System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting information input by a user; means for generating a self-introduction using a generative model based on the collected information; means for adding the generated self-introduction to the user's profile; and means for displaying the profile information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional matching services, users often have to put in a lot of effort to create self-introductions and exchange messages, which can discourage them from using the service. Furthermore, many users are often matched with users with unreliable profiles or safety concerns, which can cause anxiety. Therefore, there is a need for a system that can reduce users' psychological burden and provide a safe and smooth matching experience. [Means for solving the problem]

[0005] The present invention provides the following means:

[0006] means of collecting information entered by users;

[0007] A means for generating a self-introduction sentence using a generative model based on the collected information;

[0008] a means for adding the generated bio to the user's profile;

[0009] a means of displaying profile information to users;

[0010] A means of collecting users' past message history and other parties' profile information;

[0011] A means of suggesting message content using a generative model based on collected information;

[0012] means for displaying suggested message content to the user;

[0013] A means of accumulating user activity data;

[0014] A means of analyzing the accumulated activity data;

[0015] A means to update the user's matching algorithm based on the analysis results;

[0016] The system has the means to detect suspicious patterns of behavior and request identity verification information, reducing the burden on users and enabling a safe and efficient matching experience.

[0017] "User" means an individual who uses the System to create a profile and match with other Users.

[0018] "Means of collecting information" refers to the function of receiving data entered by the user (such as name, hobbies, occupation, etc.) and sending it to the server.

[0019] A "generative model" is an artificial intelligence or machine learning algorithm that automatically generates self-introductions and message content based on collected information.

[0020] The "means for generating a self-introduction" is a function that uses a generative model to create a self-introduction based on information input by the user.

[0021] The "means for adding to profile" is a function for integrating the generated self-introduction into the user's profile data.

[0022] The "means for displaying profile information" is a function for displaying profile information including a self-introduction created by a user on a screen.

[0023] "Message history" is a record of messages a user has sent or received in the past.

[0024] The "means for suggesting message content" is a function that uses a generative model to automatically suggest new message content based on the user's message history and the other person's profile information.

[0025] "Activity data" refers to records of a user's behavior, such as the number of messages sent, the number of profile views, and match rates.

[0026] "Means for accumulating activity data" refers to a function that records data on user behavior and stores it in a database.

[0027] "Means for analyzing activity data" refers to a function that analyzes accumulated activity data and extracts user behavior patterns and trends.

[0028] A "matching algorithm" is a calculation method for optimally matching users based on collected data.

[0029] "Means for detecting behavioral patterns" refers to the ability to analyze activity data and identify reliable behavioral patterns and suspicious behavior.

[0030] "Means to request identity verification information" refers to the ability to request additional identity verification information from the user when suspicious activity is detected. [Brief explanation of the drawings]

[0031] [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

[0032] 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.

[0033] First, the terms used in the following description will be explained.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] [First embodiment]

[0040] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0041] 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.

[0042] 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).

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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."

[0052] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[0053] Profile creation support

[0054] First, we will explain the process by which a user creates a profile.

[0055] Collecting user input information

[0056] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[0057] Generating a self-introduction

[0058] The server receives the user's input and passes it to the generative model, which then generates an appealing self-introduction based on the input.

[0059] Adding and displaying your bio

[0060] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[0061] Specific examples

[0062] When a user enters "My hobby is reading" or "My occupation is an engineer," the server uses the generative model to generate a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I often spend my days off reading books. Nice to meet you," and displays it on the device.

[0063] Message suggestions

[0064] Next, we will explain the process by which a user exchanges messages with other users.

[0065] Collection of message history and profile information

[0066] When a user opens the message input area on their device, it sends a message suggestion request to the server, which collects the user's past message history and the other person's profile information.

[0067] Generate and display message suggestions

[0068] The server passes the collected data to a generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area. The user can then review the suggested message, edit it if necessary, and send it.

[0069] Specific examples

[0070] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest a message like "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[0071] Data accumulation and analysis

[0072] Finally, we will explain how to accumulate user activity data and analyze it.

[0073] Accumulation of activity data

[0074] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[0075] Data analysis and algorithm updates

[0076] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[0077] Identity verification and security

[0078] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[0079] In this way, the present invention reduces the burden on users and provides a safe and efficient matching experience.

[0080] The processing flow will be explained below.

[0081] Profile creation support

[0082] Profile Creation Process Steps

[0083] Step 1:

[0084] Terminal: Presents a profile entry form to the user.

[0085] Step 2:

[0086] User: Enter basic information such as name, hobbies, and occupation.

[0087] Step 3:

[0088] Terminal: Sends the information entered by the user to the server.

[0089] Step 4:

[0090] Server: Receives the information entered by the user.

[0091] Step 5:

[0092] Server: Inputs the received information into the generative model and generates a self-introduction.

[0093] Step 6:

[0094] Server: Add the generated self-introduction text to the user's profile data.

[0095] Step 7:

[0096] Server: Sends updated profile information to the device.

[0097] Step 8:

[0098] On your device: Display your profile information, including a user-generated bio.

[0099] Message suggestions

[0100] Message Proposal Processing Steps

[0101] Step 1:

[0102] Users: View the profile of the person you want to message and open the message input area.

[0103] Step 2:

[0104] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[0105] Step 3:

[0106] Server: Receives the message proposal request.

[0107] Step 4:

[0108] Server: Collects the user's past message history and the other person's profile information.

[0109] Step 5:

[0110] Server: Inputs the collected information into the generative model and generates appropriate message content.

[0111] Step 6:

[0112] Server: Sends the generated message content to the terminal.

[0113] Step 7:

[0114] Terminal: Display suggested message content in the message entry area.

[0115] Step 8:

[0116] User: Review the suggested message and edit it if necessary.

[0117] Step 9:

[0118] User: Presses the send button to send the final message to the other party.

[0119] Step 10:

[0120] Terminal: Sends the final message to the server.

[0121] Step 11:

[0122] Server: Sends the last message received to the other party.

[0123] Data accumulation and analysis

[0124] Data accumulation and analysis process steps

[0125] Step 1:

[0126] Device: Collects activity data such as messages sent and profile views.

[0127] Step 2:

[0128] Device: Periodically sends collected activity data to the server.

[0129] Step 3:

[0130] Server: Stores the received activity data in a database.

[0131] Step 4:

[0132] Server: Runs the system for data analysis.

[0133] Step 5:

[0134] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[0135] Step 6:

[0136] Server: Updates the matching algorithm based on the analysis results.

[0137] Identity verification process steps

[0138] Step 7:

[0139] Server: Performs data analysis and detects suspicious behavioral patterns.

[0140] Step 8:

[0141] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[0142] Step 9:

[0143] Terminal: Receives notifications from the server and displays them to the user.

[0144] Step 10:

[0145] User: Enters requested identity verification information.

[0146] Step 11:

[0147] Terminal: Sends the entered personal identification information to the server.

[0148] Step 12:

[0149] Server: Receives identity verification information and verifies its security.

[0150] Example 1

[0151] 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."

[0152] Conventional matching services lack support for users to create attractive profiles and offer a lack of appropriate suggestions for messaging, which can lead to communication issues. Furthermore, in terms of ensuring safety, the systems for detecting suspicious behavior are insufficient, potentially undermining users' sense of security.

[0153] 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.

[0154] In this invention, the server includes means for collecting information entered by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for ensuring security by requesting personal identification information, and means for analyzing the user's activity data and detecting suspicious behavioral patterns. This allows users to easily create attractive profiles and use the matching service efficiently and safely.

[0155] "User" refers to an individual who uses the Matching Service.

[0156] "Terminal" refers to the device used by the user (smartphone, tablet, PC, etc.).

[0157] "Server" refers to a computer system that processes user requests and manages and stores data.

[0158] "Input information" refers to basic information such as name, hobbies, and occupation provided by the user through the terminal.

[0159] A "generative model" refers to an artificial intelligence model that generates self-introductions and message suggestions based on input information.

[0160] "Self-introduction" refers to an introduction created by a generative model based on user input information.

[0161] "Profile" refers to a personal information page that includes the user's basic information and self-introduction.

[0162] "Message History" means a record of messages previously sent or received by a User.

[0163] "Profile information" refers to data including basic information about the user and their contacts, self-introductions, etc.

[0164] "Proposed message" refers to a new message proposal generated by a generative model based on past message history and profile information.

[0165] "Activity data" refers to data on user usage behavior, such as sending messages or viewing profiles.

[0166] A "database" refers to a system for storing multiple data managed by a server.

[0167] "Matching algorithm" refers to a calculation method for efficiently and appropriately matching users with each other.

[0168] "Identifying Information" means additional personally identifiable information provided by you for security purposes.

[0169] "Suspicious behavioral patterns" refer to patterns of suspicious behavior that differ from normal usage.

[0170] MODE FOR CARRYING OUT THE INVENTION

[0171] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[0172] Profile creation support

[0173] First, we will explain the process by which a user creates a profile.

[0174] 1. Display the profile entry form

[0175] The server sends the profile entry section of a web page or mobile application to the user's device, for example, presented as an HTML form or app screen.

[0176] 2. Enter your user information

[0177] Users enter basic information such as their name, hobbies, and occupation into a profile form.

[0178] 3. Sending input information to the server

[0179] The terminal sends the entered information to the server, which verifies the received data and stores it in a database.

[0180] 4. Generating self-introductions

[0181] The server sends a prompt to the generative AI model based on the user's input, and the generative AI model generates a self-introduction, such as "Hello, I'm Taro Yamada, an engineer. My hobby is reading, and I often spend my days off reading books."

[0182] 5. Adding and displaying your bio to your profile

[0183] The server adds the generated self-introduction to the user profile and transmits the profile data to the terminal, which displays the updated profile information to the user.

[0184] Message suggestions

[0185] Next, the process by which a user exchanges messages with other users will be described.

[0186] 1. Submit a message suggestion request

[0187] The terminal sends a message suggestion request to the server when the user opens the message input area.

[0188] 2. Collection of message history and profile information

[0189] The server retrieves the user's past message history and the other person's profile information from a database.

[0190] 3. Message Proposal Generation

[0191] The server passes the collected data to a generative AI model to generate appropriate message content, such as "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?"

[0192] 4. View and send message suggestions

[0193] The server sends the generated message to the terminal, which displays the proposed message in the message input area and allows the user to send it as is.

[0194] Data accumulation and analysis

[0195] Finally, we will explain how to accumulate user activity data and analyze it.

[0196] 1. Collection of activity data

[0197] The device collects activity data, such as user messages sent and profile views.

[0198] 2. Sending activity data to the server

[0199] The terminal periodically transmits the collected activity data to the server.

[0200] 3. Data accumulation and storage

[0201] The server stores the received activity data in a database.

[0202] 4. Data analysis and algorithm updates

[0203] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[0204] 5. Identity verification and security

[0205] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[0206] Specific examples

[0207] 1. Create your profile

[0208] When a user enters "My hobby is reading" or "My occupation is engineer," the server uses a generative AI model to generate a self-introduction such as "Hello, I'm Yamada Taro, an engineer. My hobby is reading, and I often spend my days off reading books," and displays this on the device.

[0209] 2. Message proposal

[0210] When a user opens the message input area, the device sends a message suggestion request to the server, which uses a generative AI model to generate a message like, "Hello! I understand you're a movie lover. Can you recommend a movie you've seen recently?" and displays it on the device.

[0211] Prompt Sentence Examples

[0212] 1. "Generate a self-introduction when a user enters 'reading' as a hobby."

[0213] 2. "If a user has previously sent a message saying 'Nice to meet you, nice to meet you,' suggest a message to send next."

[0214] In this way, the present invention reduces the burden on users and enables them to use matching services efficiently and safely.

[0215] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0216] Profile creation support

[0217] Step 1:

[0218] The terminal sends a request to the server when the user opens the profile entry section.

[0219] Input: Request packet

[0220] Output: Profile input form

[0221] Specific operation: The server sends a web page or app profile entry form to the device, which then displays the form to the user.

[0222] Step 2:

[0223] Users enter basic information such as name, hobbies, and occupation into the feel input form.

[0224] Input: User enters basic information

[0225] Output: Basic information entered

[0226] What it does: The device temporarily stores the information you enter.

[0227] Step 3:

[0228] The terminal transmits the input information to the server.

[0229] Input: Basic information entered by the user

[0230] Output: Basic information sent

[0231] Specific operation: The terminal sends the data entered in the form to the server, which receives this information and stores it in a database.

[0232] Step 4:

[0233] The server passes the received basic information to a generative AI model to generate an appealing self-introduction.

[0234] Input: User basic information

[0235] Output: Generated bio

[0236] Specific operation: The generative AI model generates a self-introduction based on the prompt: "Please generate a self-introduction if the user enters 'reading' as a hobby."

[0237] Step 5:

[0238] The server adds the generated self-introduction to the user profile and transmits the updated profile data to the terminal.

[0239] Input: Generated bio, user profile information

[0240] Output: Updated profile information

[0241] Specific operation: The server adds the generated self-introduction to the user profile and sends the updated profile to the terminal, which displays this information to the user.

[0242] Message suggestions

[0243] Step 1:

[0244] When the user opens the message input area, the terminal sends a message suggestion request to the server.

[0245] Input: Message suggestion request

[0246] Output: Acknowledgement of request

[0247] Specific operation: The terminal sends a message suggestion request to the server.

[0248] Step 2:

[0249] The server collects the user's past message history and the other person's profile information from a database.

[0250] Input: User ID, other user ID

[0251] Output: Past message history, other person's profile information

[0252] Specific operation: The server retrieves the necessary data from the database.

[0253] Step 3:

[0254] The server passes the collected information to a generative AI model to generate appropriate message content.

[0255] Input: Past message history, other person's profile information

[0256] Output: Proposed message content

[0257] Specific behavior: The generative AI model generates a message based on the prompt: "If the user has previously sent the message 'Nice to meet you, nice to meet you,' please suggest the next message to send."

[0258] Step 4:

[0259] The server sends the generated message to the terminal, and the terminal displays the suggested message in the message input area.

[0260] Input: Generated message content

[0261] Output: Proposal message displayed on the terminal

[0262] Specific operation: The server sends the generated message to the terminal, which displays it in the message input area. The user can then send the suggested message as is.

[0263] Data accumulation and analysis

[0264] Step 1:

[0265] The device collects activity data, such as user messages sent and profile views.

[0266] Input: User operation log

[0267] Output: Collected activity data

[0268] Specific operation: The device records the user's actions and temporarily stores the data.

[0269] Step 2:

[0270] The terminal periodically transmits the collected activity data to the server.

[0271] Input: Collected activity data

[0272] Output: Send data to the server

[0273] Specific operation: The terminal transmits data to the server at regular intervals.

[0274] Step 3:

[0275] The server stores the received activity data in a database.

[0276] Input: Activity data

[0277] Output: Data items stored in the database

[0278] Specific operation: The server stores the received data in a database.

[0279] Step 4:

[0280] The server analyzes the accumulated activity data and extracts the user's behavioral patterns.

[0281] Input: Accumulated activity data

[0282] Output: Behavioral pattern analysis results

[0283] Specific operation: The server analyzes the user's behavioral patterns using a data analysis algorithm.

[0284] Step 5:

[0285] The server updates the matching algorithm based on the analysis results, and if it detects suspicious behavioral patterns, it requests additional identity verification information from the user via their device.

[0286] Input: Behavioral pattern analysis results

[0287] Output: Updated matching algorithm, safety confirmation request

[0288] Specific operation: The server adjusts the matching algorithm based on the analysis results and sends a message to the user requesting identity verification if suspicious behavior is detected.

[0289] Through this series of steps, users can easily create an attractive profile, receive appropriate message suggestions, and use the matching service safely and efficiently.

[0290] (Application example 1)

[0291] 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."

[0292] Conventional matching services and online shopping sites have had problems with generating user input information, message content, product reviews, etc., requiring a great deal of effort from the user and making it difficult to provide personalized information. It is also not easy to update recommendation algorithms based on collected data or ensure their safety. To solve these issues and improve the user experience, new systems and methods must be provided.

[0293] 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.

[0294] In this invention, the server includes means for collecting information entered by a user, means for generating a self-introduction or product description using a generative model based on the collected information, means for adding the generated self-introduction or product description to the user's profile or product detail page, means for displaying the profile information or product detail information to the user, means for collecting the user's past message history and other users' profile information or means for collecting the user's past reviews and purchase history, means for suggesting message content or review content using a generative model based on the collected information, means for displaying the suggested message content or review content to the user, means for accumulating user activity data or purchase data, means for analyzing the accumulated activity data or purchase data, means for updating the user's matching algorithm or recommendation algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows users to easily generate personalized self-introductions, product descriptions, messages, and reviews, and enables safe and effective matching and product suggestions.

[0295] "User" refers to any individual or legal entity that uses the system or services.

[0296] "Collection Method" refers to the software or hardware functionality used to collect particular information.

[0297] "Generative Model" refers to an artificial intelligence model used to generate appropriate messages and testimonials for users.

[0298] "About Me" refers to text that contains summarized information about the user.

[0299] "Product Description" refers to text containing a description of a particular product.

[0300] "Profile information" refers to data including a user's personal information, preferences, and past behavioral history.

[0301] "Product details" refers to data including information about a product, such as specifications, features, and price.

[0302] "Message History" refers to a record of messages that a User has previously sent or received.

[0303] "Purchase history" refers to a record of products a user has purchased in the past.

[0304] "Suggestion means" refers to a system function for suggesting content generated based on collected information to users.

[0305] "Display" refers to the functionality that visually presents suggested messages, reviews, and profile information to users.

[0306] "Activity Data" refers to data regarding a series of actions taken by a user on the System.

[0307] "Storage means" refers to the function for saving and managing collected data.

[0308] "Analysis means" refers to the function for analyzing accumulated data to extract user behavior patterns and preferences.

[0309] A "matching algorithm" refers to a calculation method for optimally connecting users.

[0310] A "recommendation algorithm" refers to a calculation method for suggesting the most suitable products and content to users.

[0311] "Identification Information" means information necessary to verify a User's identity.

[0312] This invention is a system that uses a generative AI model to generate, display, and suggest user profile information, messages, and product descriptions. The specific system configuration and processing flow for implementing this invention are described below.

[0313] Profile Creation Support Embodiment

[0314] Collecting user input information

[0315] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile input form. This information is sent from the device to the server.

[0316] Generating a self-introduction

[0317] The server passes the information sent by the user to a generative model, which generates a self-introduction based on the collected information. The generative model uses the OpenAI (registered trademark) "text-davinci-003" engine.

[0318] Adding and displaying your bio

[0319] The server adds the generated self-introduction to the user's profile data and transmits the updated profile information to the terminal, where the automatically generated self-introduction is displayed.

[0320] Specific examples

[0321] When a user inputs "My hobby is watching movies" or "My occupation is designer," the server uses the generative model to generate a self-introduction such as "Hello, I work as a designer. My hobby is watching movies, and I often go to the cinema on weekends. Nice to meet you," and displays it on the device.

[0322] Message Proposal Embodiment

[0323] Collection of message history and profile information

[0324] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[0325] Generate and display message suggestions

[0326] The server passes the collected information to the generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area.

[0327] Specific examples

[0328] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest the message "Hello! I heard you enjoy traveling. Do you have any recommendations for places you've been recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[0329] Data storage and analysis implementation

[0330] Accumulation of activity data

[0331] The device collects activity data, such as messages sent and profile views, and periodically transmits it to a server, which stores the data in a database.

[0332] Data analysis and algorithm updates

[0333] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching or recommendation algorithm based on the analysis results.

[0334] Identity verification and security

[0335] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification from the user, which will then be used to verify the user's identity and take appropriate measures.

[0336] Specific examples

[0337] The server collects information about the user's past messages, viewed products, and purchased products, and updates the recommendation algorithm based on this data. As a result, the user can find more relevant products. If suspicious behavior is detected, the server will request additional information from the user to verify their identity (e.g., resubmit ID).

[0338] Prompt Sentence Examples

[0339] Product description generation prompt:

[0340] Generate compelling product descriptions based on user information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n

[0341] Prompt for generating review suggestions:

[0342] User information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n Product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n Suggest compelling product reviews that users can write based on past reviews: ['This product has good sound quality.']\n

[0343] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0344] Step 1:

[0345] Collecting user input information

[0346] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile form. The information is then sent from the device to the server. Input is done using text fields and drop-down menus.

[0347] Input: User profile information

[0348] Output: Input sent to the server

[0349] Step 2:

[0350] Generating a self-introduction

[0351] The server receives the information sent by the user and passes it to a generative AI model (OpenAI's text-davinci-003 engine) as a prompt. The generative model generates a self-introduction based on the prompt.

[0352] Input: User profile information, prompt text

[0353] Output: Generated bio

[0354] Step 3:

[0355] Adding and displaying your bio

[0356] The server adds the generated self-introduction to the user's profile data, then transmits the updated profile information to the terminal, and the terminal displays the profile information including the self-introduction.

[0357] Input: Generated self-introduction

[0358] Output: Updated profile information sent to the device

[0359] Step 4:

[0360] Collection of message history and profile information

[0361] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[0362] Input: User message input request

[0363] Output: Collected message history and profile information

[0364] Step 5:

[0365] Generate and display message suggestions

[0366] The server passes the collected data to the generative model to generate appropriate message content, which is then sent to the device and displayed in the message input area.

[0367] Input: Message history, profile information, prompt text

[0368] Output: Generated message content

[0369] Step 6:

[0370] Accumulation of user activity data

[0371] The device periodically sends activity data, such as user messages sent and profile views, to a server, which stores this data in a database.

[0372] Input: User activity data

[0373] Output: Activity data stored in a database

[0374] Step 7:

[0375] Data analysis and algorithm updates

[0376] The server analyzes the accumulated activity data and extracts user behavior patterns. Based on the extracted data, the matching algorithm or recommendation algorithm is updated. Machine learning models and statistical analysis tools are used for data analysis.

[0377] Input: Accumulated activity data

[0378] Output: Updated algorithm

[0379] Step 8:

[0380] Identity verification and security

[0381] If the server detects suspicious behavior patterns through data analysis, it will request additional identification information from the user, verify the user's security based on the information provided, and take additional measures if necessary.

[0382] Input: Analysis results, personal identification information

[0383] Output: User confirmed safe or countermeasure information

[0384] 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.

[0385] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[0386] Profile creation support

[0387] First, we will explain the process by which a user creates a profile.

[0388] Collecting user input information

[0389] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[0390] Generating a self-introduction

[0391] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state.Then, the received information and the emotion recognition results are input into a generative model to generate a self-introduction sentence based on the user's emotions.

[0392] Adding and displaying your bio

[0393] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[0394] Specific examples

[0395] When a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you," and displays it on the device.

[0396] Message suggestions

[0397] Next, we will explain the process by which a user exchanges messages with other users.

[0398] Collection of message history and profile information

[0399] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[0400] Generate and display message suggestions

[0401] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[0402] Specific examples

[0403] If the user has previously sent "Nice to meet you, nice to meet you," the server uses the emotion engine to determine that the user is in a curious state. The generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send it.

[0404] Data accumulation and analysis

[0405] Finally, we will explain how to accumulate user activity data and analyze it.

[0406] Accumulation of activity data

[0407] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[0408] Data analysis and algorithm updates

[0409] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes their emotional state using an emotion engine. Based on the analysis results, the server updates the matching algorithm.

[0410] Identity verification and security

[0411] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[0412] In this way, by combining emotion engines, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, further improving the user experience and providing a safe matching experience.

[0413] The processing flow will be explained below.

[0414] Profile creation support

[0415] Profile Creation Process Steps

[0416] Step 1:

[0417] Terminal: Presents a profile entry form to the user.

[0418] Step 2:

[0419] User: Enter basic information such as name, hobbies, and occupation.

[0420] Step 3:

[0421] Terminal: Sends the information entered by the user to the server.

[0422] Step 4:

[0423] Server: Receives the information entered by the user.

[0424] Step 5:

[0425] Server: Uses the emotion engine to analyze the user's emotional state.

[0426] Step 6:

[0427] Server: Inputs the emotional state and input information into the generative model and generates a self-introduction sentence.

[0428] Step 7:

[0429] Server: Add the generated bio to the user's profile data.

[0430] Step 8:

[0431] Server: Sends updated profile information to the device.

[0432] Step 9:

[0433] On your device: Display your profile information, including a user-generated bio.

[0434] Message suggestions

[0435] Message Proposal Processing Steps

[0436] Step 1:

[0437] Users: View the profile of the person you want to message and open the message input area.

[0438] Step 2:

[0439] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[0440] Step 3:

[0441] Server: Receives the message proposal request.

[0442] Step 4:

[0443] Server: Collects the user's past message history and the other person's profile information.

[0444] Step 5:

[0445] Server: Utilizes the emotion engine to analyze the user's current emotional state.

[0446] Step 6:

[0447] Server: Inputs the collected information and emotional state into a generative model to generate message content with an appropriate emotional tone.

[0448] Step 7:

[0449] Server: Sends the generated message content to the terminal.

[0450] Step 8:

[0451] Terminal: Display suggested message content in the message entry area.

[0452] Step 9:

[0453] User: Review the suggested message and edit it if necessary.

[0454] Step 10:

[0455] User: Presses the send button to send the final message to the other party.

[0456] Step 11:

[0457] Terminal: Sends the final message to the server.

[0458] Step 12:

[0459] Server: Sends the last message received to the other party.

[0460] Data accumulation and analysis

[0461] Data accumulation and analysis process steps

[0462] Step 1:

[0463] Device: Collects activity data such as messages sent and profile views.

[0464] Step 2:

[0465] Device: Periodically sends collected activity data to the server.

[0466] Step 3:

[0467] Server: Stores the received activity data in a database.

[0468] Step 4:

[0469] Server: Runs the system for data analysis.

[0470] Step 5:

[0471] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[0472] Step 6:

[0473] Server: Updates the matching algorithm based on the analysis results.

[0474] Step 7:

[0475] Server: Uses the emotion engine to analyze the user's emotional state.

[0476] Step 8:

[0477] Server: Further adjusts the matching algorithm, taking into account the analyzed emotional state.

[0478] Identity verification process steps

[0479] Step 9:

[0480] Server: Performs data analysis and detects suspicious behavioral patterns.

[0481] Step 10:

[0482] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[0483] Step 11:

[0484] Terminal: Receives notifications from the server and displays them to the user.

[0485] Step 12:

[0486] User: Enters requested identity verification information.

[0487] Step 13:

[0488] Terminal: Sends the entered personal identification information to the server.

[0489] Step 14:

[0490] Server: Receives identity verification information and verifies its security.

[0491] Example 2

[0492] 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."

[0493] The objective of this invention is to reduce the psychological burden on users and provide a safe and efficient matching experience. In particular, the objective is to realize a system that is natural and easy to use for users by creating profiles and suggesting messages that take into account the user's emotional state.

[0494] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information input by the user, means for recognizing an emotional state based on the collected information, means for generating a self-introduction using a generative model based on the emotional state and the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for collecting the user's past message history and other party's profile information, means for suggesting message content using a generative model based on the collected information and the user's emotional state, means for displaying the suggested message content to the user, means for accumulating user activity data, means for analyzing the accumulated activity data and the user's emotional state, means for updating the user's matching algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows for profile creation and message suggestions that take the user's emotional state into consideration, improving the user experience and providing a safe matching experience.

[0495] "Means for collecting information entered by the user" refers to a device or system that electrically or electronically obtains basic information such as name, hobbies, and occupation entered by the user into the terminal.

[0496] A "means for recognizing emotional state" is a device or algorithm that analyzes input information and user behavioral data to identify the user's current emotional or psychological state.

[0497] The "means for generating a self-introduction using a generative model" is a device or system that automatically creates a self-introduction for a user using natural language processing technology based on the analysis of collected information and emotional state.

[0498] A "means for adding to a user's profile" is a device or system that merges the generated self-introduction and other information into the user's digital profile and stores it.

[0499] "Means for displaying profile information to a user" refers to a device or system that visually presents the information contained in a user's profile, such as through a terminal display.

[0500] "Means for collecting a user's past message history and other party profile information" refers to a device or system that obtains messages previously sent and received by a user and other party profile data.

[0501] The "means for suggesting message content using a generative model" is a device or system that automatically creates appropriate message content based on collected past message history and the other party's profile information.

[0502] The "means for displaying the proposed message content to the user" refers to a device or system that displays the generated message content on the user's terminal so that the message content can be confirmed and edited.

[0503] "Means for storing user activity data" refers to a device or system that records and stores user behavioral data such as sending messages and viewing profiles.

[0504] The "means for analyzing accumulated activity data and the user's emotional state" refers to a device or system that analyzes accumulated behavioral data and emotional data using statistical methods and machine learning algorithms.

[0505] The "means for updating the user's matching algorithm" is a device or system that improves or optimizes the algorithm of the matching system based on the analysis results.

[0506] "Means for detecting suspicious behavioral patterns and requesting identity verification information" refers to a device or system that analyzes user behavioral data and, if a suspicious behavioral pattern is found, obtains additional identity verification information from the user.

[0507] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[0508] Hardware and software used

[0509] Device: The device on which users enter information and view profiles and messages, such as a smartphone, tablet, or PC.

[0510] Server: A computer system used to process and store user information, run the emotion engine and generative AI models.

[0511] Emotion engine: Software for analyzing and recognizing the user's emotional state.

[0512] Generative AI model: Software that generates self-introductions and message content based on user input information and emotion recognition results.

[0513] Profile creation support

[0514] First, we will explain the process by which a user creates a profile.

[0515] Collecting user input information

[0516] On the device, the user enters basic information such as name, hobbies, and occupation into a profile input form. For example, the user enters "Name: Taro," "Hobbies: Reading," and "Occupation: Engineer." The entered information is sent to the server.

[0517] Generating a self-introduction

[0518] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state. For example, it may determine from the user's description that the user is in a relaxed state. The received information and the emotion recognition results are then input into a generative AI model, which generates a self-introduction based on the user's emotions. Specifically, it generates the sentence, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you."

[0519] Adding and displaying your bio

[0520] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[0521] Message suggestions

[0522] Next, we will explain the process by which a user exchanges messages with other users.

[0523] Collection of message history and profile information

[0524] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[0525] Generate and display message suggestions

[0526] The server passes the collected data and emotional state to a generative AI model to generate message content with an appropriate emotional tone. For example, if a user has previously sent "Nice to meet you, nice to meet you," the emotion engine determines that the user is in a curious state. In this case, the generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send this suggested message as is.

[0527] Data accumulation and analysis

[0528] Finally, we will explain how to accumulate and analyze user activity data.

[0529] Accumulation of activity data

[0530] The device continuously collects data on users' activities, such as sending messages and viewing profiles, and periodically transmits this data to a server, which then stores it in a database.

[0531] Data analysis and algorithm updates

[0532] The server analyzes the accumulated activity data and the user's emotional state to extract user behavior patterns. For example, if a user frequently uses a certain keyword, the server analyzes that pattern and uses it to update the algorithm.

[0533] Identity verification and security

[0534] If the server detects suspicious behavior patterns based on the analysis data, it will request additional authentication information from the user via the device. For example, if suspicious activity is detected, the server will send additional authentication information to the user, who will then provide it to confirm their safety.

[0535] In this way, by combining an emotion engine with a generative AI model, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, improving the user experience and providing a safe matching experience.

[0536] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0537] Step 1: Collect user input

[0538] The user enters basic information such as name, hobbies, and occupation into a profile input form. Specifically, the user enters the name "Taro," the hobby "reading," and the occupation "engineer." This input information is stored as digital data on the device.

[0539] Step 2: Submit your input

[0540] The terminal sends the entered user information to the server. In this case, the terminal sends the data "Name: Taro, Hobby: Reading, Occupation: Engineer" to the server. The input is the user information, and the output is the information data sent to the server.

[0541] Step 3: Recognizing your emotional state

[0542] The server starts an emotion engine based on the received user information and analyzes the user's emotional state. For example, it determines that the user is in a relaxed state based on their writing. In this case, the input is the transmitted user information data, and the output is the analyzed emotional state (relaxed state).

[0543] Step 4: Generate a self-introduction

[0544] The server uses an emotion engine and a generative AI model to integrate the received information with the emotion recognition results to generate a self-introduction. For example, the generative AI model generates a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you." The input is user information and emotion recognition results, and the output is the generated self-introduction.

[0545] Step 5: Add and submit your bio

[0546] The server adds the generated self-introduction to the profile data and sends the updated profile information to the terminal. The data received by the terminal is the updated profile information. The input is the generated self-introduction, and the output is the updated profile information.

[0547] Step 6: View your profile information

[0548] The device displays the received profile information to the user. The user can check the profile information, including the self-introduction text. The input is the updated profile information, and the output is the profile information displayed to the user.

[0549] Step 7: Submit a message suggestion request

[0550] When a user opens the message input area, the device sends a message suggestion request to the server. This request includes the user's ID and message history information. The input is the user's action, and the output is the request data to the server.

[0551] Step 8: Gather message history and profile information

[0552] The server receives the message suggestion request and collects the user's past message history and the other party's profile information. The input is the request data sent to the server, and the output is the collected message history and profile information.

[0553] Step 9: Analyze your emotional state

[0554] The server uses the collected data to analyze the user's current emotional state using an emotion engine. For example, the analysis may determine that the user is in a curious state. The input is the collected data, and the output is the analyzed emotional state.

[0555] Step 10: Generate message proposals

[0556] The server passes the analysis results from the emotion engine to the generative AI model, which generates a message with an appropriate emotional tone. For example, it generates a message like, "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?" The input is the emotional state and collected data, and the output is the generated message.

[0557] Step 11: Send and display the suggestion message to the user

[0558] The server sends the generated message to the terminal and displays it in the message input area. The user can review the suggested message and send it as is or edit it and send it. The input is the generated message and the output is the message that is displayed to the user.

[0559] Step 12: Accumulating activity data

[0560] The device continuously collects user activity data, such as message sending and profile viewing, and periodically transmits it to the server. The input is the user behavior data, and the output is the activity data transmitted to the server.

[0561] Step 13: Data analysis and algorithm updates

[0562] The server analyzes the accumulated activity data and the user's emotional state to extract the user's behavioral patterns. The matching algorithm is updated based on the analysis results. The input is the accumulated activity data and the user's emotional state, and the output is the updated algorithm.

[0563] Step 14: Request and secure identity verification information

[0564] If the server detects a suspicious behavior pattern based on the analysis data, it requests additional identity verification information from the user via the terminal. The user provides this information to confirm safety. The input is the suspicious behavior pattern and the user's identity verification information, and the output is the verification result.

[0565] (Application example 2)

[0566] 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."

[0567] Conventional matching systems do not take into account the user's emotional state, which limits their ability to reduce the user's psychological burden or provide a safe and efficient matching experience. Furthermore, they do not take into account the user's emotions when displaying advertisements, making it difficult to provide personalized advertisements.

[0568] 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 collecting information input by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying profile information to the user, emotion recognition means for recognizing the user's emotion, means for selecting targeted advertisements based on the emotion recognition results, and means for displaying the selected advertisements to the user. This allows users to enjoy an efficient matching experience with peace of mind, and makes it possible to display appropriate advertisements personalized to each user.

[0569] "Information entered by the user" refers to basic information and message history provided by the user to the system.

[0570] "Means of collection" refers to the functions on the system for obtaining user input information and activity data.

[0571] A "generative model" refers to a machine learning model that creates self-introductions and message content based on information entered by the user.

[0572] "Means for generating a self-introduction" refers to a function for creating a self-introduction based on information input by a user using a generative model.

[0573] "Means to add to profile" refers to functionality for incorporating the generated self-introduction into user profile data.

[0574] "Profile Information" refers to a collection of user data including the user's basic information, self-introduction, etc.

[0575] "Means of displaying to the user" refers to the function for displaying generated profile information, message content, advertisements, etc. on the user's device.

[0576] "Emotion recognition means" refers to a function for analyzing and determining the user's emotional state in real time.

[0577] "Means for selecting targeted advertisements" refers to a function for selecting the most suitable advertisement for a user based on the results of emotion recognition.

[0578] "Means for displaying selected advertisements to users" refers to a function for displaying selected advertising content on a user's device based on the user's emotions.

[0579] This invention relates to a matching system that combines an emotion engine that recognizes the user's emotions, thereby reducing the user's psychological burden and providing a safe and efficient matching experience. Specific means for carrying out the invention will now be described.

[0580] Profile creation support

[0581] First, we will explain the process by which a user creates a profile.

[0582] Collecting user input information

[0583] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[0584] Generating a self-introduction

[0585] The server receives the user's input information and recognizes the user's emotional state using an emotion recognition means.Then, the received information and the emotion recognition results are input into the generative model to generate a self-introduction sentence based on the emotion.

[0586] Adding and displaying your bio

[0587] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[0588] Message suggestions

[0589] Next, we will explain the process by which a user exchanges messages with other users.

[0590] Collection of message history and profile information

[0591] When a user opens the message input area on the device, a message suggestion request is sent to the server, which then collects the user's past message history and the other party's profile information, and analyzes the user's emotional state using emotion recognition means.

[0592] Generate and display message suggestions

[0593] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[0594] Advertisement display

[0595] Next, the process of displaying targeted advertisements based on the user's emotional state will be described.

[0596] Emotion Recognition and Ad Selection

[0597] While the user is watching an advertisement, the device uses its camera to analyze the user's emotional state through facial expressions and voice through emotion recognition.The server then selects an appropriate advertisement from the advertisement database based on this emotional state.

[0598] Viewing and switching ads

[0599] The selected advertisement is displayed to the user on the device. If the user's emotional state changes, the server selects a new appropriate advertisement and switches the advertisement displayed on the device.

[0600] Data accumulation and analysis

[0601] Finally, we will explain how to accumulate user activity data and analyze it.

[0602] Accumulation of activity data

[0603] The device collects user activity data, such as messages sent, profile views, and advertisement viewing, and periodically transmits this data to a server, which then stores it in a database.

[0604] Data analysis and algorithm updates

[0605] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes the user's emotional state using emotion recognition means. Based on the analysis results, the server updates the matching algorithm.

[0606] Identity verification and security

[0607] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[0608] Examples and prompts

[0609] Specific examples

[0610] For example, if a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you," and displays it on the device.

[0611] Prompt Sentence Examples

[0612] Recommended ad prompt when user is having fun:

[0613] Title: Delivering the latest entertainment experience!

[0614] Contents:

[0615] 1. Latest movie introductions

[0616] 2. New game release information

[0617] 3. Suggesting unique places to go

[0618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0619] Step 1:

[0620] The user enters basic information such as name, hobbies, and occupation into a profile input form. The entered information is sent from the device to the server. The input data includes the user's text data, and the device captures the user input in real time and sends it to the server.

[0621] Step 2:

[0622] The server receives the user's input information and analyzes the user's emotional state using an emotion recognition means. In this process, the server analyzes the text data and the corresponding emotion data, and the input data is processed by the emotion recognition algorithm. The output is the user's emotional state information.

[0623] Step 3:

[0624] The server inputs the received user input information and emotion recognition results into a generative model, which generates a self-introduction based on the emotion data. The generative model generates text using a machine learning algorithm (e.g., a neural network). The output is a self-introduction based on the emotion.

[0625] Step 4:

[0626] The generated self-introduction is added to the user's profile data by the server. The server accesses the profile database and updates the profile information, including the self-introduction. The output is the updated profile data.

[0627] Step 5:

[0628] The profile information is sent from the server to the terminal, and the terminal displays the profile information, including a self-introduction, to the user. Here, the terminal visually displays the profile data and provides it to the user. The output is the displayed profile information.

[0629] Step 6:

[0630] While a user is watching an advertisement, the device's camera function is used to capture the user's facial expressions, and the emotional state is analyzed in real time through an emotion recognition means. The emotional state data is sent from the device to a server. The input is the camera-captured image, and the output is the emotional state data.

[0631] Step 7:

[0632] The server selects the most suitable advertisement from the advertisement database based on the result of the emotion recognition means. In this process, an advertisement selection algorithm is used to select the target advertisement and generate selected advertisement data. The output is the advertisement content.

[0633] Step 8:

[0634] The selected advertisement is sent from the server to the device, which then displays it to the user. The device visually presents the advertisement content to the user and changes and updates the execution as necessary. The output is the advertisement displayed to the user.

[0635] Step 9:

[0636] The device collects activity data such as sending messages, viewing profiles, and watching advertisements, and periodically sends it to the server. The input is activity data, and the output is data stored in the database.

[0637] Step 10:

[0638] The server analyzes the accumulated activity data to analyze the user's behavioral patterns and emotional state, and updates the matching algorithm and ad selection algorithm accordingly. The output is the updated algorithm.

[0639] Step 11:

[0640] If the server detects suspicious behavior patterns through data analysis, it requests additional identity verification information from the user via the terminal. The server verifies the provided identity verification information, checks its security, and takes measures if necessary. The output is the verification result and measures.

[0641] 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.

[0642] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0643] 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.

[0644] [Second embodiment]

[0645] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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).

[0651] 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. 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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."

[0657] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[0658] Profile creation support

[0659] First, we will explain the process by which a user creates a profile.

[0660] Collecting user input information

[0661] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[0662] Generating a self-introduction

[0663] The server receives the user's input and passes it to the generative model, which then generates an appealing self-introduction based on the input.

[0664] Adding and displaying your bio

[0665] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[0666] Specific examples

[0667] When a user enters "My hobby is reading" or "My occupation is an engineer," the server uses the generative model to generate a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I often spend my days off reading books. Nice to meet you," and displays it on the device.

[0668] Message suggestions

[0669] Next, we will explain the process by which a user exchanges messages with other users.

[0670] Collection of message history and profile information

[0671] When a user opens the message input area on their device, it sends a message suggestion request to the server, which collects the user's past message history and the other person's profile information.

[0672] Generate and display message suggestions

[0673] The server passes the collected data to a generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area. The user can then review the suggested message, edit it if necessary, and send it.

[0674] Specific examples

[0675] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest a message like "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[0676] Data accumulation and analysis

[0677] Finally, we will explain how to accumulate user activity data and analyze it.

[0678] Accumulation of activity data

[0679] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[0680] Data analysis and algorithm updates

[0681] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[0682] Identity verification and security

[0683] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[0684] In this way, the present invention reduces the burden on users and provides a safe and efficient matching experience.

[0685] The processing flow will be explained below.

[0686] Profile creation support

[0687] Profile Creation Process Steps

[0688] Step 1:

[0689] Terminal: Presents a profile entry form to the user.

[0690] Step 2:

[0691] User: Enter basic information such as name, hobbies, and occupation.

[0692] Step 3:

[0693] Terminal: Sends the information entered by the user to the server.

[0694] Step 4:

[0695] Server: Receives the information entered by the user.

[0696] Step 5:

[0697] Server: Inputs the received information into the generative model and generates a self-introduction.

[0698] Step 6:

[0699] Server: Add the generated self-introduction text to the user's profile data.

[0700] Step 7:

[0701] Server: Sends updated profile information to the device.

[0702] Step 8:

[0703] On your device: Display your profile information, including a user-generated bio.

[0704] Message suggestions

[0705] Message Proposal Processing Steps

[0706] Step 1:

[0707] Users: View the profile of the person you want to message and open the message input area.

[0708] Step 2:

[0709] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[0710] Step 3:

[0711] Server: Receives the message proposal request.

[0712] Step 4:

[0713] Server: Collects the user's past message history and the other person's profile information.

[0714] Step 5:

[0715] Server: Inputs the collected information into the generative model and generates appropriate message content.

[0716] Step 6:

[0717] Server: Sends the generated message content to the terminal.

[0718] Step 7:

[0719] Terminal: Display suggested message content in the message entry area.

[0720] Step 8:

[0721] User: Review the suggested message and edit it if necessary.

[0722] Step 9:

[0723] User: Presses the send button to send the final message to the other party.

[0724] Step 10:

[0725] Terminal: Sends the final message to the server.

[0726] Step 11:

[0727] Server: Sends the last message received to the other party.

[0728] Data accumulation and analysis

[0729] Data accumulation and analysis process steps

[0730] Step 1:

[0731] Device: Collects activity data such as messages sent and profile views.

[0732] Step 2:

[0733] Device: Periodically sends collected activity data to the server.

[0734] Step 3:

[0735] Server: Stores the received activity data in a database.

[0736] Step 4:

[0737] Server: Runs the system for data analysis.

[0738] Step 5:

[0739] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[0740] Step 6:

[0741] Server: Updates the matching algorithm based on the analysis results.

[0742] Identity verification process steps

[0743] Step 7:

[0744] Server: Performs data analysis and detects suspicious behavioral patterns.

[0745] Step 8:

[0746] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[0747] Step 9:

[0748] Terminal: Receives notifications from the server and displays them to the user.

[0749] Step 10:

[0750] User: Enters requested identity verification information.

[0751] Step 11:

[0752] Terminal: Sends the entered personal identification information to the server.

[0753] Step 12:

[0754] Server: Receives identity verification information and verifies its security.

[0755] Example 1

[0756] 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."

[0757] Conventional matching services lack support for users to create attractive profiles and offer a lack of appropriate suggestions for messaging, which can lead to communication issues. Furthermore, in terms of ensuring safety, the systems for detecting suspicious behavior are insufficient, potentially undermining users' sense of security.

[0758] 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.

[0759] In this invention, the server includes means for collecting information entered by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for ensuring security by requesting personal identification information, and means for analyzing the user's activity data and detecting suspicious behavioral patterns. This allows users to easily create attractive profiles and use the matching service efficiently and safely.

[0760] "User" refers to an individual who uses the Matching Service.

[0761] "Terminal" refers to the device used by the user (smartphone, tablet, PC, etc.).

[0762] "Server" refers to a computer system that processes user requests and manages and stores data.

[0763] "Input information" refers to basic information such as name, hobbies, and occupation provided by the user through the terminal.

[0764] A "generative model" refers to an artificial intelligence model that generates self-introductions and message suggestions based on input information.

[0765] "Self-introduction" refers to an introduction created by a generative model based on user input information.

[0766] "Profile" refers to a personal information page that includes the user's basic information and self-introduction.

[0767] "Message History" means a record of messages previously sent or received by a User.

[0768] "Profile information" refers to data including basic information about the user and their contacts, self-introductions, etc.

[0769] "Proposed message" refers to a new message proposal generated by a generative model based on past message history and profile information.

[0770] "Activity data" refers to data on user usage behavior, such as sending messages or viewing profiles.

[0771] A "database" refers to a system for storing multiple data managed by a server.

[0772] "Matching algorithm" refers to a calculation method for efficiently and appropriately matching users with each other.

[0773] "Identifying Information" means additional personally identifiable information provided by you for security purposes.

[0774] "Suspicious behavioral patterns" refer to patterns of suspicious behavior that differ from normal usage.

[0775] MODE FOR CARRYING OUT THE INVENTION

[0776] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[0777] Profile creation support

[0778] First, we will explain the process by which a user creates a profile.

[0779] 1. Display the profile entry form

[0780] The server sends the profile entry section of a web page or mobile application to the user's device, for example, presented as an HTML form or app screen.

[0781] 2. Enter your user information

[0782] Users enter basic information such as their name, hobbies, and occupation into a profile form.

[0783] 3. Sending input information to the server

[0784] The terminal sends the entered information to the server, which verifies the received data and stores it in a database.

[0785] 4. Generating self-introductions

[0786] The server sends a prompt to the generative AI model based on the user's input, and the generative AI model generates a self-introduction, such as "Hello, I'm Taro Yamada, an engineer. My hobby is reading, and I often spend my days off reading books."

[0787] 5. Adding and displaying your bio to your profile

[0788] The server adds the generated self-introduction to the user profile and transmits the profile data to the terminal, which displays the updated profile information to the user.

[0789] Message suggestions

[0790] Next, the process by which a user exchanges messages with other users will be described.

[0791] 1. Submit a message suggestion request

[0792] The terminal sends a message suggestion request to the server when the user opens the message input area.

[0793] 2. Collection of message history and profile information

[0794] The server retrieves the user's past message history and the other person's profile information from a database.

[0795] 3. Message Proposal Generation

[0796] The server passes the collected data to a generative AI model to generate appropriate message content, such as "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?"

[0797] 4. View and send message suggestions

[0798] The server sends the generated message to the terminal, which displays the proposed message in the message input area and allows the user to send it as is.

[0799] Data accumulation and analysis

[0800] Finally, we will explain how to accumulate user activity data and analyze it.

[0801] 1. Collection of activity data

[0802] The device collects activity data, such as user messages sent and profile views.

[0803] 2. Sending activity data to the server

[0804] The terminal periodically transmits the collected activity data to the server.

[0805] 3. Data accumulation and storage

[0806] The server stores the received activity data in a database.

[0807] 4. Data analysis and algorithm updates

[0808] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[0809] 5. Identity verification and security

[0810] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[0811] Specific examples

[0812] 1. Create your profile

[0813] When a user enters "My hobby is reading" or "My occupation is engineer," the server uses a generative AI model to generate a self-introduction such as "Hello, I'm Yamada Taro, an engineer. My hobby is reading, and I often spend my days off reading books," and displays this on the device.

[0814] 2. Message proposal

[0815] When a user opens the message input area, the device sends a message suggestion request to the server, which uses a generative AI model to generate a message like, "Hello! I understand you're a movie lover. Can you recommend a movie you've seen recently?" and displays it on the device.

[0816] Prompt Sentence Examples

[0817] 1. "Generate a self-introduction when a user enters 'reading' as a hobby."

[0818] 2. "If a user has previously sent a message saying 'Nice to meet you, nice to meet you,' suggest a message to send next."

[0819] In this way, the present invention reduces the burden on users and enables them to use matching services efficiently and safely.

[0820] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0821] Profile creation support

[0822] Step 1:

[0823] The terminal sends a request to the server when the user opens the profile entry section.

[0824] Input: Request packet

[0825] Output: Profile input form

[0826] Specific operation: The server sends a web page or app profile entry form to the device, which then displays the form to the user.

[0827] Step 2:

[0828] Users enter basic information such as name, hobbies, and occupation into the feel input form.

[0829] Input: User enters basic information

[0830] Output: Basic information entered

[0831] What it does: The device temporarily stores the information you enter.

[0832] Step 3:

[0833] The terminal transmits the input information to the server.

[0834] Input: Basic information entered by the user

[0835] Output: Basic information sent

[0836] Specific operation: The terminal sends the data entered in the form to the server, which receives this information and stores it in a database.

[0837] Step 4:

[0838] The server passes the received basic information to a generative AI model to generate an appealing self-introduction.

[0839] Input: User basic information

[0840] Output: Generated bio

[0841] Specific operation: The generative AI model generates a self-introduction based on the prompt: "Please generate a self-introduction if the user enters 'reading' as a hobby."

[0842] Step 5:

[0843] The server adds the generated self-introduction to the user profile and transmits the updated profile data to the terminal.

[0844] Input: Generated bio, user profile information

[0845] Output: Updated profile information

[0846] Specific operation: The server adds the generated self-introduction to the user profile and sends the updated profile to the terminal, which displays this information to the user.

[0847] Message suggestions

[0848] Step 1:

[0849] When the user opens the message input area, the terminal sends a message suggestion request to the server.

[0850] Input: Message suggestion request

[0851] Output: Acknowledgement of request

[0852] Specific operation: The terminal sends a message suggestion request to the server.

[0853] Step 2:

[0854] The server collects the user's past message history and the other person's profile information from a database.

[0855] Input: User ID, other user ID

[0856] Output: Past message history, other person's profile information

[0857] Specific operation: The server retrieves the necessary data from the database.

[0858] Step 3:

[0859] The server passes the collected information to a generative AI model to generate appropriate message content.

[0860] Input: Past message history, other person's profile information

[0861] Output: Proposed message content

[0862] Specific behavior: The generative AI model generates a message based on the prompt: "If the user has previously sent the message 'Nice to meet you, nice to meet you,' please suggest the next message to send."

[0863] Step 4:

[0864] The server sends the generated message to the terminal, and the terminal displays the suggested message in the message input area.

[0865] Input: Generated message content

[0866] Output: Proposal message displayed on the terminal

[0867] Specific operation: The server sends the generated message to the terminal, which displays it in the message input area. The user can then send the suggested message as is.

[0868] Data accumulation and analysis

[0869] Step 1:

[0870] The device collects activity data, such as user messages sent and profile views.

[0871] Input: User operation log

[0872] Output: Collected activity data

[0873] Specific operation: The device records the user's actions and temporarily stores the data.

[0874] Step 2:

[0875] The terminal periodically transmits the collected activity data to the server.

[0876] Input: Collected activity data

[0877] Output: Send data to the server

[0878] Specific operation: The terminal transmits data to the server at regular intervals.

[0879] Step 3:

[0880] The server stores the received activity data in a database.

[0881] Input: Activity data

[0882] Output: Data items stored in the database

[0883] Specific operation: The server stores the received data in a database.

[0884] Step 4:

[0885] The server analyzes the accumulated activity data and extracts the user's behavioral patterns.

[0886] Input: Accumulated activity data

[0887] Output: Behavioral pattern analysis results

[0888] Specific operation: The server analyzes the user's behavioral patterns using a data analysis algorithm.

[0889] Step 5:

[0890] The server updates the matching algorithm based on the analysis results, and if it detects suspicious behavioral patterns, it requests additional identity verification information from the user via their device.

[0891] Input: Behavioral pattern analysis results

[0892] Output: Updated matching algorithm, safety confirmation request

[0893] Specific operation: The server adjusts the matching algorithm based on the analysis results and sends a message to the user requesting identity verification if suspicious behavior is detected.

[0894] Through this series of steps, users can easily create an attractive profile, receive appropriate message suggestions, and use the matching service safely and efficiently.

[0895] (Application example 1)

[0896] 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."

[0897] Conventional matching services and online shopping sites have had problems with generating user input information, message content, product reviews, etc., requiring a great deal of effort from the user and making it difficult to provide personalized information. It is also not easy to update recommendation algorithms based on collected data or ensure their safety. To solve these issues and improve the user experience, new systems and methods must be provided.

[0898] 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.

[0899] In this invention, the server includes means for collecting information entered by a user, means for generating a self-introduction or product description using a generative model based on the collected information, means for adding the generated self-introduction or product description to the user's profile or product detail page, means for displaying the profile information or product detail information to the user, means for collecting the user's past message history and other users' profile information or means for collecting the user's past reviews and purchase history, means for suggesting message content or review content using a generative model based on the collected information, means for displaying the suggested message content or review content to the user, means for accumulating user activity data or purchase data, means for analyzing the accumulated activity data or purchase data, means for updating the user's matching algorithm or recommendation algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows users to easily generate personalized self-introductions, product descriptions, messages, and reviews, and enables safe and effective matching and product suggestions.

[0900] "User" refers to any individual or legal entity that uses the system or services.

[0901] "Collection Method" refers to the software or hardware functionality used to collect particular information.

[0902] "Generative Model" refers to an artificial intelligence model used to generate appropriate messages and testimonials for users.

[0903] "About Me" refers to text that contains summarized information about the user.

[0904] "Product Description" refers to text containing a description of a particular product.

[0905] "Profile information" refers to data including a user's personal information, preferences, and past behavioral history.

[0906] "Product details" refers to data including information about a product, such as specifications, features, and price.

[0907] "Message History" refers to a record of messages that a User has previously sent or received.

[0908] "Purchase history" refers to a record of products a user has purchased in the past.

[0909] "Suggestion means" refers to a system function for suggesting content generated based on collected information to users.

[0910] "Display" refers to the functionality that visually presents suggested messages, reviews, and profile information to users.

[0911] "Activity Data" refers to data regarding a series of actions taken by a user on the System.

[0912] "Storage means" refers to the function for saving and managing collected data.

[0913] "Analysis means" refers to the function for analyzing accumulated data to extract user behavior patterns and preferences.

[0914] A "matching algorithm" refers to a calculation method for optimally connecting users.

[0915] A "recommendation algorithm" refers to a calculation method for suggesting the most suitable products and content to users.

[0916] "Identification Information" means information necessary to verify a User's identity.

[0917] This invention is a system that uses a generative AI model to generate, display, and suggest user profile information, messages, and product descriptions. The specific system configuration and processing flow for implementing this invention are described below.

[0918] Profile Creation Support Embodiment

[0919] Collecting user input information

[0920] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile input form. This information is sent from the device to the server.

[0921] Generating a self-introduction

[0922] The server passes the information submitted by the user to a generative model, which generates a self-introduction based on the collected information. The generative model uses OpenAI's "text-davinci-003" engine.

[0923] Adding and displaying your bio

[0924] The server adds the generated self-introduction to the user's profile data and transmits the updated profile information to the terminal, where the automatically generated self-introduction is displayed.

[0925] Specific examples

[0926] When a user inputs "My hobby is watching movies" or "My occupation is designer," the server uses the generative model to generate a self-introduction such as "Hello, I work as a designer. My hobby is watching movies, and I often go to the cinema on weekends. Nice to meet you," and displays it on the device.

[0927] Message Proposal Embodiment

[0928] Collection of message history and profile information

[0929] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[0930] Generate and display message suggestions

[0931] The server passes the collected information to the generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area.

[0932] Specific examples

[0933] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest the message "Hello! I heard you enjoy traveling. Do you have any recommendations for places you've been recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[0934] Data storage and analysis implementation

[0935] Accumulation of activity data

[0936] The device collects activity data, such as messages sent and profile views, and periodically transmits it to a server, which stores the data in a database.

[0937] Data analysis and algorithm updates

[0938] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching or recommendation algorithm based on the analysis results.

[0939] Identity verification and security

[0940] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification from the user, which will then be used to verify the user's identity and take appropriate measures.

[0941] Specific examples

[0942] The server collects information about the user's past messages, viewed products, and purchased products, and updates the recommendation algorithm based on this data. As a result, the user can find more relevant products. If suspicious behavior is detected, the server will request additional information from the user to verify their identity (e.g., resubmit ID).

[0943] Prompt Sentence Examples

[0944] Product description generation prompt:

[0945] Generate compelling product descriptions based on user information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n

[0946] Prompt for generating review suggestions:

[0947] User information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n Product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n Suggest compelling product reviews that users can write based on past reviews: ['This product has good sound quality.']\n

[0948] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0949] Step 1:

[0950] Collecting user input information

[0951] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile form. The information is then sent from the device to the server. Input is done using text fields and drop-down menus.

[0952] Input: User profile information

[0953] Output: Input sent to the server

[0954] Step 2:

[0955] Generating a self-introduction

[0956] The server receives the information sent by the user and passes it to a generative AI model (OpenAI's text-davinci-003 engine) as a prompt. The generative model generates a self-introduction based on the prompt.

[0957] Input: User profile information, prompt text

[0958] Output: Generated bio

[0959] Step 3:

[0960] Adding and displaying your bio

[0961] The server adds the generated self-introduction to the user's profile data, then transmits the updated profile information to the terminal, and the terminal displays the profile information including the self-introduction.

[0962] Input: Generated self-introduction

[0963] Output: Updated profile information sent to the device

[0964] Step 4:

[0965] Collection of message history and profile information

[0966] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[0967] Input: User message input request

[0968] Output: Collected message history and profile information

[0969] Step 5:

[0970] Generate and display message suggestions

[0971] The server passes the collected data to the generative model to generate appropriate message content, which is then sent to the device and displayed in the message input area.

[0972] Input: Message history, profile information, prompt text

[0973] Output: Generated message content

[0974] Step 6:

[0975] Accumulation of user activity data

[0976] The device periodically sends activity data, such as user messages sent and profile views, to a server, which stores this data in a database.

[0977] Input: User activity data

[0978] Output: Activity data stored in a database

[0979] Step 7:

[0980] Data analysis and algorithm updates

[0981] The server analyzes the accumulated activity data and extracts user behavior patterns. Based on the extracted data, the matching algorithm or recommendation algorithm is updated. Machine learning models and statistical analysis tools are used for data analysis.

[0982] Input: Accumulated activity data

[0983] Output: Updated algorithm

[0984] Step 8:

[0985] Identity verification and security

[0986] If the server detects suspicious behavior patterns through data analysis, it will request additional identification information from the user, verify the user's security based on the information provided, and take additional measures if necessary.

[0987] Input: Analysis results, personal identification information

[0988] Output: User confirmed safe or countermeasure information

[0989] 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.

[0990] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[0991] Profile creation support

[0992] First, we will explain the process by which a user creates a profile.

[0993] Collecting user input information

[0994] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[0995] Generating a self-introduction

[0996] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state.Then, the received information and the emotion recognition results are input into a generative model to generate a self-introduction sentence based on the user's emotions.

[0997] Adding and displaying your bio

[0998] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[0999] Specific examples

[1000] When a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you," and displays it on the device.

[1001] Message suggestions

[1002] Next, we will explain the process by which a user exchanges messages with other users.

[1003] Collection of message history and profile information

[1004] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[1005] Generate and display message suggestions

[1006] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[1007] Specific examples

[1008] If the user has previously sent "Nice to meet you, nice to meet you," the server uses the emotion engine to determine that the user is in a curious state. The generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send it.

[1009] Data accumulation and analysis

[1010] Finally, we will explain how to accumulate user activity data and analyze it.

[1011] Accumulation of activity data

[1012] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[1013] Data analysis and algorithm updates

[1014] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes their emotional state using an emotion engine. Based on the analysis results, the server updates the matching algorithm.

[1015] Identity verification and security

[1016] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[1017] In this way, by combining emotion engines, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, further improving the user experience and providing a safe matching experience.

[1018] The processing flow will be explained below.

[1019] Profile creation support

[1020] Profile Creation Process Steps

[1021] Step 1:

[1022] Terminal: Presents a profile entry form to the user.

[1023] Step 2:

[1024] User: Enter basic information such as name, hobbies, and occupation.

[1025] Step 3:

[1026] Terminal: Sends the information entered by the user to the server.

[1027] Step 4:

[1028] Server: Receives the information entered by the user.

[1029] Step 5:

[1030] Server: Uses the emotion engine to analyze the user's emotional state.

[1031] Step 6:

[1032] Server: Inputs the emotional state and input information into the generative model and generates a self-introduction sentence.

[1033] Step 7:

[1034] Server: Add the generated bio to the user's profile data.

[1035] Step 8:

[1036] Server: Sends updated profile information to the device.

[1037] Step 9:

[1038] On your device: Display your profile information, including a user-generated bio.

[1039] Message suggestions

[1040] Message Proposal Processing Steps

[1041] Step 1:

[1042] Users: View the profile of the person you want to message and open the message input area.

[1043] Step 2:

[1044] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[1045] Step 3:

[1046] Server: Receives the message proposal request.

[1047] Step 4:

[1048] Server: Collects the user's past message history and the other person's profile information.

[1049] Step 5:

[1050] Server: Utilizes the emotion engine to analyze the user's current emotional state.

[1051] Step 6:

[1052] Server: Inputs the collected information and emotional state into a generative model to generate message content with an appropriate emotional tone.

[1053] Step 7:

[1054] Server: Sends the generated message content to the terminal.

[1055] Step 8:

[1056] Terminal: Display suggested message content in the message entry area.

[1057] Step 9:

[1058] User: Review the suggested message and edit it if necessary.

[1059] Step 10:

[1060] User: Presses the send button to send the final message to the other party.

[1061] Step 11:

[1062] Terminal: Sends the final message to the server.

[1063] Step 12:

[1064] Server: Sends the last message received to the other party.

[1065] Data accumulation and analysis

[1066] Data accumulation and analysis process steps

[1067] Step 1:

[1068] Device: Collects activity data such as messages sent and profile views.

[1069] Step 2:

[1070] Device: Periodically sends collected activity data to the server.

[1071] Step 3:

[1072] Server: Stores the received activity data in a database.

[1073] Step 4:

[1074] Server: Runs the system for data analysis.

[1075] Step 5:

[1076] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[1077] Step 6:

[1078] Server: Updates the matching algorithm based on the analysis results.

[1079] Step 7:

[1080] Server: Uses the emotion engine to analyze the user's emotional state.

[1081] Step 8:

[1082] Server: Further adjusts the matching algorithm, taking into account the analyzed emotional state.

[1083] Identity verification process steps

[1084] Step 9:

[1085] Server: Performs data analysis and detects suspicious behavioral patterns.

[1086] Step 10:

[1087] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[1088] Step 11:

[1089] Terminal: Receives notifications from the server and displays them to the user.

[1090] Step 12:

[1091] User: Enters requested identity verification information.

[1092] Step 13:

[1093] Terminal: Sends the entered personal identification information to the server.

[1094] Step 14:

[1095] Server: Receives identity verification information and verifies its security.

[1096] Example 2

[1097] 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."

[1098] The objective of this invention is to reduce the psychological burden on users and provide a safe and efficient matching experience. In particular, the objective is to realize a system that is natural and easy to use for users by creating profiles and suggesting messages that take into account the user's emotional state.

[1099] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information input by the user, means for recognizing an emotional state based on the collected information, means for generating a self-introduction using a generative model based on the emotional state and the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for collecting the user's past message history and other party's profile information, means for suggesting message content using a generative model based on the collected information and the user's emotional state, means for displaying the suggested message content to the user, means for accumulating user activity data, means for analyzing the accumulated activity data and the user's emotional state, means for updating the user's matching algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows for profile creation and message suggestions that take the user's emotional state into consideration, improving the user experience and providing a safe matching experience.

[1100] "Means for collecting information entered by the user" refers to a device or system that electrically or electronically obtains basic information such as name, hobbies, and occupation entered by the user into the terminal.

[1101] A "means for recognizing emotional state" is a device or algorithm that analyzes input information and user behavioral data to identify the user's current emotional or psychological state.

[1102] The "means for generating a self-introduction using a generative model" is a device or system that automatically creates a self-introduction for a user using natural language processing technology based on the analysis of collected information and emotional state.

[1103] A "means for adding to a user's profile" is a device or system that merges the generated self-introduction and other information into the user's digital profile and stores it.

[1104] "Means for displaying profile information to a user" refers to a device or system that visually presents the information contained in a user's profile, such as through a terminal display.

[1105] "Means for collecting a user's past message history and other party profile information" refers to a device or system that obtains messages previously sent and received by a user and other party profile data.

[1106] The "means for suggesting message content using a generative model" is a device or system that automatically creates appropriate message content based on collected past message history and the other party's profile information.

[1107] The "means for displaying the proposed message content to the user" refers to a device or system that displays the generated message content on the user's terminal so that the message content can be confirmed and edited.

[1108] "Means for storing user activity data" refers to a device or system that records and stores user behavioral data such as sending messages and viewing profiles.

[1109] The "means for analyzing accumulated activity data and the user's emotional state" refers to a device or system that analyzes accumulated behavioral data and emotional data using statistical methods and machine learning algorithms.

[1110] The "means for updating the user's matching algorithm" is a device or system that improves or optimizes the algorithm of the matching system based on the analysis results.

[1111] "Means for detecting suspicious behavioral patterns and requesting identity verification information" refers to a device or system that analyzes user behavioral data and, if a suspicious behavioral pattern is found, obtains additional identity verification information from the user.

[1112] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[1113] Hardware and software used

[1114] Device: The device on which users enter information and view profiles and messages, such as a smartphone, tablet, or PC.

[1115] Server: A computer system used to process and store user information, run the emotion engine and generative AI models.

[1116] Emotion engine: Software for analyzing and recognizing the user's emotional state.

[1117] Generative AI model: Software that generates self-introductions and message content based on user input information and emotion recognition results.

[1118] Profile creation support

[1119] First, we will explain the process by which a user creates a profile.

[1120] Collecting user input information

[1121] On the device, the user enters basic information such as name, hobbies, and occupation into a profile input form. For example, the user enters "Name: Taro," "Hobbies: Reading," and "Occupation: Engineer." The entered information is sent to the server.

[1122] Generating a self-introduction

[1123] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state. For example, it may determine from the user's description that the user is in a relaxed state. The received information and the emotion recognition results are then input into a generative AI model, which generates a self-introduction based on the user's emotions. Specifically, it generates the sentence, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you."

[1124] Adding and displaying your bio

[1125] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[1126] Message suggestions

[1127] Next, we will explain the process by which a user exchanges messages with other users.

[1128] Collection of message history and profile information

[1129] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[1130] Generate and display message suggestions

[1131] The server passes the collected data and emotional state to a generative AI model to generate message content with an appropriate emotional tone. For example, if a user has previously sent "Nice to meet you, nice to meet you," the emotion engine determines that the user is in a curious state. In this case, the generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send this suggested message as is.

[1132] Data accumulation and analysis

[1133] Finally, we will explain how to accumulate and analyze user activity data.

[1134] Accumulation of activity data

[1135] The device continuously collects data on users' activities, such as sending messages and viewing profiles, and periodically transmits this data to a server, which then stores it in a database.

[1136] Data analysis and algorithm updates

[1137] The server analyzes the accumulated activity data and the user's emotional state to extract user behavior patterns. For example, if a user frequently uses a certain keyword, the server analyzes that pattern and uses it to update the algorithm.

[1138] Identity verification and security

[1139] If the server detects suspicious behavior patterns based on the analysis data, it will request additional authentication information from the user via the device. For example, if suspicious activity is detected, the server will send additional authentication information to the user, who will then provide it to confirm their safety.

[1140] In this way, by combining an emotion engine with a generative AI model, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, improving the user experience and providing a safe matching experience.

[1141] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1142] Step 1: Collect user input

[1143] The user enters basic information such as name, hobbies, and occupation into a profile input form. Specifically, the user enters the name "Taro," the hobby "reading," and the occupation "engineer." This input information is stored as digital data on the device.

[1144] Step 2: Submit your input

[1145] The terminal sends the entered user information to the server. In this case, the terminal sends the data "Name: Taro, Hobby: Reading, Occupation: Engineer" to the server. The input is the user information, and the output is the information data sent to the server.

[1146] Step 3: Recognizing your emotional state

[1147] The server starts an emotion engine based on the received user information and analyzes the user's emotional state. For example, it determines that the user is in a relaxed state based on their writing. In this case, the input is the transmitted user information data, and the output is the analyzed emotional state (relaxed state).

[1148] Step 4: Generate a self-introduction

[1149] The server uses an emotion engine and a generative AI model to integrate the received information with the emotion recognition results to generate a self-introduction. For example, the generative AI model generates a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you." The input is user information and emotion recognition results, and the output is the generated self-introduction.

[1150] Step 5: Add and submit your bio

[1151] The server adds the generated self-introduction to the profile data and sends the updated profile information to the terminal. The data received by the terminal is the updated profile information. The input is the generated self-introduction, and the output is the updated profile information.

[1152] Step 6: View your profile information

[1153] The device displays the received profile information to the user. The user can check the profile information, including the self-introduction text. The input is the updated profile information, and the output is the profile information displayed to the user.

[1154] Step 7: Submit a message suggestion request

[1155] When a user opens the message input area, the device sends a message suggestion request to the server. This request includes the user's ID and message history information. The input is the user's action, and the output is the request data to the server.

[1156] Step 8: Gather message history and profile information

[1157] The server receives the message suggestion request and collects the user's past message history and the other party's profile information. The input is the request data sent to the server, and the output is the collected message history and profile information.

[1158] Step 9: Analyze your emotional state

[1159] The server uses the collected data to analyze the user's current emotional state using an emotion engine. For example, the analysis may determine that the user is in a curious state. The input is the collected data, and the output is the analyzed emotional state.

[1160] Step 10: Generate message proposals

[1161] The server passes the analysis results from the emotion engine to the generative AI model, which generates a message with an appropriate emotional tone. For example, it generates a message like, "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?" The input is the emotional state and collected data, and the output is the generated message.

[1162] Step 11: Send and display the suggestion message to the user

[1163] The server sends the generated message to the terminal and displays it in the message input area. The user can review the suggested message and send it as is or edit it and send it. The input is the generated message and the output is the message that is displayed to the user.

[1164] Step 12: Accumulating activity data

[1165] The device continuously collects user activity data, such as message sending and profile viewing, and periodically transmits it to the server. The input is the user behavior data, and the output is the activity data transmitted to the server.

[1166] Step 13: Data analysis and algorithm updates

[1167] The server analyzes the accumulated activity data and the user's emotional state to extract the user's behavioral patterns. The matching algorithm is updated based on the analysis results. The input is the accumulated activity data and the user's emotional state, and the output is the updated algorithm.

[1168] Step 14: Request and secure identity verification information

[1169] If the server detects a suspicious behavior pattern based on the analysis data, it requests additional identity verification information from the user via the terminal. The user provides this information to confirm safety. The input is the suspicious behavior pattern and the user's identity verification information, and the output is the verification result.

[1170] (Application example 2)

[1171] 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."

[1172] Conventional matching systems do not take into account the user's emotional state, which limits their ability to reduce the user's psychological burden or provide a safe and efficient matching experience. Furthermore, they do not take into account the user's emotions when displaying advertisements, making it difficult to provide personalized advertisements.

[1173] 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 collecting information input by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying profile information to the user, emotion recognition means for recognizing the user's emotion, means for selecting targeted advertisements based on the emotion recognition results, and means for displaying the selected advertisements to the user. This allows users to enjoy an efficient matching experience with peace of mind, and makes it possible to display appropriate advertisements personalized to each user.

[1174] "Information entered by the user" refers to basic information and message history provided by the user to the system.

[1175] "Means of collection" refers to the functions on the system for obtaining user input information and activity data.

[1176] A "generative model" refers to a machine learning model that creates self-introductions and message content based on information entered by the user.

[1177] "Means for generating a self-introduction" refers to a function for creating a self-introduction based on information input by a user using a generative model.

[1178] "Means to add to profile" refers to functionality for incorporating the generated self-introduction into user profile data.

[1179] "Profile Information" refers to a collection of user data including the user's basic information, self-introduction, etc.

[1180] "Means of displaying to the user" refers to the function for displaying generated profile information, message content, advertisements, etc. on the user's device.

[1181] "Emotion recognition means" refers to a function for analyzing and determining the user's emotional state in real time.

[1182] "Means for selecting targeted advertisements" refers to a function for selecting the most suitable advertisement for a user based on the results of emotion recognition.

[1183] "Means for displaying selected advertisements to users" refers to a function for displaying selected advertising content on a user's device based on the user's emotions.

[1184] This invention relates to a matching system that combines an emotion engine that recognizes the user's emotions, thereby reducing the user's psychological burden and providing a safe and efficient matching experience. Specific means for carrying out the invention will now be described.

[1185] Profile creation support

[1186] First, we will explain the process by which a user creates a profile.

[1187] Collecting user input information

[1188] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[1189] Generating a self-introduction

[1190] The server receives the user's input information and recognizes the user's emotional state using an emotion recognition means.Then, the received information and the emotion recognition results are input into the generative model to generate a self-introduction sentence based on the emotion.

[1191] Adding and displaying your bio

[1192] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[1193] Message suggestions

[1194] Next, we will explain the process by which a user exchanges messages with other users.

[1195] Collection of message history and profile information

[1196] When a user opens the message input area on the device, a message suggestion request is sent to the server, which then collects the user's past message history and the other party's profile information, and analyzes the user's emotional state using emotion recognition means.

[1197] Generate and display message suggestions

[1198] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[1199] Advertisement display

[1200] Next, the process of displaying targeted advertisements based on the user's emotional state will be described.

[1201] Emotion Recognition and Ad Selection

[1202] While the user is watching an advertisement, the device uses its camera to analyze the user's emotional state through facial expressions and voice through emotion recognition.The server then selects an appropriate advertisement from the advertisement database based on this emotional state.

[1203] Viewing and switching ads

[1204] The selected advertisement is displayed to the user on the device. If the user's emotional state changes, the server selects a new appropriate advertisement and switches the advertisement displayed on the device.

[1205] Data accumulation and analysis

[1206] Finally, we will explain how to accumulate user activity data and analyze it.

[1207] Accumulation of activity data

[1208] The device collects user activity data, such as messages sent, profile views, and advertisement viewing, and periodically transmits this data to a server, which then stores it in a database.

[1209] Data analysis and algorithm updates

[1210] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes the user's emotional state using emotion recognition means. Based on the analysis results, the server updates the matching algorithm.

[1211] Identity verification and security

[1212] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[1213] Examples and prompts

[1214] Specific examples

[1215] For example, if a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you," and displays it on the device.

[1216] Prompt Sentence Examples

[1217] Recommended ad prompt when user is having fun:

[1218] Title: Delivering the latest entertainment experience!

[1219] Contents:

[1220] 1. Latest movie introductions

[1221] 2. New game release information

[1222] 3. Suggesting unique places to go

[1223] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1224] Step 1:

[1225] The user enters basic information such as name, hobbies, and occupation into a profile input form. The entered information is sent from the device to the server. The input data includes the user's text data, and the device captures the user input in real time and sends it to the server.

[1226] Step 2:

[1227] The server receives the user's input information and analyzes the user's emotional state using an emotion recognition means. In this process, the server analyzes the text data and the corresponding emotion data, and the input data is processed by the emotion recognition algorithm. The output is the user's emotional state information.

[1228] Step 3:

[1229] The server inputs the received user input information and emotion recognition results into a generative model, which generates a self-introduction based on the emotion data. The generative model generates text using a machine learning algorithm (e.g., a neural network). The output is a self-introduction based on the emotion.

[1230] Step 4:

[1231] The generated self-introduction is added to the user's profile data by the server. The server accesses the profile database and updates the profile information, including the self-introduction. The output is the updated profile data.

[1232] Step 5:

[1233] The profile information is sent from the server to the terminal, and the terminal displays the profile information, including a self-introduction, to the user. Here, the terminal visually displays the profile data and provides it to the user. The output is the displayed profile information.

[1234] Step 6:

[1235] While a user is watching an advertisement, the device's camera function is used to capture the user's facial expressions, and the emotional state is analyzed in real time through an emotion recognition means. The emotional state data is sent from the device to a server. The input is the camera-captured image, and the output is the emotional state data.

[1236] Step 7:

[1237] The server selects the most suitable advertisement from the advertisement database based on the result of the emotion recognition means. In this process, an advertisement selection algorithm is used to select the target advertisement and generate selected advertisement data. The output is the advertisement content.

[1238] Step 8:

[1239] The selected advertisement is sent from the server to the device, which then displays it to the user. The device visually presents the advertisement content to the user and changes and updates the execution as necessary. The output is the advertisement displayed to the user.

[1240] Step 9:

[1241] The device collects activity data such as sending messages, viewing profiles, and watching advertisements, and periodically sends it to the server. The input is activity data, and the output is data stored in the database.

[1242] Step 10:

[1243] The server analyzes the accumulated activity data to analyze the user's behavioral patterns and emotional state, and updates the matching algorithm and ad selection algorithm accordingly. The output is the updated algorithm.

[1244] Step 11:

[1245] If the server detects suspicious behavior patterns through data analysis, it requests additional identity verification information from the user via the terminal. The server verifies the provided identity verification information, checks its security, and takes measures if necessary. The output is the verification result and measures.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] [Third embodiment]

[1250] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1251] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1252] 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).

[1253] 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.

[1254] 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.

[1255] 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).

[1256] 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. 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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."

[1262] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[1263] Profile creation support

[1264] First, we will explain the process by which a user creates a profile.

[1265] Collecting user input information

[1266] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[1267] Generating a self-introduction

[1268] The server receives the user's input and passes it to the generative model, which then generates an appealing self-introduction based on the input.

[1269] Adding and displaying your bio

[1270] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[1271] Specific examples

[1272] When a user enters "My hobby is reading" or "My occupation is an engineer," the server uses the generative model to generate a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I often spend my days off reading books. Nice to meet you," and displays it on the device.

[1273] Message suggestions

[1274] Next, we will explain the process by which a user exchanges messages with other users.

[1275] Collection of message history and profile information

[1276] When a user opens the message input area on their device, it sends a message suggestion request to the server, which collects the user's past message history and the other person's profile information.

[1277] Generate and display message suggestions

[1278] The server passes the collected data to a generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area. The user can then review the suggested message, edit it if necessary, and send it.

[1279] Specific examples

[1280] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest a message like "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[1281] Data accumulation and analysis

[1282] Finally, we will explain how to accumulate user activity data and analyze it.

[1283] Accumulation of activity data

[1284] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[1285] Data analysis and algorithm updates

[1286] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[1287] Identity verification and security

[1288] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[1289] In this way, the present invention reduces the burden on users and provides a safe and efficient matching experience.

[1290] The processing flow will be explained below.

[1291] Profile creation support

[1292] Profile Creation Process Steps

[1293] Step 1:

[1294] Terminal: Presents a profile entry form to the user.

[1295] Step 2:

[1296] User: Enter basic information such as name, hobbies, and occupation.

[1297] Step 3:

[1298] Terminal: Sends the information entered by the user to the server.

[1299] Step 4:

[1300] Server: Receives the information entered by the user.

[1301] Step 5:

[1302] Server: Inputs the received information into the generative model and generates a self-introduction.

[1303] Step 6:

[1304] Server: Add the generated self-introduction text to the user's profile data.

[1305] Step 7:

[1306] Server: Sends updated profile information to the device.

[1307] Step 8:

[1308] On your device: Display your profile information, including a user-generated bio.

[1309] Message suggestions

[1310] Message Proposal Processing Steps

[1311] Step 1:

[1312] Users: View the profile of the person you want to message and open the message input area.

[1313] Step 2:

[1314] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[1315] Step 3:

[1316] Server: Receives the message proposal request.

[1317] Step 4:

[1318] Server: Collects the user's past message history and the other person's profile information.

[1319] Step 5:

[1320] Server: Inputs the collected information into the generative model and generates appropriate message content.

[1321] Step 6:

[1322] Server: Sends the generated message content to the terminal.

[1323] Step 7:

[1324] Terminal: Display suggested message content in the message entry area.

[1325] Step 8:

[1326] User: Review the suggested message and edit it if necessary.

[1327] Step 9:

[1328] User: Presses the send button to send the final message to the other party.

[1329] Step 10:

[1330] Terminal: Sends the final message to the server.

[1331] Step 11:

[1332] Server: Sends the last message received to the other party.

[1333] Data accumulation and analysis

[1334] Data accumulation and analysis process steps

[1335] Step 1:

[1336] Device: Collects activity data such as messages sent and profile views.

[1337] Step 2:

[1338] Device: Periodically sends collected activity data to the server.

[1339] Step 3:

[1340] Server: Stores the received activity data in a database.

[1341] Step 4:

[1342] Server: Runs the system for data analysis.

[1343] Step 5:

[1344] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[1345] Step 6:

[1346] Server: Updates the matching algorithm based on the analysis results.

[1347] Identity verification process steps

[1348] Step 7:

[1349] Server: Performs data analysis and detects suspicious behavioral patterns.

[1350] Step 8:

[1351] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[1352] Step 9:

[1353] Terminal: Receives notifications from the server and displays them to the user.

[1354] Step 10:

[1355] User: Enters requested identity verification information.

[1356] Step 11:

[1357] Terminal: Sends the entered personal identification information to the server.

[1358] Step 12:

[1359] Server: Receives identity verification information and verifies its security.

[1360] Example 1

[1361] 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."

[1362] Conventional matching services lack support for users to create attractive profiles and offer a lack of appropriate suggestions for messaging, which can lead to communication issues. Furthermore, in terms of ensuring safety, the systems for detecting suspicious behavior are insufficient, potentially undermining users' sense of security.

[1363] 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.

[1364] In this invention, the server includes means for collecting information entered by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for ensuring security by requesting personal identification information, and means for analyzing the user's activity data and detecting suspicious behavioral patterns. This allows users to easily create attractive profiles and use the matching service efficiently and safely.

[1365] "User" refers to an individual who uses the Matching Service.

[1366] "Terminal" refers to the device used by the user (smartphone, tablet, PC, etc.).

[1367] "Server" refers to a computer system that processes user requests and manages and stores data.

[1368] "Input information" refers to basic information such as name, hobbies, and occupation provided by the user through the terminal.

[1369] A "generative model" refers to an artificial intelligence model that generates self-introductions and message suggestions based on input information.

[1370] "Self-introduction" refers to an introduction created by a generative model based on user input information.

[1371] "Profile" refers to a personal information page that includes the user's basic information and self-introduction.

[1372] "Message History" means a record of messages previously sent or received by a User.

[1373] "Profile information" refers to data including basic information about the user and their contacts, self-introductions, etc.

[1374] "Proposed message" refers to a new message proposal generated by a generative model based on past message history and profile information.

[1375] "Activity data" refers to data on user usage behavior, such as sending messages or viewing profiles.

[1376] A "database" refers to a system for storing multiple data managed by a server.

[1377] "Matching algorithm" refers to a calculation method for efficiently and appropriately matching users with each other.

[1378] "Identifying Information" means additional personally identifiable information provided by you for security purposes.

[1379] "Suspicious behavioral patterns" refer to patterns of suspicious behavior that differ from normal usage.

[1380] MODE FOR CARRYING OUT THE INVENTION

[1381] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[1382] Profile creation support

[1383] First, we will explain the process by which a user creates a profile.

[1384] 1. Display the profile entry form

[1385] The server sends the profile entry section of a web page or mobile application to the user's device, for example, presented as an HTML form or app screen.

[1386] 2. Enter your user information

[1387] Users enter basic information such as their name, hobbies, and occupation into a profile form.

[1388] 3. Sending input information to the server

[1389] The terminal sends the entered information to the server, which verifies the received data and stores it in a database.

[1390] 4. Generating self-introductions

[1391] The server sends a prompt to the generative AI model based on the user's input, and the generative AI model generates a self-introduction, such as "Hello, I'm Taro Yamada, an engineer. My hobby is reading, and I often spend my days off reading books."

[1392] 5. Adding and displaying your bio to your profile

[1393] The server adds the generated self-introduction to the user profile and transmits the profile data to the terminal, which displays the updated profile information to the user.

[1394] Message suggestions

[1395] Next, the process by which a user exchanges messages with other users will be described.

[1396] 1. Submit a message suggestion request

[1397] The terminal sends a message suggestion request to the server when the user opens the message input area.

[1398] 2. Collection of message history and profile information

[1399] The server retrieves the user's past message history and the other person's profile information from a database.

[1400] 3. Message Proposal Generation

[1401] The server passes the collected data to a generative AI model to generate appropriate message content, such as "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?"

[1402] 4. View and send message suggestions

[1403] The server sends the generated message to the terminal, which displays the proposed message in the message input area and allows the user to send it as is.

[1404] Data accumulation and analysis

[1405] Finally, we will explain how to accumulate user activity data and analyze it.

[1406] 1. Collection of activity data

[1407] The device collects activity data, such as user messages sent and profile views.

[1408] 2. Sending activity data to the server

[1409] The terminal periodically transmits the collected activity data to the server.

[1410] 3. Data accumulation and storage

[1411] The server stores the received activity data in a database.

[1412] 4. Data analysis and algorithm updates

[1413] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[1414] 5. Identity verification and security

[1415] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[1416] Specific examples

[1417] 1. Create your profile

[1418] When a user enters "My hobby is reading" or "My occupation is engineer," the server uses a generative AI model to generate a self-introduction such as "Hello, I'm Yamada Taro, an engineer. My hobby is reading, and I often spend my days off reading books," and displays this on the device.

[1419] 2. Message proposal

[1420] When a user opens the message input area, the device sends a message suggestion request to the server, which uses a generative AI model to generate a message like, "Hello! I understand you're a movie lover. Can you recommend a movie you've seen recently?" and displays it on the device.

[1421] Prompt Sentence Examples

[1422] 1. "Generate a self-introduction when a user enters 'reading' as a hobby."

[1423] 2. "If a user has previously sent a message saying 'Nice to meet you, nice to meet you,' suggest a message to send next."

[1424] In this way, the present invention reduces the burden on users and enables them to use matching services efficiently and safely.

[1425] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1426] Profile creation support

[1427] Step 1:

[1428] The terminal sends a request to the server when the user opens the profile entry section.

[1429] Input: Request packet

[1430] Output: Profile input form

[1431] Specific operation: The server sends a web page or app profile entry form to the device, which then displays the form to the user.

[1432] Step 2:

[1433] Users enter basic information such as name, hobbies, and occupation into the feel input form.

[1434] Input: User enters basic information

[1435] Output: Basic information entered

[1436] What it does: The device temporarily stores the information you enter.

[1437] Step 3:

[1438] The terminal transmits the input information to the server.

[1439] Input: Basic information entered by the user

[1440] Output: Basic information sent

[1441] Specific operation: The terminal sends the data entered in the form to the server, which receives this information and stores it in a database.

[1442] Step 4:

[1443] The server passes the received basic information to a generative AI model to generate an appealing self-introduction.

[1444] Input: User basic information

[1445] Output: Generated bio

[1446] Specific operation: The generative AI model generates a self-introduction based on the prompt: "Please generate a self-introduction if the user enters 'reading' as a hobby."

[1447] Step 5:

[1448] The server adds the generated self-introduction to the user profile and transmits the updated profile data to the terminal.

[1449] Input: Generated bio, user profile information

[1450] Output: Updated profile information

[1451] Specific operation: The server adds the generated self-introduction to the user profile and sends the updated profile to the terminal, which displays this information to the user.

[1452] Message suggestions

[1453] Step 1:

[1454] When the user opens the message input area, the terminal sends a message suggestion request to the server.

[1455] Input: Message suggestion request

[1456] Output: Acknowledgement of request

[1457] Specific operation: The terminal sends a message suggestion request to the server.

[1458] Step 2:

[1459] The server collects the user's past message history and the other person's profile information from a database.

[1460] Input: User ID, other user ID

[1461] Output: Past message history, other person's profile information

[1462] Specific operation: The server retrieves the necessary data from the database.

[1463] Step 3:

[1464] The server passes the collected information to a generative AI model to generate appropriate message content.

[1465] Input: Past message history, other person's profile information

[1466] Output: Proposed message content

[1467] Specific behavior: The generative AI model generates a message based on the prompt: "If the user has previously sent the message 'Nice to meet you, nice to meet you,' please suggest the next message to send."

[1468] Step 4:

[1469] The server sends the generated message to the terminal, and the terminal displays the suggested message in the message input area.

[1470] Input: Generated message content

[1471] Output: Proposal message displayed on the terminal

[1472] Specific operation: The server sends the generated message to the terminal, which displays it in the message input area. The user can then send the suggested message as is.

[1473] Data accumulation and analysis

[1474] Step 1:

[1475] The device collects activity data, such as user messages sent and profile views.

[1476] Input: User operation log

[1477] Output: Collected activity data

[1478] Specific operation: The device records the user's actions and temporarily stores the data.

[1479] Step 2:

[1480] The terminal periodically transmits the collected activity data to the server.

[1481] Input: Collected activity data

[1482] Output: Send data to the server

[1483] Specific operation: The terminal transmits data to the server at regular intervals.

[1484] Step 3:

[1485] The server stores the received activity data in a database.

[1486] Input: Activity data

[1487] Output: Data items stored in the database

[1488] Specific operation: The server stores the received data in a database.

[1489] Step 4:

[1490] The server analyzes the accumulated activity data and extracts the user's behavioral patterns.

[1491] Input: Accumulated activity data

[1492] Output: Behavioral pattern analysis results

[1493] Specific operation: The server analyzes the user's behavioral patterns using a data analysis algorithm.

[1494] Step 5:

[1495] The server updates the matching algorithm based on the analysis results, and if it detects suspicious behavioral patterns, it requests additional identity verification information from the user via their device.

[1496] Input: Behavioral pattern analysis results

[1497] Output: Updated matching algorithm, safety confirmation request

[1498] Specific operation: The server adjusts the matching algorithm based on the analysis results and sends a message to the user requesting identity verification if suspicious behavior is detected.

[1499] Through this series of steps, users can easily create an attractive profile, receive appropriate message suggestions, and use the matching service safely and efficiently.

[1500] (Application example 1)

[1501] 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."

[1502] Conventional matching services and online shopping sites have had problems with generating user input information, message content, product reviews, etc., requiring a great deal of effort from the user and making it difficult to provide personalized information. It is also not easy to update recommendation algorithms based on collected data or ensure their safety. To solve these issues and improve the user experience, new systems and methods must be provided.

[1503] 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.

[1504] In this invention, the server includes means for collecting information entered by a user, means for generating a self-introduction or product description using a generative model based on the collected information, means for adding the generated self-introduction or product description to the user's profile or product detail page, means for displaying the profile information or product detail information to the user, means for collecting the user's past message history and other users' profile information or means for collecting the user's past reviews and purchase history, means for suggesting message content or review content using a generative model based on the collected information, means for displaying the suggested message content or review content to the user, means for accumulating user activity data or purchase data, means for analyzing the accumulated activity data or purchase data, means for updating the user's matching algorithm or recommendation algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows users to easily generate personalized self-introductions, product descriptions, messages, and reviews, and enables safe and effective matching and product suggestions.

[1505] "User" refers to any individual or legal entity that uses the system or services.

[1506] "Collection Method" refers to the software or hardware functionality used to collect particular information.

[1507] "Generative Model" refers to an artificial intelligence model used to generate appropriate messages and testimonials for users.

[1508] "About Me" refers to text that contains summarized information about the user.

[1509] "Product Description" refers to text containing a description of a particular product.

[1510] "Profile information" refers to data including a user's personal information, preferences, and past behavioral history.

[1511] "Product details" refers to data including information about a product, such as specifications, features, and price.

[1512] "Message History" refers to a record of messages that a User has previously sent or received.

[1513] "Purchase history" refers to a record of products a user has purchased in the past.

[1514] "Suggestion means" refers to a system function for suggesting content generated based on collected information to users.

[1515] "Display" refers to the functionality that visually presents suggested messages, reviews, and profile information to users.

[1516] "Activity Data" refers to data regarding a series of actions taken by a user on the System.

[1517] "Storage means" refers to the function for saving and managing collected data.

[1518] "Analysis means" refers to the function for analyzing accumulated data to extract user behavior patterns and preferences.

[1519] A "matching algorithm" refers to a calculation method for optimally connecting users.

[1520] A "recommendation algorithm" refers to a calculation method for suggesting the most suitable products and content to users.

[1521] "Identification Information" means information necessary to verify a User's identity.

[1522] This invention is a system that uses a generative AI model to generate, display, and suggest user profile information, messages, and product descriptions. The specific system configuration and processing flow for implementing this invention are described below.

[1523] Profile Creation Support Embodiment

[1524] Collecting user input information

[1525] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile input form. This information is sent from the device to the server.

[1526] Generating a self-introduction

[1527] The server passes the information submitted by the user to a generative model, which generates a self-introduction based on the collected information. The generative model uses OpenAI's "text-davinci-003" engine.

[1528] Adding and displaying your bio

[1529] The server adds the generated self-introduction to the user's profile data and transmits the updated profile information to the terminal, where the automatically generated self-introduction is displayed.

[1530] Specific examples

[1531] When a user inputs "My hobby is watching movies" or "My occupation is designer," the server uses the generative model to generate a self-introduction such as "Hello, I work as a designer. My hobby is watching movies, and I often go to the cinema on weekends. Nice to meet you," and displays it on the device.

[1532] Message Proposal Embodiment

[1533] Collection of message history and profile information

[1534] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[1535] Generate and display message suggestions

[1536] The server passes the collected information to the generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area.

[1537] Specific examples

[1538] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest the message "Hello! I heard you enjoy traveling. Do you have any recommendations for places you've been recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[1539] Data storage and analysis implementation

[1540] Accumulation of activity data

[1541] The device collects activity data, such as messages sent and profile views, and periodically transmits it to a server, which stores the data in a database.

[1542] Data analysis and algorithm updates

[1543] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching or recommendation algorithm based on the analysis results.

[1544] Identity verification and security

[1545] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification from the user, which will then be used to verify the user's identity and take appropriate measures.

[1546] Specific examples

[1547] The server collects information about the user's past messages, viewed products, and purchased products, and updates the recommendation algorithm based on this data. As a result, the user can find more relevant products. If suspicious behavior is detected, the server will request additional information from the user to verify their identity (e.g., resubmit ID).

[1548] Prompt Sentence Examples

[1549] Product description generation prompt:

[1550] Generate compelling product descriptions based on user information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n

[1551] Prompt for generating review suggestions:

[1552] User information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n Product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n Suggest compelling product reviews that users can write based on past reviews: ['This product has good sound quality.']\n

[1553] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1554] Step 1:

[1555] Collecting user input information

[1556] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile form. The information is then sent from the device to the server. Input is done using text fields and drop-down menus.

[1557] Input: User profile information

[1558] Output: Input sent to the server

[1559] Step 2:

[1560] Generating a self-introduction

[1561] The server receives the information sent by the user and passes it to a generative AI model (OpenAI's text-davinci-003 engine) as a prompt. The generative model generates a self-introduction based on the prompt.

[1562] Input: User profile information, prompt text

[1563] Output: Generated bio

[1564] Step 3:

[1565] Adding and displaying your bio

[1566] The server adds the generated self-introduction to the user's profile data, then transmits the updated profile information to the terminal, and the terminal displays the profile information including the self-introduction.

[1567] Input: Generated self-introduction

[1568] Output: Updated profile information sent to the device

[1569] Step 4:

[1570] Collection of message history and profile information

[1571] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[1572] Input: User message input request

[1573] Output: Collected message history and profile information

[1574] Step 5:

[1575] Generate and display message suggestions

[1576] The server passes the collected data to the generative model to generate appropriate message content, which is then sent to the device and displayed in the message input area.

[1577] Input: Message history, profile information, prompt text

[1578] Output: Generated message content

[1579] Step 6:

[1580] Accumulation of user activity data

[1581] The device periodically sends activity data, such as user messages sent and profile views, to a server, which stores this data in a database.

[1582] Input: User activity data

[1583] Output: Activity data stored in a database

[1584] Step 7:

[1585] Data analysis and algorithm updates

[1586] The server analyzes the accumulated activity data and extracts user behavior patterns. Based on the extracted data, the matching algorithm or recommendation algorithm is updated. Machine learning models and statistical analysis tools are used for data analysis.

[1587] Input: Accumulated activity data

[1588] Output: Updated algorithm

[1589] Step 8:

[1590] Identity verification and security

[1591] If the server detects suspicious behavior patterns through data analysis, it will request additional identification information from the user, verify the user's security based on the information provided, and take additional measures if necessary.

[1592] Input: Analysis results, personal identification information

[1593] Output: User confirmed safe or countermeasure information

[1594] 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.

[1595] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[1596] Profile creation support

[1597] First, we will explain the process by which a user creates a profile.

[1598] Collecting user input information

[1599] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[1600] Generating a self-introduction

[1601] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state.Then, the received information and the emotion recognition results are input into a generative model to generate a self-introduction sentence based on the user's emotions.

[1602] Adding and displaying your bio

[1603] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[1604] Specific examples

[1605] When a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you," and displays it on the device.

[1606] Message suggestions

[1607] Next, we will explain the process by which a user exchanges messages with other users.

[1608] Collection of message history and profile information

[1609] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[1610] Generate and display message suggestions

[1611] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[1612] Specific examples

[1613] If the user has previously sent "Nice to meet you, nice to meet you," the server uses the emotion engine to determine that the user is in a curious state. The generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send it.

[1614] Data accumulation and analysis

[1615] Finally, we will explain how to accumulate user activity data and analyze it.

[1616] Accumulation of activity data

[1617] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[1618] Data analysis and algorithm updates

[1619] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes their emotional state using an emotion engine. Based on the analysis results, the server updates the matching algorithm.

[1620] Identity verification and security

[1621] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[1622] In this way, by combining emotion engines, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, further improving the user experience and providing a safe matching experience.

[1623] The processing flow will be explained below.

[1624] Profile creation support

[1625] Profile Creation Process Steps

[1626] Step 1:

[1627] Terminal: Presents a profile entry form to the user.

[1628] Step 2:

[1629] User: Enter basic information such as name, hobbies, and occupation.

[1630] Step 3:

[1631] Terminal: Sends the information entered by the user to the server.

[1632] Step 4:

[1633] Server: Receives the information entered by the user.

[1634] Step 5:

[1635] Server: Uses the emotion engine to analyze the user's emotional state.

[1636] Step 6:

[1637] Server: Inputs the emotional state and input information into the generative model and generates a self-introduction sentence.

[1638] Step 7:

[1639] Server: Add the generated bio to the user's profile data.

[1640] Step 8:

[1641] Server: Sends updated profile information to the device.

[1642] Step 9:

[1643] On your device: Display your profile information, including a user-generated bio.

[1644] Message suggestions

[1645] Message Proposal Processing Steps

[1646] Step 1:

[1647] Users: View the profile of the person you want to message and open the message input area.

[1648] Step 2:

[1649] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[1650] Step 3:

[1651] Server: Receives the message proposal request.

[1652] Step 4:

[1653] Server: Collects the user's past message history and the other person's profile information.

[1654] Step 5:

[1655] Server: Utilizes the emotion engine to analyze the user's current emotional state.

[1656] Step 6:

[1657] Server: Inputs the collected information and emotional state into a generative model to generate message content with an appropriate emotional tone.

[1658] Step 7:

[1659] Server: Sends the generated message content to the terminal.

[1660] Step 8:

[1661] Terminal: Display suggested message content in the message entry area.

[1662] Step 9:

[1663] User: Review the suggested message and edit it if necessary.

[1664] Step 10:

[1665] User: Presses the send button to send the final message to the other party.

[1666] Step 11:

[1667] Terminal: Sends the final message to the server.

[1668] Step 12:

[1669] Server: Sends the last message received to the other party.

[1670] Data accumulation and analysis

[1671] Data accumulation and analysis process steps

[1672] Step 1:

[1673] Device: Collects activity data such as messages sent and profile views.

[1674] Step 2:

[1675] Device: Periodically sends collected activity data to the server.

[1676] Step 3:

[1677] Server: Stores the received activity data in a database.

[1678] Step 4:

[1679] Server: Runs the system for data analysis.

[1680] Step 5:

[1681] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[1682] Step 6:

[1683] Server: Updates the matching algorithm based on the analysis results.

[1684] Step 7:

[1685] Server: Uses the emotion engine to analyze the user's emotional state.

[1686] Step 8:

[1687] Server: Further adjusts the matching algorithm, taking into account the analyzed emotional state.

[1688] Identity verification process steps

[1689] Step 9:

[1690] Server: Performs data analysis and detects suspicious behavioral patterns.

[1691] Step 10:

[1692] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[1693] Step 11:

[1694] Terminal: Receives notifications from the server and displays them to the user.

[1695] Step 12:

[1696] User: Enters requested identity verification information.

[1697] Step 13:

[1698] Terminal: Sends the entered personal identification information to the server.

[1699] Step 14:

[1700] Server: Receives identity verification information and verifies its security.

[1701] Example 2

[1702] 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."

[1703] The objective of this invention is to reduce the psychological burden on users and provide a safe and efficient matching experience. In particular, the objective is to realize a system that is natural and easy to use for users by creating profiles and suggesting messages that take into account the user's emotional state.

[1704] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information input by the user, means for recognizing an emotional state based on the collected information, means for generating a self-introduction using a generative model based on the emotional state and the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for collecting the user's past message history and other party's profile information, means for suggesting message content using a generative model based on the collected information and the user's emotional state, means for displaying the suggested message content to the user, means for accumulating user activity data, means for analyzing the accumulated activity data and the user's emotional state, means for updating the user's matching algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows for profile creation and message suggestions that take the user's emotional state into consideration, improving the user experience and providing a safe matching experience.

[1705] "Means for collecting information entered by the user" refers to a device or system that electrically or electronically obtains basic information such as name, hobbies, and occupation entered by the user into the terminal.

[1706] A "means for recognizing emotional state" is a device or algorithm that analyzes input information and user behavioral data to identify the user's current emotional or psychological state.

[1707] The "means for generating a self-introduction using a generative model" is a device or system that automatically creates a self-introduction for a user using natural language processing technology based on the analysis of collected information and emotional state.

[1708] A "means for adding to a user's profile" is a device or system that merges the generated self-introduction and other information into the user's digital profile and stores it.

[1709] "Means for displaying profile information to a user" refers to a device or system that visually presents the information contained in a user's profile, such as through a terminal display.

[1710] "Means for collecting a user's past message history and other party profile information" refers to a device or system that obtains messages previously sent and received by a user and other party profile data.

[1711] The "means for suggesting message content using a generative model" is a device or system that automatically creates appropriate message content based on collected past message history and the other party's profile information.

[1712] The "means for displaying the proposed message content to the user" refers to a device or system that displays the generated message content on the user's terminal so that the message content can be confirmed and edited.

[1713] "Means for storing user activity data" refers to a device or system that records and stores user behavioral data such as sending messages and viewing profiles.

[1714] The "means for analyzing accumulated activity data and the user's emotional state" refers to a device or system that analyzes accumulated behavioral data and emotional data using statistical methods and machine learning algorithms.

[1715] The "means for updating the user's matching algorithm" is a device or system that improves or optimizes the algorithm of the matching system based on the analysis results.

[1716] "Means for detecting suspicious behavioral patterns and requesting identity verification information" refers to a device or system that analyzes user behavioral data and, if a suspicious behavioral pattern is found, obtains additional identity verification information from the user.

[1717] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[1718] Hardware and software used

[1719] Device: The device on which users enter information and view profiles and messages, such as a smartphone, tablet, or PC.

[1720] Server: A computer system used to process and store user information, run the emotion engine and generative AI models.

[1721] Emotion engine: Software for analyzing and recognizing the user's emotional state.

[1722] Generative AI model: Software that generates self-introductions and message content based on user input information and emotion recognition results.

[1723] Profile creation support

[1724] First, we will explain the process by which a user creates a profile.

[1725] Collecting user input information

[1726] On the device, the user enters basic information such as name, hobbies, and occupation into a profile input form. For example, the user enters "Name: Taro," "Hobbies: Reading," and "Occupation: Engineer." The entered information is sent to the server.

[1727] Generating a self-introduction

[1728] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state. For example, it may determine from the user's description that the user is in a relaxed state. The received information and the emotion recognition results are then input into a generative AI model, which generates a self-introduction based on the user's emotions. Specifically, it generates the sentence, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you."

[1729] Adding and displaying your bio

[1730] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[1731] Message suggestions

[1732] Next, we will explain the process by which a user exchanges messages with other users.

[1733] Collection of message history and profile information

[1734] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[1735] Generate and display message suggestions

[1736] The server passes the collected data and emotional state to a generative AI model to generate message content with an appropriate emotional tone. For example, if a user has previously sent "Nice to meet you, nice to meet you," the emotion engine determines that the user is in a curious state. In this case, the generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send this suggested message as is.

[1737] Data accumulation and analysis

[1738] Finally, we will explain how to accumulate and analyze user activity data.

[1739] Accumulation of activity data

[1740] The device continuously collects data on users' activities, such as sending messages and viewing profiles, and periodically transmits this data to a server, which then stores it in a database.

[1741] Data analysis and algorithm updates

[1742] The server analyzes the accumulated activity data and the user's emotional state to extract user behavior patterns. For example, if a user frequently uses a certain keyword, the server analyzes that pattern and uses it to update the algorithm.

[1743] Identity verification and security

[1744] If the server detects suspicious behavior patterns based on the analysis data, it will request additional authentication information from the user via the device. For example, if suspicious activity is detected, the server will send additional authentication information to the user, who will then provide it to confirm their safety.

[1745] In this way, by combining an emotion engine with a generative AI model, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, improving the user experience and providing a safe matching experience.

[1746] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1747] Step 1: Collect user input

[1748] The user enters basic information such as name, hobbies, and occupation into a profile input form. Specifically, the user enters the name "Taro," the hobby "reading," and the occupation "engineer." This input information is stored as digital data on the device.

[1749] Step 2: Submit your input

[1750] The terminal sends the entered user information to the server. In this case, the terminal sends the data "Name: Taro, Hobby: Reading, Occupation: Engineer" to the server. The input is the user information, and the output is the information data sent to the server.

[1751] Step 3: Recognizing your emotional state

[1752] The server starts an emotion engine based on the received user information and analyzes the user's emotional state. For example, it determines that the user is in a relaxed state based on their writing. In this case, the input is the transmitted user information data, and the output is the analyzed emotional state (relaxed state).

[1753] Step 4: Generate a self-introduction

[1754] The server uses an emotion engine and a generative AI model to integrate the received information with the emotion recognition results to generate a self-introduction. For example, the generative AI model generates a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you." The input is user information and emotion recognition results, and the output is the generated self-introduction.

[1755] Step 5: Add and submit your bio

[1756] The server adds the generated self-introduction to the profile data and sends the updated profile information to the terminal. The data received by the terminal is the updated profile information. The input is the generated self-introduction, and the output is the updated profile information.

[1757] Step 6: View your profile information

[1758] The device displays the received profile information to the user. The user can check the profile information, including the self-introduction text. The input is the updated profile information, and the output is the profile information displayed to the user.

[1759] Step 7: Submit a message suggestion request

[1760] When a user opens the message input area, the device sends a message suggestion request to the server. This request includes the user's ID and message history information. The input is the user's action, and the output is the request data to the server.

[1761] Step 8: Gather message history and profile information

[1762] The server receives the message suggestion request and collects the user's past message history and the other party's profile information. The input is the request data sent to the server, and the output is the collected message history and profile information.

[1763] Step 9: Analyze your emotional state

[1764] The server uses the collected data to analyze the user's current emotional state using an emotion engine. For example, the analysis may determine that the user is in a curious state. The input is the collected data, and the output is the analyzed emotional state.

[1765] Step 10: Generate message proposals

[1766] The server passes the analysis results from the emotion engine to the generative AI model, which generates a message with an appropriate emotional tone. For example, it generates a message like, "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?" The input is the emotional state and collected data, and the output is the generated message.

[1767] Step 11: Send and display the suggestion message to the user

[1768] The server sends the generated message to the terminal and displays it in the message input area. The user can review the suggested message and send it as is or edit it and send it. The input is the generated message and the output is the message that is displayed to the user.

[1769] Step 12: Accumulating activity data

[1770] The device continuously collects user activity data, such as message sending and profile viewing, and periodically transmits it to the server. The input is the user behavior data, and the output is the activity data transmitted to the server.

[1771] Step 13: Data analysis and algorithm updates

[1772] The server analyzes the accumulated activity data and the user's emotional state to extract the user's behavioral patterns. The matching algorithm is updated based on the analysis results. The input is the accumulated activity data and the user's emotional state, and the output is the updated algorithm.

[1773] Step 14: Request and secure identity verification information

[1774] If the server detects a suspicious behavior pattern based on the analysis data, it requests additional identity verification information from the user via the terminal. The user provides this information to confirm safety. The input is the suspicious behavior pattern and the user's identity verification information, and the output is the verification result.

[1775] (Application example 2)

[1776] 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."

[1777] Conventional matching systems do not take into account the user's emotional state, which limits their ability to reduce the user's psychological burden or provide a safe and efficient matching experience. Furthermore, they do not take into account the user's emotions when displaying advertisements, making it difficult to provide personalized advertisements.

[1778] 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 collecting information input by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying profile information to the user, emotion recognition means for recognizing the user's emotion, means for selecting targeted advertisements based on the emotion recognition results, and means for displaying the selected advertisements to the user. This allows users to enjoy an efficient matching experience with peace of mind, and makes it possible to display appropriate advertisements personalized to each user.

[1779] "Information entered by the user" refers to basic information and message history provided by the user to the system.

[1780] "Means of collection" refers to the functions on the system for obtaining user input information and activity data.

[1781] A "generative model" refers to a machine learning model that creates self-introductions and message content based on information entered by the user.

[1782] "Means for generating a self-introduction" refers to a function for creating a self-introduction based on information input by a user using a generative model.

[1783] "Means to add to profile" refers to functionality for incorporating the generated self-introduction into user profile data.

[1784] "Profile Information" refers to a collection of user data including the user's basic information, self-introduction, etc.

[1785] "Means of displaying to the user" refers to the function for displaying generated profile information, message content, advertisements, etc. on the user's device.

[1786] "Emotion recognition means" refers to a function for analyzing and determining the user's emotional state in real time.

[1787] "Means for selecting targeted advertisements" refers to a function for selecting the most suitable advertisement for a user based on the results of emotion recognition.

[1788] "Means for displaying selected advertisements to users" refers to a function for displaying selected advertising content on a user's device based on the user's emotions.

[1789] This invention relates to a matching system that combines an emotion engine that recognizes the user's emotions, thereby reducing the user's psychological burden and providing a safe and efficient matching experience. Specific means for carrying out the invention will now be described.

[1790] Profile creation support

[1791] First, we will explain the process by which a user creates a profile.

[1792] Collecting user input information

[1793] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[1794] Generating a self-introduction

[1795] The server receives the user's input information and recognizes the user's emotional state using an emotion recognition means.Then, the received information and the emotion recognition results are input into the generative model to generate a self-introduction sentence based on the emotion.

[1796] Adding and displaying your bio

[1797] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[1798] Message suggestions

[1799] Next, we will explain the process by which a user exchanges messages with other users.

[1800] Collection of message history and profile information

[1801] When a user opens the message input area on the device, a message suggestion request is sent to the server, which then collects the user's past message history and the other party's profile information, and analyzes the user's emotional state using emotion recognition means.

[1802] Generate and display message suggestions

[1803] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[1804] Advertisement display

[1805] Next, the process of displaying targeted advertisements based on the user's emotional state will be described.

[1806] Emotion Recognition and Ad Selection

[1807] While the user is watching an advertisement, the device uses its camera to analyze the user's emotional state through facial expressions and voice through emotion recognition.The server then selects an appropriate advertisement from the advertisement database based on this emotional state.

[1808] Viewing and switching ads

[1809] The selected advertisement is displayed to the user on the device. If the user's emotional state changes, the server selects a new appropriate advertisement and switches the advertisement displayed on the device.

[1810] Data accumulation and analysis

[1811] Finally, we will explain how to accumulate user activity data and analyze it.

[1812] Accumulation of activity data

[1813] The device collects user activity data, such as messages sent, profile views, and advertisement viewing, and periodically transmits this data to a server, which then stores it in a database.

[1814] Data analysis and algorithm updates

[1815] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes the user's emotional state using emotion recognition means. Based on the analysis results, the server updates the matching algorithm.

[1816] Identity verification and security

[1817] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[1818] Examples and prompts

[1819] Specific examples

[1820] For example, if a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you," and displays it on the device.

[1821] Prompt Sentence Examples

[1822] Recommended ad prompt when user is having fun:

[1823] Title: Delivering the latest entertainment experience!

[1824] Contents:

[1825] 1. Latest movie introductions

[1826] 2. New game release information

[1827] 3. Suggesting unique places to go

[1828] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1829] Step 1:

[1830] The user enters basic information such as name, hobbies, and occupation into a profile input form. The entered information is sent from the device to the server. The input data includes the user's text data, and the device captures the user input in real time and sends it to the server.

[1831] Step 2:

[1832] The server receives the user's input information and analyzes the user's emotional state using an emotion recognition means. In this process, the server analyzes the text data and the corresponding emotion data, and the input data is processed by the emotion recognition algorithm. The output is the user's emotional state information.

[1833] Step 3:

[1834] The server inputs the received user input information and emotion recognition results into a generative model, which generates a self-introduction based on the emotion data. The generative model generates text using a machine learning algorithm (e.g., a neural network). The output is a self-introduction based on the emotion.

[1835] Step 4:

[1836] The generated self-introduction is added to the user's profile data by the server. The server accesses the profile database and updates the profile information, including the self-introduction. The output is the updated profile data.

[1837] Step 5:

[1838] The profile information is sent from the server to the terminal, and the terminal displays the profile information, including a self-introduction, to the user. Here, the terminal visually displays the profile data and provides it to the user. The output is the displayed profile information.

[1839] Step 6:

[1840] While a user is watching an advertisement, the device's camera function is used to capture the user's facial expressions, and the emotional state is analyzed in real time through an emotion recognition means. The emotional state data is sent from the device to a server. The input is the camera-captured image, and the output is the emotional state data.

[1841] Step 7:

[1842] The server selects the most suitable advertisement from the advertisement database based on the result of the emotion recognition means. In this process, an advertisement selection algorithm is used to select the target advertisement and generate selected advertisement data. The output is the advertisement content.

[1843] Step 8:

[1844] The selected advertisement is sent from the server to the device, which then displays it to the user. The device visually presents the advertisement content to the user and changes and updates the execution as necessary. The output is the advertisement displayed to the user.

[1845] Step 9:

[1846] The device collects activity data such as sending messages, viewing profiles, and watching advertisements, and periodically sends it to the server. The input is activity data, and the output is data stored in the database.

[1847] Step 10:

[1848] The server analyzes the accumulated activity data to analyze the user's behavioral patterns and emotional state, and updates the matching algorithm and ad selection algorithm accordingly. The output is the updated algorithm.

[1849] Step 11:

[1850] If the server detects suspicious behavior patterns through data analysis, it requests additional identity verification information from the user via the terminal. The server verifies the provided identity verification information, checks its security, and takes measures if necessary. The output is the verification result and measures.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] [Fourth embodiment]

[1855] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1856] 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.

[1857] 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).

[1858] 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.

[1859] 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.

[1860] 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).

[1861] 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. 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.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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."

[1868] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[1869] Profile creation support

[1870] First, we will explain the process by which a user creates a profile.

[1871] Collecting user input information

[1872] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[1873] Generating a self-introduction

[1874] The server receives the user's input and passes it to the generative model, which then generates an appealing self-introduction based on the input.

[1875] Adding and displaying your bio

[1876] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[1877] Specific examples

[1878] When a user enters "My hobby is reading" or "My occupation is an engineer," the server uses the generative model to generate a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I often spend my days off reading books. Nice to meet you," and displays it on the device.

[1879] Message suggestions

[1880] Next, we will explain the process by which a user exchanges messages with other users.

[1881] Collection of message history and profile information

[1882] When a user opens the message input area on their device, it sends a message suggestion request to the server, which collects the user's past message history and the other person's profile information.

[1883] Generate and display message suggestions

[1884] The server passes the collected data to a generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area. The user can then review the suggested message, edit it if necessary, and send it.

[1885] Specific examples

[1886] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest a message like "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[1887] Data accumulation and analysis

[1888] Finally, we will explain how to accumulate user activity data and analyze it.

[1889] Accumulation of activity data

[1890] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[1891] Data analysis and algorithm updates

[1892] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[1893] Identity verification and security

[1894] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[1895] In this way, the present invention reduces the burden on users and provides a safe and efficient matching experience.

[1896] The processing flow will be explained below.

[1897] Profile creation support

[1898] Profile Creation Process Steps

[1899] Step 1:

[1900] Terminal: Presents a profile entry form to the user.

[1901] Step 2:

[1902] User: Enter basic information such as name, hobbies, and occupation.

[1903] Step 3:

[1904] Terminal: Sends the information entered by the user to the server.

[1905] Step 4:

[1906] Server: Receives the information entered by the user.

[1907] Step 5:

[1908] Server: Inputs the received information into the generative model and generates a self-introduction.

[1909] Step 6:

[1910] Server: Add the generated self-introduction text to the user's profile data.

[1911] Step 7:

[1912] Server: Sends updated profile information to the device.

[1913] Step 8:

[1914] On your device: Display your profile information, including a user-generated bio.

[1915] Message suggestions

[1916] Message Proposal Processing Steps

[1917] Step 1:

[1918] Users: View the profile of the person you want to message and open the message input area.

[1919] Step 2:

[1920] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[1921] Step 3:

[1922] Server: Receives the message proposal request.

[1923] Step 4:

[1924] Server: Collects the user's past message history and the other person's profile information.

[1925] Step 5:

[1926] Server: Inputs the collected information into the generative model and generates appropriate message content.

[1927] Step 6:

[1928] Server: Sends the generated message content to the terminal.

[1929] Step 7:

[1930] Terminal: Display suggested message content in the message entry area.

[1931] Step 8:

[1932] User: Review the suggested message and edit it if necessary.

[1933] Step 9:

[1934] User: Presses the send button to send the final message to the other party.

[1935] Step 10:

[1936] Terminal: Sends the final message to the server.

[1937] Step 11:

[1938] Server: Sends the last message received to the other party.

[1939] Data accumulation and analysis

[1940] Data accumulation and analysis process steps

[1941] Step 1:

[1942] Device: Collects activity data such as messages sent and profile views.

[1943] Step 2:

[1944] Device: Periodically sends collected activity data to the server.

[1945] Step 3:

[1946] Server: Stores the received activity data in a database.

[1947] Step 4:

[1948] Server: Runs the system for data analysis.

[1949] Step 5:

[1950] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[1951] Step 6:

[1952] Server: Updates the matching algorithm based on the analysis results.

[1953] Identity verification process steps

[1954] Step 7:

[1955] Server: Performs data analysis and detects suspicious behavioral patterns.

[1956] Step 8:

[1957] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[1958] Step 9:

[1959] Terminal: Receives notifications from the server and displays them to the user.

[1960] Step 10:

[1961] User: Enters requested identity verification information.

[1962] Step 11:

[1963] Terminal: Sends the entered personal identification information to the server.

[1964] Step 12:

[1965] Server: Receives identity verification information and verifies its security.

[1966] Example 1

[1967] 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."

[1968] Conventional matching services lack support for users to create attractive profiles and offer a lack of appropriate suggestions for messaging, which can lead to communication issues. Furthermore, in terms of ensuring safety, the systems for detecting suspicious behavior are insufficient, potentially undermining users' sense of security.

[1969] 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.

[1970] In this invention, the server includes means for collecting information entered by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for ensuring security by requesting personal identification information, and means for analyzing the user's activity data and detecting suspicious behavioral patterns. This allows users to easily create attractive profiles and use the matching service efficiently and safely.

[1971] "User" refers to an individual who uses the Matching Service.

[1972] "Terminal" refers to the device used by the user (smartphone, tablet, PC, etc.).

[1973] "Server" refers to a computer system that processes user requests and manages and stores data.

[1974] "Input information" refers to basic information such as name, hobbies, and occupation provided by the user through the terminal.

[1975] A "generative model" refers to an artificial intelligence model that generates self-introductions and message suggestions based on input information.

[1976] "Self-introduction" refers to an introduction created by a generative model based on user input information.

[1977] "Profile" refers to a personal information page that includes the user's basic information and self-introduction.

[1978] "Message History" means a record of messages previously sent or received by a User.

[1979] "Profile information" refers to data including basic information about the user and their contacts, self-introductions, etc.

[1980] "Proposed message" refers to a new message proposal generated by a generative model based on past message history and profile information.

[1981] "Activity data" refers to data on user usage behavior, such as sending messages or viewing profiles.

[1982] A "database" refers to a system for storing multiple data managed by a server.

[1983] "Matching algorithm" refers to a calculation method for efficiently and appropriately matching users with each other.

[1984] "Identifying Information" means additional personally identifiable information provided by you for security purposes.

[1985] "Suspicious behavioral patterns" refer to patterns of suspicious behavior that differ from normal usage.

[1986] MODE FOR CARRYING OUT THE INVENTION

[1987] The present invention aims to improve the efficiency and safety of a matching service for single men and women. Specific embodiments of the present invention will be described below.

[1988] Profile creation support

[1989] First, we will explain the process by which a user creates a profile.

[1990] 1. Display the profile entry form

[1991] The server sends the profile entry section of a web page or mobile application to the user's device, for example, presented as an HTML form or app screen.

[1992] 2. Enter your user information

[1993] Users enter basic information such as their name, hobbies, and occupation into a profile form.

[1994] 3. Sending input information to the server

[1995] The terminal sends the entered information to the server, which verifies the received data and stores it in a database.

[1996] 4. Generating self-introductions

[1997] The server sends a prompt to the generative AI model based on the user's input, and the generative AI model generates a self-introduction, such as "Hello, I'm Taro Yamada, an engineer. My hobby is reading, and I often spend my days off reading books."

[1998] 5. Adding and displaying your bio to your profile

[1999] The server adds the generated self-introduction to the user profile and transmits the profile data to the terminal, which displays the updated profile information to the user.

[2000] Message suggestions

[2001] Next, the process by which a user exchanges messages with other users will be described.

[2002] 1. Submit a message suggestion request

[2003] The terminal sends a message suggestion request to the server when the user opens the message input area.

[2004] 2. Collection of message history and profile information

[2005] The server retrieves the user's past message history and the other person's profile information from a database.

[2006] 3. Message Proposal Generation

[2007] The server passes the collected data to a generative AI model to generate appropriate message content, such as "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?"

[2008] 4. View and send message suggestions

[2009] The server sends the generated message to the terminal, which displays the proposed message in the message input area and allows the user to send it as is.

[2010] Data accumulation and analysis

[2011] Finally, we will explain how to accumulate user activity data and analyze it.

[2012] 1. Collection of activity data

[2013] The device collects activity data, such as user messages sent and profile views.

[2014] 2. Sending activity data to the server

[2015] The terminal periodically transmits the collected activity data to the server.

[2016] 3. Data accumulation and storage

[2017] The server stores the received activity data in a database.

[2018] 4. Data analysis and algorithm updates

[2019] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching algorithm based on the analysis results.

[2020] 5. Identity verification and security

[2021] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[2022] Specific examples

[2023] 1. Create your profile

[2024] When a user enters "My hobby is reading" or "My occupation is engineer," the server uses a generative AI model to generate a self-introduction such as "Hello, I'm Yamada Taro, an engineer. My hobby is reading, and I often spend my days off reading books," and displays this on the device.

[2025] 2. Message proposal

[2026] When a user opens the message input area, the device sends a message suggestion request to the server, which uses a generative AI model to generate a message like, "Hello! I understand you're a movie lover. Can you recommend a movie you've seen recently?" and displays it on the device.

[2027] Prompt Sentence Examples

[2028] 1. "Generate a self-introduction when a user enters 'reading' as a hobby."

[2029] 2. "If a user has previously sent a message saying 'Nice to meet you, nice to meet you,' suggest a message to send next."

[2030] In this way, the present invention reduces the burden on users and enables them to use matching services efficiently and safely.

[2031] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2032] Profile creation support

[2033] Step 1:

[2034] The terminal sends a request to the server when the user opens the profile entry section.

[2035] Input: Request packet

[2036] Output: Profile input form

[2037] Specific operation: The server sends a web page or app profile entry form to the device, which then displays the form to the user.

[2038] Step 2:

[2039] Users enter basic information such as name, hobbies, and occupation into the feel input form.

[2040] Input: User enters basic information

[2041] Output: Basic information entered

[2042] What it does: The device temporarily stores the information you enter.

[2043] Step 3:

[2044] The terminal transmits the input information to the server.

[2045] Input: Basic information entered by the user

[2046] Output: Basic information sent

[2047] Specific operation: The terminal sends the data entered in the form to the server, which receives this information and stores it in a database.

[2048] Step 4:

[2049] The server passes the received basic information to a generative AI model to generate an appealing self-introduction.

[2050] Input: User basic information

[2051] Output: Generated bio

[2052] Specific operation: The generative AI model generates a self-introduction based on the prompt: "Please generate a self-introduction if the user enters 'reading' as a hobby."

[2053] Step 5:

[2054] The server adds the generated self-introduction to the user profile and transmits the updated profile data to the terminal.

[2055] Input: Generated bio, user profile information

[2056] Output: Updated profile information

[2057] Specific operation: The server adds the generated self-introduction to the user profile and sends the updated profile to the terminal, which displays this information to the user.

[2058] Message suggestions

[2059] Step 1:

[2060] When the user opens the message input area, the terminal sends a message suggestion request to the server.

[2061] Input: Message suggestion request

[2062] Output: Acknowledgement of request

[2063] Specific operation: The terminal sends a message suggestion request to the server.

[2064] Step 2:

[2065] The server collects the user's past message history and the other person's profile information from a database.

[2066] Input: User ID, other user ID

[2067] Output: Past message history, other person's profile information

[2068] Specific operation: The server retrieves the necessary data from the database.

[2069] Step 3:

[2070] The server passes the collected information to a generative AI model to generate appropriate message content.

[2071] Input: Past message history, other person's profile information

[2072] Output: Proposed message content

[2073] Specific behavior: The generative AI model generates a message based on the prompt: "If the user has previously sent the message 'Nice to meet you, nice to meet you,' please suggest the next message to send."

[2074] Step 4:

[2075] The server sends the generated message to the terminal, and the terminal displays the suggested message in the message input area.

[2076] Input: Generated message content

[2077] Output: Proposal message displayed on the terminal

[2078] Specific operation: The server sends the generated message to the terminal, which displays it in the message input area. The user can then send the suggested message as is.

[2079] Data accumulation and analysis

[2080] Step 1:

[2081] The device collects activity data, such as user messages sent and profile views.

[2082] Input: User operation log

[2083] Output: Collected activity data

[2084] Specific operation: The device records the user's actions and temporarily stores the data.

[2085] Step 2:

[2086] The terminal periodically transmits the collected activity data to the server.

[2087] Input: Collected activity data

[2088] Output: Send data to the server

[2089] Specific operation: The terminal transmits data to the server at regular intervals.

[2090] Step 3:

[2091] The server stores the received activity data in a database.

[2092] Input: Activity data

[2093] Output: Data items stored in the database

[2094] Specific operation: The server stores the received data in a database.

[2095] Step 4:

[2096] The server analyzes the accumulated activity data and extracts the user's behavioral patterns.

[2097] Input: Accumulated activity data

[2098] Output: Behavioral pattern analysis results

[2099] Specific operation: The server analyzes the user's behavioral patterns using a data analysis algorithm.

[2100] Step 5:

[2101] The server updates the matching algorithm based on the analysis results, and if it detects suspicious behavioral patterns, it requests additional identity verification information from the user via their device.

[2102] Input: Behavioral pattern analysis results

[2103] Output: Updated matching algorithm, safety confirmation request

[2104] Specific operation: The server adjusts the matching algorithm based on the analysis results and sends a message to the user requesting identity verification if suspicious behavior is detected.

[2105] Through this series of steps, users can easily create an attractive profile, receive appropriate message suggestions, and use the matching service safely and efficiently.

[2106] (Application example 1)

[2107] 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."

[2108] Conventional matching services and online shopping sites have had problems with generating user input information, message content, product reviews, etc., requiring a great deal of effort from the user and making it difficult to provide personalized information. It is also not easy to update recommendation algorithms based on collected data or ensure their safety. To solve these issues and improve the user experience, new systems and methods must be provided.

[2109] 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.

[2110] In this invention, the server includes means for collecting information entered by a user, means for generating a self-introduction or product description using a generative model based on the collected information, means for adding the generated self-introduction or product description to the user's profile or product detail page, means for displaying the profile information or product detail information to the user, means for collecting the user's past message history and other users' profile information or means for collecting the user's past reviews and purchase history, means for suggesting message content or review content using a generative model based on the collected information, means for displaying the suggested message content or review content to the user, means for accumulating user activity data or purchase data, means for analyzing the accumulated activity data or purchase data, means for updating the user's matching algorithm or recommendation algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows users to easily generate personalized self-introductions, product descriptions, messages, and reviews, and enables safe and effective matching and product suggestions.

[2111] "User" refers to any individual or legal entity that uses the system or services.

[2112] "Collection Method" refers to the software or hardware functionality used to collect particular information.

[2113] "Generative Model" refers to an artificial intelligence model used to generate appropriate messages and testimonials for users.

[2114] "About Me" refers to text that contains summarized information about the user.

[2115] "Product Description" refers to text containing a description of a particular product.

[2116] "Profile information" refers to data including a user's personal information, preferences, and past behavioral history.

[2117] "Product details" refers to data including information about a product, such as specifications, features, and price.

[2118] "Message History" refers to a record of messages that a User has previously sent or received.

[2119] "Purchase history" refers to a record of products a user has purchased in the past.

[2120] "Suggestion means" refers to a system function for suggesting content generated based on collected information to users.

[2121] "Display" refers to the functionality that visually presents suggested messages, reviews, and profile information to users.

[2122] "Activity Data" refers to data regarding a series of actions taken by a user on the System.

[2123] "Storage means" refers to the function for saving and managing collected data.

[2124] "Analysis means" refers to the function for analyzing accumulated data to extract user behavior patterns and preferences.

[2125] A "matching algorithm" refers to a calculation method for optimally connecting users.

[2126] A "recommendation algorithm" refers to a calculation method for suggesting the most suitable products and content to users.

[2127] "Identification Information" means information necessary to verify a User's identity.

[2128] This invention is a system that uses a generative AI model to generate, display, and suggest user profile information, messages, and product descriptions. The specific system configuration and processing flow for implementing this invention are described below.

[2129] Profile Creation Support Embodiment

[2130] Collecting user input information

[2131] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile input form. This information is sent from the device to the server.

[2132] Generating a self-introduction

[2133] The server passes the information submitted by the user to a generative model, which generates a self-introduction based on the collected information. The generative model uses OpenAI's "text-davinci-003" engine.

[2134] Adding and displaying your bio

[2135] The server adds the generated self-introduction to the user's profile data and transmits the updated profile information to the terminal, where the automatically generated self-introduction is displayed.

[2136] Specific examples

[2137] When a user inputs "My hobby is watching movies" or "My occupation is designer," the server uses the generative model to generate a self-introduction such as "Hello, I work as a designer. My hobby is watching movies, and I often go to the cinema on weekends. Nice to meet you," and displays it on the device.

[2138] Message Proposal Embodiment

[2139] Collection of message history and profile information

[2140] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[2141] Generate and display message suggestions

[2142] The server passes the collected information to the generative model, which then suggests appropriate message content. The generated message is sent to the device and displayed in the message input area.

[2143] Specific examples

[2144] If the user has previously sent "Nice to meet you, nice to meet you," the server will use the generative model to suggest the message "Hello! I heard you enjoy traveling. Do you have any recommendations for places you've been recently?" The device will then display this in the message input area, allowing the user to send it immediately.

[2145] Data storage and analysis implementation

[2146] Accumulation of activity data

[2147] The device collects activity data, such as messages sent and profile views, and periodically transmits it to a server, which stores the data in a database.

[2148] Data analysis and algorithm updates

[2149] The server analyzes the accumulated activity data to extract user behavior patterns, and updates the matching or recommendation algorithm based on the analysis results.

[2150] Identity verification and security

[2151] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification from the user, which will then be used to verify the user's identity and take appropriate measures.

[2152] Specific examples

[2153] The server collects information about the user's past messages, viewed products, and purchased products, and updates the recommendation algorithm based on this data. As a result, the user can find more relevant products. If suspicious behavior is detected, the server will request additional information from the user to verify their identity (e.g., resubmit ID).

[2154] Prompt Sentence Examples

[2155] Product description generation prompt:

[2156] Generate compelling product descriptions based on user information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n

[2157] Prompt for generating review suggestions:

[2158] User information: {'name': 'User', 'preferences': ['Reading', 'Movies']}\n Product information: {'name': 'Wireless headphones', 'features': ['Bluetooth', 'Noise cancelling']}\n Suggest compelling product reviews that users can write based on past reviews: ['This product has good sound quality.']\n

[2159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2160] Step 1:

[2161] Collecting user input information

[2162] The user uses a device to enter basic information (such as name, hobbies, and occupation) into a profile form. The information is then sent from the device to the server. Input is done using text fields and drop-down menus.

[2163] Input: User profile information

[2164] Output: Input sent to the server

[2165] Step 2:

[2166] Generating a self-introduction

[2167] The server receives the information sent by the user and passes it to a generative AI model (OpenAI's text-davinci-003 engine) as a prompt. The generative model generates a self-introduction based on the prompt.

[2168] Input: User profile information, prompt text

[2169] Output: Generated bio

[2170] Step 3:

[2171] Adding and displaying your bio

[2172] The server adds the generated self-introduction to the user's profile data, then transmits the updated profile information to the terminal, and the terminal displays the profile information including the self-introduction.

[2173] Input: Generated self-introduction

[2174] Output: Updated profile information sent to the device

[2175] Step 4:

[2176] Collection of message history and profile information

[2177] When a user opens the message input area, the device sends a message suggestion request to the server, which collects the user's past message history and the other party's profile information.

[2178] Input: User message input request

[2179] Output: Collected message history and profile information

[2180] Step 5:

[2181] Generate and display message suggestions

[2182] The server passes the collected data to the generative model to generate appropriate message content, which is then sent to the device and displayed in the message input area.

[2183] Input: Message history, profile information, prompt text

[2184] Output: Generated message content

[2185] Step 6:

[2186] Accumulation of user activity data

[2187] The device periodically sends activity data, such as user messages sent and profile views, to a server, which stores this data in a database.

[2188] Input: User activity data

[2189] Output: Activity data stored in a database

[2190] Step 7:

[2191] Data analysis and algorithm updates

[2192] The server analyzes the accumulated activity data and extracts user behavior patterns. Based on the extracted data, the matching algorithm or recommendation algorithm is updated. Machine learning models and statistical analysis tools are used for data analysis.

[2193] Input: Accumulated activity data

[2194] Output: Updated algorithm

[2195] Step 8:

[2196] Identity verification and security

[2197] If the server detects suspicious behavior patterns through data analysis, it will request additional identification information from the user, verify the user's security based on the information provided, and take additional measures if necessary.

[2198] Input: Analysis results, personal identification information

[2199] Output: User confirmed safe or countermeasure information

[2200] 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.

[2201] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[2202] Profile creation support

[2203] First, we will explain the process by which a user creates a profile.

[2204] Collecting user input information

[2205] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[2206] Generating a self-introduction

[2207] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state.Then, the received information and the emotion recognition results are input into a generative model to generate a self-introduction sentence based on the user's emotions.

[2208] Adding and displaying your bio

[2209] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[2210] Specific examples

[2211] When a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you," and displays it on the device.

[2212] Message suggestions

[2213] Next, we will explain the process by which a user exchanges messages with other users.

[2214] Collection of message history and profile information

[2215] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[2216] Generate and display message suggestions

[2217] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[2218] Specific examples

[2219] If the user has previously sent "Nice to meet you, nice to meet you," the server uses the emotion engine to determine that the user is in a curious state. The generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send it.

[2220] Data accumulation and analysis

[2221] Finally, we will explain how to accumulate user activity data and analyze it.

[2222] Accumulation of activity data

[2223] The device collects user activity data, such as messages sent and profile views, and periodically sends it to a server, which stores the data in a database.

[2224] Data analysis and algorithm updates

[2225] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes their emotional state using an emotion engine. Based on the analysis results, the server updates the matching algorithm.

[2226] Identity verification and security

[2227] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[2228] In this way, by combining emotion engines, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, further improving the user experience and providing a safe matching experience.

[2229] The processing flow will be explained below.

[2230] Profile creation support

[2231] Profile Creation Process Steps

[2232] Step 1:

[2233] Terminal: Presents a profile entry form to the user.

[2234] Step 2:

[2235] User: Enter basic information such as name, hobbies, and occupation.

[2236] Step 3:

[2237] Terminal: Sends the information entered by the user to the server.

[2238] Step 4:

[2239] Server: Receives the information entered by the user.

[2240] Step 5:

[2241] Server: Uses the emotion engine to analyze the user's emotional state.

[2242] Step 6:

[2243] Server: Inputs the emotional state and input information into the generative model and generates a self-introduction sentence.

[2244] Step 7:

[2245] Server: Add the generated bio to the user's profile data.

[2246] Step 8:

[2247] Server: Sends updated profile information to the device.

[2248] Step 9:

[2249] On your device: Display your profile information, including a user-generated bio.

[2250] Message suggestions

[2251] Message Proposal Processing Steps

[2252] Step 1:

[2253] Users: View the profile of the person you want to message and open the message input area.

[2254] Step 2:

[2255] Device: Sends a message suggestion request to the server (including information such as the recipient's ID).

[2256] Step 3:

[2257] Server: Receives the message proposal request.

[2258] Step 4:

[2259] Server: Collects the user's past message history and the other person's profile information.

[2260] Step 5:

[2261] Server: Utilizes the emotion engine to analyze the user's current emotional state.

[2262] Step 6:

[2263] Server: Inputs the collected information and emotional state into a generative model to generate message content with an appropriate emotional tone.

[2264] Step 7:

[2265] Server: Sends the generated message content to the terminal.

[2266] Step 8:

[2267] Terminal: Display suggested message content in the message entry area.

[2268] Step 9:

[2269] User: Review the suggested message and edit it if necessary.

[2270] Step 10:

[2271] User: Presses the send button to send the final message to the other party.

[2272] Step 11:

[2273] Terminal: Sends the final message to the server.

[2274] Step 12:

[2275] Server: Sends the last message received to the other party.

[2276] Data accumulation and analysis

[2277] Data accumulation and analysis process steps

[2278] Step 1:

[2279] Device: Collects activity data such as messages sent and profile views.

[2280] Step 2:

[2281] Device: Periodically sends collected activity data to the server.

[2282] Step 3:

[2283] Server: Stores the received activity data in a database.

[2284] Step 4:

[2285] Server: Runs the system for data analysis.

[2286] Step 5:

[2287] Server: Analyzes accumulated activity data and extracts user behavior patterns.

[2288] Step 6:

[2289] Server: Updates the matching algorithm based on the analysis results.

[2290] Step 7:

[2291] Server: Uses the emotion engine to analyze the user's emotional state.

[2292] Step 8:

[2293] Server: Further adjusts the matching algorithm, taking into account the analyzed emotional state.

[2294] Identity verification process steps

[2295] Step 9:

[2296] Server: Performs data analysis and detects suspicious behavioral patterns.

[2297] Step 10:

[2298] Server: If suspicious activity is detected, it sends a notification to the user requesting identity verification information.

[2299] Step 11:

[2300] Terminal: Receives notifications from the server and displays them to the user.

[2301] Step 12:

[2302] User: Enters requested identity verification information.

[2303] Step 13:

[2304] Terminal: Sends the entered personal identification information to the server.

[2305] Step 14:

[2306] Server: Receives identity verification information and verifies its security.

[2307] Example 2

[2308] 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."

[2309] The objective of this invention is to reduce the psychological burden on users and provide a safe and efficient matching experience. In particular, the objective is to realize a system that is natural and easy to use for users by creating profiles and suggesting messages that take into account the user's emotional state.

[2310] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information input by the user, means for recognizing an emotional state based on the collected information, means for generating a self-introduction using a generative model based on the emotional state and the collected information, means for adding the generated self-introduction to the user's profile, means for displaying the profile information to the user, means for collecting the user's past message history and other party's profile information, means for suggesting message content using a generative model based on the collected information and the user's emotional state, means for displaying the suggested message content to the user, means for accumulating user activity data, means for analyzing the accumulated activity data and the user's emotional state, means for updating the user's matching algorithm based on the analysis results, and means for detecting suspicious behavioral patterns and requesting identity verification information. This allows for profile creation and message suggestions that take the user's emotional state into consideration, improving the user experience and providing a safe matching experience.

[2311] "Means for collecting information entered by the user" refers to a device or system that electrically or electronically obtains basic information such as name, hobbies, and occupation entered by the user into the terminal.

[2312] A "means for recognizing emotional state" is a device or algorithm that analyzes input information and user behavioral data to identify the user's current emotional or psychological state.

[2313] The "means for generating a self-introduction using a generative model" is a device or system that automatically creates a self-introduction for a user using natural language processing technology based on the analysis of collected information and emotional state.

[2314] A "means for adding to a user's profile" is a device or system that merges the generated self-introduction and other information into the user's digital profile and stores it.

[2315] "Means for displaying profile information to a user" refers to a device or system that visually presents the information contained in a user's profile, such as through a terminal display.

[2316] "Means for collecting a user's past message history and other party profile information" refers to a device or system that obtains messages previously sent and received by a user and other party profile data.

[2317] The "means for suggesting message content using a generative model" is a device or system that automatically creates appropriate message content based on collected past message history and the other party's profile information.

[2318] The "means for displaying the proposed message content to the user" refers to a device or system that displays the generated message content on the user's terminal so that the message content can be confirmed and edited.

[2319] "Means for storing user activity data" refers to a device or system that records and stores user behavioral data such as sending messages and viewing profiles.

[2320] The "means for analyzing accumulated activity data and the user's emotional state" refers to a device or system that analyzes accumulated behavioral data and emotional data using statistical methods and machine learning algorithms.

[2321] The "means for updating the user's matching algorithm" is a device or system that improves or optimizes the algorithm of the matching system based on the analysis results.

[2322] "Means for detecting suspicious behavioral patterns and requesting identity verification information" refers to a device or system that analyzes user behavioral data and, if a suspicious behavioral pattern is found, obtains additional identity verification information from the user.

[2323] The present invention relates to a matching system that incorporates an emotion engine that recognizes user emotions, thereby reducing the psychological burden on users and providing a safe and efficient matching experience. Specific embodiments of the present invention are described in detail below.

[2324] Hardware and software used

[2325] Device: The device on which users enter information and view profiles and messages, such as a smartphone, tablet, or PC.

[2326] Server: A computer system used to process and store user information, run the emotion engine and generative AI models.

[2327] Emotion engine: Software for analyzing and recognizing the user's emotional state.

[2328] Generative AI model: Software that generates self-introductions and message content based on user input information and emotion recognition results.

[2329] Profile creation support

[2330] First, we will explain the process by which a user creates a profile.

[2331] Collecting user input information

[2332] On the device, the user enters basic information such as name, hobbies, and occupation into a profile input form. For example, the user enters "Name: Taro," "Hobbies: Reading," and "Occupation: Engineer." The entered information is sent to the server.

[2333] Generating a self-introduction

[2334] The server receives the user's input information and uses an emotion engine to recognize the user's emotional state. For example, it may determine from the user's description that the user is in a relaxed state. The received information and the emotion recognition results are then input into a generative AI model, which generates a self-introduction based on the user's emotions. Specifically, it generates the sentence, "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you."

[2335] Adding and displaying your bio

[2336] The server adds the generated self-introduction to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[2337] Message suggestions

[2338] Next, we will explain the process by which a user exchanges messages with other users.

[2339] Collection of message history and profile information

[2340] When a user opens the message input area on their device, a message suggestion request is sent to the server, which then collects the user's past message history and the other person's profile information, and uses an emotion engine to analyze the user's emotional state.

[2341] Generate and display message suggestions

[2342] The server passes the collected data and emotional state to a generative AI model to generate message content with an appropriate emotional tone. For example, if a user has previously sent "Nice to meet you, nice to meet you," the emotion engine determines that the user is in a curious state. In this case, the generative model generates a message saying, "Hello! I heard you enjoy movies. Do you have any recommendations for movies you've seen recently?" and displays it on the device. The user can then send this suggested message as is.

[2343] Data accumulation and analysis

[2344] Finally, we will explain how to accumulate and analyze user activity data.

[2345] Accumulation of activity data

[2346] The device continuously collects data on users' activities, such as sending messages and viewing profiles, and periodically transmits this data to a server, which then stores it in a database.

[2347] Data analysis and algorithm updates

[2348] The server analyzes the accumulated activity data and the user's emotional state to extract user behavior patterns. For example, if a user frequently uses a certain keyword, the server analyzes that pattern and uses it to update the algorithm.

[2349] Identity verification and security

[2350] If the server detects suspicious behavior patterns based on the analysis data, it will request additional authentication information from the user via the device. For example, if suspicious activity is detected, the server will send additional authentication information to the user, who will then provide it to confirm their safety.

[2351] In this way, by combining an emotion engine with a generative AI model, a system can be realized that creates profiles and suggests messages that take into account the user's emotional state, improving the user experience and providing a safe matching experience.

[2352] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2353] Step 1: Collect user input

[2354] The user enters basic information such as name, hobbies, and occupation into a profile input form. Specifically, the user enters the name "Taro," the hobby "reading," and the occupation "engineer." This input information is stored as digital data on the device.

[2355] Step 2: Submit your input

[2356] The terminal sends the entered user information to the server. In this case, the terminal sends the data "Name: Taro, Hobby: Reading, Occupation: Engineer" to the server. The input is the user information, and the output is the information data sent to the server.

[2357] Step 3: Recognizing your emotional state

[2358] The server starts an emotion engine based on the received user information and analyzes the user's emotional state. For example, it determines that the user is in a relaxed state based on their writing. In this case, the input is the transmitted user information data, and the output is the analyzed emotional state (relaxed state).

[2359] Step 4: Generate a self-introduction

[2360] The server uses an emotion engine and a generative AI model to integrate the received information with the emotion recognition results to generate a self-introduction. For example, the generative AI model generates a self-introduction such as "Hello, I work as an engineer. My hobby is reading, and I relax by reading books on my days off. Nice to meet you." The input is user information and emotion recognition results, and the output is the generated self-introduction.

[2361] Step 5: Add and submit your bio

[2362] The server adds the generated self-introduction to the profile data and sends the updated profile information to the terminal. The data received by the terminal is the updated profile information. The input is the generated self-introduction, and the output is the updated profile information.

[2363] Step 6: View your profile information

[2364] The device displays the received profile information to the user. The user can check the profile information, including the self-introduction text. The input is the updated profile information, and the output is the profile information displayed to the user.

[2365] Step 7: Submit a message suggestion request

[2366] When a user opens the message input area, the device sends a message suggestion request to the server. This request includes the user's ID and message history information. The input is the user's action, and the output is the request data to the server.

[2367] Step 8: Gather message history and profile information

[2368] The server receives the message suggestion request and collects the user's past message history and the other party's profile information. The input is the request data sent to the server, and the output is the collected message history and profile information.

[2369] Step 9: Analyze your emotional state

[2370] The server uses the collected data to analyze the user's current emotional state using an emotion engine. For example, the analysis may determine that the user is in a curious state. The input is the collected data, and the output is the analyzed emotional state.

[2371] Step 10: Generate message proposals

[2372] The server passes the analysis results from the emotion engine to the generative AI model, which generates a message with an appropriate emotional tone. For example, it generates a message like, "Hello! I understand you enjoy movies. Do you have any recommendations for movies you've seen recently?" The input is the emotional state and collected data, and the output is the generated message.

[2373] Step 11: Send and display the suggestion message to the user

[2374] The server sends the generated message to the terminal and displays it in the message input area. The user can review the suggested message and send it as is or edit it and send it. The input is the generated message and the output is the message that is displayed to the user.

[2375] Step 12: Accumulating activity data

[2376] The device continuously collects user activity data, such as message sending and profile viewing, and periodically transmits it to the server. The input is the user behavior data, and the output is the activity data transmitted to the server.

[2377] Step 13: Data analysis and algorithm updates

[2378] The server analyzes the accumulated activity data and the user's emotional state to extract the user's behavioral patterns. The matching algorithm is updated based on the analysis results. The input is the accumulated activity data and the user's emotional state, and the output is the updated algorithm.

[2379] Step 14: Request and secure identity verification information

[2380] If the server detects a suspicious behavior pattern based on the analysis data, it requests additional identity verification information from the user via the terminal. The user provides this information to confirm safety. The input is the suspicious behavior pattern and the user's identity verification information, and the output is the verification result.

[2381] (Application example 2)

[2382] 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."

[2383] Conventional matching systems do not take into account the user's emotional state, which limits their ability to reduce the user's psychological burden or provide a safe and efficient matching experience. Furthermore, they do not take into account the user's emotions when displaying advertisements, making it difficult to provide personalized advertisements.

[2384] 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 collecting information input by the user, means for generating a self-introduction using a generative model based on the collected information, means for adding the generated self-introduction to the user's profile, means for displaying profile information to the user, emotion recognition means for recognizing the user's emotion, means for selecting targeted advertisements based on the emotion recognition results, and means for displaying the selected advertisements to the user. This allows users to enjoy an efficient matching experience with peace of mind, and makes it possible to display appropriate advertisements personalized to each user.

[2385] "Information entered by the user" refers to basic information and message history provided by the user to the system.

[2386] "Means of collection" refers to the functions on the system for obtaining user input information and activity data.

[2387] A "generative model" refers to a machine learning model that creates self-introductions and message content based on information entered by the user.

[2388] "Means for generating a self-introduction" refers to a function for creating a self-introduction based on information input by a user using a generative model.

[2389] "Means to add to profile" refers to functionality for incorporating the generated self-introduction into user profile data.

[2390] "Profile Information" refers to a collection of user data including the user's basic information, self-introduction, etc.

[2391] "Means of displaying to the user" refers to the function for displaying generated profile information, message content, advertisements, etc. on the user's device.

[2392] "Emotion recognition means" refers to a function for analyzing and determining the user's emotional state in real time.

[2393] "Means for selecting targeted advertisements" refers to a function for selecting the most suitable advertisement for a user based on the results of emotion recognition.

[2394] "Means for displaying selected advertisements to users" refers to a function for displaying selected advertising content on a user's device based on the user's emotions.

[2395] This invention relates to a matching system that combines an emotion engine that recognizes the user's emotions, thereby reducing the user's psychological burden and providing a safe and efficient matching experience. Specific means for carrying out the invention will now be described.

[2396] Profile creation support

[2397] First, we will explain the process by which a user creates a profile.

[2398] Collecting user input information

[2399] On the device, the user enters basic information such as name, hobbies, occupation, etc. into a profile input form, which is then sent to the server.

[2400] Generating a self-introduction

[2401] The server receives the user's input information and recognizes the user's emotional state using an emotion recognition means.Then, the received information and the emotion recognition results are input into the generative model to generate a self-introduction sentence based on the emotion.

[2402] Adding and displaying your bio

[2403] The server adds a self-introduction based on the generated emotion to the user's profile data and transmits the updated profile to the terminal, which displays the profile information including the self-introduction to the user.

[2404] Message suggestions

[2405] Next, we will explain the process by which a user exchanges messages with other users.

[2406] Collection of message history and profile information

[2407] When a user opens the message input area on the device, a message suggestion request is sent to the server, which then collects the user's past message history and the other party's profile information, and analyzes the user's emotional state using emotion recognition means.

[2408] Generate and display message suggestions

[2409] The server passes the collected data and emotional state to a generative model to generate message content with the appropriate emotional tone. The generated message is sent to the device and displayed in the message input area. The user can review the suggested message, edit it if necessary, and then send it.

[2410] Advertisement display

[2411] Next, the process of displaying targeted advertisements based on the user's emotional state will be described.

[2412] Emotion Recognition and Ad Selection

[2413] While the user is watching an advertisement, the device uses its camera to analyze the user's emotional state through facial expressions and voice through emotion recognition.The server then selects an appropriate advertisement from the advertisement database based on this emotional state.

[2414] Viewing and switching ads

[2415] The selected advertisement is displayed to the user on the device. If the user's emotional state changes, the server selects a new appropriate advertisement and switches the advertisement displayed on the device.

[2416] Data accumulation and analysis

[2417] Finally, we will explain how to accumulate user activity data and analyze it.

[2418] Accumulation of activity data

[2419] The device collects user activity data, such as messages sent, profile views, and advertisement viewing, and periodically transmits this data to a server, which then stores it in a database.

[2420] Data analysis and algorithm updates

[2421] The server analyzes the accumulated activity data to extract the user's behavioral patterns and analyzes the user's emotional state using emotion recognition means. Based on the analysis results, the server updates the matching algorithm.

[2422] Identity verification and security

[2423] If the server detects suspicious behavior patterns through data analysis, it will request additional identity verification information from the user via their device, and based on the information provided by the user, it will verify their safety and take appropriate measures.

[2424] Examples and prompts

[2425] Specific examples

[2426] For example, if a user inputs "My hobby is reading" or "My occupation is an engineer," the server uses the emotion engine to recognize that the user is relaxed. The generative model generates a self-introduction that reads, "Hello, I work as an engineer. My hobby is reading, and I rel...

Claims

1. A means of collecting information entered by users; A means for generating a self-introduction sentence using a generative model based on the collected information; a means for adding the generated bio to the user's profile; A system including a means for displaying profile information to a user.

2. A means of collecting users' past message history and other parties' profile information; A means for suggesting message content using a generative model based on the collected information; 10. The system of claim 1, further comprising: means for displaying suggested message content to a user.

3. a means for accumulating user activity data; A means for analyzing the accumulated activity data; A means to update the user's matching algorithm based on the analysis results; 10. The system of claim 1, further comprising means for detecting suspicious patterns of behavior and requesting identity verification information.

Images