System

Abstract

A system is provided.SOLUTION: The system includes a terminal for a user to input his / her preference and request, a terminal for a local producer to register commodity information, an artificial intelligence engine for storing the preference and request information of the user and the commodity information of the producer in a database, and a means for analyzing the information stored in the database and matching a commodity matched with the preference and request of the user.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] Local specialty products and niche creative products are not well known and are difficult to find on major marketplaces. This makes it difficult for users to find products that suit their tastes and needs. At the same time, it is difficult for producers to find new business opportunities, and they are not contributing to regional revitalization. Furthermore, searching for the right product from a vast amount of information takes time and effort, which is why such a system is necessary. [Means for solving the problem]

[0005] The present invention is a system that includes a terminal for users to input their preferences and requests, a terminal for local producers to register product information, a server that stores the user's preferences and requests and the producer's product information in a database, an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's preferences and requests, and a means for notifying the user's terminal of the matching results. This allows users to easily find products that suit their preferences, and producers to gain new business opportunities. As a result, it contributes to the revitalization of local areas.

[0006] "Terminal" refers to a device that users and producers use to access the system via the Internet and input information.

[0007] "User" refers to a consumer who uses the system to search for products that suit their tastes and needs.

[0008] "Producers" refer to individuals and companies that produce local specialties or niche creative products and register that information in the system.

[0009] "Taste" refers to a user's preference for a particular product or category.

[0010] "Requests" refer to the specific conditions for products or services that users require from the system.

[0011] "Product information" refers to detailed product descriptions, photos, price ranges, production methods, etc. provided by producers.

[0012] "Server" refers to a central device that stores and manages system data and responds to access and requests from other devices.

[0013] "Database" refers to a collection of information used to efficiently store, manage, and search user and product information within a system.

[0014] "Artificial intelligence engine" refers to machine learning and other AI technologies that analyze information stored in a database and appropriately match products that match users' tastes and requests.

[0015] "Matching" refers to the process in which an artificial intelligence engine selects the most suitable products and producers based on the user's tastes and requests, and presents the results.

[0016] "Tagging" refers to the process of adding keywords or category labels to information in order to classify and organize it.

[0017] "Notification" refers to the means of informing the user or producer of matching results and other important information on their device. [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] The present invention is a system that effectively matches users with local producers, and an embodiment thereof is described below. This system includes a terminal for users to input their preferences and requests, a terminal for producers to register product information, a server for storing and analyzing data, and an AI engine running on the server.

[0040] System Overview

[0041] 1. Device operation

[0042] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, and their purchase history.

[0043] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[0044] 2. Server Roles

[0045] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[0046] The database efficiently stores and manages user and producer information.

[0047] 3. Matching Process

[0048] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[0049] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[0050] Program processing explanation

[0051] Collection of User Information

[0052] Device:

[0053] Users access the system and enter information about their preferences and needs, including their name, email address, hobbies and preferences, genres of interest, and purchase history.

[0054] server:

[0055] User information is received and stored in a database, where it is tagged and categorized based on the information entered.

[0056] Collecting producer information

[0057] Device:

[0058] Producers access the system and enter product information, including company or individual names, detailed product descriptions, photos, production location information, price ranges, and production processes.

[0059] server:

[0060] Producer information is received and stored in a database, and tagging and categorization are performed based on the information entered.

[0061] AI automatic learning and matching

[0062] server:

[0063] User and producer information is read from the database and analyzed using an AI engine. The system matches users with the most suitable products, taking into account their preferences, past behavioral history, and product features.

[0064] Presenting matching results

[0065] server:

[0066] It generates matching results and creates individual notification messages, the contents of which are sent to the user's device.

[0067] Device:

[0068] It receives notifications and displays matching results to the user, including product name, producer information, price, and purchasing instructions.

[0069] Specific examples

[0070] 1. User A's actions

[0071] Register "traditional crafts" as your preference on the device and enter your tastes and requests.

[0072] Enter specific criteria that match your interests into the system.

[0073] 2. Producer B's actions

[0074] Register "Handmade Wood Carvings" as a product on the terminal.

[0075] Enter detailed product information, place of origin, price range, and production method.

[0076] 3. Server Analysis

[0077] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[0078] 4. Notification of Results

[0079] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[0080] User A's device receives the notification and displays detailed information.

[0081] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, which is expected to contribute to the revitalization of local communities.

[0082] The processing flow will be explained below.

[0083] Step 1:

[0084] Terminal: The user accesses the system and enters the necessary information into the user registration form, such as their name, email address, hobbies and preferences, genres of interest, and purchase history.

[0085] Step 2:

[0086] Terminal: The user checks the information entered and clicks the "Register" button, which sends the form data to the server.

[0087] Step 3:

[0088] Server: Receives user information sent from the device and stores it in a database, tagging and categorizing it based on the information.

[0089] Step 4:

[0090] Terminal: Producers access the system and enter the necessary information into the producer registration form, such as the company or individual name, detailed product description, photo, production area information, price range, and production process.

[0091] Step 5:

[0092] Terminal: The producer confirms the information entered and clicks the "Register" button, which sends the form data to the server.

[0093] Step 6:

[0094] Server: Receives producer information sent from the device and stores it in a database. At this time, it tags and categorizes the products based on the information.

[0095] Step 7:

[0096] Server: Reads user and producer information from the database and analyzes it using an AI engine. The analysis takes into account the user's preferences, past behavioral history, and the producer's product characteristics.

[0097] Step 8:

[0098] Server: The AI ​​engine matches the optimal products based on the user's preferences and generates matching results.

[0099] Step 9:

[0100] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[0101] Step 10:

[0102] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[0103] Step 11:

[0104] On the device: The user checks the notification and views detailed information about the product they are interested in. This allows the user to find products that suit their preferences.

[0105] In this way, the entire system can operate smoothly and effectively match users and producers.

[0106] Example 1

[0107] 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."

[0108] In modern society, it is difficult to effectively match rural producers with urban consumers. Rural producers have limited opportunities to widely promote their products, and urban consumers have few means of accessing local specialties. As a result, consumers are unable to find products that suit their tastes, and producers miss out on new business opportunities. This has also slowed the revitalization of local economies.

[0109] 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.

[0110] In this invention, the server includes a means for storing user preferences and request information and producer product information in a database, a means for tagging and categorizing based on the information stored in the database, and an artificial intelligence engine for analyzing the information and matching products that match the user's preferences and requests. This allows users to easily find products that suit their tastes, and producers to gain new business opportunities. This is likely to contribute to the revitalization of local economies.

[0111] "Terminal" means an electronic device used by a user or producer to enter or access information.

[0112] A "server" is a computer system that receives, stores, and analyzes information sent from terminals via a network.

[0113] A "database" is a system for systematically storing and managing information about users and producers.

[0114] "Tastes" refer to the user's personal preferences and interests.

[0115] "Requests" are specific conditions or requirements that users need.

[0116] "Producers" are individuals or companies that produce and supply local specialty products.

[0117] "Product information" refers to data provided by producers, such as detailed product descriptions, information on the place of origin, price, and manufacturing process.

[0118] "Tagging" is the act of labeling information to classify and organize it.

[0119] "Categorization" is the process of classifying information into specific groups.

[0120] An "artificial intelligence engine" is a program that includes machine learning algorithms to analyze data and match products that match users' tastes and requests.

[0121] "Matching" is the process of selecting and combining products that best suit the user's tastes and requests.

[0122] A "notification" is information or a message sent from the server to the user's terminal.

[0123] The present invention is a system that effectively matches users with local producers. The system includes the following elements:

[0124] 1. Device operation

[0125] The user's device provides an interface for the user to input their preferences and requests. For example, the user opens a browser and enters their hobbies, preferences, purchase history, etc. into an input form. Then, they click a button to send the entered information to the server.

[0126] The producer's terminal provides an interface for registering product information. The producer inputs detailed product descriptions, price ranges, and production area information, and sends them to the server.

[0127] 2. Server Roles

[0128] The server receives the user information and producer information sent from the terminal and stores it in a database. Specific software used for this purpose is a database management system such as MySQL (registered trademark).

[0129] The database efficiently stores and manages information about users and producers. User information includes categories of hobbies and preferences and purchase history, while producer information includes detailed product descriptions, price ranges, and production processes.

[0130] The server tags and categorizes the information it receives, using a tagging algorithm to assign appropriate tags to each piece of information.

[0131] 3. Matching Process

[0132] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests. The AI ​​engine uses machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch.

[0133] The server takes into account the user's past behavioral history and the producer's product characteristics to generate optimal matching results.

[0134] 4. Notification of Matching Results

[0135] The server generates matching results and creates a personalized notification message, for example, "Recommended Product: Handmade Wood Carvings (Producer Name)" and sends it to the user's device.

[0136] The device receives the notification and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[0137] Specific examples

[0138] User behavior examples

[0139] User A sets "traditional crafts" as a preference and inputs his / her tastes and requests. For example, he / she may input that he / she is interested in "handmade wood carvings."

[0140] Examples of producer actions

[0141] Producer B registers "handmade wood carvings" as a product. He enters detailed product information, including the place of origin, price range, and production method.

[0142] Server analysis example

[0143] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[0144] Example of notification of results

[0145] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer B)" and sends it to User A's device.

[0146] User A receives the notification on his / her device and checks the detailed information.

[0147] Prompt Sentence Examples

[0148] Prompt for User A: "Please describe your preferences or desires. For example, 'traditional crafts' or 'handmade wood carvings.'"

[0149] Prompt for Producer B: "Please enter the product information you would like to register. Include the product name, detailed description, photos, origin, price range, and production process."

[0150] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, thereby contributing to the revitalization of local economies.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Program processing flow

[0153] Step 1: Enter your user information

[0154] explanation:

[0155] Users access the system from their own devices using a dedicated application or web interface, and enter information such as their name, email address, hobbies and preferences, genres of interest, and purchase history in the displayed form.

[0156] input:

[0157] User name, email address, hobbies and preferences, genres of interest, purchase history

[0158] output:

[0159] This information is sent to the server

[0160] Specific behavior:

[0161] The user opens a browser, visits the specified website, logs in, fills in the required information on the form, and clicks the "Submit" button.

[0162] Step 2: Enter producer information

[0163] explanation:

[0164] Producers access the system using a dedicated application or web interface from their own devices, and enter detailed product descriptions, photos, information on the place of origin, price range, and production process.

[0165] input:

[0166] Company or individual name, detailed product description, photo, origin information, price range, production process

[0167] output:

[0168] This information is sent to the server

[0169] Specific behavior:

[0170] The producer opens a browser and accesses the specified website. After logging in, they enter the required product information into the form and click the "Submit" button.

[0171] Step 3: Data received and stored by the server

[0172] explanation:

[0173] The server receives the user and producer information sent from the device, then stores the received information in a database and tags and categorizes it.

[0174] input:

[0175] User information, producer information

[0176] output:

[0177] User and producer information stored in a database, along with their corresponding tags and categories

[0178] Specific behavior:

[0179] The server analyzes the data received from users' and producers' devices and stores it in a database system such as MySQL. Using a tagging algorithm, appropriate tags are assigned to each piece of information and it is then categorized.

[0180] Step 4: Database analysis and matching

[0181] explanation:

[0182] The server's AI engine reads and analyzes the user and producer information stored in the database. As a result of the analysis, it takes into account the user's preferences, past behavioral history, and the producer's product characteristics to match the optimal product.

[0183] input:

[0184] User information stored in the database, producer information stored in the database

[0185] output:

[0186] Results of product matching that matches user tastes and requests

[0187] Specific behavior:

[0188] The AI ​​engine uses machine learning libraries such as TensorFlow and PyTorch to analyze the user and producer information in the database, and connects users and producers through clustering algorithms and recommendation systems.

[0189] Step 5: Generating and notifying matching results

[0190] explanation:

[0191] The server generates a personalized notification message for the user based on the matching results obtained from the AI ​​engine, and the notification message is sent to the user's device, where the user can receive and check it.

[0192] input:

[0193] Matching results

[0194] output:

[0195] Notification message to user terminal

[0196] Specific behavior:

[0197] The server generates a notification message to User A in the form of a push notification or email with the content "Recommended Product: Handmade Wood Carving (Producer B)". The generated notification message is sent to User A's device. User A's device receives the notification and displays it on the screen.

[0198] The above is a specific processing flow of the system.

[0199] (Application example 1)

[0200] 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."

[0201] Products offered by local producers often match the tastes and requests of specific users, but effective ways of approaching consumers are limited, making appropriate purchasing matching difficult. Users also have few means of finding local products that interest them, making purchasing activities cumbersome. This makes it difficult for local producers to seize new business opportunities, and consumers also have difficulty finding products that suit their tastes.

[0202] 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.

[0203] In this invention, the server includes an artificial intelligence engine that matches products that match the user's tastes and desires. This provides a means for tagging and categorizing products based on the user's tastes and desires, a means for matching products taking into account past purchase history, and a means for notifying the user of recommended products and displaying detailed information. This allows users to easily find products that match their tastes and desires, and also provides local producers with new business opportunities.

[0204] A "terminal" is a device equipped with an interface for users to input their preferences and requests, and is used by users and producers to provide information to the system.

[0205] The "server" is a central processing unit that stores information on users' preferences and requests, as well as product information from producers, in a database, and analyzes this information to perform matching.

[0206] A "database" is a place where information is collected to efficiently store and manage user and producer information.

[0207] The "artificial intelligence engine" is a program that analyzes information stored in a database and matches products that match the user's tastes and requests.

[0208] The "notification means" is a function that transmits the matching results to the user's terminal and provides the user with information about recommended products.

[0209] "Tagging" is a technique for assigning keywords and categories related to information (such as products or interests) to make it easier to search and analyze within a database.

[0210] "Categorization" refers to the process of classifying data into specific groups to make management and searching more efficient.

[0211] "Purchase history" refers to information about products a user has purchased in the past, and analyzing this information is used to understand the user's preferences and patterns.

[0212] "Matching" refers to the process of selecting product information that best suits the user's tastes and needs and recommending it to the user.

[0213] "Recommendation" is a function that encourages purchases by presenting appropriate products based on the user's interests and past behavior.

[0214] The present invention is a system that effectively matches users with local producers. This system is designed to recommend optimal products based on the user's preferences and requests. A specific embodiment of the system is shown below.

[0215] System Overview

[0216] 1. Device operation

[0217] The user's device provides an interface for the user to input their preferences and requests, such as hobbies, tastes, local products of interest, and past purchase history.

[0218] The producer's terminal provides an interface for registering product information. The producer inputs detailed descriptions, images, price ranges, and production processes for the specialty products they produce.

[0219] 2. Server Roles

[0220] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[0221] The database efficiently stores and manages user and producer information.

[0222] 3. Matching Process

[0223] The server's artificial intelligence engine analyzes the information stored in the database and matches users with products that match their tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[0224] A notification of the results is generated by the server and sent to the appropriate user's device.

[0225] Program processing description

[0226] The hardware used is a smartphone. Examples include Apple's iPhone (registered trademark) and Samsung's Galaxy. The software uses Python to run the program, and machine learning libraries such as TensorFlow and PyTorch are used for the AI ​​engine.

[0227] The flow of data processing and calculation is as follows:

[0228] User preference input: Users input their preferences, requests, and purchase history via their smartphone. The input information is sent to the server.

[0229] Producer product registration: Producers also use smartphones or other devices to enter product information, including the product name, price, detailed description, and production process.

[0230] Tagging and categorization: The server stores the received information in a database and appropriately tags and categorizes it, taking into account the user's preferences, needs, and purchasing history.

[0231] AI analysis and matching: The server's AI engine analyzes the database and selects the products that best suit the user's tastes and needs. This process utilizes AI models.

[0232] Recommendation notification and detailed display: Matching results are generated and notified to the user's smartphone, where the user can check detailed information.

[0233] Specific examples

[0234] User A's actions: Registers "traditional crafts" as a preference on the device and inputs his / her tastes and requests.

[0235] Producer B's actions: Register "Handmade Wood Carvings" as a product on the terminal. Enter product information, place of origin, price range, and production method.

[0236] Server analysis: The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the shared tags of "traditional crafts" and "wood carving."

[0237] Result notification: The server generates a notification saying "Recommended product: Handmade wood carvings (Producer: Producer B)" and sends it to User A's device. User A checks the details.

[0238] Prompt Sentence Examples

[0239] "I would like to develop an app that recommends handmade wood carvings from local producers to users who like traditional crafts. I would like to tag user information and product information and use AI to match the best products. Please explain the basic configuration and functions of the system."

[0240] This will allow users to easily find products that suit their tastes, provide producers with new business opportunities, and is also expected to contribute to revitalizing local industries.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] Entering user information

[0244] The user uses a terminal to input their preferences, requests, past purchase history, etc. The input data is sent to the server along with the user ID. The input information includes hobbies in text format, genres of interest, and product IDs of past purchases. The server receives this information and stores it in a database.

[0245] Step 2:

[0246] Enter producer information

[0247] Producers enter product information (product name, image, detailed description, price range, place of production, etc.) into their terminal and send it to the server. The server stores the received product information in a database and assigns a product ID. At this time, the product information is tagged (for example, "traditional craft" or "wood carving").

[0248] Step 3:

[0249] Tagging and categorization

[0250] The server tags and categorizes products based on user and producer information. It generates tags based on the user's hobbies and interests, and also tags the product information. This process is managed in a database, facilitating future searches and matching.

[0251] Step 4:

[0252] Analysis and matching by AI engine

[0253] The server reads the user and producer information stored in the database and analyzes it using an AI engine. Specifically, it matches the optimal product by taking into account the user's preferences, past behavioral history, and product tag information. The AI ​​model used here is a recommender system trained using, for example, TensorFlow or PyTorch.

[0254] Step 5:

[0255] Generating matching results

[0256] The AI ​​engine generates analysis results and extracts the most relevant product information for the user ID, including the product name, image, price, and detailed description. This information is then structured into a notification message and linked to the user ID.

[0257] Step 6:

[0258] Notifications and details

[0259] The server then sends the generated notification message to the user's device. The user receives the notification and checks the details on the device. The displayed content includes the name, image, price, and detailed description of the recommended product, as well as a link to purchase it.

[0260] Through this processing flow, users can easily find products that suit their tastes and needs, and producers can efficiently promote their products.

[0261] 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.

[0262] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the user's tastes, requests, and even emotions. The system includes a terminal for users to input their preferences and requests, a terminal for local producers to register product information, a server for storing and analyzing data, an AI engine, and an emotion engine.

[0263] System Overview

[0264] 1. Device operation

[0265] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, their purchase history, and their emotional state.

[0266] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[0267] 2. Server Roles

[0268] The server receives the user and producer information sent from the terminal and stores it in a database, where it is tagged and categorized based on preference and emotion information.

[0269] The database efficiently stores and manages user and producer information.

[0270] 3. Emotion Engine

[0271] The emotion engine recognizes the user's emotional state from the user's input information and device interactions, and this information is sent to the server as additional data on the user's preferences and needs.

[0272] Emotional states include emotion categories such as happiness, excitement, surprise, and sadness.

[0273] 4. Matching Process

[0274] The server's AI engine analyzes the information stored in the database and matches users with the best products based on their preferences, needs, and even their emotional state, taking into account the user's past behavioral history and the producer's product characteristics.

[0275] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[0276] Program processing explanation

[0277] Collecting user information and emotional information

[0278] Device:

[0279] Users access the system and input their preferences, requests, and emotional state. Preferences include name, email address, hobbies, preferences, genres of interest, and purchase history. They also input their current emotional state based on an emotion input form.

[0280] server:

[0281] User information and emotion information are received and stored in a database, where they are tagged and categorized based on the information.

[0282] Collecting producer information

[0283] Device:

[0284] Producers access the system and enter product information, including the company or individual name, detailed product description, photos, production location information, price range, and production process.

[0285] server:

[0286] Producer information is received and stored in a database, which is then used to tag and categorize the produce.

[0287] Applying the Emotion Engine

[0288] Device:

[0289] The emotion engine recognizes emotions from user operations and input information and sends them to the server. Emotional states are expressed as "happiness," "surprise," "sadness," etc.

[0290] server:

[0291] The emotional information is stored in a database and used as additional data for analysis.

[0292] AI automatic learning and matching

[0293] server:

[0294] User information, producer information, and emotional information are read from the database and analyzed using an AI engine. The analysis takes into account the user's preferences, past behavioral history, the producer's product features, and the user's emotional state.

[0295] server:

[0296] The AI ​​engine matches the most suitable products based on the user's tastes and emotions and generates matching results.

[0297] Presenting matching results

[0298] server:

[0299] Based on the matching results, an individual notification message is created and sent to the user's device.

[0300] Device:

[0301] The device receives the notification sent from the server and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[0302] Specific examples

[0303] 1. User A's actions

[0304] Register "traditional crafts" as your preference on the device, and then enter your tastes, desires, and current emotional state. Enter "joy" as your emotional state.

[0305] 2. Producer B's actions

[0306] Register "Handmade Wood Carvings" as a product on the terminal and enter the product information.

[0307] 3. Server Analysis

[0308] The server uses an AI engine to analyze information about User A and Producer B, as well as emotional information, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[0309] 4. Notification of Results

[0310] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[0311] User A's device receives the notification and displays detailed information.

[0312] In this way, the system takes into account the tastes, desires, and even emotions of users to provide optimal products, opening up new business opportunities for producers and potentially contributing to the revitalization of local communities.

[0313] The processing flow will be explained below.

[0314] Step 1:

[0315] Terminal: The user accesses the system and enters information into the user registration form, specifically their name, email address, hobbies and preferences, genres of interest, and purchase history.

[0316] Step 2:

[0317] Terminal: The user checks the information they have entered and clicks the "Register" button, which sends the form data to the server.

[0318] Step 3:

[0319] Server: Receives user information sent from the device and stores it in a database. At this time, it tags and categorizes the information based on the input preferences and requests.

[0320] Step 4:

[0321] Terminal: Producers access the system and enter information into the producer registration form, including the company or individual name, detailed product description, photo, production area information, price range, and production process.

[0322] Step 5:

[0323] Terminal: The producer checks the information entered and clicks the "Register" button, which sends the form data to the server.

[0324] Step 6:

[0325] Server: Receives producer information sent from the terminal and stores it in a database. At this time, it tags and categorizes the products based on the input product information.

[0326] Step 7:

[0327] Device: The user accesses the emotion information input screen and selects their current emotional state. Emotion categories include "joy," "excitement," "surprise," and "sadness."

[0328] Step 8:

[0329] Terminal: The user inputs emotion information and clicks the "Send" button, which sends the emotion data to the server.

[0330] Step 9:

[0331] Server: Receives emotion information sent from the device and stores it in a database. This information is linked to user information for analysis.

[0332] Step 10:

[0333] Server: Reads user information, producer information, and emotional information from the database and analyzes them using an AI engine. The analysis takes into account the user's preferences and requests, past behavioral history, product features of the producer, and their emotional state.

[0334] Step 11:

[0335] Server: The AI ​​engine matches the optimal products based on the user's preferences and emotions and generates matching results.

[0336] Step 12:

[0337] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[0338] Step 13:

[0339] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[0340] Step 14:

[0341] On the device: The user checks the notification and browses detailed information about the product they are interested in. This allows the user to find products that match their tastes and emotions.

[0342] In this way, the system analyzes users' tastes, requests, and even emotions, and provides the most suitable products, thereby providing new business opportunities for producers and contributing to the revitalization of local areas.

[0343] Example 2

[0344] 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."

[0345] The objective of this invention is to accurately recognize users' tastes, desires, and even emotions, and effectively match them with local specialty products and niche creative products. Conventional systems do not take the user's emotional state into account when matching products, making it difficult to stimulate purchasing motivation or suggest appropriate products. Therefore, there is a need for a product recommendation system based on detailed information, including the user's emotions.

[0346] 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.

[0347] In this invention, the server includes a means for storing user preferences, requests, and emotional information as well as producer product information in a database, an emotion engine that recognizes the user's emotional state from user operations and input information, and an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's preferences, requests, and emotional state. This enables highly accurate product recommendations that take the user's emotional state into consideration.

[0348] A "terminal" is a device that allows users or producers to input or display information.

[0349] "Tastes" refers to the genres that users like and the things that interest them.

[0350] "Request" refers to the conditions or requests desired by the user.

[0351] "Emotional state" refers to the emotion the user is currently feeling, and includes categories such as joy, surprise, and sadness.

[0352] "Producers" refers to local producers and creators who create and sell products.

[0353] "Product information" refers to information such as detailed product descriptions, photos, price ranges, and production processes registered by producers.

[0354] A "server" is a central device that stores data, analyzes data, and manages network resources.

[0355] A "database" is a structured repository of information built within a server.

[0356] An "emotion engine" is a system for recognizing a user's emotional state from their operations and input information.

[0357] An "artificial intelligence engine" is a system that has an algorithm that analyzes information stored in a database and matches the most suitable products.

[0358] "Tagging" is the process of assigning keywords and categories to information in order to classify and organize it.

[0359] "Matching" is a method of selecting and suggesting the most suitable product based on the user's tastes, requests, and emotional state.

[0360] "Notification means" is a function for sending and displaying matching results from the server to the user's device.

[0361] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the tastes, desires, and emotions of users. An embodiment of this system will be described below.

[0362] System configuration

[0363] The system includes the following components:

[0364] 1. User's Device

[0365] This terminal provides an interface for users to input their preferences, needs, and emotional state. Users use this to input their favorite genres, local products they are interested in, their purchase history, their emotional state, and so on.

[0366] 2. Producer's terminal

[0367] This terminal provides an interface for local producers to register product information. Producers enter detailed descriptions of their specialty products, photos, price ranges, production processes, and other information.

[0368] 3. Server

[0369] It is a central device that stores information on users' tastes, desires, and emotions, as well as product information from producers, in a database that is tagged and categorized based on the stored information.

[0370] 4. Emotion Engine

[0371] This system recognizes the user's emotional state from their actions and input information. The recognized emotional information is sent to the server and used as additional data. Emotional states include categories such as joy, surprise, and sadness.

[0372] 5. Artificial Intelligence Engine

[0373] The system analyzes information stored in a database and matches users with products that match their tastes, desires, and emotional state, taking into account their past behavioral history and the characteristics of the products made by the producers.

[0374] 6. Means of notification

[0375] This function generates matching results and notifies the user's device. The user can receive the notification and check detailed information.

[0376] Example of operation

[0377] 1. User A's actions

[0378] The user registers "traditional crafts" as a preference on the device, and inputs their tastes, desires, and current emotional state. The emotional state is input as "joy."

[0379] 2. Producer B's actions

[0380] The producer registers "handmade wood carvings" as a product on the terminal and enters product information.

[0381] 3. Server Analysis

[0382] The server uses an AI engine to analyze the information and emotional information of User A and Producer B, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[0383] 4. Notification of Results

[0384] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device. User A's device receives the notification and displays detailed information.

[0385] Prompt Sentence Examples

[0386] "Using the generative AI model, please provide an example of natural language processing in this system. For example, if a user registers a preference for traditional crafts and inputs their current emotional state, please explain the steps to generate product information for handmade wood carvings that would be recommended as the most suitable item."

[0387] The above is an embodiment of the present invention. This system makes it possible to recommend products that take into account detailed information about the user, enabling the provision of products that are optimal for the user. It is also expected to provide new business opportunities for producers and contribute to the revitalization of local communities.

[0388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0389] Step 1:

[0390] Collecting user information and emotional information

[0391] User: Enters their own preferences, requests, and emotional state. Specifically, they enter their name, email address, hobbies, preferences, genres of interest, purchase history, and emotional state into the interface.

[0392] Input: Name, email address, hobbies and preferences, genre interests, purchase history, emotional state.

[0393] Output: This information is sent to the server.

[0394] Step 2:

[0395] Data transmission and storage

[0396] Terminal: Send the entered information to the server. Click the Send button to upload the data.

[0397] Server: Stores the received user information and emotion information in a database, tagging and categorizing the information.

[0398] Input: User information and emotion information entered into the interface.

[0399] Output: The tagged user information and emotion information are stored in a database.

[0400] Step 3:

[0401] Collecting producer information

[0402] Producer: Enter product information into the system. Specifically, enter the company or individual name, detailed product description, photo, production location information, price range, and production process into the interface.

[0403] Input: company / individual name, detailed product description, photo, origin information, price range, and production process.

[0404] Output: This information is sent to the server.

[0405] Step 4:

[0406] Data transmission and storage

[0407] Terminal: Send the entered product information to the server. Click the Send button to upload the data.

[0408] Server: Stores the received product information in a database, tagging and categorizing it based on the information.

[0409] Input: Product information entered into the interface.

[0410] Output: Tagged product information is stored in a database.

[0411] Step 5:

[0412] Emotion engine application and recognition

[0413] On the device: The emotion engine recognizes emotions from user actions and input information, specifically analyzing emotions based on keystroke speed and phrases in the interface.

[0414] Input: User actions and input information.

[0415] Output: Data of the recognized emotional state sent to the server.

[0416] Step 6:

[0417] Storing emotional information

[0418] Server: The received emotional information is stored in a database and used as additional data for analysis. Specifically, the emotional history of each user is recorded.

[0419] Input: Recognized emotion information.

[0420] Output: Emotion information stored in a database.

[0421] Step 7:

[0422] Loading information

[0423] Server: Reads user, producer, and sentiment information from the database, specifically extracting relevant data using SQL queries.

[0424] Input: User information, producer information, and emotion information stored in the database.

[0425] Output: Analyzed data provided to the AI ​​engine.

[0426] Step 8:

[0427] Data analysis and product matching

[0428] Server: Analyzes the information loaded by the AI ​​engine. Specifically, the AI ​​algorithm analyzes the user's preferences, past behavioral history, product features of the manufacturer, and the user's emotional state. For example, by referencing data from similar users, it predicts the best product for the current user's emotional state.

[0429] Input: Analysis data provided to the AI ​​engine.

[0430] Output: Matched product information.

[0431] Step 9:

[0432] Generating matching results

[0433] Server: Generates matching results and creates individual notification messages, specifically creating a list of candidate products and formatting them as text messages for the user.

[0434] Input: Matched product information.

[0435] Output: Informational message.

[0436] Step 10:

[0437] Sending notifications

[0438] Server: Sends the created notification message to the user's device. Specifically, it pushes the message to the user's device using a network protocol.

[0439] Input: Notification message.

[0440] output: Notification sent to the user's device.

[0441] Step 11:

[0442] Results display

[0443] Device: Receives the notification sent from the server and displays the matching results to the user. Specifically, it displays information such as "Product name: Handmade wood carving, Producer: Producer B, Price: \XXXX" on the screen.

[0444] Input: The notification sent by the server.

[0445] Output: Matching results displayed in the user interface.

[0446] (Application example 2)

[0447] 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."

[0448] In modern society, there is a demand for recommendation systems that respond to users' tastes and emotions. However, conventional systems have difficulty matching products that reflect the user's emotional state, making it difficult to effectively recommend appropriate products. In particular, when it comes to local specialties or niche creative products, it is difficult to achieve optimal matching based on the user's emotions, which makes it difficult to contribute to regional revitalization.

[0449] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal for inputting user preferences, requests, and emotional states; a terminal for local producers to register product information; a means for storing the user preferences, requests, and emotional information and producer product information in a database; an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user preferences, requests, and emotional states; a means for notifying the user terminal of the matching results; and a means for understanding the user's emotional state using emotion recognition technology. This enables appropriate product recommendations that take the user's emotional state into consideration, thereby improving the matching accuracy of local specialty products and niche creative products.

[0450] A "user" is a consumer who uses a terminal to input his or her preferences, desires, and emotional state.

[0451] "Local producers" are merchants or individuals who use terminals to register information about specialty products or niche creative goods.

[0452] A "terminal" is a device that allows users to input information about their preferences, requests, and emotional state, and a device that allows producers to register product information.

[0453] The "server" is a central device that stores information sent by users and producers in a database and analyzes it.

[0454] The "database" is an information storage system that manages information on users' preferences, requests, and emotions, as well as product information from producers, stored on a server.

[0455] An "artificial intelligence engine" is an algorithm and software that analyzes a user's tastes, requests, and emotional state with a producer's product information to match them with the most suitable product.

[0456] "Emotional state" is information that indicates the emotions that the user is feeling, and is classified into categories such as joy, surprise, and sadness.

[0457] "Emotion recognition technology" is a technology that recognizes the emotional state of a user from their input information and device interactions.

[0458] "Matching results" are information that indicates the products that best suit the user's tastes, desires, and emotional state, based on analysis by the AI ​​engine.

[0459] The "notification means" is a system in which the server generates matching results and sends them to the terminal of the corresponding user.

[0460] The present invention is a system for effectively matching local specialties and niche creative products based on the tastes, desires, and emotional state of users, and an embodiment thereof is described below. The system includes a terminal for users to input their tastes, desires, and emotional state, a terminal for local producers to register product information, a server for storing and analyzing data, an artificial intelligence engine, and emotion recognition technology.

[0461] System Overview

[0462] 1. User operations

[0463] The user terminal provides an interface for users to input their preferences, requests, and emotional state. Users input their favorite genres, local products they are interested in, their purchase history, and their emotional state (for example, joy or surprise). In addition, by using a device with a camera, emotion recognition technology can be used to analyze the user's facial expressions and automatically recognize their emotional state.

[0464] 2. Producer manipulation

[0465] The producer terminal provides an interface for producers to register product information. Producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, and so on.

[0466] 3. Server Roles

[0467] The server receives information sent from user terminals and producer terminals and stores this data in a database. At this time, it tags and categorizes the data based on the user's preferences, requests, and emotional information, and the producer's product information.

[0468] 4. Application of emotion recognition technology

[0469] Emotion recognition technology recognizes the user's emotional state from input information and device interactions and sends it to a server. Emotion recognition uses the Microsoft® Azure® emotion recognition API and other technologies.

[0470] 5. Matching Process

[0471] The AI ​​engine analyzes the user's preferences, requests, and emotional information stored in the database, as well as the product information of the producer, to match the optimal product. This is done using generative AI models such as Amazon SageMaker.

[0472] 6. Presenting the matching results

[0473] The server generates matching results based on the analysis results and notifies the relevant user's device. The user's device receives the notification and displays the product name, producer information, price, purchase method, etc.

[0474] Specific examples

[0475] 1. User A's behavior

[0476] User A accesses the app using a smartphone, enters "I like seasonal fruits," and enters his / her current emotional state as "joy." When he / she captures his / her facial expression using a camera, the emotion recognition technology automatically recognizes that information.

[0477] 2. Producer B's actions

[0478] Producer B registers "seasonal local apples" as a product and enters detailed product information and photos.

[0479] 3. Server Analysis

[0480] The server stores information and emotional information of User A and Producer B in a database, and uses a generative AI model to make optimal matches based on "fruit" and "joy."

[0481] 4. Generate and send notifications

[0482] The server generates a notification as a matching result, stating "Recommended product: Seasonal local apples (Producer: Producer B)" and sends it to User A's terminal.

[0483] 5. Receipt and confirmation by User A

[0484] User A's device receives the notification and displays detailed information. User A checks the product in the app and completes the purchase procedure.

[0485] Prompt Sentence Examples

[0486] User input: "I'm in a fun mood today, so I'm looking for something exciting to buy."

[0487] Emotion Recognition API: "Joy"

[0488] AI engine prompt: "Recommend exciting local products that match the user's tastes with emotions of joy"

[0489] This system makes it possible to recommend appropriate products that take into account the user's emotional state, improving the accuracy of matching local specialties and niche creative products.

[0490] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0491] Step 1:

[0492] The user device receives input of the user's preferences, requests, and emotional state. The user can provide their preferred genres and purchase history through text input, and their current emotional state is automatically recognized by taking a photo with a camera using emotion recognition technology. The input data (preferences, requests, and emotional state) is sent to the server.

[0493] Step 2:

[0494] The server receives information on preferences, requests, and emotional states sent from the user's device and stores it in a database. Emotion recognition technology categorizes the emotional states into categories such as "happiness," "surprise," and "sadness," and tags them as data. The stored data is used for later analysis.

[0495] Step 3:

[0496] The producer terminal receives product information (detailed description, photos, price, etc.) from the producer. The producer enters and registers the product information. The entered product information is sent to the server.

[0497] Step 4:

[0498] The server receives product information sent from the producer terminal and stores it in a database. The product information is tagged and categorized and then stored in the database.

[0499] Step 5:

[0500] The server uses a generative AI model to analyze user information (tastes, requests, emotional state) and producer information (product information) stored in a database. The purpose of the analysis is to find the product that best suits the user's tastes and emotional state. The generative AI model uses AI tools such as Amazon SageMaker.

[0501] Step 6:

[0502] The server's AI engine performs analysis and matches the optimal product based on the user's tastes, desires, and emotional state. The generative AI model compares the user information with product information to generate the optimal matching result. The matching result is expressed as a list of recommended products.

[0503] Step 7:

[0504] The server generates a notification message of the matching result and sends a list including recommended products to the user terminal. The user terminal provides an interface that displays detailed information such as recommended products, prices, and producer information, allowing the user to easily check the products.

[0505] Step 8:

[0506] Users can receive matching results through the app, check the details, and if they are interested, they can purchase the product within the app.

[0507] This series of processing steps allows users to easily find the specialty or niche product that best suits their current mood, and allows producers to effectively deliver their products to more consumers. Furthermore, the explicit operation and input / output make the system's operation clear.

[0508] 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.

[0509] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0510] 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.

[0511] [Second embodiment]

[0512] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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).

[0518] 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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."

[0524] The present invention is a system that effectively matches users with local producers, and an embodiment thereof is described below. This system includes a terminal for users to input their preferences and requests, a terminal for producers to register product information, a server for storing and analyzing data, and an AI engine running on the server.

[0525] System Overview

[0526] 1. Device operation

[0527] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, and their purchase history.

[0528] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[0529] 2. Server Roles

[0530] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[0531] The database efficiently stores and manages user and producer information.

[0532] 3. Matching Process

[0533] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[0534] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[0535] Program processing explanation

[0536] Collection of User Information

[0537] Device:

[0538] Users access the system and enter information about their preferences and needs, including their name, email address, hobbies and preferences, genres of interest, and purchase history.

[0539] server:

[0540] User information is received and stored in a database, where it is tagged and categorized based on the information entered.

[0541] Collecting producer information

[0542] Device:

[0543] Producers access the system and enter product information, including company or individual names, detailed product descriptions, photos, production location information, price ranges, and production processes.

[0544] server:

[0545] Producer information is received and stored in a database, and tagging and categorization are performed based on the information entered.

[0546] AI automatic learning and matching

[0547] server:

[0548] User and producer information is read from the database and analyzed using an AI engine. The system matches users with the most suitable products, taking into account their preferences, past behavioral history, and product features.

[0549] Presenting matching results

[0550] server:

[0551] It generates matching results and creates individual notification messages, the contents of which are sent to the user's device.

[0552] Device:

[0553] It receives notifications and displays matching results to the user, including product name, producer information, price, and purchasing instructions.

[0554] Specific examples

[0555] 1. User A's actions

[0556] Register "traditional crafts" as your preference on the device and enter your tastes and requests.

[0557] Enter specific criteria that match your interests into the system.

[0558] 2. Producer B's actions

[0559] Register "Handmade Wood Carvings" as a product on the terminal.

[0560] Enter detailed product information, place of origin, price range, and production method.

[0561] 3. Server Analysis

[0562] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[0563] 4. Notification of Results

[0564] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[0565] User A's device receives the notification and displays detailed information.

[0566] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, which is expected to contribute to the revitalization of local communities.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] Terminal: The user accesses the system and enters the necessary information into the user registration form, such as their name, email address, hobbies and preferences, genres of interest, and purchase history.

[0570] Step 2:

[0571] Terminal: The user checks the information entered and clicks the "Register" button, which sends the form data to the server.

[0572] Step 3:

[0573] Server: Receives user information sent from the device and stores it in a database, tagging and categorizing it based on the information.

[0574] Step 4:

[0575] Terminal: Producers access the system and enter the necessary information into the producer registration form, such as the company or individual name, detailed product description, photo, production area information, price range, and production process.

[0576] Step 5:

[0577] Terminal: The producer confirms the information entered and clicks the "Register" button, which sends the form data to the server.

[0578] Step 6:

[0579] Server: Receives producer information sent from the device and stores it in a database. At this time, it tags and categorizes the products based on the information.

[0580] Step 7:

[0581] Server: Reads user and producer information from the database and analyzes it using an AI engine. The analysis takes into account the user's preferences, past behavioral history, and the producer's product characteristics.

[0582] Step 8:

[0583] Server: The AI ​​engine matches the optimal products based on the user's preferences and generates matching results.

[0584] Step 9:

[0585] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[0586] Step 10:

[0587] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[0588] Step 11:

[0589] On the device: The user checks the notification and views detailed information about the product they are interested in. This allows the user to find products that suit their preferences.

[0590] In this way, the entire system can operate smoothly and effectively match users and producers.

[0591] Example 1

[0592] 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."

[0593] In modern society, it is difficult to effectively match rural producers with urban consumers. Rural producers have limited opportunities to widely promote their products, and urban consumers have few means of accessing local specialties. As a result, consumers are unable to find products that suit their tastes, and producers miss out on new business opportunities. This has also slowed the revitalization of local economies.

[0594] 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.

[0595] In this invention, the server includes a means for storing user preferences and request information and producer product information in a database, a means for tagging and categorizing based on the information stored in the database, and an artificial intelligence engine for analyzing the information and matching products that match the user's preferences and requests. This allows users to easily find products that suit their tastes, and producers to gain new business opportunities. This is likely to contribute to the revitalization of local economies.

[0596] "Terminal" means an electronic device used by a user or producer to enter or access information.

[0597] A "server" is a computer system that receives, stores, and analyzes information sent from terminals via a network.

[0598] A "database" is a system for systematically storing and managing information about users and producers.

[0599] "Tastes" refer to the user's personal preferences and interests.

[0600] "Requests" are specific conditions or requirements that users need.

[0601] "Producers" are individuals or companies that produce and supply local specialty products.

[0602] "Product information" refers to data provided by producers, such as detailed product descriptions, information on the place of origin, price, and manufacturing process.

[0603] "Tagging" is the act of labeling information to classify and organize it.

[0604] "Categorization" is the process of classifying information into specific groups.

[0605] An "artificial intelligence engine" is a program that includes machine learning algorithms to analyze data and match products that match users' tastes and requests.

[0606] "Matching" is the process of selecting and combining products that best suit the user's tastes and requests.

[0607] A "notification" is information or a message sent from the server to the user's terminal.

[0608] The present invention is a system that effectively matches users with local producers. The system includes the following elements:

[0609] 1. Device operation

[0610] The user's device provides an interface for the user to input their preferences and requests. For example, the user opens a browser and enters their hobbies, preferences, purchase history, etc. into an input form. Then, they click a button to send the entered information to the server.

[0611] The producer's terminal provides an interface for registering product information. The producer inputs detailed product descriptions, price ranges, and production area information, and sends them to the server.

[0612] 2. Server Roles

[0613] The server receives the user and producer information sent from the terminal and stores it in a database, using a database management system such as MySQL.

[0614] The database efficiently stores and manages information about users and producers. User information includes categories of hobbies and preferences and purchase history, while producer information includes detailed product descriptions, price ranges, and production processes.

[0615] The server tags and categorizes the information it receives, using a tagging algorithm to assign appropriate tags to each piece of information.

[0616] 3. Matching Process

[0617] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests. The AI ​​engine uses machine learning libraries such as TensorFlow and PyTorch.

[0618] The server takes into account the user's past behavioral history and the producer's product characteristics to generate optimal matching results.

[0619] 4. Notification of Matching Results

[0620] The server generates matching results and creates a personalized notification message, for example, "Recommended Product: Handmade Wood Carvings (Producer Name)" and sends it to the user's device.

[0621] The device receives the notification and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[0622] Specific examples

[0623] User behavior examples

[0624] User A sets "traditional crafts" as a preference and inputs his / her tastes and requests. For example, he / she may input that he / she is interested in "handmade wood carvings."

[0625] Examples of producer actions

[0626] Producer B registers "handmade wood carvings" as a product. He enters detailed product information, including the place of origin, price range, and production method.

[0627] Server analysis example

[0628] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[0629] Example of notification of results

[0630] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer B)" and sends it to User A's device.

[0631] User A receives the notification on his / her device and checks the detailed information.

[0632] Prompt Sentence Examples

[0633] Prompt for User A: "Please describe your preferences or desires. For example, 'traditional crafts' or 'handmade wood carvings.'"

[0634] Prompt for Producer B: "Please enter the product information you would like to register. Include the product name, detailed description, photos, origin, price range, and production process."

[0635] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, thereby contributing to the revitalization of local economies.

[0636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0637] Program processing flow

[0638] Step 1: Enter your user information

[0639] explanation:

[0640] Users access the system from their own devices using a dedicated application or web interface, and enter information such as their name, email address, hobbies and preferences, genres of interest, and purchase history in the displayed form.

[0641] input:

[0642] User name, email address, hobbies and preferences, genres of interest, purchase history

[0643] output:

[0644] This information is sent to the server

[0645] Specific behavior:

[0646] The user opens a browser, visits the specified website, logs in, fills in the required information on the form, and clicks the "Submit" button.

[0647] Step 2: Enter producer information

[0648] explanation:

[0649] Producers access the system using a dedicated application or web interface from their own devices, and enter detailed product descriptions, photos, information on the place of origin, price range, and production process.

[0650] input:

[0651] Company or individual name, detailed product description, photo, origin information, price range, production process

[0652] output:

[0653] This information is sent to the server

[0654] Specific behavior:

[0655] The producer opens a browser and accesses the specified website. After logging in, they enter the required product information into the form and click the "Submit" button.

[0656] Step 3: Data received and stored by the server

[0657] explanation:

[0658] The server receives the user and producer information sent from the device, then stores the received information in a database and tags and categorizes it.

[0659] input:

[0660] User information, producer information

[0661] output:

[0662] User and producer information stored in a database, along with their corresponding tags and categories

[0663] Specific behavior:

[0664] The server analyzes the data received from users' and producers' devices and stores it in a database system such as MySQL. Using a tagging algorithm, appropriate tags are assigned to each piece of information and it is then categorized.

[0665] Step 4: Database analysis and matching

[0666] explanation:

[0667] The server's AI engine reads and analyzes the user and producer information stored in the database. As a result of the analysis, it takes into account the user's preferences, past behavioral history, and the producer's product characteristics to match the optimal product.

[0668] input:

[0669] User information stored in the database, producer information stored in the database

[0670] output:

[0671] Results of product matching that matches user tastes and requests

[0672] Specific behavior:

[0673] The AI ​​engine uses machine learning libraries such as TensorFlow and PyTorch to analyze the user and producer information in the database, and connects users and producers through clustering algorithms and recommendation systems.

[0674] Step 5: Generating and notifying matching results

[0675] explanation:

[0676] The server generates a personalized notification message for the user based on the matching results obtained from the AI ​​engine, and the notification message is sent to the user's device, where the user can receive and check it.

[0677] input:

[0678] Matching results

[0679] output:

[0680] Notification message to user terminal

[0681] Specific behavior:

[0682] The server generates a notification message to User A in the form of a push notification or email with the content "Recommended Product: Handmade Wood Carving (Producer B)". The generated notification message is sent to User A's device. User A's device receives the notification and displays it on the screen.

[0683] The above is a specific processing flow of the system.

[0684] (Application example 1)

[0685] 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."

[0686] Products offered by local producers often match the tastes and requests of specific users, but effective ways of approaching consumers are limited, making appropriate purchasing matching difficult. Users also have few means of finding local products that interest them, making purchasing activities cumbersome. This makes it difficult for local producers to seize new business opportunities, and consumers also have difficulty finding products that suit their tastes.

[0687] 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.

[0688] In this invention, the server includes an artificial intelligence engine that matches products that match the user's tastes and desires. This provides a means for tagging and categorizing products based on the user's tastes and desires, a means for matching products taking into account past purchase history, and a means for notifying the user of recommended products and displaying detailed information. This allows users to easily find products that match their tastes and desires, and also provides local producers with new business opportunities.

[0689] A "terminal" is a device equipped with an interface for users to input their preferences and requests, and is used by users and producers to provide information to the system.

[0690] The "server" is a central processing unit that stores information on users' preferences and requests, as well as product information from producers, in a database, and analyzes this information to perform matching.

[0691] A "database" is a place where information is collected to efficiently store and manage user and producer information.

[0692] The "artificial intelligence engine" is a program that analyzes information stored in a database and matches products that match the user's tastes and requests.

[0693] The "notification means" is a function that transmits the matching results to the user's terminal and provides the user with information about recommended products.

[0694] "Tagging" is a technique for assigning keywords and categories related to information (such as products or interests) to make it easier to search and analyze within a database.

[0695] "Categorization" refers to the process of classifying data into specific groups to make management and searching more efficient.

[0696] "Purchase history" refers to information about products a user has purchased in the past, and analyzing this information is used to understand the user's preferences and patterns.

[0697] "Matching" refers to the process of selecting product information that best suits the user's tastes and needs and recommending it to the user.

[0698] "Recommendation" is a function that encourages purchases by presenting appropriate products based on the user's interests and past behavior.

[0699] The present invention is a system that effectively matches users with local producers. This system is designed to recommend optimal products based on the user's preferences and requests. A specific embodiment of the system is shown below.

[0700] System Overview

[0701] 1. Device operation

[0702] The user's device provides an interface for the user to input their preferences and requests, such as hobbies, tastes, local products of interest, and past purchase history.

[0703] The producer's terminal provides an interface for registering product information. The producer inputs detailed descriptions, images, price ranges, and production processes for the specialty products they produce.

[0704] 2. Server Roles

[0705] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[0706] The database efficiently stores and manages user and producer information.

[0707] 3. Matching Process

[0708] The server's artificial intelligence engine analyzes the information stored in the database and matches users with products that match their tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[0709] A notification of the results is generated by the server and sent to the appropriate user's device.

[0710] Program processing description

[0711] The hardware used is a smartphone, such as the Apple iPhone or Samsung Galaxy. The software uses Python to run the program, and machine learning libraries such as TensorFlow and PyTorch are used for the AI ​​engine.

[0712] The flow of data processing and calculation is as follows:

[0713] User preference input: Users input their preferences, requests, and purchase history via their smartphone. The input information is sent to the server.

[0714] Producer product registration: Producers also use smartphones or other devices to enter product information, including the product name, price, detailed description, and production process.

[0715] Tagging and categorization: The server stores the received information in a database and appropriately tags and categorizes it, taking into account the user's preferences, needs, and purchasing history.

[0716] AI analysis and matching: The server's AI engine analyzes the database and selects the products that best suit the user's tastes and needs. This process utilizes AI models.

[0717] Recommendation notification and detailed display: Matching results are generated and notified to the user's smartphone, where the user can check detailed information.

[0718] Specific examples

[0719] User A's actions: Registers "traditional crafts" as a preference on the device and inputs his / her tastes and requests.

[0720] Producer B's actions: Register "Handmade Wood Carvings" as a product on the terminal. Enter product information, place of origin, price range, and production method.

[0721] Server analysis: The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the shared tags of "traditional crafts" and "wood carving."

[0722] Result notification: The server generates a notification saying "Recommended product: Handmade wood carvings (Producer: Producer B)" and sends it to User A's device. User A checks the details.

[0723] Prompt Sentence Examples

[0724] "I would like to develop an app that recommends handmade wood carvings from local producers to users who like traditional crafts. I would like to tag user information and product information and use AI to match the best products. Please explain the basic configuration and functions of the system."

[0725] This will allow users to easily find products that suit their tastes, provide producers with new business opportunities, and is also expected to contribute to revitalizing local industries.

[0726] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0727] Step 1:

[0728] Entering user information

[0729] The user uses a terminal to input their preferences, requests, past purchase history, etc. The input data is sent to the server along with the user ID. The input information includes hobbies in text format, genres of interest, and product IDs of past purchases. The server receives this information and stores it in a database.

[0730] Step 2:

[0731] Enter producer information

[0732] Producers enter product information (product name, image, detailed description, price range, place of production, etc.) into their terminal and send it to the server. The server stores the received product information in a database and assigns a product ID. At this time, the product information is tagged (for example, "traditional craft" or "wood carving").

[0733] Step 3:

[0734] Tagging and categorization

[0735] The server tags and categorizes products based on user and producer information. It generates tags based on the user's hobbies and interests, and also tags the product information. This process is managed in a database, facilitating future searches and matching.

[0736] Step 4:

[0737] Analysis and matching by AI engine

[0738] The server reads the user and producer information stored in the database and analyzes it using an AI engine. Specifically, it matches the optimal product by taking into account the user's preferences, past behavioral history, and product tag information. The AI ​​model used here is a recommender system trained using, for example, TensorFlow or PyTorch.

[0739] Step 5:

[0740] Generating matching results

[0741] The AI ​​engine generates analysis results and extracts the most relevant product information for the user ID, including the product name, image, price, and detailed description. This information is then structured into a notification message and linked to the user ID.

[0742] Step 6:

[0743] Notifications and details

[0744] The server then sends the generated notification message to the user's device. The user receives the notification and checks the details on the device. The displayed content includes the name, image, price, and detailed description of the recommended product, as well as a link to purchase it.

[0745] Through this processing flow, users can easily find products that suit their tastes and needs, and producers can efficiently promote their products.

[0746] 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.

[0747] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the user's tastes, requests, and even emotions. The system includes a terminal for users to input their preferences and requests, a terminal for local producers to register product information, a server for storing and analyzing data, an AI engine, and an emotion engine.

[0748] System Overview

[0749] 1. Device operation

[0750] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, their purchase history, and their emotional state.

[0751] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[0752] 2. Server Roles

[0753] The server receives the user and producer information sent from the terminal and stores it in a database, where it is tagged and categorized based on preference and emotion information.

[0754] The database efficiently stores and manages user and producer information.

[0755] 3. Emotion Engine

[0756] The emotion engine recognizes the user's emotional state from the user's input information and device interactions, and this information is sent to the server as additional data on the user's preferences and needs.

[0757] Emotional states include emotion categories such as happiness, excitement, surprise, and sadness.

[0758] 4. Matching Process

[0759] The server's AI engine analyzes the information stored in the database and matches users with the best products based on their preferences, needs, and even their emotional state, taking into account the user's past behavioral history and the producer's product characteristics.

[0760] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[0761] Program processing explanation

[0762] Collecting user information and emotional information

[0763] Device:

[0764] Users access the system and input their preferences, requests, and emotional state. Preferences include name, email address, hobbies, preferences, genres of interest, and purchase history. They also input their current emotional state based on an emotion input form.

[0765] server:

[0766] User information and emotion information are received and stored in a database, where they are tagged and categorized based on the information.

[0767] Collecting producer information

[0768] Device:

[0769] Producers access the system and enter product information, including the company or individual name, detailed product description, photos, production location information, price range, and production process.

[0770] server:

[0771] Producer information is received and stored in a database, which is then used to tag and categorize the produce.

[0772] Applying the Emotion Engine

[0773] Device:

[0774] The emotion engine recognizes emotions from user operations and input information and sends them to the server. Emotional states are expressed as "happiness," "surprise," "sadness," etc.

[0775] server:

[0776] The emotional information is stored in a database and used as additional data for analysis.

[0777] AI automatic learning and matching

[0778] server:

[0779] User information, producer information, and emotional information are read from the database and analyzed using an AI engine. The analysis takes into account the user's preferences, past behavioral history, the producer's product features, and the user's emotional state.

[0780] server:

[0781] The AI ​​engine matches the most suitable products based on the user's tastes and emotions and generates matching results.

[0782] Presenting matching results

[0783] server:

[0784] Based on the matching results, an individual notification message is created and sent to the user's device.

[0785] Device:

[0786] The device receives the notification sent from the server and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[0787] Specific examples

[0788] 1. User A's actions

[0789] Register "traditional crafts" as your preference on the device, and then enter your tastes, desires, and current emotional state. Enter "joy" as your emotional state.

[0790] 2. Producer B's actions

[0791] Register "Handmade Wood Carvings" as a product on the terminal and enter the product information.

[0792] 3. Server Analysis

[0793] The server uses an AI engine to analyze information about User A and Producer B, as well as emotional information, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[0794] 4. Notification of Results

[0795] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[0796] User A's device receives the notification and displays detailed information.

[0797] In this way, the system takes into account the tastes, desires, and even emotions of users to provide optimal products, opening up new business opportunities for producers and potentially contributing to the revitalization of local communities.

[0798] The processing flow will be explained below.

[0799] Step 1:

[0800] Terminal: The user accesses the system and enters information into the user registration form, specifically their name, email address, hobbies and preferences, genres of interest, and purchase history.

[0801] Step 2:

[0802] Terminal: The user checks the information they have entered and clicks the "Register" button, which sends the form data to the server.

[0803] Step 3:

[0804] Server: Receives user information sent from the device and stores it in a database. At this time, it tags and categorizes the information based on the input preferences and requests.

[0805] Step 4:

[0806] Terminal: Producers access the system and enter information into the producer registration form, including the company or individual name, detailed product description, photo, production area information, price range, and production process.

[0807] Step 5:

[0808] Terminal: The producer checks the information entered and clicks the "Register" button, which sends the form data to the server.

[0809] Step 6:

[0810] Server: Receives producer information sent from the terminal and stores it in a database. At this time, it tags and categorizes the products based on the input product information.

[0811] Step 7:

[0812] Device: The user accesses the emotion information input screen and selects their current emotional state. Emotion categories include "joy," "excitement," "surprise," and "sadness."

[0813] Step 8:

[0814] Terminal: The user inputs emotion information and clicks the "Send" button, which sends the emotion data to the server.

[0815] Step 9:

[0816] Server: Receives emotion information sent from the device and stores it in a database. This information is linked to user information for analysis.

[0817] Step 10:

[0818] Server: Reads user information, producer information, and emotional information from the database and analyzes them using an AI engine. The analysis takes into account the user's preferences and requests, past behavioral history, product features of the producer, and their emotional state.

[0819] Step 11:

[0820] Server: The AI ​​engine matches the optimal products based on the user's preferences and emotions and generates matching results.

[0821] Step 12:

[0822] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[0823] Step 13:

[0824] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[0825] Step 14:

[0826] On the device: The user checks the notification and browses detailed information about the product they are interested in. This allows the user to find products that match their tastes and emotions.

[0827] In this way, the system analyzes users' tastes, requests, and even emotions, and provides the most suitable products, thereby providing new business opportunities for producers and contributing to the revitalization of local areas.

[0828] Example 2

[0829] 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."

[0830] The objective of this invention is to accurately recognize users' tastes, desires, and even emotions, and effectively match them with local specialty products and niche creative products. Conventional systems do not take the user's emotional state into account when matching products, making it difficult to stimulate purchasing motivation or suggest appropriate products. Therefore, there is a need for a product recommendation system based on detailed information, including the user's emotions.

[0831] 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.

[0832] In this invention, the server includes a means for storing user preferences, requests, and emotional information as well as producer product information in a database, an emotion engine that recognizes the user's emotional state from user operations and input information, and an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's preferences, requests, and emotional state. This enables highly accurate product recommendations that take the user's emotional state into consideration.

[0833] A "terminal" is a device that allows users or producers to input or display information.

[0834] "Tastes" refers to the genres that users like and the things that interest them.

[0835] "Request" refers to the conditions or requests desired by the user.

[0836] "Emotional state" refers to the emotion the user is currently feeling, and includes categories such as joy, surprise, and sadness.

[0837] "Producers" refers to local producers and creators who create and sell products.

[0838] "Product information" refers to information such as detailed product descriptions, photos, price ranges, and production processes registered by producers.

[0839] A "server" is a central device that stores data, analyzes data, and manages network resources.

[0840] A "database" is a structured repository of information built within a server.

[0841] An "emotion engine" is a system for recognizing a user's emotional state from their operations and input information.

[0842] An "artificial intelligence engine" is a system that has an algorithm that analyzes information stored in a database and matches the most suitable products.

[0843] "Tagging" is the process of assigning keywords and categories to information in order to classify and organize it.

[0844] "Matching" is a method of selecting and suggesting the most suitable product based on the user's tastes, requests, and emotional state.

[0845] "Notification means" is a function for sending and displaying matching results from the server to the user's device.

[0846] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the tastes, desires, and emotions of users. An embodiment of this system will be described below.

[0847] System configuration

[0848] The system includes the following components:

[0849] 1. User's Device

[0850] This terminal provides an interface for users to input their preferences, needs, and emotional state. Users use this to input their favorite genres, local products they are interested in, their purchase history, their emotional state, and so on.

[0851] 2. Producer's terminal

[0852] This terminal provides an interface for local producers to register product information. Producers enter detailed descriptions of their specialty products, photos, price ranges, production processes, and other information.

[0853] 3. Server

[0854] It is a central device that stores information on users' tastes, desires, and emotions, as well as product information from producers, in a database that is tagged and categorized based on the stored information.

[0855] 4. Emotion Engine

[0856] This system recognizes the user's emotional state from their actions and input information. The recognized emotional information is sent to the server and used as additional data. Emotional states include categories such as joy, surprise, and sadness.

[0857] 5. Artificial Intelligence Engine

[0858] The system analyzes information stored in a database and matches users with products that match their tastes, desires, and emotional state, taking into account their past behavioral history and the characteristics of the products made by the producers.

[0859] 6. Means of notification

[0860] This function generates matching results and notifies the user's device. The user can receive the notification and check detailed information.

[0861] Example of operation

[0862] 1. User A's actions

[0863] The user registers "traditional crafts" as a preference on the device, and inputs their tastes, desires, and current emotional state. The emotional state is input as "joy."

[0864] 2. Producer B's actions

[0865] The producer registers "handmade wood carvings" as a product on the terminal and enters product information.

[0866] 3. Server Analysis

[0867] The server uses an AI engine to analyze the information and emotional information of User A and Producer B, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[0868] 4. Notification of Results

[0869] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device. User A's device receives the notification and displays detailed information.

[0870] Prompt Sentence Examples

[0871] "Using the generative AI model, please provide an example of natural language processing in this system. For example, if a user registers a preference for traditional crafts and inputs their current emotional state, please explain the steps to generate product information for handmade wood carvings that would be recommended as the most suitable item."

[0872] The above is an embodiment of the present invention. This system makes it possible to recommend products that take into account detailed information about the user, enabling the provision of products that are optimal for the user. It is also expected to provide new business opportunities for producers and contribute to the revitalization of local communities.

[0873] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0874] Step 1:

[0875] Collecting user information and emotional information

[0876] User: Enters their own preferences, requests, and emotional state. Specifically, they enter their name, email address, hobbies, preferences, genres of interest, purchase history, and emotional state into the interface.

[0877] Input: Name, email address, hobbies and preferences, genre interests, purchase history, emotional state.

[0878] Output: This information is sent to the server.

[0879] Step 2:

[0880] Data transmission and storage

[0881] Terminal: Send the entered information to the server. Click the Send button to upload the data.

[0882] Server: Stores the received user information and emotion information in a database, tagging and categorizing the information.

[0883] Input: User information and emotion information entered into the interface.

[0884] Output: The tagged user information and emotion information are stored in a database.

[0885] Step 3:

[0886] Collecting producer information

[0887] Producer: Enter product information into the system. Specifically, enter the company or individual name, detailed product description, photo, production location information, price range, and production process into the interface.

[0888] Input: company / individual name, detailed product description, photo, origin information, price range, and production process.

[0889] Output: This information is sent to the server.

[0890] Step 4:

[0891] Data transmission and storage

[0892] Terminal: Send the entered product information to the server. Click the Send button to upload the data.

[0893] Server: Stores the received product information in a database, tagging and categorizing it based on the information.

[0894] Input: Product information entered into the interface.

[0895] Output: Tagged product information is stored in a database.

[0896] Step 5:

[0897] Emotion engine application and recognition

[0898] On the device: The emotion engine recognizes emotions from user actions and input information, specifically analyzing emotions based on keystroke speed and phrases in the interface.

[0899] Input: User actions and input information.

[0900] Output: Data of the recognized emotional state sent to the server.

[0901] Step 6:

[0902] Storing emotional information

[0903] Server: The received emotional information is stored in a database and used as additional data for analysis. Specifically, the emotional history of each user is recorded.

[0904] Input: Recognized emotion information.

[0905] Output: Emotion information stored in a database.

[0906] Step 7:

[0907] Loading information

[0908] Server: Reads user, producer, and sentiment information from the database, specifically extracting relevant data using SQL queries.

[0909] Input: User information, producer information, and emotion information stored in the database.

[0910] Output: Analyzed data provided to the AI ​​engine.

[0911] Step 8:

[0912] Data analysis and product matching

[0913] Server: Analyzes the information loaded by the AI ​​engine. Specifically, the AI ​​algorithm analyzes the user's preferences, past behavioral history, product features of the manufacturer, and the user's emotional state. For example, by referencing data from similar users, it predicts the best product for the current user's emotional state.

[0914] Input: Analysis data provided to the AI ​​engine.

[0915] Output: Matched product information.

[0916] Step 9:

[0917] Generating matching results

[0918] Server: Generates matching results and creates individual notification messages, specifically creating a list of candidate products and formatting them as text messages for the user.

[0919] Input: Matched product information.

[0920] Output: Informational message.

[0921] Step 10:

[0922] Sending notifications

[0923] Server: Sends the created notification message to the user's device. Specifically, it pushes the message to the user's device using a network protocol.

[0924] Input: Notification message.

[0925] output: Notification sent to the user's device.

[0926] Step 11:

[0927] Results display

[0928] Device: Receives the notification sent from the server and displays the matching results to the user. Specifically, it displays information such as "Product name: Handmade wood carving, Producer: Producer B, Price: \XXXX" on the screen.

[0929] Input: The notification sent by the server.

[0930] Output: Matching results displayed in the user interface.

[0931] (Application example 2)

[0932] 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."

[0933] In modern society, there is a demand for recommendation systems that respond to users' tastes and emotions. However, conventional systems have difficulty matching products that reflect the user's emotional state, making it difficult to effectively recommend appropriate products. In particular, when it comes to local specialties or niche creative products, it is difficult to achieve optimal matching based on the user's emotions, which makes it difficult to contribute to regional revitalization.

[0934] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal for inputting user preferences, requests, and emotional states; a terminal for local producers to register product information; a means for storing the user preferences, requests, and emotional information and producer product information in a database; an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user preferences, requests, and emotional states; a means for notifying the user terminal of the matching results; and a means for understanding the user's emotional state using emotion recognition technology. This enables appropriate product recommendations that take the user's emotional state into consideration, thereby improving the matching accuracy of local specialty products and niche creative products.

[0935] A "user" is a consumer who uses a terminal to input his or her preferences, desires, and emotional state.

[0936] "Local producers" are merchants or individuals who use terminals to register information about specialty products or niche creative goods.

[0937] A "terminal" is a device that allows users to input information about their preferences, requests, and emotional state, and a device that allows producers to register product information.

[0938] The "server" is a central device that stores information sent by users and producers in a database and analyzes it.

[0939] The "database" is an information storage system that manages information on users' preferences, requests, and emotions, as well as product information from producers, stored on a server.

[0940] An "artificial intelligence engine" is an algorithm and software that analyzes a user's tastes, requests, and emotional state with a producer's product information to match them with the most suitable product.

[0941] "Emotional state" is information that indicates the emotions that the user is feeling, and is classified into categories such as joy, surprise, and sadness.

[0942] "Emotion recognition technology" is a technology that recognizes the emotional state of a user from their input information and device interactions.

[0943] "Matching results" are information that indicates the products that best suit the user's tastes, desires, and emotional state, based on analysis by the AI ​​engine.

[0944] The "notification means" is a system in which the server generates matching results and sends them to the terminal of the corresponding user.

[0945] The present invention is a system for effectively matching local specialties and niche creative products based on the tastes, desires, and emotional state of users, and an embodiment thereof is described below. The system includes a terminal for users to input their tastes, desires, and emotional state, a terminal for local producers to register product information, a server for storing and analyzing data, an artificial intelligence engine, and emotion recognition technology.

[0946] System Overview

[0947] 1. User operations

[0948] The user terminal provides an interface for users to input their preferences, requests, and emotional state. Users input their favorite genres, local products they are interested in, their purchase history, and their emotional state (for example, joy or surprise). In addition, by using a device with a camera, emotion recognition technology can be used to analyze the user's facial expressions and automatically recognize their emotional state.

[0949] 2. Producer manipulation

[0950] The producer terminal provides an interface for producers to register product information. Producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, and so on.

[0951] 3. Server Roles

[0952] The server receives information sent from user terminals and producer terminals and stores this data in a database. At this time, it tags and categorizes the data based on the user's preferences, requests, and emotional information, and the producer's product information.

[0953] 4. Application of emotion recognition technology

[0954] Emotion recognition technology recognizes the user's emotional state from input information and device interactions and sends it to a server, using Microsoft Azure's emotion recognition API and other technologies.

[0955] 5. Matching Process

[0956] The AI ​​engine analyzes the user's preferences, requests, and emotional information stored in the database, as well as the product information of the producer, to match the optimal product. This is done using generative AI models such as Amazon SageMaker.

[0957] 6. Presenting the matching results

[0958] The server generates matching results based on the analysis results and notifies the relevant user's device. The user's device receives the notification and displays the product name, producer information, price, purchase method, etc.

[0959] Specific examples

[0960] 1. User A's behavior

[0961] User A accesses the app using a smartphone, enters "I like seasonal fruits," and enters his / her current emotional state as "joy." When he / she captures his / her facial expression using a camera, the emotion recognition technology automatically recognizes that information.

[0962] 2. Producer B's actions

[0963] Producer B registers "seasonal local apples" as a product and enters detailed product information and photos.

[0964] 3. Server Analysis

[0965] The server stores information and emotional information of User A and Producer B in a database, and uses a generative AI model to make optimal matches based on "fruit" and "joy."

[0966] 4. Generate and send notifications

[0967] The server generates a notification as a matching result, stating "Recommended product: Seasonal local apples (Producer: Producer B)" and sends it to User A's terminal.

[0968] 5. Receipt and confirmation by User A

[0969] User A's device receives the notification and displays detailed information. User A checks the product in the app and completes the purchase procedure.

[0970] Prompt Sentence Examples

[0971] User input: "I'm in a fun mood today, so I'm looking for something exciting to buy."

[0972] Emotion Recognition API: "Joy"

[0973] AI engine prompt: "Recommend exciting local products that match the user's tastes with emotions of joy"

[0974] This system makes it possible to recommend appropriate products that take into account the user's emotional state, improving the accuracy of matching local specialties and niche creative products.

[0975] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0976] Step 1:

[0977] The user device receives input of the user's preferences, requests, and emotional state. The user can provide their preferred genres and purchase history through text input, and their current emotional state is automatically recognized by taking a photo with a camera using emotion recognition technology. The input data (preferences, requests, and emotional state) is sent to the server.

[0978] Step 2:

[0979] The server receives information on preferences, requests, and emotional states sent from the user's device and stores it in a database. Emotion recognition technology categorizes the emotional states into categories such as "happiness," "surprise," and "sadness," and tags them as data. The stored data is used for later analysis.

[0980] Step 3:

[0981] The producer terminal receives product information (detailed description, photos, price, etc.) from the producer. The producer enters and registers the product information. The entered product information is sent to the server.

[0982] Step 4:

[0983] The server receives product information sent from the producer terminal and stores it in a database. The product information is tagged and categorized and then stored in the database.

[0984] Step 5:

[0985] The server uses a generative AI model to analyze user information (tastes, requests, emotional state) and producer information (product information) stored in a database. The purpose of the analysis is to find the product that best suits the user's tastes and emotional state. The generative AI model uses AI tools such as Amazon SageMaker.

[0986] Step 6:

[0987] The server's AI engine performs analysis and matches the optimal product based on the user's tastes, desires, and emotional state. The generative AI model compares the user information with product information to generate the optimal matching result. The matching result is expressed as a list of recommended products.

[0988] Step 7:

[0989] The server generates a notification message of the matching result and sends a list including recommended products to the user terminal. The user terminal provides an interface that displays detailed information such as recommended products, prices, and producer information, allowing the user to easily check the products.

[0990] Step 8:

[0991] Users can receive matching results through the app, check the details, and if they are interested, they can purchase the product within the app.

[0992] This series of processing steps allows users to easily find the specialty or niche product that best suits their current mood, and allows producers to effectively deliver their products to more consumers. Furthermore, the explicit operation and input / output make the system's operation clear.

[0993] 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.

[0994] 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.

[0995] 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.

[0996] [Third embodiment]

[0997] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0998] 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.

[0999] 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).

[1000] 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.

[1001] 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.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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.

[1007] 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.

[1008] 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."

[1009] The present invention is a system that effectively matches users with local producers, and an embodiment thereof is described below. This system includes a terminal for users to input their preferences and requests, a terminal for producers to register product information, a server for storing and analyzing data, and an AI engine running on the server.

[1010] System Overview

[1011] 1. Device operation

[1012] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, and their purchase history.

[1013] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[1014] 2. Server Roles

[1015] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[1016] The database efficiently stores and manages user and producer information.

[1017] 3. Matching Process

[1018] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[1019] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[1020] Program processing explanation

[1021] Collection of User Information

[1022] Device:

[1023] Users access the system and enter information about their preferences and needs, including their name, email address, hobbies and preferences, genres of interest, and purchase history.

[1024] server:

[1025] User information is received and stored in a database, where it is tagged and categorized based on the information entered.

[1026] Collecting producer information

[1027] Device:

[1028] Producers access the system and enter product information, including company or individual names, detailed product descriptions, photos, production location information, price ranges, and production processes.

[1029] server:

[1030] Producer information is received and stored in a database, and tagging and categorization are performed based on the information entered.

[1031] AI automatic learning and matching

[1032] server:

[1033] User and producer information is read from the database and analyzed using an AI engine. The system matches users with the most suitable products, taking into account their preferences, past behavioral history, and product features.

[1034] Presenting matching results

[1035] server:

[1036] It generates matching results and creates individual notification messages, the contents of which are sent to the user's device.

[1037] Device:

[1038] It receives notifications and displays matching results to the user, including product name, producer information, price, and purchasing instructions.

[1039] Specific examples

[1040] 1. User A's actions

[1041] Register "traditional crafts" as your preference on the device and enter your tastes and requests.

[1042] Enter specific criteria that match your interests into the system.

[1043] 2. Producer B's actions

[1044] Register "Handmade Wood Carvings" as a product on the terminal.

[1045] Enter detailed product information, place of origin, price range, and production method.

[1046] 3. Server Analysis

[1047] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[1048] 4. Notification of Results

[1049] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[1050] User A's device receives the notification and displays detailed information.

[1051] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, which is expected to contribute to the revitalization of local communities.

[1052] The processing flow will be explained below.

[1053] Step 1:

[1054] Terminal: The user accesses the system and enters the necessary information into the user registration form, such as their name, email address, hobbies and preferences, genres of interest, and purchase history.

[1055] Step 2:

[1056] Terminal: The user checks the information entered and clicks the "Register" button, which sends the form data to the server.

[1057] Step 3:

[1058] Server: Receives user information sent from the device and stores it in a database, tagging and categorizing it based on the information.

[1059] Step 4:

[1060] Terminal: Producers access the system and enter the necessary information into the producer registration form, such as the company or individual name, detailed product description, photo, production area information, price range, and production process.

[1061] Step 5:

[1062] Terminal: The producer confirms the information entered and clicks the "Register" button, which sends the form data to the server.

[1063] Step 6:

[1064] Server: Receives producer information sent from the device and stores it in a database. At this time, it tags and categorizes the products based on the information.

[1065] Step 7:

[1066] Server: Reads user and producer information from the database and analyzes it using an AI engine. The analysis takes into account the user's preferences, past behavioral history, and the producer's product characteristics.

[1067] Step 8:

[1068] Server: The AI ​​engine matches the optimal products based on the user's preferences and generates matching results.

[1069] Step 9:

[1070] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[1071] Step 10:

[1072] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[1073] Step 11:

[1074] On the device: The user checks the notification and views detailed information about the product they are interested in. This allows the user to find products that suit their preferences.

[1075] In this way, the entire system can operate smoothly and effectively match users and producers.

[1076] Example 1

[1077] 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."

[1078] In modern society, it is difficult to effectively match rural producers with urban consumers. Rural producers have limited opportunities to widely promote their products, and urban consumers have few means of accessing local specialties. As a result, consumers are unable to find products that suit their tastes, and producers miss out on new business opportunities. This has also slowed the revitalization of local economies.

[1079] 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.

[1080] In this invention, the server includes a means for storing user preferences and request information and producer product information in a database, a means for tagging and categorizing based on the information stored in the database, and an artificial intelligence engine for analyzing the information and matching products that match the user's preferences and requests. This allows users to easily find products that suit their tastes, and producers to gain new business opportunities. This is likely to contribute to the revitalization of local economies.

[1081] "Terminal" means an electronic device used by a user or producer to enter or access information.

[1082] A "server" is a computer system that receives, stores, and analyzes information sent from terminals via a network.

[1083] A "database" is a system for systematically storing and managing information about users and producers.

[1084] "Tastes" refer to the user's personal preferences and interests.

[1085] "Requests" are specific conditions or requirements that users need.

[1086] "Producers" are individuals or companies that produce and supply local specialty products.

[1087] "Product information" refers to data provided by producers, such as detailed product descriptions, information on the place of origin, price, and manufacturing process.

[1088] "Tagging" is the act of labeling information to classify and organize it.

[1089] "Categorization" is the process of classifying information into specific groups.

[1090] An "artificial intelligence engine" is a program that includes machine learning algorithms to analyze data and match products that match users' tastes and requests.

[1091] "Matching" is the process of selecting and combining products that best suit the user's tastes and requests.

[1092] A "notification" is information or a message sent from the server to the user's terminal.

[1093] The present invention is a system that effectively matches users with local producers. The system includes the following elements:

[1094] 1. Device operation

[1095] The user's device provides an interface for the user to input their preferences and requests. For example, the user opens a browser and enters their hobbies, preferences, purchase history, etc. into an input form. Then, they click a button to send the entered information to the server.

[1096] The producer's terminal provides an interface for registering product information. The producer inputs detailed product descriptions, price ranges, and production area information, and sends them to the server.

[1097] 2. Server Roles

[1098] The server receives the user and producer information sent from the terminal and stores it in a database, using a database management system such as MySQL.

[1099] The database efficiently stores and manages information about users and producers. User information includes categories of hobbies and preferences and purchase history, while producer information includes detailed product descriptions, price ranges, and production processes.

[1100] The server tags and categorizes the information it receives, using a tagging algorithm to assign appropriate tags to each piece of information.

[1101] 3. Matching Process

[1102] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests. The AI ​​engine uses machine learning libraries such as TensorFlow and PyTorch.

[1103] The server takes into account the user's past behavioral history and the producer's product characteristics to generate optimal matching results.

[1104] 4. Notification of Matching Results

[1105] The server generates matching results and creates a personalized notification message, for example, "Recommended Product: Handmade Wood Carvings (Producer Name)" and sends it to the user's device.

[1106] The device receives the notification and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[1107] Specific examples

[1108] User behavior examples

[1109] User A sets "traditional crafts" as a preference and inputs his / her tastes and requests. For example, he / she may input that he / she is interested in "handmade wood carvings."

[1110] Examples of producer actions

[1111] Producer B registers "handmade wood carvings" as a product. He enters detailed product information, including the place of origin, price range, and production method.

[1112] Server analysis example

[1113] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[1114] Example of notification of results

[1115] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer B)" and sends it to User A's device.

[1116] User A receives the notification on his / her device and checks the detailed information.

[1117] Prompt Sentence Examples

[1118] Prompt for User A: "Please describe your preferences or desires. For example, 'traditional crafts' or 'handmade wood carvings.'"

[1119] Prompt for Producer B: "Please enter the product information you would like to register. Include the product name, detailed description, photos, origin, price range, and production process."

[1120] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, thereby contributing to the revitalization of local economies.

[1121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1122] Program processing flow

[1123] Step 1: Enter your user information

[1124] explanation:

[1125] Users access the system from their own devices using a dedicated application or web interface, and enter information such as their name, email address, hobbies and preferences, genres of interest, and purchase history in the displayed form.

[1126] input:

[1127] User name, email address, hobbies and preferences, genres of interest, purchase history

[1128] output:

[1129] This information is sent to the server

[1130] Specific behavior:

[1131] The user opens a browser, visits the specified website, logs in, fills in the required information on the form, and clicks the "Submit" button.

[1132] Step 2: Enter producer information

[1133] explanation:

[1134] Producers access the system using a dedicated application or web interface from their own devices, and enter detailed product descriptions, photos, information on the place of origin, price range, and production process.

[1135] input:

[1136] Company or individual name, detailed product description, photo, origin information, price range, production process

[1137] output:

[1138] This information is sent to the server

[1139] Specific behavior:

[1140] The producer opens a browser and accesses the specified website. After logging in, they enter the required product information into the form and click the "Submit" button.

[1141] Step 3: Data received and stored by the server

[1142] explanation:

[1143] The server receives the user and producer information sent from the device, then stores the received information in a database and tags and categorizes it.

[1144] input:

[1145] User information, producer information

[1146] output:

[1147] User and producer information stored in a database, along with their corresponding tags and categories

[1148] Specific behavior:

[1149] The server analyzes the data received from users' and producers' devices and stores it in a database system such as MySQL. Using a tagging algorithm, appropriate tags are assigned to each piece of information and it is then categorized.

[1150] Step 4: Database analysis and matching

[1151] explanation:

[1152] The server's AI engine reads and analyzes the user and producer information stored in the database. As a result of the analysis, it takes into account the user's preferences, past behavioral history, and the producer's product characteristics to match the optimal product.

[1153] input:

[1154] User information stored in the database, producer information stored in the database

[1155] output:

[1156] Results of product matching that matches user tastes and requests

[1157] Specific behavior:

[1158] The AI ​​engine uses machine learning libraries such as TensorFlow and PyTorch to analyze the user and producer information in the database, and connects users and producers through clustering algorithms and recommendation systems.

[1159] Step 5: Generating and notifying matching results

[1160] explanation:

[1161] The server generates a personalized notification message for the user based on the matching results obtained from the AI ​​engine, and the notification message is sent to the user's device, where the user can receive and check it.

[1162] input:

[1163] Matching results

[1164] output:

[1165] Notification message to user terminal

[1166] Specific behavior:

[1167] The server generates a notification message to User A in the form of a push notification or email with the content "Recommended Product: Handmade Wood Carving (Producer B)". The generated notification message is sent to User A's device. User A's device receives the notification and displays it on the screen.

[1168] The above is a specific processing flow of the system.

[1169] (Application example 1)

[1170] 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."

[1171] Products offered by local producers often match the tastes and requests of specific users, but effective ways of approaching consumers are limited, making appropriate purchasing matching difficult. Users also have few means of finding local products that interest them, making purchasing activities cumbersome. This makes it difficult for local producers to seize new business opportunities, and consumers also have difficulty finding products that suit their tastes.

[1172] 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.

[1173] In this invention, the server includes an artificial intelligence engine that matches products that match the user's tastes and desires. This provides a means for tagging and categorizing products based on the user's tastes and desires, a means for matching products taking into account past purchase history, and a means for notifying the user of recommended products and displaying detailed information. This allows users to easily find products that match their tastes and desires, and also provides local producers with new business opportunities.

[1174] A "terminal" is a device equipped with an interface for users to input their preferences and requests, and is used by users and producers to provide information to the system.

[1175] The "server" is a central processing unit that stores information on users' preferences and requests, as well as product information from producers, in a database, and analyzes this information to perform matching.

[1176] A "database" is a place where information is collected to efficiently store and manage user and producer information.

[1177] The "artificial intelligence engine" is a program that analyzes information stored in a database and matches products that match the user's tastes and requests.

[1178] The "notification means" is a function that transmits the matching results to the user's terminal and provides the user with information about recommended products.

[1179] "Tagging" is a technique for assigning keywords and categories related to information (such as products or interests) to make it easier to search and analyze within a database.

[1180] "Categorization" refers to the process of classifying data into specific groups to make management and searching more efficient.

[1181] "Purchase history" refers to information about products a user has purchased in the past, and analyzing this information is used to understand the user's preferences and patterns.

[1182] "Matching" refers to the process of selecting product information that best suits the user's tastes and needs and recommending it to the user.

[1183] "Recommendation" is a function that encourages purchases by presenting appropriate products based on the user's interests and past behavior.

[1184] The present invention is a system that effectively matches users with local producers. This system is designed to recommend optimal products based on the user's preferences and requests. A specific embodiment of the system is shown below.

[1185] System Overview

[1186] 1. Device operation

[1187] The user's device provides an interface for the user to input their preferences and requests, such as hobbies, tastes, local products of interest, and past purchase history.

[1188] The producer's terminal provides an interface for registering product information. The producer inputs detailed descriptions, images, price ranges, and production processes for the specialty products they produce.

[1189] 2. Server Roles

[1190] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[1191] The database efficiently stores and manages user and producer information.

[1192] 3. Matching Process

[1193] The server's artificial intelligence engine analyzes the information stored in the database and matches users with products that match their tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[1194] A notification of the results is generated by the server and sent to the appropriate user's device.

[1195] Program processing description

[1196] The hardware used is a smartphone, such as the Apple iPhone or Samsung Galaxy. The software uses Python to run the program, and machine learning libraries such as TensorFlow and PyTorch are used for the AI ​​engine.

[1197] The flow of data processing and calculation is as follows:

[1198] User preference input: Users input their preferences, requests, and purchase history via their smartphone. The input information is sent to the server.

[1199] Producer product registration: Producers also use smartphones or other devices to enter product information, including the product name, price, detailed description, and production process.

[1200] Tagging and categorization: The server stores the received information in a database and appropriately tags and categorizes it, taking into account the user's preferences, needs, and purchasing history.

[1201] AI analysis and matching: The server's AI engine analyzes the database and selects the products that best suit the user's tastes and needs. This process utilizes AI models.

[1202] Recommendation notification and detailed display: Matching results are generated and notified to the user's smartphone, where the user can check detailed information.

[1203] Specific examples

[1204] User A's actions: Registers "traditional crafts" as a preference on the device and inputs his / her tastes and requests.

[1205] Producer B's actions: Register "Handmade Wood Carvings" as a product on the terminal. Enter product information, place of origin, price range, and production method.

[1206] Server analysis: The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the shared tags of "traditional crafts" and "wood carving."

[1207] Result notification: The server generates a notification saying "Recommended product: Handmade wood carvings (Producer: Producer B)" and sends it to User A's device. User A checks the details.

[1208] Prompt Sentence Examples

[1209] "I would like to develop an app that recommends handmade wood carvings from local producers to users who like traditional crafts. I would like to tag user information and product information and use AI to match the best products. Please explain the basic configuration and functions of the system."

[1210] This will allow users to easily find products that suit their tastes, provide producers with new business opportunities, and is also expected to contribute to revitalizing local industries.

[1211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1212] Step 1:

[1213] Entering user information

[1214] The user uses a terminal to input their preferences, requests, past purchase history, etc. The input data is sent to the server along with the user ID. The input information includes hobbies in text format, genres of interest, and product IDs of past purchases. The server receives this information and stores it in a database.

[1215] Step 2:

[1216] Enter producer information

[1217] Producers enter product information (product name, image, detailed description, price range, place of production, etc.) into their terminal and send it to the server. The server stores the received product information in a database and assigns a product ID. At this time, the product information is tagged (for example, "traditional craft" or "wood carving").

[1218] Step 3:

[1219] Tagging and categorization

[1220] The server tags and categorizes products based on user and producer information. It generates tags based on the user's hobbies and interests, and also tags the product information. This process is managed in a database, facilitating future searches and matching.

[1221] Step 4:

[1222] Analysis and matching by AI engine

[1223] The server reads the user and producer information stored in the database and analyzes it using an AI engine. Specifically, it matches the optimal product by taking into account the user's preferences, past behavioral history, and product tag information. The AI ​​model used here is a recommender system trained using, for example, TensorFlow or PyTorch.

[1224] Step 5:

[1225] Generating matching results

[1226] The AI ​​engine generates analysis results and extracts the most relevant product information for the user ID, including the product name, image, price, and detailed description. This information is then structured into a notification message and linked to the user ID.

[1227] Step 6:

[1228] Notifications and details

[1229] The server then sends the generated notification message to the user's device. The user receives the notification and checks the details on the device. The displayed content includes the name, image, price, and detailed description of the recommended product, as well as a link to purchase it.

[1230] Through this processing flow, users can easily find products that suit their tastes and needs, and producers can efficiently promote their products.

[1231] 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.

[1232] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the user's tastes, requests, and even emotions. The system includes a terminal for users to input their preferences and requests, a terminal for local producers to register product information, a server for storing and analyzing data, an AI engine, and an emotion engine.

[1233] System Overview

[1234] 1. Device operation

[1235] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, their purchase history, and their emotional state.

[1236] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[1237] 2. Server Roles

[1238] The server receives the user and producer information sent from the terminal and stores it in a database, where it is tagged and categorized based on preference and emotion information.

[1239] The database efficiently stores and manages user and producer information.

[1240] 3. Emotion Engine

[1241] The emotion engine recognizes the user's emotional state from the user's input information and device interactions, and this information is sent to the server as additional data on the user's preferences and needs.

[1242] Emotional states include emotion categories such as happiness, excitement, surprise, and sadness.

[1243] 4. Matching Process

[1244] The server's AI engine analyzes the information stored in the database and matches users with the best products based on their preferences, needs, and even their emotional state, taking into account the user's past behavioral history and the producer's product characteristics.

[1245] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[1246] Program processing explanation

[1247] Collecting user information and emotional information

[1248] Device:

[1249] Users access the system and input their preferences, requests, and emotional state. Preferences include name, email address, hobbies, preferences, genres of interest, and purchase history. They also input their current emotional state based on an emotion input form.

[1250] server:

[1251] User information and emotion information are received and stored in a database, where they are tagged and categorized based on the information.

[1252] Collecting producer information

[1253] Device:

[1254] Producers access the system and enter product information, including the company or individual name, detailed product description, photos, production location information, price range, and production process.

[1255] server:

[1256] Producer information is received and stored in a database, which is then used to tag and categorize the produce.

[1257] Applying the Emotion Engine

[1258] Device:

[1259] The emotion engine recognizes emotions from user operations and input information and sends them to the server. Emotional states are expressed as "happiness," "surprise," "sadness," etc.

[1260] server:

[1261] The emotional information is stored in a database and used as additional data for analysis.

[1262] AI automatic learning and matching

[1263] server:

[1264] User information, producer information, and emotional information are read from the database and analyzed using an AI engine. The analysis takes into account the user's preferences, past behavioral history, the producer's product features, and the user's emotional state.

[1265] server:

[1266] The AI ​​engine matches the most suitable products based on the user's tastes and emotions and generates matching results.

[1267] Presenting matching results

[1268] server:

[1269] Based on the matching results, an individual notification message is created and sent to the user's device.

[1270] Device:

[1271] The device receives the notification sent from the server and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[1272] Specific examples

[1273] 1. User A's actions

[1274] Register "traditional crafts" as your preference on the device, and then enter your tastes, desires, and current emotional state. Enter "joy" as your emotional state.

[1275] 2. Producer B's actions

[1276] Register "Handmade Wood Carvings" as a product on the terminal and enter the product information.

[1277] 3. Server Analysis

[1278] The server uses an AI engine to analyze information about User A and Producer B, as well as emotional information, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[1279] 4. Notification of Results

[1280] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[1281] User A's device receives the notification and displays detailed information.

[1282] In this way, the system takes into account the tastes, desires, and even emotions of users to provide optimal products, opening up new business opportunities for producers and potentially contributing to the revitalization of local communities.

[1283] The processing flow will be explained below.

[1284] Step 1:

[1285] Terminal: The user accesses the system and enters information into the user registration form, specifically their name, email address, hobbies and preferences, genres of interest, and purchase history.

[1286] Step 2:

[1287] Terminal: The user checks the information they have entered and clicks the "Register" button, which sends the form data to the server.

[1288] Step 3:

[1289] Server: Receives user information sent from the device and stores it in a database. At this time, it tags and categorizes the information based on the input preferences and requests.

[1290] Step 4:

[1291] Terminal: Producers access the system and enter information into the producer registration form, including the company or individual name, detailed product description, photo, production area information, price range, and production process.

[1292] Step 5:

[1293] Terminal: The producer checks the information entered and clicks the "Register" button, which sends the form data to the server.

[1294] Step 6:

[1295] Server: Receives producer information sent from the terminal and stores it in a database. At this time, it tags and categorizes the products based on the input product information.

[1296] Step 7:

[1297] Device: The user accesses the emotion information input screen and selects their current emotional state. Emotion categories include "joy," "excitement," "surprise," and "sadness."

[1298] Step 8:

[1299] Terminal: The user inputs emotion information and clicks the "Send" button, which sends the emotion data to the server.

[1300] Step 9:

[1301] Server: Receives emotion information sent from the device and stores it in a database. This information is linked to user information for analysis.

[1302] Step 10:

[1303] Server: Reads user information, producer information, and emotional information from the database and analyzes them using an AI engine. The analysis takes into account the user's preferences and requests, past behavioral history, product features of the producer, and their emotional state.

[1304] Step 11:

[1305] Server: The AI ​​engine matches the optimal products based on the user's preferences and emotions and generates matching results.

[1306] Step 12:

[1307] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[1308] Step 13:

[1309] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[1310] Step 14:

[1311] On the device: The user checks the notification and browses detailed information about the product they are interested in. This allows the user to find products that match their tastes and emotions.

[1312] In this way, the system analyzes users' tastes, requests, and even emotions, and provides the most suitable products, thereby providing new business opportunities for producers and contributing to the revitalization of local areas.

[1313] Example 2

[1314] 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."

[1315] The objective of this invention is to accurately recognize users' tastes, desires, and even emotions, and effectively match them with local specialty products and niche creative products. Conventional systems do not take the user's emotional state into account when matching products, making it difficult to stimulate purchasing motivation or suggest appropriate products. Therefore, there is a need for a product recommendation system based on detailed information, including the user's emotions.

[1316] 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.

[1317] In this invention, the server includes a means for storing user preferences, requests, and emotional information as well as producer product information in a database, an emotion engine that recognizes the user's emotional state from user operations and input information, and an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's preferences, requests, and emotional state. This enables highly accurate product recommendations that take the user's emotional state into consideration.

[1318] A "terminal" is a device that allows users or producers to input or display information.

[1319] "Tastes" refers to the genres that users like and the things that interest them.

[1320] "Request" refers to the conditions or requests desired by the user.

[1321] "Emotional state" refers to the emotion the user is currently feeling, and includes categories such as joy, surprise, and sadness.

[1322] "Producers" refers to local producers and creators who create and sell products.

[1323] "Product information" refers to information such as detailed product descriptions, photos, price ranges, and production processes registered by producers.

[1324] A "server" is a central device that stores data, analyzes data, and manages network resources.

[1325] A "database" is a structured repository of information built within a server.

[1326] An "emotion engine" is a system for recognizing a user's emotional state from their operations and input information.

[1327] An "artificial intelligence engine" is a system that has an algorithm that analyzes information stored in a database and matches the most suitable products.

[1328] "Tagging" is the process of assigning keywords and categories to information in order to classify and organize it.

[1329] "Matching" is a method of selecting and suggesting the most suitable product based on the user's tastes, requests, and emotional state.

[1330] "Notification means" is a function for sending and displaying matching results from the server to the user's device.

[1331] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the tastes, desires, and emotions of users. An embodiment of this system will be described below.

[1332] System configuration

[1333] The system includes the following components:

[1334] 1. User's Device

[1335] This terminal provides an interface for users to input their preferences, needs, and emotional state. Users use this to input their favorite genres, local products they are interested in, their purchase history, their emotional state, and so on.

[1336] 2. Producer's terminal

[1337] This terminal provides an interface for local producers to register product information. Producers enter detailed descriptions of their specialty products, photos, price ranges, production processes, and other information.

[1338] 3. Server

[1339] It is a central device that stores information on users' tastes, desires, and emotions, as well as product information from producers, in a database that is tagged and categorized based on the stored information.

[1340] 4. Emotion Engine

[1341] This system recognizes the user's emotional state from their actions and input information. The recognized emotional information is sent to the server and used as additional data. Emotional states include categories such as joy, surprise, and sadness.

[1342] 5. Artificial Intelligence Engine

[1343] The system analyzes information stored in a database and matches users with products that match their tastes, desires, and emotional state, taking into account their past behavioral history and the characteristics of the products made by the producers.

[1344] 6. Means of notification

[1345] This function generates matching results and notifies the user's device. The user can receive the notification and check detailed information.

[1346] Example of operation

[1347] 1. User A's actions

[1348] The user registers "traditional crafts" as a preference on the device, and inputs their tastes, desires, and current emotional state. The emotional state is input as "joy."

[1349] 2. Producer B's actions

[1350] The producer registers "handmade wood carvings" as a product on the terminal and enters product information.

[1351] 3. Server Analysis

[1352] The server uses an AI engine to analyze the information and emotional information of User A and Producer B, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[1353] 4. Notification of Results

[1354] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device. User A's device receives the notification and displays detailed information.

[1355] Prompt Sentence Examples

[1356] "Using the generative AI model, please provide an example of natural language processing in this system. For example, if a user registers a preference for traditional crafts and inputs their current emotional state, please explain the steps to generate product information for handmade wood carvings that would be recommended as the most suitable item."

[1357] The above is an embodiment of the present invention. This system makes it possible to recommend products that take into account detailed information about the user, enabling the provision of products that are optimal for the user. It is also expected to provide new business opportunities for producers and contribute to the revitalization of local communities.

[1358] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1359] Step 1:

[1360] Collecting user information and emotional information

[1361] User: Enters their own preferences, requests, and emotional state. Specifically, they enter their name, email address, hobbies, preferences, genres of interest, purchase history, and emotional state into the interface.

[1362] Input: Name, email address, hobbies and preferences, genre interests, purchase history, emotional state.

[1363] Output: This information is sent to the server.

[1364] Step 2:

[1365] Data transmission and storage

[1366] Terminal: Send the entered information to the server. Click the Send button to upload the data.

[1367] Server: Stores the received user information and emotion information in a database, tagging and categorizing the information.

[1368] Input: User information and emotion information entered into the interface.

[1369] Output: The tagged user information and emotion information are stored in a database.

[1370] Step 3:

[1371] Collecting producer information

[1372] Producer: Enter product information into the system. Specifically, enter the company or individual name, detailed product description, photo, production location information, price range, and production process into the interface.

[1373] Input: company / individual name, detailed product description, photo, origin information, price range, and production process.

[1374] Output: This information is sent to the server.

[1375] Step 4:

[1376] Data transmission and storage

[1377] Terminal: Send the entered product information to the server. Click the Send button to upload the data.

[1378] Server: Stores the received product information in a database, tagging and categorizing it based on the information.

[1379] Input: Product information entered into the interface.

[1380] Output: Tagged product information is stored in a database.

[1381] Step 5:

[1382] Emotion engine application and recognition

[1383] On the device: The emotion engine recognizes emotions from user actions and input information, specifically analyzing emotions based on keystroke speed and phrases in the interface.

[1384] Input: User actions and input information.

[1385] Output: Data of the recognized emotional state sent to the server.

[1386] Step 6:

[1387] Storing emotional information

[1388] Server: The received emotional information is stored in a database and used as additional data for analysis. Specifically, the emotional history of each user is recorded.

[1389] Input: Recognized emotion information.

[1390] Output: Emotion information stored in a database.

[1391] Step 7:

[1392] Loading information

[1393] Server: Reads user, producer, and sentiment information from the database, specifically extracting relevant data using SQL queries.

[1394] Input: User information, producer information, and emotion information stored in the database.

[1395] Output: Analyzed data provided to the AI ​​engine.

[1396] Step 8:

[1397] Data analysis and product matching

[1398] Server: Analyzes the information loaded by the AI ​​engine. Specifically, the AI ​​algorithm analyzes the user's preferences, past behavioral history, product features of the manufacturer, and the user's emotional state. For example, by referencing data from similar users, it predicts the best product for the current user's emotional state.

[1399] Input: Analysis data provided to the AI ​​engine.

[1400] Output: Matched product information.

[1401] Step 9:

[1402] Generating matching results

[1403] Server: Generates matching results and creates individual notification messages, specifically creating a list of candidate products and formatting them as text messages for the user.

[1404] Input: Matched product information.

[1405] Output: Informational message.

[1406] Step 10:

[1407] Sending notifications

[1408] Server: Sends the created notification message to the user's device. Specifically, it pushes the message to the user's device using a network protocol.

[1409] Input: Notification message.

[1410] output: Notification sent to the user's device.

[1411] Step 11:

[1412] Results display

[1413] Device: Receives the notification sent from the server and displays the matching results to the user. Specifically, it displays information such as "Product name: Handmade wood carving, Producer: Producer B, Price: \XXXX" on the screen.

[1414] Input: The notification sent by the server.

[1415] Output: Matching results displayed in the user interface.

[1416] (Application example 2)

[1417] 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."

[1418] In modern society, there is a demand for recommendation systems that respond to users' tastes and emotions. However, conventional systems have difficulty matching products that reflect the user's emotional state, making it difficult to effectively recommend appropriate products. In particular, when it comes to local specialties or niche creative products, it is difficult to achieve optimal matching based on the user's emotions, which makes it difficult to contribute to regional revitalization.

[1419] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal for inputting user preferences, requests, and emotional states; a terminal for local producers to register product information; a means for storing the user preferences, requests, and emotional information and producer product information in a database; an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user preferences, requests, and emotional states; a means for notifying the user terminal of the matching results; and a means for understanding the user's emotional state using emotion recognition technology. This enables appropriate product recommendations that take the user's emotional state into consideration, thereby improving the matching accuracy of local specialty products and niche creative products.

[1420] A "user" is a consumer who uses a terminal to input his or her preferences, desires, and emotional state.

[1421] "Local producers" are merchants or individuals who use terminals to register information about specialty products or niche creative goods.

[1422] A "terminal" is a device that allows users to input information about their preferences, requests, and emotional state, and a device that allows producers to register product information.

[1423] The "server" is a central device that stores information sent by users and producers in a database and analyzes it.

[1424] The "database" is an information storage system that manages information on users' preferences, requests, and emotions, as well as product information from producers, stored on a server.

[1425] An "artificial intelligence engine" is an algorithm and software that analyzes a user's tastes, requests, and emotional state with a producer's product information to match them with the most suitable product.

[1426] "Emotional state" is information that indicates the emotions that the user is feeling, and is classified into categories such as joy, surprise, and sadness.

[1427] "Emotion recognition technology" is a technology that recognizes the emotional state of a user from their input information and device interactions.

[1428] "Matching results" are information that indicates the products that best suit the user's tastes, desires, and emotional state, based on analysis by the AI ​​engine.

[1429] The "notification means" is a system in which the server generates matching results and sends them to the terminal of the corresponding user.

[1430] The present invention is a system for effectively matching local specialties and niche creative products based on the tastes, desires, and emotional state of users, and an embodiment thereof is described below. The system includes a terminal for users to input their tastes, desires, and emotional state, a terminal for local producers to register product information, a server for storing and analyzing data, an artificial intelligence engine, and emotion recognition technology.

[1431] System Overview

[1432] 1. User operations

[1433] The user terminal provides an interface for users to input their preferences, requests, and emotional state. Users input their favorite genres, local products they are interested in, their purchase history, and their emotional state (for example, joy or surprise). In addition, by using a device with a camera, emotion recognition technology can be used to analyze the user's facial expressions and automatically recognize their emotional state.

[1434] 2. Producer manipulation

[1435] The producer terminal provides an interface for producers to register product information. Producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, and so on.

[1436] 3. Server Roles

[1437] The server receives information sent from user terminals and producer terminals and stores this data in a database. At this time, it tags and categorizes the data based on the user's preferences, requests, and emotional information, and the producer's product information.

[1438] 4. Application of emotion recognition technology

[1439] Emotion recognition technology recognizes the user's emotional state from input information and device interactions and sends it to a server, using Microsoft Azure's emotion recognition API and other technologies.

[1440] 5. Matching Process

[1441] The AI ​​engine analyzes the user's preferences, requests, and emotional information stored in the database, as well as the product information of the producer, to match the optimal product. This is done using generative AI models such as Amazon SageMaker.

[1442] 6. Presenting the matching results

[1443] The server generates matching results based on the analysis results and notifies the relevant user's device. The user's device receives the notification and displays the product name, producer information, price, purchase method, etc.

[1444] Specific examples

[1445] 1. User A's behavior

[1446] User A accesses the app using a smartphone, enters "I like seasonal fruits," and enters his / her current emotional state as "joy." When he / she captures his / her facial expression using a camera, the emotion recognition technology automatically recognizes that information.

[1447] 2. Producer B's actions

[1448] Producer B registers "seasonal local apples" as a product and enters detailed product information and photos.

[1449] 3. Server Analysis

[1450] The server stores information and emotional information of User A and Producer B in a database, and uses a generative AI model to make optimal matches based on "fruit" and "joy."

[1451] 4. Generate and send notifications

[1452] The server generates a notification as a matching result, stating "Recommended product: Seasonal local apples (Producer: Producer B)" and sends it to User A's terminal.

[1453] 5. Receipt and confirmation by User A

[1454] User A's device receives the notification and displays detailed information. User A checks the product in the app and completes the purchase procedure.

[1455] Prompt Sentence Examples

[1456] User input: "I'm in a fun mood today, so I'm looking for something exciting to buy."

[1457] Emotion Recognition API: "Joy"

[1458] AI engine prompt: "Recommend exciting local products that match the user's tastes with emotions of joy"

[1459] This system makes it possible to recommend appropriate products that take into account the user's emotional state, improving the accuracy of matching local specialties and niche creative products.

[1460] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1461] Step 1:

[1462] The user device receives input of the user's preferences, requests, and emotional state. The user can provide their preferred genres and purchase history through text input, and their current emotional state is automatically recognized by taking a photo with a camera using emotion recognition technology. The input data (preferences, requests, and emotional state) is sent to the server.

[1463] Step 2:

[1464] The server receives information on preferences, requests, and emotional states sent from the user's device and stores it in a database. Emotion recognition technology categorizes the emotional states into categories such as "happiness," "surprise," and "sadness," and tags them as data. The stored data is used for later analysis.

[1465] Step 3:

[1466] The producer terminal receives product information (detailed description, photos, price, etc.) from the producer. The producer enters and registers the product information. The entered product information is sent to the server.

[1467] Step 4:

[1468] The server receives product information sent from the producer terminal and stores it in a database. The product information is tagged and categorized and then stored in the database.

[1469] Step 5:

[1470] The server uses a generative AI model to analyze user information (tastes, requests, emotional state) and producer information (product information) stored in a database. The purpose of the analysis is to find the product that best suits the user's tastes and emotional state. The generative AI model uses AI tools such as Amazon SageMaker.

[1471] Step 6:

[1472] The server's AI engine performs analysis and matches the optimal product based on the user's tastes, desires, and emotional state. The generative AI model compares the user information with product information to generate the optimal matching result. The matching result is expressed as a list of recommended products.

[1473] Step 7:

[1474] The server generates a notification message of the matching result and sends a list including recommended products to the user terminal. The user terminal provides an interface that displays detailed information such as recommended products, prices, and producer information, allowing the user to easily check the products.

[1475] Step 8:

[1476] Users can receive matching results through the app, check the details, and if they are interested, they can purchase the product within the app.

[1477] This series of processing steps allows users to easily find the specialty or niche product that best suits their current mood, and allows producers to effectively deliver their products to more consumers. Furthermore, the explicit operation and input / output make the system's operation clear.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] [Fourth embodiment]

[1482] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1483] 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.

[1484] 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).

[1485] 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.

[1486] 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.

[1487] 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).

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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.

[1494] 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."

[1495] The present invention is a system that effectively matches users with local producers, and an embodiment thereof is described below. This system includes a terminal for users to input their preferences and requests, a terminal for producers to register product information, a server for storing and analyzing data, and an AI engine running on the server.

[1496] System Overview

[1497] 1. Device operation

[1498] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, and their purchase history.

[1499] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[1500] 2. Server Roles

[1501] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[1502] The database efficiently stores and manages user and producer information.

[1503] 3. Matching Process

[1504] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[1505] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[1506] Program processing explanation

[1507] Collection of User Information

[1508] Device:

[1509] Users access the system and enter information about their preferences and needs, including their name, email address, hobbies and preferences, genres of interest, and purchase history.

[1510] server:

[1511] User information is received and stored in a database, where it is tagged and categorized based on the information entered.

[1512] Collecting producer information

[1513] Device:

[1514] Producers access the system and enter product information, including company or individual names, detailed product descriptions, photos, production location information, price ranges, and production processes.

[1515] server:

[1516] Producer information is received and stored in a database, and tagging and categorization are performed based on the information entered.

[1517] AI automatic learning and matching

[1518] server:

[1519] User and producer information is read from the database and analyzed using an AI engine. The system matches users with the most suitable products, taking into account their preferences, past behavioral history, and product features.

[1520] Presenting matching results

[1521] server:

[1522] It generates matching results and creates individual notification messages, the contents of which are sent to the user's device.

[1523] Device:

[1524] It receives notifications and displays matching results to the user, including product name, producer information, price, and purchasing instructions.

[1525] Specific examples

[1526] 1. User A's actions

[1527] Register "traditional crafts" as your preference on the device and enter your tastes and requests.

[1528] Enter specific criteria that match your interests into the system.

[1529] 2. Producer B's actions

[1530] Register "Handmade Wood Carvings" as a product on the terminal.

[1531] Enter detailed product information, place of origin, price range, and production method.

[1532] 3. Server Analysis

[1533] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[1534] 4. Notification of Results

[1535] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[1536] User A's device receives the notification and displays detailed information.

[1537] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, which is expected to contribute to the revitalization of local communities.

[1538] The processing flow will be explained below.

[1539] Step 1:

[1540] Terminal: The user accesses the system and enters the necessary information into the user registration form, such as their name, email address, hobbies and preferences, genres of interest, and purchase history.

[1541] Step 2:

[1542] Terminal: The user checks the information entered and clicks the "Register" button, which sends the form data to the server.

[1543] Step 3:

[1544] Server: Receives user information sent from the device and stores it in a database, tagging and categorizing it based on the information.

[1545] Step 4:

[1546] Terminal: Producers access the system and enter the necessary information into the producer registration form, such as the company or individual name, detailed product description, photo, production area information, price range, and production process.

[1547] Step 5:

[1548] Terminal: The producer confirms the information entered and clicks the "Register" button, which sends the form data to the server.

[1549] Step 6:

[1550] Server: Receives producer information sent from the device and stores it in a database. At this time, it tags and categorizes the products based on the information.

[1551] Step 7:

[1552] Server: Reads user and producer information from the database and analyzes it using an AI engine. The analysis takes into account the user's preferences, past behavioral history, and the producer's product characteristics.

[1553] Step 8:

[1554] Server: The AI ​​engine matches the optimal products based on the user's preferences and generates matching results.

[1555] Step 9:

[1556] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[1557] Step 10:

[1558] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[1559] Step 11:

[1560] On the device: The user checks the notification and views detailed information about the product they are interested in. This allows the user to find products that suit their preferences.

[1561] In this way, the entire system can operate smoothly and effectively match users and producers.

[1562] Example 1

[1563] 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."

[1564] In modern society, it is difficult to effectively match rural producers with urban consumers. Rural producers have limited opportunities to widely promote their products, and urban consumers have few means of accessing local specialties. As a result, consumers are unable to find products that suit their tastes, and producers miss out on new business opportunities. This has also slowed the revitalization of local economies.

[1565] 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.

[1566] In this invention, the server includes a means for storing user preferences and request information and producer product information in a database, a means for tagging and categorizing based on the information stored in the database, and an artificial intelligence engine for analyzing the information and matching products that match the user's preferences and requests. This allows users to easily find products that suit their tastes, and producers to gain new business opportunities. This is likely to contribute to the revitalization of local economies.

[1567] "Terminal" means an electronic device used by a user or producer to enter or access information.

[1568] A "server" is a computer system that receives, stores, and analyzes information sent from terminals via a network.

[1569] A "database" is a system for systematically storing and managing information about users and producers.

[1570] "Tastes" refer to the user's personal preferences and interests.

[1571] "Requests" are specific conditions or requirements that users need.

[1572] "Producers" are individuals or companies that produce and supply local specialty products.

[1573] "Product information" refers to data provided by producers, such as detailed product descriptions, information on the place of origin, price, and manufacturing process.

[1574] "Tagging" is the act of labeling information to classify and organize it.

[1575] "Categorization" is the process of classifying information into specific groups.

[1576] An "artificial intelligence engine" is a program that includes machine learning algorithms to analyze data and match products that match users' tastes and requests.

[1577] "Matching" is the process of selecting and combining products that best suit the user's tastes and requests.

[1578] A "notification" is information or a message sent from the server to the user's terminal.

[1579] The present invention is a system that effectively matches users with local producers. The system includes the following elements:

[1580] 1. Device operation

[1581] The user's device provides an interface for the user to input their preferences and requests. For example, the user opens a browser and enters their hobbies, preferences, purchase history, etc. into an input form. Then, they click a button to send the entered information to the server.

[1582] The producer's terminal provides an interface for registering product information. The producer inputs detailed product descriptions, price ranges, and production area information, and sends them to the server.

[1583] 2. Server Roles

[1584] The server receives the user and producer information sent from the terminal and stores it in a database, using a database management system such as MySQL.

[1585] The database efficiently stores and manages information about users and producers. User information includes categories of hobbies and preferences and purchase history, while producer information includes detailed product descriptions, price ranges, and production processes.

[1586] The server tags and categorizes the information it receives, using a tagging algorithm to assign appropriate tags to each piece of information.

[1587] 3. Matching Process

[1588] The server's AI engine analyzes the information stored in the database and matches products that match the user's tastes and requests. The AI ​​engine uses machine learning libraries such as TensorFlow and PyTorch.

[1589] The server takes into account the user's past behavioral history and the producer's product characteristics to generate optimal matching results.

[1590] 4. Notification of Matching Results

[1591] The server generates matching results and creates a personalized notification message, for example, "Recommended Product: Handmade Wood Carvings (Producer Name)" and sends it to the user's device.

[1592] The device receives the notification and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[1593] Specific examples

[1594] User behavior examples

[1595] User A sets "traditional crafts" as a preference and inputs his / her tastes and requests. For example, he / she may input that he / she is interested in "handmade wood carvings."

[1596] Examples of producer actions

[1597] Producer B registers "handmade wood carvings" as a product. He enters detailed product information, including the place of origin, price range, and production method.

[1598] Server analysis example

[1599] The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the common tags of "traditional crafts" and "wood carving."

[1600] Example of notification of results

[1601] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer B)" and sends it to User A's device.

[1602] User A receives the notification on his / her device and checks the detailed information.

[1603] Prompt Sentence Examples

[1604] Prompt for User A: "Please describe your preferences or desires. For example, 'traditional crafts' or 'handmade wood carvings.'"

[1605] Prompt for Producer B: "Please enter the product information you would like to register. Include the product name, detailed description, photos, origin, price range, and production process."

[1606] This embodiment allows users to easily find products that suit their tastes and provides producers with new business opportunities, thereby contributing to the revitalization of local economies.

[1607] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1608] Program processing flow

[1609] Step 1: Enter your user information

[1610] explanation:

[1611] Users access the system from their own devices using a dedicated application or web interface, and enter information such as their name, email address, hobbies and preferences, genres of interest, and purchase history in the displayed form.

[1612] input:

[1613] User name, email address, hobbies and preferences, genres of interest, purchase history

[1614] output:

[1615] This information is sent to the server

[1616] Specific behavior:

[1617] The user opens a browser, visits the specified website, logs in, fills in the required information on the form, and clicks the "Submit" button.

[1618] Step 2: Enter producer information

[1619] explanation:

[1620] Producers access the system using a dedicated application or web interface from their own devices, and enter detailed product descriptions, photos, information on the place of origin, price range, and production process.

[1621] input:

[1622] Company or individual name, detailed product description, photo, origin information, price range, production process

[1623] output:

[1624] This information is sent to the server

[1625] Specific behavior:

[1626] The producer opens a browser and accesses the specified website. After logging in, they enter the required product information into the form and click the "Submit" button.

[1627] Step 3: Data received and stored by the server

[1628] explanation:

[1629] The server receives the user and producer information sent from the device, then stores the received information in a database and tags and categorizes it.

[1630] input:

[1631] User information, producer information

[1632] output:

[1633] User and producer information stored in a database, along with their corresponding tags and categories

[1634] Specific behavior:

[1635] The server analyzes the data received from users' and producers' devices and stores it in a database system such as MySQL. Using a tagging algorithm, appropriate tags are assigned to each piece of information and it is then categorized.

[1636] Step 4: Database analysis and matching

[1637] explanation:

[1638] The server's AI engine reads and analyzes the user and producer information stored in the database. As a result of the analysis, it takes into account the user's preferences, past behavioral history, and the producer's product characteristics to match the optimal product.

[1639] input:

[1640] User information stored in the database, producer information stored in the database

[1641] output:

[1642] Results of product matching that matches user tastes and requests

[1643] Specific behavior:

[1644] The AI ​​engine uses machine learning libraries such as TensorFlow and PyTorch to analyze the user and producer information in the database, and connects users and producers through clustering algorithms and recommendation systems.

[1645] Step 5: Generating and notifying matching results

[1646] explanation:

[1647] The server generates a personalized notification message for the user based on the matching results obtained from the AI ​​engine, and the notification message is sent to the user's device, where the user can receive and check it.

[1648] input:

[1649] Matching results

[1650] output:

[1651] Notification message to user terminal

[1652] Specific behavior:

[1653] The server generates a notification message to User A in the form of a push notification or email with the content "Recommended Product: Handmade Wood Carving (Producer B)". The generated notification message is sent to User A's device. User A's device receives the notification and displays it on the screen.

[1654] The above is a specific processing flow of the system.

[1655] (Application example 1)

[1656] 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."

[1657] Products offered by local producers often match the tastes and requests of specific users, but effective ways of approaching consumers are limited, making appropriate purchasing matching difficult. Users also have few means of finding local products that interest them, making purchasing activities cumbersome. This makes it difficult for local producers to seize new business opportunities, and consumers also have difficulty finding products that suit their tastes.

[1658] 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.

[1659] In this invention, the server includes an artificial intelligence engine that matches products that match the user's tastes and desires. This provides a means for tagging and categorizing products based on the user's tastes and desires, a means for matching products taking into account past purchase history, and a means for notifying the user of recommended products and displaying detailed information. This allows users to easily find products that match their tastes and desires, and also provides local producers with new business opportunities.

[1660] A "terminal" is a device equipped with an interface for users to input their preferences and requests, and is used by users and producers to provide information to the system.

[1661] The "server" is a central processing unit that stores information on users' preferences and requests, as well as product information from producers, in a database, and analyzes this information to perform matching.

[1662] A "database" is a place where information is collected to efficiently store and manage user and producer information.

[1663] The "artificial intelligence engine" is a program that analyzes information stored in a database and matches products that match the user's tastes and requests.

[1664] The "notification means" is a function that transmits the matching results to the user's terminal and provides the user with information about recommended products.

[1665] "Tagging" is a technique for assigning keywords and categories related to information (such as products or interests) to make it easier to search and analyze within a database.

[1666] "Categorization" refers to the process of classifying data into specific groups to make management and searching more efficient.

[1667] "Purchase history" refers to information about products a user has purchased in the past, and analyzing this information is used to understand the user's preferences and patterns.

[1668] "Matching" refers to the process of selecting product information that best suits the user's tastes and needs and recommending it to the user.

[1669] "Recommendation" is a function that encourages purchases by presenting appropriate products based on the user's interests and past behavior.

[1670] The present invention is a system that effectively matches users with local producers. This system is designed to recommend optimal products based on the user's preferences and requests. A specific embodiment of the system is shown below.

[1671] System Overview

[1672] 1. Device operation

[1673] The user's device provides an interface for the user to input their preferences and requests, such as hobbies, tastes, local products of interest, and past purchase history.

[1674] The producer's terminal provides an interface for registering product information. The producer inputs detailed descriptions, images, price ranges, and production processes for the specialty products they produce.

[1675] 2. Server Roles

[1676] The server receives the user and producer information sent from the device and stores it in a database, where it is tagged and categorized.

[1677] The database efficiently stores and manages user and producer information.

[1678] 3. Matching Process

[1679] The server's artificial intelligence engine analyzes the information stored in the database and matches users with products that match their tastes and requests, taking into account the user's past behavioral history and the producer's product characteristics.

[1680] A notification of the results is generated by the server and sent to the appropriate user's device.

[1681] Program processing description

[1682] The hardware used is a smartphone, such as the Apple iPhone or Samsung Galaxy. The software uses Python to run the program, and machine learning libraries such as TensorFlow and PyTorch are used for the AI ​​engine.

[1683] The flow of data processing and calculation is as follows:

[1684] User preference input: Users input their preferences, requests, and purchase history via their smartphone. The input information is sent to the server.

[1685] Producer product registration: Producers also use smartphones or other devices to enter product information, including the product name, price, detailed description, and production process.

[1686] Tagging and categorization: The server stores the received information in a database and appropriately tags and categorizes it, taking into account the user's preferences, needs, and purchasing history.

[1687] AI analysis and matching: The server's AI engine analyzes the database and selects the products that best suit the user's tastes and needs. This process utilizes AI models.

[1688] Recommendation notification and detailed display: Matching results are generated and notified to the user's smartphone, where the user can check detailed information.

[1689] Specific examples

[1690] User A's actions: Registers "traditional crafts" as a preference on the device and inputs his / her tastes and requests.

[1691] Producer B's actions: Register "Handmade Wood Carvings" as a product on the terminal. Enter product information, place of origin, price range, and production method.

[1692] Server analysis: The server analyzes the information of User A and Producer B using an AI engine and makes the best match based on the shared tags of "traditional crafts" and "wood carving."

[1693] Result notification: The server generates a notification saying "Recommended product: Handmade wood carvings (Producer: Producer B)" and sends it to User A's device. User A checks the details.

[1694] Prompt Sentence Examples

[1695] "I would like to develop an app that recommends handmade wood carvings from local producers to users who like traditional crafts. I would like to tag user information and product information and use AI to match the best products. Please explain the basic configuration and functions of the system."

[1696] This will allow users to easily find products that suit their tastes, provide producers with new business opportunities, and is also expected to contribute to revitalizing local industries.

[1697] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1698] Step 1:

[1699] Entering user information

[1700] The user uses a terminal to input their preferences, requests, past purchase history, etc. The input data is sent to the server along with the user ID. The input information includes hobbies in text format, genres of interest, and product IDs of past purchases. The server receives this information and stores it in a database.

[1701] Step 2:

[1702] Enter producer information

[1703] Producers enter product information (product name, image, detailed description, price range, place of production, etc.) into their terminal and send it to the server. The server stores the received product information in a database and assigns a product ID. At this time, the product information is tagged (for example, "traditional craft" or "wood carving").

[1704] Step 3:

[1705] Tagging and categorization

[1706] The server tags and categorizes products based on user and producer information. It generates tags based on the user's hobbies and interests, and also tags the product information. This process is managed in a database, facilitating future searches and matching.

[1707] Step 4:

[1708] Analysis and matching by AI engine

[1709] The server reads the user and producer information stored in the database and analyzes it using an AI engine. Specifically, it matches the optimal product by taking into account the user's preferences, past behavioral history, and product tag information. The AI ​​model used here is a recommender system trained using, for example, TensorFlow or PyTorch.

[1710] Step 5:

[1711] Generating matching results

[1712] The AI ​​engine generates analysis results and extracts the most relevant product information for the user ID, including the product name, image, price, and detailed description. This information is then structured into a notification message and linked to the user ID.

[1713] Step 6:

[1714] Notifications and details

[1715] The server then sends the generated notification message to the user's device. The user receives the notification and checks the details on the device. The displayed content includes the name, image, price, and detailed description of the recommended product, as well as a link to purchase it.

[1716] Through this processing flow, users can easily find products that suit their tastes and needs, and producers can efficiently promote their products.

[1717] 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.

[1718] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the user's tastes, requests, and even emotions. The system includes a terminal for users to input their preferences and requests, a terminal for local producers to register product information, a server for storing and analyzing data, an AI engine, and an emotion engine.

[1719] System Overview

[1720] 1. Device operation

[1721] The user's terminal provides an interface for the user to input their preferences and requests, such as their favorite genres, local products they are interested in, their purchase history, and their emotional state.

[1722] The producer's terminal provides an interface for registering product information, where producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, etc.

[1723] 2. Server Roles

[1724] The server receives the user and producer information sent from the terminal and stores it in a database, where it is tagged and categorized based on preference and emotion information.

[1725] The database efficiently stores and manages user and producer information.

[1726] 3. Emotion Engine

[1727] The emotion engine recognizes the user's emotional state from the user's input information and device interactions, and this information is sent to the server as additional data on the user's preferences and needs.

[1728] Emotional states include emotion categories such as happiness, excitement, surprise, and sadness.

[1729] 4. Matching Process

[1730] The server's AI engine analyzes the information stored in the database and matches users with the best products based on their preferences, needs, and even their emotional state, taking into account the user's past behavioral history and the producer's product characteristics.

[1731] A notification of the results is generated by the server and sent to the appropriate user's terminal.

[1732] Program processing explanation

[1733] Collecting user information and emotional information

[1734] Device:

[1735] Users access the system and input their preferences, requests, and emotional state. Preferences include name, email address, hobbies, preferences, genres of interest, and purchase history. They also input their current emotional state based on an emotion input form.

[1736] server:

[1737] User information and emotion information are received and stored in a database, where they are tagged and categorized based on the information.

[1738] Collecting producer information

[1739] Device:

[1740] Producers access the system and enter product information, including the company or individual name, detailed product description, photos, production location information, price range, and production process.

[1741] server:

[1742] Producer information is received and stored in a database, which is then used to tag and categorize the produce.

[1743] Applying the Emotion Engine

[1744] Device:

[1745] The emotion engine recognizes emotions from user operations and input information and sends them to the server. Emotional states are expressed as "happiness," "surprise," "sadness," etc.

[1746] server:

[1747] The emotional information is stored in a database and used as additional data for analysis.

[1748] AI automatic learning and matching

[1749] server:

[1750] User information, producer information, and emotional information are read from the database and analyzed using an AI engine. The analysis takes into account the user's preferences, past behavioral history, the producer's product features, and the user's emotional state.

[1751] server:

[1752] The AI ​​engine matches the most suitable products based on the user's tastes and emotions and generates matching results.

[1753] Presenting matching results

[1754] server:

[1755] Based on the matching results, an individual notification message is created and sent to the user's device.

[1756] Device:

[1757] The device receives the notification sent from the server and displays the matching results to the user, including the product name, manufacturer information, price, and purchase method.

[1758] Specific examples

[1759] 1. User A's actions

[1760] Register "traditional crafts" as your preference on the device, and then enter your tastes, desires, and current emotional state. Enter "joy" as your emotional state.

[1761] 2. Producer B's actions

[1762] Register "Handmade Wood Carvings" as a product on the terminal and enter the product information.

[1763] 3. Server Analysis

[1764] The server uses an AI engine to analyze information about User A and Producer B, as well as emotional information, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[1765] 4. Notification of Results

[1766] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device.

[1767] User A's device receives the notification and displays detailed information.

[1768] In this way, the system takes into account the tastes, desires, and even emotions of users to provide optimal products, opening up new business opportunities for producers and potentially contributing to the revitalization of local communities.

[1769] The processing flow will be explained below.

[1770] Step 1:

[1771] Terminal: The user accesses the system and enters information into the user registration form, specifically their name, email address, hobbies and preferences, genres of interest, and purchase history.

[1772] Step 2:

[1773] Terminal: The user checks the information they have entered and clicks the "Register" button, which sends the form data to the server.

[1774] Step 3:

[1775] Server: Receives user information sent from the device and stores it in a database. At this time, it tags and categorizes the information based on the input preferences and requests.

[1776] Step 4:

[1777] Terminal: Producers access the system and enter information into the producer registration form, including the company or individual name, detailed product description, photo, production area information, price range, and production process.

[1778] Step 5:

[1779] Terminal: The producer checks the information entered and clicks the "Register" button, which sends the form data to the server.

[1780] Step 6:

[1781] Server: Receives producer information sent from the terminal and stores it in a database. At this time, it tags and categorizes the products based on the input product information.

[1782] Step 7:

[1783] Device: The user accesses the emotion information input screen and selects their current emotional state. Emotion categories include "joy," "excitement," "surprise," and "sadness."

[1784] Step 8:

[1785] Terminal: The user inputs emotion information and clicks the "Send" button, which sends the emotion data to the server.

[1786] Step 9:

[1787] Server: Receives emotion information sent from the device and stores it in a database. This information is linked to user information for analysis.

[1788] Step 10:

[1789] Server: Reads user information, producer information, and emotional information from the database and analyzes them using an AI engine. The analysis takes into account the user's preferences and requests, past behavioral history, product features of the producer, and their emotional state.

[1790] Step 11:

[1791] Server: The AI ​​engine matches the optimal products based on the user's preferences and emotions and generates matching results.

[1792] Step 12:

[1793] Server: Based on the generated matching results, it creates an individual notification message and sends the contents of that message to the user's device.

[1794] Step 13:

[1795] Device: The device receives the notification sent from the server. The notification contains detailed information about the matched product (product name, manufacturer information, price, purchase method, etc.).

[1796] Step 14:

[1797] On the device: The user checks the notification and browses detailed information about the product they are interested in. This allows the user to find products that match their tastes and emotions.

[1798] In this way, the system analyzes users' tastes, requests, and even emotions, and provides the most suitable products, thereby providing new business opportunities for producers and contributing to the revitalization of local areas.

[1799] Example 2

[1800] 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."

[1801] The objective of this invention is to accurately recognize users' tastes, desires, and even emotions, and effectively match them with local specialty products and niche creative products. Conventional systems do not take the user's emotional state into account when matching products, making it difficult to stimulate purchasing motivation or suggest appropriate products. Therefore, there is a need for a product recommendation system based on detailed information, including the user's emotions.

[1802] 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.

[1803] In this invention, the server includes a means for storing user preferences, requests, and emotional information as well as producer product information in a database, an emotion engine that recognizes the user's emotional state from user operations and input information, and an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's preferences, requests, and emotional state. This enables highly accurate product recommendations that take the user's emotional state into consideration.

[1804] A "terminal" is a device that allows users or producers to input or display information.

[1805] "Tastes" refers to the genres that users like and the things that interest them.

[1806] "Request" refers to the conditions or requests desired by the user.

[1807] "Emotional state" refers to the emotion the user is currently feeling, and includes categories such as joy, surprise, and sadness.

[1808] "Producers" refers to local producers and creators who create and sell products.

[1809] "Product information" refers to information such as detailed product descriptions, photos, price ranges, and production processes registered by producers.

[1810] A "server" is a central device that stores data, analyzes data, and manages network resources.

[1811] A "database" is a structured repository of information built within a server.

[1812] An "emotion engine" is a system for recognizing a user's emotional state from their operations and input information.

[1813] An "artificial intelligence engine" is a system that has an algorithm that analyzes information stored in a database and matches the most suitable products.

[1814] "Tagging" is the process of assigning keywords and categories to information in order to classify and organize it.

[1815] "Matching" is a method of selecting and suggesting the most suitable product based on the user's tastes, requests, and emotional state.

[1816] "Notification means" is a function for sending and displaying matching results from the server to the user's device.

[1817] The present invention is a system that effectively matches local specialty products and niche creative products by recognizing the tastes, desires, and emotions of users. An embodiment of this system will be described below.

[1818] System configuration

[1819] The system includes the following components:

[1820] 1. User's Device

[1821] This terminal provides an interface for users to input their preferences, needs, and emotional state. Users use this to input their favorite genres, local products they are interested in, their purchase history, their emotional state, and so on.

[1822] 2. Producer's terminal

[1823] This terminal provides an interface for local producers to register product information. Producers enter detailed descriptions of their specialty products, photos, price ranges, production processes, and other information.

[1824] 3. Server

[1825] It is a central device that stores information on users' tastes, desires, and emotions, as well as product information from producers, in a database that is tagged and categorized based on the stored information.

[1826] 4. Emotion Engine

[1827] This system recognizes the user's emotional state from their actions and input information. The recognized emotional information is sent to the server and used as additional data. Emotional states include categories such as joy, surprise, and sadness.

[1828] 5. Artificial Intelligence Engine

[1829] The system analyzes information stored in a database and matches users with products that match their tastes, desires, and emotional state, taking into account their past behavioral history and the characteristics of the products made by the producers.

[1830] 6. Means of notification

[1831] This function generates matching results and notifies the user's device. The user can receive the notification and check detailed information.

[1832] Example of operation

[1833] 1. User A's actions

[1834] The user registers "traditional crafts" as a preference on the device, and inputs their tastes, desires, and current emotional state. The emotional state is input as "joy."

[1835] 2. Producer B's actions

[1836] The producer registers "handmade wood carvings" as a product on the terminal and enters product information.

[1837] 3. Server Analysis

[1838] The server uses an AI engine to analyze the information and emotional information of User A and Producer B, and makes the optimal match based on the common tags of "traditional crafts" and "wood carving" and the user's emotion of "joy."

[1839] 4. Notification of Results

[1840] The server generates a notification saying "Recommended Product: Handmade Wood Carvings (Producer: Producer B)" and sends it to User A's device. User A's device receives the notification and displays detailed information.

[1841] Prompt Sentence Examples

[1842] "Using the generative AI model, please provide an example of natural language processing in this system. For example, if a user registers a preference for traditional crafts and inputs their current emotional state, please explain the steps to generate product information for handmade wood carvings that would be recommended as the most suitable item."

[1843] The above is an embodiment of the present invention. This system makes it possible to recommend products that take into account detailed information about the user, enabling the provision of products that are optimal for the user. It is also expected to provide new business opportunities for producers and contribute to the revitalization of local communities.

[1844] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1845] Step 1:

[1846] Collecting user information and emotional information

[1847] User: Enters their own preferences, requests, and emotional state. Specifically, they enter their name, email address, hobbies, preferences, genres of interest, purchase history, and emotional state into the interface.

[1848] Input: Name, email address, hobbies and preferences, genre interests, purchase history, emotional state.

[1849] Output: This information is sent to the server.

[1850] Step 2:

[1851] Data transmission and storage

[1852] Terminal: Send the entered information to the server. Click the Send button to upload the data.

[1853] Server: Stores the received user information and emotion information in a database, tagging and categorizing the information.

[1854] Input: User information and emotion information entered into the interface.

[1855] Output: The tagged user information and emotion information are stored in a database.

[1856] Step 3:

[1857] Collecting producer information

[1858] Producer: Enter product information into the system. Specifically, enter the company or individual name, detailed product description, photo, production location information, price range, and production process into the interface.

[1859] Input: company / individual name, detailed product description, photo, origin information, price range, and production process.

[1860] Output: This information is sent to the server.

[1861] Step 4:

[1862] Data transmission and storage

[1863] Terminal: Send the entered product information to the server. Click the Send button to upload the data.

[1864] Server: Stores the received product information in a database, tagging and categorizing it based on the information.

[1865] Input: Product information entered into the interface.

[1866] Output: Tagged product information is stored in a database.

[1867] Step 5:

[1868] Emotion engine application and recognition

[1869] On the device: The emotion engine recognizes emotions from user actions and input information, specifically analyzing emotions based on keystroke speed and phrases in the interface.

[1870] Input: User actions and input information.

[1871] Output: Data of the recognized emotional state sent to the server.

[1872] Step 6:

[1873] Storing emotional information

[1874] Server: The received emotional information is stored in a database and used as additional data for analysis. Specifically, the emotional history of each user is recorded.

[1875] Input: Recognized emotion information.

[1876] Output: Emotion information stored in a database.

[1877] Step 7:

[1878] Loading information

[1879] Server: Reads user, producer, and sentiment information from the database, specifically extracting relevant data using SQL queries.

[1880] Input: User information, producer information, and emotion information stored in the database.

[1881] Output: Analyzed data provided to the AI ​​engine.

[1882] Step 8:

[1883] Data analysis and product matching

[1884] Server: Analyzes the information loaded by the AI ​​engine. Specifically, the AI ​​algorithm analyzes the user's preferences, past behavioral history, product features of the manufacturer, and the user's emotional state. For example, by referencing data from similar users, it predicts the best product for the current user's emotional state.

[1885] Input: Analysis data provided to the AI ​​engine.

[1886] Output: Matched product information.

[1887] Step 9:

[1888] Generating matching results

[1889] Server: Generates matching results and creates individual notification messages, specifically creating a list of candidate products and formatting them as text messages for the user.

[1890] Input: Matched product information.

[1891] Output: Informational message.

[1892] Step 10:

[1893] Sending notifications

[1894] Server: Sends the created notification message to the user's device. Specifically, it pushes the message to the user's device using a network protocol.

[1895] Input: Notification message.

[1896] output: Notification sent to the user's device.

[1897] Step 11:

[1898] Results display

[1899] Device: Receives the notification sent from the server and displays the matching results to the user. Specifically, it displays information such as "Product name: Handmade wood carving, Producer: Producer B, Price: \XXXX" on the screen.

[1900] Input: The notification sent by the server.

[1901] Output: Matching results displayed in the user interface.

[1902] (Application example 2)

[1903] 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."

[1904] In modern society, there is a demand for recommendation systems that respond to users' tastes and emotions. However, conventional systems have difficulty matching products that reflect the user's emotional state, making it difficult to effectively recommend appropriate products. In particular, when it comes to local specialties or niche creative products, it is difficult to achieve optimal matching based on the user's emotions, which makes it difficult to contribute to regional revitalization.

[1905] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a terminal for inputting user preferences, requests, and emotional states; a terminal for local producers to register product information; a means for storing the user preferences, requests, and emotional information and producer product information in a database; an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user preferences, requests, and emotional states; a means for notifying the user terminal of the matching results; and a means for understanding the user's emotional state using emotion recognition technology. This enables appropriate product recommendations that take the user's emotional state into consideration, thereby improving the matching accuracy of local specialty products and niche creative products.

[1906] A "user" is a consumer who uses a terminal to input his or her preferences, desires, and emotional state.

[1907] "Local producers" are merchants or individuals who use terminals to register information about specialty products or niche creative goods.

[1908] A "terminal" is a device that allows users to input information about their preferences, requests, and emotional state, and a device that allows producers to register product information.

[1909] The "server" is a central device that stores information sent by users and producers in a database and analyzes it.

[1910] The "database" is an information storage system that manages information on users' preferences, requests, and emotions, as well as product information from producers, stored on a server.

[1911] An "artificial intelligence engine" is an algorithm and software that analyzes a user's tastes, requests, and emotional state with a producer's product information to match them with the most suitable product.

[1912] "Emotional state" is information that indicates the emotions that the user is feeling, and is classified into categories such as joy, surprise, and sadness.

[1913] "Emotion recognition technology" is a technology that recognizes the emotional state of a user from their input information and device interactions.

[1914] "Matching results" are information that indicates the products that best suit the user's tastes, desires, and emotional state, based on analysis by the AI ​​engine.

[1915] The "notification means" is a system in which the server generates matching results and sends them to the terminal of the corresponding user.

[1916] The present invention is a system for effectively matching local specialties and niche creative products based on the tastes, desires, and emotional state of users, and an embodiment thereof is described below. The system includes a terminal for users to input their tastes, desires, and emotional state, a terminal for local producers to register product information, a server for storing and analyzing data, an artificial intelligence engine, and emotion recognition technology.

[1917] System Overview

[1918] 1. User operations

[1919] The user terminal provides an interface for users to input their preferences, requests, and emotional state. Users input their favorite genres, local products they are interested in, their purchase history, and their emotional state (for example, joy or surprise). In addition, by using a device with a camera, emotion recognition technology can be used to analyze the user's facial expressions and automatically recognize their emotional state.

[1920] 2. Producer manipulation

[1921] The producer terminal provides an interface for producers to register product information. Producers input detailed descriptions of the specialty products they produce, photos, price ranges, production processes, and so on.

[1922] 3. Server Roles

[1923] The server receives information sent from user terminals and producer terminals and stores this data in a database. At this time, it tags and categorizes the data based on the user's preferences, requests, and emotional information, and the producer's product information.

[1924] 4. Application of emotion recognition technology

[1925] Emotion recognition technology recognizes the user's emotional state from input information and device interactions and sends it to a server, using Microsoft Azure's emotion recognition API and other technologies.

[1926] 5. Matching Process

[1927] The AI ​​engine analyzes the user's preferences, requests, and emotional information stored in the database, as well as the product information of the producer, to match the optimal product. This is done using generative AI models such as Amazon SageMaker.

[1928] 6. Presenting the matching results

[1929] The server generates matching results based on the analysis results and notifies the relevant user's device. The user's device receives the notification and displays the product name, producer information, price, purchase method, etc.

[1930] Specific examples

[1931] 1. User A's behavior

[1932] User A accesses the app using a smartphone, enters "I like seasonal fruits," and enters his / her current emotional state as "joy." When he / she captures his / her facial expression using a camera, the emotion recognition technology automatically recognizes that information.

[1933] 2. Producer B's actions

[1934] Producer B registers "seasonal local apples" as a product and enters detailed product information and photos.

[1935] 3. Server Analysis

[1936] The server stores information and emotional information of User A and Producer B in a database, and uses a generative AI model to make optimal matches based on "fruit" and "joy."

[1937] 4. Generate and send notifications

[1938] The server generates a notification as a matching result, stating "Recommended product: Seasonal local apples (Producer: Producer B)" and sends it to User A's terminal.

[1939] 5. Receipt and confirmation by User A

[1940] User A's device receives the notification and displays detailed information. User A checks the product in the app and completes the purchase procedure.

[1941] Prompt Sentence Examples

[1942] User input: "I'm in a fun mood today, so I'm looking for something exciting to buy."

[1943] Emotion Recognition API: "Joy"

[1944] AI engine prompt: "Recommend exciting local products that match the user's tastes with emotions of joy"

[1945] This system makes it possible to recommend appropriate products that take into account the user's emotional state, improving the accuracy of matching local specialties and niche creative products.

[1946] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1947] Step 1:

[1948] The user device receives input of the user's preferences, requests, and emotional state. The user can provide their preferred genres and purchase history through text input, and their current emotional state is automatically recognized by taking a photo with a camera using emotion recognition technology. The input data (preferences, requests, and emotional state) is sent to the server.

[1949] Step 2:

[1950] The server receives information on preferences, requests, and emotional states sent from the user's device and stores it in a database. Emotion recognition technology categorizes the emotional states into categories such as "happiness," "surprise," and "sadness," and tags them as data. The stored data is used for later analysis.

[1951] Step 3:

[1952] The producer terminal receives product information (detailed description, photos, price, etc.) from the producer. The producer enters and registers the product information. The entered product information is sent to the server.

[1953] Step 4:

[1954] The server receives product information sent from the producer terminal and stores it in a database. The product information is tagged and categorized and then stored in the database.

[1955] Step 5:

[1956] The server uses a generative AI model to analyze user information (tastes, requests, emotional state) and producer information (product information) stored in a database. The purpose of the analysis is to find the product that best suits the user's tastes and emotional state. The generative AI model uses AI tools such as Amazon SageMaker.

[1957] Step 6:

[1958] The server's AI engine performs analysis and matches the optimal product based on the user's tastes, desires, and emotional state. The generative AI model compares the user information with product information to generate the optimal matching result. The matching result is expressed as a list of recommended products.

[1959] Step 7:

[1960] The server generates a notification message of the matching result and sends a list including recommended products to the user terminal. The user terminal provides an interface that displays detailed information such as recommended products, prices, and producer information, allowing the user to easily check the products.

[1961] Step 8:

[1962] Users can receive matching results through the app, check the details, and if they are interested, they can purchase the product within the app.

[1963] This series of processing steps allows users to easily find the specialty or niche product that best suits their current mood, and allows producers to effectively deliver their products to more consumers. Furthermore, the explicit operation and input / output make the system's operation clear.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] 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.

[1970] 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).

[1971] 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.

[1972] 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."

[1973] 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.

[1974] 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).

[1975] 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.

[1976] 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.

[1977] 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.

[1978] 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.

[1979] 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.

[1980] 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.

[1981] 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.

[1982] 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.

[1983] 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.

[1984] 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.

[1985] The following is further disclosed regarding the above embodiment.

[1986] (Claim 1)

[1987] a terminal for users to input their preferences and requests;

[1988] A terminal for local producers to register product information;

[1989] a server that stores the user's preferences and requests and the producer's product information in a database;

[1990] An artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's tastes and requests;

[1991] means for notifying a user terminal of the matching result;

[1992] A system including:

[1993] (Claim 2)

[1994] 2. The system according to claim 1, further comprising means for tagging based on the user's preferences and requests and storing the tagged images in a database.

[1995] (Claim 3)

[1996] 2. The system according to claim 1, further comprising means for tagging based on product information of a producer and storing the tagged product information in a database.

[1997] "Example 1"

[1998] (Claim 1)

[1999] a terminal for users to input their preferences and requests;

[2000] A terminal for local producers to register product information;

[2001] a means for storing the user's preferences and requests and the producer's product information in a database;

[2002] means for tagging and categorizing based on the information stored in said database;

[2003] An artificial intelligence engine that analyzes the information and matches products that match the user's tastes and requests;

[2004] means for notifying a user terminal of the matching result;

[2005] means for displaying the notification on a user terminal;

[2006] A system including:

[2007] (Claim 2)

[2008] 2. The system according to claim 1, further comprising means for tagging based on the user's preferences and requests and storing the tagged images in a database.

[2009] (Claim 3)

[2010] 2. The system according to claim 1, further comprising means for tagging based on product information of a producer and storing the tagged product information in a database.

[2011] "Application Example 1"

[2012] (Claim 1)

[2013] a terminal for users to input their preferences and requests;

[2014] A terminal for local producers to register product information;

[2015] a server that stores the user's preferences and requests and the producer's product information in a database;

[2016] An artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's tastes and requests;

[2017] means for notifying a user terminal of the matching result;

[2018] A means to tag and categorize products based on user preferences and needs;

[2019] A means of matching products by taking into account past purchase history;

[2020] A means for notifying users of product recommendations and displaying detailed information;

[2021] A system including:

[2022] (Claim 2)

[2023] 2. The system according to claim 1, further comprising means for tagging based on the user's preferences and requests and storing the tagged images in a database.

[2024] (Claim 3)

[2025] 2. The system according to claim 1, further comprising means for tagging based on product information of a producer and storing the tagged product information in a database.

[2026] "Example 2: Combining Emotion Engines"

[2027] (Claim 1)

[2028] a terminal for a user to input his / her preferences, requests and emotional state;

[2029] A terminal for local producers to register product information;

[2030] a server that stores the user's preferences, requests, and emotional information and the producer's product information in a database;

[2031] An emotion engine that recognizes the user's emotional state from their operations and input information;

[2032] an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's tastes, requests, and emotional state;

[2033] means for notifying a user terminal of the matching result;

[2034] A system including:

[2035] (Claim 2)

[2036] 2. The system according to claim 1, further comprising means for tagging based on user preferences, desires and emotional information and storing the tagged information in a database.

[2037] (Claim 3)

[2038] 2. The system according to claim 1, further comprising means for tagging based on product information of a producer and storing the tagged product information in a database.

[2039] "Application example 2 when combining emotion engines"

[2040] (Claim 1)

[2041] a terminal for users to input their preferences, requests, and emotional state;

[2042] A terminal for local producers to register product information;

[2043] a server that stores the user's preferences, request information, and emotion information, and product information of producers in a database;

[2044] an artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's tastes, desires, and emotional state;

[2045] means for notifying a user terminal of the matching result;

[2046] A means for understanding a user's emotional state using emotion recognition technology;

[2047] A system including:

[2048] (Claim 2)

[2049] 2. The system according to claim 1, further comprising means for tagging based on the user's preferences, desires, and emotional state, and storing the tagged images in a database.

[2050] (Claim 3)

[2051] 2. The system according to claim 1, further comprising means for tagging based on product information of a producer and storing the tagged product information in a database. [Explanation of symbols]

[2052] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal for users to input their preferences and requests; A terminal for local producers to register product information; a server that stores the user's preferences and requests and the producer's product information in a database; An artificial intelligence engine that analyzes the information stored in the database and matches products that match the user's tastes and requests; means for notifying a user terminal of the matching result; A system including:

2. 2. The system according to claim 1, further comprising means for tagging based on the user's preferences and requests and storing the tagged images in a database.

3. 2. The system according to claim 1, further comprising means for tagging based on product information of a producer and storing the tagged product information in a database.

Images