obtaining detailed product information

[1097] The server retrieves product details from the database based on the adjusted solution list, such as the price, rating, description, etc. of "Knife Shield, Silicone Kitchen Mitt, Vegetable Cutter." The input is the solution list, and the output is a list of product details.

[1098] Step 7:

[1099] Obtaining user location information

[1100] The device acquires the user's location information using GPS data and IP address and sends it to the server. The input is the trigger for location acquisition, and the output is the location data.

[1101] Step 8:

[1102] Identifying nearby stores

[1103] The server references a geographic information database based on the user's location information to identify nearby 100-yen shops. For example, it generates a list of shops in Shinjuku Ward. The input is location data, and the output is a list of identified shops.

[1104] Step 9:

[1105] Check stock information

[1106] The server then references the inventory database of each store based on the identified product and checks stock information. For example, it obtains information such as "Knife shields are on the 2nd and 3rd floors of the Shinjuku store, and silicone kitchen mitts are on the 1st floor of the Shinjuku Nishi-chome store." The input is store and product data, and the output is the stock check result.

[1107] Step 10:

[1108] Providing results

[1109] The server organizes and formats the data to be provided to the user based on the acquired inventory information and sends it to the terminal. The input is inventory information, and the output is the information provided to the user.

[1110] Step 11:

[1111] Results display

[1112] The terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shields: Shinjuku store 2-3F, silicone kitchen mitts: Shinjuku Nishi-chome store 1F, vegetable cutters: Shibuya store 3F." The input is formatted inventory information, and the output is a visual display to the user.

[1113] (Application example 2)

[1114] 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."

[1115] Modern users face various problems in their daily lives and often find it difficult to find solutions quickly. In particular, in situations where it is necessary to find optimal solutions based on emotions and moods, the challenge is that conventional systems cannot provide personalized support by making suggestions that take into account the emotions of individual users.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1117] In this invention, the server includes means for analyzing a question entered by a user using a generative AI model and proposing an optimal solution, means for recognizing the user's emotions and generating emotion data, and means for proposing products based on the user's emotions, thereby providing an appropriate solution corresponding to the user's emotions and enabling more personalized support.

[1118] A "terminal" is an electronic device that a user uses to input and display information.

[1119] A "question" is text information that a user inputs into a terminal in search of a solution or suggestion.

[1120] A "server" is a computer system that processes information received from users and provides appropriate solutions or information.

[1121] A "generative AI model" is an artificial intelligence model that runs on a server and uses natural language processing to analyze text data entered by a user.

[1122] A "database" is a collection of information that stores product information, inventory information, geographical information, etc. and is referenced by the server.

[1123] "Location information" is geographic data about a user's current location, obtained through GPS or IP address.

[1124] "Emotion data" is information that represents an emotional state identified from the user's input text.

[1125] "Products" are available items stored in the database that are the subject of suggestions.

[1126] A "store" is a place that sells products and holds inventory, and is identified based on the user's location.

[1127] "Inventory information" is information indicating the availability of a product at a particular store.

[1128] This invention is a support system for users to solve problems in daily life, especially in the food delivery field. This system realizes more personalized support by recognizing the user's emotions and providing appropriate solutions based on those emotions.

[1129] The system is configured as follows: A user inputs a question using a device (such as a smartphone). This input text information is sent from the device to a server. The server analyzes the question using a generative AI model and proposes an appropriate solution to the user's problem. The device also has an emotion recognition engine that recognizes emotions from the user's input text, generating user emotion data.

[1130] The server then suggests the optimal delivery menu based on the analysis results and emotional data. For example, if a user enters "I'm very tired today," this text is sent to the server, and the emotion recognition engine recognizes the emotion "tired." Based on this information, the server suggests dishes that will help relieve stress, such as "soup" or "warm bread."

[1131] The server then retrieves detailed information about the proposed menu from the database and identifies nearby restaurants based on the user's location. This process also references a geographical database. The server then checks the inventory information of the identified restaurants and lists restaurants that stock the proposed menu. This information is then sent back to the device, which displays the results to the user.

[1132] Specifically, the following food delivery assistant implementations are possible:

[1133] Program processing explanation

[1134] Hardware: Smartphone, server, GPS module

[1135] Software: Flask (web application framework), Transformers (natural language processing model), database management system

[1136] For example, if a user types "I'm very tired today," the text is sent to the server. The server uses an emotion recognition engine to analyze the input text and recognize the emotion "tired." Based on this emotion, the server generates a list of options such as "soup" and "warm bread." This allows the user to find appropriate delivery options based on their emotional state.

[1137] Prompt Sentence Examples

[1138] User input: "I'm very tired today"

[1139] AI Prompt: "Identify the sentiment from the above input and suggest dishes the user might like."

[1140] This system allows users to quickly find the best solution that takes their emotions into consideration, improving the experience of using delivery services.

[1141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1142] Step 1:

[1143] The user inputs a question into the terminal. The question includes the specific content for which the user wants a solution and the user's current feelings. For example, the user might input "I'm very tired today."

[1144] Input: Question text

[1145] Output: Question text entered into the terminal

[1146] Step 2:

[1147] The device sends the entered question to the server, and the text data is sent to the server via the Internet.

[1148] Input: Question text typed into the terminal

[1149] Output: The question text sent to the server

[1150] Step 3:

[1151] The server analyzes the question using a generative AI model and creates a proposal to solve the problem. It uses an emotion recognition engine to generate emotion data from the user's text. It then makes a proposal based on the analysis results and emotion data.

[1152] Input: Question text

[1153] Output: Analysis results, emotion data, suggestions

[1154] Step 4:

[1155] The server identifies product information corresponding to the proposed solution from a database, including details of the proposed menu.

[1156] Input: Analysis results, emotion data

[1157] Output: Product information list

[1158] Step 5:

[1159] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[1160] Input: User location (GPS data or IP address)

[1161] Output: Location information sent to the server

[1162] Step 6:

[1163] The server identifies nearby stores based on the location information and references a geographic information database to find the store closest to the user's current location.

[1164] Input:Location

[1165] Output: List of nearby stores

[1166] Step 7:

[1167] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[1168] Input: Nearby store list, product information list

[1169] Output: Stock information list

[1170] Step 8:

[1171] The server compiles and formats inventory information and sends it to the device, providing data including a list of stores that stock the product and its location.

[1172] Input: Stock Information List

[1173] Output: Organized inventory information sent to the device

[1174] Step 9:

[1175] The terminal displays the inventory information received from the server to the user. The display screen shows a list of stores that have the product in stock and their locations within those stores.

[1176] Input: Organized inventory information

[1177] Output: Inventory information displayed to the user

[1178] 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.

[1179] 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.

[1180] 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.

[1181] [Fourth embodiment]

[1182] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1183] 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.

[1184] 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).

[1185] 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.

[1186] 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.

[1187] 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).

[1188] 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.

[1189] 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.

[1190] 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.

[1191] 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.

[1192] 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.

[1193] 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.

[1194] 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."

[1195] This invention is a system that allows users to efficiently solve everyday problems using 100-yen items. This system automatically performs a series of procedures: it accepts questions entered by users, analyzes them, proposes appropriate solutions, identifies the necessary products at 100-yen stores, and displays inventory information for nearby stores.

[1196] Program processing explanation

[1197] Questions about assignments

[1198] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[1199] Sending questions to the server

[1200] The terminal sends the question entered by the user to the server. This transmission is carried out via the Internet, and the question content reaches the server.

[1201] Analyzing questions and proposing solutions

[1202] The server passes the received question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as "knife shields, silicone kitchen mitts, and vegetable cutters."

[1203] Identifying products from the database

[1204] Based on the solution proposed by the generative AI model, the server identifies the corresponding product information from the database, for example, retrieving detailed information about knife shields, silicone kitchen mitts, and vegetable cutters from the database.

[1205] Obtaining user location information

[1206] The device then obtains the user's location, which is obtained using GPS data and / or IP address, and sends it to the server.

[1207] Identifying nearby stores

[1208] The server identifies nearby 100-yen stores based on the user's location information, and references a geographic information database to find the store closest to the user's location.

[1209] Check stock information

[1210] The server checks the inventory information of the identified store, retrieves the inventory status of the relevant product from the database, and lists the stores that have the product in stock.

[1211] Providing results to users

[1212] The server organizes and formats the inventory information and sends it to the terminal, which displays it to the user.

[1213] Specific examples

[1214] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server then retrieves detailed information about these products from the database and identifies 100-yen shops in Shinjuku Ward based on the user's location information. It then retrieves information about the stores that stock these products, and informs the user that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store, silicone kitchen mitts on the 1st floor of the Shinjuku Nishi-chome store, and vegetable cutters on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to quickly and efficiently find and purchase the products they need.

[1215] In this way, the present invention helps users quickly and efficiently locate materials and tools to solve life's problems.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] The user inputs a question into the terminal. For example, the user inputs, "Please tell me about some useful tools for cooking."

[1219] Step 2:

[1220] The device sends a question to the server, which receives the question via the Internet.

[1221] Step 3:

[1222] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and generates a corresponding solution. For example, it suggests "knife shield, silicone kitchen mitt, vegetable cutter."

[1223] Step 4:

[1224] Based on the generated solution, the server identifies the relevant product from the database and retrieves detailed information about "knife shield, silicone kitchen mitt, vegetable cutter" from the database.

[1225] Step 5:

[1226] The device obtains the user's location information using GPS data and IP address, and sends this information to the server.

[1227] Step 6:

[1228] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[1229] Step 7:

[1230] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[1231] Step 8:

[1232] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[1233] Step 9:

[1234] The terminal displays the received inventory information to the user. The display screen shows information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishichome store 1F, vegetable cutter: Shibuya store 3F."

[1235] This allows users to efficiently find the products they need and purchase them at the nearest store.

[1236] Example 1

[1237] 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."

[1238] In conventional systems, users have had to spend a lot of time and effort finding products and services that efficiently solve problems in their daily lives. Even when an appropriate solution is proposed, it is the user's responsibility to identify where to purchase the necessary products, which can be time-consuming. The present invention aims to solve these problems.

[1239] 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.

[1240] In this invention, the server includes means for accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for the server to identify a product corresponding to the solution from a database, means for the terminal to acquire location information of the user and transmit the information to the server, means for the server to identify nearby stores based on the location information, means for the server to check inventory information of the identified store based on the product, means for the server to provide the inventory information to the user, and means for the terminal to display the inventory information. This allows users to efficiently find solutions and quickly purchase the necessary products.

[1241] A "terminal" is an electronic device that is directly operated by a user to input questions and transmit location information.

[1242] A "question" is information that a user inputs into a terminal and sends to a server, asking about the details of a problem and a solution.

[1243] A "server" is a central computer system that receives and analyzes questions sent from terminals via the Internet, proposes solutions, identifies products, and checks inventory information.

[1244] A "generative AI model" is an artificial intelligence model that uses natural language processing technology to analyze questions and propose corresponding solutions.

[1245] The "database" is a system that stores and manages related data such as products that correspond to the solutions proposed by the generative AI model, information on nearby stores, and inventory information.

[1246] "Location information" is data that indicates the user's current location and is obtained using GPS data or an IP address.

[1247] The "nearby store" is the nearest commercial facility that sells the products necessary for the solution, identified based on the user's location information.

[1248] "Stock information" is data that indicates whether a particular product is in stock at a nearby store.

[1249] A "solution" is a specific means or item for solving a problem that the generative AI model proposes as a result of analyzing the question.

[1250] This invention is a system that allows users to efficiently solve problems in their daily lives, focusing in particular on solving problems using 100-yen items. This system automatically performs a series of processes: analyzing questions entered by users, proposing solutions, identifying needed products, and providing inventory information for nearby stores.

[1251] Hardware and Software

[1252] Hardware used

[1253] This system uses the following hardware:

[1254] Device (smartphone, tablet, computer, etc.): Used by users to enter questions and obtain location information.

[1255] Server: A central computer system that analyzes questions, proposes solutions, identifies products, and manages inventory information.

[1256] Software used

[1257] This system uses the following software:

[1258] Generative AI model: Uses natural language processing techniques to analyze questions and suggest solutions.

[1259] Database: Stores and manages product information, store information, inventory information, etc.

[1260] Geographic information database: Provides geographic data for identifying nearby stores based on the user's location information.

[1261] GPS and IP location services: Technologies for obtaining a user's location.

[1262] Processing flow explanation

[1263] The device accepts questions from users and sends them to a server. The server then passes the questions to a generative AI model for analysis. The generative AI model uses natural language processing technology to understand the questions and propose corresponding solutions. For example, it may propose a "knife shield," a "silicone kitchen mitt," or a "vegetable cutter."

[1264] The server then identifies the corresponding product information from the database based on the proposed solution. The device obtains the user's location information and sends this information to the server. The server then references the geographic information database to identify the nearest 100-yen store based on the user's location information. It also obtains the inventory information of the proposed product from the corresponding store.

[1265] Finally, the server sends the organized inventory information to the terminal, which displays it to the user, allowing the user to quickly and efficiently find the product they need.

[1266] Specific examples

[1267] For example, if a user types "Please tell me about some useful cooking tools" into their device, the question is sent to the server. The server uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters. The server retrieves detailed information about each product from a database and identifies nearby 100-yen stores based on the user's location. It also retrieves information about stores that stock each product.

[1268] Example prompt sentence:

[1269] "Please suggest some convenient 100 yen items to use in cooking."

[1270] By using this system, users can quickly and efficiently find and purchase the products they need. The system automates the entire process by linking the terminal and server, significantly reducing the amount of work required by the user.

[1271] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1272] Step 1:

[1273] The user inputs a question into the device. The input question is in text format, such as "Please tell me about some useful cooking tools." The input question is acquired through the input interface in the device and temporarily stored in memory.

[1274] (Input) Questions entered by the user

[1275] (Output) Text data of the question

[1276] Specifically, the user enters text into the input form on the terminal and presses the enter key to confirm the question.

[1277] Step 2:

[1278] The device sends the entered question to the server. A POST request is sent to the server's API endpoint via the HTTP protocol. This request includes the text data of the entered question.

[1279] (Input) Text data of the question

[1280] (Output) POST request sent to the server

[1281] Specifically, the terminal creates a POST request to the URL, includes the text data of the question in the request body, and sends it to the server.

[1282] Step 3:

[1283] The server receives questions sent from the device. The received questions are stored in internal memory and then passed to the generative AI model for analysis. The generative AI model uses natural language processing techniques to analyze the questions and generate appropriate solutions. For example, it may suggest a "knife shield," "silicone kitchen mitt," or "vegetable cutter."

[1284] (Input) Text data of the question sent to the server

[1285] (Output) A list of solutions as analysis results

[1286] Specifically, the server passes the text data of the question to the API of the generative AI model and receives the analysis results.

[1287] Step 4:

[1288] Based on the solutions proposed by the generative AI model, the server identifies the corresponding product information from the database. This is the process of searching the database using the solution list as a query to obtain detailed information about knife shields, silicone kitchen mitts, and vegetable cutters.

[1289] (Input) Solution List

[1290] (Output) Product details

[1291] Specifically, the server creates an SQL query, queries the database, and retrieves detailed product information.

[1292] Step 5:

[1293] The device acquires the user's location information. This location information is accurately acquired using GPS data and IP address. The acquired location information is sent to the server.

[1294] (Input) None (user's current location)

[1295] (Output) Location data

[1296] Specifically, the device calls the location information service, obtains GPS data, and sends it to the server as a POST request.

[1297] Step 6:

[1298] The server identifies nearby 100-yen stores based on the user's location information, and in the process references a geographic information database to find the store closest to the user's location.

[1299] (Input) Location data

[1300] (Output) Nearby store information

[1301] Specifically, the server sends a query including location information to a geographic information database to obtain information about the nearest store.

[1302] Step 7:

[1303] The server checks the inventory information of nearby stores, queries the database for the availability of the suggested product, and generates a list of stores that have it in stock.

[1304] (Input) Nearby store information, detailed product information

[1305] (Output) Inventory information list

[1306] Specifically, the server sends a query to the inventory management system to obtain the product's inventory status.

[1307] Step 8:

[1308] The server organizes inventory information and sends it to the terminal. The terminal displays the received information to the user. For example, it displays information that knife shields are in stock on the 2nd and 3rd floors of the Shinjuku store.

[1309] (Input) Inventory information list

[1310] (Output) Inventory information displayed to the user

[1311] Specifically, the server formats the inventory information into JSON format and sends it to the device as an HTTP response. The device receives this and prepares it to be displayed on the user interface.

[1312] These are the specific processing steps of this system. By clarifying the specific operations and inputs / outputs performed at each step, it becomes easier to understand the operation of the entire system.

[1313] (Application example 1)

[1314] 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."

[1315] Currently, there are insufficient ways for users to efficiently solve specific problems in their daily lives using products from 100-yen stores. For example, users cannot efficiently check which stores have a particular product in stock, forcing them to visit multiple stores. There is also a lack of an easy way for users to find out which products are suitable for their problem. This makes the user's shopping experience inconvenient and time-consuming.

[1316] 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.

[1317] In this invention, the server includes means for accepting a question input by a user to a terminal, means for transmitting the question to the server, means for the server to analyze the question using a generative AI model and propose a corresponding solution, means for identifying a product corresponding to the solution from the database, means for transmitting the information to the server, means for identifying a nearby sales outlet based on the location information, means for checking inventory information of the identified sales outlet based on the product, means for providing the inventory information to the user, means for displaying the inventory information, means for the user to check detailed information about the suggested product, and means for providing the generative AI model with a specific prompt sentence suggested based on the content of the user's question. This allows a user to quickly and efficiently find a solution to a specific problem and check information about products in stock at nearby stores.

[1318] A "terminal" is an electronic device that a user uses to input data and display information.

[1319] A "server" is a computer system that processes data sent by users and provides related information.

[1320] A "question" is text data that a user inputs into a terminal regarding a specific problem or piece of information.

[1321] A "generative AI model" is an artificial intelligence model that uses machine learning technology to analyze natural language and propose optimal solutions.

[1322] A "solution" is a product or method for solving a problem that the generative AI model suggests in response to a user's question.

[1323] "Products" is a general term for items sold at 100 yen stores.

[1324] "Location information" is data indicating the user's current location, and is obtained using GPS, IP address, etc.

[1325] A "sales point" is a physical location where products are sold, such as a 100 yen store.

[1326] "Inventory information" is data indicating the inventory status of a product at a specific sales point.

[1327] A "prompt" is text data that describes specific instructions or questions that are passed to a generative AI model.

[1328] The system for realizing this invention automates the process of accepting and analyzing user inquiries and providing appropriate product and inventory information. This system is built using a smartphone, a natural language processing library (e.g., spaCy), a generative AI model (e.g., OpenAI GPT), a location information acquisition API (e.g., Google Maps API), a database (e.g., MySQL), and a web server (e.g., Node.js).

[1329] First, the user inputs a question into their device (smartphone). For example, they input a question such as, "Please tell me about some useful cleaning products." This information is sent to the server via a form on the smartphone. The submitted question is received by the web server (Node.js) and analyzed using a generative AI model (OpenAI GPT).

[1330] The server uses a generative AI model and a natural language processing library (spaCy) to analyze the question and propose appropriate solutions. Solutions could include products such as a duster cloth, a roller cleaner, or a spray bottle. Detailed information about these suggested products is retrieved from a database (MySQL), and the information is sent to the user's device.

[1331] Next, the user's smartphone obtains their current location using a location information acquisition API (Google Maps API) and sends it to the server. The server then references the geographic information database based on this location information to identify nearby sales locations. After identifying the sales locations, it retrieves inventory information for the suggested products from the database and organizes and formats the information to provide to the user.

[1332] Finally, inventory information is displayed on the user's device, allowing them to check the availability of the suggested products at nearby sales locations. For example, information such as "Duster cloths are in stock on the 3rd floor of the Shinjuku store," "Duster cleaners are in stock on the 2nd floor of the Shinjuku West store," and "Spray bottles are in stock on the 1st floor of the Shinjuku South store" is provided. This allows users to quickly and efficiently find the products they need.

[1333] Examples of specific prompts include:

[1334] "I'd like to know what cleaning products are available at the 100 yen store. Would you recommend a duster cloth, a dustpan, a spray bottle, etc.? Please search for product information and availability information for these items."

[1335] Through this entire process, users can quickly and efficiently find the best solution to their problem and then get the product at the nearest point of sale, thereby improving the convenience of their lives.

[1336] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1337] Step 1:

[1338] The user inputs a question into the terminal.

[1339] Input: The user enters a specific question in text format into the device's input form. For example, "Please tell me about some useful cleaning products."

[1340] Data processing: The user's question text is received and temporarily stored within the device.

[1341] Output: The question text data is ready to be sent to the server.

[1342] Step 2:

[1343] The terminal sends a question to the server.

[1344] Input: A submitting action by the user (such as pressing a button).

[1345] Data processing: The question text data is converted into HTTP request format and sent to the server.

[1346] Output: The server receives the query.

[1347] Step 3:

[1348] The server analyzes the question using a generative AI model.

[1349] Input: The query text data received by the server.

[1350] Data processing: The question text is passed to a generative AI model (e.g., OpenAI GPT) and analyzed using a natural language processing library (e.g., spaCy).

[1351] Output: A solution suggested by the generative AI model (e.g., a duster cloth, a dust roller, a spray bottle, etc.).

[1352] Step 4:

[1353] The server identifies a product corresponding to the solution from the database.

[1354] Input: A list of solutions proposed by the generative AI model.

[1355] Data processing: Query the MySQL database to obtain detailed information about the relevant product (such as name, category, image link, etc.).

[1356] Output: A list of details about the identified products.

[1357] Step 5:

[1358] The device acquires the user's location information and sends it to the server.

[1359] Input: A request to obtain the device's location.

[1360] Data processing: GPS data is obtained using the Google Maps API and sent to the server in the form of an HTTP request.

[1361] Output: The server receives the user's location.

[1362] Step 6:

[1363] The server identifies nearby sales locations based on the location information.

[1364] Input: The user's location data received by the server.

[1365] Data processing: Refer to a geographical information database to find the nearest sales point based on location information.

[1366] Output: A list of identified sales locations.

[1367] Step 7:

[1368] The server checks the inventory information of the identified sales base based on the product.

[1369] Input: A list of identified points of sale and a list of suggested products.

[1370] Data processing: Obtain inventory information for proposed products at each sales location from the MySQL database.

[1371] Output: A detailed list of inventory information.

[1372] Step 8:

[1373] The server provides inventory information to the user.

[1374] Input: A detailed list of retrieved inventory information.

[1375] Data processing: Convert the inventory information into a format that is easy for the user to understand and send it to the terminal in the form of an HTTP response.

[1376] Output: Stock information is provided to the terminal.

[1377] Step 9:

[1378] The device displays inventory information.

[1379] Input: Inventory information sent from the server.

[1380] Data processing: Converting inventory information into the data structure required for display in the user interface.

[1381] Output: The inventory information is displayed on the terminal display for the user to review.

[1382] Through these steps, users can quickly and efficiently find the products they need and purchase them at a nearby 100-yen store.

[1383] 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.

[1384] This invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support.

[1385] Program processing explanation

[1386] Questions about assignments

[1387] First, the user inputs a question into the terminal. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the system via the terminal.

[1388] Sending questions to the server

[1389] The terminal sends the question entered by the user to the server, which receives the question via the Internet.

[1390] Question analysis and emotion recognition

[1391] The server passes the question to the generative AI model for analysis, while the device recognizes emotions from the user's input text and generates user emotion data using an emotion engine.

[1392] Proposed solutions

[1393] The server generates solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that will help relieve stress.

[1394] Identifying products from the database

[1395] Based on the generated solution, the server identifies the relevant product from the database, for example, obtaining detailed information on "knife shield, silicone kitchen mitt, vegetable cutter."

[1396] Obtaining user location information

[1397] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[1398] Identifying nearby stores

[1399] The server identifies nearby 100-yen stores based on the user's location, and then references a geographic database to find the store closest to the user's location.

[1400] Check stock information

[1401] The server checks the inventory information of nearby stores based on the identified product, and then references each store's inventory database to create a list of stores that stock the suggested product.

[1402] Providing results to users

[1403] The server organizes and formats the inventory information and sends it to the device, including a list of stores that stock the product and its location.

[1404] Displaying results to the user

[1405] The terminal displays the received inventory information to the user, such as "Knife shield: Shinjuku store 2nd-3rd floor, Silicone kitchen mitt: Shinjuku Nishichome store 1st floor, Vegetable cutter: Shibuya store 3rd floor."

[1406] Specific examples

[1407] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes the emotion from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[1408] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need.

[1409] This invention allows users to get appropriate solutions depending on their emotional state, providing more personalized support.

[1410] The processing flow will be explained below.

[1411] Step 1:

[1412] The user inputs a question into the terminal. For example, "Please tell me about some useful tools for cooking."

[1413] Step 2:

[1414] The device sends a question to the server, which receives the question via the Internet.

[1415] Step 3:

[1416] The device activates an emotion engine to recognize emotions from the user's input text. The emotion engine analyzes the user's input text using natural language processing and generates emotion data such as "stress."

[1417] Step 4:

[1418] Emotion data is sent to the server, which receives it.

[1419] Step 5:

[1420] The server passes the question to a generative AI model for analysis. The generative AI model analyzes the question using natural language processing technology and suggests solutions such as a knife shield, a silicone kitchen knife mitt, and a vegetable cutter.

[1421] Step 6:

[1422] The server combines emotional data with the generated solutions and adjusts the priority of solutions according to the emotion. For example, if it detects "stress," it prioritizes products that are easy to use and reduce stress.

[1423] Step 7:

[1424] The server identifies the products corresponding to the preferred solution from its database, for example, retrieving details for "knife shields, silicone kitchen mitts, vegetable cutters."

[1425] Step 8:

[1426] The device obtains the user's location information using GPS data and IP address and sends it to the server.

[1427] Step 9:

[1428] The server then uses the location information to refer to a geographical information database to identify nearby 100-yen shops. For example, it finds shops in Shinjuku Ward.

[1429] Step 10:

[1430] The server checks the inventory information for the identified products. It queries each store's inventory database and creates a list of stores that stock "knife shields, silicone kitchen mitts, and vegetable cutters."

[1431] Step 11:

[1432] The server organizes and formats the inventory information and sends a solution list that takes into account the user's emotional data to the terminal. For example, it generates a list including information such as "Knife shield: Shinjuku store 2-3F, silicone kitchen mitt: Shinjuku Nishi-chome store 1F, vegetable cutter: Shibuya store 3F."

[1433] Step 12:

[1434] The terminal displays the received inventory information and solution list to the user, allowing the user to efficiently find the product they need and purchase it at the nearest store.

[1435] Example 2

[1436] 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."

[1437] In conventional systems, when users receive suggestions to solve problems in their daily lives, the suggestions do not take into account the user's emotional state, making it difficult to provide optimal solutions. Furthermore, the process of identifying nearby stores based on the user's location information and checking inventory information is cumbersome, placing a heavy burden on the user. The purpose of this invention is to solve these problems and provide more personalized and efficient support.

[1438] 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.

[1439] In this invention, the server includes: means for accepting a question entered by a user into the terminal; means for transmitting the question to the server; means for the server to analyze the question using a generative AI model and propose a corresponding solution; means for the server to identify a product corresponding to the solution from a database; means for the terminal to recognize the user's emotions, generate emotion data and transmit it to the server; means for the server to tailor a solution based on the emotion data; means for the terminal to acquire the user's location information and transmit the information to the server; means for the server to identify nearby stores based on the location information; means for the server to check inventory information of the identified stores based on the product; means for the server to provide the inventory information to the user; and means for the terminal to display the inventory information. This allows for suggestions that take the user's emotional state into consideration and provides an optimal solution. Furthermore, combining location information and inventory information allows users to efficiently find products.

[1440] "User" refers to a person who uses the system to enter questions and receive suggestions.

[1441] "Terminal" refers to the device a user uses to enter questions and view results, such as a personal computer or smartphone.

[1442] "Server" refers to a computer system that plays a central role in analyzing user queries and providing appropriate solutions.

[1443] A "question" refers to an input that expresses a problem that a user wants the system to solve or something that the user wants to know.

[1444] "Generative AI model" refers to an artificial intelligence model used to analyze a question and generate an appropriate solution.

[1445] "Analysis" refers to the process of understanding the content of the question entered by the user and grasping its meaning.

[1446] "Solution" refers to a specific answer or suggestion provided to the analyzed question.

[1447] A "database" refers to a collection of systematically accumulated data such as product and store information that corresponds to a solution.

[1448] "Emotion" refers to the psychological state inferred from the user's input, and appropriate solutions are suggested based on this.

[1449] "Emotion data" refers to data that recognizes emotions from the user's input text and expresses that state in numerical values ​​or categories.

[1450] "Location information" refers to data that indicates a user's current physical location and is obtained from GPS data, IP address, etc.

[1451] "Nearby stores" refers to stores located within an available area identified based on the user's location information.

[1452] "Inventory information" refers to data regarding the availability and quantity of a product at a particular store.

[1453] "Display" refers to the visual presentation of information on the screen of a device.

[1454] The present invention adds a user emotion recognition function to a system that allows users to efficiently solve everyday problems using 100-yen store items. This system takes the user's emotions into consideration and proposes appropriate solutions, providing more personalized support. The following describes an embodiment of the present invention in detail.

[1455] First, the user inputs a question into a terminal. The terminal used here can be a personal computer or smartphone. For example, the user might input, "Please tell me about some useful tools for cooking." This information is sent to the server via the terminal.

[1456] The question received by the server is sent to a generative AI model (e.g., ChatGPT) for analysis. The generative AI model uses natural language processing to understand the content of the question and generate a relevant solution. Here, a device with emotion recognition capabilities generates emotion data from the user's input text and sends it to the server. An emotion engine (e.g., Microsoft Azure Emotion API) is used for this emotion recognition.

[1457] The server adjusts the appropriate solution based on the solutions provided by the generative AI model and the emotional data sent from the device. For example, if the user is feeling stressed, the server will prioritize suggesting products that help relieve stress. Possible solutions include a knife shield, a silicone kitchen mitt, and a vegetable cutter.

[1458] The server then retrieves detailed information about the suggested products from a database, which includes product names, prices, ratings, etc. Specific product information can improve the quality of the solutions provided to users.

[1459] The device then acquires the user's location information using GPS data and IP address and sends it to the server. The server then references the geographic information database based on the location information to identify 100-yen shops near the user. For example, it identifies "100-yen shops in Shinjuku Ward."

[1460] The server then checks the inventory information of each store for the identified items. The server then compiles the inventory information for each item and provides it to the user. This information includes details of which items are in stock at which stores.

[1461] Finally, the terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shield: Shinjuku store 2-3F, Silicone kitchen mitt: Shinjuku Nishichome store 1F, Vegetable cutter: Shibuya store 3F." This allows users to efficiently find the products they need.

[1462] For example, if a user types, "Please tell me about some useful cooking tools," the question is sent to the server. During this process, the device's emotion engine recognizes emotions from the user's input text and generates metadata such as "I'm feeling stressed." The server then uses a generative AI model to analyze the question and suggests knife shields, silicone kitchen mitts, and vegetable cutters, but prioritizes products that help relieve stress if the user is feeling stressed.

[1463] The server retrieves detailed information about these products from the database and identifies 100-yen stores in Shinjuku Ward based on the user's location information. It then checks inventory information for related products and informs the user that the knife shield is in stock on the 2nd and 3rd floors of the Shinjuku store, the silicone kitchen mitt on the 1st floor of the Shinjuku Nishi-chome store, and the vegetable cutter on the 3rd floor of the Shibuya store. The terminal displays this information to the user, allowing them to efficiently find the products they need. This invention allows users to receive appropriate solutions based on their emotional state, providing more personalized support.

[1464] Keywords include generative AI model and prompt sentence.

[1465] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1466] Step 1:

[1467] User question input

[1468] The user inputs a question into the terminal, for example, "Please tell me some useful tools for cooking." The input data is a question in the form of a string, and this information is used in the next processing step.

[1469] Step 2:

[1470] Submit a Question

[1471] The terminal transmits the input question to the server, using the Internet Protocol to transmit the user's input data, generating question data that arrives at the server as output.

[1472] Step 3:

[1473] Question Analysis

[1474] The server passes the received question to the generative AI model, which analyzes the question. The generative AI model uses natural language processing technology to understand the question and generate a solution prompt. For example, in response to the question "Please tell me about some useful cooking tools," it generates a command to search for "100 yen cooking tools." A list of candidate solutions is generated as the output.

[1475] Step 4:

[1476] emotion recognition

[1477] The device recognizes emotions from the user's input text. Using the emotion engine, it generates emotion data (e.g., stress, joy, etc.) from the user's input and sends it to the server. The input is the user's text data, and the output is the meta information of the emotion data.

[1478] Step 5:

[1479] Adjusting the solution

[1480] The server tailors solutions based on the analysis results and emotional data. For example, if the user is feeling stressed, it will prioritize products that help relieve stress. As an output, it generates a prioritized list of solutions.

[1481] Step 6:

[1482] Obtaining detailed product information

[1483] The server retrieves product details from the database based on the adjusted solution list, such as the price, rating, description, etc. of "Knife Shield, Silicone Kitchen Mitt, Vegetable Cutter." The input is the solution list, and the output is a list of product details.

[1484] Step 7:

[1485] Obtaining user location information

[1486] The device acquires the user's location information using GPS data and IP address and sends it to the server. The input is the trigger for location acquisition, and the output is the location data.

[1487] Step 8:

[1488] Identifying nearby stores

[1489] The server references a geographic information database based on the user's location information to identify nearby 100-yen shops. For example, it generates a list of shops in Shinjuku Ward. The input is location data, and the output is a list of identified shops.

[1490] Step 9:

[1491] Check stock information

[1492] The server then references the inventory database of each store based on the identified product and checks stock information. For example, it obtains information such as "Knife shields are on the 2nd and 3rd floors of the Shinjuku store, and silicone kitchen mitts are on the 1st floor of the Shinjuku Nishi-chome store." The input is store and product data, and the output is the stock check result.

[1493] Step 10:

[1494] Providing results

[1495] The server organizes and formats the data to be provided to the user based on the acquired inventory information and sends it to the terminal. The input is inventory information, and the output is the information provided to the user.

[1496] Step 11:

[1497] Results display

[1498] The terminal displays the inventory information received from the server to the user. For example, it displays information such as "Knife shields: Shinjuku store 2-3F, silicone kitchen mitts: Shinjuku Nishi-chome store 1F, vegetable cutters: Shibuya store 3F." The input is formatted inventory information, and the output is a visual display to the user.

[1499] (Application example 2)

[1500] 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."

[1501] Modern users face various problems in their daily lives and often find it difficult to find solutions quickly. In particular, in situations where it is necessary to find optimal solutions based on emotions and moods, the challenge is that conventional systems cannot provide personalized support by making suggestions that take into account the emotions of individual users.

[1502] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1503] In this invention, the server includes means for analyzing a question entered by a user using a generative AI model and proposing an optimal solution, means for recognizing the user's emotions and generating emotion data, and means for proposing products based on the user's emotions, thereby providing an appropriate solution corresponding to the user's emotions and enabling more personalized support.

[1504] A "terminal" is an electronic device that a user uses to input and display information.

[1505] A "question" is text information that a user inputs into a terminal in search of a solution or suggestion.

[1506] A "server" is a computer system that processes information received from users and provides appropriate solutions or information.

[1507] A "generative AI model" is an artificial intelligence model that runs on a server and uses natural language processing to analyze text data entered by a user.

[1508] A "database" is a collection of information that stores product information, inventory information, geographical information, etc. and is referenced by the server.

[1509] "Location information" is geographic data about a user's current location, obtained through GPS or IP address.

[1510] "Emotion data" is information that represents an emotional state identified from the user's input text.

[1511] "Products" are available items stored in the database that are the subject of suggestions.

[1512] A "store" is a place that sells products and holds inventory, and is identified based on the user's location.

[1513] "Inventory information" is information indicating the availability of a product at a particular store.

[1514] This invention is a support system for users to solve problems in daily life, especially in the food delivery field. This system realizes more personalized support by recognizing the user's emotions and providing appropriate solutions based on those emotions.

[1515] The system is configured as follows: A user inputs a question using a device (such as a smartphone). This input text information is sent from the device to a server. The server analyzes the question using a generative AI model and proposes an appropriate solution to the user's problem. The device also has an emotion recognition engine that recognizes emotions from the user's input text, generating user emotion data.

[1516] The server then suggests the optimal delivery menu based on the analysis results and emotional data. For example, if a user enters "I'm very tired today," this text is sent to the server, and the emotion recognition engine recognizes the emotion "tired." Based on this information, the server suggests dishes that will help relieve stress, such as "soup" or "warm bread."

[1517] The server then retrieves detailed information about the proposed menu from the database and identifies nearby restaurants based on the user's location. This process also references a geographical database. The server then checks the inventory information of the identified restaurants and lists restaurants that stock the proposed menu. This information is then sent back to the device, which displays the results to the user.

[1518] Specifically, the following food delivery assistant implementations are possible:

[1519] Program processing explanation

[1520] Hardware: Smartphone, server, GPS module

[1521] Software: Flask (web application framework), Transformers (natural language processing model), database management system

[1522] For example, if a user types "I'm very tired today," the text is sent to the server. The server uses an emotion recognition engine to analyze the input text and recognize the emotion "tired." Based on this emotion, the server generates a list of options such as "soup" and "warm bread." This allows the user to find appropriate delivery options based on their emotional state.

[1523] Prompt Sentence Examples

[1524] User input: "I'm very tired today"

[1525] AI Prompt: "Identify the sentiment from the above input and suggest dishes the user might like."

[1526] This system allows users to quickly find the best solution that takes their emotions into consideration, improving the experience of using delivery services.

[1527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1528] Step 1:

[1529] The user inputs a question into the terminal. The question includes the specific content for which the user wants a solution and the user's current feelings. For example, the user might input "I'm very tired today."

[1530] Input: Question text

[1531] Output: Question text entered into the terminal

[1532] Step 2:

[1533] The device sends the entered question to the server, and the text data is sent to the server via the Internet.

[1534] Input: Question text typed into the terminal

[1535] Output: The question text sent to the server

[1536] Step 3:

[1537] The server analyzes the question using a generative AI model and creates a proposal to solve the problem. It uses an emotion recognition engine to generate emotion data from the user's text. It then makes a proposal based on the analysis results and emotion data.

[1538] Input: Question text

[1539] Output: Analysis results, emotion data, suggestions

[1540] Step 4:

[1541] The server identifies product information corresponding to the proposed solution from a database, including details of the proposed menu.

[1542] Input: Analysis results, emotion data

[1543] Output: Product information list

[1544] Step 5:

[1545] The device acquires the user's location information and sends it to the server. Location information is acquired using GPS data and IP address.

[1546] Input: User location (GPS data or IP address)

[1547] Output: Location information sent to the server

[1548] Step 6:

[1549] The server identifies nearby stores based on the location information and references a geographic information database to find the store closest to the user's current location.

[1550] Input:Location

[1551] Output: List of nearby stores

[1552] Step 7:

[1553] The server checks the inventory information of nearby stores based on the identified product, and references each store's inventory database to create a list of stores that stock the suggested product.

[1554] Input: Nearby store list, product information list

[1555] Output: Stock information list

[1556] Step 8:

[1557] The server compiles and formats inventory information and sends it to the device, providing data including a list of stores that stock the product and its location.

[1558] Input: Stock Information List

[1559] Output: Organized inventory information sent to the device

[1560] Step 9:

[1561] The terminal displays the inventory information received from the server to the user. The display screen shows a list of stores that have the product in stock and their locations within those stores.

[1562] Input: Organized inventory information

[1563] Output: Inventory information displayed to the user

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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).

[1571] 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.

[1572] 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."

[1573] 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.

[1574] 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).

[1575] 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.

[1576] 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.

[1577] 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.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] The following is further disclosed regarding the above embodiment.

[1586] (Claim 1)

[1587] means for accepting a question input by a user into a terminal;

[1588] means for sending said query to a server;

[1589] means for the server to analyze the question using a generative AI model and propose a corresponding solution;

[1590] means for the server to identify a product corresponding to the solution from a database;

[1591] a means for the terminal to acquire user location information and transmit the information to a server;

[1592] A means for the server to identify nearby stores based on the location information;

[1593] a means for the server to check inventory information of the store identified based on the product;

[1594] means for the server to provide the inventory information to a user;

[1595] a means for displaying the inventory information on the terminal;

[1596] A system including:

[1597] (Claim 2)

[1598] 10. The system of claim 1, wherein the generative AI model comprises means for parsing the question using natural language processing.

[1599] (Claim 3)

[1600] The system according to claim 1, wherein the server comprises means for identifying nearby stores by referring to a geographic information database.

[1601] "Example 1"

[1602] (Claim 1)

[1603] means for accepting a question input by a user into a terminal;

[1604] means for sending said query to a server;

[1605] means for the server to analyze the question using a generative AI model and propose a corresponding solution;

[1606] means for the server to identify a product corresponding to the solution from a database;

[1607] a means for the terminal to acquire user location information and transmit the information to a server;

[1608] A means for the server to identify nearby stores based on the location information;

[1609] a means for the server to check inventory information of the store identified based on the product;

[1610] means for the server to provide the inventory information to a user;

[1611] a means for displaying the inventory information on the terminal;

[1612] A system including:

[1613] (Claim 2)

[1614] 10. The system of claim 1, wherein the generative AI model comprises means for parsing the question using natural language processing.

[1615] (Claim 3)

[1616] The system according to claim 1, wherein the server comprises means for identifying nearby stores by referring to a geographic information database.

[1617] "Application Example 1"

[1618] (Claim 1)

[1619] means for accepting a question input by a user into a terminal;

[1620] means for sending said query to a server;

[1621] means for the server to analyze the question using a generative AI model and propose a corresponding solution;

[1622] means for the server to identify a product corresponding to the solution from a database;

[1623] a means for the terminal to acquire user location information and transmit the information to a server;

[1624] a means for the server to identify nearby sales locations based on the location information;

[1625] a means for the server to check inventory information of the sales base identified based on the product;

[1626] means for the server to provide the inventory information to a user;

[1627] a means for displaying the inventory information on the terminal;

[1628] a means by which the user can view detailed information about the suggested products;

[1629] A means for providing a generative AI model with specific prompt sentences suggested based on the content of the user's question;

[1630] A system including:

[1631] (Claim 2)

[1632] 10. The system of claim 1, wherein the generative AI model comprises means for parsing the question using natural language processing.

[1633] (Claim 3)

[1634] 2. The system according to claim 1, wherein the server comprises means for identifying nearby points of sale by referring to a geographic information database.

[1635] "Example 2: Combining Emotion Engines"

[1636] (Claim 1)

[1637] means for accepting a question input by a user into a terminal;

[1638] means for sending said query to a server;

[1639] means for the server to analyze the question using a generative AI model and propose a corresponding solution;

[1640] means for the server to identify a product corresponding to the solution from a database;

[1641] means for the terminal to recognize the user's emotion, generate emotion data, and transmit the emotion data to a server;

[1642] means for the server to adjust a solution based on the emotion data;

[1643] a means for the terminal to acquire user location information and transmit the information to a server;

[1644] A means for the server to identify nearby stores based on the location information;

[1645] a means for the server to check inventory information of the store identified based on the product;

[1646] means for the server to provide the inventory information to a user;

[1647] a means for displaying the inventory information on the terminal;

[1648] A system including:

[1649] (Claim 2)

[1650] 10. The system of claim 1, wherein the generative AI model comprises means for parsing the question using natural language processing.

[1651] (Claim 3)

[1652] The system according to claim 1, wherein the server comprises means for identifying nearby stores by referring to a geographic information database.

[1653] "Application example 2 when combining emotion engines"

[1654] (Claim 1)

[1655] means for accepting a question input by a user into a terminal;

[1656] means for sending said query to a server;

[1657] means for the server to analyze the question using a generative AI model and propose a corresponding solution;

[1658] means for the server to identify a product corresponding to the solution from a database;

[1659] a means for the terminal to acquire user location information and transmit the information to a server;

[1660] A means for the server to identify nearby stores based on the location information;

[1661] a means for the server to check inventory information of the store identified based on the product;

[1662] means for the server to provide the inventory information to a user;

[1663] a means for displaying the inventory information on the terminal;

[1664] means for recognizing emotions from a user's input text and generating emotion data;

[1665] means for the server to suggest a product corresponding to the user's emotion based on the emotion data;

[1666] A system including:

[1667] (Claim 2)

[1668] 10. The system of claim 1, wherein the generative AI model comprises means for parsing the question using natural language processing.

[1669] (Claim 3)

[1670] The system according to claim 1, wherein the server comprises means for identifying nearby stores by referring to a geographic information database. [Explanation of symbols]

[1671] 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. means for accepting a question input by a user into a terminal; means for sending said query to a server; means for the server to analyze the question using a generative AI model and propose a corresponding solution; means for the server to identify a product corresponding to the solution from a database; means for the terminal to acquire user location information and transmit the information to a server; A means for the server to identify nearby stores based on the location information; a means for the server to check inventory information of the store identified based on the product; means for the server to provide the inventory information to a user; means for displaying the inventory information on the terminal; A system including:

2. The system of claim 1 , wherein the generative AI model comprises means for parsing the question using natural language processing.

3. The system according to claim 1 , wherein the server comprises means for identifying nearby stores by referring to a geographic information database.

Images