System

Abstract

A system is provided.SOLUTION: A system comprising: means for allowing a user to input his / her own attribute information; means for receiving the attribute information and storing the attribute information in a database; means for analyzing the attribute information and attribute information of other users using a generated AI; means for calculating an appropriate matching candidate based on the analysis result; and means for notifying the user of the matching candidate.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] As people age and change their life stages, friendships often become weaker. It is particularly difficult for adults who find it difficult to find time to spend with friends due to child-rearing or family circumstances to build new connections. Furthermore, busy lives mean limited time and opportunities to find new friends who share similar hobbies and interests. The present invention aims to solve these problems and provide a system that allows adults to smoothly build new connections. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input their own attribute information, a means for receiving and storing the attribute information in a database, a means for analyzing the attribute information and attribute information of other users using a generation AI, a means for calculating suitable match candidates based on the analysis results, and a means for notifying the user of the match candidates. In particular, the attribute information includes hobbies, preferences, interests, and place of residence, and the degree of match between users is calculated as a reliability score based on the analysis results. This allows adults to effectively build new connections.

[0006] "User" refers to an individual who uses the system to seek new connections.

[0007] "Attribute information" refers to data related to an individual, such as hobbies, preferences, interests, and place of residence, entered by the user.

[0008] "Means" refers to the method or device used by the system to achieve a particular function.

[0009] A "database" refers to a collection of information for storing user attribute information and analysis results.

[0010] "Generative AI" refers to a program that uses artificial intelligence technology to analyze and match data.

[0011] "Analysis" refers to the process of processing the user's attribute information and attribute information of other users to calculate match candidates.

[0012] "Matching candidates" refer to other users suggested by the generation AI as suitable connections.

[0013] "Notification" refers to the action of informing the user of the matching results.

[0014] The "reliability score" refers to an evaluation value that expresses the degree of agreement between users as a numerical value. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that provides new connections to adults whose friendships become weaker as they age or change life stages. The system acquires and analyzes user attribute information to provide appropriate match candidates. The specific processing flow is explained below.

[0037] Overall system overview

[0038] The system uses the information users enter to match them with other users and provide them with appropriate connections. The system mainly consists of the following components:

[0039] A terminal for users to input attribute information

[0040] Server that receives and stores attribute information

[0041] A server module that uses generative AI to analyze attribute information

[0042] A mechanism for notifying users of matching results

[0043] User Registration Process

[0044] User: First, the user enters their demographic information (e.g., hobbies, preferences, interests, and location). This involves accessing the account creation page on the user's device and entering information in the required fields.

[0045] Terminal: The entered information is sent from the terminal to the server when the user presses the "Register" button.

[0046] Receiving and storing data

[0047] Server: The server receives the user attribute information sent from the device. The received information is saved in a database, and each attribute is classified and stored in the appropriate table.

[0048] Analysis and Matching

[0049] Server: Passes user information stored in the database to the generation AI, which analyzes the user's attribute information and calculates the most suitable match with other users.

[0050] For example, matching is done based on commonalities such as "User A and User B both like basketball and live in Tokyo."

[0051] The generation AI evaluates the degree of match of attribute information and calculates a confidence score, which is a numerical representation of the suitability of the match.

[0052] Notification of matching results

[0053] Server: Based on the matching results calculated by the generation AI, the server selects the optimal connection candidates for a specific user. This information is sent to the device.

[0054] Device: The user's device will notify the user based on the matching results received. For example, a notification such as "Mr. Tanaka, we have found Mr. Sato who has the same hobbies as you" will be displayed.

[0055] Specific examples

[0056] For example, Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, can enter his own attribute information into the system, and the generative AI will analyze and match him with users who have similar attribute information.

[0057] As a result of the analysis, a user named Sato who also shares the same hobbies of basketball and cooking and lives in the same area is found.

[0058] The server notifies Tanaka that Sato is a potential match, and the notification also displays a trust score, making it easier for users to make new connections.

[0059] This allows the user to effectively find new connections that match their interests and preferences. Furthermore, because the entire system is automated, the burden on the user is reduced and convenience is greatly improved.

[0060] The processing flow will be explained below.

[0061] Step 1: User Input

[0062] User: A new user accesses the system and enters their attribute information on the account creation screen, such as name, age, hobbies, preferences, interests, and place of residence.

[0063] Terminal: Checks the information entered by the user and prepares it for transmission.

[0064] Step 2: Send data

[0065] Terminal: Sends the request data including the entered attribute information to the server. This transmission is triggered when the user presses the "Register" button.

[0066] Step 3: Receiving the Server

[0067] Server: Receives user attribute information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[0068] Step 4: Saving to the Database

[0069] Server: Connects to the database and stores the received user attribute information in the corresponding tables. For example, store basic information in the Users table and information about hobbies and interests in the Interests table.

[0070] Step 5: Analysis by generative AI

[0071] Server: Retrieves user information from the database and inputs it into the generation AI, which analyzes the data and finds suitable match candidates based on each user's hobbies, preferences, place of residence, etc.

[0072] Step 6: Calculating match candidates

[0073] Server: The generation AI goes through a process to calculate matching candidates. Specifically, it calculates the degree of match between users as a reliability score based on similarities in hobbies, proximity of residence, etc.

[0074] Step 7: Generate matching results

[0075] Server: Creates matching results for users based on the analysis results obtained from the generation AI. For example, it may organize the results as "The most suitable match candidate for User A is User B (reliability 90%)."

[0076] Step 8: Prepare for notification

[0077] Server: Generates notification data for the matching results and prepares to send it to the corresponding user's device.

[0078] Step 9: Sending notifications

[0079] Server: Sends notification data to the user's device. Sends it securely using a communication encryption protocol.

[0080] Step 10: Receiving and Viewing Notifications

[0081] Terminal: Receives the matching results sent from the server, analyzes the received data, and displays it in a user-friendly format.

[0082] User: Check the matching results displayed on the device. For example, a notification will appear saying, "We've found a user with similar interests. Would you like to get in touch?"

[0083] The above is the specific flow of program processing on the system.

[0084] Example 1

[0085] 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."

[0086] Conventional friendship maintenance systems have been insufficient in providing effective means for adults whose friendships become weaker as they age and change in life stages to create new connections. Furthermore, the process of entering attribute information was cumbersome, and matching accuracy was low, making it difficult to increase user satisfaction. This resulted in a lack of convenience and motivation for building new friendships.

[0087] 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.

[0088] In this invention, the server includes a means for a user to input his / her own attribute information, a means for receiving the attribute information and storing it in a database, a means for analyzing the attribute information and attribute information of other users using a generative AI model, a means for inputting a prompt sentence to the generative AI model to calculate optimal matching candidates, and a means for notifying the user of the matching candidates together with their reliability scores. This makes it possible to analyze the user's attribute information with high accuracy, realize optimal matching with other users who share common hobbies and interests, and effectively build new friendships.

[0089] A "user" is someone who uses the system to input their own attribute information and seeks to be matched with other users.

[0090] "Attribute information" refers to information about an individual, such as hobbies, preferences, interests, and place of residence, entered by the user.

[0091] A "database" is a collection of information that stores received user attribute information and is used for analysis.

[0092] A "generative AI model" is an artificial intelligence model that analyzes user attribute information and derives the most suitable matching candidates.

[0093] A "prompt sentence" is a text-based command sentence that is input into the generative AI model and includes attribute information of the user to be analyzed.

[0094] "Matching Candidates" means other users analyzed by the generative AI model and recommended to the user as suitable connections.

[0095] The "confidence score" is a numerical value that indicates the suitability of a match calculated by the generative AI model, and is an index for evaluating the reliability of a matching candidate.

[0096] "Notification" refers to the means or process used to communicate information about potential matches to a user.

[0097] The present invention is a system that allows users to input their own attribute information, match with other users, and provide new connections. This system is implemented using a terminal, a server, and a generative AI model.

[0098] First, the user enters attribute information using a device, which can be a smartphone, tablet, or PC. The user accesses a dedicated application or web page and enters information such as hobbies, preferences, interests, and place of residence.

[0099] Next, the device sends the entered attribute information to the server, which receives it as an HTTP POST request and receives it in a standard data format such as JSON.

[0100] The server stores the received attribute information in a database. The database has a mechanism for appropriately classifying and storing attribute information for each user. Cloud services such as AWS (Amazon Web Services), Google Cloud Platform, and Microsoft Azure can be used to store the data.

[0101] The stored attribute information is analyzed using a generative AI model. Examples of generative AI models used include GPT-4 (OpenAI) and BERT (Google). Analysis is initiated by entering a prompt into this model. Examples of prompts include the following:

[0102] User Attribute Information:

[0103] User ID: Tanaka

[0104] Hobbies: Basketball, cooking

[0105] Place of residence: Tokyo

[0106] Use this information to analyze potential matches.

[0107] The generative AI model analyzes this prompt to find other users with common hobbies and interests. The analysis results in the calculation of the best match candidates and their confidence scores. The confidence score is a numerical value that indicates the suitability of the match and is used to evaluate the degree of match with the user.

[0108] Finally, the server sends the calculated matching results to the user's device. The device receives the results and notifies the user. The notification displays specific information such as, "Mr. Tanaka, you have found Mr. Sato, who has the same hobbies. Trust score: 95."

[0109] For example, after Mr. Tanaka uses his smartphone to input attribute information such as "basketball, cooking, Tokyo," the generative AI model analyzes this information and finds a user named Mr. Sato who shares the same hobbies. The server then notifies Mr. Tanaka's smartphone of the results, allowing him to take specific actions to build new friendships.

[0110] This allows users to effectively find new connections that match their hobbies and preferences. The present invention supports new connections by performing advanced analysis based on information entered by the user and providing optimal matching candidates.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1: User enters attribute information

[0113] Users access the system's account creation page using a device such as a smartphone or PC. They enter information such as hobbies, preferences, interests, and place of residence. The entered information (e.g., "basketball, cooking, Tokyo") is stored in the account creation form.

[0114] Input: Attribute information entered by the user in the input form

[0115] Output: Attribute information stored in the input form

[0116] Step 2: Send attribute information

[0117] When the user presses the "Register" button, the device sends the entered attribute information to the server. When sending, the data is sent in JSON format via an HTTP POST request.

[0118] Input: Attribute information stored in the input form

[0119] Output: JSON formatted attribute information sent to the server

[0120] Step 3: Receiving and storing attribute information

[0121] The server receives the attribute information sent from the terminal and stores it in a database. The received information is inserted into the database using an SQL query.

[0122] Input: JSON formatted attribute information sent to the server

[0123] Output: Attribute information stored in a database

[0124] Specific behavior:

[0125] The server parses the POST request, extracts the JSON data, converts it into a SQL query, and inserts it into the database.

[0126] Step 4: Analysis of attribute information by generative AI

[0127] The server generates a prompt to pass the user's attribute information stored in the database to the generative AI model, which then analyzes the attribute information based on the prompt and finds the best matching candidate.

[0128] Input: Attribute information stored in the database

[0129] Output: Match candidates and confidence scores as analysis results

[0130] Specific behavior:

[0131] The server generates a prompt sentence and inputs it into the generative AI model, which analyzes the attribute information and calculates matching candidates and their confidence scores.

[0132] Step 5: Generate matching results

[0133] The generative AI model outputs the analysis results and returns them to the server, which receives the analysis results and generates a list of matching candidates.

[0134] Input: Prompt sentences and saved attribute information entered into the AI ​​model

[0135] Output: A list of potential matches and their confidence scores

[0136] Specific behavior:

[0137] The server retrieves the analysis results and compiles them into a list, which includes the IDs of the matching users, common interests, and a trust score.

[0138] Step 6: Submit your match results

[0139] The server then sends the generated matching results to the user's device, usually as an HTTP response, in JSON format.

[0140] Input: Matching results stored on the server

[0141] Output: Matching results sent to the user's device

[0142] Specific behavior:

[0143] The server converts the matching results into JSON format and sends them to the user's device as an HTTP response.

[0144] Step 7: Viewing the matching results

[0145] The user's device will then notify them based on the matching results received from the server. Specifically, a pop-up notification will be displayed saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Trust score: 95."

[0146] Input: Matching results sent from the server

[0147] Output: The notification displayed to the user

[0148] Specific behavior:

[0149] The device parses the received JSON data and displays a notification pop-up on the screen. The user can then confirm the notification and take action to establish a new friendship.

[0150] (Application example 1)

[0151] 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."

[0152] In modern society, as people become adults, friendships tend to weaken as they age and their life stages change. In addition, it is not easy to form new connections with people from the same area or who share the same hobbies. Furthermore, physical stores have limited means of effectively forming and maintaining communities for their customers. This creates a challenge: there are few opportunities to build new relationships between individuals. Furthermore, it is difficult for individual users to form new friendships through participating in events.

[0153] 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.

[0154] In this invention, the server includes means for a user to input his or her own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, and means for acquiring event information at physical stores and providing that information to the user. This enables users to automatically match with other users who share the same hobbies, preferences, and place of residence, and to build new friendships through participation in events held at physical stores.

[0155] A "user" is an individual who accesses the system and enters their own attribute information.

[0156] "Attribute information" is information that a user inputs into the system, and includes hobbies, preferences, interests, place of residence, and the like.

[0157] "Receiving" is the act of transferring attribute information entered by the user from the terminal to the server.

[0158] "Database" refers to a storage device within the system that stores and manages received attribute information.

[0159] "Generative AI" is an artificial intelligence technology that analyzes a user's attribute information and calculates suitable matching candidates with other users.

[0160] "Analysis" is the process of using generative AI to evaluate attribute information and calculate commonalities and similarities between users.

[0161] "Matching candidates" are other users who are selected based on the analysis results and who may have friendships with a particular user.

[0162] "Notification" refers to the act of sending calculated match candidates and event information to the user's device to inform them.

[0163] A "physical store" is a physical commercial facility where customers can visit in person to enjoy products and services.

[0164] "Event information" is detailed information about social activities and events held at physical stores, and is provided to users.

[0165] The system that realizes this application example consists of the following components: a device where the user inputs their own attribute information, a server that receives the attribute information and stores it in a database, a module that uses generative AI to analyze the user's attribute information, a system that calculates appropriate matching candidates and notifies the user, and a mechanism that obtains event information from physical stores and provides it to the user.

[0166] Users use their smartphones to input attribute information, such as hobbies, preferences, interests, and place of residence. Once the user inputs the information and presses the registration button, the smartphone sends the information to the server.

[0167] The server is built using the Flask framework and has the ability to store attribute information received from users in an SQLite database, which is then used for analysis by the generative AI.

[0168] The generation AI is implemented in Python and is responsible for analyzing user attribute information. Specifically, it calculates commonalities and similarities with other users based on information such as each user's hobbies, interests, and place of residence. The generation AI calculates the similarity between each user as a reliability score and selects other users with high scores as matching candidates.

[0169] The server notifies the user of the matching results based on the matching candidates calculated by the generation AI. The notification is displayed on the user's smartphone in the form of "A user with the same hobbies has been found." The server also obtains information about events held at physical stores and provides it to the user. This allows users to build new friendships through actual events.

[0170] As a concrete example, consider the case where a user enters the attribute information "My hobbies are basketball and cooking, and I live in Tokyo." The server receives this information and stores it in a database. The generation AI analyzes the attribute information and finds other users who live in Tokyo and also like basketball and cooking. The server then notifies the user of the matching results and also provides information about basketball-related events at physical stores.

[0171] Examples of prompt sentences to input to a generative AI model include the following:

[0172] "We want to create a system that matches users with the best possible friends based on their hobbies and location. User information includes the following attributes:

[0173] name

[0174] hobby

[0175] place of residence

[0176] After registration, there needs to be a function that automatically finds other users who share the same hobbies and location and displays matching results.

[0177] In this way, the present invention provides a system that supports users in easily building new friendships.

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

[0179] Step 1:

[0180] User enters attribute information

[0181] The user uses their smartphone to enter attribute information such as name, hobbies, preferences, interests, and place of residence into the application form. Once the input is complete, the user presses the "Register" button. This input data is sent to the server by the application. The input data includes attribute information in text format.

[0182] Step 2:

[0183] The server receives and stores the attribute information

[0184] The server receives the attribute information sent from the device. The server uses the Flask framework to receive this information and store it in an SQLite database. As part of the processing, the received data is sorted into the appropriate table and an insert operation is performed. The input data is received in JSON format, and as output, a new record is added to the database.

[0185] Step 3:

[0186] Generative AI analyzes attribute information

[0187] The server passes the attribute information of multiple users stored in the database to the generation AI. The generation AI is implemented in Python and calculates commonalities and similarities with other users based on information such as each user's hobbies, preferences, interests, and place of residence. The input data is the user's attribute information obtained from the database, and the output data is a reliability score indicating the degree of similarity between each user.

[0188] Step 4:

[0189] Calculating match candidates

[0190] The server selects appropriate match candidates based on the reliability score calculated by the generation AI. The input data is the reliability score from the generation AI, and the output data is a list of optimal match candidates. The server prioritizes users with high reliability scores and lists them as match candidates.

[0191] Step 5:

[0192] Notify users of matching results

[0193] The server notifies the user of the calculated match candidates. The matching results are sent as a notification to the user's smartphone. The input data is a list of match candidates, and the output data is a notification message displayed on the user's smartphone. The notification message contains details of the match candidates and their confidence scores.

[0194] Step 6:

[0195] Acquire and provide information about events at physical stores

[0196] The server also retrieves information about events held at physical stores and provides it to users. This information is updated periodically and stored in a database. When a user requests event information from the application, the server sends the latest event information to the user. The input data is a request for event information, and the output data is detailed information about the event. The event information is displayed on the user's smartphone, allowing the user to decide whether or not to attend the event.

[0197] In this way, by combining the processes performed at each step, users can easily build new friendships and deepen their interactions through events at physical stores.

[0198] 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.

[0199] This invention is a system that uses user attribute information and emotional information to provide more appropriate and emotionally supported match candidates. In addition to the basic function of inputting and saving user attribute information and analyzing that information to perform matching, this system also incorporates an emotion engine that recognizes user emotions, thereby improving matching accuracy.

[0200] Overall system overview

[0201] The system consists of the following main elements:

[0202] A terminal for user input

[0203] A server that receives and stores attribute information and emotion information

[0204] Server module that performs analysis using generative AI

[0205] Emotion engine that recognizes user emotions

[0206] A mechanism for notifying users of matching results

[0207] User Registration Process

[0208] User: A user accesses the system and enters their own attribute information, such as name, age, hobbies, preferences, interests, and place of residence.

[0209] Terminal: Checks the information entered by the user and prepares it for transmission. The emotion engine also collects the user's emotional information.

[0210] Receiving and storing data

[0211] Server: Receives user attribute information and emotion information sent from the device. The received information is saved in a database, with attribute information stored in the Users table and emotion information stored in the Emotions table.

[0212] Analysis of attribute information and emotional information

[0213] Server: Inputs the user's attribute information and emotional information from the database into the generation AI. The generation AI analyzes this data and finds the most suitable match candidate, taking into account the user's current emotional state and past emotional patterns.

[0214] For example, if a user is feeling stressed, the system will prioritize matching with users who have relaxing hobbies or who have the same hobbies and a calm personality.

[0215] Calculating match candidates

[0216] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[0217] The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[0218] Matching result generation and notification

[0219] Server: Based on the analysis results obtained from the generation AI, it generates appropriate matching results for the user. For example, "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[0220] Server: Generates notification data of the matching results and sends it to the corresponding user's device. Communication is performed using an encrypted protocol.

[0221] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Would you like to get in touch?"

[0222] Specific examples

[0223] In the case of Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, Mr. Tanaka enters his attribute information into the system, and the emotion engine collects his emotional information (for example, whether he is relaxed or stressed). As a result of the analysis, Mr. Sato, who has similar attribute information and is in the same emotional state, is found. The server notifies Mr. Tanaka that Mr. Sato is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[0224] As a result, the present invention takes into account the user's emotions, making it possible to provide connections that are suited to the user's psychological state, rather than just attribute information, thereby enabling the user to effectively build new connections.

[0225] The processing flow will be explained below.

[0226] Step 1: Collecting user input and sentiment

[0227] User: A new user accesses the system and enters their demographic information (such as name, age, hobbies, preferences, interests, and place of residence). During the registration process, they also interact with an interface that displays the user's current emotional state.

[0228] Terminal: Checks the attribute information entered by the user and the emotion information collected by the emotion engine, and prepares for transmission.

[0229] Step 2: Send data

[0230] Device: Sends request data including the entered attribute information and emotion information to the server. This transmission is triggered when the user presses the "Register" button.

[0231] Step 3: Receiving the Server

[0232] Server: Receives the user's attribute information and emotion information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[0233] Step 4: Saving to the Database

[0234] Server: Connects to the database and stores the received user attribute information in the Users table and emotion information in the Emotions table. This allows for centralized management of user information.

[0235] Step 5: Analysis by generative AI and emotion engine

[0236] Server: Obtains user attribute information and emotional information from the database and inputs it into the generation AI. The generation AI analyzes the user's attribute information and emotional information, taking into account their current emotional state and past emotional patterns, and finds suitable matching candidates.

[0237] For example, if a user is feeling stressed, the system is set to prioritize matching with users who have relaxing hobbies or who have the same hobbies and calm personalities.

[0238] Step 6: Calculating match candidates

[0239] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[0240] The AI ​​generator calculates the degree of similarity between users as a reliability score and selects matching candidates based on this. For example, it calculates the score based on information such as "User A and User B both like basketball and are currently relaxing."

[0241] Step 7: Generate matching results

[0242] Server: Based on the analysis results obtained from the generation AI, the server generates appropriate matching results for the target user. For example, it may organize the results as "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[0243] Step 8: Prepare for notification

[0244] Server: Generates notification data of the matching results and prepares to send it to the corresponding user's device. Communication is performed using an encryption protocol.

[0245] Step 9: Sending notifications

[0246] Server: Sends notification data to the user's device, including information about match candidates and their confidence scores.

[0247] Step 10: Receiving and Viewing Notifications

[0248] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may be displayed saying, "Sato also likes basketball and is currently relaxing. Why not get in touch?"

[0249] User: Check the matching results displayed on the device and take the next step (e.g., decide whether to contact them).

[0250] The above is the flow of specific processing steps in the invention incorporating an emotion engine. This system allows users to find the optimal connection based on their own emotional state.

[0251] Example 2

[0252] 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."

[0253] Conventional matching systems select match candidates based on user attribute information, but do not take the user's emotional state into consideration, resulting in insufficient matching accuracy. Matching that ignores the user's psychological state results in difficulty in obtaining results that are satisfactory to the user. The present invention aims to provide more accurate matching and improve user satisfaction by taking emotional information into consideration.

[0254] 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.

[0255] In this invention, the server includes a means for a user to input their own attribute information, a means for receiving the attribute information and emotion information and storing them in a database, and a means for analyzing the attribute information and emotion information using a generation AI, thereby enabling the selection of appropriate matching candidates taking into consideration the user's current emotional state and past emotion patterns.

[0256] "Attribute information" is information about personal characteristics and interests provided by a user, and specifically includes name, age, hobbies, preferences, interests, place of residence, and the like.

[0257] "Emotional Information" refers to information that indicates the user's current psychological and emotional state, including data collected through the Emotion Engine, such as whether they are relaxed or stressed.

[0258] An "emotion engine" is a software or hardware mechanism that analyzes a user's facial expressions, voice, input patterns, etc. to identify their emotional state.

[0259] "Generative AI" is an artificial intelligence model that analyzes input data and generates matching candidates and other results suitable for the user.

[0260] The "confidence score" is a numerical value calculated by the generation AI that indicates the degree of match between users, and is an index for evaluating the suitability of matching candidates.

[0261] The "database" is a digital storage system for storing and managing received user attribute information and emotion information.

[0262] A "terminal" is an electronic device, such as a computer device or smartphone, that a user uses to input and send information.

[0263] "Server" refers to the back-end computer system that processes and analyzes the received data, calculates the matching results, and notifies the user.

[0264] "Matching candidates" are potential contacts that may be suitable for the user, selected based on the analysis results of the generation AI.

[0265] The present invention is a system that uses a user's attribute information and emotional information to provide more appropriate and emotionally supportive match candidates, and its specific implementation method is described below. This system has the function of inputting and saving a user's attribute information and emotional information, analyzing that information, and notifying the user of the most suitable match candidates.

[0266] Key elements of the system

[0267] The system consists of the following main elements:

[0268] A terminal for user input

[0269] A server that receives and stores attribute information and emotion information

[0270] Server module that performs analysis using generative AI

[0271] Emotion engine that recognizes user emotions

[0272] A mechanism for notifying users of matching results

[0273] Enter user information

[0274] User: The user accesses the system and inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence, as well as their current emotional state through the emotion engine.

[0275] Device: The device temporarily stores the information entered by the user and sends it to the server when it is ready. The emotion engine analyzes the user's facial expressions and keyboard input to collect emotional information.

[0276] Data transmission and storage

[0277] Device: When the user clicks the send button, the device sends all input information, including attribute information and emotion information, to the server.

[0278] Server: The server stores the data received from the device in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[0279] Analysis of attribute information and emotional information

[0280] Server: The server extracts the user's attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model analyzes this data and finds the best match candidates, taking into account the user's current emotional state and past emotional patterns.

[0281] Example prompt sentence:

[0282] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[0283] Calculating match candidates

[0284] Server: The generative AI model analyzes the user's emotional patterns and attribute information based on the input information to find the best match. For example, the degree of match between users is calculated as a confidence score.

[0285] Matching result generation and notification

[0286] Server: Generates matching results including match candidates and reliability scores based on the results obtained from the generative AI model. For example, the result might be "For User A, the most suitable match candidate is User B (reliability 90%)."

[0287] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is secure using encryption protocols such as SSL / TLS.

[0288] Device: Receives the matching results sent from the server and displays them to the user in an easy-to-understand format. For example, a notification like "Mr. Tanaka, we've found user C who has the same hobbies as you. Would you like to get in touch?" is displayed.

[0289] As a result, the present invention provides highly accurate matching candidates that take the user's emotions into consideration, helping the user build appropriate connections.

[0290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0291] Step 1:

[0292] The user inputs their own attribute information and emotional information.

[0293] User: Accesses the system and enters his / her demographic information, including name, age, hobbies, preferences, interests, and place of residence, and also answers a questionnaire to input his / her current emotional state.

[0294] Terminal: Checks the information entered by the user and temporarily stores the input data. The emotion engine analyzes the user's facial expressions and input speed to collect emotional information.

[0295] Input: User attribute information and emotion information

[0296] Output: Temporarily stored user information and analyzed emotion information

[0297] Step 2:

[0298] Data transmission and storage

[0299] Terminal: When the user clicks the submit button, the terminal sends all input information that was temporarily saved to the server.

[0300] Server: Receives data sent from the device and stores it in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[0301] Input: User attribute information and emotion information

[0302] Output: User information and emotion information stored in a database

[0303] Step 3:

[0304] Analysis of attribute information and emotional information

[0305] Server: Extracts user attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model receives this data via a prompt and begins analysis.

[0306] Input: User attribute information and emotion information stored in the database

[0307] Output: Input data and analysis results for the generative AI model

[0308] Specific prompt examples:

[0309] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[0310] Step 4:

[0311] Calculating match candidates

[0312] Server: The generative AI model analyzes the user's emotional patterns and attributes based on the input information to find the best match candidates. During this process, a confidence score is calculated.

[0313] Input: Input data to the generative AI model

[0314] Output: Match candidates with confidence scores

[0315] Step 5:

[0316] Matching result generation and notification

[0317] Server: Based on the analysis results obtained from the generative AI model, it generates matching results including match candidates and confidence scores.

[0318] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is securely performed using an encryption protocol.

[0319] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user.

[0320] Input: Analysis results and matching candidate information

[0321] Output: Matching results reported to the user

[0322] This allows users to find suitable matching candidates that suit their emotional state, while also protecting their privacy.

[0323] (Application example 2)

[0324] 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."

[0325] Conventional matching systems make recommendations based on user attribute information, but do not take the user's emotional state into consideration, making it difficult to make recommendations that are appropriate for the user's psychological state. Furthermore, there is a need for a method that effectively utilizes the user's emotional information to provide highly accurate matching candidates. Meanwhile, online shopping sites also do not recommend products that reflect the user's emotional state, limiting the extent to which they can improve user satisfaction. The present invention addresses these issues.

[0326] 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 means for a user to input their own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, means for collecting user emotion information, means for analyzing the emotion information and performing matching in conjunction with the attribute information, and means for inputting the attribute information and emotion information using a generation AI model and generating a prompt sentence. This enables highly accurate match candidates and product recommendations that reflect the user's emotional state.

[0327] "Attribute information" refers to information such as a user's hobbies, preferences, interests, and place of residence.

[0328] "Emotion information" is data that indicates the user's emotional state, and includes, for example, the degree of stress or relaxation.

[0329] "Generative AI" refers to artificial intelligence technology that analyzes a user's attribute information and emotional information to provide optimal matching candidates and product recommendations.

[0330] "Matching candidates" refer to other users or products that are suitable for the user, found based on the user's attribute information and emotional information.

[0331] A "prompt sentence" refers to a text-based input sentence used by the generation AI when performing analysis.

[0332] "Database" refers to an information management system for storing attribute information and emotion information.

[0333] "Emotion engine" refers to technology for collecting and analyzing user emotional information.

[0334] "Push notification" refers to a method for a system to send information to a user in real time.

[0335] The "confidence score" is a numerical evaluation of the degree of match between users, and is an index indicating the accuracy of a matching candidate.

[0336] The system for implementing this invention utilizes user attribute information and emotion information to provide optimal matching candidates and product recommendations. The configuration and operation of the system will be described in detail below.

[0337] System configuration

[0338] The system consists of the following elements:

[0339] 1. User terminal: A device used by the user for input, including smartphones.

[0340] 2. Server: The back-end system that manages the database and runs the generative AI and emotion engine.

[0341] 3. Database: A system for storing user attribute information and emotion information.

[0342] 4. Generative AI model: An artificial intelligence system that analyzes the user's attribute information and emotional information and generates prompt sentences.

[0343] 5. Emotion engine: A software module for collecting and analyzing user emotional information.

[0344] User Registration Process

[0345] Users access the system and input their own attribute information, such as their name, age, hobbies, preferences, interests, and place of residence. The user's device then checks the information and prepares for transmission. The emotion engine also collects the user's emotional information at this time.

[0346] Receiving and storing data

[0347] The server receives the user's attribute information and emotion information sent from the device. The received information is stored in a database, with the attribute information stored in the Users table and the emotion information stored in the Emotions table.

[0348] Analysis of attribute information and emotional information

[0349] The server inputs the user's attribute information and emotional information from the database into the generative AI model. The generative AI analyzes this data and finds the most suitable match candidates and products, taking into account the user's current emotional state and past emotional patterns. For example, if a user is feeling stressed, it will prioritize matching with other users who have relaxing hobbies or who have the same hobbies and calm personalities.

[0350] Calculating match candidates

[0351] The server calculates matching candidates for the user based on the analysis results from the generation AI. At this time, by taking into account not only attribute information but also emotional information, more accurate matching is possible. The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[0352] Matching result generation and notification

[0353] The server generates appropriate matching results for the user based on the analysis results obtained from the generation AI. For example, "The most suitable match candidate for User A is User B (reliability 90%)." The server generates notification data of the matching results and sends it to the corresponding user's device. Communication is carried out using an encrypted protocol. The user's device receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "We have found other users who share the same hobbies as you. Would you like to get in touch?"

[0354] Specific examples

[0355] Example: User A, whose hobbies are basketball and cooking and who lives in a large city, enters his or her own attribute information into the system, and the emotion engine collects User A's emotional information (for example, whether he or she is relaxed or stressed). As a result of the analysis, another User B is found who has similar attribute information and is in the same emotional state. The server notifies User A that User B is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[0356] Prompt Sentence Examples

[0357] An example of a prompt for a generative AI model is:

[0358] "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[0359] In this way, the present invention takes the user's emotions into consideration, and can provide connections and products that are suited to the user's psychological state, rather than just attribute information.

[0360] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0361] Step 1:

[0362] The user enters attribute information

[0363] The user inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence. The terminal receives this information and prepares it to be sent to the database.

[0364] Input: Name, age, hobbies, preferences, interests, place of residence

[0365] Output: Attribute information saved on the device

[0366] Step 2:

[0367] Collecting emotional information

[0368] The camera and microphone on the user's device are used to collect information about the user's emotions, which are then analyzed by an emotion engine to identify states such as relaxed or stressed.

[0369] Input: User's facial image and voice data

[0370] Output: Analyzed emotional information (relaxed, stressed, etc.)

[0371] Step 3:

[0372] Receiving and storing data

[0373] The server receives the user's attribute information and emotion information transmitted from the terminal.

[0374] Input: attribute information, emotion information

[0375] Output: Attribute information and emotion information stored in the database

[0376] Step 4:

[0377] Analysis of attribute information and emotional information

[0378] The server sends attribute and emotional information from the database to the generative AI model, which analyzes this data and identifies the best match candidates and products, taking into account the user's current emotional state.

[0379] Input: Attribute information and emotion information stored in the database

[0380] Output: Analysis results (best matching candidates and products)

[0381] Step 5:

[0382] Generate prompt statement

[0383] The server uses a generative AI model to generate a prompt sentence and create a text-format input sentence for analysis.

[0384] Input: attribute information, emotion information

[0385] Output: Generated prompt statement

[0386] Step 6:

[0387] Calculating match candidates

[0388] Based on the analysis results of the generative AI model, the server calculates matching candidates and products, evaluating the degree of match between users as a reliability score.

[0389] Input: prompt statement

[0390] Output: Match candidates and products based on confidence scores

[0391] Step 7:

[0392] Notification of matching results

[0393] The server generates notification data of the matching results and sends it to the corresponding user's device. The communication uses an encrypted protocol. The device displays the matching results received in an easy-to-understand format for the user.

[0394] Input: Match candidates and products based on confidence scores

[0395] Output: Matching results and product recommendations displayed on the user's device

[0396] As a concrete example, a prompt for a generative AI model might look like this: "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Second embodiment]

[0401] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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."

[0413] The present invention is a system that provides new connections to adults whose friendships become weaker as they age or change life stages. The system acquires and analyzes user attribute information to provide appropriate match candidates. The specific processing flow is explained below.

[0414] Overall system overview

[0415] The system uses the information users enter to match them with other users and provide them with appropriate connections. The system mainly consists of the following components:

[0416] A terminal for users to input attribute information

[0417] Server that receives and stores attribute information

[0418] A server module that uses generative AI to analyze attribute information

[0419] A mechanism for notifying users of matching results

[0420] User Registration Process

[0421] User: First, the user enters their demographic information (e.g., hobbies, preferences, interests, and location). This involves accessing the account creation page on the user's device and entering information in the required fields.

[0422] Terminal: The entered information is sent from the terminal to the server when the user presses the "Register" button.

[0423] Receiving and storing data

[0424] Server: The server receives the user attribute information sent from the device. The received information is saved in a database, and each attribute is classified and stored in the appropriate table.

[0425] Analysis and Matching

[0426] Server: Passes user information stored in the database to the generation AI, which analyzes the user's attribute information and calculates the most suitable match with other users.

[0427] For example, matching is done based on commonalities such as "User A and User B both like basketball and live in Tokyo."

[0428] The generation AI evaluates the degree of match of attribute information and calculates a confidence score, which is a numerical representation of the suitability of the match.

[0429] Notification of matching results

[0430] Server: Based on the matching results calculated by the generation AI, the server selects the optimal connection candidates for a specific user. This information is sent to the device.

[0431] Device: The user's device will notify the user based on the matching results received. For example, a notification such as "Mr. Tanaka, we have found Mr. Sato who has the same hobbies as you" will be displayed.

[0432] Specific examples

[0433] For example, Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, can enter his own attribute information into the system, and the generative AI will analyze and match him with users who have similar attribute information.

[0434] As a result of the analysis, a user named Sato who also shares the same hobbies of basketball and cooking and lives in the same area is found.

[0435] The server notifies Tanaka that Sato is a potential match, and the notification also displays a trust score, making it easier for users to make new connections.

[0436] This allows the user to effectively find new connections that match their interests and preferences. Furthermore, because the entire system is automated, the burden on the user is reduced and convenience is greatly improved.

[0437] The processing flow will be explained below.

[0438] Step 1: User Input

[0439] User: A new user accesses the system and enters their attribute information on the account creation screen, such as name, age, hobbies, preferences, interests, and place of residence.

[0440] Terminal: Checks the information entered by the user and prepares it for transmission.

[0441] Step 2: Send data

[0442] Terminal: Sends the request data including the entered attribute information to the server. This transmission is triggered when the user presses the "Register" button.

[0443] Step 3: Receiving the Server

[0444] Server: Receives user attribute information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[0445] Step 4: Saving to the Database

[0446] Server: Connects to the database and stores the received user attribute information in the corresponding tables. For example, store basic information in the Users table and information about hobbies and interests in the Interests table.

[0447] Step 5: Analysis by generative AI

[0448] Server: Retrieves user information from the database and inputs it into the generation AI, which analyzes the data and finds suitable match candidates based on each user's hobbies, preferences, place of residence, etc.

[0449] Step 6: Calculating match candidates

[0450] Server: The generation AI goes through a process to calculate matching candidates. Specifically, it calculates the degree of match between users as a reliability score based on similarities in hobbies, proximity of residence, etc.

[0451] Step 7: Generate matching results

[0452] Server: Creates matching results for users based on the analysis results obtained from the generation AI. For example, it may organize the results as "The most suitable match candidate for User A is User B (reliability 90%)."

[0453] Step 8: Prepare for notification

[0454] Server: Generates notification data for the matching results and prepares to send it to the corresponding user's device.

[0455] Step 9: Sending notifications

[0456] Server: Sends notification data to the user's device. Sends it securely using a communication encryption protocol.

[0457] Step 10: Receiving and Viewing Notifications

[0458] Terminal: Receives the matching results sent from the server, analyzes the received data, and displays it in a user-friendly format.

[0459] User: Check the matching results displayed on the device. For example, a notification will appear saying, "We've found a user with similar interests. Would you like to get in touch?"

[0460] The above is the specific flow of program processing on the system.

[0461] Example 1

[0462] 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."

[0463] Conventional friendship maintenance systems have been insufficient in providing effective means for adults whose friendships become weaker as they age and change in life stages to create new connections. Furthermore, the process of entering attribute information was cumbersome, and matching accuracy was low, making it difficult to increase user satisfaction. This resulted in a lack of convenience and motivation for building new friendships.

[0464] 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.

[0465] In this invention, the server includes a means for a user to input his / her own attribute information, a means for receiving the attribute information and storing it in a database, a means for analyzing the attribute information and attribute information of other users using a generative AI model, a means for inputting a prompt sentence to the generative AI model to calculate optimal matching candidates, and a means for notifying the user of the matching candidates together with their reliability scores. This makes it possible to analyze the user's attribute information with high accuracy, realize optimal matching with other users who share common hobbies and interests, and effectively build new friendships.

[0466] A "user" is someone who uses the system to input their own attribute information and seeks to be matched with other users.

[0467] "Attribute information" refers to information about an individual, such as hobbies, preferences, interests, and place of residence, entered by the user.

[0468] A "database" is a collection of information that stores received user attribute information and is used for analysis.

[0469] A "generative AI model" is an artificial intelligence model that analyzes user attribute information and derives the most suitable matching candidates.

[0470] A "prompt sentence" is a text-based command sentence that is input into the generative AI model and includes attribute information of the user to be analyzed.

[0471] "Matching Candidates" means other users analyzed by the generative AI model and recommended to the user as suitable connections.

[0472] The "confidence score" is a numerical value that indicates the suitability of a match calculated by the generative AI model, and is an index for evaluating the reliability of a matching candidate.

[0473] "Notification" refers to the means or process used to communicate information about potential matches to a user.

[0474] The present invention is a system that allows users to input their own attribute information, match with other users, and provide new connections. This system is implemented using a terminal, a server, and a generative AI model.

[0475] First, the user enters attribute information using a device, which can be a smartphone, tablet, or PC. The user accesses a dedicated application or web page and enters information such as hobbies, preferences, interests, and place of residence.

[0476] Next, the device sends the entered attribute information to the server, which receives it as an HTTP POST request and receives it in a standard data format such as JSON.

[0477] The server stores the received attribute information in a database. The database has a mechanism for appropriately classifying and storing attribute information for each user. Cloud services such as AWS (Amazon Web Services), Google Cloud Platform, and Microsoft Azure can be used to store the data.

[0478] The stored attribute information is analyzed using a generative AI model. Examples of generative AI models used include GPT-4 (OpenAI) and BERT (Google). Analysis is initiated by entering a prompt into this model. Examples of prompts include the following:

[0479] User Attribute Information:

[0480] User ID: Tanaka

[0481] Hobbies: Basketball, cooking

[0482] Place of residence: Tokyo

[0483] Use this information to analyze potential matches.

[0484] The generative AI model analyzes this prompt to find other users with common hobbies and interests. The analysis results in the calculation of the best match candidates and their confidence scores. The confidence score is a numerical value that indicates the suitability of the match and is used to evaluate the degree of match with the user.

[0485] Finally, the server sends the calculated matching results to the user's device. The device receives the results and notifies the user. The notification displays specific information such as, "Mr. Tanaka, you have found Mr. Sato, who has the same hobbies. Trust score: 95."

[0486] For example, after Mr. Tanaka uses his smartphone to input attribute information such as "basketball, cooking, Tokyo," the generative AI model analyzes this information and finds a user named Mr. Sato who shares the same hobbies. The server then notifies Mr. Tanaka's smartphone of the results, allowing him to take specific actions to build new friendships.

[0487] This allows users to effectively find new connections that match their hobbies and preferences. The present invention supports new connections by performing advanced analysis based on information entered by the user and providing optimal matching candidates.

[0488] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0489] Step 1: User enters attribute information

[0490] Users access the system's account creation page using a device such as a smartphone or PC. They enter information such as hobbies, preferences, interests, and place of residence. The entered information (e.g., "basketball, cooking, Tokyo") is stored in the account creation form.

[0491] Input: Attribute information entered by the user in the input form

[0492] Output: Attribute information stored in the input form

[0493] Step 2: Send attribute information

[0494] When the user presses the "Register" button, the device sends the entered attribute information to the server. When sending, the data is sent in JSON format via an HTTP POST request.

[0495] Input: Attribute information stored in the input form

[0496] Output: JSON formatted attribute information sent to the server

[0497] Step 3: Receiving and storing attribute information

[0498] The server receives the attribute information sent from the terminal and stores it in a database. The received information is inserted into the database using an SQL query.

[0499] Input: JSON formatted attribute information sent to the server

[0500] Output: Attribute information stored in a database

[0501] Specific behavior:

[0502] The server parses the POST request, extracts the JSON data, converts it into a SQL query, and inserts it into the database.

[0503] Step 4: Analysis of attribute information by generative AI

[0504] The server generates a prompt to pass the user's attribute information stored in the database to the generative AI model, which then analyzes the attribute information based on the prompt and finds the best matching candidate.

[0505] Input: Attribute information stored in the database

[0506] Output: Match candidates and confidence scores as analysis results

[0507] Specific behavior:

[0508] The server generates a prompt sentence and inputs it into the generative AI model, which analyzes the attribute information and calculates matching candidates and their confidence scores.

[0509] Step 5: Generate matching results

[0510] The generative AI model outputs the analysis results and returns them to the server, which receives the analysis results and generates a list of matching candidates.

[0511] Input: Prompt sentences and saved attribute information entered into the AI ​​model

[0512] Output: A list of potential matches and their confidence scores

[0513] Specific behavior:

[0514] The server retrieves the analysis results and compiles them into a list, which includes the IDs of the matching users, common interests, and a trust score.

[0515] Step 6: Submit your match results

[0516] The server then sends the generated matching results to the user's device, usually as an HTTP response, in JSON format.

[0517] Input: Matching results stored on the server

[0518] Output: Matching results sent to the user's device

[0519] Specific behavior:

[0520] The server converts the matching results into JSON format and sends them to the user's device as an HTTP response.

[0521] Step 7: Viewing the matching results

[0522] The user's device will then notify them based on the matching results received from the server. Specifically, a pop-up notification will be displayed saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Trust score: 95."

[0523] Input: Matching results sent from the server

[0524] Output: The notification displayed to the user

[0525] Specific behavior:

[0526] The device parses the received JSON data and displays a notification pop-up on the screen. The user can then confirm the notification and take action to establish a new friendship.

[0527] (Application example 1)

[0528] 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."

[0529] In modern society, as people become adults, friendships tend to weaken as they age and their life stages change. In addition, it is not easy to form new connections with people from the same area or who share the same hobbies. Furthermore, physical stores have limited means of effectively forming and maintaining communities for their customers. This creates a challenge: there are few opportunities to build new relationships between individuals. Furthermore, it is difficult for individual users to form new friendships through participating in events.

[0530] 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.

[0531] In this invention, the server includes means for a user to input his or her own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, and means for acquiring event information at physical stores and providing that information to the user. This enables users to automatically match with other users who share the same hobbies, preferences, and place of residence, and to build new friendships through participation in events held at physical stores.

[0532] A "user" is an individual who accesses the system and enters their own attribute information.

[0533] "Attribute information" is information that a user inputs into the system, and includes hobbies, preferences, interests, place of residence, and the like.

[0534] "Receiving" is the act of transferring attribute information entered by the user from the terminal to the server.

[0535] "Database" refers to a storage device within the system that stores and manages received attribute information.

[0536] "Generative AI" is an artificial intelligence technology that analyzes a user's attribute information and calculates suitable matching candidates with other users.

[0537] "Analysis" is the process of using generative AI to evaluate attribute information and calculate commonalities and similarities between users.

[0538] "Matching candidates" are other users who are selected based on the analysis results and who may have friendships with a particular user.

[0539] "Notification" refers to the act of sending calculated match candidates and event information to the user's device to inform them.

[0540] A "physical store" is a physical commercial facility where customers can visit in person to enjoy products and services.

[0541] "Event information" is detailed information about social activities and events held at physical stores, and is provided to users.

[0542] The system that realizes this application example consists of the following components: a device where the user inputs their own attribute information, a server that receives the attribute information and stores it in a database, a module that uses generative AI to analyze the user's attribute information, a system that calculates appropriate matching candidates and notifies the user, and a mechanism that obtains event information from physical stores and provides it to the user.

[0543] Users use their smartphones to input attribute information, such as hobbies, preferences, interests, and place of residence. Once the user inputs the information and presses the registration button, the smartphone sends the information to the server.

[0544] The server is built using the Flask framework and has the ability to store attribute information received from users in an SQLite database, which is then used for analysis by the generative AI.

[0545] The generation AI is implemented in Python and is responsible for analyzing user attribute information. Specifically, it calculates commonalities and similarities with other users based on information such as each user's hobbies, interests, and place of residence. The generation AI calculates the similarity between each user as a reliability score and selects other users with high scores as matching candidates.

[0546] The server notifies the user of the matching results based on the matching candidates calculated by the generation AI. The notification is displayed on the user's smartphone in the form of "A user with the same hobbies has been found." The server also obtains information about events held at physical stores and provides it to the user. This allows users to build new friendships through actual events.

[0547] As a concrete example, consider the case where a user enters the attribute information "My hobbies are basketball and cooking, and I live in Tokyo." The server receives this information and stores it in a database. The generation AI analyzes the attribute information and finds other users who live in Tokyo and also like basketball and cooking. The server then notifies the user of the matching results and also provides information about basketball-related events at physical stores.

[0548] Examples of prompt sentences to input to a generative AI model include the following:

[0549] "We want to create a system that matches users with the best possible friends based on their hobbies and location. User information includes the following attributes:

[0550] name

[0551] hobby

[0552] place of residence

[0553] After registration, there needs to be a function that automatically finds other users who share the same hobbies and location and displays matching results.

[0554] In this way, the present invention provides a system that supports users in easily building new friendships.

[0555] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0556] Step 1:

[0557] User enters attribute information

[0558] The user uses their smartphone to enter attribute information such as name, hobbies, preferences, interests, and place of residence into the application form. Once the input is complete, the user presses the "Register" button. This input data is sent to the server by the application. The input data includes attribute information in text format.

[0559] Step 2:

[0560] The server receives and stores the attribute information

[0561] The server receives the attribute information sent from the device. The server uses the Flask framework to receive this information and store it in an SQLite database. As part of the processing, the received data is sorted into the appropriate table and an insert operation is performed. The input data is received in JSON format, and as output, a new record is added to the database.

[0562] Step 3:

[0563] Generative AI analyzes attribute information

[0564] The server passes the attribute information of multiple users stored in the database to the generation AI. The generation AI is implemented in Python and calculates commonalities and similarities with other users based on information such as each user's hobbies, preferences, interests, and place of residence. The input data is the user's attribute information obtained from the database, and the output data is a reliability score indicating the degree of similarity between each user.

[0565] Step 4:

[0566] Calculating match candidates

[0567] The server selects appropriate match candidates based on the reliability score calculated by the generation AI. The input data is the reliability score from the generation AI, and the output data is a list of optimal match candidates. The server prioritizes users with high reliability scores and lists them as match candidates.

[0568] Step 5:

[0569] Notify users of matching results

[0570] The server notifies the user of the calculated match candidates. The matching results are sent as a notification to the user's smartphone. The input data is a list of match candidates, and the output data is a notification message displayed on the user's smartphone. The notification message contains details of the match candidates and their confidence scores.

[0571] Step 6:

[0572] Acquire and provide information about events at physical stores

[0573] The server also retrieves information about events held at physical stores and provides it to users. This information is updated periodically and stored in a database. When a user requests event information from the application, the server sends the latest event information to the user. The input data is a request for event information, and the output data is detailed information about the event. The event information is displayed on the user's smartphone, allowing the user to decide whether or not to attend the event.

[0574] In this way, by combining the processes performed at each step, users can easily build new friendships and deepen their interactions through events at physical stores.

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

[0576] This invention is a system that uses user attribute information and emotional information to provide more appropriate and emotionally supported match candidates. In addition to the basic function of inputting and saving user attribute information and analyzing that information to perform matching, this system also incorporates an emotion engine that recognizes user emotions, thereby improving matching accuracy.

[0577] Overall system overview

[0578] The system consists of the following main elements:

[0579] A terminal for user input

[0580] A server that receives and stores attribute information and emotion information

[0581] Server module that performs analysis using generative AI

[0582] Emotion engine that recognizes user emotions

[0583] A mechanism for notifying users of matching results

[0584] User Registration Process

[0585] User: A user accesses the system and enters their own attribute information, such as name, age, hobbies, preferences, interests, and place of residence.

[0586] Terminal: Checks the information entered by the user and prepares it for transmission. The emotion engine also collects the user's emotional information.

[0587] Receiving and storing data

[0588] Server: Receives user attribute information and emotion information sent from the device. The received information is saved in a database, with attribute information stored in the Users table and emotion information stored in the Emotions table.

[0589] Analysis of attribute information and emotional information

[0590] Server: Inputs the user's attribute information and emotional information from the database into the generation AI. The generation AI analyzes this data and finds the most suitable match candidate, taking into account the user's current emotional state and past emotional patterns.

[0591] For example, if a user is feeling stressed, the system will prioritize matching with users who have relaxing hobbies or who have the same hobbies and a calm personality.

[0592] Calculating match candidates

[0593] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[0594] The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[0595] Matching result generation and notification

[0596] Server: Based on the analysis results obtained from the generation AI, it generates appropriate matching results for the user. For example, "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[0597] Server: Generates notification data of the matching results and sends it to the corresponding user's device. Communication is performed using an encrypted protocol.

[0598] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Would you like to get in touch?"

[0599] Specific examples

[0600] In the case of Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, Mr. Tanaka enters his attribute information into the system, and the emotion engine collects his emotional information (for example, whether he is relaxed or stressed). As a result of the analysis, Mr. Sato, who has similar attribute information and is in the same emotional state, is found. The server notifies Mr. Tanaka that Mr. Sato is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[0601] As a result, the present invention takes into account the user's emotions, making it possible to provide connections that are suited to the user's psychological state, rather than just attribute information, thereby enabling the user to effectively build new connections.

[0602] The processing flow will be explained below.

[0603] Step 1: Collecting user input and sentiment

[0604] User: A new user accesses the system and enters their demographic information (such as name, age, hobbies, preferences, interests, and place of residence). During the registration process, they also interact with an interface that displays the user's current emotional state.

[0605] Terminal: Checks the attribute information entered by the user and the emotion information collected by the emotion engine, and prepares for transmission.

[0606] Step 2: Send data

[0607] Device: Sends request data including the entered attribute information and emotion information to the server. This transmission is triggered when the user presses the "Register" button.

[0608] Step 3: Receiving the Server

[0609] Server: Receives the user's attribute information and emotion information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[0610] Step 4: Saving to the Database

[0611] Server: Connects to the database and stores the received user attribute information in the Users table and emotion information in the Emotions table. This allows for centralized management of user information.

[0612] Step 5: Analysis by generative AI and emotion engine

[0613] Server: Obtains user attribute information and emotional information from the database and inputs it into the generation AI. The generation AI analyzes the user's attribute information and emotional information, taking into account their current emotional state and past emotional patterns, and finds suitable matching candidates.

[0614] For example, if a user is feeling stressed, the system is set to prioritize matching with users who have relaxing hobbies or who have the same hobbies and calm personalities.

[0615] Step 6: Calculating match candidates

[0616] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[0617] The AI ​​generator calculates the degree of similarity between users as a reliability score and selects matching candidates based on this. For example, it calculates the score based on information such as "User A and User B both like basketball and are currently relaxing."

[0618] Step 7: Generate matching results

[0619] Server: Based on the analysis results obtained from the generation AI, the server generates appropriate matching results for the target user. For example, it may organize the results as "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[0620] Step 8: Prepare for notification

[0621] Server: Generates notification data of the matching results and prepares to send it to the corresponding user's device. Communication is performed using an encryption protocol.

[0622] Step 9: Sending notifications

[0623] Server: Sends notification data to the user's device, including information about match candidates and their confidence scores.

[0624] Step 10: Receiving and Viewing Notifications

[0625] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may be displayed saying, "Sato also likes basketball and is currently relaxing. Why not get in touch?"

[0626] User: Check the matching results displayed on the device and take the next step (e.g., decide whether to contact them).

[0627] The above is the flow of specific processing steps in the invention incorporating an emotion engine. This system allows users to find the optimal connection based on their own emotional state.

[0628] Example 2

[0629] 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."

[0630] Conventional matching systems select match candidates based on user attribute information, but do not take the user's emotional state into consideration, resulting in insufficient matching accuracy. Matching that ignores the user's psychological state results in difficulty in obtaining results that are satisfactory to the user. The present invention aims to provide more accurate matching and improve user satisfaction by taking emotional information into consideration.

[0631] 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.

[0632] In this invention, the server includes a means for a user to input their own attribute information, a means for receiving the attribute information and emotion information and storing them in a database, and a means for analyzing the attribute information and emotion information using a generation AI, thereby enabling the selection of appropriate matching candidates taking into consideration the user's current emotional state and past emotion patterns.

[0633] "Attribute information" is information about personal characteristics and interests provided by a user, and specifically includes name, age, hobbies, preferences, interests, place of residence, and the like.

[0634] "Emotional Information" refers to information that indicates the user's current psychological and emotional state, including data collected through the Emotion Engine, such as whether they are relaxed or stressed.

[0635] An "emotion engine" is a software or hardware mechanism that analyzes a user's facial expressions, voice, input patterns, etc. to identify their emotional state.

[0636] "Generative AI" is an artificial intelligence model that analyzes input data and generates matching candidates and other results suitable for the user.

[0637] The "confidence score" is a numerical value calculated by the generation AI that indicates the degree of match between users, and is an index for evaluating the suitability of matching candidates.

[0638] The "database" is a digital storage system for storing and managing received user attribute information and emotion information.

[0639] A "terminal" is an electronic device, such as a computer device or smartphone, that a user uses to input and send information.

[0640] "Server" refers to the back-end computer system that processes and analyzes the received data, calculates the matching results, and notifies the user.

[0641] "Matching candidates" are potential contacts that may be suitable for the user, selected based on the analysis results of the generation AI.

[0642] The present invention is a system that uses a user's attribute information and emotional information to provide more appropriate and emotionally supportive match candidates, and its specific implementation method is described below. This system has the function of inputting and saving a user's attribute information and emotional information, analyzing that information, and notifying the user of the most suitable match candidates.

[0643] Key elements of the system

[0644] The system consists of the following main elements:

[0645] A terminal for user input

[0646] A server that receives and stores attribute information and emotion information

[0647] Server module that performs analysis using generative AI

[0648] Emotion engine that recognizes user emotions

[0649] A mechanism for notifying users of matching results

[0650] Enter user information

[0651] User: The user accesses the system and inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence, as well as their current emotional state through the emotion engine.

[0652] Device: The device temporarily stores the information entered by the user and sends it to the server when it is ready. The emotion engine analyzes the user's facial expressions and keyboard input to collect emotional information.

[0653] Data transmission and storage

[0654] Device: When the user clicks the send button, the device sends all input information, including attribute information and emotion information, to the server.

[0655] Server: The server stores the data received from the device in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[0656] Analysis of attribute information and emotional information

[0657] Server: The server extracts the user's attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model analyzes this data and finds the best match candidates, taking into account the user's current emotional state and past emotional patterns.

[0658] Example prompt sentence:

[0659] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[0660] Calculating match candidates

[0661] Server: The generative AI model analyzes the user's emotional patterns and attribute information based on the input information to find the best match. For example, the degree of match between users is calculated as a confidence score.

[0662] Matching result generation and notification

[0663] Server: Generates matching results including match candidates and reliability scores based on the results obtained from the generative AI model. For example, the result might be "For User A, the most suitable match candidate is User B (reliability 90%)."

[0664] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is secure using encryption protocols such as SSL / TLS.

[0665] Device: Receives the matching results sent from the server and displays them to the user in an easy-to-understand format. For example, a notification like "Mr. Tanaka, we've found user C who has the same hobbies as you. Would you like to get in touch?" is displayed.

[0666] As a result, the present invention provides highly accurate matching candidates that take the user's emotions into consideration, helping the user build appropriate connections.

[0667] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0668] Step 1:

[0669] The user inputs their own attribute information and emotional information.

[0670] User: Accesses the system and enters his / her demographic information, including name, age, hobbies, preferences, interests, and place of residence, and also answers a questionnaire to input his / her current emotional state.

[0671] Terminal: Checks the information entered by the user and temporarily stores the input data. The emotion engine analyzes the user's facial expressions and input speed to collect emotional information.

[0672] Input: User attribute information and emotion information

[0673] Output: Temporarily stored user information and analyzed emotion information

[0674] Step 2:

[0675] Data transmission and storage

[0676] Terminal: When the user clicks the submit button, the terminal sends all input information that was temporarily saved to the server.

[0677] Server: Receives data sent from the device and stores it in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[0678] Input: User attribute information and emotion information

[0679] Output: User information and emotion information stored in a database

[0680] Step 3:

[0681] Analysis of attribute information and emotional information

[0682] Server: Extracts user attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model receives this data via a prompt and begins analysis.

[0683] Input: User attribute information and emotion information stored in the database

[0684] Output: Input data and analysis results for the generative AI model

[0685] Specific prompt examples:

[0686] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[0687] Step 4:

[0688] Calculating match candidates

[0689] Server: The generative AI model analyzes the user's emotional patterns and attributes based on the input information to find the best match candidates. During this process, a confidence score is calculated.

[0690] Input: Input data to the generative AI model

[0691] Output: Match candidates with confidence scores

[0692] Step 5:

[0693] Matching result generation and notification

[0694] Server: Based on the analysis results obtained from the generative AI model, it generates matching results including match candidates and confidence scores.

[0695] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is securely performed using an encryption protocol.

[0696] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user.

[0697] Input: Analysis results and matching candidate information

[0698] Output: Matching results reported to the user

[0699] This allows users to find suitable matching candidates that suit their emotional state, while also protecting their privacy.

[0700] (Application example 2)

[0701] 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."

[0702] Conventional matching systems make recommendations based on user attribute information, but do not take the user's emotional state into consideration, making it difficult to make recommendations that are appropriate for the user's psychological state. Furthermore, there is a need for a method that effectively utilizes the user's emotional information to provide highly accurate matching candidates. Meanwhile, online shopping sites also do not recommend products that reflect the user's emotional state, limiting the extent to which they can improve user satisfaction. The present invention addresses these issues.

[0703] 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 means for a user to input their own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, means for collecting user emotion information, means for analyzing the emotion information and performing matching in conjunction with the attribute information, and means for inputting the attribute information and emotion information using a generation AI model and generating a prompt sentence. This enables highly accurate match candidates and product recommendations that reflect the user's emotional state.

[0704] "Attribute information" refers to information such as a user's hobbies, preferences, interests, and place of residence.

[0705] "Emotion information" is data that indicates the user's emotional state, and includes, for example, the degree of stress or relaxation.

[0706] "Generative AI" refers to artificial intelligence technology that analyzes a user's attribute information and emotional information to provide optimal matching candidates and product recommendations.

[0707] "Matching candidates" refer to other users or products that are suitable for the user, found based on the user's attribute information and emotional information.

[0708] A "prompt sentence" refers to a text-based input sentence used by the generation AI when performing analysis.

[0709] "Database" refers to an information management system for storing attribute information and emotion information.

[0710] "Emotion engine" refers to technology for collecting and analyzing user emotional information.

[0711] "Push notification" refers to a method for a system to send information to a user in real time.

[0712] The "confidence score" is a numerical evaluation of the degree of match between users, and is an index indicating the accuracy of a matching candidate.

[0713] The system for implementing this invention utilizes user attribute information and emotion information to provide optimal matching candidates and product recommendations. The configuration and operation of the system will be described in detail below.

[0714] System configuration

[0715] The system consists of the following elements:

[0716] 1. User terminal: A device used by the user for input, including smartphones.

[0717] 2. Server: The back-end system that manages the database and runs the generative AI and emotion engine.

[0718] 3. Database: A system for storing user attribute information and emotion information.

[0719] 4. Generative AI model: An artificial intelligence system that analyzes the user's attribute information and emotional information and generates prompt sentences.

[0720] 5. Emotion engine: A software module for collecting and analyzing user emotional information.

[0721] User Registration Process

[0722] Users access the system and input their own attribute information, such as their name, age, hobbies, preferences, interests, and place of residence. The user's device then checks the information and prepares for transmission. The emotion engine also collects the user's emotional information at this time.

[0723] Receiving and storing data

[0724] The server receives the user's attribute information and emotion information sent from the device. The received information is stored in a database, with the attribute information stored in the Users table and the emotion information stored in the Emotions table.

[0725] Analysis of attribute information and emotional information

[0726] The server inputs the user's attribute information and emotional information from the database into the generative AI model. The generative AI analyzes this data and finds the most suitable match candidates and products, taking into account the user's current emotional state and past emotional patterns. For example, if a user is feeling stressed, it will prioritize matching with other users who have relaxing hobbies or who have the same hobbies and calm personalities.

[0727] Calculating match candidates

[0728] The server calculates matching candidates for the user based on the analysis results from the generation AI. At this time, by taking into account not only attribute information but also emotional information, more accurate matching is possible. The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[0729] Matching result generation and notification

[0730] The server generates appropriate matching results for the user based on the analysis results obtained from the generation AI. For example, "The most suitable match candidate for User A is User B (reliability 90%)." The server generates notification data of the matching results and sends it to the corresponding user's device. Communication is carried out using an encrypted protocol. The user's device receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "We have found other users who share the same hobbies as you. Would you like to get in touch?"

[0731] Specific examples

[0732] Example: User A, whose hobbies are basketball and cooking and who lives in a large city, enters his or her own attribute information into the system, and the emotion engine collects User A's emotional information (for example, whether he or she is relaxed or stressed). As a result of the analysis, another User B is found who has similar attribute information and is in the same emotional state. The server notifies User A that User B is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[0733] Prompt Sentence Examples

[0734] An example of a prompt for a generative AI model is:

[0735] "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[0736] In this way, the present invention takes the user's emotions into consideration, and can provide connections and products that are suited to the user's psychological state, rather than just attribute information.

[0737] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0738] Step 1:

[0739] The user enters attribute information

[0740] The user inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence. The terminal receives this information and prepares it to be sent to the database.

[0741] Input: Name, age, hobbies, preferences, interests, place of residence

[0742] Output: Attribute information saved on the device

[0743] Step 2:

[0744] Collecting emotional information

[0745] The camera and microphone on the user's device are used to collect information about the user's emotions, which are then analyzed by an emotion engine to identify states such as relaxed or stressed.

[0746] Input: User's facial image and voice data

[0747] Output: Analyzed emotional information (relaxed, stressed, etc.)

[0748] Step 3:

[0749] Receiving and storing data

[0750] The server receives the user's attribute information and emotion information transmitted from the terminal.

[0751] Input: attribute information, emotion information

[0752] Output: Attribute information and emotion information stored in the database

[0753] Step 4:

[0754] Analysis of attribute information and emotional information

[0755] The server sends attribute and emotional information from the database to the generative AI model, which analyzes this data and identifies the best match candidates and products, taking into account the user's current emotional state.

[0756] Input: Attribute information and emotion information stored in the database

[0757] Output: Analysis results (best matching candidates and products)

[0758] Step 5:

[0759] Generate prompt statement

[0760] The server uses a generative AI model to generate a prompt sentence and create a text-format input sentence for analysis.

[0761] Input: attribute information, emotion information

[0762] Output: Generated prompt statement

[0763] Step 6:

[0764] Calculating match candidates

[0765] Based on the analysis results of the generative AI model, the server calculates matching candidates and products, evaluating the degree of match between users as a reliability score.

[0766] Input: prompt statement

[0767] Output: Match candidates and products based on confidence scores

[0768] Step 7:

[0769] Notification of matching results

[0770] The server generates notification data of the matching results and sends it to the corresponding user's device. The communication uses an encrypted protocol. The device displays the matching results received in an easy-to-understand format for the user.

[0771] Input: Match candidates and products based on confidence scores

[0772] Output: Matching results and product recommendations displayed on the user's device

[0773] As a concrete example, a prompt for a generative AI model might look like this: "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[0774] 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.

[0775] 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.

[0776] 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.

[0777] [Third embodiment]

[0778] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0779] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0780] 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).

[0781] 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.

[0782] 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.

[0783] 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).

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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."

[0790] The present invention is a system that provides new connections to adults whose friendships become weaker as they age or change life stages. The system acquires and analyzes user attribute information to provide appropriate match candidates. The specific processing flow is explained below.

[0791] Overall system overview

[0792] The system uses the information users enter to match them with other users and provide them with appropriate connections. The system mainly consists of the following components:

[0793] A terminal for users to input attribute information

[0794] Server that receives and stores attribute information

[0795] A server module that uses generative AI to analyze attribute information

[0796] A mechanism for notifying users of matching results

[0797] User Registration Process

[0798] User: First, the user enters their demographic information (e.g., hobbies, preferences, interests, and location). This involves accessing the account creation page on the user's device and entering information in the required fields.

[0799] Terminal: The entered information is sent from the terminal to the server when the user presses the "Register" button.

[0800] Receiving and storing data

[0801] Server: The server receives the user attribute information sent from the device. The received information is saved in a database, and each attribute is classified and stored in the appropriate table.

[0802] Analysis and Matching

[0803] Server: Passes user information stored in the database to the generation AI, which analyzes the user's attribute information and calculates the most suitable match with other users.

[0804] For example, matching is done based on commonalities such as "User A and User B both like basketball and live in Tokyo."

[0805] The generation AI evaluates the degree of match of attribute information and calculates a confidence score, which is a numerical representation of the suitability of the match.

[0806] Notification of matching results

[0807] Server: Based on the matching results calculated by the generation AI, the server selects the optimal connection candidates for a specific user. This information is sent to the device.

[0808] Device: The user's device will notify the user based on the matching results received. For example, a notification such as "Mr. Tanaka, we have found Mr. Sato who has the same hobbies as you" will be displayed.

[0809] Specific examples

[0810] For example, Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, can enter his own attribute information into the system, and the generative AI will analyze and match him with users who have similar attribute information.

[0811] As a result of the analysis, a user named Sato who also shares the same hobbies of basketball and cooking and lives in the same area is found.

[0812] The server notifies Tanaka that Sato is a potential match, and the notification also displays a trust score, making it easier for users to make new connections.

[0813] This allows the user to effectively find new connections that match their interests and preferences. Furthermore, because the entire system is automated, the burden on the user is reduced and convenience is greatly improved.

[0814] The processing flow will be explained below.

[0815] Step 1: User Input

[0816] User: A new user accesses the system and enters their attribute information on the account creation screen, such as name, age, hobbies, preferences, interests, and place of residence.

[0817] Terminal: Checks the information entered by the user and prepares it for transmission.

[0818] Step 2: Send data

[0819] Terminal: Sends the request data including the entered attribute information to the server. This transmission is triggered when the user presses the "Register" button.

[0820] Step 3: Receiving the Server

[0821] Server: Receives user attribute information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[0822] Step 4: Saving to the Database

[0823] Server: Connects to the database and stores the received user attribute information in the corresponding tables. For example, store basic information in the Users table and information about hobbies and interests in the Interests table.

[0824] Step 5: Analysis by generative AI

[0825] Server: Retrieves user information from the database and inputs it into the generation AI, which analyzes the data and finds suitable match candidates based on each user's hobbies, preferences, place of residence, etc.

[0826] Step 6: Calculating match candidates

[0827] Server: The generation AI goes through a process to calculate matching candidates. Specifically, it calculates the degree of match between users as a reliability score based on similarities in hobbies, proximity of residence, etc.

[0828] Step 7: Generate matching results

[0829] Server: Creates matching results for users based on the analysis results obtained from the generation AI. For example, it may organize the results as "The most suitable match candidate for User A is User B (reliability 90%)."

[0830] Step 8: Prepare for notification

[0831] Server: Generates notification data for the matching results and prepares to send it to the corresponding user's device.

[0832] Step 9: Sending notifications

[0833] Server: Sends notification data to the user's device. Sends it securely using a communication encryption protocol.

[0834] Step 10: Receiving and Viewing Notifications

[0835] Terminal: Receives the matching results sent from the server, analyzes the received data, and displays it in a user-friendly format.

[0836] User: Check the matching results displayed on the device. For example, a notification will appear saying, "We've found a user with similar interests. Would you like to get in touch?"

[0837] The above is the specific flow of program processing on the system.

[0838] Example 1

[0839] 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."

[0840] Conventional friendship maintenance systems have been insufficient in providing effective means for adults whose friendships become weaker as they age and change in life stages to create new connections. Furthermore, the process of entering attribute information was cumbersome, and matching accuracy was low, making it difficult to increase user satisfaction. This resulted in a lack of convenience and motivation for building new friendships.

[0841] 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.

[0842] In this invention, the server includes a means for a user to input his / her own attribute information, a means for receiving the attribute information and storing it in a database, a means for analyzing the attribute information and attribute information of other users using a generative AI model, a means for inputting a prompt sentence to the generative AI model to calculate optimal matching candidates, and a means for notifying the user of the matching candidates together with their reliability scores. This makes it possible to analyze the user's attribute information with high accuracy, realize optimal matching with other users who share common hobbies and interests, and effectively build new friendships.

[0843] A "user" is someone who uses the system to input their own attribute information and seeks to be matched with other users.

[0844] "Attribute information" refers to information about an individual, such as hobbies, preferences, interests, and place of residence, entered by the user.

[0845] A "database" is a collection of information that stores received user attribute information and is used for analysis.

[0846] A "generative AI model" is an artificial intelligence model that analyzes user attribute information and derives the most suitable matching candidates.

[0847] A "prompt sentence" is a text-based command sentence that is input into the generative AI model and includes attribute information of the user to be analyzed.

[0848] "Matching Candidates" means other users analyzed by the generative AI model and recommended to the user as suitable connections.

[0849] The "confidence score" is a numerical value that indicates the suitability of a match calculated by the generative AI model, and is an index for evaluating the reliability of a matching candidate.

[0850] "Notification" refers to the means or process used to communicate information about potential matches to a user.

[0851] The present invention is a system that allows users to input their own attribute information, match with other users, and provide new connections. This system is implemented using a terminal, a server, and a generative AI model.

[0852] First, the user enters attribute information using a device, which can be a smartphone, tablet, or PC. The user accesses a dedicated application or web page and enters information such as hobbies, preferences, interests, and place of residence.

[0853] Next, the device sends the entered attribute information to the server, which receives it as an HTTP POST request and receives it in a standard data format such as JSON.

[0854] The server stores the received attribute information in a database. The database has a mechanism for appropriately classifying and storing attribute information for each user. Cloud services such as AWS (Amazon Web Services), Google Cloud Platform, and Microsoft Azure can be used to store the data.

[0855] The stored attribute information is analyzed using a generative AI model. Examples of generative AI models used include GPT-4 (OpenAI) and BERT (Google). Analysis is initiated by entering a prompt into this model. Examples of prompts include the following:

[0856] User Attribute Information:

[0857] User ID: Tanaka

[0858] Hobbies: Basketball, cooking

[0859] Place of residence: Tokyo

[0860] Use this information to analyze potential matches.

[0861] The generative AI model analyzes this prompt to find other users with common hobbies and interests. The analysis results in the calculation of the best match candidates and their confidence scores. The confidence score is a numerical value that indicates the suitability of the match and is used to evaluate the degree of match with the user.

[0862] Finally, the server sends the calculated matching results to the user's device. The device receives the results and notifies the user. The notification displays specific information such as, "Mr. Tanaka, you have found Mr. Sato, who has the same hobbies. Trust score: 95."

[0863] For example, after Mr. Tanaka uses his smartphone to input attribute information such as "basketball, cooking, Tokyo," the generative AI model analyzes this information and finds a user named Mr. Sato who shares the same hobbies. The server then notifies Mr. Tanaka's smartphone of the results, allowing him to take specific actions to build new friendships.

[0864] This allows users to effectively find new connections that match their hobbies and preferences. The present invention supports new connections by performing advanced analysis based on information entered by the user and providing optimal matching candidates.

[0865] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0866] Step 1: User enters attribute information

[0867] Users access the system's account creation page using a device such as a smartphone or PC. They enter information such as hobbies, preferences, interests, and place of residence. The entered information (e.g., "basketball, cooking, Tokyo") is stored in the account creation form.

[0868] Input: Attribute information entered by the user in the input form

[0869] Output: Attribute information stored in the input form

[0870] Step 2: Send attribute information

[0871] When the user presses the "Register" button, the device sends the entered attribute information to the server. When sending, the data is sent in JSON format via an HTTP POST request.

[0872] Input: Attribute information stored in the input form

[0873] Output: JSON formatted attribute information sent to the server

[0874] Step 3: Receiving and storing attribute information

[0875] The server receives the attribute information sent from the terminal and stores it in a database. The received information is inserted into the database using an SQL query.

[0876] Input: JSON formatted attribute information sent to the server

[0877] Output: Attribute information stored in a database

[0878] Specific behavior:

[0879] The server parses the POST request, extracts the JSON data, converts it into a SQL query, and inserts it into the database.

[0880] Step 4: Analysis of attribute information by generative AI

[0881] The server generates a prompt to pass the user's attribute information stored in the database to the generative AI model, which then analyzes the attribute information based on the prompt and finds the best matching candidate.

[0882] Input: Attribute information stored in the database

[0883] Output: Match candidates and confidence scores as analysis results

[0884] Specific behavior:

[0885] The server generates a prompt sentence and inputs it into the generative AI model, which analyzes the attribute information and calculates matching candidates and their confidence scores.

[0886] Step 5: Generate matching results

[0887] The generative AI model outputs the analysis results and returns them to the server, which receives the analysis results and generates a list of matching candidates.

[0888] Input: Prompt sentences and saved attribute information entered into the AI ​​model

[0889] Output: A list of potential matches and their confidence scores

[0890] Specific behavior:

[0891] The server retrieves the analysis results and compiles them into a list, which includes the IDs of the matching users, common interests, and a trust score.

[0892] Step 6: Submit your match results

[0893] The server then sends the generated matching results to the user's device, usually as an HTTP response, in JSON format.

[0894] Input: Matching results stored on the server

[0895] Output: Matching results sent to the user's device

[0896] Specific behavior:

[0897] The server converts the matching results into JSON format and sends them to the user's device as an HTTP response.

[0898] Step 7: Viewing the matching results

[0899] The user's device will then notify them based on the matching results received from the server. Specifically, a pop-up notification will be displayed saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Trust score: 95."

[0900] Input: Matching results sent from the server

[0901] Output: The notification displayed to the user

[0902] Specific behavior:

[0903] The device parses the received JSON data and displays a notification pop-up on the screen. The user can then confirm the notification and take action to establish a new friendship.

[0904] (Application example 1)

[0905] 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."

[0906] In modern society, as people become adults, friendships tend to weaken as they age and their life stages change. In addition, it is not easy to form new connections with people from the same area or who share the same hobbies. Furthermore, physical stores have limited means of effectively forming and maintaining communities for their customers. This creates a challenge: there are few opportunities to build new relationships between individuals. Furthermore, it is difficult for individual users to form new friendships through participating in events.

[0907] 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.

[0908] In this invention, the server includes means for a user to input his or her own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, and means for acquiring event information at physical stores and providing that information to the user. This enables users to automatically match with other users who share the same hobbies, preferences, and place of residence, and to build new friendships through participation in events held at physical stores.

[0909] A "user" is an individual who accesses the system and enters their own attribute information.

[0910] "Attribute information" is information that a user inputs into the system, and includes hobbies, preferences, interests, place of residence, and the like.

[0911] "Receiving" is the act of transferring attribute information entered by the user from the terminal to the server.

[0912] "Database" refers to a storage device within the system that stores and manages received attribute information.

[0913] "Generative AI" is an artificial intelligence technology that analyzes a user's attribute information and calculates suitable matching candidates with other users.

[0914] "Analysis" is the process of using generative AI to evaluate attribute information and calculate commonalities and similarities between users.

[0915] "Matching candidates" are other users who are selected based on the analysis results and who may have friendships with a particular user.

[0916] "Notification" refers to the act of sending calculated match candidates and event information to the user's device to inform them.

[0917] A "physical store" is a physical commercial facility where customers can visit in person to enjoy products and services.

[0918] "Event information" is detailed information about social activities and events held at physical stores, and is provided to users.

[0919] The system that realizes this application example consists of the following components: a device where the user inputs their own attribute information, a server that receives the attribute information and stores it in a database, a module that uses generative AI to analyze the user's attribute information, a system that calculates appropriate matching candidates and notifies the user, and a mechanism that obtains event information from physical stores and provides it to the user.

[0920] Users use their smartphones to input attribute information, such as hobbies, preferences, interests, and place of residence. Once the user inputs the information and presses the registration button, the smartphone sends the information to the server.

[0921] The server is built using the Flask framework and has the ability to store attribute information received from users in an SQLite database, which is then used for analysis by the generative AI.

[0922] The generation AI is implemented in Python and is responsible for analyzing user attribute information. Specifically, it calculates commonalities and similarities with other users based on information such as each user's hobbies, interests, and place of residence. The generation AI calculates the similarity between each user as a reliability score and selects other users with high scores as matching candidates.

[0923] The server notifies the user of the matching results based on the matching candidates calculated by the generation AI. The notification is displayed on the user's smartphone in the form of "A user with the same hobbies has been found." The server also obtains information about events held at physical stores and provides it to the user. This allows users to build new friendships through actual events.

[0924] As a concrete example, consider the case where a user enters the attribute information "My hobbies are basketball and cooking, and I live in Tokyo." The server receives this information and stores it in a database. The generation AI analyzes the attribute information and finds other users who live in Tokyo and also like basketball and cooking. The server then notifies the user of the matching results and also provides information about basketball-related events at physical stores.

[0925] Examples of prompt sentences to input to a generative AI model include the following:

[0926] "We want to create a system that matches users with the best possible friends based on their hobbies and location. User information includes the following attributes:

[0927] name

[0928] hobby

[0929] place of residence

[0930] After registration, there needs to be a function that automatically finds other users who share the same hobbies and location and displays matching results.

[0931] In this way, the present invention provides a system that supports users in easily building new friendships.

[0932] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0933] Step 1:

[0934] User enters attribute information

[0935] The user uses their smartphone to enter attribute information such as name, hobbies, preferences, interests, and place of residence into the application form. Once the input is complete, the user presses the "Register" button. This input data is sent to the server by the application. The input data includes attribute information in text format.

[0936] Step 2:

[0937] The server receives and stores the attribute information

[0938] The server receives the attribute information sent from the device. The server uses the Flask framework to receive this information and store it in an SQLite database. As part of the processing, the received data is sorted into the appropriate table and an insert operation is performed. The input data is received in JSON format, and as output, a new record is added to the database.

[0939] Step 3:

[0940] Generative AI analyzes attribute information

[0941] The server passes the attribute information of multiple users stored in the database to the generation AI. The generation AI is implemented in Python and calculates commonalities and similarities with other users based on information such as each user's hobbies, preferences, interests, and place of residence. The input data is the user's attribute information obtained from the database, and the output data is a reliability score indicating the degree of similarity between each user.

[0942] Step 4:

[0943] Calculating match candidates

[0944] The server selects appropriate match candidates based on the reliability score calculated by the generation AI. The input data is the reliability score from the generation AI, and the output data is a list of optimal match candidates. The server prioritizes users with high reliability scores and lists them as match candidates.

[0945] Step 5:

[0946] Notify users of matching results

[0947] The server notifies the user of the calculated match candidates. The matching results are sent as a notification to the user's smartphone. The input data is a list of match candidates, and the output data is a notification message displayed on the user's smartphone. The notification message contains details of the match candidates and their confidence scores.

[0948] Step 6:

[0949] Acquire and provide information about events at physical stores

[0950] The server also retrieves information about events held at physical stores and provides it to users. This information is updated periodically and stored in a database. When a user requests event information from the application, the server sends the latest event information to the user. The input data is a request for event information, and the output data is detailed information about the event. The event information is displayed on the user's smartphone, allowing the user to decide whether or not to attend the event.

[0951] In this way, by combining the processes performed at each step, users can easily build new friendships and deepen their interactions through events at physical stores.

[0952] 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.

[0953] This invention is a system that uses user attribute information and emotional information to provide more appropriate and emotionally supported match candidates. In addition to the basic function of inputting and saving user attribute information and analyzing that information to perform matching, this system also incorporates an emotion engine that recognizes user emotions, thereby improving matching accuracy.

[0954] Overall system overview

[0955] The system consists of the following main elements:

[0956] A terminal for user input

[0957] A server that receives and stores attribute information and emotion information

[0958] Server module that performs analysis using generative AI

[0959] Emotion engine that recognizes user emotions

[0960] A mechanism for notifying users of matching results

[0961] User Registration Process

[0962] User: A user accesses the system and enters their own attribute information, such as name, age, hobbies, preferences, interests, and place of residence.

[0963] Terminal: Checks the information entered by the user and prepares it for transmission. The emotion engine also collects the user's emotional information.

[0964] Receiving and storing data

[0965] Server: Receives user attribute information and emotion information sent from the device. The received information is saved in a database, with attribute information stored in the Users table and emotion information stored in the Emotions table.

[0966] Analysis of attribute information and emotional information

[0967] Server: Inputs the user's attribute information and emotional information from the database into the generation AI. The generation AI analyzes this data and finds the most suitable match candidate, taking into account the user's current emotional state and past emotional patterns.

[0968] For example, if a user is feeling stressed, the system will prioritize matching with users who have relaxing hobbies or who have the same hobbies and a calm personality.

[0969] Calculating match candidates

[0970] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[0971] The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[0972] Matching result generation and notification

[0973] Server: Based on the analysis results obtained from the generation AI, it generates appropriate matching results for the user. For example, "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[0974] Server: Generates notification data of the matching results and sends it to the corresponding user's device. Communication is performed using an encrypted protocol.

[0975] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Would you like to get in touch?"

[0976] Specific examples

[0977] In the case of Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, Mr. Tanaka enters his attribute information into the system, and the emotion engine collects his emotional information (for example, whether he is relaxed or stressed). As a result of the analysis, Mr. Sato, who has similar attribute information and is in the same emotional state, is found. The server notifies Mr. Tanaka that Mr. Sato is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[0978] As a result, the present invention takes into account the user's emotions, making it possible to provide connections that are suited to the user's psychological state, rather than just attribute information, thereby enabling the user to effectively build new connections.

[0979] The processing flow will be explained below.

[0980] Step 1: Collecting user input and sentiment

[0981] User: A new user accesses the system and enters their demographic information (such as name, age, hobbies, preferences, interests, and place of residence). During the registration process, they also interact with an interface that displays the user's current emotional state.

[0982] Terminal: Checks the attribute information entered by the user and the emotion information collected by the emotion engine, and prepares for transmission.

[0983] Step 2: Send data

[0984] Device: Sends request data including the entered attribute information and emotion information to the server. This transmission is triggered when the user presses the "Register" button.

[0985] Step 3: Receiving the Server

[0986] Server: Receives the user's attribute information and emotion information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[0987] Step 4: Saving to the Database

[0988] Server: Connects to the database and stores the received user attribute information in the Users table and emotion information in the Emotions table. This allows for centralized management of user information.

[0989] Step 5: Analysis by generative AI and emotion engine

[0990] Server: Obtains user attribute information and emotional information from the database and inputs it into the generation AI. The generation AI analyzes the user's attribute information and emotional information, taking into account their current emotional state and past emotional patterns, and finds suitable matching candidates.

[0991] For example, if a user is feeling stressed, the system is set to prioritize matching with users who have relaxing hobbies or who have the same hobbies and calm personalities.

[0992] Step 6: Calculating match candidates

[0993] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[0994] The AI ​​generator calculates the degree of similarity between users as a reliability score and selects matching candidates based on this. For example, it calculates the score based on information such as "User A and User B both like basketball and are currently relaxing."

[0995] Step 7: Generate matching results

[0996] Server: Based on the analysis results obtained from the generation AI, the server generates appropriate matching results for the target user. For example, it may organize the results as "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[0997] Step 8: Prepare for notification

[0998] Server: Generates notification data of the matching results and prepares to send it to the corresponding user's device. Communication is performed using an encryption protocol.

[0999] Step 9: Sending notifications

[1000] Server: Sends notification data to the user's device, including information about match candidates and their confidence scores.

[1001] Step 10: Receiving and Viewing Notifications

[1002] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may be displayed saying, "Sato also likes basketball and is currently relaxing. Why not get in touch?"

[1003] User: Check the matching results displayed on the device and take the next step (e.g., decide whether to contact them).

[1004] The above is the flow of specific processing steps in the invention incorporating an emotion engine. This system allows users to find the optimal connection based on their own emotional state.

[1005] Example 2

[1006] 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."

[1007] Conventional matching systems select match candidates based on user attribute information, but do not take the user's emotional state into consideration, resulting in insufficient matching accuracy. Matching that ignores the user's psychological state results in difficulty in obtaining results that are satisfactory to the user. The present invention aims to provide more accurate matching and improve user satisfaction by taking emotional information into consideration.

[1008] 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.

[1009] In this invention, the server includes a means for a user to input their own attribute information, a means for receiving the attribute information and emotion information and storing them in a database, and a means for analyzing the attribute information and emotion information using a generation AI, thereby enabling the selection of appropriate matching candidates taking into consideration the user's current emotional state and past emotion patterns.

[1010] "Attribute information" is information about personal characteristics and interests provided by a user, and specifically includes name, age, hobbies, preferences, interests, place of residence, and the like.

[1011] "Emotional Information" refers to information that indicates the user's current psychological and emotional state, including data collected through the Emotion Engine, such as whether they are relaxed or stressed.

[1012] An "emotion engine" is a software or hardware mechanism that analyzes a user's facial expressions, voice, input patterns, etc. to identify their emotional state.

[1013] "Generative AI" is an artificial intelligence model that analyzes input data and generates matching candidates and other results suitable for the user.

[1014] The "confidence score" is a numerical value calculated by the generation AI that indicates the degree of match between users, and is an index for evaluating the suitability of matching candidates.

[1015] The "database" is a digital storage system for storing and managing received user attribute information and emotion information.

[1016] A "terminal" is an electronic device, such as a computer device or smartphone, that a user uses to input and send information.

[1017] "Server" refers to the back-end computer system that processes and analyzes the received data, calculates the matching results, and notifies the user.

[1018] "Matching candidates" are potential contacts that may be suitable for the user, selected based on the analysis results of the generation AI.

[1019] The present invention is a system that uses a user's attribute information and emotional information to provide more appropriate and emotionally supportive match candidates, and its specific implementation method is described below. This system has the function of inputting and saving a user's attribute information and emotional information, analyzing that information, and notifying the user of the most suitable match candidates.

[1020] Key elements of the system

[1021] The system consists of the following main elements:

[1022] A terminal for user input

[1023] A server that receives and stores attribute information and emotion information

[1024] Server module that performs analysis using generative AI

[1025] Emotion engine that recognizes user emotions

[1026] A mechanism for notifying users of matching results

[1027] Enter user information

[1028] User: The user accesses the system and inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence, as well as their current emotional state through the emotion engine.

[1029] Device: The device temporarily stores the information entered by the user and sends it to the server when it is ready. The emotion engine analyzes the user's facial expressions and keyboard input to collect emotional information.

[1030] Data transmission and storage

[1031] Device: When the user clicks the send button, the device sends all input information, including attribute information and emotion information, to the server.

[1032] Server: The server stores the data received from the device in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[1033] Analysis of attribute information and emotional information

[1034] Server: The server extracts the user's attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model analyzes this data and finds the best match candidates, taking into account the user's current emotional state and past emotional patterns.

[1035] Example prompt sentence:

[1036] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[1037] Calculating match candidates

[1038] Server: The generative AI model analyzes the user's emotional patterns and attribute information based on the input information to find the best match. For example, the degree of match between users is calculated as a confidence score.

[1039] Matching result generation and notification

[1040] Server: Generates matching results including match candidates and reliability scores based on the results obtained from the generative AI model. For example, the result might be "For User A, the most suitable match candidate is User B (reliability 90%)."

[1041] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is secure using encryption protocols such as SSL / TLS.

[1042] Device: Receives the matching results sent from the server and displays them to the user in an easy-to-understand format. For example, a notification like "Mr. Tanaka, we've found user C who has the same hobbies as you. Would you like to get in touch?" is displayed.

[1043] As a result, the present invention provides highly accurate matching candidates that take the user's emotions into consideration, helping the user build appropriate connections.

[1044] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1045] Step 1:

[1046] The user inputs their own attribute information and emotional information.

[1047] User: Accesses the system and enters his / her demographic information, including name, age, hobbies, preferences, interests, and place of residence, and also answers a questionnaire to input his / her current emotional state.

[1048] Terminal: Checks the information entered by the user and temporarily stores the input data. The emotion engine analyzes the user's facial expressions and input speed to collect emotional information.

[1049] Input: User attribute information and emotion information

[1050] Output: Temporarily stored user information and analyzed emotion information

[1051] Step 2:

[1052] Data transmission and storage

[1053] Terminal: When the user clicks the submit button, the terminal sends all input information that was temporarily saved to the server.

[1054] Server: Receives data sent from the device and stores it in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[1055] Input: User attribute information and emotion information

[1056] Output: User information and emotion information stored in a database

[1057] Step 3:

[1058] Analysis of attribute information and emotional information

[1059] Server: Extracts user attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model receives this data via a prompt and begins analysis.

[1060] Input: User attribute information and emotion information stored in the database

[1061] Output: Input data and analysis results for the generative AI model

[1062] Specific prompt examples:

[1063] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[1064] Step 4:

[1065] Calculating match candidates

[1066] Server: The generative AI model analyzes the user's emotional patterns and attributes based on the input information to find the best match candidates. During this process, a confidence score is calculated.

[1067] Input: Input data to the generative AI model

[1068] Output: Match candidates with confidence scores

[1069] Step 5:

[1070] Matching result generation and notification

[1071] Server: Based on the analysis results obtained from the generative AI model, it generates matching results including match candidates and confidence scores.

[1072] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is securely performed using an encryption protocol.

[1073] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user.

[1074] Input: Analysis results and matching candidate information

[1075] Output: Matching results reported to the user

[1076] This allows users to find suitable matching candidates that suit their emotional state, while also protecting their privacy.

[1077] (Application example 2)

[1078] 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."

[1079] Conventional matching systems make recommendations based on user attribute information, but do not take the user's emotional state into consideration, making it difficult to make recommendations that are appropriate for the user's psychological state. Furthermore, there is a need for a method that effectively utilizes the user's emotional information to provide highly accurate matching candidates. Meanwhile, online shopping sites also do not recommend products that reflect the user's emotional state, limiting the extent to which they can improve user satisfaction. The present invention addresses these issues.

[1080] 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 means for a user to input their own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, means for collecting user emotion information, means for analyzing the emotion information and performing matching in conjunction with the attribute information, and means for inputting the attribute information and emotion information using a generation AI model and generating a prompt sentence. This enables highly accurate match candidates and product recommendations that reflect the user's emotional state.

[1081] "Attribute information" refers to information such as a user's hobbies, preferences, interests, and place of residence.

[1082] "Emotion information" is data that indicates the user's emotional state, and includes, for example, the degree of stress or relaxation.

[1083] "Generative AI" refers to artificial intelligence technology that analyzes a user's attribute information and emotional information to provide optimal matching candidates and product recommendations.

[1084] "Matching candidates" refer to other users or products that are suitable for the user, found based on the user's attribute information and emotional information.

[1085] A "prompt sentence" refers to a text-based input sentence used by the generation AI when performing analysis.

[1086] "Database" refers to an information management system for storing attribute information and emotion information.

[1087] "Emotion engine" refers to technology for collecting and analyzing user emotional information.

[1088] "Push notification" refers to a method for a system to send information to a user in real time.

[1089] The "confidence score" is a numerical evaluation of the degree of match between users, and is an index indicating the accuracy of a matching candidate.

[1090] The system for implementing this invention utilizes user attribute information and emotion information to provide optimal matching candidates and product recommendations. The configuration and operation of the system will be described in detail below.

[1091] System configuration

[1092] The system consists of the following elements:

[1093] 1. User terminal: A device used by the user for input, including smartphones.

[1094] 2. Server: The back-end system that manages the database and runs the generative AI and emotion engine.

[1095] 3. Database: A system for storing user attribute information and emotion information.

[1096] 4. Generative AI model: An artificial intelligence system that analyzes the user's attribute information and emotional information and generates prompt sentences.

[1097] 5. Emotion engine: A software module for collecting and analyzing user emotional information.

[1098] User Registration Process

[1099] Users access the system and input their own attribute information, such as their name, age, hobbies, preferences, interests, and place of residence. The user's device then checks the information and prepares for transmission. The emotion engine also collects the user's emotional information at this time.

[1100] Receiving and storing data

[1101] The server receives the user's attribute information and emotion information sent from the device. The received information is stored in a database, with the attribute information stored in the Users table and the emotion information stored in the Emotions table.

[1102] Analysis of attribute information and emotional information

[1103] The server inputs the user's attribute information and emotional information from the database into the generative AI model. The generative AI analyzes this data and finds the most suitable match candidates and products, taking into account the user's current emotional state and past emotional patterns. For example, if a user is feeling stressed, it will prioritize matching with other users who have relaxing hobbies or who have the same hobbies and calm personalities.

[1104] Calculating match candidates

[1105] The server calculates matching candidates for the user based on the analysis results from the generation AI. At this time, by taking into account not only attribute information but also emotional information, more accurate matching is possible. The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[1106] Matching result generation and notification

[1107] The server generates appropriate matching results for the user based on the analysis results obtained from the generation AI. For example, "The most suitable match candidate for User A is User B (reliability 90%)." The server generates notification data of the matching results and sends it to the corresponding user's device. Communication is carried out using an encrypted protocol. The user's device receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "We have found other users who share the same hobbies as you. Would you like to get in touch?"

[1108] Specific examples

[1109] Example: User A, whose hobbies are basketball and cooking and who lives in a large city, enters his or her own attribute information into the system, and the emotion engine collects User A's emotional information (for example, whether he or she is relaxed or stressed). As a result of the analysis, another User B is found who has similar attribute information and is in the same emotional state. The server notifies User A that User B is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[1110] Prompt Sentence Examples

[1111] An example of a prompt for a generative AI model is:

[1112] "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[1113] In this way, the present invention takes the user's emotions into consideration, and can provide connections and products that are suited to the user's psychological state, rather than just attribute information.

[1114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1115] Step 1:

[1116] The user enters attribute information

[1117] The user inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence. The terminal receives this information and prepares it to be sent to the database.

[1118] Input: Name, age, hobbies, preferences, interests, place of residence

[1119] Output: Attribute information saved on the device

[1120] Step 2:

[1121] Collecting emotional information

[1122] The camera and microphone on the user's device are used to collect information about the user's emotions, which are then analyzed by an emotion engine to identify states such as relaxed or stressed.

[1123] Input: User's facial image and voice data

[1124] Output: Analyzed emotional information (relaxed, stressed, etc.)

[1125] Step 3:

[1126] Receiving and storing data

[1127] The server receives the user's attribute information and emotion information transmitted from the terminal.

[1128] Input: attribute information, emotion information

[1129] Output: Attribute information and emotion information stored in the database

[1130] Step 4:

[1131] Analysis of attribute information and emotional information

[1132] The server sends attribute and emotional information from the database to the generative AI model, which analyzes this data and identifies the best match candidates and products, taking into account the user's current emotional state.

[1133] Input: Attribute information and emotion information stored in the database

[1134] Output: Analysis results (best matching candidates and products)

[1135] Step 5:

[1136] Generate prompt statement

[1137] The server uses a generative AI model to generate a prompt sentence and create a text-format input sentence for analysis.

[1138] Input: attribute information, emotion information

[1139] Output: Generated prompt statement

[1140] Step 6:

[1141] Calculating match candidates

[1142] Based on the analysis results of the generative AI model, the server calculates matching candidates and products, evaluating the degree of match between users as a reliability score.

[1143] Input: prompt statement

[1144] Output: Match candidates and products based on confidence scores

[1145] Step 7:

[1146] Notification of matching results

[1147] The server generates notification data of the matching results and sends it to the corresponding user's device. The communication uses an encrypted protocol. The device displays the matching results received in an easy-to-understand format for the user.

[1148] Input: Match candidates and products based on confidence scores

[1149] Output: Matching results and product recommendations displayed on the user's device

[1150] As a concrete example, a prompt for a generative AI model might look like this: "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[1151] 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.

[1152] 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.

[1153] 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.

[1154] [Fourth embodiment]

[1155] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1156] 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.

[1157] 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).

[1158] 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.

[1159] 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.

[1160] 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).

[1161] 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.

[1162] 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.

[1163] 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.

[1164] 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.

[1165] 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.

[1166] 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.

[1167] 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."

[1168] The present invention is a system that provides new connections to adults whose friendships become weaker as they age or change life stages. The system acquires and analyzes user attribute information to provide appropriate match candidates. The specific processing flow is explained below.

[1169] Overall system overview

[1170] The system uses the information users enter to match them with other users and provide them with appropriate connections. The system mainly consists of the following components:

[1171] A terminal for users to input attribute information

[1172] Server that receives and stores attribute information

[1173] A server module that uses generative AI to analyze attribute information

[1174] A mechanism for notifying users of matching results

[1175] User Registration Process

[1176] User: First, the user enters their demographic information (e.g., hobbies, preferences, interests, and location). This involves accessing the account creation page on the user's device and entering information in the required fields.

[1177] Terminal: The entered information is sent from the terminal to the server when the user presses the "Register" button.

[1178] Receiving and storing data

[1179] Server: The server receives the user attribute information sent from the device. The received information is saved in a database, and each attribute is classified and stored in the appropriate table.

[1180] Analysis and Matching

[1181] Server: Passes user information stored in the database to the generation AI, which analyzes the user's attribute information and calculates the most suitable match with other users.

[1182] For example, matching is done based on commonalities such as "User A and User B both like basketball and live in Tokyo."

[1183] The generation AI evaluates the degree of match of attribute information and calculates a confidence score, which is a numerical representation of the suitability of the match.

[1184] Notification of matching results

[1185] Server: Based on the matching results calculated by the generation AI, the server selects the optimal connection candidates for a specific user. This information is sent to the device.

[1186] Device: The user's device will notify the user based on the matching results received. For example, a notification such as "Mr. Tanaka, we have found Mr. Sato who has the same hobbies as you" will be displayed.

[1187] Specific examples

[1188] For example, Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, can enter his own attribute information into the system, and the generative AI will analyze and match him with users who have similar attribute information.

[1189] As a result of the analysis, a user named Sato who also shares the same hobbies of basketball and cooking and lives in the same area is found.

[1190] The server notifies Tanaka that Sato is a potential match, and the notification also displays a trust score, making it easier for users to make new connections.

[1191] This allows the user to effectively find new connections that match their interests and preferences. Furthermore, because the entire system is automated, the burden on the user is reduced and convenience is greatly improved.

[1192] The processing flow will be explained below.

[1193] Step 1: User Input

[1194] User: A new user accesses the system and enters their attribute information on the account creation screen, such as name, age, hobbies, preferences, interests, and place of residence.

[1195] Terminal: Checks the information entered by the user and prepares it for transmission.

[1196] Step 2: Send data

[1197] Terminal: Sends the request data including the entered attribute information to the server. This transmission is triggered when the user presses the "Register" button.

[1198] Step 3: Receiving the Server

[1199] Server: Receives user attribute information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[1200] Step 4: Saving to the Database

[1201] Server: Connects to the database and stores the received user attribute information in the corresponding tables. For example, store basic information in the Users table and information about hobbies and interests in the Interests table.

[1202] Step 5: Analysis by generative AI

[1203] Server: Retrieves user information from the database and inputs it into the generation AI, which analyzes the data and finds suitable match candidates based on each user's hobbies, preferences, place of residence, etc.

[1204] Step 6: Calculating match candidates

[1205] Server: The generation AI goes through a process to calculate matching candidates. Specifically, it calculates the degree of match between users as a reliability score based on similarities in hobbies, proximity of residence, etc.

[1206] Step 7: Generate matching results

[1207] Server: Creates matching results for users based on the analysis results obtained from the generation AI. For example, it may organize the results as "The most suitable match candidate for User A is User B (reliability 90%)."

[1208] Step 8: Prepare for notification

[1209] Server: Generates notification data for the matching results and prepares to send it to the corresponding user's device.

[1210] Step 9: Sending notifications

[1211] Server: Sends notification data to the user's device. Sends it securely using a communication encryption protocol.

[1212] Step 10: Receiving and Viewing Notifications

[1213] Terminal: Receives the matching results sent from the server, analyzes the received data, and displays it in a user-friendly format.

[1214] User: Check the matching results displayed on the device. For example, a notification will appear saying, "We've found a user with similar interests. Would you like to get in touch?"

[1215] The above is the specific flow of program processing on the system.

[1216] Example 1

[1217] 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."

[1218] Conventional friendship maintenance systems have been insufficient in providing effective means for adults whose friendships become weaker as they age and change in life stages to create new connections. Furthermore, the process of entering attribute information was cumbersome, and matching accuracy was low, making it difficult to increase user satisfaction. This resulted in a lack of convenience and motivation for building new friendships.

[1219] 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.

[1220] In this invention, the server includes a means for a user to input his / her own attribute information, a means for receiving the attribute information and storing it in a database, a means for analyzing the attribute information and attribute information of other users using a generative AI model, a means for inputting a prompt sentence to the generative AI model to calculate optimal matching candidates, and a means for notifying the user of the matching candidates together with their reliability scores. This makes it possible to analyze the user's attribute information with high accuracy, realize optimal matching with other users who share common hobbies and interests, and effectively build new friendships.

[1221] A "user" is someone who uses the system to input their own attribute information and seeks to be matched with other users.

[1222] "Attribute information" refers to information about an individual, such as hobbies, preferences, interests, and place of residence, entered by the user.

[1223] A "database" is a collection of information that stores received user attribute information and is used for analysis.

[1224] A "generative AI model" is an artificial intelligence model that analyzes user attribute information and derives the most suitable matching candidates.

[1225] A "prompt sentence" is a text-based command sentence that is input into the generative AI model and includes attribute information of the user to be analyzed.

[1226] "Matching Candidates" means other users analyzed by the generative AI model and recommended to the user as suitable connections.

[1227] The "confidence score" is a numerical value that indicates the suitability of a match calculated by the generative AI model, and is an index for evaluating the reliability of a matching candidate.

[1228] "Notification" refers to the means or process used to communicate information about potential matches to a user.

[1229] The present invention is a system that allows users to input their own attribute information, match with other users, and provide new connections. This system is implemented using a terminal, a server, and a generative AI model.

[1230] First, the user enters attribute information using a device, which can be a smartphone, tablet, or PC. The user accesses a dedicated application or web page and enters information such as hobbies, preferences, interests, and place of residence.

[1231] Next, the device sends the entered attribute information to the server, which receives it as an HTTP POST request and receives it in a standard data format such as JSON.

[1232] The server stores the received attribute information in a database. The database has a mechanism for appropriately classifying and storing attribute information for each user. Cloud services such as AWS (Amazon Web Services), Google Cloud Platform, and Microsoft Azure can be used to store the data.

[1233] The stored attribute information is analyzed using a generative AI model. Examples of generative AI models used include GPT-4 (OpenAI) and BERT (Google). Analysis is initiated by entering a prompt into this model. Examples of prompts include the following:

[1234] User Attribute Information:

[1235] User ID: Tanaka

[1236] Hobbies: Basketball, cooking

[1237] Place of residence: Tokyo

[1238] Use this information to analyze potential matches.

[1239] The generative AI model analyzes this prompt to find other users with common hobbies and interests. The analysis results in the calculation of the best match candidates and their confidence scores. The confidence score is a numerical value that indicates the suitability of the match and is used to evaluate the degree of match with the user.

[1240] Finally, the server sends the calculated matching results to the user's device. The device receives the results and notifies the user. The notification displays specific information such as, "Mr. Tanaka, you have found Mr. Sato, who has the same hobbies. Trust score: 95."

[1241] For example, after Mr. Tanaka uses his smartphone to input attribute information such as "basketball, cooking, Tokyo," the generative AI model analyzes this information and finds a user named Mr. Sato who shares the same hobbies. The server then notifies Mr. Tanaka's smartphone of the results, allowing him to take specific actions to build new friendships.

[1242] This allows users to effectively find new connections that match their hobbies and preferences. The present invention supports new connections by performing advanced analysis based on information entered by the user and providing optimal matching candidates.

[1243] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1244] Step 1: User enters attribute information

[1245] Users access the system's account creation page using a device such as a smartphone or PC. They enter information such as hobbies, preferences, interests, and place of residence. The entered information (e.g., "basketball, cooking, Tokyo") is stored in the account creation form.

[1246] Input: Attribute information entered by the user in the input form

[1247] Output: Attribute information stored in the input form

[1248] Step 2: Send attribute information

[1249] When the user presses the "Register" button, the device sends the entered attribute information to the server. When sending, the data is sent in JSON format via an HTTP POST request.

[1250] Input: Attribute information stored in the input form

[1251] Output: JSON formatted attribute information sent to the server

[1252] Step 3: Receiving and storing attribute information

[1253] The server receives the attribute information sent from the terminal and stores it in a database. The received information is inserted into the database using an SQL query.

[1254] Input: JSON formatted attribute information sent to the server

[1255] Output: Attribute information stored in a database

[1256] Specific behavior:

[1257] The server parses the POST request, extracts the JSON data, converts it into a SQL query, and inserts it into the database.

[1258] Step 4: Analysis of attribute information by generative AI

[1259] The server generates a prompt to pass the user's attribute information stored in the database to the generative AI model, which then analyzes the attribute information based on the prompt and finds the best matching candidate.

[1260] Input: Attribute information stored in the database

[1261] Output: Match candidates and confidence scores as analysis results

[1262] Specific behavior:

[1263] The server generates a prompt sentence and inputs it into the generative AI model, which analyzes the attribute information and calculates matching candidates and their confidence scores.

[1264] Step 5: Generate matching results

[1265] The generative AI model outputs the analysis results and returns them to the server, which receives the analysis results and generates a list of matching candidates.

[1266] Input: Prompt sentences and saved attribute information entered into the AI ​​model

[1267] Output: A list of potential matches and their confidence scores

[1268] Specific behavior:

[1269] The server retrieves the analysis results and compiles them into a list, which includes the IDs of the matching users, common interests, and a trust score.

[1270] Step 6: Submit your match results

[1271] The server then sends the generated matching results to the user's device, usually as an HTTP response, in JSON format.

[1272] Input: Matching results stored on the server

[1273] Output: Matching results sent to the user's device

[1274] Specific behavior:

[1275] The server converts the matching results into JSON format and sends them to the user's device as an HTTP response.

[1276] Step 7: Viewing the matching results

[1277] The user's device will then notify them based on the matching results received from the server. Specifically, a pop-up notification will be displayed saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Trust score: 95."

[1278] Input: Matching results sent from the server

[1279] Output: The notification displayed to the user

[1280] Specific behavior:

[1281] The device parses the received JSON data and displays a notification pop-up on the screen. The user can then confirm the notification and take action to establish a new friendship.

[1282] (Application example 1)

[1283] 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."

[1284] In modern society, as people become adults, friendships tend to weaken as they age and their life stages change. In addition, it is not easy to form new connections with people from the same area or who share the same hobbies. Furthermore, physical stores have limited means of effectively forming and maintaining communities for their customers. This creates a challenge: there are few opportunities to build new relationships between individuals. Furthermore, it is difficult for individual users to form new friendships through participating in events.

[1285] 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.

[1286] In this invention, the server includes means for a user to input his or her own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, and means for acquiring event information at physical stores and providing that information to the user. This enables users to automatically match with other users who share the same hobbies, preferences, and place of residence, and to build new friendships through participation in events held at physical stores.

[1287] A "user" is an individual who accesses the system and enters their own attribute information.

[1288] "Attribute information" is information that a user inputs into the system, and includes hobbies, preferences, interests, place of residence, and the like.

[1289] "Receiving" is the act of transferring attribute information entered by the user from the terminal to the server.

[1290] "Database" refers to a storage device within the system that stores and manages received attribute information.

[1291] "Generative AI" is an artificial intelligence technology that analyzes a user's attribute information and calculates suitable matching candidates with other users.

[1292] "Analysis" is the process of using generative AI to evaluate attribute information and calculate commonalities and similarities between users.

[1293] "Matching candidates" are other users who are selected based on the analysis results and who may have friendships with a particular user.

[1294] "Notification" refers to the act of sending calculated match candidates and event information to the user's device to inform them.

[1295] A "physical store" is a physical commercial facility where customers can visit in person to enjoy products and services.

[1296] "Event information" is detailed information about social activities and events held at physical stores, and is provided to users.

[1297] The system that realizes this application example consists of the following components: a device where the user inputs their own attribute information, a server that receives the attribute information and stores it in a database, a module that uses generative AI to analyze the user's attribute information, a system that calculates appropriate matching candidates and notifies the user, and a mechanism that obtains event information from physical stores and provides it to the user.

[1298] Users use their smartphones to input attribute information, such as hobbies, preferences, interests, and place of residence. Once the user inputs the information and presses the registration button, the smartphone sends the information to the server.

[1299] The server is built using the Flask framework and has the ability to store attribute information received from users in an SQLite database, which is then used for analysis by the generative AI.

[1300] The generation AI is implemented in Python and is responsible for analyzing user attribute information. Specifically, it calculates commonalities and similarities with other users based on information such as each user's hobbies, interests, and place of residence. The generation AI calculates the similarity between each user as a reliability score and selects other users with high scores as matching candidates.

[1301] The server notifies the user of the matching results based on the matching candidates calculated by the generation AI. The notification is displayed on the user's smartphone in the form of "A user with the same hobbies has been found." The server also obtains information about events held at physical stores and provides it to the user. This allows users to build new friendships through actual events.

[1302] As a concrete example, consider the case where a user enters the attribute information "My hobbies are basketball and cooking, and I live in Tokyo." The server receives this information and stores it in a database. The generation AI analyzes the attribute information and finds other users who live in Tokyo and also like basketball and cooking. The server then notifies the user of the matching results and also provides information about basketball-related events at physical stores.

[1303] Examples of prompt sentences to input to a generative AI model include the following:

[1304] "We want to create a system that matches users with the best possible friends based on their hobbies and location. User information includes the following attributes:

[1305] name

[1306] hobby

[1307] place of residence

[1308] After registration, there needs to be a function that automatically finds other users who share the same hobbies and location and displays matching results.

[1309] In this way, the present invention provides a system that supports users in easily building new friendships.

[1310] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1311] Step 1:

[1312] User enters attribute information

[1313] The user uses their smartphone to enter attribute information such as name, hobbies, preferences, interests, and place of residence into the application form. Once the input is complete, the user presses the "Register" button. This input data is sent to the server by the application. The input data includes attribute information in text format.

[1314] Step 2:

[1315] The server receives and stores the attribute information

[1316] The server receives the attribute information sent from the device. The server uses the Flask framework to receive this information and store it in an SQLite database. As part of the processing, the received data is sorted into the appropriate table and an insert operation is performed. The input data is received in JSON format, and as output, a new record is added to the database.

[1317] Step 3:

[1318] Generative AI analyzes attribute information

[1319] The server passes the attribute information of multiple users stored in the database to the generation AI. The generation AI is implemented in Python and calculates commonalities and similarities with other users based on information such as each user's hobbies, preferences, interests, and place of residence. The input data is the user's attribute information obtained from the database, and the output data is a reliability score indicating the degree of similarity between each user.

[1320] Step 4:

[1321] Calculating match candidates

[1322] The server selects appropriate match candidates based on the reliability score calculated by the generation AI. The input data is the reliability score from the generation AI, and the output data is a list of optimal match candidates. The server prioritizes users with high reliability scores and lists them as match candidates.

[1323] Step 5:

[1324] Notify users of matching results

[1325] The server notifies the user of the calculated match candidates. The matching results are sent as a notification to the user's smartphone. The input data is a list of match candidates, and the output data is a notification message displayed on the user's smartphone. The notification message contains details of the match candidates and their confidence scores.

[1326] Step 6:

[1327] Acquire and provide information about events at physical stores

[1328] The server also retrieves information about events held at physical stores and provides it to users. This information is updated periodically and stored in a database. When a user requests event information from the application, the server sends the latest event information to the user. The input data is a request for event information, and the output data is detailed information about the event. The event information is displayed on the user's smartphone, allowing the user to decide whether or not to attend the event.

[1329] In this way, by combining the processes performed at each step, users can easily build new friendships and deepen their interactions through events at physical stores.

[1330] 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.

[1331] This invention is a system that uses user attribute information and emotional information to provide more appropriate and emotionally supported match candidates. In addition to the basic function of inputting and saving user attribute information and analyzing that information to perform matching, this system also incorporates an emotion engine that recognizes user emotions, thereby improving matching accuracy.

[1332] Overall system overview

[1333] The system consists of the following main elements:

[1334] A terminal for user input

[1335] A server that receives and stores attribute information and emotion information

[1336] Server module that performs analysis using generative AI

[1337] Emotion engine that recognizes user emotions

[1338] A mechanism for notifying users of matching results

[1339] User Registration Process

[1340] User: A user accesses the system and enters their own attribute information, such as name, age, hobbies, preferences, interests, and place of residence.

[1341] Terminal: Checks the information entered by the user and prepares it for transmission. The emotion engine also collects the user's emotional information.

[1342] Receiving and storing data

[1343] Server: Receives user attribute information and emotion information sent from the device. The received information is saved in a database, with attribute information stored in the Users table and emotion information stored in the Emotions table.

[1344] Analysis of attribute information and emotional information

[1345] Server: Inputs the user's attribute information and emotional information from the database into the generation AI. The generation AI analyzes this data and finds the most suitable match candidate, taking into account the user's current emotional state and past emotional patterns.

[1346] For example, if a user is feeling stressed, the system will prioritize matching with users who have relaxing hobbies or who have the same hobbies and a calm personality.

[1347] Calculating match candidates

[1348] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[1349] The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[1350] Matching result generation and notification

[1351] Server: Based on the analysis results obtained from the generation AI, it generates appropriate matching results for the user. For example, "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[1352] Server: Generates notification data of the matching results and sends it to the corresponding user's device. Communication is performed using an encrypted protocol.

[1353] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "Mr. Tanaka, we've found Mr. Sato who has the same hobbies as you. Would you like to get in touch?"

[1354] Specific examples

[1355] In the case of Mr. Tanaka, whose hobbies are basketball and cooking and who lives in Tokyo, Mr. Tanaka enters his attribute information into the system, and the emotion engine collects his emotional information (for example, whether he is relaxed or stressed). As a result of the analysis, Mr. Sato, who has similar attribute information and is in the same emotional state, is found. The server notifies Mr. Tanaka that Mr. Sato is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[1356] As a result, the present invention takes into account the user's emotions, making it possible to provide connections that are suited to the user's psychological state, rather than just attribute information, thereby enabling the user to effectively build new connections.

[1357] The processing flow will be explained below.

[1358] Step 1: Collecting user input and sentiment

[1359] User: A new user accesses the system and enters their demographic information (such as name, age, hobbies, preferences, interests, and place of residence). During the registration process, they also interact with an interface that displays the user's current emotional state.

[1360] Terminal: Checks the attribute information entered by the user and the emotion information collected by the emotion engine, and prepares for transmission.

[1361] Step 2: Send data

[1362] Device: Sends request data including the entered attribute information and emotion information to the server. This transmission is triggered when the user presses the "Register" button.

[1363] Step 3: Receiving the Server

[1364] Server: Receives the user's attribute information and emotion information sent from the device. First, it checks the integrity of the received data and converts it into the required format.

[1365] Step 4: Saving to the Database

[1366] Server: Connects to the database and stores the received user attribute information in the Users table and emotion information in the Emotions table. This allows for centralized management of user information.

[1367] Step 5: Analysis by generative AI and emotion engine

[1368] Server: Obtains user attribute information and emotional information from the database and inputs it into the generation AI. The generation AI analyzes the user's attribute information and emotional information, taking into account their current emotional state and past emotional patterns, and finds suitable matching candidates.

[1369] For example, if a user is feeling stressed, the system is set to prioritize matching with users who have relaxing hobbies or who have the same hobbies and calm personalities.

[1370] Step 6: Calculating match candidates

[1371] Server: The generation AI calculates matching candidates for the user based on the analysis results. At this time, not only attribute information but also emotional information is taken into account, enabling more accurate matching.

[1372] The AI ​​generator calculates the degree of similarity between users as a reliability score and selects matching candidates based on this. For example, it calculates the score based on information such as "User A and User B both like basketball and are currently relaxing."

[1373] Step 7: Generate matching results

[1374] Server: Based on the analysis results obtained from the generation AI, the server generates appropriate matching results for the target user. For example, it may organize the results as "The most suitable match candidate for User A is User B (with a reliability of 90%)."

[1375] Step 8: Prepare for notification

[1376] Server: Generates notification data of the matching results and prepares to send it to the corresponding user's device. Communication is performed using an encryption protocol.

[1377] Step 9: Sending notifications

[1378] Server: Sends notification data to the user's device, including information about match candidates and their confidence scores.

[1379] Step 10: Receiving and Viewing Notifications

[1380] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may be displayed saying, "Sato also likes basketball and is currently relaxing. Why not get in touch?"

[1381] User: Check the matching results displayed on the device and take the next step (e.g., decide whether to contact them).

[1382] The above is the flow of specific processing steps in the invention incorporating an emotion engine. This system allows users to find the optimal connection based on their own emotional state.

[1383] Example 2

[1384] 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."

[1385] Conventional matching systems select match candidates based on user attribute information, but do not take the user's emotional state into consideration, resulting in insufficient matching accuracy. Matching that ignores the user's psychological state results in difficulty in obtaining results that are satisfactory to the user. The present invention aims to provide more accurate matching and improve user satisfaction by taking emotional information into consideration.

[1386] 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.

[1387] In this invention, the server includes a means for a user to input their own attribute information, a means for receiving the attribute information and emotion information and storing them in a database, and a means for analyzing the attribute information and emotion information using a generation AI, thereby enabling the selection of appropriate matching candidates taking into consideration the user's current emotional state and past emotion patterns.

[1388] "Attribute information" is information about personal characteristics and interests provided by a user, and specifically includes name, age, hobbies, preferences, interests, place of residence, and the like.

[1389] "Emotional Information" refers to information that indicates the user's current psychological and emotional state, including data collected through the Emotion Engine, such as whether they are relaxed or stressed.

[1390] An "emotion engine" is a software or hardware mechanism that analyzes a user's facial expressions, voice, input patterns, etc. to identify their emotional state.

[1391] "Generative AI" is an artificial intelligence model that analyzes input data and generates matching candidates and other results suitable for the user.

[1392] The "confidence score" is a numerical value calculated by the generation AI that indicates the degree of match between users, and is an index for evaluating the suitability of matching candidates.

[1393] The "database" is a digital storage system for storing and managing received user attribute information and emotion information.

[1394] A "terminal" is an electronic device, such as a computer device or smartphone, that a user uses to input and send information.

[1395] "Server" refers to the back-end computer system that processes and analyzes the received data, calculates the matching results, and notifies the user.

[1396] "Matching candidates" are potential contacts that may be suitable for the user, selected based on the analysis results of the generation AI.

[1397] The present invention is a system that uses a user's attribute information and emotional information to provide more appropriate and emotionally supportive match candidates, and its specific implementation method is described below. This system has the function of inputting and saving a user's attribute information and emotional information, analyzing that information, and notifying the user of the most suitable match candidates.

[1398] Key elements of the system

[1399] The system consists of the following main elements:

[1400] A terminal for user input

[1401] A server that receives and stores attribute information and emotion information

[1402] Server module that performs analysis using generative AI

[1403] Emotion engine that recognizes user emotions

[1404] A mechanism for notifying users of matching results

[1405] Enter user information

[1406] User: The user accesses the system and inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence, as well as their current emotional state through the emotion engine.

[1407] Device: The device temporarily stores the information entered by the user and sends it to the server when it is ready. The emotion engine analyzes the user's facial expressions and keyboard input to collect emotional information.

[1408] Data transmission and storage

[1409] Device: When the user clicks the send button, the device sends all input information, including attribute information and emotion information, to the server.

[1410] Server: The server stores the data received from the device in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[1411] Analysis of attribute information and emotional information

[1412] Server: The server extracts the user's attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model analyzes this data and finds the best match candidates, taking into account the user's current emotional state and past emotional patterns.

[1413] Example prompt sentence:

[1414] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[1415] Calculating match candidates

[1416] Server: The generative AI model analyzes the user's emotional patterns and attribute information based on the input information to find the best match. For example, the degree of match between users is calculated as a confidence score.

[1417] Matching result generation and notification

[1418] Server: Generates matching results including match candidates and reliability scores based on the results obtained from the generative AI model. For example, the result might be "For User A, the most suitable match candidate is User B (reliability 90%)."

[1419] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is secure using encryption protocols such as SSL / TLS.

[1420] Device: Receives the matching results sent from the server and displays them to the user in an easy-to-understand format. For example, a notification like "Mr. Tanaka, we've found user C who has the same hobbies as you. Would you like to get in touch?" is displayed.

[1421] As a result, the present invention provides highly accurate matching candidates that take the user's emotions into consideration, helping the user build appropriate connections.

[1422] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1423] Step 1:

[1424] The user inputs their own attribute information and emotional information.

[1425] User: Accesses the system and enters his / her demographic information, including name, age, hobbies, preferences, interests, and place of residence, and also answers a questionnaire to input his / her current emotional state.

[1426] Terminal: Checks the information entered by the user and temporarily stores the input data. The emotion engine analyzes the user's facial expressions and input speed to collect emotional information.

[1427] Input: User attribute information and emotion information

[1428] Output: Temporarily stored user information and analyzed emotion information

[1429] Step 2:

[1430] Data transmission and storage

[1431] Terminal: When the user clicks the submit button, the terminal sends all input information that was temporarily saved to the server.

[1432] Server: Receives data sent from the device and stores it in a database. Specifically, attribute information is stored in the Users table, and emotion information is stored in the Emotions table.

[1433] Input: User attribute information and emotion information

[1434] Output: User information and emotion information stored in a database

[1435] Step 3:

[1436] Analysis of attribute information and emotional information

[1437] Server: Extracts user attribute information and emotional information from the database and inputs it into the generative AI model. The generative AI model receives this data via a prompt and begins analysis.

[1438] Input: User attribute information and emotion information stored in the database

[1439] Output: Input data and analysis results for the generative AI model

[1440] Specific prompt examples:

[1441] Find the best match for Tanaka based on his profile and emotional information. Tanaka's profile: Age: 30, Hobbies: Basketball, Cooking, Location: Tokyo. Emotional state: Relaxed. Please also show the candidate's profile and trust score.

[1442] Step 4:

[1443] Calculating match candidates

[1444] Server: The generative AI model analyzes the user's emotional patterns and attributes based on the input information to find the best match candidates. During this process, a confidence score is calculated.

[1445] Input: Input data to the generative AI model

[1446] Output: Match candidates with confidence scores

[1447] Step 5:

[1448] Matching result generation and notification

[1449] Server: Based on the analysis results obtained from the generative AI model, it generates matching results including match candidates and confidence scores.

[1450] Server: Generates notification data of the matching results and sends the notification data to the corresponding user's device. This communication is securely performed using an encryption protocol.

[1451] Device: Receives the matching results sent from the server and displays them in an easy-to-understand manner for the user.

[1452] Input: Analysis results and matching candidate information

[1453] Output: Matching results reported to the user

[1454] This allows users to find suitable matching candidates that suit their emotional state, while also protecting their privacy.

[1455] (Application example 2)

[1456] 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."

[1457] Conventional matching systems make recommendations based on user attribute information, but do not take the user's emotional state into consideration, making it difficult to make recommendations that are appropriate for the user's psychological state. Furthermore, there is a need for a method that effectively utilizes the user's emotional information to provide highly accurate matching candidates. Meanwhile, online shopping sites also do not recommend products that reflect the user's emotional state, limiting the extent to which they can improve user satisfaction. The present invention addresses these issues.

[1458] 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 means for a user to input their own attribute information, means for receiving the attribute information and storing it in a database, means for analyzing the attribute information and attribute information of other users using a generation AI, means for calculating appropriate match candidates based on the analysis results, means for notifying the user of the match candidates, means for collecting user emotion information, means for analyzing the emotion information and performing matching in conjunction with the attribute information, and means for inputting the attribute information and emotion information using a generation AI model and generating a prompt sentence. This enables highly accurate match candidates and product recommendations that reflect the user's emotional state.

[1459] "Attribute information" refers to information such as a user's hobbies, preferences, interests, and place of residence.

[1460] "Emotion information" is data that indicates the user's emotional state, and includes, for example, the degree of stress or relaxation.

[1461] "Generative AI" refers to artificial intelligence technology that analyzes a user's attribute information and emotional information to provide optimal matching candidates and product recommendations.

[1462] "Matching candidates" refer to other users or products that are suitable for the user, found based on the user's attribute information and emotional information.

[1463] A "prompt sentence" refers to a text-based input sentence used by the generation AI when performing analysis.

[1464] "Database" refers to an information management system for storing attribute information and emotion information.

[1465] "Emotion engine" refers to technology for collecting and analyzing user emotional information.

[1466] "Push notification" refers to a method for a system to send information to a user in real time.

[1467] The "confidence score" is a numerical evaluation of the degree of match between users, and is an index indicating the accuracy of a matching candidate.

[1468] The system for implementing this invention utilizes user attribute information and emotion information to provide optimal matching candidates and product recommendations. The configuration and operation of the system will be described in detail below.

[1469] System configuration

[1470] The system consists of the following elements:

[1471] 1. User terminal: A device used by the user for input, including smartphones.

[1472] 2. Server: The back-end system that manages the database and runs the generative AI and emotion engine.

[1473] 3. Database: A system for storing user attribute information and emotion information.

[1474] 4. Generative AI model: An artificial intelligence system that analyzes the user's attribute information and emotional information and generates prompt sentences.

[1475] 5. Emotion engine: A software module for collecting and analyzing user emotional information.

[1476] User Registration Process

[1477] Users access the system and input their own attribute information, such as their name, age, hobbies, preferences, interests, and place of residence. The user's device then checks the information and prepares for transmission. The emotion engine also collects the user's emotional information at this time.

[1478] Receiving and storing data

[1479] The server receives the user's attribute information and emotion information sent from the device. The received information is stored in a database, with the attribute information stored in the Users table and the emotion information stored in the Emotions table.

[1480] Analysis of attribute information and emotional information

[1481] The server inputs the user's attribute information and emotional information from the database into the generative AI model. The generative AI analyzes this data and finds the most suitable match candidates and products, taking into account the user's current emotional state and past emotional patterns. For example, if a user is feeling stressed, it will prioritize matching with other users who have relaxing hobbies or who have the same hobbies and calm personalities.

[1482] Calculating match candidates

[1483] The server calculates matching candidates for the user based on the analysis results from the generation AI. At this time, by taking into account not only attribute information but also emotional information, more accurate matching is possible. The generation AI calculates the degree of match between users as a reliability score and selects matching candidates based on this.

[1484] Matching result generation and notification

[1485] The server generates appropriate matching results for the user based on the analysis results obtained from the generation AI. For example, "The most suitable match candidate for User A is User B (reliability 90%)." The server generates notification data of the matching results and sends it to the corresponding user's device. Communication is carried out using an encrypted protocol. The user's device receives the matching results sent from the server and displays them in an easy-to-understand manner for the user. For example, a notification may appear saying, "We have found other users who share the same hobbies as you. Would you like to get in touch?"

[1486] Specific examples

[1487] Example: User A, whose hobbies are basketball and cooking and who lives in a large city, enters his or her own attribute information into the system, and the emotion engine collects User A's emotional information (for example, whether he or she is relaxed or stressed). As a result of the analysis, another User B is found who has similar attribute information and is in the same emotional state. The server notifies User A that User B is a potential match. At this time, additional information such as "we share a relaxing hobby" is also provided.

[1488] Prompt Sentence Examples

[1489] An example of a prompt for a generative AI model is:

[1490] "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[1491] In this way, the present invention takes the user's emotions into consideration, and can provide connections and products that are suited to the user's psychological state, rather than just attribute information.

[1492] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1493] Step 1:

[1494] The user enters attribute information

[1495] The user inputs their own attribute information, including name, age, hobbies, preferences, interests, and place of residence. The terminal receives this information and prepares it to be sent to the database.

[1496] Input: Name, age, hobbies, preferences, interests, place of residence

[1497] Output: Attribute information saved on the device

[1498] Step 2:

[1499] Collecting emotional information

[1500] The camera and microphone on the user's device are used to collect information about the user's emotions, which are then analyzed by an emotion engine to identify states such as relaxed or stressed.

[1501] Input: User's facial image and voice data

[1502] Output: Analyzed emotional information (relaxed, stressed, etc.)

[1503] Step 3:

[1504] Receiving and storing data

[1505] The server receives the user's attribute information and emotion information transmitted from the terminal.

[1506] Input: attribute information, emotion information

[1507] Output: Attribute information and emotion information stored in the database

[1508] Step 4:

[1509] Analysis of attribute information and emotional information

[1510] The server sends attribute and emotional information from the database to the generative AI model, which analyzes this data and identifies the best match candidates and products, taking into account the user's current emotional state.

[1511] Input: Attribute information and emotion information stored in the database

[1512] Output: Analysis results (best matching candidates and products)

[1513] Step 5:

[1514] Generate prompt statement

[1515] The server uses a generative AI model to generate a prompt sentence and create a text-format input sentence for analysis.

[1516] Input: attribute information, emotion information

[1517] Output: Generated prompt statement

[1518] Step 6:

[1519] Calculating match candidates

[1520] Based on the analysis results of the generative AI model, the server calculates matching candidates and products, evaluating the degree of match between users as a reliability score.

[1521] Input: prompt statement

[1522] Output: Match candidates and products based on confidence scores

[1523] Step 7:

[1524] Notification of matching results

[1525] The server generates notification data of the matching results and sends it to the corresponding user's device. The communication uses an encrypted protocol. The device displays the matching results received in an easy-to-understand format for the user.

[1526] Input: Match candidates and products based on confidence scores

[1527] Output: Matching results and product recommendations displayed on the user's device

[1528] As a concrete example, a prompt for a generative AI model might look like this: "User A is 30 years old, lives in a big city, and his hobbies are cooking and watching movies. According to the sentiment analysis engine, he is currently in a relaxed state. Based on this information, please recommend the best match candidate for User A."

[1529] 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.

[1530] 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.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] 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.

[1535] 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).

[1536] 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.

[1537] 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."

[1538] 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.

[1539] 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).

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] 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.

[1545] 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.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] The following is further disclosed regarding the above embodiment.

[1551] (Claim 1)

[1552] A means for a user to input his / her own attribute information;

[1553] means for receiving the attribute information and storing it in a database;

[1554] A means for analyzing the attribute information and attribute information of other users using a generation AI;

[1555] means for calculating suitable matching candidates based on the analysis results;

[1556] means for notifying a user of the match candidates;

[1557] A system including:

[1558] (Claim 2)

[1559] 2. The system according to claim 1, wherein the attribute information includes hobbies, preferences, interests, and place of residence.

[1560] (Claim 3)

[1561] 2. The system according to claim 1, wherein the calculation of the matching candidates includes calculating a degree of match between users as a reliability score.

[1562] "Example 1"

[1563] (Claim 1)

[1564] A means for a user to input his / her own attribute information;

[1565] means for receiving the attribute information and storing it in a database;

[1566] A means for analyzing the attribute information and attribute information of other users using a generative AI model;

[1567] A means for inputting a prompt sentence into the generative AI model and calculating an optimal matching candidate;

[1568] means for notifying a user of the match candidates along with their confidence scores;

[1569] A system including:

[1570] (Claim 2)

[1571] 2. The system according to claim 1, wherein the attribute information includes hobbies, preferences, interests, and place of residence.

[1572] (Claim 3)

[1573] 2. The system according to claim 1, wherein the calculation of the matching candidates includes calculating a degree of match between users as a reliability score.

[1574] "Application Example 1"

[1575] (Claim 1)

[1576] A means for a user to input his / her own attribute information;

[1577] means for receiving the attribute information and storing it in a database;

[1578] A means for analyzing the attribute information and attribute information of other users using a generation AI;

[1579] means for calculating suitable matching candidates based on the analysis results;

[1580] means for notifying a user of the match candidates;

[1581] A means for acquiring event information at a physical store and providing the information to a user;

[1582] A system including:

[1583] (Claim 2)

[1584] 2. The system according to claim 1, wherein the attribute information includes hobbies, preferences, interests, and place of residence.

[1585] (Claim 3)

[1586] 2. The system according to claim 1, wherein the calculation of the matching candidates includes calculating a degree of match between users as a reliability score.

[1587] "Example 2: Combining Emotion Engines"

[1588] (Claim 1)

[1589] A means for a user to input his / her own attribute information;

[1590] means for receiving the attribute information and emotion information and storing them in a database;

[1591] A means for analyzing the attribute information and emotion information using a generation AI;

[1592] a means for calculating appropriate matching candidates based on the analysis results, taking into consideration the user's current emotional state and past emotional patterns;

[1593] means for notifying a user of the match candidates;

[1594] A system including:

[1595] (Claim 2)

[1596] 2. The system according to claim 1, wherein the attribute information includes hobbies, preferences, interests, and place of residence.

[1597] (Claim 3)

[1598] 2. The system according to claim 1, wherein the calculation of the matching candidates includes calculating a degree of match between users as a reliability score.

[1599] "Application example 2 when combining emotion engines"

[1600] (Claim 1)

[1601] A means for a user to input his / her own attribute information;

[1602] means for receiving the attribute information and storing it in a database;

[1603] A means for analyzing the attribute information and attribute information of other users using a generation AI;

[1604] means for calculating suitable matching candidates based on the analysis results;

[1605] means for notifying a user of the match candidates;

[1606] A means for collecting user emotion information;

[1607] means for analyzing the emotion information and performing matching in combination with the attribute information;

[1608] a means for inputting the attribute information and emotion information and generating a prompt sentence using a generative AI model;

[1609] A system including:

[1610] (Claim 2)

[1611] 2. The system according to claim 1, wherein the attribute information includes hobbies, preferences, interests, and place of residence.

[1612] (Claim 3)

[1613] 2. The system according to claim 1, wherein the calculation of the matching candidates includes calculating a degree of match between users as a reliability score. [Explanation of symbols]

[1614] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to input his / her own attribute information; means for receiving the attribute information and storing it in a database; A means for analyzing the attribute information and attribute information of other users using a generation AI; means for calculating suitable matching candidates based on the analysis results; means for notifying a user of the match candidates; A system including:

2. The system according to claim 1 , wherein the attribute information includes hobbies, preferences, interests, and place of residence.

3. The system according to claim 1 , wherein the calculation of the matching candidates includes calculating a degree of match between users as a reliability score.

Images