system

Abstract

We provide the system. [Solution] A means to automatically identify common interests between registered users based on their hobbies and activity history, A means for generating an initial message that facilitates the initiation of a dialogue based on the aforementioned shared interests, A means for sending and receiving generated messages between users, A means of naturally continuing or ending a conversation based on the user's response, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] When using a matching app, many users feel a psychological burden regarding communication with a person they meet for the first time. Such a burden is particularly prominent in scenarios such as creating the first message or determining the progress and end of a conversation. As a result, users may avoid direct communication and hesitate to use the app. To solve this problem, a system that enables users to communicate naturally and smoothly is required.

Means for Solving the Problems

[0005] This invention provides a system that enables smooth initiation of initial communication by having AI identify common interests based on the user's registration information and behavioral history, and automatically generating an initial message based on those interests. Furthermore, it naturally generates responses during the conversation and suggests messages that suit the user's style, ensuring that the conversation continues without stalling. It also provides means for users to end the conversation smoothly at the appropriate time and with appropriate expression. In this way, it reduces the psychological burden on the user and enables a better communication experience.

[0006] "User" refers to a person who uses the services of a dating app.

[0007] "Hobbies and activity history" refers to the user's registered interests and records of actions they have taken within the app in the past.

[0008] "Shared interests" is a concept that refers to the shared interests or concerns of two or more users.

[0009] "Initial message" refers to the first message exchanged between a user and another user.

[0010] "Response" refers to the content of a user's reply to a message they have received.

[0011] An "AI agent" refers to an automated program within a system that uses artificial intelligence to support communication between users.

[0012] "Automatic generation" refers to the process of generating content through a system or program without human intervention.

[0013] "Conversation progression" is a concept that refers to the process by which a dialogue initiated between users develops and progresses.

[0014] "Natural conversation" refers to smooth and flowing communication that allows users to participate without discomfort.

[0015] "Message proposal" refers to the act of presenting content determined to be appropriate as a message to be sent by the AI agent to the user.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0030] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention provides a system to support natural communication between users in a matching application. This system uses an AI agent to analyze users' hobbies and behavioral history, and automatically generates messages to facilitate smooth communication between users. Specific embodiments are described below.

[0038] The server stores information about users when they register for the app in a database. This information includes hobbies, areas of interest, and past message exchange history. Based on this information, the server uses an AI agent to identify common interests among users.

[0039] The terminal displays information sent from the server to the user. During this process, the user can view messages generated by the AI. If the user's interests align with the AI's, the AI ​​automatically generates an initial message tailored to those interests, making it easy for the user to send.

[0040] As a concrete example, suppose user A is interested in movies, and user B also likes movies. In this case, the server identifies that their interests match and has the AI ​​agent generate an initial message such as, "Hi, I heard you like movies too. What's the best movie you've seen recently?" The user can then send this message directly to the other person, starting a conversation in a natural way.

[0041] As the conversation progresses, the server uses an AI agent to analyze the user's response patterns and the progress of the dialogue, and generates the next message to send. Furthermore, if the user indicates an intention to end the conversation, the AI ​​suggests a message to conclude the conversation in an appropriate manner. This allows users to maintain a natural flow of communication while reducing psychological burden.

[0042] As described above, the system according to the present invention aims to provide smooth communication based on users' hobbies and behaviors, and to improve the user experience of matching applications.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server stores in a database the hobbies, interests, and past activity history that users entered when registering for the app.

[0046] Step 2:

[0047] The server uses an AI agent to analyze and automatically identify common interests between matched users. If common interests are found, it prepares to utilize that information.

[0048] Step 3:

[0049] The server instructs the AI ​​agent to generate the initial message based on the analysis results. For example, if the common interest is movies, it will generate a message such as, "I heard you like movies too."

[0050] Step 4:

[0051] The server sends the initial message it generates to the user's terminal.

[0052] Step 5:

[0053] The device displays the first message received to the user. The user can review the message, edit it as needed, and then send it to the recipient.

[0054] Step 6:

[0055] When a user receives a reply, the device displays the message to the user.

[0056] Step 7:

[0057] The server uses an AI agent to create appropriate responses to generate the next message based on the progress of the user interaction. In doing so, it takes into account the user's conversation style and tone.

[0058] Step 8:

[0059] The server sends the generated response back to the terminal, which then presents it to the user.

[0060] Step 9:

[0061] If the user indicates their intention to end the conversation, the server has the AI ​​agent generate an end message. The device then displays this message, and the user's approval allows the conversation to be smoothly concluded.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Current communication support systems have a problem in that it is difficult for users to initiate and continue conversations in a natural way. Furthermore, even once a conversation has started, there is a lack of means to appropriately maintain its flow and to naturally end it when necessary. In this situation, there is a need to provide smoother communication based on users' interests and behaviors.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes data management means for storing information of registered users, analysis means for identifying common interests based on the information, and message generation means for generating an initial message based on the common interests. This enables the initiation and maintenance of natural and effective communication tailored to the user's background.

[0067] A "registered user" refers to an individual or organization that provides necessary information for a service or system, and whose information is stored in the database.

[0068] "Data management means" refers to a technical element that has a process or function for collecting, organizing, storing, and managing information.

[0069] "Analysis tools" refer to elements that analyze information based on collected data and have the function of clarifying trends and patterns necessary for a specific purpose, such as identifying common interests.

[0070] "Shared interests" refers to connections based on hobbies, interests, and behaviors shared by two or more users.

[0071] "Message generation means" refers to a technical element that has the function of automatically creating text or messages for a specific purpose, such as facilitating the initiation of communication by users.

[0072] "Display means" refers to a technical device or function that plays a role in visually presenting generated information or messages to the user.

[0073] "Conversation support means" refers to a technical element that analyzes an ongoing conversation and has the function of assisting the user in continuing or ending the conversation naturally.

[0074] "Response patterns" refer to information used to predict the flow of a conversation, based on the user's past reactions and response tendencies.

[0075] This invention is a system for facilitating communication among users in a matching service. This system consists of three main elements: a server, a terminal, and a user, each playing a specific role.

[0076] Server Role

[0077] The server first collects information provided by users and stores it in a database. This includes users' hobbies and activity history. Next, it uses a generative AI model to analyze common interests among users based on the collected information. By sending the analysis results to the AI ​​using prompts, it generates an initial message based on the identified interests. The technologies used include a database management system and a generative AI engine.

[0078] Terminal role

[0079] A terminal is a device that visually presents generated messages received from a server to the user. The terminal displays the generated messages through a user interface, allowing the user to review the messages and, if necessary, send them immediately without editing. This display is typically done via a smartphone or computer screen.

[0080] User roles

[0081] Users can view messages displayed on their devices and send them to other users to initiate conversations. Furthermore, they can consider suggested messages generated by the server to help them continue or end the conversation.

[0082] Specific example

[0083] For example, if the server identifies "movies" as a common interest of users A and B, it will have the AI ​​generate an initial message such as, "Hi, I heard you like movies too. What's the best movie you've seen recently?" An example of a prompt would be, "If users A and B share a common interest in movies, please create an initial message." Such messages allow users to start a conversation naturally, and with the support of the AI, the conversation can be kept flowing without interruption.

[0084] In this way, the system aims to improve the user's communication experience and enhance the overall value of the service.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The server stores profile information and past message history entered by users when they register for the application in a database. This input data includes the user's hobbies, interests, and message exchange history. This information is stored as foundational data for future analysis.

[0088] Step 2:

[0089] The server uses accumulated data to send prompt messages to a generating AI model, which analyzes common interests among users. The input data includes users' hobbies and past behavioral history. As a result of the analysis, hobbies shared by two or more users are identified. For example, it might be found that both users like "science fiction movies."

[0090] Step 3:

[0091] The server uses a generative AI model based on identified common interests to automatically generate the initial message. The input data is the common interest information obtained in step 2. The output is a message suitable for starting a conversation. Specifically, a message like "I heard you like science fiction movies, what movie have you seen recently?" is generated.

[0092] Step 4:

[0093] The terminal displays a generated message sent from the server to the user. The input is the generated message from the server, and the output of the terminal is the display of the message. Through the user interface, the user can review the generated message and send it as is without editing.

[0094] Step 5:

[0095] The user initiates a conversation by selecting a message received via their device and sending it to the other party. Specifically, the user reviews the displayed message and, if necessary, sends it with a single click. This action allows the conversation to begin naturally.

[0096] Step 6:

[0097] The server analyzes the ongoing conversation using a generative AI model and suggests the next message to send. The input is the user's response data, and the output suggested by the AI ​​model based on the analysis is either a new message to continue the conversation or a message to end it. For example, if the conversation seems to be winding down, it might suggest, "What are your plans for next weekend?"

[0098] This allows the system to perform a series of processes to facilitate smooth communication among users.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] In modern families, the increasing busyness of daily life leads to a weakening of communication among family members. In this situation, it is necessary to consider the individual hobbies and interests of each family member and promote natural conversation. However, manually suggesting specific conversation topics based on each member's hobbies and past conversation history is time-consuming, making a system that efficiently supports communication necessary.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for automatically identifying common interests within a family based on the hobbies and activity history of registered users, means for learning the individual hobbies and conversation history of family members and providing common topics, and means for generating topic suggestion sentences using a generative AI model. This makes it possible to facilitate communication within the family and revitalize everyday conversations.

[0104] "Registered users" refers to individuals who have provided their information to the system and have been granted permission to use it.

[0105] "Hobbies and activity history" refers to areas and activities that the user is personally interested in, as well as records related to those activities.

[0106] "Shared interests" refer to the concerns or areas of interest that multiple users share.

[0107] "Message generation means" refers to an element that has the function of automatically creating text for initiating or advancing conversations between users.

[0108] "Transmission and reception means" refers to the function of sending and receiving generated messages between users.

[0109] A "generative AI model" refers to an artificial intelligence system that uses large amounts of data to perform tasks such as text generation and decision support.

[0110] A "topic proposal document" refers to a document that contains a specific topic suggested to initiate or advance a dialogue.

[0111] The system of this invention is designed to revitalize communication within families. The server first stores the hobbies and activity history of registered users in a database. This database contains information about each family member's interests and past conversation history. The server analyzes this information and runs an algorithm to identify common interests and concerns within the family.

[0112] The server uses a generative AI model to generate topic suggestions based on identified common interests. These generated messages are presented to family members via a device, such as a home robot, which supports natural conversation. At this point, the user can initiate a conversation on the suggested topic and enjoy further communication.

[0113] For example, if a family member enjoys watching new movies, the server can identify other family members' interest in movies and suggest topics such as, "Why don't we all talk about the latest movies?"

[0114] An example of a prompt generated by the AI ​​model is an input such as, "Family profile data: {Personality information}." Based on such prompts, messages are generated to start a conversation in a natural way.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server collects user hobby and activity history data and stores it in a database. This database contains user-registered profile information and past conversation history. The server analyzes this data to organize each user's hobbies and interests.

[0118] Step 2:

[0119] The server identifies common interests shared within a family based on data in the database. The input here is the hobby data of each user stored in the database. The server uses an algorithm to compare and analyze the data, extracting and outputting commonalities.

[0120] Step 3:

[0121] Based on the identified common interests, the server generates appropriate topic suggestions using a generative AI model. In this step, the common interest data is received as input, the AI ​​model performs natural language processing to create suggestion sentences, and these are output.

[0122] Step 4:

[0123] The generated topic suggestion is sent to the terminal, which then presents it to the user. The input here is the generated suggestion, which the terminal displays to the user in an appropriate format.

[0124] Step 5:

[0125] The user initiates a conversation based on the suggested topic. The user can use the provided topic suggestions to communicate with other family members. The output is a natural start to a conversation.

[0126] Step 6:

[0127] During the conversation, the server monitors the user's responses and updates the data as needed to generate the next message. The input here is the progress of the conversation and the user's responses, which the server uses to output information that supports the next dialogue step.

[0128] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0129] This invention is a system for gaining a deeper understanding of and facilitating communication between users in a matching application. This system uses an emotion engine to analyze users' emotions and improves communication by responding based on the analysis results.

[0130] The server identifies common interests based on the user's registration information and behavioral history, and an AI agent generates an initial message. The generated message is presented to the user from their device, and the user can send this message as is or edit it as needed.

[0131] As a concrete example, if both user A and user B are interested in cooking, the server uses AI to generate an initial message such as "Do you have any recommendations for dishes you've made recently?" and presents it to user A via their device. When user A sends this message to user B, a natural conversation begins.

[0132] The emotion engine analyzes the content of messages sent by users and evaluates their emotional state. For example, if user A's response is positive, the AI ​​agent returns a message that maintains that tone. If the user's message is judged to be negative, it generates a response that guides the conversation in a positive direction. In this way, it enables communication that takes the user's emotions into consideration.

[0133] Furthermore, the server considers past conversational styles and the user's emotional tendencies to generate more personalized messages. For example, it will respond to users who previously preferred calm conversations in a gentle tone, and to users who preferred lively conversations in a more energetic tone.

[0134] Thus, by incorporating an emotion engine, the system of the present invention can provide a sophisticated communication experience that is attentive to the user's emotions, significantly improving the value of using a matching app.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The server stores the registration information and past activity history entered by the user into the app in a database.

[0138] Step 2:

[0139] The server uses an AI agent to analyze and automatically identify common interests among matched users.

[0140] Step 3:

[0141] The server prompts the AI ​​agent to generate an initial message based on identified common interests. For example, if the common interest is cooking, it will create an appropriate message.

[0142] Step 4:

[0143] The terminal displays the initial message sent from the server to the user. The user reviews the message, edits it as needed, and then sends it to the recipient.

[0144] Step 5:

[0145] The server analyzes received messages using an emotion engine and evaluates the user's emotional state. This evaluation result is used to generate response messages.

[0146] Step 6:

[0147] Based on the evaluation of the emotion engine, the server uses an AI agent to generate response messages that are tailored to the user's emotions. For example, if the emotion is positive, it will create a response that maintains that emotion; if the emotion is negative, it will create a response that shifts it in a positive direction.

[0148] Step 7:

[0149] The server sends the generated response message to the terminal, which then presents it to the user.

[0150] Step 8:

[0151] When a user indicates they wish to end a conversation, the device displays an appropriate ending message based on the sentiment engine's evaluation. Based on this, the user can smoothly conclude the conversation with the other party.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] Traditional matching applications have faced challenges such as superficial communication between users and difficulty in forming emotional connections. Furthermore, they lacked sufficient personalized support based on users' hobbies and behaviors, requiring the creation of nuanced conversations tailored to each individual. In particular, there was a need to achieve more natural and human-like communication by being sensitive to changes in emotions.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes means for automatically identifying common interests among users based on registered user attribute data and behavioral history; means for using a generation algorithm to generate initial communication content based on the common interests; and means for evaluating communication content sent by users using an emotion analysis engine and generating a response based on the results. This enables users to communicate in a way that suits their individual emotions and conversational style.

[0157] A "registered user" is an individual who provides their information to a matching service or application and is authorized to use it.

[0158] "Attribute data" refers to information that indicates an individual's characteristics, including the user's profile information.

[0159] "Activity history" refers to information that records the history of actions and interactions that a user has performed in the past.

[0160] "Shared interests" refer to information that indicates similar interests or hobbies shared by multiple users.

[0161] A "generative algorithm" is a computational method used to generate an output based on specific input information.

[0162] "Communication content" is a term that refers to information and messages exchanged between users.

[0163] An "emotion analysis engine" is a software tool that reads emotions from a user's messages and actions.

[0164] A "response" is a message or reaction that is generated based on specific input information and presented to the user.

[0165] A "user terminal" is a device used by a user to access and operate an application.

[0166] Personalization refers to providing information and services that are optimized based on an individual's characteristics and past behavior.

[0167] A "suggestion" is a guideline that presents users with specific actions or options to support their decision-making.

[0168] This invention provides a system that highly personalizes communication between users in a matching application and enables emotion-based dialogue. In this system, a server plays a central role, and a specific embodiment of this system is shown below.

[0169] The server first analyzes attribute data from user registration information and behavioral history. This process utilizes databases and data analysis tools to build user profiles. Machine learning techniques are used to automatically identify common interests among users. At this time, libraries such as Scikit-learn are used to calculate Euclidean distance and cosine similarity, and users with similar interests are clustered.

[0170] As a generation algorithm, the server uses a generation AI model. Examples of AI models used include GPT-4®. By inputting a prompt to this model, an initial message is generated to facilitate the start of a dialogue. For example, the prompt might be, "Generate a message to initiate a conversation between users A and B about their shared hobby, music." This generated message is then presented to the users via their terminal.

[0171] When a user receives this message, the device displays the message through the UI, allowing the user to choose whether to send or edit it. This enables flexible communication that reflects the user's intentions.

[0172] Furthermore, the server uses an emotion analysis engine to analyze the content of messages sent by users and evaluate their emotional state. Specifically, it utilizes TextBlob and Watson® Natural Language Understanding to determine whether a message is positive or negative. Based on this evaluation, it adjusts the tone of the next message delivered. In this way, it enables natural and friendly communication that is attentive to the user's emotions.

[0173] Ultimately, the server generates more personalized messages by considering the user's past conversational style and emotional tendencies. This process provides an optimized experience for each individual user. In this way, the present invention provides users of matching applications with communication that fosters deeper emotional connections.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The server collects user registration information and behavioral history. The input for this process is the profile data and past behavioral history provided by the user to the application. The server stores this data in a database and analyzes it using machine learning algorithms to output user attribute data. Specifically, it sends a request to the data server to retrieve user profiles and log data.

[0177] Step 2:

[0178] The server identifies common interests based on the acquired attribute data. The input here is the user attribute data obtained in step 1. The server uses a clustering algorithm to group users with similar hobbies and interests and outputs the common interests. Specifically, it uses Scikit-learn functions to classify common items into clusters.

[0179] Step 3:

[0180] The server generates an initial message based on shared interests. The input is the shared interests identified in step 2. Based on this information, the server inputs a prompt sentence into the generating AI model to generate a message. The output is an initial message such as "What are your recent hobbies?". The specific operation involves inputting text into the AI ​​model and performing natural language generation.

[0181] Step 4:

[0182] The terminal presents the user with a message generated by the server. The input here is the message output in step 3. The terminal displays this message through the user interface, informing the user of its contents. The output is the screen display that the user confirms. Specifically, the display is popped up using the device's notification function.

[0183] Step 5:

[0184] The user reviews the message displayed on the terminal and either sends or edits it. The input is the message displayed in step 4, and the output is either the message sent as is or the edited message. Specifically, the user either clicks the send button or opens the editing screen.

[0185] Step 6:

[0186] The server performs sentiment analysis on the messages sent. The input is the message sent by the user, and the output is sentiment data evaluated by the sentiment analysis engine. The server uses this data to adjust the tone of the next message. Specifically, it performs sentiment analysis using the TextBlob library to obtain a positive or negative evaluation.

[0187] Step 7:

[0188] The server personalizes the next message by considering sentiment data and past interaction history. The input is the sentiment data and past interaction history obtained in step 6, and the output is the personalized response message. The specific operation involves a process of referring to past database history, analyzing features, and generating an appropriate response.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Robots used in homes are required to maintain more natural and positive communication with residents. However, conventional technology struggles to appropriately understand residents' emotions and communicate accordingly, failing to meet residents' expectations. Furthermore, smooth communication and suggestions within the home require considering each resident's past conversational tendencies and shared interests, but this has not been adequately achieved.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for automatically identifying common interests among multiple users based on the attributes and behavioral history of registered users, means for generating an initial message to facilitate the initiation of a conversation based on the common interests, and means for sending and receiving the generated message between users. This enables appropriate communication based on the users' emotions and facilitates smooth conversations within the home.

[0194] A "registered user" refers to an individual who is recognized within the system and whose attributes and behavioral history are stored.

[0195] "Attributes" refer to information that indicates a user's hobbies, interests, or other personal characteristics.

[0196] "Activity history" refers to a record of past actions and operations performed by a user on the system.

[0197] "Shared interests" refer to themes such as interests and hobbies that are shared among multiple users.

[0198] An "initial message" is the first message the system generates to initiate a conversation.

[0199] A "generated message" is text created by the system based on the input information.

[0200] "User emotions" refers to the state and tone of emotions expressed by the user.

[0201] "Communication" refers to a means of exchanging information between users or with a system.

[0202] A "suggestion" is information provided by the system to offer advice or recommendations to the user.

[0203] A "robot" is an autonomous or semi-autonomous electronic device used to interact with residents within the home.

[0204] The system for realizing this invention is a robot designed to facilitate communication within the home. This robot operates using an embedded computer (e.g., Raspberry Pi) and high-performance emotion analysis software.

[0205] The server performs data analysis to identify common interests based on user attributes and behavioral history. This extracts interests shared among multiple users within a household and generates an initial message based on them. This message generation utilizes a generative AI model (e.g., OpenAI® GPT-3®) to initiate a natural conversation.

[0206] The generated messages are presented to residents through the robot's communication capabilities. Speech recognition software (e.g., Google® Cloud Speech-to-Text) and natural language processing libraries (e.g., TENSORFLOW®) are incorporated to analyze residents' responses and emotions. This allows the robot to understand the user's feelings and make appropriate suggestions.

[0207] For example, if the server identifies gardening as a common interest, the robot can use its AI model to generate suggestions such as, "The weather's been nice lately, how about tidying up the garden with your family this weekend?", thereby promoting family togetherness.

[0208] An example of a prompt message is: "User A and User B are spending time together at home. Their current interest is gardening. Assuming it's a sunny day, generate a positive message about gardening." Such prompts enable flexible communication tailored to the user's situation.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server retrieves user attributes and behavioral history from a database. Based on this information, it runs an algorithm to identify common interests and pinpoint shared interests among users. The input is user attributes and behavioral history, and the output is information on common interests.

[0212] Step 2:

[0213] The server generates an initial message based on identified common interests. It utilizes a generative AI model to construct prompts and generate the message. The input is the common interest information and prompts, and the output is the generated initial message.

[0214] Step 3:

[0215] The terminal displays the generated message to the user. The user can either send this message as is or edit it to finalize it. The input is the initial generated message, and the output is the final message from the user.

[0216] Step 4:

[0217] The robot receives messages sent and received between users, analyzes them using natural language processing, and evaluates the users' emotions. The input is the sent and received messages, and the output is the users' emotional state.

[0218] Step 5:

[0219] The robot generates the next suggestion or dialogue based on the analyzed emotional state. Here again, a generative AI model is used to create messages that continue the conversation in a natural flow. The input is the user's emotional state, and the output is the next dialogue message.

[0220] Step 6:

[0221] The user receives a new message and chooses to accept the suggestion or end the conversation. The robot continues or ends the conversation according to the user's choice. The input is the next dialogue message and the user's choice, and the output is the continuation or termination of the conversation.

[0222] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0223] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0224] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0228] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0229] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0230] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0231] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0232] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0233] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0234] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0235] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0236] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0237] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0238] This invention provides a system to support natural communication between users in a matching application. This system uses an AI agent to analyze users' hobbies and behavioral history, and automatically generates messages to facilitate smooth communication between users. Specific embodiments are described below.

[0239] The server stores information about users when they register for the app in a database. This information includes hobbies, areas of interest, and past message exchange history. Based on this information, the server uses an AI agent to identify common interests among users.

[0240] The terminal displays information sent from the server to the user. During this process, the user can view messages generated by the AI. If the user's interests align with the AI's, the AI ​​automatically generates an initial message tailored to those interests, making it easy for the user to send.

[0241] As a concrete example, suppose user A is interested in movies, and user B also likes movies. In this case, the server identifies that their interests match and has the AI ​​agent generate an initial message such as, "Hi, I heard you like movies too. What's the best movie you've seen recently?" The user can then send this message directly to the other person, starting a conversation in a natural way.

[0242] As the conversation progresses, the server uses an AI agent to analyze the user's response patterns and the progress of the dialogue, and generates the next message to send. Furthermore, if the user indicates an intention to end the conversation, the AI ​​suggests a message to conclude the conversation in an appropriate manner. This allows users to maintain a natural flow of communication while reducing psychological burden.

[0243] As described above, the system according to the present invention aims to provide smooth communication based on users' hobbies and behaviors, and to improve the user experience of matching applications.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The server stores in a database the hobbies, interests, and past activity history that users entered when registering for the app.

[0247] Step 2:

[0248] The server uses an AI agent to analyze and automatically identify common interests between matched users. If common interests are found, it prepares to utilize that information.

[0249] Step 3:

[0250] The server instructs the AI ​​agent to generate the initial message based on the analysis results. For example, if the common interest is movies, it will generate a message such as, "I heard you like movies too."

[0251] Step 4:

[0252] The server sends the initial message it generates to the user's terminal.

[0253] Step 5:

[0254] The device displays the first message received to the user. The user can review the message, edit it as needed, and then send it to the recipient.

[0255] Step 6:

[0256] When a user receives a reply, the device displays the message to the user.

[0257] Step 7:

[0258] The server uses an AI agent to create appropriate responses to generate the next message based on the progress of the user interaction. In doing so, it takes into account the user's conversation style and tone.

[0259] Step 8:

[0260] The server sends the generated response back to the terminal, which then presents it to the user.

[0261] Step 9:

[0262] If the user indicates their intention to end the conversation, the server has the AI ​​agent generate an end message. The device then displays this message, and the user's approval allows the conversation to be smoothly concluded.

[0263] (Example 1)

[0264] Next, we will describe Example 1. 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."

[0265] Current communication support systems have a problem in that it is difficult for users to initiate and continue conversations in a natural way. Furthermore, even once a conversation has started, there is a lack of means to appropriately maintain its flow and to naturally end it when necessary. In this situation, there is a need to provide smoother communication based on users' interests and behaviors.

[0266] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0267] In this invention, the server includes data management means for storing information of registered users, analysis means for identifying common interests based on the information, and message generation means for generating an initial message based on the common interests. This enables the initiation and maintenance of natural and effective communication tailored to the user's background.

[0268] A "registered user" refers to an individual or organization that provides necessary information for a service or system, and whose information is stored in the database.

[0269] "Data management means" refers to a technical element that has a process or function for collecting, organizing, storing, and managing information.

[0270] "Analysis tools" refer to elements that analyze information based on collected data and have the function of clarifying trends and patterns necessary for a specific purpose, such as identifying common interests.

[0271] "Shared interests" refers to connections based on hobbies, interests, and behaviors shared by two or more users.

[0272] "Message generation means" refers to a technical element that has the function of automatically creating text or messages for a specific purpose, such as facilitating the initiation of communication by users.

[0273] "Display means" refers to a technical device or function that plays a role in visually presenting generated information or messages to the user.

[0274] "Conversation support means" refers to a technical element that analyzes an ongoing conversation and has the function of assisting the user in continuing or ending the conversation naturally.

[0275] "Response patterns" refer to information used to predict the flow of a conversation, based on the user's past reactions and response tendencies.

[0276] This invention is a system for facilitating communication among users in a matching service. This system consists of three main elements: a server, a terminal, and a user, each playing a specific role.

[0277] Server Role

[0278] The server first collects information provided by users and stores it in a database. This includes users' hobbies and activity history. Next, it uses a generative AI model to analyze common interests among users based on the collected information. By sending the analysis results to the AI ​​using prompts, it generates an initial message based on the identified interests. The technologies used include a database management system and a generative AI engine.

[0279] Role of the terminal

[0280] The terminal is a device that visually presents the generated message received from the server to the user. The terminal displays the generated message via the user interface, enabling the user to view the message and, if necessary, immediately send it without editing. The display is typically done through the screen of a smartphone or computer.

[0281] Role of the user

[0282] The user checks the message displayed on the terminal and sends it to another user to start a conversation. Additionally, the user can consider the proposed message generated by the server and use it to continue or end the conversation.

[0283] Specific example

[0284] For example, when the server identifies "movies" as a common interest between users A and B, it causes the AI to generate an initial message such as "Hello, I heard you like movies too. What was the best movie you've seen recently?" An example of a prompt sentence is "If the common hobby of user A and user B is movies, create an initial message." Such a message enables the user to naturally start a conversation and, with the support of the AI, maintain the flow of the conversation without interruption.

[0285] In this way, the purpose of this system is to improve the communication experience of users and enhance the utilization value of the entire service.

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The server stores profile information and past message history entered by users when they register for the application in a database. This input data includes the user's hobbies, interests, and message exchange history. This information is stored as foundational data for future analysis.

[0289] Step 2:

[0290] The server uses accumulated data to send prompt messages to a generating AI model, which analyzes common interests among users. The input data includes users' hobbies and past behavioral history. As a result of the analysis, hobbies shared by two or more users are identified. For example, it might be found that both users like "science fiction movies."

[0291] Step 3:

[0292] The server uses a generative AI model based on identified common interests to automatically generate the initial message. The input data is the common interest information obtained in step 2. The output is a message suitable for starting a conversation. Specifically, a message like "I heard you like science fiction movies, what movie have you seen recently?" is generated.

[0293] Step 4:

[0294] The terminal displays a generated message sent from the server to the user. The input is the generated message from the server, and the output of the terminal is the display of the message. Through the user interface, the user can review the generated message and send it as is without editing.

[0295] Step 5:

[0296] The user initiates a conversation by selecting a message received via their device and sending it to the other party. Specifically, the user reviews the displayed message and, if necessary, sends it with a single click. This action allows the conversation to begin naturally.

[0297] Step 6:

[0298] The server analyzes the ongoing conversation using a generative AI model and suggests the next message to send. The input is the user's response data, and the output suggested by the AI ​​model based on the analysis is either a new message to continue the conversation or a message to end it. For example, if the conversation seems to be winding down, it might suggest, "What are your plans for next weekend?"

[0299] This allows the system to perform a series of processes to facilitate smooth communication among users.

[0300] (Application Example 1)

[0301] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0302] In modern families, the increasing busyness of daily life leads to a weakening of communication among family members. In this situation, it is necessary to consider the individual hobbies and interests of each family member and promote natural conversation. However, manually suggesting specific conversation topics based on each member's hobbies and past conversation history is time-consuming, making a system that efficiently supports communication necessary.

[0303] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0304] In this invention, the server includes means for automatically identifying common interests within the family based on the hobbies and behavior histories of registered users, means for learning the individual hobbies and conversation histories of family members and providing common topics, and means for generating topic proposal texts using a generative AI model. This enables smooth communication within the family and activates daily conversations.

[0305] "Registered users" refers to people who have provided their own information to the system and are permitted to use it.

[0306] "Hobbies and behavior histories" refers to the fields and activities in which users have personal interests and the records related to those activities.

[0307] "Common interests" refers to the interests and fields of interest shared by multiple users.

[0308] "Message generation means" refers to an element having a function of automatically creating texts for starting or advancing conversations between users.

[0309] "Transmission and reception means" refers to the function of sending and receiving the generated messages between users.

[0310] "Generative AI model" refers to an artificial intelligence system that plays a role in text generation and decision-making support based on large-scale data.

[0311] "Topic proposal text" refers to a text containing specific topics proposed for starting or advancing a conversation.

[0312] The system of this invention is made to activate communication within the family. The server first stores the hobbies and behavior histories of registered users in a database. This database contains information about the interests and past conversation histories of each family member. The server analyzes this information and executes an algorithm for identifying common interests and concerns within the family.

[0313] The server uses a generative AI model to generate topic suggestions based on identified common interests. These generated messages are presented to family members via a device, such as a home robot, which supports natural conversation. At this point, the user can initiate a conversation on the suggested topic and enjoy further communication.

[0314] For example, if a family member enjoys watching new movies, the server can identify other family members' interest in movies and suggest topics such as, "Why don't we all talk about the latest movies?"

[0315] An example of a prompt generated by the AI ​​model is an input such as, "Family profile data: {Personality information}." Based on such prompts, messages are generated to start a conversation in a natural way.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The server collects user hobby and activity history data and stores it in a database. This database contains user-registered profile information and past conversation history. The server analyzes this data to organize each user's hobbies and interests.

[0319] Step 2:

[0320] The server identifies common interests shared within a family based on data in the database. The input here is the hobby data of each user stored in the database. The server uses an algorithm to compare and analyze the data, extracting and outputting commonalities.

[0321] Step 3:

[0322] Based on the identified common interests, the server generates appropriate topic suggestions using a generative AI model. In this step, the common interest data is received as input, the AI ​​model performs natural language processing to create suggestion sentences, and these are output.

[0323] Step 4:

[0324] The generated topic suggestion is sent to the terminal, which then presents it to the user. The input here is the generated suggestion, which the terminal displays to the user in an appropriate format.

[0325] Step 5:

[0326] The user initiates a conversation based on the suggested topic. The user can use the provided topic suggestions to communicate with other family members. The output is a natural start to a conversation.

[0327] Step 6:

[0328] During the conversation, the server monitors the user's responses and updates the data as needed to generate the next message. The input here is the progress of the conversation and the user's responses, which the server uses to output information that supports the next dialogue step.

[0329] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0330] This invention is a system for gaining a deeper understanding of and facilitating communication between users in a matching application. This system uses an emotion engine to analyze users' emotions and improves communication by responding based on the analysis results.

[0331] The server identifies common interests based on the user's registration information and behavioral history, and an AI agent generates an initial message. The generated message is presented to the user from their device, and the user can send this message as is or edit it as needed.

[0332] As a concrete example, if both user A and user B are interested in cooking, the server uses AI to generate an initial message such as "Do you have any recommendations for dishes you've made recently?" and presents it to user A via their device. When user A sends this message to user B, a natural conversation begins.

[0333] The emotion engine analyzes the content of messages sent by users and evaluates their emotional state. For example, if user A's response is positive, the AI ​​agent returns a message that maintains that tone. If the user's message is judged to be negative, it generates a response that guides the conversation in a positive direction. In this way, it enables communication that takes the user's emotions into consideration.

[0334] Furthermore, the server considers past conversational styles and the user's emotional tendencies to generate more personalized messages. For example, it will respond to users who previously preferred calm conversations in a gentle tone, and to users who preferred lively conversations in a more energetic tone.

[0335] Thus, by incorporating an emotion engine, the system of the present invention can provide a sophisticated communication experience that is attentive to the user's emotions, significantly improving the value of using a matching app.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The server stores the registration information and past activity history entered by the user into the app in a database.

[0339] Step 2:

[0340] The server uses an AI agent to analyze and automatically identify common interests among matched users.

[0341] Step 3:

[0342] The server prompts the AI ​​agent to generate an initial message based on identified common interests. For example, if the common interest is cooking, it will create an appropriate message.

[0343] Step 4:

[0344] The terminal displays the initial message sent from the server to the user. The user reviews the message, edits it as needed, and then sends it to the recipient.

[0345] Step 5:

[0346] The server analyzes received messages using an emotion engine and evaluates the user's emotional state. This evaluation result is used to generate response messages.

[0347] Step 6:

[0348] Based on the evaluation of the emotion engine, the server uses an AI agent to generate response messages that are tailored to the user's emotions. For example, if the emotion is positive, it will create a response that maintains that emotion; if the emotion is negative, it will create a response that shifts it in a positive direction.

[0349] Step 7:

[0350] The server sends the generated response message to the terminal, which then presents it to the user.

[0351] Step 8:

[0352] When a user indicates they wish to end a conversation, the device displays an appropriate ending message based on the sentiment engine's evaluation. Based on this, the user can smoothly conclude the conversation with the other party.

[0353] (Example 2)

[0354] Next, we will describe Example 2. 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".

[0355] Traditional matching applications have faced challenges such as superficial communication between users and difficulty in forming emotional connections. Furthermore, they lacked sufficient personalized support based on users' hobbies and behaviors, requiring the creation of nuanced conversations tailored to each individual. In particular, there was a need to achieve more natural and human-like communication by being sensitive to changes in emotions.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes means for automatically identifying common interests among users based on registered user attribute data and behavioral history; means for using a generation algorithm to generate initial communication content based on the common interests; and means for evaluating communication content sent by users using an emotion analysis engine and generating a response based on the results. This enables users to communicate in a way that suits their individual emotions and conversational style.

[0358] A "registered user" is an individual who provides their information to a matching service or application and is authorized to use it.

[0359] "Attribute data" refers to information that indicates an individual's characteristics, including the user's profile information.

[0360] "Activity history" refers to information that records the history of actions and interactions that a user has performed in the past.

[0361] "Shared interests" refer to information that indicates similar interests or hobbies shared by multiple users.

[0362] A "generative algorithm" is a computational method used to generate an output based on specific input information.

[0363] "Communication content" is a term that refers to information and messages exchanged between users.

[0364] An "emotion analysis engine" is a software tool that reads emotions from a user's messages and actions.

[0365] A "response" is a message or reaction that is generated based on specific input information and presented to the user.

[0366] A "user terminal" is a device used by a user to access and operate an application.

[0367] Personalization refers to providing information and services that are optimized based on an individual's characteristics and past behavior.

[0368] A "suggestion" is a guideline that presents users with specific actions or options to support their decision-making.

[0369] This invention provides a system that highly personalizes communication between users in a matching application and enables emotion-based dialogue. In this system, a server plays a central role, and a specific embodiment of this system is shown below.

[0370] The server first analyzes attribute data from user registration information and behavioral history. This process utilizes databases and data analysis tools to build user profiles. Machine learning techniques are used to automatically identify common interests among users. At this time, libraries such as Scikit-learn are used to calculate Euclidean distance and cosine similarity, and users with similar interests are clustered.

[0371] As a generation algorithm, the server uses a generation AI model. Examples of AI models used include GPT-4. By inputting a prompt, the server generates an initial message to facilitate the start of a dialogue. For example, the prompt might be, "Generate a message to initiate a conversation between users A and B about their shared hobby, music." This generated message is then presented to the users via their terminal.

[0372] When a user receives this message, the device displays the message through the UI, allowing the user to choose whether to send or edit it. This enables flexible communication that reflects the user's intentions.

[0373] Furthermore, the server uses an emotion analysis engine to analyze the content of messages sent by users and evaluate their emotional state. Specifically, it utilizes TextBlob and Watson Natural Language Understanding to determine whether a message is positive or negative. Based on this evaluation, it adjusts the tone of the next message delivered. In this way, it enables natural and friendly communication that is attentive to the user's emotions.

[0374] Ultimately, the server generates more personalized messages by considering the user's past conversational style and emotional tendencies. This process provides an optimized experience for each individual user. In this way, the present invention provides users of matching applications with communication that fosters deeper emotional connections.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The server collects user registration information and behavioral history. The input for this process is the profile data and past behavioral history provided by the user to the application. The server stores this data in a database and analyzes it using machine learning algorithms to output user attribute data. Specifically, it sends a request to the data server to retrieve user profiles and log data.

[0378] Step 2:

[0379] The server identifies common interests based on the acquired attribute data. The input here is the user attribute data obtained in step 1. The server uses a clustering algorithm to group users with similar hobbies and interests and outputs the common interests. Specifically, it uses Scikit-learn functions to classify common items into clusters.

[0380] Step 3:

[0381] The server generates an initial message based on shared interests. The input is the shared interests identified in step 2. Based on this information, the server inputs a prompt sentence into the generating AI model to generate a message. The output is an initial message such as "What are your recent hobbies?". The specific operation involves inputting text into the AI ​​model and performing natural language generation.

[0382] Step 4:

[0383] The terminal presents the user with a message generated by the server. The input here is the message output in step 3. The terminal displays this message through the user interface, informing the user of its contents. The output is the screen display that the user confirms. Specifically, the display is popped up using the device's notification function.

[0384] Step 5:

[0385] The user reviews the message displayed on the terminal and either sends or edits it. The input is the message displayed in step 4, and the output is either the message sent as is or the edited message. Specifically, the user either clicks the send button or opens the editing screen.

[0386] Step 6:

[0387] The server performs sentiment analysis on the messages sent. The input is the message sent by the user, and the output is sentiment data evaluated by the sentiment analysis engine. The server uses this data to adjust the tone of the next message. Specifically, it performs sentiment analysis using the TextBlob library to obtain a positive or negative evaluation.

[0388] Step 7:

[0389] The server personalizes the next message by considering sentiment data and past interaction history. The input is the sentiment data and past interaction history obtained in step 6, and the output is the personalized response message. The specific operation involves a process of referring to past database history, analyzing features, and generating an appropriate response.

[0390] (Application Example 2)

[0391] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0392] Robots used in homes are required to maintain more natural and positive communication with residents. However, conventional technology struggles to appropriately understand residents' emotions and communicate accordingly, failing to meet residents' expectations. Furthermore, smooth communication and suggestions within the home require considering each resident's past conversational tendencies and shared interests, but this has not been adequately achieved.

[0393] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0394] In this invention, the server includes means for automatically identifying common interests among multiple users based on the attributes and behavioral history of registered users, means for generating an initial message to facilitate the initiation of a conversation based on the common interests, and means for sending and receiving the generated message between users. This enables appropriate communication based on the users' emotions and facilitates smooth conversations within the home.

[0395] A "registered user" refers to an individual who is recognized within the system and whose attributes and behavioral history are stored.

[0396] "Attributes" refer to information that indicates a user's hobbies, interests, or other personal characteristics.

[0397] "Activity history" refers to a record of past actions and operations performed by a user on the system.

[0398] "Shared interests" refer to themes such as interests and hobbies that are shared among multiple users.

[0399] An "initial message" is the first message the system generates to initiate a conversation.

[0400] A "generated message" is text created by the system based on the input information.

[0401] "User emotions" refers to the state and tone of emotions expressed by the user.

[0402] "Communication" refers to a means of exchanging information between users or with a system.

[0403] A "suggestion" is information provided by the system to offer advice or recommendations to the user.

[0404] A "robot" is an autonomous or semi-autonomous electronic device used to interact with residents within the home.

[0405] The system for realizing this invention is a robot designed to facilitate communication within the home. This robot operates using an embedded computer (e.g., Raspberry Pi) and high-performance emotion analysis software.

[0406] The server performs data analysis to identify common interests based on user attributes and behavioral history. This extracts interests shared among multiple users within a household and generates an initial message based on them. This message generation utilizes a generative AI model (e.g., OpenAI GPT-3) to initiate a natural conversation.

[0407] The generated messages are presented to residents through the robot's communication capabilities. Speech recognition software (e.g., Google Cloud Speech-to-Text) and natural language processing libraries (e.g., TensorFlow) are incorporated to analyze residents' responses and emotions. This allows the robot to understand the user's feelings and make appropriate suggestions.

[0408] For example, if the server identifies gardening as a common interest, the robot can use its AI model to generate suggestions such as, "The weather's been nice lately, how about tidying up the garden with your family this weekend?", thereby promoting family togetherness.

[0409] An example of a prompt message is: "User A and User B are spending time together at home. Their current interest is gardening. Assuming it's a sunny day, generate a positive message about gardening." Such prompts enable flexible communication tailored to the user's situation.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The server retrieves user attributes and behavioral history from a database. Based on this information, it runs an algorithm to identify common interests and pinpoint shared interests among users. The input is user attributes and behavioral history, and the output is information on common interests.

[0413] Step 2:

[0414] The server generates an initial message based on identified common interests. It utilizes a generative AI model to construct prompts and generate the message. The input is the common interest information and prompts, and the output is the generated initial message.

[0415] Step 3:

[0416] The terminal displays the generated message to the user. The user can either send this message as is or edit it to finalize it. The input is the initial generated message, and the output is the final message from the user.

[0417] Step 4:

[0418] The robot receives messages sent and received between users, analyzes them using natural language processing, and evaluates the users' emotions. The input is the sent and received messages, and the output is the users' emotional state.

[0419] Step 5:

[0420] The robot generates the next suggestion or dialogue based on the analyzed emotional state. Here again, a generative AI model is used to create messages that continue the conversation in a natural flow. The input is the user's emotional state, and the output is the next dialogue message.

[0421] Step 6:

[0422] The user receives a new message and chooses to accept the suggestion or end the conversation. The robot continues or ends the conversation according to the user's choice. The input is the next dialogue message and the user's choice, and the output is the continuation or termination of the conversation.

[0423] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0424] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0426] [Third Embodiment]

[0427] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0428] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0429] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0430] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0431] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0433] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0434] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0435] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0436] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0437] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0438] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0439] This invention provides a system to support natural communication between users in a matching application. This system uses an AI agent to analyze users' hobbies and behavioral history, and automatically generates messages to facilitate smooth communication between users. Specific embodiments are described below.

[0440] The server stores information about users when they register for the app in a database. This information includes hobbies, areas of interest, and past message exchange history. Based on this information, the server uses an AI agent to identify common interests among users.

[0441] The terminal displays information sent from the server to the user. During this process, the user can view messages generated by the AI. If the user's interests align with the AI's, the AI ​​automatically generates an initial message tailored to those interests, making it easy for the user to send.

[0442] As a concrete example, suppose user A is interested in movies, and user B also likes movies. In this case, the server identifies that their interests match and has the AI ​​agent generate an initial message such as, "Hi, I heard you like movies too. What's the best movie you've seen recently?" The user can then send this message directly to the other person, starting a conversation in a natural way.

[0443] As the conversation progresses, the server uses an AI agent to analyze the user's response patterns and the progress of the dialogue, and generates the next message to send. Furthermore, if the user indicates an intention to end the conversation, the AI ​​suggests a message to conclude the conversation in an appropriate manner. This allows users to maintain a natural flow of communication while reducing psychological burden.

[0444] As described above, the system according to the present invention aims to provide smooth communication based on users' hobbies and behaviors, and to improve the user experience of matching applications.

[0445] The following describes the processing flow.

[0446] Step 1:

[0447] The server stores in a database the hobbies, interests, and past activity history that users entered when registering for the app.

[0448] Step 2:

[0449] The server uses an AI agent to analyze and automatically identify common interests between matched users. If common interests are found, it prepares to utilize that information.

[0450] Step 3:

[0451] The server instructs the AI ​​agent to generate the initial message based on the analysis results. For example, if the common interest is movies, it will generate a message such as, "I heard you like movies too."

[0452] Step 4:

[0453] The server sends the initial message it generates to the user's terminal.

[0454] Step 5:

[0455] The device displays the first message received to the user. The user can review the message, edit it as needed, and then send it to the recipient.

[0456] Step 6:

[0457] When a user receives a reply, the device displays the message to the user.

[0458] Step 7:

[0459] The server uses an AI agent to create appropriate responses to generate the next message based on the progress of the user interaction. In doing so, it takes into account the user's conversation style and tone.

[0460] Step 8:

[0461] The server sends the generated response back to the terminal, which then presents it to the user.

[0462] Step 9:

[0463] If the user indicates their intention to end the conversation, the server has the AI ​​agent generate an end message. The device then displays this message, and the user's approval allows the conversation to be smoothly concluded.

[0464] (Example 1)

[0465] Next, we will describe Example 1. 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."

[0466] Current communication support systems have a problem in that it is difficult for users to initiate and continue conversations in a natural way. Furthermore, even once a conversation has started, there is a lack of means to appropriately maintain its flow and to naturally end it when necessary. In this situation, there is a need to provide smoother communication based on users' interests and behaviors.

[0467] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0468] In this invention, the server includes data management means for storing information of registered users, analysis means for identifying common interests based on the information, and message generation means for generating an initial message based on the common interests. This enables the initiation and maintenance of natural and effective communication tailored to the user's background.

[0469] A "registered user" refers to an individual or organization that provides necessary information for a service or system, and whose information is stored in the database.

[0470] "Data management means" refers to a technical element that has a process or function for collecting, organizing, storing, and managing information.

[0471] "Analysis tools" refer to elements that analyze information based on collected data and have the function of clarifying trends and patterns necessary for a specific purpose, such as identifying common interests.

[0472] "Shared interests" refers to connections based on hobbies, interests, and behaviors shared by two or more users.

[0473] "Message generation means" refers to a technical element that has the function of automatically creating text or messages for a specific purpose, such as facilitating the initiation of communication by users.

[0474] "Display means" refers to a technical device or function that plays a role in visually presenting generated information or messages to the user.

[0475] "Conversation support means" refers to a technical element that analyzes an ongoing conversation and has the function of assisting the user in continuing or ending the conversation naturally.

[0476] "Response patterns" refer to information used to predict the flow of a conversation, based on the user's past reactions and response tendencies.

[0477] This invention is a system for facilitating communication among users in a matching service. This system consists of three main elements: a server, a terminal, and a user, each playing a specific role.

[0478] Server Role

[0479] The server first collects information provided by users and stores it in a database. This includes users' hobbies and activity history. Next, it uses a generative AI model to analyze common interests among users based on the collected information. By sending the analysis results to the AI ​​using prompts, it generates an initial message based on the identified interests. The technologies used include a database management system and a generative AI engine.

[0480] Terminal role

[0481] A terminal is a device that visually presents generated messages received from a server to the user. The terminal displays the generated messages through a user interface, allowing the user to review the messages and, if necessary, send them immediately without editing. This display is typically done via a smartphone or computer screen.

[0482] User roles

[0483] Users can view messages displayed on their devices and send them to other users to initiate conversations. Furthermore, they can consider suggested messages generated by the server to help them continue or end the conversation.

[0484] Specific example

[0485] For example, if the server identifies "movies" as a common interest of users A and B, it will have the AI ​​generate an initial message such as, "Hi, I heard you like movies too. What's the best movie you've seen recently?" An example of a prompt would be, "If users A and B share a common interest in movies, please create an initial message." Such messages allow users to start a conversation naturally, and with the support of the AI, the conversation can be kept flowing without interruption.

[0486] In this way, the system aims to improve the user's communication experience and enhance the overall value of the service.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The server stores profile information and past message history entered by users when they register for the application in a database. This input data includes the user's hobbies, interests, and message exchange history. This information is stored as foundational data for future analysis.

[0490] Step 2:

[0491] The server uses accumulated data to send prompt messages to a generating AI model, which analyzes common interests among users. The input data includes users' hobbies and past behavioral history. As a result of the analysis, hobbies shared by two or more users are identified. For example, it might be found that both users like "science fiction movies."

[0492] Step 3:

[0493] The server uses a generative AI model based on identified common interests to automatically generate the initial message. The input data is the common interest information obtained in step 2. The output is a message suitable for starting a conversation. Specifically, a message like "I heard you like science fiction movies, what movie have you seen recently?" is generated.

[0494] Step 4:

[0495] The terminal displays a generated message sent from the server to the user. The input is the generated message from the server, and the output of the terminal is the display of the message. Through the user interface, the user can review the generated message and send it as is without editing.

[0496] Step 5:

[0497] The user initiates a conversation by selecting a message received via their device and sending it to the other party. Specifically, the user reviews the displayed message and, if necessary, sends it with a single click. This action allows the conversation to begin naturally.

[0498] Step 6:

[0499] The server analyzes the ongoing conversation using a generative AI model and suggests the next message to send. The input is the user's response data, and the output suggested by the AI ​​model based on the analysis is either a new message to continue the conversation or a message to end it. For example, if the conversation seems to be winding down, it might suggest, "What are your plans for next weekend?"

[0500] This allows the system to perform a series of processes to facilitate smooth communication among users.

[0501] (Application Example 1)

[0502] Next, we will explain Application Example 1. In the following explanation, 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."

[0503] In modern families, the increasing busyness of daily life leads to a weakening of communication among family members. In this situation, it is necessary to consider the individual hobbies and interests of each family member and promote natural conversation. However, manually suggesting specific conversation topics based on each member's hobbies and past conversation history is time-consuming, making a system that efficiently supports communication necessary.

[0504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0505] In this invention, the server includes means for automatically identifying common interests within a family based on the hobbies and activity history of registered users, means for learning the individual hobbies and conversation history of family members and providing common topics, and means for generating topic suggestion sentences using a generative AI model. This makes it possible to facilitate communication within the family and revitalize everyday conversations.

[0506] "Registered users" refers to individuals who have provided their information to the system and have been granted permission to use it.

[0507] "Hobbies and activity history" refers to areas and activities that the user is personally interested in, as well as records related to those activities.

[0508] "Shared interests" refer to the concerns or areas of interest that multiple users share.

[0509] "Message generation means" refers to an element that has the function of automatically creating text for initiating or advancing conversations between users.

[0510] "Transmission and reception means" refers to the function of sending and receiving generated messages between users.

[0511] A "generative AI model" refers to an artificial intelligence system that uses large amounts of data to perform tasks such as text generation and decision support.

[0512] A "topic proposal document" refers to a document that contains a specific topic suggested to initiate or advance a dialogue.

[0513] The system of this invention is designed to revitalize communication within families. The server first stores the hobbies and activity history of registered users in a database. This database contains information about each family member's interests and past conversation history. The server analyzes this information and runs an algorithm to identify common interests and concerns within the family.

[0514] The server uses a generative AI model to generate topic suggestions based on identified common interests. These generated messages are presented to family members via a device, such as a home robot, which supports natural conversation. At this point, the user can initiate a conversation on the suggested topic and enjoy further communication.

[0515] For example, if a family member enjoys watching new movies, the server can identify other family members' interest in movies and suggest topics such as, "Why don't we all talk about the latest movies?"

[0516] An example of a prompt generated by the AI ​​model is an input such as, "Family profile data: {Personality information}." Based on such prompts, messages are generated to start a conversation in a natural way.

[0517] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0518] Step 1:

[0519] The server collects user hobby and activity history data and stores it in a database. This database contains user-registered profile information and past conversation history. The server analyzes this data to organize each user's hobbies and interests.

[0520] Step 2:

[0521] The server identifies common interests shared within a family based on data in the database. The input here is the hobby data of each user stored in the database. The server uses an algorithm to compare and analyze the data, extracting and outputting commonalities.

[0522] Step 3:

[0523] Based on the identified common interests, the server generates appropriate topic suggestions using a generative AI model. In this step, the common interest data is received as input, the AI ​​model performs natural language processing to create suggestion sentences, and these are output.

[0524] Step 4:

[0525] The generated topic suggestion is sent to the terminal, which then presents it to the user. The input here is the generated suggestion, which the terminal displays to the user in an appropriate format.

[0526] Step 5:

[0527] The user initiates a conversation based on the suggested topic. The user can use the provided topic suggestions to communicate with other family members. The output is a natural start to a conversation.

[0528] Step 6:

[0529] During the conversation, the server monitors the user's responses and updates the data as needed to generate the next message. The input here is the progress of the conversation and the user's responses, which the server uses to output information that supports the next dialogue step.

[0530] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0531] This invention is a system for gaining a deeper understanding of and facilitating communication between users in a matching application. This system uses an emotion engine to analyze users' emotions and improves communication by responding based on the analysis results.

[0532] The server identifies common interests based on the user's registration information and behavioral history, and an AI agent generates an initial message. The generated message is presented to the user from their device, and the user can send this message as is or edit it as needed.

[0533] As a concrete example, if both user A and user B are interested in cooking, the server uses AI to generate an initial message such as "Do you have any recommendations for dishes you've made recently?" and presents it to user A via their device. When user A sends this message to user B, a natural conversation begins.

[0534] The emotion engine analyzes the content of messages sent by users and evaluates their emotional state. For example, if user A's response is positive, the AI ​​agent returns a message that maintains that tone. If the user's message is judged to be negative, it generates a response that guides the conversation in a positive direction. In this way, it enables communication that takes the user's emotions into consideration.

[0535] Furthermore, the server considers past conversational styles and the user's emotional tendencies to generate more personalized messages. For example, it will respond to users who previously preferred calm conversations in a gentle tone, and to users who preferred lively conversations in a more energetic tone.

[0536] Thus, by incorporating an emotion engine, the system of the present invention can provide a sophisticated communication experience that is attentive to the user's emotions, significantly improving the value of using a matching app.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The server stores the registration information and past activity history entered by the user into the app in a database.

[0540] Step 2:

[0541] The server uses an AI agent to analyze and automatically identify common interests among matched users.

[0542] Step 3:

[0543] The server prompts the AI ​​agent to generate an initial message based on identified common interests. For example, if the common interest is cooking, it will create an appropriate message.

[0544] Step 4:

[0545] The terminal displays the initial message sent from the server to the user. The user reviews the message, edits it as needed, and then sends it to the recipient.

[0546] Step 5:

[0547] The server analyzes received messages using an emotion engine and evaluates the user's emotional state. This evaluation result is used to generate response messages.

[0548] Step 6:

[0549] Based on the evaluation of the emotion engine, the server uses an AI agent to generate response messages that are tailored to the user's emotions. For example, if the emotion is positive, it will create a response that maintains that emotion; if the emotion is negative, it will create a response that shifts it in a positive direction.

[0550] Step 7:

[0551] The server sends the generated response message to the terminal, which then presents it to the user.

[0552] Step 8:

[0553] When a user indicates they wish to end a conversation, the device displays an appropriate ending message based on the sentiment engine's evaluation. Based on this, the user can smoothly conclude the conversation with the other party.

[0554] (Example 2)

[0555] Next, we will describe Example 2. 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."

[0556] Traditional matching applications have faced challenges such as superficial communication between users and difficulty in forming emotional connections. Furthermore, they lacked sufficient personalized support based on users' hobbies and behaviors, requiring the creation of nuanced conversations tailored to each individual. In particular, there was a need to achieve more natural and human-like communication by being sensitive to changes in emotions.

[0557] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0558] In this invention, the server includes means for automatically identifying common interests among users based on registered user attribute data and behavioral history; means for using a generation algorithm to generate initial communication content based on the common interests; and means for evaluating communication content sent by users using an emotion analysis engine and generating a response based on the results. This enables users to communicate in a way that suits their individual emotions and conversational style.

[0559] A "registered user" is an individual who provides their information to a matching service or application and is authorized to use it.

[0560] "Attribute data" refers to information that indicates an individual's characteristics, including the user's profile information.

[0561] "Activity history" refers to information that records the history of actions and interactions that a user has performed in the past.

[0562] "Shared interests" refer to information that indicates similar interests or hobbies shared by multiple users.

[0563] A "generative algorithm" is a computational method used to generate an output based on specific input information.

[0564] "Communication content" is a term that refers to information and messages exchanged between users.

[0565] An "emotion analysis engine" is a software tool that reads emotions from a user's messages and actions.

[0566] A "response" is a message or reaction that is generated based on specific input information and presented to the user.

[0567] A "user terminal" is a device used by a user to access and operate an application.

[0568] Personalization refers to providing information and services that are optimized based on an individual's characteristics and past behavior.

[0569] A "suggestion" is a guideline that presents users with specific actions or options to support their decision-making.

[0570] This invention provides a system that highly personalizes communication between users in a matching application and enables emotion-based dialogue. In this system, a server plays a central role, and a specific embodiment of this system is shown below.

[0571] The server first analyzes attribute data from user registration information and behavioral history. This process utilizes databases and data analysis tools to build user profiles. Machine learning techniques are used to automatically identify common interests among users. At this time, libraries such as Scikit-learn are used to calculate Euclidean distance and cosine similarity, and users with similar interests are clustered.

[0572] As a generation algorithm, the server uses a generation AI model. Examples of AI models used include GPT-4. By inputting a prompt, the server generates an initial message to facilitate the start of a dialogue. For example, the prompt might be, "Generate a message to initiate a conversation between users A and B about their shared hobby, music." This generated message is then presented to the users via their terminal.

[0573] When a user receives this message, the device displays the message through the UI, allowing the user to choose whether to send or edit it. This enables flexible communication that reflects the user's intentions.

[0574] Furthermore, the server uses an emotion analysis engine to analyze the content of messages sent by users and evaluate their emotional state. Specifically, it utilizes TextBlob and Watson Natural Language Understanding to determine whether a message is positive or negative. Based on this evaluation, it adjusts the tone of the next message delivered. In this way, it enables natural and friendly communication that is attentive to the user's emotions.

[0575] Ultimately, the server generates more personalized messages by considering the user's past conversational style and emotional tendencies. This process provides an optimized experience for each individual user. In this way, the present invention provides users of matching applications with communication that fosters deeper emotional connections.

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] The server collects user registration information and behavioral history. The input for this process is the profile data and past behavioral history provided by the user to the application. The server stores this data in a database and analyzes it using machine learning algorithms to output user attribute data. Specifically, it sends a request to the data server to retrieve user profiles and log data.

[0579] Step 2:

[0580] The server identifies common interests based on the acquired attribute data. The input here is the user attribute data obtained in step 1. The server uses a clustering algorithm to group users with similar hobbies and interests and outputs the common interests. Specifically, it uses Scikit-learn functions to classify common items into clusters.

[0581] Step 3:

[0582] The server generates an initial message based on shared interests. The input is the shared interests identified in step 2. Based on this information, the server inputs a prompt sentence into the generating AI model to generate a message. The output is an initial message such as "What are your recent hobbies?". The specific operation involves inputting text into the AI ​​model and performing natural language generation.

[0583] Step 4:

[0584] The terminal presents the user with a message generated by the server. The input here is the message output in step 3. The terminal displays this message through the user interface, informing the user of its contents. The output is the screen display that the user confirms. Specifically, the display is popped up using the device's notification function.

[0585] Step 5:

[0586] The user reviews the message displayed on the terminal and either sends or edits it. The input is the message displayed in step 4, and the output is either the message sent as is or the edited message. Specifically, the user either clicks the send button or opens the editing screen.

[0587] Step 6:

[0588] The server performs sentiment analysis on the messages sent. The input is the message sent by the user, and the output is sentiment data evaluated by the sentiment analysis engine. The server uses this data to adjust the tone of the next message. Specifically, it performs sentiment analysis using the TextBlob library to obtain a positive or negative evaluation.

[0589] Step 7:

[0590] The server personalizes the next message by considering sentiment data and past interaction history. The input is the sentiment data and past interaction history obtained in step 6, and the output is the personalized response message. The specific operation involves a process of referring to past database history, analyzing features, and generating an appropriate response.

[0591] (Application Example 2)

[0592] Next, we will explain application example 2. In the following explanation, 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."

[0593] Robots used in homes are required to maintain more natural and positive communication with residents. However, conventional technology struggles to appropriately understand residents' emotions and communicate accordingly, failing to meet residents' expectations. Furthermore, smooth communication and suggestions within the home require considering each resident's past conversational tendencies and shared interests, but this has not been adequately achieved.

[0594] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0595] In this invention, the server includes means for automatically identifying common interests among multiple users based on the attributes and behavioral history of registered users, means for generating an initial message to facilitate the initiation of a conversation based on the common interests, and means for sending and receiving the generated message between users. This enables appropriate communication based on the users' emotions and facilitates smooth conversations within the home.

[0596] A "registered user" refers to an individual who is recognized within the system and whose attributes and behavioral history are stored.

[0597] "Attributes" refer to information that indicates a user's hobbies, interests, or other personal characteristics.

[0598] "Activity history" refers to a record of past actions and operations performed by a user on the system.

[0599] "Shared interests" refer to themes such as interests and hobbies that are shared among multiple users.

[0600] An "initial message" is the first message the system generates to initiate a conversation.

[0601] A "generated message" is text created by the system based on the input information.

[0602] "User emotions" refers to the state and tone of emotions expressed by the user.

[0603] "Communication" refers to a means of exchanging information between users or with a system.

[0604] A "suggestion" is information provided by the system to offer advice or recommendations to the user.

[0605] A "robot" is an autonomous or semi-autonomous electronic device used to interact with residents within the home.

[0606] The system for realizing this invention is a robot designed to facilitate communication within the home. This robot operates using an embedded computer (e.g., Raspberry Pi) and high-performance emotion analysis software.

[0607] The server performs data analysis to identify common interests based on user attributes and behavioral history. This extracts interests shared among multiple users within a household and generates an initial message based on them. This message generation utilizes a generative AI model (e.g., OpenAI GPT-3) to initiate a natural conversation.

[0608] The generated messages are presented to residents through the robot's communication capabilities. Speech recognition software (e.g., Google Cloud Speech-to-Text) and natural language processing libraries (e.g., TensorFlow) are incorporated to analyze residents' responses and emotions. This allows the robot to understand the user's feelings and make appropriate suggestions.

[0609] For example, if the server identifies gardening as a common interest, the robot can use its AI model to generate suggestions such as, "The weather's been nice lately, how about tidying up the garden with your family this weekend?", thereby promoting family togetherness.

[0610] An example of a prompt message is: "User A and User B are spending time together at home. Their current interest is gardening. Assuming it's a sunny day, generate a positive message about gardening." Such prompts enable flexible communication tailored to the user's situation.

[0611] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0612] Step 1:

[0613] The server retrieves user attributes and behavioral history from a database. Based on this information, it runs an algorithm to identify common interests and pinpoint shared interests among users. The input is user attributes and behavioral history, and the output is information on common interests.

[0614] Step 2:

[0615] The server generates an initial message based on identified common interests. It utilizes a generative AI model to construct prompts and generate the message. The input is the common interest information and prompts, and the output is the generated initial message.

[0616] Step 3:

[0617] The terminal displays the generated message to the user. The user can either send this message as is or edit it to finalize it. The input is the initial generated message, and the output is the final message from the user.

[0618] Step 4:

[0619] The robot receives messages sent and received between users, analyzes them using natural language processing, and evaluates the users' emotions. The input is the sent and received messages, and the output is the users' emotional state.

[0620] Step 5:

[0621] The robot generates the next suggestion or dialogue based on the analyzed emotional state. Here again, a generative AI model is used to create messages that continue the conversation in a natural flow. The input is the user's emotional state, and the output is the next dialogue message.

[0622] Step 6:

[0623] The user receives a new message and chooses to accept the suggestion or end the conversation. The robot continues or ends the conversation according to the user's choice. The input is the next dialogue message and the user's choice, and the output is the continuation or termination of the conversation.

[0624] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0625] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0626] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0627] [Fourth Embodiment]

[0628] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0629] As shown in Figure 7, the 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.

[0630] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0631] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0632] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0633] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0634] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0635] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0636] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0637] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0638] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0639] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0640] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0641] This invention provides a system to support natural communication between users in a matching application. This system uses an AI agent to analyze users' hobbies and behavioral history, and automatically generates messages to facilitate smooth communication between users. Specific embodiments are described below.

[0642] The server stores information about users when they register for the app in a database. This information includes hobbies, areas of interest, and past message exchange history. Based on this information, the server uses an AI agent to identify common interests among users.

[0643] The terminal displays information sent from the server to the user. During this process, the user can view messages generated by the AI. If the user's interests align with the AI's, the AI ​​automatically generates an initial message tailored to those interests, making it easy for the user to send.

[0644] As a concrete example, suppose user A is interested in movies, and user B also likes movies. In this case, the server identifies that their interests match and has the AI ​​agent generate an initial message such as, "Hi, I heard you like movies too. What's the best movie you've seen recently?" The user can then send this message directly to the other person, starting a conversation in a natural way.

[0645] As the conversation progresses, the server uses an AI agent to analyze the user's response patterns and the progress of the dialogue, and generates the next message to send. Furthermore, if the user indicates an intention to end the conversation, the AI ​​suggests a message to conclude the conversation in an appropriate manner. This allows users to maintain a natural flow of communication while reducing psychological burden.

[0646] As described above, the system according to the present invention aims to provide smooth communication based on users' hobbies and behaviors, and to improve the user experience of matching applications.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] The server stores in a database the hobbies, interests, and past activity history that users entered when registering for the app.

[0650] Step 2:

[0651] The server uses an AI agent to analyze and automatically identify common interests between matched users. If common interests are found, it prepares to utilize that information.

[0652] Step 3:

[0653] The server instructs the AI ​​agent to generate the initial message based on the analysis results. For example, if the common interest is movies, it will generate a message such as, "I heard you like movies too."

[0654] Step 4:

[0655] The server sends the initial message it generates to the user's terminal.

[0656] Step 5:

[0657] The device displays the first message received to the user. The user can review the message, edit it as needed, and then send it to the recipient.

[0658] Step 6:

[0659] When a user receives a reply, the device displays the message to the user.

[0660] Step 7:

[0661] The server uses an AI agent to create appropriate responses to generate the next message based on the progress of the user interaction. In doing so, it takes into account the user's conversation style and tone.

[0662] Step 8:

[0663] The server sends the generated response back to the terminal, which then presents it to the user.

[0664] Step 9:

[0665] If the user indicates their intention to end the conversation, the server has the AI ​​agent generate an end message. The device then displays this message, and the user's approval allows the conversation to be smoothly concluded.

[0666] (Example 1)

[0667] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] Current communication support systems have a problem in that it is difficult for users to initiate and continue conversations in a natural way. Furthermore, even once a conversation has started, there is a lack of means to appropriately maintain its flow and to naturally end it when necessary. In this situation, there is a need to provide smoother communication based on users' interests and behaviors.

[0669] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0670] In this invention, the server includes data management means for storing information of registered users, analysis means for identifying common interests based on the information, and message generation means for generating an initial message based on the common interests. This enables the initiation and maintenance of natural and effective communication tailored to the user's background.

[0671] A "registered user" refers to an individual or organization that provides necessary information for a service or system, and whose information is stored in the database.

[0672] "Data management means" refers to a technical element that has a process or function for collecting, organizing, storing, and managing information.

[0673] "Analysis tools" refer to elements that analyze information based on collected data and have the function of clarifying trends and patterns necessary for a specific purpose, such as identifying common interests.

[0674] "Shared interests" refers to connections based on hobbies, interests, and behaviors shared by two or more users.

[0675] "Message generation means" refers to a technical element that has the function of automatically creating text or messages for a specific purpose, such as facilitating the initiation of communication by users.

[0676] "Display means" refers to a technical device or function that plays a role in visually presenting generated information or messages to the user.

[0677] "Conversation support means" refers to a technical element that analyzes an ongoing conversation and has the function of assisting the user in continuing or ending the conversation naturally.

[0678] "Response patterns" refer to information used to predict the flow of a conversation, based on the user's past reactions and response tendencies.

[0679] This invention is a system for facilitating communication among users in a matching service. This system consists of three main elements: a server, a terminal, and a user, each playing a specific role.

[0680] Server Role

[0681] The server first collects information provided by users and stores it in a database. This includes users' hobbies and activity history. Next, it uses a generative AI model to analyze common interests among users based on the collected information. By sending the analysis results to the AI ​​using prompts, it generates an initial message based on the identified interests. The technologies used include a database management system and a generative AI engine.

[0682] Terminal role

[0683] A terminal is a device that visually presents generated messages received from a server to the user. The terminal displays the generated messages through a user interface, allowing the user to review the messages and, if necessary, send them immediately without editing. This display is typically done via a smartphone or computer screen.

[0684] User roles

[0685] Users can view messages displayed on their devices and send them to other users to initiate conversations. Furthermore, they can consider suggested messages generated by the server to help them continue or end the conversation.

[0686] Specific example

[0687] For example, if the server identifies "movies" as a common interest of users A and B, it will have the AI ​​generate an initial message such as, "Hi, I heard you like movies too. What's the best movie you've seen recently?" An example of a prompt would be, "If users A and B share a common interest in movies, please create an initial message." Such messages allow users to start a conversation naturally, and with the support of the AI, the conversation can be kept flowing without interruption.

[0688] In this way, the system aims to improve the user's communication experience and enhance the overall value of the service.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The server stores profile information and past message history entered by users when they register for the application in a database. This input data includes the user's hobbies, interests, and message exchange history. This information is stored as foundational data for future analysis.

[0692] Step 2:

[0693] The server uses accumulated data to send prompt messages to a generating AI model, which analyzes common interests among users. The input data includes users' hobbies and past behavioral history. As a result of the analysis, hobbies shared by two or more users are identified. For example, it might be found that both users like "science fiction movies."

[0694] Step 3:

[0695] The server uses a generative AI model based on identified common interests to automatically generate the initial message. The input data is the common interest information obtained in step 2. The output is a message suitable for starting a conversation. Specifically, a message like "I heard you like science fiction movies, what movie have you seen recently?" is generated.

[0696] Step 4:

[0697] The terminal displays a generated message sent from the server to the user. The input is the generated message from the server, and the output of the terminal is the display of the message. Through the user interface, the user can review the generated message and send it as is without editing.

[0698] Step 5:

[0699] The user initiates a conversation by selecting a message received via their device and sending it to the other party. Specifically, the user reviews the displayed message and, if necessary, sends it with a single click. This action allows the conversation to begin naturally.

[0700] Step 6:

[0701] The server analyzes the ongoing conversation using a generative AI model and suggests the next message to send. The input is the user's response data, and the output suggested by the AI ​​model based on the analysis is either a new message to continue the conversation or a message to end it. For example, if the conversation seems to be winding down, it might suggest, "What are your plans for next weekend?"

[0702] This allows the system to perform a series of processes to facilitate smooth communication among users.

[0703] (Application Example 1)

[0704] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0705] In modern families, the increasing busyness of daily life leads to a weakening of communication among family members. In this situation, it is necessary to consider the individual hobbies and interests of each family member and promote natural conversation. However, manually suggesting specific conversation topics based on each member's hobbies and past conversation history is time-consuming, making a system that efficiently supports communication necessary.

[0706] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0707] In this invention, the server includes means for automatically identifying common interests within a family based on the hobbies and activity history of registered users, means for learning the individual hobbies and conversation history of family members and providing common topics, and means for generating topic suggestion sentences using a generative AI model. This makes it possible to facilitate communication within the family and revitalize everyday conversations.

[0708] "Registered users" refers to individuals who have provided their information to the system and have been granted permission to use it.

[0709] "Hobbies and activity history" refers to areas and activities that the user is personally interested in, as well as records related to those activities.

[0710] "Shared interests" refer to the concerns or areas of interest that multiple users share.

[0711] "Message generation means" refers to an element that has the function of automatically creating text for initiating or advancing conversations between users.

[0712] "Transmission and reception means" refers to the function of sending and receiving generated messages between users.

[0713] A "generative AI model" refers to an artificial intelligence system that uses large amounts of data to perform tasks such as text generation and decision support.

[0714] A "topic proposal document" refers to a document that contains a specific topic suggested to initiate or advance a dialogue.

[0715] The system of this invention is designed to revitalize communication within families. The server first stores the hobbies and activity history of registered users in a database. This database contains information about each family member's interests and past conversation history. The server analyzes this information and runs an algorithm to identify common interests and concerns within the family.

[0716] The server uses a generative AI model to generate topic suggestions based on identified common interests. These generated messages are presented to family members via a device, such as a home robot, which supports natural conversation. At this point, the user can initiate a conversation on the suggested topic and enjoy further communication.

[0717] For example, if a family member enjoys watching new movies, the server can identify other family members' interest in movies and suggest topics such as, "Why don't we all talk about the latest movies?"

[0718] An example of a prompt generated by the AI ​​model is an input such as, "Family profile data: {Personality information}." Based on such prompts, messages are generated to start a conversation in a natural way.

[0719] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0720] Step 1:

[0721] The server collects user hobby and activity history data and stores it in a database. This database contains user-registered profile information and past conversation history. The server analyzes this data to organize each user's hobbies and interests.

[0722] Step 2:

[0723] The server identifies common interests shared within a family based on data in the database. The input here is the hobby data of each user stored in the database. The server uses an algorithm to compare and analyze the data, extracting and outputting commonalities.

[0724] Step 3:

[0725] Based on the identified common interests, the server generates appropriate topic suggestions using a generative AI model. In this step, the common interest data is received as input, the AI ​​model performs natural language processing to create suggestion sentences, and these are output.

[0726] Step 4:

[0727] The generated topic suggestion is sent to the terminal, which then presents it to the user. The input here is the generated suggestion, which the terminal displays to the user in an appropriate format.

[0728] Step 5:

[0729] The user initiates a conversation based on the suggested topic. The user can use the provided topic suggestions to communicate with other family members. The output is a natural start to a conversation.

[0730] Step 6:

[0731] During the conversation, the server monitors the user's responses and updates the data as needed to generate the next message. The input here is the progress of the conversation and the user's responses, which the server uses to output information that supports the next dialogue step.

[0732] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0733] This invention is a system for gaining a deeper understanding of and facilitating communication between users in a matching application. This system uses an emotion engine to analyze users' emotions and improves communication by responding based on the analysis results.

[0734] The server identifies common interests based on the user's registration information and behavioral history, and an AI agent generates an initial message. The generated message is presented to the user from their device, and the user can send this message as is or edit it as needed.

[0735] As a concrete example, if both user A and user B are interested in cooking, the server uses AI to generate an initial message such as "Do you have any recommendations for dishes you've made recently?" and presents it to user A via their device. When user A sends this message to user B, a natural conversation begins.

[0736] The emotion engine analyzes the content of messages sent by users and evaluates their emotional state. For example, if user A's response is positive, the AI ​​agent returns a message that maintains that tone. If the user's message is judged to be negative, it generates a response that guides the conversation in a positive direction. In this way, it enables communication that takes the user's emotions into consideration.

[0737] Furthermore, the server considers past conversational styles and the user's emotional tendencies to generate more personalized messages. For example, it will respond to users who previously preferred calm conversations in a gentle tone, and to users who preferred lively conversations in a more energetic tone.

[0738] Thus, by incorporating an emotion engine, the system of the present invention can provide a sophisticated communication experience that is attentive to the user's emotions, significantly improving the value of using a matching app.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] The server stores the registration information and past activity history entered by the user into the app in a database.

[0742] Step 2:

[0743] The server uses an AI agent to analyze and automatically identify common interests among matched users.

[0744] Step 3:

[0745] The server prompts the AI ​​agent to generate an initial message based on identified common interests. For example, if the common interest is cooking, it will create an appropriate message.

[0746] Step 4:

[0747] The terminal displays the initial message sent from the server to the user. The user reviews the message, edits it as needed, and then sends it to the recipient.

[0748] Step 5:

[0749] The server analyzes received messages using an emotion engine and evaluates the user's emotional state. This evaluation result is used to generate response messages.

[0750] Step 6:

[0751] Based on the evaluation of the emotion engine, the server uses an AI agent to generate response messages that are tailored to the user's emotions. For example, if the emotion is positive, it will create a response that maintains that emotion; if the emotion is negative, it will create a response that shifts it in a positive direction.

[0752] Step 7:

[0753] The server sends the generated response message to the terminal, which then presents it to the user.

[0754] Step 8:

[0755] When a user indicates they wish to end a conversation, the device displays an appropriate ending message based on the sentiment engine's evaluation. Based on this, the user can smoothly conclude the conversation with the other party.

[0756] (Example 2)

[0757] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] Traditional matching applications have faced challenges such as superficial communication between users and difficulty in forming emotional connections. Furthermore, they lacked sufficient personalized support based on users' hobbies and behaviors, requiring the creation of nuanced conversations tailored to each individual. In particular, there was a need to achieve more natural and human-like communication by being sensitive to changes in emotions.

[0759] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0760] In this invention, the server includes means for automatically identifying common interests among users based on registered user attribute data and behavioral history; means for using a generation algorithm to generate initial communication content based on the common interests; and means for evaluating communication content sent by users using an emotion analysis engine and generating a response based on the results. This enables users to communicate in a way that suits their individual emotions and conversational style.

[0761] A "registered user" is an individual who provides their information to a matching service or application and is authorized to use it.

[0762] "Attribute data" refers to information that indicates an individual's characteristics, including the user's profile information.

[0763] "Activity history" refers to information that records the history of actions and interactions that a user has performed in the past.

[0764] "Shared interests" refer to information that indicates similar interests or hobbies shared by multiple users.

[0765] A "generative algorithm" is a computational method used to generate an output based on specific input information.

[0766] "Communication content" is a term that refers to information and messages exchanged between users.

[0767] An "emotion analysis engine" is a software tool that reads emotions from a user's messages and actions.

[0768] A "response" is a message or reaction that is generated based on specific input information and presented to the user.

[0769] A "user terminal" is a device used by a user to access and operate an application.

[0770] Personalization refers to providing information and services that are optimized based on an individual's characteristics and past behavior.

[0771] A "suggestion" is a guideline that presents users with specific actions or options to support their decision-making.

[0772] This invention provides a system that highly personalizes communication between users in a matching application and enables emotion-based dialogue. In this system, a server plays a central role, and a specific embodiment of this system is shown below.

[0773] The server first analyzes attribute data from user registration information and behavioral history. This process utilizes databases and data analysis tools to build user profiles. Machine learning techniques are used to automatically identify common interests among users. At this time, libraries such as Scikit-learn are used to calculate Euclidean distance and cosine similarity, and users with similar interests are clustered.

[0774] As a generation algorithm, the server uses a generation AI model. Examples of AI models used include GPT-4. By inputting a prompt, the server generates an initial message to facilitate the start of a dialogue. For example, the prompt might be, "Generate a message to initiate a conversation between users A and B about their shared hobby, music." This generated message is then presented to the users via their terminal.

[0775] When a user receives this message, the device displays the message through the UI, allowing the user to choose whether to send or edit it. This enables flexible communication that reflects the user's intentions.

[0776] Furthermore, the server uses an emotion analysis engine to analyze the content of messages sent by users and evaluate their emotional state. Specifically, it utilizes TextBlob and Watson Natural Language Understanding to determine whether a message is positive or negative. Based on this evaluation, it adjusts the tone of the next message delivered. In this way, it enables natural and friendly communication that is attentive to the user's emotions.

[0777] Ultimately, the server generates more personalized messages by considering the user's past conversational style and emotional tendencies. This process provides an optimized experience for each individual user. In this way, the present invention provides users of matching applications with communication that fosters deeper emotional connections.

[0778] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0779] Step 1:

[0780] The server collects user registration information and behavioral history. The input for this process is the profile data and past behavioral history provided by the user to the application. The server stores this data in a database and analyzes it using machine learning algorithms to output user attribute data. Specifically, it sends a request to the data server to retrieve user profiles and log data.

[0781] Step 2:

[0782] The server identifies common interests based on the acquired attribute data. The input here is the user attribute data obtained in step 1. The server uses a clustering algorithm to group users with similar hobbies and interests and outputs the common interests. Specifically, it uses Scikit-learn functions to classify common items into clusters.

[0783] Step 3:

[0784] The server generates an initial message based on shared interests. The input is the shared interests identified in step 2. Based on this information, the server inputs a prompt sentence into the generating AI model to generate a message. The output is an initial message such as "What are your recent hobbies?". The specific operation involves inputting text into the AI ​​model and performing natural language generation.

[0785] Step 4:

[0786] The terminal presents the user with a message generated by the server. The input here is the message output in step 3. The terminal displays this message through the user interface, informing the user of its contents. The output is the screen display that the user confirms. Specifically, the display is popped up using the device's notification function.

[0787] Step 5:

[0788] The user reviews the message displayed on the terminal and either sends or edits it. The input is the message displayed in step 4, and the output is either the message sent as is or the edited message. Specifically, the user either clicks the send button or opens the editing screen.

[0789] Step 6:

[0790] The server performs sentiment analysis on the messages sent. The input is the message sent by the user, and the output is sentiment data evaluated by the sentiment analysis engine. The server uses this data to adjust the tone of the next message. Specifically, it performs sentiment analysis using the TextBlob library to obtain a positive or negative evaluation.

[0791] Step 7:

[0792] The server personalizes the next message by considering sentiment data and past interaction history. The input is the sentiment data and past interaction history obtained in step 6, and the output is the personalized response message. The specific operation involves a process of referring to past database history, analyzing features, and generating an appropriate response.

[0793] (Application Example 2)

[0794] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0795] Robots used in homes are required to maintain more natural and positive communication with residents. However, conventional technology struggles to appropriately understand residents' emotions and communicate accordingly, failing to meet residents' expectations. Furthermore, smooth communication and suggestions within the home require considering each resident's past conversational tendencies and shared interests, but this has not been adequately achieved.

[0796] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0797] In this invention, the server includes means for automatically identifying common interests among multiple users based on the attributes and behavioral history of registered users, means for generating an initial message to facilitate the initiation of a conversation based on the common interests, and means for sending and receiving the generated message between users. This enables appropriate communication based on the users' emotions and facilitates smooth conversations within the home.

[0798] A "registered user" refers to an individual who is recognized within the system and whose attributes and behavioral history are stored.

[0799] "Attributes" refer to information that indicates a user's hobbies, interests, or other personal characteristics.

[0800] "Activity history" refers to a record of past actions and operations performed by a user on the system.

[0801] "Shared interests" refer to themes such as interests and hobbies that are shared among multiple users.

[0802] An "initial message" is the first message the system generates to initiate a conversation.

[0803] A "generated message" is text created by the system based on the input information.

[0804] "User emotions" refers to the state and tone of emotions expressed by the user.

[0805] "Communication" refers to a means of exchanging information between users or with a system.

[0806] A "suggestion" is information provided by the system to offer advice or recommendations to the user.

[0807] A "robot" is an autonomous or semi-autonomous electronic device used to interact with residents within the home.

[0808] The system for realizing this invention is a robot designed to facilitate communication within the home. This robot operates using an embedded computer (e.g., Raspberry Pi) and high-performance emotion analysis software.

[0809] The server performs data analysis to identify common interests based on user attributes and behavioral history. This extracts interests shared among multiple users within a household and generates an initial message based on them. This message generation utilizes a generative AI model (e.g., OpenAI GPT-3) to initiate a natural conversation.

[0810] The generated messages are presented to residents through the robot's communication capabilities. Speech recognition software (e.g., Google Cloud Speech-to-Text) and natural language processing libraries (e.g., TensorFlow) are incorporated to analyze residents' responses and emotions. This allows the robot to understand the user's feelings and make appropriate suggestions.

[0811] For example, if the server identifies gardening as a common interest, the robot can use its AI model to generate suggestions such as, "The weather's been nice lately, how about tidying up the garden with your family this weekend?", thereby promoting family togetherness.

[0812] An example of a prompt message is: "User A and User B are spending time together at home. Their current interest is gardening. Assuming it's a sunny day, generate a positive message about gardening." Such prompts enable flexible communication tailored to the user's situation.

[0813] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0814] Step 1:

[0815] The server retrieves user attributes and behavioral history from a database. Based on this information, it runs an algorithm to identify common interests and pinpoint shared interests among users. The input is user attributes and behavioral history, and the output is information on common interests.

[0816] Step 2:

[0817] The server generates an initial message based on identified common interests. It utilizes a generative AI model to construct prompts and generate the message. The input is the common interest information and prompts, and the output is the generated initial message.

[0818] Step 3:

[0819] The terminal displays the generated message to the user. The user can either send this message as is or edit it to finalize it. The input is the initial generated message, and the output is the final message from the user.

[0820] Step 4:

[0821] The robot receives messages sent and received between users, analyzes them using natural language processing, and evaluates the users' emotions. The input is the sent and received messages, and the output is the users' emotional state.

[0822] Step 5:

[0823] The robot generates the next suggestion or dialogue based on the analyzed emotional state. Here again, a generative AI model is used to create messages that continue the conversation in a natural flow. The input is the user's emotional state, and the output is the next dialogue message.

[0824] Step 6:

[0825] The user receives a new message and chooses to accept the suggestion or end the conversation. The robot continues or ends the conversation according to the user's choice. The input is the next dialogue message and the user's choice, and the output is the continuation or termination of the conversation.

[0826] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0827] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0828] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0829] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0830] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0831] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0832] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0833] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0834] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0835] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0836] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0837] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0838] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0839] 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.

[0840] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0841] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0842] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0843] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0844] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0845] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0846] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0847] The following is further disclosed regarding the embodiments described above.

[0848] (Claim 1)

[0849] A means to automatically identify common interests between registered users based on their hobbies and activity history,

[0850] A means for generating an initial message that facilitates the initiation of a dialogue based on the aforementioned shared interests,

[0851] A means for sending and receiving generated messages between users,

[0852] A means of naturally continuing or ending a conversation based on the user's response,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the generated message further comprises means for generating a response that takes into account the user's past conversational style.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for suggesting a message when the user chooses whether to continue or end the conversation.

[0858] "Example 1"

[0859] (Claim 1)

[0860] A data management system for storing information of registered users,

[0861] An analytical means for identifying common interests based on the aforementioned information,

[0862] A message generation means that generates an initial message based on the aforementioned common interest,

[0863] A display means for presenting the generated message to the user,

[0864] A conversation progression support means that analyzes the user's response patterns and assists in continuing or ending the conversation,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, further comprising a message generation means that generates the generated message taking into account the user's past dialogue patterns.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising a suggestion means for suggesting an appropriate termination message when a user indicates an intention to end a conversation.

[0870] "Application Example 1"

[0871] (Claim 1)

[0872] A means to automatically identify common interests between registered users based on their hobbies and activity history,

[0873] A means for generating an initial message that facilitates the initiation of a dialogue based on the aforementioned shared interests,

[0874] A means for sending and receiving generated messages between users,

[0875] A means of naturally continuing or ending a conversation based on the user's response,

[0876] A means of learning the individual hobbies and conversation history of family members and providing common topics of conversation,

[0877] A means of suggesting topics to revitalize conversations within the family,

[0878] A means of generating topic suggestion sentences using a generative AI model,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, wherein the generated message further comprises means for generating a response that takes into account the user's past conversational style.

[0882] (Claim 3)

[0883] The system according to claim 1, further comprising means for suggesting a message when the user chooses whether to continue or end the conversation.

[0884] "Example 2 of combining an emotion engine"

[0885] (Claim 1)

[0886] A means to automatically identify common interests among users based on the attribute data and behavioral history of registered users,

[0887] To generate the initial communication content based on the aforementioned common interests, a means of using a generation algorithm,

[0888] A means for evaluating the content of communications sent by a user using an emotion analysis engine and generating a response based on the results,

[0889] A means of presenting the generated response to the user's terminal and allowing it to be edited,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, wherein the generated communication content is personalized taking into account the user's past conversational tendencies and emotional state.

[0893] (Claim 3)

[0894] The system according to claim 1, further comprising means for making suggestions to facilitate the continuation of a conversation when the user selects or modifies the presented communication content.

[0895] "Application example 2 when combining with an emotional engine"

[0896] (Claim 1)

[0897] A means for automatically identifying common interests among multiple users based on the attributes and behavioral history of registered users,

[0898] A means for generating an initial message that facilitates the initiation of a dialogue, based on the aforementioned shared interests,

[0899] A means for sending and receiving generated messages between users,

[0900] A means of analyzing the user's emotions and, based on the analysis results, naturally continuing or ending the conversation,

[0901] Means of making proposals via communication within the residence,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, wherein the generated message further comprises means for generating a response that takes into account the user's past conversational tendencies.

[0905] (Claim 3)

[0906] The system according to claim 1, further comprising means for suggesting a message when the user chooses whether to continue or end the conversation. [Explanation of Symbols]

[0907] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to automatically identify common interests between registered users based on their hobbies and activity history, A means for generating an initial message that facilitates the initiation of a dialogue based on the aforementioned shared interests, A means for sending and receiving generated messages between users, A means of naturally continuing or ending a conversation based on the user's response, A system that includes this.

2. The system according to claim 1, wherein the generated message further comprises means for generating a response that takes into account the user's past conversational style.

3. The system according to claim 1, further comprising means for suggesting a message when the user chooses whether to continue or end the conversation.

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