system

Abstract

Provide a system. A means for a user to input personal information; A means for storing the personal information entered; a means for matching users; A means for matching users to chat with each other; a means for storing chat history data; means for analyzing the stored chat history data; A means for generating recommendation sentences based on the analyzed data; The system includes a means for presenting the generated recommendation sentences to a user.

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] Dating apps are widespread these days, and many users are using them. However, there are many users who are not good at chatting or find it troublesome, so they stop using the apps midway. These users miss out on potential dating opportunities and are unable to enjoy the true value of dating apps. Furthermore, there is a lack of systems that automatically analyze chat data and generate appropriate recommendation messages, which places a heavy burden on users. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a means for users to input and save personal information when logging in for the first time. It also includes a means for matching users and allowing matched users to chat with each other, and is equipped with a means for saving chat history data. It also provides a means for analyzing the saved chat history data and incorporates a means for generating recommended sentences based on the analyzed data. The system is equipped with a means for presenting the generated recommended sentences to the user, making it easy for even users who are not good at chatting to continue communicating.

[0006] Specifically, chat history data is analyzed using natural language processing technology, and appropriate recommendation sentences are automatically generated based on the analysis results. This allows users to be provided with appropriate topics and smoothly progress in the chat. The generated recommendation sentences are displayed on the display screen of the user's device, allowing users to use them intuitively. In this way, the present invention reduces the burden on users of dating apps and prevents opportunity loss.

[0007] A "user" is an individual who uses a dating app.

[0008] "Personal information" refers to information relating to privacy such as the user's age, gender, hobbies, etc.

[0009] "Matching" is the process of pairing users who are likely to like each other based on a certain algorithm.

[0010] "Chat" is a communication method in which users exchange messages in real time.

[0011] "Chat history data" refers to data that includes the contents of messages exchanged between users and their timestamps.

[0012] "Storage" means recording and storing input data in a storage device such as a database.

[0013] "Analysis" is the process of analyzing stored data using certain algorithms or analytical models.

[0014] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0015] "Recommendation sentences" are template sentences automatically generated by AI to facilitate smooth chats between users.

[0016] "Presenting" means showing the generated recommendation sentences to the user. [Brief explanation of the drawings]

[0017] [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 illustrating 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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system in which users enter personal information when registering, and then manually chat with a certain number of people of the opposite sex after registration. The system analyzes the user's chat patterns and uses generative AI to create recommendation sentences. The system aims to reduce the user's chat burden and prevent opportunity loss.

[0039] System configuration

[0040] 1. Registration Phase

[0041] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[0042] Terminal: Sends personal information submitted through the form to the server.

[0043] Server: Stores the received user information in a database.

[0044] Examples:

[0045] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[0046] 2. Manual Chat Phase

[0047] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[0048] Terminal: Display the chat screen to users A and B.

[0049] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[0050] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0051] 3. Data analysis phase

[0052] Server: Once a certain amount of chat data has been accumulated, analysis begins. Natural language processing (NLP) technology is used for analysis.

[0053] Examples:

[0054] Once a certain amount of chat data between users A and B has been accumulated, this data is analyzed to determine that user A has a strong interest in movies and books.

[0055] 4. Recommendation text creation phase

[0056] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, if User A likes watching movies, the recommendation generated would be, "What movie have you seen recently?"

[0057] Device: Display the recommended text on User A's chat screen.

[0058] Users can easily select a suggested sentence to use in their next chat.

[0059] Details of the working example

[0060] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B through an automatic matching algorithm, and the device displays a chat screen. Users A and B then chat manually, enjoying topics such as movies and books. The server saves this chat data in a database and analyzes it once a certain amount of chat data has been collected. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[0061] In this way, the present invention provides a system that allows users to chat smoothly without feeling any difficulty and continue the conversation.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user opens the personal information form.

[0065] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[0066] Step 2:

[0067] The terminal acquires the user's input data and sends it to the server.

[0068] The device sends the input data to the appropriate endpoint on the server.

[0069] Step 3:

[0070] The server stores the received user information in a database.

[0071] The server analyzes the personal information sent and stores it in a database in an appropriate format.

[0072] Step 4:

[0073] The server matches users with suitable members of the opposite sex.

[0074] The server uses a matching algorithm to find people of the opposite sex who share common hobbies and interests.

[0075] Step 5:

[0076] A chat screen is displayed between users whose devices have been matched.

[0077] The device generates and displays an interface that allows you to chat with the matched person.

[0078] Step 6:

[0079] A user starts a chat and sends a message.

[0080] The user enters a message on the chat screen and clicks the "Send" button.

[0081] Step 7:

[0082] The terminal sends the user's message to the server.

[0083] The device sends the sent message to the server's chat save endpoint.

[0084] Step 8:

[0085] The server stores the received messages in a database.

[0086] The server stores the message content, sender, sent time, etc. in a database.

[0087] Step 9:

[0088] The server waits for a certain amount of chat data to accumulate.

[0089] When the server receives a certain amount of chat data, it begins analyzing the data.

[0090] Step 10:

[0091] The server analyzes the chat history data using natural language processing technology.

[0092] The server uses NLP models to analyze users' chat patterns and interests.

[0093] Step 11:

[0094] Based on the server's analysis results, a generative AI generates recommendation sentences.

[0095] The server inputs the analysis results into an AI model and automatically generates appropriate recommendation sentences.

[0096] Step 12:

[0097] The terminal presents the generated recommendation sentences to the user.

[0098] The device displays the recommendation text and allows the user to select and use it.

[0099] Step 13:

[0100] The user continues chatting using the recommended sentences provided.

[0101] The user selects the presented sentence and pastes it directly into the chat to continue.

[0102] Example 1

[0103] 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."

[0104] There is a need for a system that reduces the burden users feel when chatting with members of the opposite sex and prevents missed chat opportunities. Conventional chat systems require users to think up topics and input them themselves, which creates obstacles to communication. Furthermore, there is a lack of technology that can analyze chat content and present appropriate recommended sentences. This makes it difficult for users to continue chatting smoothly.

[0105] 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.

[0106] In this invention, the server includes: means for a user to input personal information; means for saving the input personal information; means for matching users with members of the opposite sex; means for matched users to chat with each other; means for saving chat history data; means for analyzing the saved chat history data; means for generating recommendation sentences based on the analyzed data; means for presenting the generated recommendation sentences to the user; means for the user to use the presented recommendation sentences in the next chat; means for starting analysis when a certain amount of chat data has been accumulated; means for using timestamps of the saved chat data; and means for linking and analyzing the chat data and the user's personal information. This reduces the burden on the user when chatting and enables the conversation to continue smoothly.

[0107] "User" refers to a person who uses this system to enter personal information and chat.

[0108] "Personal information" refers to detailed information relating to a user's identity, such as the user's age, gender, hobbies, etc.

[0109] "Matching" refers to the process by which the server pairs users with suitable members of the opposite sex based on their personal information.

[0110] "Chat" refers to the act of matched users communicating with each other in real time through messages.

[0111] "Historical data" refers to records of chat content, message timestamps, etc.

[0112] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[0113] "Recommended sentences" refer to chat messages that the server generates based on analysis data and suggests to users.

[0114] A "generative AI model" refers to an artificial intelligence system that automatically generates new sentences based on collected data.

[0115] "Timestamp" refers to information about the time at which a chat message was sent or received.

[0116] "Database" refers to the information management system that the system uses to store personal information, chat history data, etc.

[0117] "Analysis" refers to the process of processing stored historical data and personal information to identify user interests and concerns.

[0118] This invention is a system in which a user enters personal information when registering, chats with a certain number of people, analyzes the user's chat patterns, and a generative AI creates recommendation sentences. This reduces the user's chat burden and prevents opportunity loss. The form for realizing this system is described in detail below.

[0119] System configuration

[0120] Registration Phase

[0121] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. The entered information is sent via the form.

[0122] Example: User A is a 25-year-old male and enters on the registration screen that his hobbies are watching movies and reading.

[0123] Terminal: Sends the entered personal information to a server. This information is usually sent using a dedicated communication protocol.

[0124] Server: Stores the received personal information in a database. The database can be a relational database management system such as an SQL database.

[0125] Manual Chat Phase

[0126] Server: Based on the registered personal information, the server matches users with members of the opposite sex. For example, User A, a 25-year-old man, is matched with User B, a 24-year-old woman.

[0127] Device: A chat screen is displayed to matched users A and B. This display is done using a dedicated chat application.

[0128] User: User A and User B chat manually and send messages. At this stage, it is assumed that User A will send a message to User B saying, "Hello, do you like movies?"

[0129] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0130] Data analysis phase

[0131] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing (NLP) technology. This analysis can be done using Python libraries (e.g., NLTK or spaCy).

[0132] Example: Analysis reveals that user A has a strong interest in movies.

[0133] Recommendation sentence creation phase

[0134] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, the generated recommendation sentences might be in the form of "What movie have you seen recently?"

[0135] Device: This recommendation text is displayed on User A's chat screen.

[0136] User: Select the suggested sentence and use it in the next chat.

[0137] Example: User A clicks on the recommendation text "What movie have you seen recently?" and sends it to the chat.

[0138] Example prompts to be input to the generative AI model

[0139] "Assuming that user A is interested in movies, please create a recommendation sentence for the next chat."

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] Enter and submit user information

[0143] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. Specifically, User A enters his / her name, age (25 years old), gender (male), and hobbies (watching movies and reading) on ​​the registration screen.

[0144] Terminal: The entered personal information is sent to the server via the form. Specifically, the submit button is clicked and the entered information is sent as an HTTP POST request.

[0145] Input: Personal information such as age, gender, hobbies, etc.

[0146] Output: Personal information sent to the server

[0147] Step 2:

[0148] User information stored in a database

[0149] Server: Stores the received personal information in a database. The database used may be a relational database management system such as MySQL (registered trademark).

[0150] Specific operation: The server stores the received information in the appropriate table using an SQL query.

[0151] Input: User A's personal information (name, age, gender, hobbies)

[0152] Output: User information stored in the database

[0153] Step 3:

[0154] User Matching

[0155] Server: Based on the registration information, the server matches User A with a suitable member of the opposite sex. For example, User A, a 25-year-old man whose hobby is watching movies, is matched with User B, a 24-year-old woman whose hobby is traveling.

[0156] Specific operation: The server uses a matching algorithm to search for and respond to users of the opposite sex who share the same hobbies and interests.

[0157] Input: User A's personal information and other users' information in the database

[0158] Output: Matched user pairs (users A and B)

[0159] Step 4:

[0160] Displaying the chat screen

[0161] Device: Display the chat screen to matched users A and B.

[0162] Specific behavior: The device launches the chat application and the chat interface is displayed.

[0163] Input: Matching information (User A and B)

[0164] Output: Chat screen

[0165] Step 5:

[0166] Manually starting a chat

[0167] User: User A and User B manually start a chat and send and receive messages. For example, User A sends a message to User B saying, "Hello, do you like movies?"

[0168] Input: User A's message content

[0169] Output: Message displayed on B's chat screen

[0170] Step 6:

[0171] Saving chat data

[0172] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0173] What it does: When the server receives a chat message, it writes the historical data to a database using an SQL query.

[0174] Input: Chat history data between users A and B

[0175] Output: Chat history data stored in a database

[0176] Step 7:

[0177] Chat data analysis

[0178] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing techniques, using Python's NLTK and spaCy libraries.

[0179] What happens: The server sends chat data to a text analysis module to identify user interests.

[0180] Input: Accumulated chat data

[0181] Output: Analysis results for User A (interests in movies and reading)

[0182] Step 8:

[0183] Generating recommendation sentences

[0184] Server: Based on the analysis results, generative AI automatically generates recommendation sentences.

[0185] Specific behavior: The generative AI begins the process of creating a recommendation sentence such as, "What movie have you seen recently?"

[0186] Input: User A's analysis results

[0187] Output: Generated recommendation sentences

[0188] Step 9:

[0189] Displaying recommended sentences

[0190] Terminal: The generated recommendation text is displayed on User A's chat screen.

[0191] Specific behavior: A recommended sentence will pop up in the chat interface.

[0192] Input: Generated recommendation sentences

[0193] Output: Recommended sentences displayed on the chat screen

[0194] Step 10:

[0195] Selection and use of recommendation text

[0196] User: Select the suggested recommendation sentence and use it as the next chat message. For example, User A clicks on the recommendation sentence "What movie have you seen recently?" and sends it to the chat.

[0197] Input: Recommended sentences displayed on the chat screen

[0198] Output: Suggested sentence sent as next chat message

[0199] (Application example 1)

[0200] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0201] There is a need for an effective method for quickly and appropriately responding to a wide range of user inquiries when chatting with users. There is also a need for a system that reduces the burden on customer support representatives and improves the user experience. Conventional methods require representatives to respond to each inquiry individually, which takes time and effort, and the quality of the response is unstable.

[0202] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0203] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for generating recommendation sentences based on the analyzed data, means for presenting the generated recommendation sentences to the user, and means for analyzing inquiry patterns and generating appropriate answers and questions for customer support, thereby enabling customer support staff to respond to user inquiries quickly and appropriately.

[0204] "Means for users to input personal information" refers to devices or software that provide a form or interface for users to input personal information such as name, age, hobbies, etc.

[0205] "Means for storing input personal information" refers to devices or software that record the personal information input by the user in a database or file system and make it accessible as needed.

[0206] A "user matching method" is an algorithm or system that connects users with each other based on shared hobbies or interests.

[0207] "Means for chatting between matched users" refers to a real-time communication interface or application that allows matched users to exchange messages.

[0208] The "means for storing chat history data" refers to a device or software that records chat content and message history between users in a database or log file.

[0209] The "means for analyzing the stored chat history data" refers to algorithms or software that analyze the stored chat history data using natural language processing techniques or the like to extract patterns or interests.

[0210] The "means for generating recommendation text based on analyzed data" refers to a generative AI model or text generation engine that automatically generates appropriate messages and questions based on the analysis results.

[0211] The "means for presenting the generated recommendation sentences to the user" refers to a device or software that displays the generated recommendation sentences on an interface such as a user's chat screen or dashboard.

[0212] "Means for analyzing inquiry patterns and generating appropriate answers and questions for customer support" refers to natural language processing technology and generative AI models that analyze past inquiry data and automatically generate appropriate answers and follow-up questions so that customer support staff can respond quickly.

[0213] To realize the system of the present invention, the following hardware and software are used.

[0214] The main technologies and hardware used are:

[0215] Python (mainly data processing and AI model implementation)

[0216] Django (Web application framework)

[0217] Google (registered trademark) Cloud Natural Language API (natural language processing technology)

[0218] TENSORFLOW® (training and running AI models)

[0219] MySQL (database management system)

[0220] When a user enters personal information, the device sends this information to the server via a form, and the server stores the received information in a MySQL database. For example, a user enters their name, age, hobbies, etc., and the information is sent to the server and stored.

[0221] In the matching phase, the server automatically matches users based on common hobbies and interests—for example, users who share a common hobby of watching movies—and the device provides an interface for users to chat with each other in real time.

[0222] Chat history data is recorded by the server and analyzed using the Google Cloud Natural Language API. This analysis identifies specific inquiry patterns and areas of user interest. For example, the analysis identifies that many inquiries are about "videos not playing."

[0223] In the analysis phase, the generative AI model on TensorFlow generates appropriate recommendation sentences based on the analysis results. The generated recommendation sentences are presented to the user via the device. For example, a recommendation such as "Please clear your cache and try again" is generated and displayed on the user's chat screen.

[0224] As a concrete example, the following prompt sentence can be used to generate an appropriate answer from the generative AI model:

[0225] What's the best response when a user says "The video won't play"?

[0226] In this way, the present invention provides a system that enables customer support personnel to respond to user inquiries quickly and appropriately, allowing users to smoothly continue conversations without experiencing difficulties in chatting, thereby improving the quality and efficiency of customer support.

[0227] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0228] Step 1:

[0229] The user enters personal information. The user fills in information such as name, age, and hobbies in an input form.

[0230] Input: Personal information (name, age, hobbies, etc.)

[0231] Output: Personal information entered in the input form

[0232] Specific operation: A user enters the required information into a form on a web application and presses the "Submit" button.

[0233] Step 2:

[0234] The terminal transmits the personal information entered by the user to the server.

[0235] Input: Personal information entered in the input form

[0236] Output: Personal information sent to the server

[0237] Specific operation: The terminal sends the form data to the server as an HTTP request.

[0238] Step 3:

[0239] The server stores the received personal information in a MySQL database.

[0240] Input: Personal information sent to the server

[0241] Output: Personal information stored in a MySQL database

[0242] Specific operation: The server analyzes the received data, converts it into an appropriate format, and stores it in the database.

[0243] Step 4:

[0244] The server automatically matches users with common hobbies and interests.

[0245] Input: Personal information stored in a MySQL database

[0246] Output: Matched user pairs

[0247] How it works: The server uses an algorithm to select users with common hobbies and interests and create pairs.

[0248] Step 5:

[0249] The device provides an interface that allows matched users to chat in real time.

[0250] Input: Matched user pairs

[0251] Output: Chat interface

[0252] Specific operation: The device displays a chat screen on the web application, allowing users to exchange messages.

[0253] Step 6:

[0254] The server stores chat history data in a MySQL database.

[0255] Input: Chat message between users

[0256] Output: Chat history stored in the database

[0257] Specific operation: The server records the content and timestamp of the chat message in a database.

[0258] Step 7:

[0259] The server analyzes the saved chat history data using the Google Cloud Natural Language API.

[0260] Input: Chat history stored in the database

[0261] Output: Analysis results (user areas of interest, inquiry patterns, etc.)

[0262] How it works: The server uses natural language processing techniques to extract important topics and patterns from chat data.

[0263] Step 8:

[0264] The server uses a generative AI model on TensorFlow to generate recommendation sentences based on the analysis results.

[0265] Input: Analysis results

[0266] Output: Recommendation sentence

[0267] Specific behavior: Runs TensorFlow to generate appropriate questions and answers based on the user's interests and inquiries.

[0268] Step 9:

[0269] The terminal displays the generated recommendation sentences on the user's chat screen.

[0270] Input: Recommendation sentence

[0271] Output: Recommendation sentences displayed on the chat screen

[0272] Specific operation: The device displays the recommended sentences on the chat interface so that the user can use them.

[0273] ---

[0274] The above are the specific processing steps of the system program that realizes this application example.

[0275] 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.

[0276] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. Furthermore, by combining it with an emotion engine, it provides a function to recognize and evaluate the user's emotions and adjust the recommendation sentences based on the results. This system aims to improve the user's chat experience and facilitate smooth communication.

[0277] System configuration

[0278] 1. Registration Phase

[0279] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[0280] Device: The personal information submitted in the form is sent to the server, and registration is completed.

[0281] Server: Stores the received user information in a database.

[0282] Examples:

[0283] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[0284] 2. Manual Chat Phase

[0285] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[0286] Terminal: Display the chat screen to users A and B.

[0287] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[0288] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0289] 3. Data analysis phase

[0290] Server: Once a certain amount of chat data has been accumulated, analysis begins. Analysis uses natural language processing (NLP) technology and an emotion engine.

[0291] Examples:

[0292] Once a certain amount of chat data between User A and User B has been accumulated, this data is analyzed to recognize User A's emotional state. For example, if there is a lot of positive vocabulary in the conversation, User A is evaluated as having a happy emotion.

[0293] 4. Recommendation text creation phase

[0294] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences, reflecting the evaluation of the emotion engine. For example, if User A has a happy emotion, the recommendation generated would be, "What fun movie have you seen recently?"

[0295] Device: Display the recommended text on User A's chat screen.

[0296] Users can easily select a suggested sentence to use in their next chat.

[0297] Details of the working example

[0298] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B via an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, and get excited about topics such as movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided. In addition, the emotion engine provides recommendations that are adapted to User A's emotional state, enabling more personalized communication.

[0299] In this way, the present invention provides a system that allows users to chat smoothly without difficulty and continues conversations smoothly through appropriate recommendation sentences based on emotions.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] The user opens the personal information form.

[0303] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[0304] Step 2:

[0305] The terminal acquires the user's input data and sends it to the server.

[0306] The device sends personal information data to the server using a RESTful API.

[0307] Step 3:

[0308] The server stores the received user information in a database.

[0309] The server records the input data in the appropriate table.

[0310] Step 4:

[0311] The server matches users with suitable members of the opposite sex.

[0312] The server uses a pre-set algorithm to select compatible opposite-sex partners based on the user's hobbies and interests.

[0313] Step 5:

[0314] A chat screen is displayed between users whose devices have been matched.

[0315] The terminal renders the chat interface and displays it to the user.

[0316] Step 6:

[0317] A user starts a chat and sends a message.

[0318] The user enters text on the chat screen and clicks the "Send" button.

[0319] Step 7:

[0320] The terminal sends the user's message to the server.

[0321] The device sends message data to the server's chat API endpoint via a POST request.

[0322] Step 8:

[0323] The server stores the received messages in a database.

[0324] The server stores the message content, sending time, sender ID, etc.

[0325] Step 9:

[0326] The server waits for a certain amount of chat data to accumulate.

[0327] The server monitors the count of chat data and triggers analysis when a threshold is reached.

[0328] Step 10:

[0329] The server analyzes the chat history data using natural language processing technology.

[0330] The server applies NLP models to extract the user's conversation patterns and interests.

[0331] Step 11:

[0332] The server evaluates the user's emotions using an emotion engine.

[0333] The server uses a sentiment analysis model to assess the user's emotional state (e.g., positive, negative, neutral).

[0334] Step 12:

[0335] Based on the server's analysis and emotion evaluation results, a generative AI generates recommendation sentences.

[0336] Based on the data obtained by the server, AI generates recommendation sentences that are appropriate for the user.

[0337] Step 13:

[0338] The terminal presents the generated recommendation sentences to the user.

[0339] The terminal displays the generated text on the chat screen, allowing the user to select it.

[0340] Step 14:

[0341] The user continues chatting using the recommended sentences provided.

[0342] The user selects a recommendation and clicks to send it as a message.

[0343] Examples:

[0344] For example, user A registers as a new user, and information about his hobbies and interests is saved in a database. Afterwards, person A is matched with person B, and they chat about movies and books. Once a certain number of chat histories have been accumulated, the server analyzes the content of the conversation through natural language processing, and the emotion engine detects happy emotions from person A's conversation. Then, the generative AI creates recommendation sentences such as "What fun movies have you seen recently?" and the device presents these to person A. person A can easily select this recommendation sentence and continue chatting. In this way, this system improves the user's chat experience and supports smooth communication.

[0345] Example 2

[0346] 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."

[0347] Current matching systems often experience interruptions when users chat, preventing smooth communication. Furthermore, they provide uniform recommendations without considering users' feelings, making it difficult to provide recommendations that are appropriate for each individual user. This can lead to a poor user experience.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0349] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for evaluating the user's emotions using emotion recognition technology, means for generating recommendation sentences based on the analyzed data and the emotion evaluation, and means for presenting the generated recommendation sentences to the user.

[0350] This makes it possible to provide appropriate recommended sentences based on the user's emotions, prevent conversations from stalling, and achieve smoother, more satisfying communication.

[0351] A "user" is an individual who uses the system, inputs personal information, and chats with others.

[0352] "Personal information" refers to individual data such as age, gender, and hobbies that users enter into the system.

[0353] "Input means" refers to an interface for users to input personal information into the system, and includes methods such as forms.

[0354] "Storage means" refers to the database or storage used to hold entered personal information and chat history data.

[0355] "Matching method" refers to an algorithm or method that links multiple users together based on the personal information they input.

[0356] "Chat means" refers to a communication interface through which matched users can exchange messages in real time.

[0357] "Chat history data" refers to records of message content, timestamps, etc. in chats between users.

[0358] "Analysis means" refers to technology and software for analyzing saved chat history data, including natural language processing technology.

[0359] "Emotion recognition technology" refers to methods and algorithms for analyzing a user's chat history data and identifying and evaluating the user's emotions from that data.

[0360] "Recommendation sentence" refers to an automatically generated message generated by analytical means and emotion recognition technology to encourage the user's next conversation.

[0361] "Presentation means" refers to the method or interface for showing the generated recommendation text to the user, and includes the display screen of the user's terminal, etc.

[0362] MODE FOR CARRYING OUT THE INVENTION

[0363] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. It also utilizes emotion recognition technology to evaluate the user's emotions and provides recommendation sentences based on the results, thereby achieving smooth communication.

[0364] 1. Hardware and Software Used

[0365] The entire system consists of a server, user devices, a database, natural language processing technology (NLP), an emotion recognition engine, and a generative AI model. Specifically, the server is installed on the cloud, and user devices include smartphones and PCs. The database can be a relational database such as MySQL or PostgreSQL. The natural language processing technology utilizes the NLTK library implemented in Python or TensorFlow. The emotion recognition engine may use the Emotion API from Azure (registered trademark). The generative AI model may use GPT from OpenAI (registered trademark).

[0366] 2. Program processing explanation

[0367] First, the user enters their personal information. The information entered via a device (the user's smartphone or computer) is sent to the server via a form, and the server stores it in a database.

[0368] The server then uses an automatic matching algorithm to select suitable individuals based on the stored personal information. The matching results are then sent back to the user's device, where an interface is displayed for the matched users to chat with each other.

[0369] When users start chatting, the server stores each message in a database in real time, and each message contains a timestamp, allowing the flow of conversation between users to be tracked.

[0370] Once a certain amount of chat data has been accumulated, the server analyzes the data using natural language processing (NLP) and an emotion recognition engine. For example, if User A chats, "I recently saw a really fun movie," NLP technology analyzes this message and extracts keywords. The emotion recognition engine recognizes positive emotions and evaluates that User A is feeling happy.

[0371] Based on the analysis results, a generative AI model (e.g., GPT-3 (registered trademark)) creates a recommendation sentence for the next conversation, such as, "Tell me about a good movie you saw recently." The generated recommendation sentence is displayed on the user's device.

[0372] Users can select recommended sentences and use them in their next chat. This allows the conversation to proceed smoothly by providing appropriate topics even when users are struggling with a conversation.

[0373] 3. Examples and prompts

[0374] As a concrete example, consider the case where users A and B get excited talking about movies. For example, user A sends a message saying, "Have you seen Inception?", and user B replies, "Yes, it was really interesting." Once this historical data is accumulated and analyzed, the generative AI model will provide a recommendation sentence like the following: "What is your favorite scene in Inception?"

[0375] An example prompt is:

[0376] 1. "Tell me about the last movie you saw."

[0377] 2. "Do you have any recommended topics related to your hobbies?"

[0378] 3. "Let's talk about the book you read recently."

[0379] As described above, the present invention is a system that makes users' chat experiences smoother and provides appropriate recommended sentences based on individual emotions, thereby facilitating communication.

[0380] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0381] Step 1:

[0382] The user enters personal information.

[0383] Input: User's age, gender, hobbies, etc.

[0384] Data processing: Enter information into the form on the user's device and press the send button.

[0385] Output: The form sends personal information to the server.

[0386] Specific operation: User A enters "Age 25, Gender male, Hobbies watching movies and reading" into the form on the device and clicks the send button.

[0387] Step 2:

[0388] The terminal transmits the entered personal information to the server.

[0389] Input: Personal information entered by the user into a form.

[0390] Data processing: Send form data as an HTTP request.

[0391] Output: Personal information is delivered to the server.

[0392] Specific operation: The device (smartphone or PC) sends the form data to the server via an HTTP POST request.

[0393] Step 3:

[0394] The server stores the received user information in a database.

[0395] Input: Personal information sent from your device.

[0396] Data processing: Save to database using SQL queries.

[0397] Output: User information is recorded in the database.

[0398] What happens: The server executes an SQL query like "INSERT INTO users (age, gender, hobbies) VALUES (25, 'Male', 'Watching movies, Reading')" and saves the information in the database.

[0399] Step 4:

[0400] The server matches users based on the stored personal information.

[0401] Input: Multiple user information stored in a database.

[0402] Data processing: Apply a matching algorithm to select a partner.

[0403] Output: Generates the match results and prepares them to be sent to the device.

[0404] Specific operation: The server executes an "algorithm for matching users with similar hobbies" and selects a 24-year-old woman (Ms. B) whose hobbies are "travel and music" for User A.

[0405] Step 5:

[0406] A chat screen is displayed between users whose devices have been matched.

[0407] Input: Matching results sent by the server.

[0408] Data processing: Generates a chat screen and displays it to the user.

[0409] Output: The chat screen is displayed on the user's device.

[0410] Specific operation: The device creates a "chat room between users A and B" and displays the chat screen on each device.

[0411] Step 6:

[0412] A user manually starts a chat and sends a message.

[0413] Input: The chat message typed by the user.

[0414] Data processing: Send the message content to the server.

[0415] Output: The chat message is sent to the server.

[0416] Specific behavior: User A types "What movie have you seen recently?" in the chat box and clicks the send button.

[0417] Step 7:

[0418] The server stores the chat history data in a database.

[0419] Input: Chat messages sent from the device.

[0420] Data processing: Save to database using SQL queries.

[0421] Output: Chat history is stored in a database.

[0422] What happens: The server executes an SQL query like "INSERT INTO chat_history (user_id, message, timestamp) VALUES (A, 'What movie did you see recently?', CURRENT_TIMESTAMP)".

[0423] Step 8:

[0424] The server analyzes the stored chat history data.

[0425] Input: Chat history data stored in a database.

[0426] Data processing: Applying natural language processing and emotion recognition technologies.

[0427] Output: Analysis results and sentiment ratings.

[0428] Specific operation: The server analyzes the message using an NLP engine (e.g., Python's NLTK) and evaluates that User A is feeling "fun" using an emotion recognition engine (e.g., Azure Emotion API).

[0429] Step 9:

[0430] The server generates recommendation sentences based on the analysis results.

[0431] Input: Analysis results and emotion ratings.

[0432] Data processing: Generate recommendation sentences using a generative AI model (e.g., GPT-3).

[0433] Output: Recommendation sentences.

[0434] Specific operation: The server generates a recommendation sentence such as "What good movies have you seen recently?" and makes it available for the next conversation.

[0435] Step 10:

[0436] The terminal presents the generated recommendation sentences to the user.

[0437] Input: Recommendation text sent from the server.

[0438] Data processing: Displayed on the chat screen.

[0439] Output: The recommendation text is displayed on the user's device.

[0440] Specific operation: The device displays a recommendation message on the chat screen, such as "What good movies have you seen recently?", and allows the user to select one.

[0441] The above is the flow of processing of the program of this system.

[0442] (Application example 2)

[0443] 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."

[0444] Conventional self-driving vehicles have not provided sufficient mechanisms to encourage communication between passengers and allow them to spend their time on board meaningfully. Furthermore, the lack of communication methods that respond to passenger emotions can sometimes hinder smooth communication. This has made it difficult to improve passenger satisfaction.

[0445] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0446] In this invention, the server includes: a means for a user to input personal information; a means for saving the input personal information; a means for matching users; a means for matched users to chat with each other; a means for saving chat history data; a means for analyzing the saved chat history data; a means for generating recommendation sentences based on the analyzed data; a means for presenting the generated recommendation sentences to the user; a means for evaluating an emotional state based on the analyzed data; a means for adjusting the recommendation sentences based on the emotional state; and a means for matching users and chatting with them in an autonomous vehicle. This allows smooth communication between passengers and enables them to spend their time on board meaningfully. Furthermore, by providing recommendation sentences based on emotions, personalized communication is realized.

[0447] "Personal information" is information that can identify a specific individual, including the user's age, gender, hobbies, etc.

[0448] "Matching" is the process by which the system selects a suitable partner based on the personal information of multiple users.

[0449] "Chat" is a means for users to communicate with each other through text messages.

[0450] "Chat history data" refers to data that records the content of conversations between users and the time at which they occurred.

[0451] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0452] An "emotion engine" is a software component for recognizing and assessing a user's emotional state from text data.

[0453] "Recommendation sentences" are phrases generated by the system and presented to users to facilitate communication.

[0454] An "autonomous vehicle" is a vehicle whose driving is controlled by a system without human intervention.

[0455] "Personalization" refers to tailoring specific services or content to the specific needs and emotional state of individual users.

[0456] This invention is a system for facilitating communication between passengers in an autonomous vehicle and allowing them to spend their time on board meaningfully. The configuration and operation of the system are described below.

[0457] System configuration

[0458] 1. Registration Phase

[0459] The user enters personal information when starting the system. The entered personal information is sent to the server via the terminal and stored by the server. This process includes the user's age, gender, hobbies, etc. For example, if User A enters that he is a 25-year-old male whose hobbies are watching movies and reading, this information is sent to the server and stored.

[0460] 2. Matching Phase

[0461] The server uses the input personal information to match users with suitable partners. For example, when user A gets into an autonomous vehicle, she is matched with person B, who is also in the vehicle. Person B is a 24-year-old woman whose hobbies are traveling and music.

[0462] 3. Chat Phase

[0463] If the match is successful, the device displays a chat screen for user A and user B. Here, users can freely send and receive text messages and communicate with each other. Chat history data, including the message content and the time it occurred, is saved on the server. For example, if user A asks, "What movie did you see recently?" and user B replies, "I saw Inception! It was amazing!", this exchange is saved as chat history data.

[0464] 4. Data analysis phase

[0465] Once a certain amount of chat data has been accumulated, the server analyzes it. Natural language processing (NLP) and an emotion engine are used to evaluate the user's emotional state. For example, if the chat content contains a lot of positive vocabulary, the server evaluates the user as having a happy emotion.

[0466] 5. Recommendation text creation phase

[0467] Based on the analysis results, the server uses a generative AI model to generate recommendation sentences, taking into account the evaluation of the emotion engine. For example, if the user is feeling happy, a recommendation such as "What movie would you like to see next?" will be generated. The generated recommendation sentences are displayed on the device, allowing the user to easily select and use them in the next chat.

[0468] Specific examples

[0469] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with Person B through an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, enjoying discussions of movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generation AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[0470] Prompt Sentence Examples

[0471] User A's chat data:

[0472] 1. What's the last movie you saw?

[0473] 2. I saw Inception! It was amazing!

[0474] Recommended articles:

[0475] What movie do you want to see next?

[0476] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0477] Step 1:

[0478] The user enters personal information.

[0479] Input: Personal information such as age, gender, and hobbies entered by the user.

[0480] Output: The entered personal information is sent to the terminal.

[0481] Specific operation: The user enters information such as their age, gender, and hobbies into a form on the terminal and sends it to the server by pressing the confirm button.

[0482] Step 2:

[0483] The device sends personal information to a server, and the server stores the personal information.

[0484] Input: Personal information sent from your device.

[0485] Output: Personal information stored in the server's database.

[0486] Specific operation: The terminal sends the information entered through the form to the server using an API, and the server stores it in a database.

[0487] Step 3:

[0488] The server matches appropriate users based on the stored personal information.

[0489] Input: Personal information of multiple users stored in a database.

[0490] Output: Matched user pairs.

[0491] How it works: The server uses an algorithm to compare multiple user data and select the best pair based on criteria such as hobbies and age.

[0492] Step 4:

[0493] Matched users chat with each other.

[0494] Input: Matched user pairs.

[0495] Output: Chat messages sent and received between users.

[0496] Specific operation: The device displays a chat screen, allowing users to freely type and send messages. Sent messages are immediately displayed on the other device.

[0497] Step 5:

[0498] Chat history data is sent to the server and stored by the server.

[0499] Input: The chat message sent from the device along with the timestamp.

[0500] Output: Chat history data stored on the server.

[0501] Specific operation: Every time each device sends a chat message, the data is sent to the server and stored in a database along with a timestamp.

[0502] Step 6:

[0503] After accumulating a certain amount of chat data, the server analyzes it.

[0504] Input: Chat history data stored in a database.

[0505] Output: Analysis results, especially the user's emotional state.

[0506] Specific operation: The server uses natural language processing (NLP) technology and an emotion engine to analyze the content of chat data and evaluate the emotional state, such as positive or negative.

[0507] Step 7:

[0508] Based on the analysis results, the server generates recommendation sentences.

[0509] Input: The emotional state assessed by the emotion engine.

[0510] Output: Recommendation sentences generated by the generative AI model.

[0511] Specific operation: Based on the emotional state, the server uses a generative AI model to automatically generate the next recommendation sentence that the user should send.

[0512] Step 8:

[0513] The generated recommendation sentences are presented to the user's terminal.

[0514] Input: Generated recommendation sentences.

[0515] Output: Recommendation text displayed on the chat screen of the user's device.

[0516] Specific operation: The server sends the generated recommendation sentence to the user's device, which displays it on the chat screen. The user can click on the presented sentence to send it as the next message.

[0517] As a specific example of how it works, User A asks, "What movie did you see recently?" and User B replies, "I saw Inception! It was amazing!" This chat data is saved on the server and analyzed using NLP and an emotion engine. Based on the analysis results, a recommendation sentence such as "What movie do you want to see next?" is generated and displayed on User A's chat screen.

[0518] 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.

[0519] 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.

[0520] 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.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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).

[0528] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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."

[0534] This invention is a system in which users enter personal information when registering, and then manually chat with a certain number of people of the opposite sex after registration. The system analyzes the user's chat patterns and uses generative AI to create recommendation sentences. The system aims to reduce the user's chat burden and prevent opportunity loss.

[0535] System configuration

[0536] 1. Registration Phase

[0537] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[0538] Terminal: Sends personal information submitted through the form to the server.

[0539] Server: Stores the received user information in a database.

[0540] Examples:

[0541] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[0542] 2. Manual Chat Phase

[0543] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[0544] Terminal: Display the chat screen to users A and B.

[0545] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[0546] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0547] 3. Data analysis phase

[0548] Server: Once a certain amount of chat data has been accumulated, analysis begins. Natural language processing (NLP) technology is used for analysis.

[0549] Examples:

[0550] Once a certain amount of chat data between users A and B has been accumulated, this data is analyzed to determine that user A has a strong interest in movies and books.

[0551] 4. Recommendation text creation phase

[0552] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, if User A likes watching movies, the recommendation generated would be, "What movie have you seen recently?"

[0553] Device: Display the recommended text on User A's chat screen.

[0554] Users can easily select a suggested sentence to use in their next chat.

[0555] Details of the working example

[0556] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B through an automatic matching algorithm, and the device displays a chat screen. Users A and B then chat manually, enjoying topics such as movies and books. The server saves this chat data in a database and analyzes it once a certain amount of chat data has been collected. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[0557] In this way, the present invention provides a system that allows users to chat smoothly without feeling any difficulty and continue the conversation.

[0558] The processing flow will be explained below.

[0559] Step 1:

[0560] The user opens the personal information form.

[0561] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[0562] Step 2:

[0563] The terminal acquires the user's input data and sends it to the server.

[0564] The device sends the input data to the appropriate endpoint on the server.

[0565] Step 3:

[0566] The server stores the received user information in a database.

[0567] The server analyzes the personal information sent and stores it in a database in an appropriate format.

[0568] Step 4:

[0569] The server matches users with suitable members of the opposite sex.

[0570] The server uses a matching algorithm to find people of the opposite sex who share common hobbies and interests.

[0571] Step 5:

[0572] A chat screen is displayed between users whose devices have been matched.

[0573] The device generates and displays an interface that allows you to chat with the matched person.

[0574] Step 6:

[0575] A user starts a chat and sends a message.

[0576] The user enters a message on the chat screen and clicks the "Send" button.

[0577] Step 7:

[0578] The terminal sends the user's message to the server.

[0579] The device sends the sent message to the server's chat save endpoint.

[0580] Step 8:

[0581] The server stores the received messages in a database.

[0582] The server stores the message content, sender, sent time, etc. in a database.

[0583] Step 9:

[0584] The server waits for a certain amount of chat data to accumulate.

[0585] When the server receives a certain amount of chat data, it begins analyzing the data.

[0586] Step 10:

[0587] The server analyzes the chat history data using natural language processing technology.

[0588] The server uses NLP models to analyze users' chat patterns and interests.

[0589] Step 11:

[0590] Based on the server's analysis results, a generative AI generates recommendation sentences.

[0591] The server inputs the analysis results into an AI model and automatically generates appropriate recommendation sentences.

[0592] Step 12:

[0593] The terminal presents the generated recommendation sentences to the user.

[0594] The device displays the recommendation text and allows the user to select and use it.

[0595] Step 13:

[0596] The user continues chatting using the recommended sentences provided.

[0597] The user selects the presented sentence and pastes it directly into the chat to continue.

[0598] Example 1

[0599] 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."

[0600] There is a need for a system that reduces the burden users feel when chatting with members of the opposite sex and prevents missed chat opportunities. Conventional chat systems require users to think up topics and input them themselves, which creates obstacles to communication. Furthermore, there is a lack of technology that can analyze chat content and present appropriate recommended sentences. This makes it difficult for users to continue chatting smoothly.

[0601] 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.

[0602] In this invention, the server includes: means for a user to input personal information; means for saving the input personal information; means for matching users with members of the opposite sex; means for matched users to chat with each other; means for saving chat history data; means for analyzing the saved chat history data; means for generating recommendation sentences based on the analyzed data; means for presenting the generated recommendation sentences to the user; means for the user to use the presented recommendation sentences in the next chat; means for starting analysis when a certain amount of chat data has been accumulated; means for using timestamps of the saved chat data; and means for linking and analyzing the chat data and the user's personal information. This reduces the burden on the user when chatting and enables the conversation to continue smoothly.

[0603] "User" refers to a person who uses this system to enter personal information and chat.

[0604] "Personal information" refers to detailed information relating to a user's identity, such as the user's age, gender, hobbies, etc.

[0605] "Matching" refers to the process by which the server pairs users with suitable members of the opposite sex based on their personal information.

[0606] "Chat" refers to the act of matched users communicating with each other in real time through messages.

[0607] "Historical data" refers to records of chat content, message timestamps, etc.

[0608] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[0609] "Recommended sentences" refer to chat messages that the server generates based on analysis data and suggests to users.

[0610] A "generative AI model" refers to an artificial intelligence system that automatically generates new sentences based on collected data.

[0611] "Timestamp" refers to information about the time at which a chat message was sent or received.

[0612] "Database" refers to the information management system that the system uses to store personal information, chat history data, etc.

[0613] "Analysis" refers to the process of processing stored historical data and personal information to identify user interests and concerns.

[0614] This invention is a system in which a user enters personal information when registering, chats with a certain number of people, analyzes the user's chat patterns, and a generative AI creates recommendation sentences. This reduces the user's chat burden and prevents opportunity loss. The form for realizing this system is described in detail below.

[0615] System configuration

[0616] Registration Phase

[0617] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. The entered information is sent via the form.

[0618] Example: User A is a 25-year-old male and enters on the registration screen that his hobbies are watching movies and reading.

[0619] Terminal: Sends the entered personal information to a server. This information is usually sent using a dedicated communication protocol.

[0620] Server: Stores the received personal information in a database. The database can be a relational database management system such as an SQL database.

[0621] Manual Chat Phase

[0622] Server: Based on the registered personal information, the server matches users with members of the opposite sex. For example, User A, a 25-year-old man, is matched with User B, a 24-year-old woman.

[0623] Device: A chat screen is displayed to matched users A and B. This display is done using a dedicated chat application.

[0624] User: User A and User B chat manually and send messages. At this stage, it is assumed that User A will send a message to User B saying, "Hello, do you like movies?"

[0625] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0626] Data analysis phase

[0627] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing (NLP) technology. This analysis can be done using Python libraries (e.g., NLTK or spaCy).

[0628] Example: Analysis reveals that user A has a strong interest in movies.

[0629] Recommendation sentence creation phase

[0630] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, the generated recommendation sentences might be in the form of "What movie have you seen recently?"

[0631] Device: This recommendation text is displayed on User A's chat screen.

[0632] User: Select the suggested sentence and use it in the next chat.

[0633] Example: User A clicks on the recommendation text "What movie have you seen recently?" and sends it to the chat.

[0634] Example prompts to be input to the generative AI model

[0635] "Assuming that user A is interested in movies, please create a recommendation sentence for the next chat."

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

[0637] Step 1:

[0638] Enter and submit user information

[0639] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. Specifically, User A enters his / her name, age (25 years old), gender (male), and hobbies (watching movies and reading) on ​​the registration screen.

[0640] Terminal: The entered personal information is sent to the server via the form. Specifically, the submit button is clicked and the entered information is sent as an HTTP POST request.

[0641] Input: Personal information such as age, gender, hobbies, etc.

[0642] Output: Personal information sent to the server

[0643] Step 2:

[0644] User information stored in a database

[0645] Server: The received personal information is stored in a database, such as a relational database management system like MySQL.

[0646] Specific operation: The server stores the received information in the appropriate table using an SQL query.

[0647] Input: User A's personal information (name, age, gender, hobbies)

[0648] Output: User information stored in the database

[0649] Step 3:

[0650] User Matching

[0651] Server: Based on the registration information, the server matches User A with a suitable member of the opposite sex. For example, User A, a 25-year-old man whose hobby is watching movies, is matched with User B, a 24-year-old woman whose hobby is traveling.

[0652] Specific operation: The server uses a matching algorithm to search for and respond to users of the opposite sex who share the same hobbies and interests.

[0653] Input: User A's personal information and other users' information in the database

[0654] Output: Matched user pairs (users A and B)

[0655] Step 4:

[0656] Displaying the chat screen

[0657] Device: Display the chat screen to matched users A and B.

[0658] Specific behavior: The device launches the chat application and the chat interface is displayed.

[0659] Input: Matching information (User A and B)

[0660] Output: Chat screen

[0661] Step 5:

[0662] Manually starting a chat

[0663] User: User A and User B manually start a chat and send and receive messages. For example, User A sends a message to User B saying, "Hello, do you like movies?"

[0664] Input: User A's message content

[0665] Output: Message displayed on B's chat screen

[0666] Step 6:

[0667] Saving chat data

[0668] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0669] What it does: When the server receives a chat message, it writes the historical data to a database using an SQL query.

[0670] Input: Chat history data between users A and B

[0671] Output: Chat history data stored in a database

[0672] Step 7:

[0673] Chat data analysis

[0674] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing techniques, using Python's NLTK and spaCy libraries.

[0675] What happens: The server sends chat data to a text analysis module to identify user interests.

[0676] Input: Accumulated chat data

[0677] Output: Analysis results for User A (interests in movies and reading)

[0678] Step 8:

[0679] Generating recommendation sentences

[0680] Server: Based on the analysis results, generative AI automatically generates recommendation sentences.

[0681] Specific behavior: The generative AI begins the process of creating a recommendation sentence such as, "What movie have you seen recently?"

[0682] Input: User A's analysis results

[0683] Output: Generated recommendation sentences

[0684] Step 9:

[0685] Displaying recommended sentences

[0686] Terminal: The generated recommendation text is displayed on User A's chat screen.

[0687] Specific behavior: A recommended sentence will pop up in the chat interface.

[0688] Input: Generated recommendation sentences

[0689] Output: Recommended sentences displayed on the chat screen

[0690] Step 10:

[0691] Selection and use of recommendation text

[0692] User: Select the suggested recommendation sentence and use it as the next chat message. For example, User A clicks on the recommendation sentence "What movie have you seen recently?" and sends it to the chat.

[0693] Input: Recommended sentences displayed on the chat screen

[0694] Output: Suggested sentence sent as next chat message

[0695] (Application example 1)

[0696] 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."

[0697] There is a need for an effective method for quickly and appropriately responding to a wide range of user inquiries when chatting with users. There is also a need for a system that reduces the burden on customer support representatives and improves the user experience. Conventional methods require representatives to respond to each inquiry individually, which takes time and effort, and the quality of the response is unstable.

[0698] 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.

[0699] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for generating recommendation sentences based on the analyzed data, means for presenting the generated recommendation sentences to the user, and means for analyzing inquiry patterns and generating appropriate answers and questions for customer support, thereby enabling customer support staff to respond to user inquiries quickly and appropriately.

[0700] "Means for users to input personal information" refers to devices or software that provide a form or interface for users to input personal information such as name, age, hobbies, etc.

[0701] "Means for storing input personal information" refers to devices or software that record the personal information input by the user in a database or file system and make it accessible as needed.

[0702] A "user matching method" is an algorithm or system that connects users with each other based on shared hobbies or interests.

[0703] "Means for chatting between matched users" refers to a real-time communication interface or application that allows matched users to exchange messages.

[0704] The "means for storing chat history data" refers to a device or software that records chat content and message history between users in a database or log file.

[0705] The "means for analyzing the stored chat history data" refers to algorithms or software that analyze the stored chat history data using natural language processing techniques or the like to extract patterns or interests.

[0706] The "means for generating recommendation text based on analyzed data" refers to a generative AI model or text generation engine that automatically generates appropriate messages and questions based on the analysis results.

[0707] The "means for presenting the generated recommendation sentences to the user" refers to a device or software that displays the generated recommendation sentences on an interface such as a user's chat screen or dashboard.

[0708] "Means for analyzing inquiry patterns and generating appropriate answers and questions for customer support" refers to natural language processing technology and generative AI models that analyze past inquiry data and automatically generate appropriate answers and follow-up questions so that customer support staff can respond quickly.

[0709] To realize the system of the present invention, the following hardware and software are used.

[0710] The main technologies and hardware used are:

[0711] Python (mainly data processing and AI model implementation)

[0712] Django (Web application framework)

[0713] Google Cloud Natural Language API (natural language processing technology)

[0714] TensorFlow (training and running AI models)

[0715] MySQL (database management system)

[0716] When a user enters personal information, the device sends this information to the server via a form, and the server stores the received information in a MySQL database. For example, a user enters their name, age, hobbies, etc., and the information is sent to the server and stored.

[0717] In the matching phase, the server automatically matches users based on common hobbies and interests—for example, users who share a common hobby of watching movies—and the device provides an interface for users to chat with each other in real time.

[0718] Chat history data is recorded by the server and analyzed using the Google Cloud Natural Language API. This analysis identifies specific inquiry patterns and areas of user interest. For example, the analysis identifies that many inquiries are about "videos not playing."

[0719] In the analysis phase, the generative AI model on TensorFlow generates appropriate recommendation sentences based on the analysis results. The generated recommendation sentences are presented to the user via the device. For example, a recommendation such as "Please clear your cache and try again" is generated and displayed on the user's chat screen.

[0720] As a concrete example, the following prompt sentence can be used to generate an appropriate answer from the generative AI model:

[0721] What's the best response when a user says "The video won't play"?

[0722] In this way, the present invention provides a system that enables customer support personnel to respond to user inquiries quickly and appropriately, allowing users to smoothly continue conversations without experiencing difficulties in chatting, thereby improving the quality and efficiency of customer support.

[0723] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0724] Step 1:

[0725] The user enters personal information. The user fills in information such as name, age, and hobbies in an input form.

[0726] Input: Personal information (name, age, hobbies, etc.)

[0727] Output: Personal information entered in the input form

[0728] Specific operation: A user enters the required information into a form on a web application and presses the "Submit" button.

[0729] Step 2:

[0730] The terminal transmits the personal information entered by the user to the server.

[0731] Input: Personal information entered in the input form

[0732] Output: Personal information sent to the server

[0733] Specific operation: The terminal sends the form data to the server as an HTTP request.

[0734] Step 3:

[0735] The server stores the received personal information in a MySQL database.

[0736] Input: Personal information sent to the server

[0737] Output: Personal information stored in a MySQL database

[0738] Specific operation: The server analyzes the received data, converts it into an appropriate format, and stores it in the database.

[0739] Step 4:

[0740] The server automatically matches users with common hobbies and interests.

[0741] Input: Personal information stored in a MySQL database

[0742] Output: Matched user pairs

[0743] How it works: The server uses an algorithm to select users with common hobbies and interests and create pairs.

[0744] Step 5:

[0745] The device provides an interface that allows matched users to chat in real time.

[0746] Input: Matched user pairs

[0747] Output: Chat interface

[0748] Specific operation: The device displays a chat screen on the web application, allowing users to exchange messages.

[0749] Step 6:

[0750] The server stores chat history data in a MySQL database.

[0751] Input: Chat message between users

[0752] Output: Chat history stored in the database

[0753] Specific operation: The server records the content and timestamp of the chat message in a database.

[0754] Step 7:

[0755] The server analyzes the saved chat history data using the Google Cloud Natural Language API.

[0756] Input: Chat history stored in the database

[0757] Output: Analysis results (user areas of interest, inquiry patterns, etc.)

[0758] How it works: The server uses natural language processing techniques to extract important topics and patterns from chat data.

[0759] Step 8:

[0760] The server uses a generative AI model on TensorFlow to generate recommendation sentences based on the analysis results.

[0761] Input: Analysis results

[0762] Output: Recommendation sentence

[0763] Specific behavior: Runs TensorFlow to generate appropriate questions and answers based on the user's interests and inquiries.

[0764] Step 9:

[0765] The terminal displays the generated recommendation sentences on the user's chat screen.

[0766] Input: Recommendation sentence

[0767] Output: Recommendation sentences displayed on the chat screen

[0768] Specific operation: The device displays the recommended sentences on the chat interface so that the user can use them.

[0769] ---

[0770] The above are the specific processing steps of the system program that realizes this application example.

[0771] 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.

[0772] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. Furthermore, by combining it with an emotion engine, it provides a function to recognize and evaluate the user's emotions and adjust the recommendation sentences based on the results. This system aims to improve the user's chat experience and facilitate smooth communication.

[0773] System configuration

[0774] 1. Registration Phase

[0775] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[0776] Device: The personal information submitted in the form is sent to the server, and registration is completed.

[0777] Server: Stores the received user information in a database.

[0778] Examples:

[0779] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[0780] 2. Manual Chat Phase

[0781] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[0782] Terminal: Display the chat screen to users A and B.

[0783] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[0784] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[0785] 3. Data analysis phase

[0786] Server: Once a certain amount of chat data has been accumulated, analysis begins. Analysis uses natural language processing (NLP) technology and an emotion engine.

[0787] Examples:

[0788] Once a certain amount of chat data between User A and User B has been accumulated, this data is analyzed to recognize User A's emotional state. For example, if there is a lot of positive vocabulary in the conversation, User A is evaluated as having a happy emotion.

[0789] 4. Recommendation text creation phase

[0790] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences, reflecting the evaluation of the emotion engine. For example, if User A has a happy emotion, the recommendation generated would be, "What fun movie have you seen recently?"

[0791] Device: Display the recommended text on User A's chat screen.

[0792] Users can easily select a suggested sentence to use in their next chat.

[0793] Details of the working example

[0794] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B via an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, and get excited about topics such as movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided. In addition, the emotion engine provides recommendations that are adapted to User A's emotional state, enabling more personalized communication.

[0795] In this way, the present invention provides a system that allows users to chat smoothly without difficulty and continues conversations smoothly through appropriate recommendation sentences based on emotions.

[0796] The processing flow will be explained below.

[0797] Step 1:

[0798] The user opens the personal information form.

[0799] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[0800] Step 2:

[0801] The terminal acquires the user's input data and sends it to the server.

[0802] The device sends personal information data to the server using a RESTful API.

[0803] Step 3:

[0804] The server stores the received user information in a database.

[0805] The server records the input data in the appropriate table.

[0806] Step 4:

[0807] The server matches users with suitable members of the opposite sex.

[0808] The server uses a pre-set algorithm to select compatible opposite-sex partners based on the user's hobbies and interests.

[0809] Step 5:

[0810] A chat screen is displayed between users whose devices have been matched.

[0811] The terminal renders the chat interface and displays it to the user.

[0812] Step 6:

[0813] A user starts a chat and sends a message.

[0814] The user enters text on the chat screen and clicks the "Send" button.

[0815] Step 7:

[0816] The terminal sends the user's message to the server.

[0817] The device sends message data to the server's chat API endpoint via a POST request.

[0818] Step 8:

[0819] The server stores the received messages in a database.

[0820] The server stores the message content, sending time, sender ID, etc.

[0821] Step 9:

[0822] The server waits for a certain amount of chat data to accumulate.

[0823] The server monitors the count of chat data and triggers analysis when a threshold is reached.

[0824] Step 10:

[0825] The server analyzes the chat history data using natural language processing technology.

[0826] The server applies NLP models to extract the user's conversation patterns and interests.

[0827] Step 11:

[0828] The server evaluates the user's emotions using an emotion engine.

[0829] The server uses a sentiment analysis model to assess the user's emotional state (e.g., positive, negative, neutral).

[0830] Step 12:

[0831] Based on the server's analysis and emotion evaluation results, a generative AI generates recommendation sentences.

[0832] Based on the data obtained by the server, AI generates recommendation sentences that are appropriate for the user.

[0833] Step 13:

[0834] The terminal presents the generated recommendation sentences to the user.

[0835] The terminal displays the generated text on the chat screen, allowing the user to select it.

[0836] Step 14:

[0837] The user continues chatting using the recommended sentences provided.

[0838] The user selects a recommendation and clicks to send it as a message.

[0839] Examples:

[0840] For example, user A registers as a new user, and information about his hobbies and interests is saved in a database. Afterwards, person A is matched with person B, and they chat about movies and books. Once a certain number of chat histories have been accumulated, the server analyzes the content of the conversation through natural language processing, and the emotion engine detects happy emotions from person A's conversation. Then, the generative AI creates recommendation sentences such as "What fun movies have you seen recently?" and the device presents these to person A. person A can easily select this recommendation sentence and continue chatting. In this way, this system improves the user's chat experience and supports smooth communication.

[0841] Example 2

[0842] 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."

[0843] Current matching systems often experience interruptions when users chat, preventing smooth communication. Furthermore, they provide uniform recommendations without considering users' feelings, making it difficult to provide recommendations that are appropriate for each individual user. This can lead to a poor user experience.

[0844] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0845] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for evaluating the user's emotions using emotion recognition technology, means for generating recommendation sentences based on the analyzed data and the emotion evaluation, and means for presenting the generated recommendation sentences to the user.

[0846] This makes it possible to provide appropriate recommended sentences based on the user's emotions, prevent conversations from stalling, and achieve smoother, more satisfying communication.

[0847] A "user" is an individual who uses the system, inputs personal information, and chats with others.

[0848] "Personal information" refers to individual data such as age, gender, and hobbies that users enter into the system.

[0849] "Input means" refers to an interface for users to input personal information into the system, and includes methods such as forms.

[0850] "Storage means" refers to the database or storage used to hold entered personal information and chat history data.

[0851] "Matching method" refers to an algorithm or method that links multiple users together based on the personal information they input.

[0852] "Chat means" refers to a communication interface through which matched users can exchange messages in real time.

[0853] "Chat history data" refers to records of message content, timestamps, etc. in chats between users.

[0854] "Analysis means" refers to technology and software for analyzing saved chat history data, including natural language processing technology.

[0855] "Emotion recognition technology" refers to methods and algorithms for analyzing a user's chat history data and identifying and evaluating the user's emotions from that data.

[0856] "Recommendation sentence" refers to an automatically generated message generated by analytical means and emotion recognition technology to encourage the user's next conversation.

[0857] "Presentation means" refers to the method or interface for showing the generated recommendation text to the user, and includes the display screen of the user's terminal, etc.

[0858] MODE FOR CARRYING OUT THE INVENTION

[0859] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. It also utilizes emotion recognition technology to evaluate the user's emotions and provides recommendation sentences based on the results, thereby achieving smooth communication.

[0860] 1. Hardware and Software Used

[0861] The entire system consists of a server, user devices, a database, natural language processing technology (NLP), an emotion recognition engine, and a generative AI model. Specifically, the server is installed on the cloud, and user devices include smartphones and PCs. The database can be a relational database such as MySQL or PostgreSQL. The natural language processing technology utilizes the NLTK library implemented in Python or TensorFlow. The emotion recognition engine may use Azure's Emotion API, for example. The generative AI model uses OpenAI's GPT, for example.

[0862] 2. Program processing explanation

[0863] First, the user enters their personal information. The information entered via a device (the user's smartphone or computer) is sent to the server via a form, and the server stores it in a database.

[0864] The server then uses an automatic matching algorithm to select suitable individuals based on the stored personal information. The matching results are then sent back to the user's device, where an interface is displayed for the matched users to chat with each other.

[0865] When users start chatting, the server stores each message in a database in real time, and each message contains a timestamp, allowing the flow of conversation between users to be tracked.

[0866] Once a certain amount of chat data has been accumulated, the server analyzes the data using natural language processing (NLP) and an emotion recognition engine. For example, if User A chats, "I recently saw a really fun movie," NLP technology analyzes this message and extracts keywords. The emotion recognition engine recognizes positive emotions and evaluates that User A is feeling happy.

[0867] Based on the analysis results, a generative AI model (e.g., GPT-3) creates a recommendation sentence for the next conversation, such as, "Tell me about a good movie you saw recently." The generated recommendation sentence is then displayed on the user's device.

[0868] Users can select recommended sentences and use them in their next chat. This allows the conversation to proceed smoothly by providing appropriate topics even when users are struggling with a conversation.

[0869] 3. Examples and prompts

[0870] As a concrete example, consider the case where users A and B get excited talking about movies. For example, user A sends a message saying, "Have you seen Inception?", and user B replies, "Yes, it was really interesting." Once this historical data is accumulated and analyzed, the generative AI model will provide a recommendation sentence like the following: "What is your favorite scene in Inception?"

[0871] An example prompt is:

[0872] 1. "Tell me about the last movie you saw."

[0873] 2. "Do you have any recommended topics related to your hobbies?"

[0874] 3. "Let's talk about the book you read recently."

[0875] As described above, the present invention is a system that makes users' chat experiences smoother and provides appropriate recommended sentences based on individual emotions, thereby facilitating communication.

[0876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] The user enters personal information.

[0879] Input: User's age, gender, hobbies, etc.

[0880] Data processing: Enter information into the form on the user's device and press the send button.

[0881] Output: The form sends personal information to the server.

[0882] Specific operation: User A enters "Age 25, Gender male, Hobbies watching movies and reading" into the form on the device and clicks the send button.

[0883] Step 2:

[0884] The terminal transmits the entered personal information to the server.

[0885] Input: Personal information entered by the user into a form.

[0886] Data processing: Send form data as an HTTP request.

[0887] Output: Personal information is delivered to the server.

[0888] Specific operation: The device (smartphone or PC) sends the form data to the server via an HTTP POST request.

[0889] Step 3:

[0890] The server stores the received user information in a database.

[0891] Input: Personal information sent from your device.

[0892] Data processing: Save to database using SQL queries.

[0893] Output: User information is recorded in the database.

[0894] What happens: The server executes an SQL query like "INSERT INTO users (age, gender, hobbies) VALUES (25, 'Male', 'Watching movies, Reading')" and saves the information in the database.

[0895] Step 4:

[0896] The server matches users based on the stored personal information.

[0897] Input: Multiple user information stored in a database.

[0898] Data processing: Apply a matching algorithm to select a partner.

[0899] Output: Generates the match results and prepares them to be sent to the device.

[0900] Specific operation: The server executes an "algorithm for matching users with similar hobbies" and selects a 24-year-old woman (Ms. B) whose hobbies are "travel and music" for User A.

[0901] Step 5:

[0902] A chat screen is displayed between users whose devices have been matched.

[0903] Input: Matching results sent by the server.

[0904] Data processing: Generates a chat screen and displays it to the user.

[0905] Output: The chat screen is displayed on the user's device.

[0906] Specific operation: The device creates a "chat room between users A and B" and displays the chat screen on each device.

[0907] Step 6:

[0908] A user manually starts a chat and sends a message.

[0909] Input: The chat message typed by the user.

[0910] Data processing: Send the message content to the server.

[0911] Output: The chat message is sent to the server.

[0912] Specific behavior: User A types "What movie have you seen recently?" in the chat box and clicks the send button.

[0913] Step 7:

[0914] The server stores the chat history data in a database.

[0915] Input: Chat messages sent from the device.

[0916] Data processing: Save to database using SQL queries.

[0917] Output: Chat history is stored in a database.

[0918] What happens: The server executes an SQL query like "INSERT INTO chat_history (user_id, message, timestamp) VALUES (A, 'What movie did you see recently?', CURRENT_TIMESTAMP)".

[0919] Step 8:

[0920] The server analyzes the stored chat history data.

[0921] Input: Chat history data stored in a database.

[0922] Data processing: Applying natural language processing and emotion recognition technologies.

[0923] Output: Analysis results and sentiment ratings.

[0924] Specific operation: The server analyzes the message using an NLP engine (e.g., Python's NLTK) and evaluates that User A is feeling "fun" using an emotion recognition engine (e.g., Azure Emotion API).

[0925] Step 9:

[0926] The server generates recommendation sentences based on the analysis results.

[0927] Input: Analysis results and emotion ratings.

[0928] Data processing: Generate recommendation sentences using a generative AI model (e.g., GPT-3).

[0929] Output: Recommendation sentences.

[0930] Specific operation: The server generates a recommendation sentence such as "What good movies have you seen recently?" and makes it available for the next conversation.

[0931] Step 10:

[0932] The terminal presents the generated recommendation sentences to the user.

[0933] Input: Recommendation text sent from the server.

[0934] Data processing: Displayed on the chat screen.

[0935] Output: The recommendation text is displayed on the user's device.

[0936] Specific operation: The device displays a recommendation message on the chat screen, such as "What good movies have you seen recently?", and allows the user to select one.

[0937] The above is the flow of processing of the program of this system.

[0938] (Application example 2)

[0939] 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."

[0940] Conventional self-driving vehicles have not provided sufficient mechanisms to encourage communication between passengers and allow them to spend their time on board meaningfully. Furthermore, the lack of communication methods that respond to passenger emotions can sometimes hinder smooth communication. This has made it difficult to improve passenger satisfaction.

[0941] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0942] In this invention, the server includes: a means for a user to input personal information; a means for saving the input personal information; a means for matching users; a means for matched users to chat with each other; a means for saving chat history data; a means for analyzing the saved chat history data; a means for generating recommendation sentences based on the analyzed data; a means for presenting the generated recommendation sentences to the user; a means for evaluating an emotional state based on the analyzed data; a means for adjusting the recommendation sentences based on the emotional state; and a means for matching users and chatting with them in an autonomous vehicle. This allows smooth communication between passengers and enables them to spend their time on board meaningfully. Furthermore, by providing recommendation sentences based on emotions, personalized communication is realized.

[0943] "Personal information" is information that can identify a specific individual, including the user's age, gender, hobbies, etc.

[0944] "Matching" is the process by which the system selects a suitable partner based on the personal information of multiple users.

[0945] "Chat" is a means for users to communicate with each other through text messages.

[0946] "Chat history data" refers to data that records the content of conversations between users and the time at which they occurred.

[0947] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[0948] An "emotion engine" is a software component for recognizing and assessing a user's emotional state from text data.

[0949] "Recommendation sentences" are phrases generated by the system and presented to users to facilitate communication.

[0950] An "autonomous vehicle" is a vehicle whose driving is controlled by a system without human intervention.

[0951] "Personalization" refers to tailoring specific services or content to the specific needs and emotional state of individual users.

[0952] This invention is a system for facilitating communication between passengers in an autonomous vehicle and allowing them to spend their time on board meaningfully. The configuration and operation of the system are described below.

[0953] System configuration

[0954] 1. Registration Phase

[0955] The user enters personal information when starting the system. The entered personal information is sent to the server via the terminal and stored by the server. This process includes the user's age, gender, hobbies, etc. For example, if User A enters that he is a 25-year-old male whose hobbies are watching movies and reading, this information is sent to the server and stored.

[0956] 2. Matching Phase

[0957] The server uses the input personal information to match users with suitable partners. For example, when user A gets into an autonomous vehicle, she is matched with person B, who is also in the vehicle. Person B is a 24-year-old woman whose hobbies are traveling and music.

[0958] 3. Chat Phase

[0959] If the match is successful, the device displays a chat screen for user A and user B. Here, users can freely send and receive text messages and communicate with each other. Chat history data, including the message content and the time it occurred, is saved on the server. For example, if user A asks, "What movie did you see recently?" and user B replies, "I saw Inception! It was amazing!", this exchange is saved as chat history data.

[0960] 4. Data analysis phase

[0961] Once a certain amount of chat data has been accumulated, the server analyzes it. Natural language processing (NLP) and an emotion engine are used to evaluate the user's emotional state. For example, if the chat content contains a lot of positive vocabulary, the server evaluates the user as having a happy emotion.

[0962] 5. Recommendation text creation phase

[0963] Based on the analysis results, the server uses a generative AI model to generate recommendation sentences, taking into account the evaluation of the emotion engine. For example, if the user is feeling happy, a recommendation such as "What movie would you like to see next?" will be generated. The generated recommendation sentences are displayed on the device, allowing the user to easily select and use them in the next chat.

[0964] Specific examples

[0965] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with Person B through an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, enjoying discussions of movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generation AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[0966] Prompt Sentence Examples

[0967] User A's chat data:

[0968] 1. What's the last movie you saw?

[0969] 2. I saw Inception! It was amazing!

[0970] Recommended articles:

[0971] What movie do you want to see next?

[0972] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0973] Step 1:

[0974] The user enters personal information.

[0975] Input: Personal information such as age, gender, and hobbies entered by the user.

[0976] Output: The entered personal information is sent to the terminal.

[0977] Specific operation: The user enters information such as their age, gender, and hobbies into a form on the terminal and sends it to the server by pressing the confirm button.

[0978] Step 2:

[0979] The device sends personal information to a server, and the server stores the personal information.

[0980] Input: Personal information sent from your device.

[0981] Output: Personal information stored in the server's database.

[0982] Specific operation: The terminal sends the information entered through the form to the server using an API, and the server stores it in a database.

[0983] Step 3:

[0984] The server matches appropriate users based on the stored personal information.

[0985] Input: Personal information of multiple users stored in a database.

[0986] Output: Matched user pairs.

[0987] How it works: The server uses an algorithm to compare multiple user data and select the best pair based on criteria such as hobbies and age.

[0988] Step 4:

[0989] Matched users chat with each other.

[0990] Input: Matched user pairs.

[0991] Output: Chat messages sent and received between users.

[0992] Specific operation: The device displays a chat screen, allowing users to freely type and send messages. Sent messages are immediately displayed on the other device.

[0993] Step 5:

[0994] Chat history data is sent to the server and stored by the server.

[0995] Input: The chat message sent from the device along with the timestamp.

[0996] Output: Chat history data stored on the server.

[0997] Specific operation: Every time each device sends a chat message, the data is sent to the server and stored in a database along with a timestamp.

[0998] Step 6:

[0999] After accumulating a certain amount of chat data, the server analyzes it.

[1000] Input: Chat history data stored in a database.

[1001] Output: Analysis results, especially the user's emotional state.

[1002] Specific operation: The server uses natural language processing (NLP) technology and an emotion engine to analyze the content of chat data and evaluate the emotional state, such as positive or negative.

[1003] Step 7:

[1004] Based on the analysis results, the server generates recommendation sentences.

[1005] Input: The emotional state assessed by the emotion engine.

[1006] Output: Recommendation sentences generated by the generative AI model.

[1007] Specific operation: Based on the emotional state, the server uses a generative AI model to automatically generate the next recommendation sentence that the user should send.

[1008] Step 8:

[1009] The generated recommendation sentences are presented to the user's terminal.

[1010] Input: Generated recommendation sentences.

[1011] Output: Recommendation text displayed on the chat screen of the user's device.

[1012] Specific operation: The server sends the generated recommendation sentence to the user's device, which displays it on the chat screen. The user can click on the presented sentence to send it as the next message.

[1013] As a specific example of how it works, User A asks, "What movie did you see recently?" and User B replies, "I saw Inception! It was amazing!" This chat data is saved on the server and analyzed using NLP and an emotion engine. Based on the analysis results, a recommendation sentence such as "What movie do you want to see next?" is generated and displayed on User A's chat screen.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] [Third embodiment]

[1018] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1019] 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.

[1020] 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).

[1021] 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.

[1022] 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.

[1023] 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).

[1024] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1025] 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.

[1026] 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.

[1027] 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.

[1028] 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.

[1029] 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."

[1030] This invention is a system in which users enter personal information when registering, and then manually chat with a certain number of people of the opposite sex after registration. The system analyzes the user's chat patterns and uses generative AI to create recommendation sentences. The system aims to reduce the user's chat burden and prevent opportunity loss.

[1031] System configuration

[1032] 1. Registration Phase

[1033] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[1034] Terminal: Sends personal information submitted through the form to the server.

[1035] Server: Stores the received user information in a database.

[1036] Examples:

[1037] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[1038] 2. Manual Chat Phase

[1039] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[1040] Terminal: Display the chat screen to users A and B.

[1041] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[1042] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1043] 3. Data analysis phase

[1044] Server: Once a certain amount of chat data has been accumulated, analysis begins. Natural language processing (NLP) technology is used for analysis.

[1045] Examples:

[1046] Once a certain amount of chat data between users A and B has been accumulated, this data is analyzed to determine that user A has a strong interest in movies and books.

[1047] 4. Recommendation text creation phase

[1048] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, if User A likes watching movies, the recommendation generated would be, "What movie have you seen recently?"

[1049] Device: Display the recommended text on User A's chat screen.

[1050] Users can easily select a suggested sentence to use in their next chat.

[1051] Details of the working example

[1052] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B through an automatic matching algorithm, and the device displays a chat screen. Users A and B then chat manually, enjoying topics such as movies and books. The server saves this chat data in a database and analyzes it once a certain amount of chat data has been collected. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[1053] In this way, the present invention provides a system that allows users to chat smoothly without feeling any difficulty and continue the conversation.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] The user opens the personal information form.

[1057] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[1058] Step 2:

[1059] The terminal acquires the user's input data and sends it to the server.

[1060] The device sends the input data to the appropriate endpoint on the server.

[1061] Step 3:

[1062] The server stores the received user information in a database.

[1063] The server analyzes the personal information sent and stores it in a database in an appropriate format.

[1064] Step 4:

[1065] The server matches users with suitable members of the opposite sex.

[1066] The server uses a matching algorithm to find people of the opposite sex who share common hobbies and interests.

[1067] Step 5:

[1068] A chat screen is displayed between users whose devices have been matched.

[1069] The device generates and displays an interface that allows you to chat with the matched person.

[1070] Step 6:

[1071] A user starts a chat and sends a message.

[1072] The user enters a message on the chat screen and clicks the "Send" button.

[1073] Step 7:

[1074] The terminal sends the user's message to the server.

[1075] The device sends the sent message to the server's chat save endpoint.

[1076] Step 8:

[1077] The server stores the received messages in a database.

[1078] The server stores the message content, sender, sent time, etc. in a database.

[1079] Step 9:

[1080] The server waits for a certain amount of chat data to accumulate.

[1081] When the server receives a certain amount of chat data, it begins analyzing the data.

[1082] Step 10:

[1083] The server analyzes the chat history data using natural language processing technology.

[1084] The server uses NLP models to analyze users' chat patterns and interests.

[1085] Step 11:

[1086] Based on the server's analysis results, a generative AI generates recommendation sentences.

[1087] The server inputs the analysis results into an AI model and automatically generates appropriate recommendation sentences.

[1088] Step 12:

[1089] The terminal presents the generated recommendation sentences to the user.

[1090] The device displays the recommendation text and allows the user to select and use it.

[1091] Step 13:

[1092] The user continues chatting using the recommended sentences provided.

[1093] The user selects the presented sentence and pastes it directly into the chat to continue.

[1094] Example 1

[1095] 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."

[1096] There is a need for a system that reduces the burden users feel when chatting with members of the opposite sex and prevents missed chat opportunities. Conventional chat systems require users to think up topics and input them themselves, which creates obstacles to communication. Furthermore, there is a lack of technology that can analyze chat content and present appropriate recommended sentences. This makes it difficult for users to continue chatting smoothly.

[1097] 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.

[1098] In this invention, the server includes: means for a user to input personal information; means for saving the input personal information; means for matching users with members of the opposite sex; means for matched users to chat with each other; means for saving chat history data; means for analyzing the saved chat history data; means for generating recommendation sentences based on the analyzed data; means for presenting the generated recommendation sentences to the user; means for the user to use the presented recommendation sentences in the next chat; means for starting analysis when a certain amount of chat data has been accumulated; means for using timestamps of the saved chat data; and means for linking and analyzing the chat data and the user's personal information. This reduces the burden on the user when chatting and enables the conversation to continue smoothly.

[1099] "User" refers to a person who uses this system to enter personal information and chat.

[1100] "Personal information" refers to detailed information relating to a user's identity, such as the user's age, gender, hobbies, etc.

[1101] "Matching" refers to the process by which the server pairs users with suitable members of the opposite sex based on their personal information.

[1102] "Chat" refers to the act of matched users communicating with each other in real time through messages.

[1103] "Historical data" refers to records of chat content, message timestamps, etc.

[1104] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[1105] "Recommended sentences" refer to chat messages that the server generates based on analysis data and suggests to users.

[1106] A "generative AI model" refers to an artificial intelligence system that automatically generates new sentences based on collected data.

[1107] "Timestamp" refers to information about the time at which a chat message was sent or received.

[1108] "Database" refers to the information management system that the system uses to store personal information, chat history data, etc.

[1109] "Analysis" refers to the process of processing stored historical data and personal information to identify user interests and concerns.

[1110] This invention is a system in which a user enters personal information when registering, chats with a certain number of people, analyzes the user's chat patterns, and a generative AI creates recommendation sentences. This reduces the user's chat burden and prevents opportunity loss. The form for realizing this system is described in detail below.

[1111] System configuration

[1112] Registration Phase

[1113] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. The entered information is sent via the form.

[1114] Example: User A is a 25-year-old male and enters on the registration screen that his hobbies are watching movies and reading.

[1115] Terminal: Sends the entered personal information to a server. This information is usually sent using a dedicated communication protocol.

[1116] Server: Stores the received personal information in a database. The database can be a relational database management system such as an SQL database.

[1117] Manual Chat Phase

[1118] Server: Based on the registered personal information, the server matches users with members of the opposite sex. For example, User A, a 25-year-old man, is matched with User B, a 24-year-old woman.

[1119] Device: A chat screen is displayed to matched users A and B. This display is done using a dedicated chat application.

[1120] User: User A and User B chat manually and send messages. At this stage, it is assumed that User A will send a message to User B saying, "Hello, do you like movies?"

[1121] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1122] Data analysis phase

[1123] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing (NLP) technology. This analysis can be done using Python libraries (e.g., NLTK or spaCy).

[1124] Example: Analysis reveals that user A has a strong interest in movies.

[1125] Recommendation sentence creation phase

[1126] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, the generated recommendation sentences might be in the form of "What movie have you seen recently?"

[1127] Device: This recommendation text is displayed on User A's chat screen.

[1128] User: Select the suggested sentence and use it in the next chat.

[1129] Example: User A clicks on the recommendation text "What movie have you seen recently?" and sends it to the chat.

[1130] Example prompts to be input to the generative AI model

[1131] "Assuming that user A is interested in movies, please create a recommendation sentence for the next chat."

[1132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] Enter and submit user information

[1135] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. Specifically, User A enters his / her name, age (25 years old), gender (male), and hobbies (watching movies and reading) on ​​the registration screen.

[1136] Terminal: The entered personal information is sent to the server via the form. Specifically, the submit button is clicked and the entered information is sent as an HTTP POST request.

[1137] Input: Personal information such as age, gender, hobbies, etc.

[1138] Output: Personal information sent to the server

[1139] Step 2:

[1140] User information stored in a database

[1141] Server: The received personal information is stored in a database, such as a relational database management system like MySQL.

[1142] Specific operation: The server stores the received information in the appropriate table using an SQL query.

[1143] Input: User A's personal information (name, age, gender, hobbies)

[1144] Output: User information stored in the database

[1145] Step 3:

[1146] User Matching

[1147] Server: Based on the registration information, the server matches User A with a suitable member of the opposite sex. For example, User A, a 25-year-old man whose hobby is watching movies, is matched with User B, a 24-year-old woman whose hobby is traveling.

[1148] Specific operation: The server uses a matching algorithm to search for and respond to users of the opposite sex who share the same hobbies and interests.

[1149] Input: User A's personal information and other users' information in the database

[1150] Output: Matched user pairs (users A and B)

[1151] Step 4:

[1152] Displaying the chat screen

[1153] Device: Display the chat screen to matched users A and B.

[1154] Specific behavior: The device launches the chat application and the chat interface is displayed.

[1155] Input: Matching information (User A and B)

[1156] Output: Chat screen

[1157] Step 5:

[1158] Manually starting a chat

[1159] User: User A and User B manually start a chat and send and receive messages. For example, User A sends a message to User B saying, "Hello, do you like movies?"

[1160] Input: User A's message content

[1161] Output: Message displayed on B's chat screen

[1162] Step 6:

[1163] Saving chat data

[1164] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1165] What it does: When the server receives a chat message, it writes the historical data to a database using an SQL query.

[1166] Input: Chat history data between users A and B

[1167] Output: Chat history data stored in a database

[1168] Step 7:

[1169] Chat data analysis

[1170] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing techniques, using Python's NLTK and spaCy libraries.

[1171] What happens: The server sends chat data to a text analysis module to identify user interests.

[1172] Input: Accumulated chat data

[1173] Output: Analysis results for User A (interests in movies and reading)

[1174] Step 8:

[1175] Generating recommendation sentences

[1176] Server: Based on the analysis results, generative AI automatically generates recommendation sentences.

[1177] Specific behavior: The generative AI begins the process of creating a recommendation sentence such as, "What movie have you seen recently?"

[1178] Input: User A's analysis results

[1179] Output: Generated recommendation sentences

[1180] Step 9:

[1181] Displaying recommended sentences

[1182] Terminal: The generated recommendation text is displayed on User A's chat screen.

[1183] Specific behavior: A recommended sentence will pop up in the chat interface.

[1184] Input: Generated recommendation sentences

[1185] Output: Recommended sentences displayed on the chat screen

[1186] Step 10:

[1187] Selection and use of recommendation text

[1188] User: Select the suggested recommendation sentence and use it as the next chat message. For example, User A clicks on the recommendation sentence "What movie have you seen recently?" and sends it to the chat.

[1189] Input: Recommended sentences displayed on the chat screen

[1190] Output: Suggested sentence sent as next chat message

[1191] (Application example 1)

[1192] 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."

[1193] There is a need for an effective method for quickly and appropriately responding to a wide range of user inquiries when chatting with users. There is also a need for a system that reduces the burden on customer support representatives and improves the user experience. Conventional methods require representatives to respond to each inquiry individually, which takes time and effort, and the quality of the response is unstable.

[1194] 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.

[1195] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for generating recommendation sentences based on the analyzed data, means for presenting the generated recommendation sentences to the user, and means for analyzing inquiry patterns and generating appropriate answers and questions for customer support, thereby enabling customer support staff to respond to user inquiries quickly and appropriately.

[1196] "Means for users to input personal information" refers to devices or software that provide a form or interface for users to input personal information such as name, age, hobbies, etc.

[1197] "Means for storing input personal information" refers to devices or software that record the personal information input by the user in a database or file system and make it accessible as needed.

[1198] A "user matching method" is an algorithm or system that connects users with each other based on shared hobbies or interests.

[1199] "Means for chatting between matched users" refers to a real-time communication interface or application that allows matched users to exchange messages.

[1200] The "means for storing chat history data" refers to a device or software that records chat content and message history between users in a database or log file.

[1201] The "means for analyzing the stored chat history data" refers to algorithms or software that analyze the stored chat history data using natural language processing techniques or the like to extract patterns or interests.

[1202] The "means for generating recommendation text based on analyzed data" refers to a generative AI model or text generation engine that automatically generates appropriate messages and questions based on the analysis results.

[1203] The "means for presenting the generated recommendation sentences to the user" refers to a device or software that displays the generated recommendation sentences on an interface such as a user's chat screen or dashboard.

[1204] "Means for analyzing inquiry patterns and generating appropriate answers and questions for customer support" refers to natural language processing technology and generative AI models that analyze past inquiry data and automatically generate appropriate answers and follow-up questions so that customer support staff can respond quickly.

[1205] To realize the system of the present invention, the following hardware and software are used.

[1206] The main technologies and hardware used are:

[1207] Python (mainly data processing and AI model implementation)

[1208] Django (Web application framework)

[1209] Google Cloud Natural Language API (natural language processing technology)

[1210] TensorFlow (training and running AI models)

[1211] MySQL (database management system)

[1212] When a user enters personal information, the device sends this information to the server via a form, and the server stores the received information in a MySQL database. For example, a user enters their name, age, hobbies, etc., and the information is sent to the server and stored.

[1213] In the matching phase, the server automatically matches users based on common hobbies and interests—for example, users who share a common hobby of watching movies—and the device provides an interface for users to chat with each other in real time.

[1214] Chat history data is recorded by the server and analyzed using the Google Cloud Natural Language API. This analysis identifies specific inquiry patterns and areas of user interest. For example, the analysis identifies that many inquiries are about "videos not playing."

[1215] In the analysis phase, the generative AI model on TensorFlow generates appropriate recommendation sentences based on the analysis results. The generated recommendation sentences are presented to the user via the device. For example, a recommendation such as "Please clear your cache and try again" is generated and displayed on the user's chat screen.

[1216] As a concrete example, the following prompt sentence can be used to generate an appropriate answer from the generative AI model:

[1217] What's the best response when a user says "The video won't play"?

[1218] In this way, the present invention provides a system that enables customer support personnel to respond to user inquiries quickly and appropriately, allowing users to smoothly continue conversations without experiencing difficulties in chatting, thereby improving the quality and efficiency of customer support.

[1219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1220] Step 1:

[1221] The user enters personal information. The user fills in information such as name, age, and hobbies in an input form.

[1222] Input: Personal information (name, age, hobbies, etc.)

[1223] Output: Personal information entered in the input form

[1224] Specific operation: A user enters the required information into a form on a web application and presses the "Submit" button.

[1225] Step 2:

[1226] The terminal transmits the personal information entered by the user to the server.

[1227] Input: Personal information entered in the input form

[1228] Output: Personal information sent to the server

[1229] Specific operation: The terminal sends the form data to the server as an HTTP request.

[1230] Step 3:

[1231] The server stores the received personal information in a MySQL database.

[1232] Input: Personal information sent to the server

[1233] Output: Personal information stored in a MySQL database

[1234] Specific operation: The server analyzes the received data, converts it into an appropriate format, and stores it in the database.

[1235] Step 4:

[1236] The server automatically matches users with common hobbies and interests.

[1237] Input: Personal information stored in a MySQL database

[1238] Output: Matched user pairs

[1239] How it works: The server uses an algorithm to select users with common hobbies and interests and create pairs.

[1240] Step 5:

[1241] The device provides an interface that allows matched users to chat in real time.

[1242] Input: Matched user pairs

[1243] Output: Chat interface

[1244] Specific operation: The device displays a chat screen on the web application, allowing users to exchange messages.

[1245] Step 6:

[1246] The server stores chat history data in a MySQL database.

[1247] Input: Chat message between users

[1248] Output: Chat history stored in the database

[1249] Specific operation: The server records the content and timestamp of the chat message in a database.

[1250] Step 7:

[1251] The server analyzes the saved chat history data using the Google Cloud Natural Language API.

[1252] Input: Chat history stored in the database

[1253] Output: Analysis results (user areas of interest, inquiry patterns, etc.)

[1254] How it works: The server uses natural language processing techniques to extract important topics and patterns from chat data.

[1255] Step 8:

[1256] The server uses a generative AI model on TensorFlow to generate recommendation sentences based on the analysis results.

[1257] Input: Analysis results

[1258] Output: Recommendation sentence

[1259] Specific behavior: Runs TensorFlow to generate appropriate questions and answers based on the user's interests and inquiries.

[1260] Step 9:

[1261] The terminal displays the generated recommendation sentences on the user's chat screen.

[1262] Input: Recommendation sentence

[1263] Output: Recommendation sentences displayed on the chat screen

[1264] Specific operation: The device displays the recommended sentences on the chat interface so that the user can use them.

[1265] ---

[1266] The above are the specific processing steps of the system program that realizes this application example.

[1267] 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.

[1268] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. Furthermore, by combining it with an emotion engine, it provides a function to recognize and evaluate the user's emotions and adjust the recommendation sentences based on the results. This system aims to improve the user's chat experience and facilitate smooth communication.

[1269] System configuration

[1270] 1. Registration Phase

[1271] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[1272] Device: The personal information submitted in the form is sent to the server, and registration is completed.

[1273] Server: Stores the received user information in a database.

[1274] Examples:

[1275] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[1276] 2. Manual Chat Phase

[1277] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[1278] Terminal: Display the chat screen to users A and B.

[1279] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[1280] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1281] 3. Data analysis phase

[1282] Server: Once a certain amount of chat data has been accumulated, analysis begins. Analysis uses natural language processing (NLP) technology and an emotion engine.

[1283] Examples:

[1284] Once a certain amount of chat data between User A and User B has been accumulated, this data is analyzed to recognize User A's emotional state. For example, if there is a lot of positive vocabulary in the conversation, User A is evaluated as having a happy emotion.

[1285] 4. Recommendation text creation phase

[1286] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences, reflecting the evaluation of the emotion engine. For example, if User A has a happy emotion, the recommendation generated would be, "What fun movie have you seen recently?"

[1287] Device: Display the recommended text on User A's chat screen.

[1288] Users can easily select a suggested sentence to use in their next chat.

[1289] Details of the working example

[1290] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B via an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, and get excited about topics such as movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided. In addition, the emotion engine provides recommendations that are adapted to User A's emotional state, enabling more personalized communication.

[1291] In this way, the present invention provides a system that allows users to chat smoothly without difficulty and continues conversations smoothly through appropriate recommendation sentences based on emotions.

[1292] The processing flow will be explained below.

[1293] Step 1:

[1294] The user opens the personal information form.

[1295] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[1296] Step 2:

[1297] The terminal acquires the user's input data and sends it to the server.

[1298] The device sends personal information data to the server using a RESTful API.

[1299] Step 3:

[1300] The server stores the received user information in a database.

[1301] The server records the input data in the appropriate table.

[1302] Step 4:

[1303] The server matches users with suitable members of the opposite sex.

[1304] The server uses a pre-set algorithm to select compatible opposite-sex partners based on the user's hobbies and interests.

[1305] Step 5:

[1306] A chat screen is displayed between users whose devices have been matched.

[1307] The terminal renders the chat interface and displays it to the user.

[1308] Step 6:

[1309] A user starts a chat and sends a message.

[1310] The user enters text on the chat screen and clicks the "Send" button.

[1311] Step 7:

[1312] The terminal sends the user's message to the server.

[1313] The device sends message data to the server's chat API endpoint via a POST request.

[1314] Step 8:

[1315] The server stores the received messages in a database.

[1316] The server stores the message content, sending time, sender ID, etc.

[1317] Step 9:

[1318] The server waits for a certain amount of chat data to accumulate.

[1319] The server monitors the count of chat data and triggers analysis when a threshold is reached.

[1320] Step 10:

[1321] The server analyzes the chat history data using natural language processing technology.

[1322] The server applies NLP models to extract the user's conversation patterns and interests.

[1323] Step 11:

[1324] The server evaluates the user's emotions using an emotion engine.

[1325] The server uses a sentiment analysis model to assess the user's emotional state (e.g., positive, negative, neutral).

[1326] Step 12:

[1327] Based on the server's analysis and emotion evaluation results, a generative AI generates recommendation sentences.

[1328] Based on the data obtained by the server, AI generates recommendation sentences that are appropriate for the user.

[1329] Step 13:

[1330] The terminal presents the generated recommendation sentences to the user.

[1331] The terminal displays the generated text on the chat screen, allowing the user to select it.

[1332] Step 14:

[1333] The user continues chatting using the recommended sentences provided.

[1334] The user selects a recommendation and clicks to send it as a message.

[1335] Examples:

[1336] For example, user A registers as a new user, and information about his hobbies and interests is saved in a database. Afterwards, person A is matched with person B, and they chat about movies and books. Once a certain number of chat histories have been accumulated, the server analyzes the content of the conversation through natural language processing, and the emotion engine detects happy emotions from person A's conversation. Then, the generative AI creates recommendation sentences such as "What fun movies have you seen recently?" and the device presents these to person A. person A can easily select this recommendation sentence and continue chatting. In this way, this system improves the user's chat experience and supports smooth communication.

[1337] Example 2

[1338] 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."

[1339] Current matching systems often experience interruptions when users chat, preventing smooth communication. Furthermore, they provide uniform recommendations without considering users' feelings, making it difficult to provide recommendations that are appropriate for each individual user. This can lead to a poor user experience.

[1340] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1341] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for evaluating the user's emotions using emotion recognition technology, means for generating recommendation sentences based on the analyzed data and the emotion evaluation, and means for presenting the generated recommendation sentences to the user.

[1342] This makes it possible to provide appropriate recommended sentences based on the user's emotions, prevent conversations from stalling, and achieve smoother, more satisfying communication.

[1343] A "user" is an individual who uses the system, inputs personal information, and chats with others.

[1344] "Personal information" refers to individual data such as age, gender, and hobbies that users enter into the system.

[1345] "Input means" refers to an interface for users to input personal information into the system, and includes methods such as forms.

[1346] "Storage means" refers to the database or storage used to hold entered personal information and chat history data.

[1347] "Matching method" refers to an algorithm or method that links multiple users together based on the personal information they input.

[1348] "Chat means" refers to a communication interface through which matched users can exchange messages in real time.

[1349] "Chat history data" refers to records of message content, timestamps, etc. in chats between users.

[1350] "Analysis means" refers to technology and software for analyzing saved chat history data, including natural language processing technology.

[1351] "Emotion recognition technology" refers to methods and algorithms for analyzing a user's chat history data and identifying and evaluating the user's emotions from that data.

[1352] "Recommendation sentence" refers to an automatically generated message generated by analytical means and emotion recognition technology to encourage the user's next conversation.

[1353] "Presentation means" refers to the method or interface for showing the generated recommendation text to the user, and includes the display screen of the user's terminal, etc.

[1354] MODE FOR CARRYING OUT THE INVENTION

[1355] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. It also utilizes emotion recognition technology to evaluate the user's emotions and provides recommendation sentences based on the results, thereby achieving smooth communication.

[1356] 1. Hardware and Software Used

[1357] The entire system consists of a server, user devices, a database, natural language processing technology (NLP), an emotion recognition engine, and a generative AI model. Specifically, the server is installed on the cloud, and user devices include smartphones and PCs. The database can be a relational database such as MySQL or PostgreSQL. The natural language processing technology utilizes the NLTK library implemented in Python or TensorFlow. The emotion recognition engine may use Azure's Emotion API, for example. The generative AI model uses OpenAI's GPT, for example.

[1358] 2. Program processing explanation

[1359] First, the user enters their personal information. The information entered via a device (the user's smartphone or computer) is sent to the server via a form, and the server stores it in a database.

[1360] The server then uses an automatic matching algorithm to select suitable individuals based on the stored personal information. The matching results are then sent back to the user's device, where an interface is displayed for the matched users to chat with each other.

[1361] When users start chatting, the server stores each message in a database in real time, and each message contains a timestamp, allowing the flow of conversation between users to be tracked.

[1362] Once a certain amount of chat data has been accumulated, the server analyzes the data using natural language processing (NLP) and an emotion recognition engine. For example, if User A chats, "I recently saw a really fun movie," NLP technology analyzes this message and extracts keywords. The emotion recognition engine recognizes positive emotions and evaluates that User A is feeling happy.

[1363] Based on the analysis results, a generative AI model (e.g., GPT-3) creates a recommendation sentence for the next conversation, such as, "Tell me about a good movie you saw recently." The generated recommendation sentence is then displayed on the user's device.

[1364] Users can select recommended sentences and use them in their next chat. This allows the conversation to proceed smoothly by providing appropriate topics even when users are struggling with a conversation.

[1365] 3. Examples and prompts

[1366] As a concrete example, consider the case where users A and B get excited talking about movies. For example, user A sends a message saying, "Have you seen Inception?", and user B replies, "Yes, it was really interesting." Once this historical data is accumulated and analyzed, the generative AI model will provide a recommendation sentence like the following: "What is your favorite scene in Inception?"

[1367] An example prompt is:

[1368] 1. "Tell me about the last movie you saw."

[1369] 2. "Do you have any recommended topics related to your hobbies?"

[1370] 3. "Let's talk about the book you read recently."

[1371] As described above, the present invention is a system that makes users' chat experiences smoother and provides appropriate recommended sentences based on individual emotions, thereby facilitating communication.

[1372] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1373] Step 1:

[1374] The user enters personal information.

[1375] Input: User's age, gender, hobbies, etc.

[1376] Data processing: Enter information into the form on the user's device and press the send button.

[1377] Output: The form sends personal information to the server.

[1378] Specific operation: User A enters "Age 25, Gender male, Hobbies watching movies and reading" into the form on the device and clicks the send button.

[1379] Step 2:

[1380] The terminal transmits the entered personal information to the server.

[1381] Input: Personal information entered by the user into a form.

[1382] Data processing: Send form data as an HTTP request.

[1383] Output: Personal information is delivered to the server.

[1384] Specific operation: The device (smartphone or PC) sends the form data to the server via an HTTP POST request.

[1385] Step 3:

[1386] The server stores the received user information in a database.

[1387] Input: Personal information sent from your device.

[1388] Data processing: Save to database using SQL queries.

[1389] Output: User information is recorded in the database.

[1390] What happens: The server executes an SQL query like "INSERT INTO users (age, gender, hobbies) VALUES (25, 'Male', 'Watching movies, Reading')" and saves the information in the database.

[1391] Step 4:

[1392] The server matches users based on the stored personal information.

[1393] Input: Multiple user information stored in a database.

[1394] Data processing: Apply a matching algorithm to select a partner.

[1395] Output: Generates the match results and prepares them to be sent to the device.

[1396] Specific operation: The server executes an "algorithm for matching users with similar hobbies" and selects a 24-year-old woman (Ms. B) whose hobbies are "travel and music" for User A.

[1397] Step 5:

[1398] A chat screen is displayed between users whose devices have been matched.

[1399] Input: Matching results sent by the server.

[1400] Data processing: Generates a chat screen and displays it to the user.

[1401] Output: The chat screen is displayed on the user's device.

[1402] Specific operation: The device creates a "chat room between users A and B" and displays the chat screen on each device.

[1403] Step 6:

[1404] A user manually starts a chat and sends a message.

[1405] Input: The chat message typed by the user.

[1406] Data processing: Send the message content to the server.

[1407] Output: The chat message is sent to the server.

[1408] Specific behavior: User A types "What movie have you seen recently?" in the chat box and clicks the send button.

[1409] Step 7:

[1410] The server stores the chat history data in a database.

[1411] Input: Chat messages sent from the device.

[1412] Data processing: Save to database using SQL queries.

[1413] Output: Chat history is stored in a database.

[1414] What happens: The server executes an SQL query like "INSERT INTO chat_history (user_id, message, timestamp) VALUES (A, 'What movie did you see recently?', CURRENT_TIMESTAMP)".

[1415] Step 8:

[1416] The server analyzes the stored chat history data.

[1417] Input: Chat history data stored in a database.

[1418] Data processing: Applying natural language processing and emotion recognition technologies.

[1419] Output: Analysis results and sentiment ratings.

[1420] Specific operation: The server analyzes the message using an NLP engine (e.g., Python's NLTK) and evaluates that User A is feeling "fun" using an emotion recognition engine (e.g., Azure Emotion API).

[1421] Step 9:

[1422] The server generates recommendation sentences based on the analysis results.

[1423] Input: Analysis results and emotion ratings.

[1424] Data processing: Generate recommendation sentences using a generative AI model (e.g., GPT-3).

[1425] Output: Recommendation sentences.

[1426] Specific operation: The server generates a recommendation sentence such as "What good movies have you seen recently?" and makes it available for the next conversation.

[1427] Step 10:

[1428] The terminal presents the generated recommendation sentences to the user.

[1429] Input: Recommendation text sent from the server.

[1430] Data processing: Displayed on the chat screen.

[1431] Output: The recommendation text is displayed on the user's device.

[1432] Specific operation: The device displays a recommendation message on the chat screen, such as "What good movies have you seen recently?", and allows the user to select one.

[1433] The above is the flow of processing of the program of this system.

[1434] (Application example 2)

[1435] 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."

[1436] Conventional self-driving vehicles have not provided sufficient mechanisms to encourage communication between passengers and allow them to spend their time on board meaningfully. Furthermore, the lack of communication methods that respond to passenger emotions can sometimes hinder smooth communication. This has made it difficult to improve passenger satisfaction.

[1437] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1438] In this invention, the server includes: a means for a user to input personal information; a means for saving the input personal information; a means for matching users; a means for matched users to chat with each other; a means for saving chat history data; a means for analyzing the saved chat history data; a means for generating recommendation sentences based on the analyzed data; a means for presenting the generated recommendation sentences to the user; a means for evaluating an emotional state based on the analyzed data; a means for adjusting the recommendation sentences based on the emotional state; and a means for matching users and chatting with them in an autonomous vehicle. This allows smooth communication between passengers and enables them to spend their time on board meaningfully. Furthermore, by providing recommendation sentences based on emotions, personalized communication is realized.

[1439] "Personal information" is information that can identify a specific individual, including the user's age, gender, hobbies, etc.

[1440] "Matching" is the process by which the system selects a suitable partner based on the personal information of multiple users.

[1441] "Chat" is a means for users to communicate with each other through text messages.

[1442] "Chat history data" refers to data that records the content of conversations between users and the time at which they occurred.

[1443] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[1444] An "emotion engine" is a software component for recognizing and assessing a user's emotional state from text data.

[1445] "Recommendation sentences" are phrases generated by the system and presented to users to facilitate communication.

[1446] An "autonomous vehicle" is a vehicle whose driving is controlled by a system without human intervention.

[1447] "Personalization" refers to tailoring specific services or content to the specific needs and emotional state of individual users.

[1448] This invention is a system for facilitating communication between passengers in an autonomous vehicle and allowing them to spend their time on board meaningfully. The configuration and operation of the system are described below.

[1449] System configuration

[1450] 1. Registration Phase

[1451] The user enters personal information when starting the system. The entered personal information is sent to the server via the terminal and stored by the server. This process includes the user's age, gender, hobbies, etc. For example, if User A enters that he is a 25-year-old male whose hobbies are watching movies and reading, this information is sent to the server and stored.

[1452] 2. Matching Phase

[1453] The server uses the input personal information to match users with suitable partners. For example, when user A gets into an autonomous vehicle, she is matched with person B, who is also in the vehicle. Person B is a 24-year-old woman whose hobbies are traveling and music.

[1454] 3. Chat Phase

[1455] If the match is successful, the device displays a chat screen for user A and user B. Here, users can freely send and receive text messages and communicate with each other. Chat history data, including the message content and the time it occurred, is saved on the server. For example, if user A asks, "What movie did you see recently?" and user B replies, "I saw Inception! It was amazing!", this exchange is saved as chat history data.

[1456] 4. Data analysis phase

[1457] Once a certain amount of chat data has been accumulated, the server analyzes it. Natural language processing (NLP) and an emotion engine are used to evaluate the user's emotional state. For example, if the chat content contains a lot of positive vocabulary, the server evaluates the user as having a happy emotion.

[1458] 5. Recommendation text creation phase

[1459] Based on the analysis results, the server uses a generative AI model to generate recommendation sentences, taking into account the evaluation of the emotion engine. For example, if the user is feeling happy, a recommendation such as "What movie would you like to see next?" will be generated. The generated recommendation sentences are displayed on the device, allowing the user to easily select and use them in the next chat.

[1460] Specific examples

[1461] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with Person B through an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, enjoying discussions of movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generation AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[1462] Prompt Sentence Examples

[1463] User A's chat data:

[1464] 1. What's the last movie you saw?

[1465] 2. I saw Inception! It was amazing!

[1466] Recommended articles:

[1467] What movie do you want to see next?

[1468] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1469] Step 1:

[1470] The user enters personal information.

[1471] Input: Personal information such as age, gender, and hobbies entered by the user.

[1472] Output: The entered personal information is sent to the terminal.

[1473] Specific operation: The user enters information such as their age, gender, and hobbies into a form on the terminal and sends it to the server by pressing the confirm button.

[1474] Step 2:

[1475] The device sends personal information to a server, and the server stores the personal information.

[1476] Input: Personal information sent from your device.

[1477] Output: Personal information stored in the server's database.

[1478] Specific operation: The terminal sends the information entered through the form to the server using an API, and the server stores it in a database.

[1479] Step 3:

[1480] The server matches appropriate users based on the stored personal information.

[1481] Input: Personal information of multiple users stored in a database.

[1482] Output: Matched user pairs.

[1483] How it works: The server uses an algorithm to compare multiple user data and select the best pair based on criteria such as hobbies and age.

[1484] Step 4:

[1485] Matched users chat with each other.

[1486] Input: Matched user pairs.

[1487] Output: Chat messages sent and received between users.

[1488] Specific operation: The device displays a chat screen, allowing users to freely type and send messages. Sent messages are immediately displayed on the other device.

[1489] Step 5:

[1490] Chat history data is sent to the server and stored by the server.

[1491] Input: The chat message sent from the device along with the timestamp.

[1492] Output: Chat history data stored on the server.

[1493] Specific operation: Every time each device sends a chat message, the data is sent to the server and stored in a database along with a timestamp.

[1494] Step 6:

[1495] After accumulating a certain amount of chat data, the server analyzes it.

[1496] Input: Chat history data stored in a database.

[1497] Output: Analysis results, especially the user's emotional state.

[1498] Specific operation: The server uses natural language processing (NLP) technology and an emotion engine to analyze the content of chat data and evaluate the emotional state, such as positive or negative.

[1499] Step 7:

[1500] Based on the analysis results, the server generates recommendation sentences.

[1501] Input: The emotional state assessed by the emotion engine.

[1502] Output: Recommendation sentences generated by the generative AI model.

[1503] Specific operation: Based on the emotional state, the server uses a generative AI model to automatically generate the next recommendation sentence that the user should send.

[1504] Step 8:

[1505] The generated recommendation sentences are presented to the user's terminal.

[1506] Input: Generated recommendation sentences.

[1507] Output: Recommendation text displayed on the chat screen of the user's device.

[1508] Specific operation: The server sends the generated recommendation sentence to the user's device, which displays it on the chat screen. The user can click on the presented sentence to send it as the next message.

[1509] As a specific example of how it works, User A asks, "What movie did you see recently?" and User B replies, "I saw Inception! It was amazing!" This chat data is saved on the server and analyzed using NLP and an emotion engine. Based on the analysis results, a recommendation sentence such as "What movie do you want to see next?" is generated and displayed on User A's chat screen.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] [Fourth embodiment]

[1514] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1515] 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.

[1516] 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).

[1517] 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.

[1518] 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.

[1519] 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).

[1520] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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.

[1526] 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."

[1527] This invention is a system in which users enter personal information when registering, and then manually chat with a certain number of people of the opposite sex after registration. The system analyzes the user's chat patterns and uses generative AI to create recommendation sentences. The system aims to reduce the user's chat burden and prevent opportunity loss.

[1528] System configuration

[1529] 1. Registration Phase

[1530] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[1531] Terminal: Sends personal information submitted through the form to the server.

[1532] Server: Stores the received user information in a database.

[1533] Examples:

[1534] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[1535] 2. Manual Chat Phase

[1536] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[1537] Terminal: Display the chat screen to users A and B.

[1538] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[1539] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1540] 3. Data analysis phase

[1541] Server: Once a certain amount of chat data has been accumulated, analysis begins. Natural language processing (NLP) technology is used for analysis.

[1542] Examples:

[1543] Once a certain amount of chat data between users A and B has been accumulated, this data is analyzed to determine that user A has a strong interest in movies and books.

[1544] 4. Recommendation text creation phase

[1545] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, if User A likes watching movies, the recommendation generated would be, "What movie have you seen recently?"

[1546] Device: Display the recommended text on User A's chat screen.

[1547] Users can easily select a suggested sentence to use in their next chat.

[1548] Details of the working example

[1549] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B through an automatic matching algorithm, and the device displays a chat screen. Users A and B then chat manually, enjoying topics such as movies and books. The server saves this chat data in a database and analyzes it once a certain amount of chat data has been collected. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[1550] In this way, the present invention provides a system that allows users to chat smoothly without feeling any difficulty and continue the conversation.

[1551] The processing flow will be explained below.

[1552] Step 1:

[1553] The user opens the personal information form.

[1554] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[1555] Step 2:

[1556] The terminal acquires the user's input data and sends it to the server.

[1557] The device sends the input data to the appropriate endpoint on the server.

[1558] Step 3:

[1559] The server stores the received user information in a database.

[1560] The server analyzes the personal information sent and stores it in a database in an appropriate format.

[1561] Step 4:

[1562] The server matches users with suitable members of the opposite sex.

[1563] The server uses a matching algorithm to find people of the opposite sex who share common hobbies and interests.

[1564] Step 5:

[1565] A chat screen is displayed between users whose devices have been matched.

[1566] The device generates and displays an interface that allows you to chat with the matched person.

[1567] Step 6:

[1568] A user starts a chat and sends a message.

[1569] The user enters a message on the chat screen and clicks the "Send" button.

[1570] Step 7:

[1571] The terminal sends the user's message to the server.

[1572] The device sends the sent message to the server's chat save endpoint.

[1573] Step 8:

[1574] The server stores the received messages in a database.

[1575] The server stores the message content, sender, sent time, etc. in a database.

[1576] Step 9:

[1577] The server waits for a certain amount of chat data to accumulate.

[1578] When the server receives a certain amount of chat data, it begins analyzing the data.

[1579] Step 10:

[1580] The server analyzes the chat history data using natural language processing technology.

[1581] The server uses NLP models to analyze users' chat patterns and interests.

[1582] Step 11:

[1583] Based on the server's analysis results, a generative AI generates recommendation sentences.

[1584] The server inputs the analysis results into an AI model and automatically generates appropriate recommendation sentences.

[1585] Step 12:

[1586] The terminal presents the generated recommendation sentences to the user.

[1587] The device displays the recommendation text and allows the user to select and use it.

[1588] Step 13:

[1589] The user continues chatting using the recommended sentences provided.

[1590] The user selects the presented sentence and pastes it directly into the chat to continue.

[1591] Example 1

[1592] 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."

[1593] There is a need for a system that reduces the burden users feel when chatting with members of the opposite sex and prevents missed chat opportunities. Conventional chat systems require users to think up topics and input them themselves, which creates obstacles to communication. Furthermore, there is a lack of technology that can analyze chat content and present appropriate recommended sentences. This makes it difficult for users to continue chatting smoothly.

[1594] 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.

[1595] In this invention, the server includes: means for a user to input personal information; means for saving the input personal information; means for matching users with members of the opposite sex; means for matched users to chat with each other; means for saving chat history data; means for analyzing the saved chat history data; means for generating recommendation sentences based on the analyzed data; means for presenting the generated recommendation sentences to the user; means for the user to use the presented recommendation sentences in the next chat; means for starting analysis when a certain amount of chat data has been accumulated; means for using timestamps of the saved chat data; and means for linking and analyzing the chat data and the user's personal information. This reduces the burden on the user when chatting and enables the conversation to continue smoothly.

[1596] "User" refers to a person who uses this system to enter personal information and chat.

[1597] "Personal information" refers to detailed information relating to a user's identity, such as the user's age, gender, hobbies, etc.

[1598] "Matching" refers to the process by which the server pairs users with suitable members of the opposite sex based on their personal information.

[1599] "Chat" refers to the act of matched users communicating with each other in real time through messages.

[1600] "Historical data" refers to records of chat content, message timestamps, etc.

[1601] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language.

[1602] "Recommended sentences" refer to chat messages that the server generates based on analysis data and suggests to users.

[1603] A "generative AI model" refers to an artificial intelligence system that automatically generates new sentences based on collected data.

[1604] "Timestamp" refers to information about the time at which a chat message was sent or received.

[1605] "Database" refers to the information management system that the system uses to store personal information, chat history data, etc.

[1606] "Analysis" refers to the process of processing stored historical data and personal information to identify user interests and concerns.

[1607] This invention is a system in which a user enters personal information when registering, chats with a certain number of people, analyzes the user's chat patterns, and a generative AI creates recommendation sentences. This reduces the user's chat burden and prevents opportunity loss. The form for realizing this system is described in detail below.

[1608] System configuration

[1609] Registration Phase

[1610] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. The entered information is sent via the form.

[1611] Example: User A is a 25-year-old male and enters on the registration screen that his hobbies are watching movies and reading.

[1612] Terminal: Sends the entered personal information to a server. This information is usually sent using a dedicated communication protocol.

[1613] Server: Stores the received personal information in a database. The database can be a relational database management system such as an SQL database.

[1614] Manual Chat Phase

[1615] Server: Based on the registered personal information, the server matches users with members of the opposite sex. For example, User A, a 25-year-old man, is matched with User B, a 24-year-old woman.

[1616] Device: A chat screen is displayed to matched users A and B. This display is done using a dedicated chat application.

[1617] User: User A and User B chat manually and send messages. At this stage, it is assumed that User A will send a message to User B saying, "Hello, do you like movies?"

[1618] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1619] Data analysis phase

[1620] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing (NLP) technology. This analysis can be done using Python libraries (e.g., NLTK or spaCy).

[1621] Example: Analysis reveals that user A has a strong interest in movies.

[1622] Recommendation sentence creation phase

[1623] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences. For example, the generated recommendation sentences might be in the form of "What movie have you seen recently?"

[1624] Device: This recommendation text is displayed on User A's chat screen.

[1625] User: Select the suggested sentence and use it in the next chat.

[1626] Example: User A clicks on the recommendation text "What movie have you seen recently?" and sends it to the chat.

[1627] Example prompts to be input to the generative AI model

[1628] "Assuming that user A is interested in movies, please create a recommendation sentence for the next chat."

[1629] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1630] Step 1:

[1631] Enter and submit user information

[1632] User: Enter personal information such as age, gender, hobbies, etc. on the registration screen. Specifically, User A enters his / her name, age (25 years old), gender (male), and hobbies (watching movies and reading) on ​​the registration screen.

[1633] Terminal: The entered personal information is sent to the server via the form. Specifically, the submit button is clicked and the entered information is sent as an HTTP POST request.

[1634] Input: Personal information such as age, gender, hobbies, etc.

[1635] Output: Personal information sent to the server

[1636] Step 2:

[1637] User information stored in a database

[1638] Server: The received personal information is stored in a database, such as a relational database management system like MySQL.

[1639] Specific operation: The server stores the received information in the appropriate table using an SQL query.

[1640] Input: User A's personal information (name, age, gender, hobbies)

[1641] Output: User information stored in the database

[1642] Step 3:

[1643] User Matching

[1644] Server: Based on the registration information, the server matches User A with a suitable member of the opposite sex. For example, User A, a 25-year-old man whose hobby is watching movies, is matched with User B, a 24-year-old woman whose hobby is traveling.

[1645] Specific operation: The server uses a matching algorithm to search for and respond to users of the opposite sex who share the same hobbies and interests.

[1646] Input: User A's personal information and other users' information in the database

[1647] Output: Matched user pairs (users A and B)

[1648] Step 4:

[1649] Displaying the chat screen

[1650] Device: Display the chat screen to matched users A and B.

[1651] Specific behavior: The device launches the chat application and the chat interface is displayed.

[1652] Input: Matching information (User A and B)

[1653] Output: Chat screen

[1654] Step 5:

[1655] Manually starting a chat

[1656] User: User A and User B manually start a chat and send and receive messages. For example, User A sends a message to User B saying, "Hello, do you like movies?"

[1657] Input: User A's message content

[1658] Output: Message displayed on B's chat screen

[1659] Step 6:

[1660] Saving chat data

[1661] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1662] What it does: When the server receives a chat message, it writes the historical data to a database using an SQL query.

[1663] Input: Chat history data between users A and B

[1664] Output: Chat history data stored in a database

[1665] Step 7:

[1666] Chat data analysis

[1667] Server: Once a certain amount of chat data has been accumulated, the data is analyzed using natural language processing techniques, using Python's NLTK and spaCy libraries.

[1668] What happens: The server sends chat data to a text analysis module to identify user interests.

[1669] Input: Accumulated chat data

[1670] Output: Analysis results for User A (interests in movies and reading)

[1671] Step 8:

[1672] Generating recommendation sentences

[1673] Server: Based on the analysis results, generative AI automatically generates recommendation sentences.

[1674] Specific behavior: The generative AI begins the process of creating a recommendation sentence such as, "What movie have you seen recently?"

[1675] Input: User A's analysis results

[1676] Output: Generated recommendation sentences

[1677] Step 9:

[1678] Displaying recommended sentences

[1679] Terminal: The generated recommendation text is displayed on User A's chat screen.

[1680] Specific behavior: A recommended sentence will pop up in the chat interface.

[1681] Input: Generated recommendation sentences

[1682] Output: Recommended sentences displayed on the chat screen

[1683] Step 10:

[1684] Selection and use of recommendation text

[1685] User: Select the suggested recommendation sentence and use it as the next chat message. For example, User A clicks on the recommendation sentence "What movie have you seen recently?" and sends it to the chat.

[1686] Input: Recommended sentences displayed on the chat screen

[1687] Output: Suggested sentence sent as next chat message

[1688] (Application example 1)

[1689] 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."

[1690] There is a need for an effective method for quickly and appropriately responding to a wide range of user inquiries when chatting with users. There is also a need for a system that reduces the burden on customer support representatives and improves the user experience. Conventional methods require representatives to respond to each inquiry individually, which takes time and effort, and the quality of the response is unstable.

[1691] 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.

[1692] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for generating recommendation sentences based on the analyzed data, means for presenting the generated recommendation sentences to the user, and means for analyzing inquiry patterns and generating appropriate answers and questions for customer support, thereby enabling customer support staff to respond to user inquiries quickly and appropriately.

[1693] "Means for users to input personal information" refers to devices or software that provide a form or interface for users to input personal information such as name, age, hobbies, etc.

[1694] "Means for storing input personal information" refers to devices or software that record the personal information input by the user in a database or file system and make it accessible as needed.

[1695] A "user matching method" is an algorithm or system that connects users with each other based on shared hobbies or interests.

[1696] "Means for chatting between matched users" refers to a real-time communication interface or application that allows matched users to exchange messages.

[1697] The "means for storing chat history data" refers to a device or software that records chat content and message history between users in a database or log file.

[1698] The "means for analyzing the stored chat history data" refers to algorithms or software that analyze the stored chat history data using natural language processing techniques or the like to extract patterns or interests.

[1699] The "means for generating recommendation text based on analyzed data" refers to a generative AI model or text generation engine that automatically generates appropriate messages and questions based on the analysis results.

[1700] The "means for presenting the generated recommendation sentences to the user" refers to a device or software that displays the generated recommendation sentences on an interface such as a user's chat screen or dashboard.

[1701] "Means for analyzing inquiry patterns and generating appropriate answers and questions for customer support" refers to natural language processing technology and generative AI models that analyze past inquiry data and automatically generate appropriate answers and follow-up questions so that customer support staff can respond quickly.

[1702] To realize the system of the present invention, the following hardware and software are used.

[1703] The main technologies and hardware used are:

[1704] Python (mainly data processing and AI model implementation)

[1705] Django (Web application framework)

[1706] Google Cloud Natural Language API (natural language processing technology)

[1707] TensorFlow (training and running AI models)

[1708] MySQL (database management system)

[1709] When a user enters personal information, the device sends this information to the server via a form, and the server stores the received information in a MySQL database. For example, a user enters their name, age, hobbies, etc., and the information is sent to the server and stored.

[1710] In the matching phase, the server automatically matches users based on common hobbies and interests—for example, users who share a common hobby of watching movies—and the device provides an interface for users to chat with each other in real time.

[1711] Chat history data is recorded by the server and analyzed using the Google Cloud Natural Language API. This analysis identifies specific inquiry patterns and areas of user interest. For example, the analysis identifies that many inquiries are about "videos not playing."

[1712] In the analysis phase, the generative AI model on TensorFlow generates appropriate recommendation sentences based on the analysis results. The generated recommendation sentences are presented to the user via the device. For example, a recommendation such as "Please clear your cache and try again" is generated and displayed on the user's chat screen.

[1713] As a concrete example, the following prompt sentence can be used to generate an appropriate answer from the generative AI model:

[1714] What's the best response when a user says "The video won't play"?

[1715] In this way, the present invention provides a system that enables customer support personnel to respond to user inquiries quickly and appropriately, allowing users to smoothly continue conversations without experiencing difficulties in chatting, thereby improving the quality and efficiency of customer support.

[1716] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1717] Step 1:

[1718] The user enters personal information. The user fills in information such as name, age, and hobbies in an input form.

[1719] Input: Personal information (name, age, hobbies, etc.)

[1720] Output: Personal information entered in the input form

[1721] Specific operation: A user enters the required information into a form on a web application and presses the "Submit" button.

[1722] Step 2:

[1723] The terminal transmits the personal information entered by the user to the server.

[1724] Input: Personal information entered in the input form

[1725] Output: Personal information sent to the server

[1726] Specific operation: The terminal sends the form data to the server as an HTTP request.

[1727] Step 3:

[1728] The server stores the received personal information in a MySQL database.

[1729] Input: Personal information sent to the server

[1730] Output: Personal information stored in a MySQL database

[1731] Specific operation: The server analyzes the received data, converts it into an appropriate format, and stores it in the database.

[1732] Step 4:

[1733] The server automatically matches users with common hobbies and interests.

[1734] Input: Personal information stored in a MySQL database

[1735] Output: Matched user pairs

[1736] How it works: The server uses an algorithm to select users with common hobbies and interests and create pairs.

[1737] Step 5:

[1738] The device provides an interface that allows matched users to chat in real time.

[1739] Input: Matched user pairs

[1740] Output: Chat interface

[1741] Specific operation: The device displays a chat screen on the web application, allowing users to exchange messages.

[1742] Step 6:

[1743] The server stores chat history data in a MySQL database.

[1744] Input: Chat message between users

[1745] Output: Chat history stored in the database

[1746] Specific operation: The server records the content and timestamp of the chat message in a database.

[1747] Step 7:

[1748] The server analyzes the saved chat history data using the Google Cloud Natural Language API.

[1749] Input: Chat history stored in the database

[1750] Output: Analysis results (user areas of interest, inquiry patterns, etc.)

[1751] How it works: The server uses natural language processing techniques to extract important topics and patterns from chat data.

[1752] Step 8:

[1753] The server uses a generative AI model on TensorFlow to generate recommendation sentences based on the analysis results.

[1754] Input: Analysis results

[1755] Output: Recommendation sentence

[1756] Specific behavior: Runs TensorFlow to generate appropriate questions and answers based on the user's interests and inquiries.

[1757] Step 9:

[1758] The terminal displays the generated recommendation sentences on the user's chat screen.

[1759] Input: Recommendation sentence

[1760] Output: Recommendation sentences displayed on the chat screen

[1761] Specific operation: The device displays the recommended sentences on the chat interface so that the user can use them.

[1762] ---

[1763] The above are the specific processing steps of the system program that realizes this application example.

[1764] 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.

[1765] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. Furthermore, by combining it with an emotion engine, it provides a function to recognize and evaluate the user's emotions and adjust the recommendation sentences based on the results. This system aims to improve the user's chat experience and facilitate smooth communication.

[1766] System configuration

[1767] 1. Registration Phase

[1768] User: When logging in for the first time, enter personal information (age, gender, hobbies, etc.). The entered information is sent via a form.

[1769] Device: The personal information submitted in the form is sent to the server, and registration is completed.

[1770] Server: Stores the received user information in a database.

[1771] Examples:

[1772] User A enters that he is a 25-year-old male and that his hobbies are watching movies and reading, and this is sent to the server and saved.

[1773] 2. Manual Chat Phase

[1774] Server: Matches a suitable opposite-sex person to User A. The matched opposite-sex person is, for example, Ms. B (a 24-year-old woman whose hobbies are travel and music).

[1775] Terminal: Display the chat screen to users A and B.

[1776] User: User A and User B manually start a chat and send messages to each other. For example, they might chat about movies or books they've recently read.

[1777] Server: Stores chat history data (message content, timestamps, etc.) in a database.

[1778] 3. Data analysis phase

[1779] Server: Once a certain amount of chat data has been accumulated, analysis begins. Analysis uses natural language processing (NLP) technology and an emotion engine.

[1780] Examples:

[1781] Once a certain amount of chat data between User A and User B has been accumulated, this data is analyzed to recognize User A's emotional state. For example, if there is a lot of positive vocabulary in the conversation, User A is evaluated as having a happy emotion.

[1782] 4. Recommendation text creation phase

[1783] Server: Based on the analysis results, the generative AI automatically generates recommendation sentences, reflecting the evaluation of the emotion engine. For example, if User A has a happy emotion, the recommendation generated would be, "What fun movie have you seen recently?"

[1784] Device: Display the recommended text on User A's chat screen.

[1785] Users can easily select a suggested sentence to use in their next chat.

[1786] Details of the working example

[1787] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with User B via an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, and get excited about topics such as movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generative AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided. In addition, the emotion engine provides recommendations that are adapted to User A's emotional state, enabling more personalized communication.

[1788] In this way, the present invention provides a system that allows users to chat smoothly without difficulty and continues conversations smoothly through appropriate recommendation sentences based on emotions.

[1789] The processing flow will be explained below.

[1790] Step 1:

[1791] The user opens the personal information form.

[1792] The user enters personal information such as name, age, gender, and hobbies, and clicks the "Register" button.

[1793] Step 2:

[1794] The terminal acquires the user's input data and sends it to the server.

[1795] The device sends personal information data to the server using a RESTful API.

[1796] Step 3:

[1797] The server stores the received user information in a database.

[1798] The server records the input data in the appropriate table.

[1799] Step 4:

[1800] The server matches users with suitable members of the opposite sex.

[1801] The server uses a pre-set algorithm to select compatible opposite-sex partners based on the user's hobbies and interests.

[1802] Step 5:

[1803] A chat screen is displayed between users whose devices have been matched.

[1804] The terminal renders the chat interface and displays it to the user.

[1805] Step 6:

[1806] A user starts a chat and sends a message.

[1807] The user enters text on the chat screen and clicks the "Send" button.

[1808] Step 7:

[1809] The terminal sends the user's message to the server.

[1810] The device sends message data to the server's chat API endpoint via a POST request.

[1811] Step 8:

[1812] The server stores the received messages in a database.

[1813] The server stores the message content, sending time, sender ID, etc.

[1814] Step 9:

[1815] The server waits for a certain amount of chat data to accumulate.

[1816] The server monitors the count of chat data and triggers analysis when a threshold is reached.

[1817] Step 10:

[1818] The server analyzes the chat history data using natural language processing technology.

[1819] The server applies NLP models to extract the user's conversation patterns and interests.

[1820] Step 11:

[1821] The server evaluates the user's emotions using an emotion engine.

[1822] The server uses a sentiment analysis model to assess the user's emotional state (e.g., positive, negative, neutral).

[1823] Step 12:

[1824] Based on the server's analysis and emotion evaluation results, a generative AI generates recommendation sentences.

[1825] Based on the data obtained by the server, AI generates recommendation sentences that are appropriate for the user.

[1826] Step 13:

[1827] The terminal presents the generated recommendation sentences to the user.

[1828] The terminal displays the generated text on the chat screen, allowing the user to select it.

[1829] Step 14:

[1830] The user continues chatting using the recommended sentences provided.

[1831] The user selects a recommendation and clicks to send it as a message.

[1832] Examples:

[1833] For example, user A registers as a new user, and information about his hobbies and interests is saved in a database. Afterwards, person A is matched with person B, and they chat about movies and books. Once a certain number of chat histories have been accumulated, the server analyzes the content of the conversation through natural language processing, and the emotion engine detects happy emotions from person A's conversation. Then, the generative AI creates recommendation sentences such as "What fun movies have you seen recently?" and the device presents these to person A. person A can easily select this recommendation sentence and continue chatting. In this way, this system improves the user's chat experience and supports smooth communication.

[1834] Example 2

[1835] 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."

[1836] Current matching systems often experience interruptions when users chat, preventing smooth communication. Furthermore, they provide uniform recommendations without considering users' feelings, making it difficult to provide recommendations that are appropriate for each individual user. This can lead to a poor user experience.

[1837] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1838] In this invention, the server includes means for a user to input personal information, means for saving the input personal information, means for matching users, means for matched users to chat with each other, means for saving chat history data, means for analyzing the saved chat history data, means for evaluating the user's emotions using emotion recognition technology, means for generating recommendation sentences based on the analyzed data and the emotion evaluation, and means for presenting the generated recommendation sentences to the user.

[1839] This makes it possible to provide appropriate recommended sentences based on the user's emotions, prevent conversations from stalling, and achieve smoother, more satisfying communication.

[1840] A "user" is an individual who uses the system, inputs personal information, and chats with others.

[1841] "Personal information" refers to individual data such as age, gender, and hobbies that users enter into the system.

[1842] "Input means" refers to an interface for users to input personal information into the system, and includes methods such as forms.

[1843] "Storage means" refers to the database or storage used to hold entered personal information and chat history data.

[1844] "Matching method" refers to an algorithm or method that links multiple users together based on the personal information they input.

[1845] "Chat means" refers to a communication interface through which matched users can exchange messages in real time.

[1846] "Chat history data" refers to records of message content, timestamps, etc. in chats between users.

[1847] "Analysis means" refers to technology and software for analyzing saved chat history data, including natural language processing technology.

[1848] "Emotion recognition technology" refers to methods and algorithms for analyzing a user's chat history data and identifying and evaluating the user's emotions from that data.

[1849] "Recommendation sentence" refers to an automatically generated message generated by analytical means and emotion recognition technology to encourage the user's next conversation.

[1850] "Presentation means" refers to the method or interface for showing the generated recommendation text to the user, and includes the display screen of the user's terminal, etc.

[1851] MODE FOR CARRYING OUT THE INVENTION

[1852] This system matches users with members of the opposite sex based on personal information entered by the user, collects and analyzes chat data, and generates recommendation sentences. It also utilizes emotion recognition technology to evaluate the user's emotions and provides recommendation sentences based on the results, thereby achieving smooth communication.

[1853] 1. Hardware and Software Used

[1854] The entire system consists of a server, user devices, a database, natural language processing technology (NLP), an emotion recognition engine, and a generative AI model. Specifically, the server is installed on the cloud, and user devices include smartphones and PCs. The database can be a relational database such as MySQL or PostgreSQL. The natural language processing technology utilizes the NLTK library implemented in Python or TensorFlow. The emotion recognition engine may use Azure's Emotion API, for example. The generative AI model uses OpenAI's GPT, for example.

[1855] 2. Program processing explanation

[1856] First, the user enters their personal information. The information entered via a device (the user's smartphone or computer) is sent to the server via a form, and the server stores it in a database.

[1857] The server then uses an automatic matching algorithm to select suitable individuals based on the stored personal information. The matching results are then sent back to the user's device, where an interface is displayed for the matched users to chat with each other.

[1858] When users start chatting, the server stores each message in a database in real time, and each message contains a timestamp, allowing the flow of conversation between users to be tracked.

[1859] Once a certain amount of chat data has been accumulated, the server analyzes the data using natural language processing (NLP) and an emotion recognition engine. For example, if User A chats, "I recently saw a really fun movie," NLP technology analyzes this message and extracts keywords. The emotion recognition engine recognizes positive emotions and evaluates that User A is feeling happy.

[1860] Based on the analysis results, a generative AI model (e.g., GPT-3) creates a recommendation sentence for the next conversation, such as, "Tell me about a good movie you saw recently." The generated recommendation sentence is then displayed on the user's device.

[1861] Users can select recommended sentences and use them in their next chat. This allows the conversation to proceed smoothly by providing appropriate topics even when users are struggling with a conversation.

[1862] 3. Examples and prompts

[1863] As a concrete example, consider the case where users A and B get excited talking about movies. For example, user A sends a message saying, "Have you seen Inception?", and user B replies, "Yes, it was really interesting." Once this historical data is accumulated and analyzed, the generative AI model will provide a recommendation sentence like the following: "What is your favorite scene in Inception?"

[1864] An example prompt is:

[1865] 1. "Tell me about the last movie you saw."

[1866] 2. "Do you have any recommended topics related to your hobbies?"

[1867] 3. "Let's talk about the book you read recently."

[1868] As described above, the present invention is a system that makes users' chat experiences smoother and provides appropriate recommended sentences based on individual emotions, thereby facilitating communication.

[1869] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1870] Step 1:

[1871] The user enters personal information.

[1872] Input: User's age, gender, hobbies, etc.

[1873] Data processing: Enter information into the form on the user's device and press the send button.

[1874] Output: The form sends personal information to the server.

[1875] Specific operation: User A enters "Age 25, Gender male, Hobbies watching movies and reading" into the form on the device and clicks the send button.

[1876] Step 2:

[1877] The terminal transmits the entered personal information to the server.

[1878] Input: Personal information entered by the user into a form.

[1879] Data processing: Send form data as an HTTP request.

[1880] Output: Personal information is delivered to the server.

[1881] Specific operation: The device (smartphone or PC) sends the form data to the server via an HTTP POST request.

[1882] Step 3:

[1883] The server stores the received user information in a database.

[1884] Input: Personal information sent from your device.

[1885] Data processing: Save to database using SQL queries.

[1886] Output: User information is recorded in the database.

[1887] What happens: The server executes an SQL query like "INSERT INTO users (age, gender, hobbies) VALUES (25, 'Male', 'Watching movies, Reading')" and saves the information in the database.

[1888] Step 4:

[1889] The server matches users based on the stored personal information.

[1890] Input: Multiple user information stored in a database.

[1891] Data processing: Apply a matching algorithm to select a partner.

[1892] Output: Generates the match results and prepares them to be sent to the device.

[1893] Specific operation: The server executes an "algorithm for matching users with similar hobbies" and selects a 24-year-old woman (Ms. B) whose hobbies are "travel and music" for User A.

[1894] Step 5:

[1895] A chat screen is displayed between users whose devices have been matched.

[1896] Input: Matching results sent by the server.

[1897] Data processing: Generates a chat screen and displays it to the user.

[1898] Output: The chat screen is displayed on the user's device.

[1899] Specific operation: The device creates a "chat room between users A and B" and displays the chat screen on each device.

[1900] Step 6:

[1901] A user manually starts a chat and sends a message.

[1902] Input: The chat message typed by the user.

[1903] Data processing: Send the message content to the server.

[1904] Output: The chat message is sent to the server.

[1905] Specific behavior: User A types "What movie have you seen recently?" in the chat box and clicks the send button.

[1906] Step 7:

[1907] The server stores the chat history data in a database.

[1908] Input: Chat messages sent from the device.

[1909] Data processing: Save to database using SQL queries.

[1910] Output: Chat history is stored in a database.

[1911] What happens: The server executes an SQL query like "INSERT INTO chat_history (user_id, message, timestamp) VALUES (A, 'What movie did you see recently?', CURRENT_TIMESTAMP)".

[1912] Step 8:

[1913] The server analyzes the stored chat history data.

[1914] Input: Chat history data stored in a database.

[1915] Data processing: Applying natural language processing and emotion recognition technologies.

[1916] Output: Analysis results and sentiment ratings.

[1917] Specific operation: The server analyzes the message using an NLP engine (e.g., Python's NLTK) and evaluates that User A is feeling "fun" using an emotion recognition engine (e.g., Azure Emotion API).

[1918] Step 9:

[1919] The server generates recommendation sentences based on the analysis results.

[1920] Input: Analysis results and emotion ratings.

[1921] Data processing: Generate recommendation sentences using a generative AI model (e.g., GPT-3).

[1922] Output: Recommendation sentences.

[1923] Specific operation: The server generates a recommendation sentence such as "What good movies have you seen recently?" and makes it available for the next conversation.

[1924] Step 10:

[1925] The terminal presents the generated recommendation sentences to the user.

[1926] Input: Recommendation text sent from the server.

[1927] Data processing: Displayed on the chat screen.

[1928] Output: The recommendation text is displayed on the user's device.

[1929] Specific operation: The device displays a recommendation message on the chat screen, such as "What good movies have you seen recently?", and allows the user to select one.

[1930] The above is the flow of processing of the program of this system.

[1931] (Application example 2)

[1932] 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."

[1933] Conventional self-driving vehicles have not provided sufficient mechanisms to encourage communication between passengers and allow them to spend their time on board meaningfully. Furthermore, the lack of communication methods that respond to passenger emotions can sometimes hinder smooth communication. This has made it difficult to improve passenger satisfaction.

[1934] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1935] In this invention, the server includes: a means for a user to input personal information; a means for saving the input personal information; a means for matching users; a means for matched users to chat with each other; a means for saving chat history data; a means for analyzing the saved chat history data; a means for generating recommendation sentences based on the analyzed data; a means for presenting the generated recommendation sentences to the user; a means for evaluating an emotional state based on the analyzed data; a means for adjusting the recommendation sentences based on the emotional state; and a means for matching users and chatting with them in an autonomous vehicle. This allows smooth communication between passengers and enables them to spend their time on board meaningfully. Furthermore, by providing recommendation sentences based on emotions, personalized communication is realized.

[1936] "Personal information" is information that can identify a specific individual, including the user's age, gender, hobbies, etc.

[1937] "Matching" is the process by which the system selects a suitable partner based on the personal information of multiple users.

[1938] "Chat" is a means for users to communicate with each other through text messages.

[1939] "Chat history data" refers to data that records the content of conversations between users and the time at which they occurred.

[1940] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language.

[1941] An "emotion engine" is a software component for recognizing and assessing a user's emotional state from text data.

[1942] "Recommendation sentences" are phrases generated by the system and presented to users to facilitate communication.

[1943] An "autonomous vehicle" is a vehicle whose driving is controlled by a system without human intervention.

[1944] "Personalization" refers to tailoring specific services or content to the specific needs and emotional state of individual users.

[1945] This invention is a system for facilitating communication between passengers in an autonomous vehicle and allowing them to spend their time on board meaningfully. The configuration and operation of the system are described below.

[1946] System configuration

[1947] 1. Registration Phase

[1948] The user enters personal information when starting the system. The entered personal information is sent to the server via the terminal and stored by the server. This process includes the user's age, gender, hobbies, etc. For example, if User A enters that he is a 25-year-old male whose hobbies are watching movies and reading, this information is sent to the server and stored.

[1949] 2. Matching Phase

[1950] The server uses the input personal information to match users with suitable partners. For example, when user A gets into an autonomous vehicle, she is matched with person B, who is also in the vehicle. Person B is a 24-year-old woman whose hobbies are traveling and music.

[1951] 3. Chat Phase

[1952] If the match is successful, the device displays a chat screen for user A and user B. Here, users can freely send and receive text messages and communicate with each other. Chat history data, including the message content and the time it occurred, is saved on the server. For example, if user A asks, "What movie did you see recently?" and user B replies, "I saw Inception! It was amazing!", this exchange is saved as chat history data.

[1953] 4. Data analysis phase

[1954] Once a certain amount of chat data has been accumulated, the server analyzes it. Natural language processing (NLP) and an emotion engine are used to evaluate the user's emotional state. For example, if the chat content contains a lot of positive vocabulary, the server evaluates the user as having a happy emotion.

[1955] 5. Recommendation text creation phase

[1956] Based on the analysis results, the server uses a generative AI model to generate recommendation sentences, taking into account the evaluation of the emotion engine. For example, if the user is feeling happy, a recommendation such as "What movie would you like to see next?" will be generated. The generated recommendation sentences are displayed on the device, allowing the user to easily select and use them in the next chat.

[1957] Specific examples

[1958] For example, when User A registers for the first time, the device sends User A's personal information to the server via a form, and the server saves this information in a database. After that, User A is matched with Person B through an automatic matching algorithm, and the device displays a chat screen. Users A and B chat manually, enjoying discussions of movies and books. The server saves this chat data in a database, and once a certain amount of chat data has been collected, it analyzes it using NLP technology and an emotion engine. Based on the results of this analysis, a generation AI generates recommended sentences suitable for User A, which are then displayed on the device. User A can continue the conversation using the recommended sentences provided.

[1959] Prompt Sentence Examples

[1960] User A's chat data:

[1961] 1. What's the last movie you saw?

[1962] 2. I saw Inception! It was amazing!

[1963] Recommended articles:

[1964] What movie do you want to see next?

[1965] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1966] Step 1:

[1967] The user enters personal information.

[1968] Input: Personal information such as age, gender, and hobbies entered by the user.

[1969] Output: The entered personal information is sent to the terminal.

[1970] Specific operation: The user enters information such as their age, gender, and hobbies into a form on the terminal and sends it to the server by pressing the confirm button.

[1971] Step 2:

[1972] The device sends personal information to a server, and the server stores the personal information.

[1973] Input: Personal information sent from your device.

[1974] Output: Personal information stored in the server's database.

[1975] Specific operation: The terminal sends the information entered through the form to the server using an API, and the server stores it in a database.

[1976] Step 3:

[1977] The server matches appropriate users based on the stored personal information.

[1978] Input: Personal information of multiple users stored in a database.

[1979] Output: Matched user pairs.

[1980] How it works: The server uses an algorithm to compare multiple user data and select the best pair based on criteria such as hobbies and age.

[1981] Step 4:

[1982] Matched users chat with each other.

[1983] Input: Matched user pairs.

[1984] Output: Chat messages sent and received between users.

[1985] Specific operation: The device displays a chat screen, allowing users to freely type and send messages. Sent messages are immediately displayed on the other device.

[1986] Step 5:

[1987] Chat history data is sent to the server and stored by the server.

[1988] Input: The chat message sent from the device along with the timestamp.

[1989] Output: Chat history data stored on the server.

[1990] Specific operation: Every time each device sends a chat message, the data is sent to the server and stored in a database along with a timestamp.

[1991] Step 6:

[1992] After accumulating a certain amount of chat data, the server analyzes it.

[1993] Input: Chat history data stored in a database.

[1994] Output: Analysis results, especially the user's emotional state.

[1995] Specific operation: The server uses natural language processing (NLP) technology and an emotion engine to analyze the content of chat data and evaluate the emotional state, such as positive or negative.

[1996] Step 7:

[1997] Based on the analysis results, the server generates recommendation sentences.

[1998] Input: The emotional state assessed by the emotion engine.

[1999] Output: Recommendation sentences generated by the generative AI model.

[2000] Specific operation: Based on the emotional state, the server uses a generative AI model to automatically generate the next recommendation sentence that the user should send.

[2001] Step 8:

[2002] The generated recommendation sentences are presented to the user's terminal.

[2003] Input: Generated recommendation sentences.

[2004] Output: Recommendation text displayed on the chat screen of the user's device.

[2005] Specific operation: The server sends the generated recommendation sentence to the user's device, which displays it on the chat screen. The user can click on the presented sentence to send it as the next message.

[2006] As a specific example of how it works, User A asks, "What movie did you see recently?" and User B replies, "I saw Inception! It was amazing!" This chat data is saved on the server and analyzed using NLP and an emotion engine. Based on the analysis results, a recommendation sentence such as "What movie do you want to see next?" is generated and displayed on User A's chat screen.

[2007] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2008] 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.

[2009] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2010] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2011] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2012] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2013] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2014] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2015] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2016] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2017] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2018] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2019] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2020] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2021] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2022] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2023] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2024] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2025] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2026] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2027] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2028] The following is further disclosed regarding the above embodiment.

[2029] (Claim 1)

[2030] a means for a user to input personal information;

[2031] A means for storing the personal information entered;

[2032] a means for matching users;

[2033] A means for matching users to chat with each other;

[2034] a means for storing chat history data;

[2035] means for analyzing the stored chat history data;

[2036] A means for generating recommendation sentences based on the analyzed data;

[2037] The system includes a means for presenting the generated recommendation sentences to a user.

[2038] (Claim 2)

[2039] 2. The system according to claim 1, further comprising means for analyzing chat history data using natural language processing techniques.

[2040] (Claim 3)

[2041] 2. The system according to claim 1, further comprising means for displaying the generated recommendation sentence on a display screen of a user terminal.

[2042] "Example 1"

[2043] (Claim 1)

[2044] a means for a user to input personal information;

[2045] A means for storing the personal information entered;

[2046] a means for matching users with members of the opposite sex;

[2047] A means for matching users to chat with each other;

[2048] a means for storing chat history data;

[2049] means for analyzing the stored chat history data;

[2050] A means for generating recommendation sentences based on the analyzed data;

[2051] A means for presenting the generated recommendation sentences to a user;

[2052] A means for the user to use the suggested recommended sentences in the next chat;

[2053] A method for starting analysis when a certain amount of chat data has been accumulated, and

[2054] A means of utilizing timestamps of stored chat data;

[2055] A means for linking and analyzing chat data with user personal information;

[2056] A system including:

[2057] (Claim 2)

[2058] 2. The system according to claim 1, further comprising means for analyzing chat history data using natural language processing techniques.

[2059] (Claim 3)

[2060] The system of claim 1, further comprising means for generating recommendation sentences using a generative AI model.

[2061] "Application Example 1"

[2062] (Claim 1)

[2063] a means for a user to input personal information;

[2064] A means for storing the personal information entered;

[2065] a means for matching users;

[2066] A means for matching users to chat with each other;

[2067] a means for storing chat history data;

[2068] means for analyzing the stored chat history data;

[2069] A means for generating recommendation sentences based on the analyzed data;

[2070] A means for presenting the generated recommendation sentences to a user;

[2071] A system that includes a means for analyzing inquiry patterns and generating appropriate answers and questions for customer support.

[2072] (Claim 2)

[2073] 2. The system according to claim 1, further comprising means for analyzing chat history data using natural language processing techniques.

[2074] (Claim 3)

[2075] 2. The system according to claim 1, further comprising means for displaying the generated recommendation sentence on a display screen of a user terminal.

[2076] "Example 2: Combining Emotion Engines"

[2077] (Claim 1)

[2078] a means for a user to input personal information;

[2079] A means for storing the personal information entered;

[2080] a means for matching users;

[2081] A means for matching users to chat with each other;

[2082] a means for storing chat history data;

[2083] means for analyzing the stored chat history data;

[2084] means for assessing a user's emotions using emotion recognition technology;

[2085] A means for generating recommendation sentences based on the analyzed data and the sentiment evaluation;

[2086] The system includes a means for presenting the generated recommendation sentences to a user.

[2087] (Claim 2)

[2088] 2. The system according to claim 1, further comprising: means for analyzing chat history data using natural language processing technology; and means for evaluating emotions using emotion recognition technology.

[2089] (Claim 3)

[2090] 2. The system according to claim 1, further comprising means for displaying the generated recommendation sentence on a display screen of a user terminal.

[2091] "Application example 2 when combining emotion engines"

[2092] (Claim 1)

[2093] a means for a user to input personal information;

[2094] A means for storing the personal information entered;

[2095] a means for matching users;

[2096] A means for matching users to chat with each other;

[2097] a means for storing chat history data;

[2098] means for analyzing the stored chat history data;

[2099] A means for generating recommendation sentences based on the analyzed data;

[2100] A means for presenting the generated recommendation sentences to a user;

[2101] means for assessing an emotional state based on the analyzed data;

[2102] means for adjusting the recommendation text based on the emotional state;

[2103] A system including a means for matching and chatting with users within an autonomous vehicle.

[2104] (Claim 2)

[2105] 2. The system according to claim 1, further comprising means for analyzing chat history data using natural language processing techniques.

[2106] (Claim 3)

[2107] 2. The system according to claim 1, further comprising means for displaying the generated recommendation sentence on a display screen of a user terminal. [Explanation of symbols]

[2108] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input personal information; A means for storing the personal information entered; a means for matching users; A means for matching users to chat with each other; a means for storing chat history data; means for analyzing the stored chat history data; A means for generating recommendation sentences based on the analyzed data; The system includes a means for presenting the generated recommendation sentences to a user.

2. 2. The system according to claim 1, further comprising means for analyzing chat history data using natural language processing techniques.

3. The system according to claim 1, further comprising means for displaying the generated recommendation sentences on a display screen of a user terminal.

Images