system

Abstract

We provide the system. [Solution] A means for analyzing a user's communication history and identifying the user's personality traits and interests, A means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits and interest tendencies, Each generated virtual agent interacts with one another and explores common interests, A means of suggesting the most suitable interaction candidates to the user based on the results of conversations between virtual agents, 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Users cannot find a partner who is essentially compatible with their personality and hobbies, and there is a problem that it is difficult to develop long-term communication in conventional matching services. Behind this problem, the profile information and questionnaire answers filled in by users themselves may not necessarily accurately reflect the actual personality and interests of users. As a result, even if initial matching is achieved, there is a problem that it is difficult to attract the continuous interest of users.

Means for Solving the Problems

[0005] This invention provides a system that accurately identifies a user's personality traits and interests by analyzing their communication history. Based on this identified information, a virtual agent simulating the user's characteristics is generated, and these agents automatically interact with each other, enabling a more accurate search for partners with shared interests. The system also includes a function to calculate a compatibility score based on the interaction results and suggest the most suitable interaction candidates to the user. Furthermore, the operation of the virtual agents and the suggestions are continuously improved by incorporating user feedback. This provides users with more opportunities to find hobby friends who are a better match for them and promotes the formation of long-term relationships.

[0006] A "user" refers to an individual who uses this system, and a virtual agent is generated based on their hobbies and personality traits.

[0007] "Communication history" refers to a record of messages and conversations that users engage in on a daily basis, and is a collection of data used to analyze personality traits and interests.

[0008] "Personality traits" refer to an individual's characteristics and tendencies that are estimated based on the user's communication style and emotional expression.

[0009] "Interest tendencies" refer to characteristics that indicate a user's preferences and tendencies related to topics that interest them.

[0010] A "virtual agent" refers to a program or system that simulates a user's personality traits and interests, and automatically interacts with other virtual agents.

[0011] "Shared interests" refer to areas of interest or concern that are consistent among multiple users and serve as the basis for dialogue.

[0012] "Dialogue results" refer to the outcome of information exchange between virtual agents obtained through dialogue.

[0013] The "compatibility score" is an indicator of the degree of compatibility between users, calculated based on information obtained through dialogue between virtual agents.

[0014] "Potential matchmakers" refer to people suggested to users based on their compatibility score who may share similar hobbies and interests.

[0015] "Feedback" refers to the act of providing users with their experiences and opinions gained while using this system, and this information is used to improve the virtual agent. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a 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, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered 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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[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, 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 is a system that uses a communication platform to enable matching users based on their hobbies and interests. This system analyzes the user's conversation history using messaging apps such as LINE to understand their personality traits and interests. Based on this analysis, a user-specific virtual agent is generated, which automatically interacts with other users' virtual agents. Finally, based on the results of the virtual agent interactions, the system presents suitable interaction candidates to the user.

[0038] Specifically, users first add the system's LINE bot as a friend and enter basic settings, hobby categories, and personality assessment results to begin using the service. As the user's daily messages accumulate, the server collects them, and a generating AI analyzes them. This analysis reveals the user's unique personality traits and interests, and a virtual agent is generated based on this data.

[0039] The generated virtual agents simulate interactions with other virtual agents, exploring common interests and characteristics. The interaction information obtained during this process is analyzed by the server, and a compatibility score is calculated. This prepares the server to suggest the most suitable partner to the user.

[0040] For example, suppose user A is interested in cooking, and user B is also interested in cooking and travel. Their virtual agents will converse through topics related to cooking, exchanging information about common recipes and favorite dishes. Based on this information exchange, the server will determine whether the two users are suitable as hobby friends and make suggestions based on the high compatibility score.

[0041] Thus, this invention aims to provide more appropriate user matching and natural interaction based on realistic communication data. User feedback after interaction is sent to the server and used to improve the virtual agent and recommendation algorithm. This continuously improves the overall effectiveness of the system.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users become friends with a bot on messaging apps like LINE and complete the initial registration. This registration includes entering basic profile information, hobby categories, and answering personality tests.

[0045] Step 2:

[0046] The server periodically collects the user's communication history. This history data includes the user's everyday conversations and messaging activity.

[0047] Step 3:

[0048] The server applies generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to automatically extract the user's personality traits and interests.

[0049] Step 4:

[0050] The terminal generates a user-specific virtual agent based on the analysis results received from the server. This virtual agent is designed to simulate the user's characteristics and preferences.

[0051] Step 5:

[0052] The server automatically matches the generated virtual agents with the virtual agents of other users and allows them to interact. The interaction is programmatically controlled, with questions and information exchange taking place to explore common interests.

[0053] Step 6:

[0054] The server calculates a compatibility score based on the content of the conversations between virtual agents. The compatibility score is determined by multiple indicators, such as the number of common interests and the activity level of the conversation.

[0055] Step 7:

[0056] The server presents users with potential partners who have high compatibility scores. These suggestions include shared hobbies and related information.

[0057] Step 8:

[0058] Users can review the suggested candidates and begin interacting with those they are interested in. They can also send feedback about this interaction experience to the server.

[0059] Step 9:

[0060] Based on feedback received from users, the server improves the operation of virtual agents and matching algorithms, and uses this information for future matching.

[0061] (Example 1)

[0062] 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."

[0063] Traditional communication platforms lack the technology to efficiently and effectively match users based on their hobbies and interests. This prevents users from fully utilizing opportunities to interact with other users who potentially share similar interests. Furthermore, there are insufficient means to improve the overall system accuracy by leveraging feedback from interactions.

[0064] 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.

[0065] In this invention, the server includes means for analyzing the communication history and identifying the characteristics and interests of the person who initiated the communication; means for generating a virtual entity that simulates the characteristics of the person who initiated the communication based on the identified characteristics and interests; and means for each generated virtual entity to exchange information with each other and explore common interests. This enables the effective suggestion of optimal interaction candidates based on common hobbies and interests among users, and further allows for continuous improvement of the system based on feedback.

[0066] "Communication history" refers to the recorded data of messages and conversations that took place between users.

[0067] "Characteristic traits" refer to an individual's personality, behavioral patterns, and psychological characteristics.

[0068] "Interest" refers to the areas or topics that an individual is particularly interested in.

[0069] A "virtual entity" is a digital agent simulated based on the user's characteristics and interests.

[0070] "Information exchange" refers to the act of virtual entities sharing data and messages with each other.

[0071] A "compatibility index" is an evaluation criterion that quantifies the commonalities and interesting aspects between two parties.

[0072] A "potential interaction partner" refers to a user who is deemed to be a good match for another user and is recommended for interaction.

[0073] This system facilitates matching users based on their hobbies and interests through a communication platform.

[0074] First, the user accesses a messaging app on their device and adds the system's bot as a friend. The user then enters basic information, such as their hobby categories and personality test results, and sends this information from their device to the server.

[0075] The server collects the user's daily message history via a messaging API and stores it in a database. The collected data is analyzed using a generative AI model to extract the user's characteristics and interests. The prompt used for this is "Analyze the user's personality and interests."

[0076] Based on the analysis results, the server creates a virtual entity specifically for the user. This virtual entity simulates the user's personality and interests and exchanges information through interaction with other virtual entities.

[0077] Specifically, for example, if a user is interested in cooking, the server connects them with virtual representations of other users who are also interested in cooking, and these virtual representations interact about common recipes and favorite dishes. By analyzing this information exchange, the server suggests suitable interaction candidates for the user. Based on the generated compatibility index, it connects the user with users who have a high compatibility, thereby supporting more natural and effective interactions.

[0078] Furthermore, users send feedback to the server after their interactions, and the system continuously improves its virtual entities and recommendation algorithms based on this feedback. This promotes mutual understanding among users and improves the overall accuracy and effectiveness of the system.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user uses their device to add the system's bot as a friend and enters their hobby categories and personality test results. This basic information is sent from the device to the server, thus completing the user's initial profile data.

[0082] Step 2:

[0083] The server collects the user's daily message history through a messaging API. The entered message data is stored in a database in text format. This allows the user's communication history to be accumulated.

[0084] Step 3:

[0085] The server uses a generative AI model to analyze the accumulated message data. Based on the specified prompt "Analyze the user's personality and interests," it analyzes the data using natural language processing techniques to extract the user's characteristics and interests. The analysis results are obtained as output.

[0086] Step 4:

[0087] Based on the analysis results, the server generates a virtual entity specifically for the user. This virtual entity reflects the user's characteristics and interests. This virtual entity is then ready for simulation within the system.

[0088] Step 5:

[0089] The server connects the generated virtual entities and performs dialogue simulations. The virtual entities exchange information about common interests and personality traits. The dialogue results are obtained as output and are used to select potential partners for subsequent interactions.

[0090] Step 6:

[0091] The server analyzes the conversation results and calculates a compatibility index using a generative AI model. This creates a compatibility score between users. Users with the best compatibility can then be suggested as potential interaction partners.

[0092] Step 7:

[0093] Users interact with the suggested interaction candidates and send feedback to the server. Based on this feedback, the server uses it to improve virtual entities and recommendation algorithms, and to enhance the overall accuracy of the system.

[0094] (Application Example 1)

[0095] 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."

[0096] In today's world, where users are exposed to vast amounts of information online, finding content that best suits their interests and personality is not easy. Furthermore, naturally initiating interactions with other users who share similar interests is also difficult. In this context, there is a need to provide content tailored to each individual user and to facilitate interaction.

[0097] 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.

[0098] In this invention, the server includes means for analyzing a user's communication history and identifying the user's characteristics and interests; means for generating a virtual agent that simulates the user's personality based on the identified characteristics and interests; means for each generated virtual agent to interact with each other and explore common interests; and data processing means for providing content based on the user's interests. This allows the user to receive suggestions for content that matches their interests and facilitates natural interaction with other users.

[0099] A "user" refers to an individual whose communication history is analyzed using the system.

[0100] "Communication history" refers to the record of conversations and messages that a user has exchanged online.

[0101] "Characteristics" refer to features that indicate a user's personality and behavioral tendencies.

[0102] "Interest" refers to themes or topics that a user is interested in.

[0103] A "virtual agent" refers to a simulated entity that is generated based on the user's characteristics and interests and interacts with other agents.

[0104] "Hobbies" refer to activities or areas of interest that users enjoy personally.

[0105] "Data processing means" refers to technologies and methods for collecting and analyzing user-related information.

[0106] "Content" refers to media materials such as information, videos, and news articles provided to users.

[0107] "Compatibility rating" refers to a numerical value that represents the degree of compatibility in the relationship between users, calculated based on the results of conversations between virtual agents.

[0108] The system that realizes this invention includes a communication terminal and a server. First, the user uses the communication terminal to communicate daily through a messaging application. At this time, the communication terminal automatically sends the user's communication history to the server. The server analyzes the user's characteristics and interests based on the received communication history. A generative AI model is used for this analysis, making it possible to understand the user's personality traits and interest tendencies. As a specific example, the analysis is performed by inputting a prompt sentence such as "Please give me information on the latest technology trends" into the model.

[0109] Based on the analysis results, the server generates a virtual agent optimized for the user. This virtual agent engages in simulated conversations with other users' virtual agents, searching for agents with shared hobbies and interests. The server analyzes the results of these conversations and calculates a compatibility rating between each agent. Based on this, the server suggests new content and interactions that the user might be interested in.

[0110] For example, if a user enjoys cooking and traveling, and these topics frequently appear in their conversation history, the server will recommend media content to the user, including the latest cooking recipes and travel information. It will also suggest opportunities to interact with other users who share similar interests, enabling the user to build new relationships. This personalizes the user's online experience and supports the creation of richer relationship networks.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server receives the user's communication history sent from the terminal. The input is a record of the user's interactions in the messaging application. The server saves this data and prepares it for analysis. The output is a set of saved communication history data.

[0114] Step 2:

[0115] The server analyzes user characteristics and interests based on stored communication history data. This analysis utilizes a generative AI model. The input is communication history data, which the AI ​​model analyzes to generate a profile of characteristics and interests. The output is a profile of the user's specific personality traits and interest tendencies.

[0116] Step 3:

[0117] The server generates virtual agents based on the generated user characteristics and interest profiles. The input is the characteristics and interest profiles. Based on these characteristics and interests, the server defines the virtual agent's behavioral parameters and interaction style. The output is an individual virtual agent.

[0118] Step 4:

[0119] The server simulates interaction between other users' virtual agents using the generated virtual agents. The input consists of multiple virtual agents, which explore common interests and hobbies through virtual dialogue. The output is a dialogue log and a list of commonalities between each agent.

[0120] Step 5:

[0121] The server analyzes the results of conversations between virtual agents and calculates a compatibility rating between users. The input consists of conversation logs and a list of commonalities. Data calculations are performed to obtain a numerical compatibility score. The output is the compatibility score between users.

[0122] Step 6:

[0123] The server suggests suitable content and interaction options for the user based on their compatibility score and interest profile. The input is the compatibility score and interest profile, and it generates a list of content and interactions to provide to the user. The output is the suggested content for the user.

[0124] 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.

[0125] This invention is a system that analyzes a user's communication history and combines it with an emotion engine to comprehensively understand their personality traits, interests, and emotional state. This system enables user-centric matching and provides more accurate suggestions for potential interactions.

[0126] First, users register with the system using messaging apps such as LINE. Registration includes basic profile information, hobby categories, and answers to a personality assessment. Once registration is complete, the server periodically collects the user's communication history. This history data contains detailed records of how the user exchanges messages and the content of those messages.

[0127] The server activates an emotion engine along with a generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to identify the user's personality traits and interests. Meanwhile, the emotion engine recognizes the user's emotional state from the text data and estimates how the user felt during the period of the messages.

[0128] Next, the terminal generates a user-specific virtual agent based on the analysis results obtained from the server. This agent simulates the user's personality traits, interests, and emotional state, and reflects this information when interacting with other virtual agents. It also adjusts the content of the conversation as appropriate according to the emotional state recognized by the emotion engine.

[0129] The server matches the generated virtual agents with other users' virtual agents and automatically initiates conversations. These conversations involve dynamic communication based on emotional states. The server collects the conversation results and calculates a compatibility score. This score considers not only shared interests but also emotional compatibility.

[0130] Users are presented with optimal interaction candidates and can initiate interactions with those they are interested in. Once an interaction takes place, feedback is sent back to the server to help refine the virtual agent and emotion engine.

[0131] For example, if user C is interested in music and technology and is generally in a calm emotional state, and user D has similar interests and emotional characteristics, the virtual agent will lead the conversation through relaxed topics and news about the latest technology. In this way, interactions based on the users' actual states are formed, fostering connections that are both trustworthy and enjoyable.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] Users add official bots for LINE and other messaging platforms and register an account. Registration involves providing basic profile information, selecting hobby categories, and answering personality assessment questions.

[0135] Step 2:

[0136] The server receives user registration information and stores it in the database. Once registration is complete, it prepares to periodically monitor user communication records.

[0137] Step 3:

[0138] Every time a user communicates via LINE or other messaging apps, their chat history is collected on the server. This history includes information such as the content of the messages and the time they were sent and received.

[0139] Step 4:

[0140] The server passes the collected communication history to a generating AI to identify the user's personality traits and interests. Using natural language processing technology, the AI ​​extracts the user's hobbies and personality from frequently occurring words and topics.

[0141] Step 5:

[0142] Simultaneously, the emotion engine activates and analyzes the user's emotional state from the message content. This analysis helps recognize the user's emotions during the conversation.

[0143] Step 6:

[0144] The terminal generates a virtual agent that simulates the user's characteristics based on analysis results from the server. This agent incorporates the user's personality, interests, and emotional state.

[0145] Step 7:

[0146] The server automatically matches the generated virtual agent with another user's agent and initiates a conversation. The conversation consists of questions to explore common interests and includes adjustments that respond to emotional states.

[0147] Step 8:

[0148] The server aggregates the results of conversations between virtual agents and calculates a compatibility score. This score is calculated based on factors such as shared hobbies, the activity level of the conversation, and emotional compatibility.

[0149] Step 9:

[0150] The server presents the user with a list of highly compatible interaction candidates. The user can then select an interaction with someone they are interested in from the suggested candidates and begin actual communication.

[0151] Step 10:

[0152] The server analyzes feedback collected from users and uses it to adjust the behavior of the virtual agent and the emotion engine. This allows for improvements so that the next match will be even more accurate.

[0153] (Example 2)

[0154] 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".

[0155] In modern society, communication between users takes place through a variety of means, but selecting the most suitable interaction partners for each user based on their communication history is a challenging task. In particular, conventional systems lack the ability to suggest interaction partners that take into account not only personality and interests, but also emotional states, thus requiring matching based on a deeper understanding.

[0156] 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.

[0157] In this invention, the server includes means for analyzing the user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common topics based on their emotional state. This makes it possible to propose interaction candidates to the user that take into account a more accurate emotional compatibility.

[0158] "Communication history" refers to the history of messages sent and received by a user through messaging applications or other communication methods, and includes information such as text content, date and time of sending, and recipient.

[0159] "Personality traits" are indicators that show a user's personality and behavioral tendencies, and are based on data extracted from their past actions and statements.

[0160] "Interest tendencies" refer to the preferences and directions of interest that a user shows towards specific topics or activities, and are analyzed from communication history and other sources.

[0161] "Emotional state" refers to the state of a user's emotions during communication, and is the mood or emotional state inferred from the content and expression of the message.

[0162] A "virtual agent" is a computer program that simulates the characteristics of a user and is a pseudo-entity created to interact with other virtual agents.

[0163] The "compatibility score" is a numerical value calculated based on the results of conversations between virtual agents, and it is an indicator of the compatibility of shared interests and emotions.

[0164] The term "emotion engine" refers to a set of functions that encompass algorithms and technologies for analyzing a user's emotional state from their messages.

[0165] This invention provides a system that analyzes a user's communication history to comprehensively understand the user's personality traits, interests, and emotional state. The embodiments thereof are described in detail below.

[0166] First, users register for the system using a messaging application. During this process, their profile information, hobby-related information, and personality assessment responses are stored in a database. The user interface is implemented using a standard smartphone or computer.

[0167] The server periodically collects the communication history of registered users. This history includes detailed data such as message content, sending date and time, and recipient user. This data is transferred to the server via an API.

[0168] The server uses a generative AI model and an emotion engine to analyze the collected data. The generative AI model uses natural language processing techniques (e.g., BERT and GPT) to identify the user's personality traits and interests. Meanwhile, the emotion engine performs sentiment analysis on the text of the communication history to understand the user's emotional state.

[0169] Based on the analysis results, the terminal generates a virtual agent specifically for the user. This agent follows prompts created by the generated AI model and simulates conversations that reflect the user's characteristics. The virtual agent interacts with other users' agents and searches for appropriate topics based on their emotional state.

[0170] For example, if a user's past communication history reveals a high level of interest in "music" and "latest technology," and that they are in a "calm" emotional state, this user's virtual agent will engage in "relaxed conversations about music" and "topics about new technologies" with agents possessing similar characteristics. An example of a prompt given to the generative AI model would be: "Analyze the user's personality traits from their communication history and derive their interest nodes. Consider the user's emotional state and output the results."

[0171] This embodiment makes it possible to propose the optimal interaction candidate to the user, taking into account emotional compatibility.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] Users perform initial registration using a messaging application. Input includes user profile information, information about hobbies, and responses to a personality assessment. Based on this input data, the system saves the user's initial profile to a database. Specifically, the user fills in information in an input form and presses the submit button, at which point the data is transferred to the server.

[0175] Step 2:

[0176] The server periodically collects the communication history of registered users. Input includes the user's past message content, sending date and time, and recipient. The server extracts this data using an API and automatically stores it in a database. The output is a communication history dataset prepared for analysis. Specifically, the server periodically makes API calls to collect and store the necessary data.

[0177] Step 3:

[0178] The server activates a generative AI model and an emotion engine to analyze the collected communication history data. The input data is the communication history obtained in step 2. The generative AI model uses natural language processing techniques to analyze the user's personality traits and interests. The emotion engine evaluates the emotional state from the text data. The output is the analysis results, including each user's personality traits, interests, and emotional state. Specifically, the server sends prompt messages to the AI ​​model and receives the analysis results.

[0179] Step 4:

[0180] The terminal generates a user-specific virtual agent based on the analysis results obtained from the server. The input is the analysis results obtained in step 3. From these results, the virtual agent incorporates data to simulate the user's characteristics. The output is a virtual agent customized for each user. Specifically, the terminal sets the metadata of the virtual agent based on the analysis results and prepares a dialogue scenario based on it.

[0181] Step 5:

[0182] The server matches multiple virtual agents and initiates dialogue. The input includes characteristic information of the generated virtual agents. Based on this information, the server calculates a compatibility score and searches for common topics based on emotional states. The output is the optimal interaction candidates and dialogue content. Specifically, the server schedules dialogues between virtual agents, taking emotional compatibility into consideration, and monitors the communication.

[0183] Step 6:

[0184] The system presents the user with the most suitable interaction candidates. The input is the compatibility score calculated in step 5 and the conversation results. The user can then start an actual interaction with someone they are interested in from the presented candidates. The output is a list of interaction candidates provided to the user. Specifically, the terminal displays the list via the user interface and presents the user with choices.

[0185] (Application Example 2)

[0186] 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".

[0187] In today's information-saturated society, users often find it difficult to quickly find content that suits their interests and emotional state. Furthermore, in user interactions, it is desirable that not only shared interests but also emotional compatibility be considered. This invention aims to solve these problems and provide users with a personalized and enriching content experience and optimal matching with other users.

[0188] 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.

[0189] In this invention, the server includes means for analyzing a user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common interests and emotional compatibility. This enables the suggestion of more appropriate and personalized content to the user and the selection of emotionally appropriate interaction candidates.

[0190] "User communication history" refers to a collection of information including records of messages sent and received by individual users in the past, as well as the content of those conversations.

[0191] "Personality traits" refer to a set of specific psychological characteristics that characterize the behavior and thought patterns of individual users.

[0192] "Interest tendencies" refer to information that indicates the subjects and activities that an individual user is particularly interested in.

[0193] "Emotional state" refers to information that describes the characteristics of the emotions and moods a user is experiencing at a particular point in time.

[0194] A "virtual agent" is a software entity generated to simulate a user's personality traits, interests, and emotional state.

[0195] "Emotional compatibility" is an indicator that shows the emotional compatibility between different users and is a criterion for promoting good communication.

[0196] "Content recommendation" refers to the act of determining the content of information and entertainment that users should watch or read, based on their characteristics and circumstances.

[0197] "Potential interaction partners" are other users who may be of interest to the user, and who are people with whom the user should attempt to communicate.

[0198] The system realizing this invention includes a function to analyze the user's communication history and identify personality traits, interest tendencies, and emotional states. The server uses this information to generate a virtual agent. The virtual agent can represent the user's characteristics and interact with other virtual agents. Based on the results of this interaction, the system suggests optimal interaction candidates and content to the user based on common interests and emotional compatibility.

[0199] This system uses devices such as smartphones and tablets as hardware, and utilizes libraries for natural language processing (e.g., Transformers by Hugging Face) and SQL or NoSQL (such as MongoDB) for database management as software. In addition, machine learning engines (such as Scikit-learn and TENSORFLOW®) are used to enable content recommendations based on user interests.

[0200] As a concrete example, if user A, who is interested in sports, wants to relax, the system can automatically recommend relaxing music or video content based on their past communication history and current emotional state. This allows the user to easily enjoy appropriate content.

[0201] Because it utilizes a generative AI model, it can use prompts such as, "Based on user A's historical data, generate content that suggests the best music playlist for sports that corresponds to a relaxed emotional state. Consider past behavioral patterns and the results of sentiment analysis, and present recommendations that are updated in real time."

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The server periodically collects communication history data from the user's device. The input is text data obtained from messaging applications. This data includes the content of messages sent and received by the user. The output is the raw data of the collected text.

[0205] Step 2:

[0206] The server analyzes the collected communication history using a generative AI model. The input is the text data obtained in step 1. Natural language processing is performed on this data to identify the user's personality traits, interests, and emotional state. The output is data on the identified personality traits and interests.

[0207] Step 3:

[0208] The server generates a virtual agent based on identified personality traits, interests, and emotional states. The input is the trait data, which is the output of step 2. This data is used to create a virtual agent that simulates the user's characteristics. The output is a profile of the generated virtual agent.

[0209] Step 4:

[0210] The server initiates a process in which each generated virtual agent interacts with one another. The input consists of profiles of multiple virtual agents. These profiles communicate with each other to explore common interests and emotional compatibility. The output is the collection of relevant information derived from these interactions.

[0211] Step 5:

[0212] The server analyzes the results of conversations between virtual agents and suggests the most suitable interaction candidates and content for the user. The input is the conversation result data obtained from step 4. Using this, it calculates indicators of common interests and emotional compatibility and generates suggestions. The output presents recommendations for the most suitable interactions and content for the user.

[0213] Step 6:

[0214] The user's device displays the most suitable content and interaction suggestions sent from the server. The input is the suggestion data provided by the server. The output is a list of suggested content and interaction suggestions presented to the user.

[0215] This process allows users to quickly find content and the most suitable people to interact with that match their interests and emotional state.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention is a system that uses a communication platform to enable matching users based on their hobbies and interests. This system analyzes the user's conversation history using messaging apps such as LINE to understand their personality traits and interests. Based on this analysis, a user-specific virtual agent is generated, which automatically interacts with other users' virtual agents. Finally, based on the results of the virtual agent interactions, the system presents suitable interaction candidates to the user.

[0233] Specifically, users first add the system's LINE bot as a friend and enter basic settings, hobby categories, and personality assessment results to begin using the service. As the user's daily messages accumulate, the server collects them, and a generating AI analyzes them. This analysis reveals the user's unique personality traits and interests, and a virtual agent is generated based on this data.

[0234] The generated virtual agents simulate interactions with other virtual agents, exploring common interests and characteristics. The interaction information obtained during this process is analyzed by the server, and a compatibility score is calculated. This prepares the server to suggest the most suitable partner to the user.

[0235] For example, suppose user A is interested in cooking, and user B is also interested in cooking and travel. Their virtual agents will converse through topics related to cooking, exchanging information about common recipes and favorite dishes. Based on this information exchange, the server will determine whether the two users are suitable as hobby friends and make suggestions based on the high compatibility score.

[0236] Thus, this invention aims to provide more appropriate user matching and natural interaction based on realistic communication data. User feedback after interaction is sent to the server and used to improve the virtual agent and recommendation algorithm. This continuously improves the overall effectiveness of the system.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] Users become friends with a bot on messaging apps like LINE and complete the initial registration. This registration includes entering basic profile information, hobby categories, and answering personality tests.

[0240] Step 2:

[0241] The server periodically collects the user's communication history. This history data includes the user's everyday conversations and messaging activity.

[0242] Step 3:

[0243] The server applies generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to automatically extract the user's personality traits and interests.

[0244] Step 4:

[0245] The terminal generates a user-specific virtual agent based on the analysis results received from the server. This virtual agent is designed to simulate the user's characteristics and preferences.

[0246] Step 5:

[0247] The server automatically matches the generated virtual agents with the virtual agents of other users and allows them to interact. The interaction is programmatically controlled, with questions and information exchange taking place to explore common interests.

[0248] Step 6:

[0249] The server calculates a compatibility score based on the content of the conversations between virtual agents. The compatibility score is determined by multiple indicators, such as the number of common interests and the activity level of the conversation.

[0250] Step 7:

[0251] The server presents users with potential partners who have high compatibility scores. These suggestions include shared hobbies and related information.

[0252] Step 8:

[0253] Users can review the suggested candidates and begin interacting with those they are interested in. They can also send feedback about this interaction experience to the server.

[0254] Step 9:

[0255] Based on feedback received from users, the server improves the operation of virtual agents and matching algorithms, and uses this information for future matching.

[0256] (Example 1)

[0257] 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."

[0258] Traditional communication platforms lack the technology to efficiently and effectively match users based on their hobbies and interests. This prevents users from fully utilizing opportunities to interact with other users who potentially share similar interests. Furthermore, there are insufficient means to improve the overall system accuracy by leveraging feedback from interactions.

[0259] 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.

[0260] In this invention, the server includes means for analyzing the communication history and identifying the characteristics and interests of the person who initiated the communication; means for generating a virtual entity that simulates the characteristics of the person who initiated the communication based on the identified characteristics and interests; and means for each generated virtual entity to exchange information with each other and explore common interests. This enables the effective suggestion of optimal interaction candidates based on common hobbies and interests among users, and further allows for continuous improvement of the system based on feedback.

[0261] "Communication history" refers to the recorded data of messages and conversations that took place between users.

[0262] "Characteristic traits" refer to an individual's personality, behavioral patterns, and psychological characteristics.

[0263] "Interest" refers to the areas or topics that an individual is particularly interested in.

[0264] A "virtual entity" is a digital agent simulated based on the user's characteristics and interests.

[0265] "Information exchange" refers to the act of virtual entities sharing data and messages with each other.

[0266] A "compatibility index" is an evaluation criterion that quantifies the commonalities and interesting aspects between two parties.

[0267] A "potential interaction partner" refers to a user who is deemed to be a good match for another user and is recommended for interaction.

[0268] This system facilitates matching users based on their hobbies and interests through a communication platform.

[0269] First, the user accesses a messaging app on their device and adds the system's bot as a friend. The user then enters basic information, such as their hobby categories and personality test results, and sends this information from their device to the server.

[0270] The server collects the user's daily message history via a messaging API and stores it in a database. The collected data is analyzed using a generative AI model to extract the user's characteristics and interests. The prompt used for this is "Analyze the user's personality and interests."

[0271] Based on the analysis results, the server creates a virtual entity specifically for the user. This virtual entity simulates the user's personality and interests and exchanges information through interaction with other virtual entities.

[0272] Specifically, for example, if a user is interested in cooking, the server connects them with virtual representations of other users who are also interested in cooking, and these virtual representations interact about common recipes and favorite dishes. By analyzing this information exchange, the server suggests suitable interaction candidates for the user. Based on the generated compatibility index, it connects the user with users who have a high compatibility, thereby supporting more natural and effective interactions.

[0273] Furthermore, users send feedback to the server after their interactions, and the system continuously improves its virtual entities and recommendation algorithms based on this feedback. This promotes mutual understanding among users and improves the overall accuracy and effectiveness of the system.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] The user uses their device to add the system's bot as a friend and enters their hobby categories and personality test results. This basic information is sent from the device to the server, thus completing the user's initial profile data.

[0277] Step 2:

[0278] The server collects the user's daily message history through a messaging API. The entered message data is stored in a database in text format. This allows the user's communication history to be accumulated.

[0279] Step 3:

[0280] The server uses a generative AI model to analyze the accumulated message data. Based on the specified prompt "Analyze the user's personality and interests," it analyzes the data using natural language processing techniques to extract the user's characteristics and interests. The analysis results are obtained as output.

[0281] Step 4:

[0282] Based on the analysis results, the server generates a virtual presence dedicated to the user. The virtual presence reflects the user's nature, characteristics, and interests. This virtual presence is ready to be simulated within the system.

[0283] Step 5:

[0284] The server links the generated virtual presences to conduct an interaction simulation. Information regarding common interests and personality characteristics is exchanged between the virtual presences. As an output, an interaction result is obtained and used for subsequent selection of communication candidates.

[0285] Step 6:

[0286] The server analyzes the interaction result and calculates a compatibility index using the generated AI model. As a result, a compatibility score between users is created. This enables the proposal of users with optimal compatibility as communication candidates.

[0287] Step 7:

[0288] The user has an actual interaction with the proposed communication candidate and sends feedback to the server. Based on the feedback, the server improves the virtual presence and the recommendation algorithm and utilizes it as data to improve the overall accuracy of the system.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In modern times, it is not easy for users to find the most suitable content for their interests and personalities while being exposed to a large amount of information online. Also, it is difficult to naturally initiate communication with other users who have similar interests. In such a situation, there is a demand to provide content suitable for individual users and promote communication.

[0292] 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.

[0293] In this invention, the server includes means for analyzing a user's communication history and identifying the user's characteristics and interests; means for generating a virtual agent that simulates the user's personality based on the identified characteristics and interests; means for each generated virtual agent to interact with each other and explore common interests; and data processing means for providing content based on the user's interests. This allows the user to receive suggestions for content that matches their interests and facilitates natural interaction with other users.

[0294] A "user" refers to an individual whose communication history is analyzed using the system.

[0295] "Communication history" refers to the record of conversations and messages that a user has exchanged online.

[0296] "Characteristics" refer to features that indicate a user's personality and behavioral tendencies.

[0297] "Interest" refers to themes or topics that a user is interested in.

[0298] A "virtual agent" refers to a simulated entity that is generated based on the user's characteristics and interests and interacts with other agents.

[0299] "Hobbies" refer to activities or areas of interest that users enjoy personally.

[0300] "Data processing means" refers to technologies and methods for collecting and analyzing user-related information.

[0301] "Content" refers to media materials such as information, videos, and news articles provided to users.

[0302] "Compatibility evaluation" refers to a numerical value representing the degree of compatibility of the relationship between users, which is calculated based on the dialogue results between virtual agents.

[0303] The system for realizing this invention includes a communication terminal and a server. First, the user uses the communication terminal to conduct daily communication through a messaging application. At this time, the communication terminal automatically sends the user's communication history to the server. The server analyzes the user's characteristics and interests based on the received communication history. In this analysis, a generative AI model is used, and it is possible to grasp the user's personality characteristics and interest trends. As a specific example, analysis is performed by inputting a prompt sentence such as "Please provide information on the latest technology trends" into the model.

[0304] Based on the analysis results, the server generates an optimal virtual agent for the user. This virtual agent has the role of conducting simulation dialogues with the virtual agents of other users and exploring agents with common hobbies and interests. The server analyzes the results of this dialogue and calculates the compatibility evaluation between each agent. As a result, new content and communication proposals that the user is interested in are made to the user.

[0305] For example, if a certain user likes cooking and traveling, and these topics frequently appear in the conversation history, the server recommends media content including the latest cooking recipes and travel information to the user. In addition, opportunities for communication with other users with similar interests are also proposed, and the user can build new relationships. This realizes the support for individualizing the user's online experience and constructing a richer relationship network.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The server receives the user's communication history sent from the terminal. The input is a record of the user's interactions in the messaging application. The server saves this data and prepares it for analysis. The output is a set of saved communication history data.

[0309] Step 2:

[0310] The server analyzes user characteristics and interests based on stored communication history data. This analysis utilizes a generative AI model. The input is communication history data, which the AI ​​model analyzes to generate a profile of characteristics and interests. The output is a profile of the user's specific personality traits and interest tendencies.

[0311] Step 3:

[0312] The server generates virtual agents based on the generated user characteristics and interest profiles. The input is the characteristics and interest profiles. Based on these characteristics and interests, the server defines the virtual agent's behavioral parameters and interaction style. The output is an individual virtual agent.

[0313] Step 4:

[0314] The server simulates interaction between other users' virtual agents using the generated virtual agents. The input consists of multiple virtual agents, which explore common interests and hobbies through virtual dialogue. The output is a dialogue log and a list of commonalities between each agent.

[0315] Step 5:

[0316] The server analyzes the results of conversations between virtual agents and calculates a compatibility rating between users. The input consists of conversation logs and a list of commonalities. Data calculations are performed to obtain a numerical compatibility score. The output is the compatibility score between users.

[0317] Step 6:

[0318] The server suggests suitable content and interaction options for the user based on their compatibility score and interest profile. The input is the compatibility score and interest profile, and it generates a list of content and interactions to provide to the user. The output is the suggested content for the user.

[0319] 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.

[0320] This invention is a system that analyzes a user's communication history and combines it with an emotion engine to comprehensively understand their personality traits, interests, and emotional state. This system enables user-centric matching and provides more accurate suggestions for potential interactions.

[0321] First, users register with the system using messaging apps such as LINE. Registration includes basic profile information, hobby categories, and answers to a personality assessment. Once registration is complete, the server periodically collects the user's communication history. This history data contains detailed records of how the user exchanges messages and the content of those messages.

[0322] The server activates an emotion engine along with a generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to identify the user's personality traits and interests. Meanwhile, the emotion engine recognizes the user's emotional state from the text data and estimates how the user felt during the period of the messages.

[0323] Next, the terminal generates a user-specific virtual agent based on the analysis results obtained from the server. This agent simulates the user's personality traits, interests, and emotional state, and reflects this information when interacting with other virtual agents. It also adjusts the content of the conversation as appropriate according to the emotional state recognized by the emotion engine.

[0324] The server matches the generated virtual agents with other users' virtual agents and automatically initiates conversations. These conversations involve dynamic communication based on emotional states. The server collects the conversation results and calculates a compatibility score. This score considers not only shared interests but also emotional compatibility.

[0325] Users are presented with optimal interaction candidates and can initiate interactions with those they are interested in. Once an interaction takes place, feedback is sent back to the server to help refine the virtual agent and emotion engine.

[0326] For example, if user C is interested in music and technology and is generally in a calm emotional state, and user D has similar interests and emotional characteristics, the virtual agent will lead the conversation through relaxed topics and news about the latest technology. In this way, interactions based on the users' actual states are formed, fostering connections that are both trustworthy and enjoyable.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] Users add official bots for LINE and other messaging platforms and register an account. Registration involves providing basic profile information, selecting hobby categories, and answering personality assessment questions.

[0330] Step 2:

[0331] The server receives user registration information and stores it in the database. Once registration is complete, it prepares to periodically monitor user communication records.

[0332] Step 3:

[0333] Every time a user communicates via LINE or other messaging apps, their chat history is collected on the server. This history includes information such as the content of the messages and the time they were sent and received.

[0334] Step 4:

[0335] The server passes the collected communication history to a generating AI to identify the user's personality traits and interests. Using natural language processing technology, the AI ​​extracts the user's hobbies and personality from frequently occurring words and topics.

[0336] Step 5:

[0337] Simultaneously, the emotion engine activates and analyzes the user's emotional state from the message content. This analysis helps recognize the user's emotions during the conversation.

[0338] Step 6:

[0339] The terminal generates a virtual agent that simulates the user's characteristics based on analysis results from the server. This agent incorporates the user's personality, interests, and emotional state.

[0340] Step 7:

[0341] The server automatically matches the generated virtual agent with another user's agent and initiates a conversation. The conversation consists of questions to explore common interests and includes adjustments that respond to emotional states.

[0342] Step 8:

[0343] The server aggregates the results of conversations between virtual agents and calculates a compatibility score. This score is calculated based on factors such as shared hobbies, the activity level of the conversation, and emotional compatibility.

[0344] Step 9:

[0345] The server presents the user with a list of highly compatible interaction candidates. The user can then select an interaction with someone they are interested in from the suggested candidates and begin actual communication.

[0346] Step 10:

[0347] The server analyzes feedback collected from users and uses it to adjust the behavior of the virtual agent and the emotion engine. This allows for improvements so that the next match will be even more accurate.

[0348] (Example 2)

[0349] 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".

[0350] In modern society, communication between users takes place through a variety of means, but selecting the most suitable interaction partners for each user based on their communication history is a challenging task. In particular, conventional systems lack the ability to suggest interaction partners that take into account not only personality and interests, but also emotional states, thus requiring matching based on a deeper understanding.

[0351] 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.

[0352] In this invention, the server includes means for analyzing the user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common topics based on their emotional state. This makes it possible to propose interaction candidates to the user that take into account a more accurate emotional compatibility.

[0353] "Communication history" refers to the history of messages sent and received by a user through messaging applications or other communication methods, and includes information such as text content, date and time of sending, and recipient.

[0354] "Personality traits" are indicators that show a user's personality and behavioral tendencies, and are based on data extracted from their past actions and statements.

[0355] "Interest tendencies" refer to the preferences and directions of interest that a user shows towards specific topics or activities, and are analyzed from communication history and other sources.

[0356] "Emotional state" refers to the state of a user's emotions during communication, and is the mood or emotional state inferred from the content and expression of the message.

[0357] A "virtual agent" is a computer program that simulates the characteristics of a user and is a pseudo-entity created to interact with other virtual agents.

[0358] The "compatibility score" is a numerical value calculated based on the results of conversations between virtual agents, and it is an indicator of the compatibility of shared interests and emotions.

[0359] The term "emotion engine" refers to a set of functions that encompass algorithms and technologies for analyzing a user's emotional state from their messages.

[0360] This invention provides a system that analyzes a user's communication history to comprehensively understand the user's personality traits, interests, and emotional state. The embodiments thereof are described in detail below.

[0361] First, users register for the system using a messaging application. During this process, their profile information, hobby-related information, and personality assessment responses are stored in a database. The user interface is implemented using a standard smartphone or computer.

[0362] The server periodically collects the communication history of registered users. This history includes detailed data such as message content, sending date and time, and recipient user. This data is transferred to the server via an API.

[0363] The server uses a generative AI model and an emotion engine to analyze the collected data. The generative AI model uses natural language processing techniques (e.g., BERT and GPT) to identify the user's personality traits and interests. Meanwhile, the emotion engine performs sentiment analysis on the text of the communication history to understand the user's emotional state.

[0364] Based on the analysis results, the terminal generates a virtual agent specifically for the user. This agent follows prompts created by the generated AI model and simulates conversations that reflect the user's characteristics. The virtual agent interacts with other users' agents and searches for appropriate topics based on their emotional state.

[0365] For example, if a user's past communication history reveals a high level of interest in "music" and "latest technology," and that they are in a "calm" emotional state, this user's virtual agent will engage in "relaxed conversations about music" and "topics about new technologies" with agents possessing similar characteristics. An example of a prompt given to the generative AI model would be: "Analyze the user's personality traits from their communication history and derive their interest nodes. Consider the user's emotional state and output the results."

[0366] This embodiment makes it possible to propose the optimal interaction candidate to the user, taking into account emotional compatibility.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users perform initial registration using a messaging application. Input includes user profile information, information about hobbies, and responses to a personality assessment. Based on this input data, the system saves the user's initial profile to a database. Specifically, the user fills in information in an input form and presses the submit button, at which point the data is transferred to the server.

[0370] Step 2:

[0371] The server periodically collects the communication history of registered users. Input includes the user's past message content, sending date and time, and recipient. The server extracts this data using an API and automatically stores it in a database. The output is a communication history dataset prepared for analysis. Specifically, the server periodically makes API calls to collect and store the necessary data.

[0372] Step 3:

[0373] The server activates a generative AI model and an emotion engine to analyze the collected communication history data. The input data is the communication history obtained in step 2. The generative AI model uses natural language processing techniques to analyze the user's personality traits and interests. The emotion engine evaluates the emotional state from the text data. The output is the analysis results, including each user's personality traits, interests, and emotional state. Specifically, the server sends prompt messages to the AI ​​model and receives the analysis results.

[0374] Step 4:

[0375] The terminal generates a user-specific virtual agent based on the analysis results obtained from the server. The input is the analysis results obtained in step 3. From these results, the virtual agent incorporates data to simulate the user's characteristics. The output is a virtual agent customized for each user. Specifically, the terminal sets the metadata of the virtual agent based on the analysis results and prepares a dialogue scenario based on it.

[0376] Step 5:

[0377] The server matches multiple virtual agents and initiates dialogue. The input includes characteristic information of the generated virtual agents. Based on this information, the server calculates a compatibility score and searches for common topics based on emotional states. The output is the optimal interaction candidates and dialogue content. Specifically, the server schedules dialogues between virtual agents, taking emotional compatibility into consideration, and monitors the communication.

[0378] Step 6:

[0379] The system presents the user with the most suitable interaction candidates. The input is the compatibility score calculated in step 5 and the conversation results. The user can then start an actual interaction with someone they are interested in from the presented candidates. The output is a list of interaction candidates provided to the user. Specifically, the terminal displays the list via the user interface and presents the user with choices.

[0380] (Application Example 2)

[0381] 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."

[0382] In today's information-saturated society, users often find it difficult to quickly find content that suits their interests and emotional state. Furthermore, in user interactions, it is desirable that not only shared interests but also emotional compatibility be considered. This invention aims to solve these problems and provide users with a personalized and enriching content experience and optimal matching with other users.

[0383] 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.

[0384] In this invention, the server includes means for analyzing a user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common interests and emotional compatibility. This enables the suggestion of more appropriate and personalized content to the user and the selection of emotionally appropriate interaction candidates.

[0385] "User communication history" refers to a collection of information including records of messages sent and received by individual users in the past, as well as the content of those conversations.

[0386] "Personality traits" refer to a set of specific psychological characteristics that characterize the behavior and thought patterns of individual users.

[0387] "Interest tendencies" refer to information that indicates the subjects and activities that an individual user is particularly interested in.

[0388] "Emotional state" refers to information that describes the characteristics of the emotions and moods a user is experiencing at a particular point in time.

[0389] A "virtual agent" is a software entity generated to simulate a user's personality traits, interests, and emotional state.

[0390] "Emotional compatibility" is an indicator that shows the emotional compatibility between different users and is a criterion for promoting good communication.

[0391] "Content recommendation" refers to the act of determining the content of information and entertainment that users should watch or read, based on their characteristics and circumstances.

[0392] "Potential interaction partners" are other users who may be of interest to the user, and who are people with whom the user should attempt to communicate.

[0393] The system realizing this invention includes a function to analyze the user's communication history and identify personality traits, interest tendencies, and emotional states. The server uses this information to generate a virtual agent. The virtual agent can represent the user's characteristics and interact with other virtual agents. Based on the results of this interaction, the system suggests optimal interaction candidates and content to the user based on common interests and emotional compatibility.

[0394] This system uses devices such as smartphones and tablets as hardware, and utilizes libraries for natural language processing (e.g., Transformers by Hugging Face) and SQL or NoSQL (such as MongoDB) for database management as software. Furthermore, by using machine learning engines (such as Scikit-learn or TensorFlow), content recommendations based on user interests are realized.

[0395] As a concrete example, if user A, who is interested in sports, wants to relax, the system can automatically recommend relaxing music or video content based on their past communication history and current emotional state. This allows the user to easily enjoy appropriate content.

[0396] Because it utilizes a generative AI model, it can use prompts such as, "Based on user A's historical data, generate content that suggests the best music playlist for sports that corresponds to a relaxed emotional state. Consider past behavioral patterns and the results of sentiment analysis, and present recommendations that are updated in real time."

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The server periodically collects communication history data from the user's device. The input is text data obtained from messaging applications. This data includes the content of messages sent and received by the user. The output is the raw data of the collected text.

[0400] Step 2:

[0401] The server analyzes the collected communication history using a generative AI model. The input is the text data obtained in step 1. Natural language processing is performed on this data to identify the user's personality traits, interests, and emotional state. The output is data on the identified personality traits and interests.

[0402] Step 3:

[0403] The server generates a virtual agent based on identified personality traits, interests, and emotional states. The input is the trait data, which is the output of step 2. This data is used to create a virtual agent that simulates the user's characteristics. The output is a profile of the generated virtual agent.

[0404] Step 4:

[0405] The server initiates a process in which each generated virtual agent interacts with one another. The input consists of profiles of multiple virtual agents. These profiles communicate with each other to explore common interests and emotional compatibility. The output is the collection of relevant information derived from these interactions.

[0406] Step 5:

[0407] The server analyzes the results of conversations between virtual agents and suggests the most suitable interaction candidates and content for the user. The input is the conversation result data obtained from step 4. Using this, it calculates indicators of common interests and emotional compatibility and generates suggestions. The output presents recommendations for the most suitable interactions and content for the user.

[0408] Step 6:

[0409] The user's device displays the most suitable content and interaction suggestions sent from the server. The input is the suggestion data provided by the server. The output is a list of suggested content and interaction suggestions presented to the user.

[0410] This process allows users to quickly find content and the most suitable people to interact with that match their interests and emotional state.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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".

[0427] This invention is a system that uses a communication platform to enable matching users based on their hobbies and interests. This system analyzes the user's conversation history using messaging apps such as LINE to understand their personality traits and interests. Based on this analysis, a user-specific virtual agent is generated, which automatically interacts with other users' virtual agents. Finally, based on the results of the virtual agent interactions, the system presents suitable interaction candidates to the user.

[0428] Specifically, users first add the system's LINE bot as a friend and enter basic settings, hobby categories, and personality assessment results to begin using the service. As the user's daily messages accumulate, the server collects them, and a generating AI analyzes them. This analysis reveals the user's unique personality traits and interests, and a virtual agent is generated based on this data.

[0429] The generated virtual agents simulate interactions with other virtual agents, exploring common interests and characteristics. The interaction information obtained during this process is analyzed by the server, and a compatibility score is calculated. This prepares the server to suggest the most suitable partner to the user.

[0430] For example, suppose user A is interested in cooking, and user B is also interested in cooking and travel. Their virtual agents will converse through topics related to cooking, exchanging information about common recipes and favorite dishes. Based on this information exchange, the server will determine whether the two users are suitable as hobby friends and make suggestions based on the high compatibility score.

[0431] Thus, this invention aims to provide more appropriate user matching and natural interaction based on realistic communication data. User feedback after interaction is sent to the server and used to improve the virtual agent and recommendation algorithm. This continuously improves the overall effectiveness of the system.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] Users become friends with a bot on messaging apps like LINE and complete the initial registration. This registration includes entering basic profile information, hobby categories, and answering personality tests.

[0435] Step 2:

[0436] The server periodically collects the user's communication history. This history data includes the user's everyday conversations and messaging activity.

[0437] Step 3:

[0438] The server applies generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to automatically extract the user's personality traits and interests.

[0439] Step 4:

[0440] The terminal generates a user-specific virtual agent based on the analysis results received from the server. This virtual agent is designed to simulate the user's characteristics and preferences.

[0441] Step 5:

[0442] The server automatically matches the generated virtual agents with the virtual agents of other users and allows them to interact. The interaction is programmatically controlled, with questions and information exchange taking place to explore common interests.

[0443] Step 6:

[0444] The server calculates a compatibility score based on the content of the conversations between virtual agents. The compatibility score is determined by multiple indicators, such as the number of common interests and the activity level of the conversation.

[0445] Step 7:

[0446] The server presents users with potential partners who have high compatibility scores. These suggestions include shared hobbies and related information.

[0447] Step 8:

[0448] Users can review the suggested candidates and begin interacting with those they are interested in. They can also send feedback about this interaction experience to the server.

[0449] Step 9:

[0450] Based on feedback received from users, the server improves the operation of virtual agents and matching algorithms, and uses this information for future matching.

[0451] (Example 1)

[0452] 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."

[0453] Traditional communication platforms lack the technology to efficiently and effectively match users based on their hobbies and interests. This prevents users from fully utilizing opportunities to interact with other users who potentially share similar interests. Furthermore, there are insufficient means to improve the overall system accuracy by leveraging feedback from interactions.

[0454] 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.

[0455] In this invention, the server includes means for analyzing the communication history and identifying the characteristics and interests of the person who initiated the communication; means for generating a virtual entity that simulates the characteristics of the person who initiated the communication based on the identified characteristics and interests; and means for each generated virtual entity to exchange information with each other and explore common interests. This enables the effective suggestion of optimal interaction candidates based on common hobbies and interests among users, and further allows for continuous improvement of the system based on feedback.

[0456] "Communication history" refers to the recorded data of messages and conversations that took place between users.

[0457] "Characteristic traits" refer to an individual's personality, behavioral patterns, and psychological characteristics.

[0458] "Interest" refers to the areas or topics that an individual is particularly interested in.

[0459] A "virtual entity" is a digital agent simulated based on the user's characteristics and interests.

[0460] "Information exchange" refers to the act of virtual entities sharing data and messages with each other.

[0461] A "compatibility index" is an evaluation criterion that quantifies the commonalities and interesting aspects between two parties.

[0462] A "potential interaction partner" refers to a user who is deemed to be a good match for another user and is recommended for interaction.

[0463] This system facilitates matching users based on their hobbies and interests through a communication platform.

[0464] First, the user accesses a messaging app on their device and adds the system's bot as a friend. The user then enters basic information, such as their hobby categories and personality test results, and sends this information from their device to the server.

[0465] The server collects the user's daily message history via a messaging API and stores it in a database. The collected data is analyzed using a generative AI model to extract the user's characteristics and interests. The prompt used for this is "Analyze the user's personality and interests."

[0466] Based on the analysis results, the server creates a virtual entity specifically for the user. This virtual entity simulates the user's personality and interests and exchanges information through interaction with other virtual entities.

[0467] Specifically, for example, if a user is interested in cooking, the server connects them with virtual representations of other users who are also interested in cooking, and these virtual representations interact about common recipes and favorite dishes. By analyzing this information exchange, the server suggests suitable interaction candidates for the user. Based on the generated compatibility index, it connects the user with users who have a high compatibility, thereby supporting more natural and effective interactions.

[0468] Furthermore, users send feedback to the server after their interactions, and the system continuously improves its virtual entities and recommendation algorithms based on this feedback. This promotes mutual understanding among users and improves the overall accuracy and effectiveness of the system.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user uses their device to add the system's bot as a friend and enters their hobby categories and personality test results. This basic information is sent from the device to the server, thus completing the user's initial profile data.

[0472] Step 2:

[0473] The server collects the user's daily message history through a messaging API. The entered message data is stored in a database in text format. This allows the user's communication history to be accumulated.

[0474] Step 3:

[0475] The server uses a generative AI model to analyze the accumulated message data. Based on the specified prompt "Analyze the user's personality and interests," it analyzes the data using natural language processing techniques to extract the user's characteristics and interests. The analysis results are obtained as output.

[0476] Step 4:

[0477] Based on the analysis results, the server generates a virtual entity specifically for the user. This virtual entity reflects the user's characteristics and interests. This virtual entity is then ready for simulation within the system.

[0478] Step 5:

[0479] The server connects the generated virtual entities and performs dialogue simulations. The virtual entities exchange information about common interests and personality traits. The dialogue results are obtained as output and are used to select potential partners for subsequent interactions.

[0480] Step 6:

[0481] The server analyzes the conversation results and calculates a compatibility index using a generative AI model. This creates a compatibility score between users. Users with the best compatibility can then be suggested as potential interaction partners.

[0482] Step 7:

[0483] Users interact with the suggested interaction candidates and send feedback to the server. Based on this feedback, the server uses it to improve virtual entities and recommendation algorithms, and to enhance the overall accuracy of the system.

[0484] (Application Example 1)

[0485] 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."

[0486] In today's world, where users are exposed to vast amounts of information online, finding content that best suits their interests and personality is not easy. Furthermore, naturally initiating interactions with other users who share similar interests is also difficult. In this context, there is a need to provide content tailored to each individual user and to facilitate interaction.

[0487] 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.

[0488] In this invention, the server includes means for analyzing a user's communication history and identifying the user's characteristics and interests; means for generating a virtual agent that simulates the user's personality based on the identified characteristics and interests; means for each generated virtual agent to interact with each other and explore common interests; and data processing means for providing content based on the user's interests. This allows the user to receive suggestions for content that matches their interests and facilitates natural interaction with other users.

[0489] A "user" refers to an individual whose communication history is analyzed using the system.

[0490] "Communication history" refers to the record of conversations and messages that a user has exchanged online.

[0491] "Characteristics" refer to features that indicate a user's personality and behavioral tendencies.

[0492] "Interest" refers to themes or topics that a user is interested in.

[0493] A "virtual agent" refers to a simulated entity that is generated based on the user's characteristics and interests and interacts with other agents.

[0494] "Hobbies" refer to activities or areas of interest that users enjoy personally.

[0495] "Data processing means" refers to technologies and methods for collecting and analyzing user-related information.

[0496] "Content" refers to media materials such as information, videos, and news articles provided to users.

[0497] "Compatibility rating" refers to a numerical value that represents the degree of compatibility in the relationship between users, calculated based on the results of conversations between virtual agents.

[0498] The system that realizes this invention includes a communication terminal and a server. First, the user uses the communication terminal to communicate daily through a messaging application. At this time, the communication terminal automatically sends the user's communication history to the server. The server analyzes the user's characteristics and interests based on the received communication history. A generative AI model is used for this analysis, making it possible to understand the user's personality traits and interest tendencies. As a specific example, the analysis is performed by inputting a prompt sentence such as "Please give me information on the latest technology trends" into the model.

[0499] Based on the analysis results, the server generates a virtual agent optimized for the user. This virtual agent engages in simulated conversations with other users' virtual agents, searching for agents with shared hobbies and interests. The server analyzes the results of these conversations and calculates a compatibility rating between each agent. Based on this, the server suggests new content and interactions that the user might be interested in.

[0500] For example, if a user enjoys cooking and traveling, and these topics frequently appear in their conversation history, the server will recommend media content to the user, including the latest cooking recipes and travel information. It will also suggest opportunities to interact with other users who share similar interests, enabling the user to build new relationships. This personalizes the user's online experience and supports the creation of richer relationship networks.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The server receives the user's communication history sent from the terminal. The input is a record of the user's interactions in the messaging application. The server saves this data and prepares it for analysis. The output is a set of saved communication history data.

[0504] Step 2:

[0505] The server analyzes user characteristics and interests based on stored communication history data. This analysis utilizes a generative AI model. The input is communication history data, which the AI ​​model analyzes to generate a profile of characteristics and interests. The output is a profile of the user's specific personality traits and interest tendencies.

[0506] Step 3:

[0507] The server generates virtual agents based on the generated user characteristics and interest profiles. The input is the characteristics and interest profiles. Based on these characteristics and interests, the server defines the virtual agent's behavioral parameters and interaction style. The output is an individual virtual agent.

[0508] Step 4:

[0509] The server simulates interaction between other users' virtual agents using the generated virtual agents. The input consists of multiple virtual agents, which explore common interests and hobbies through virtual dialogue. The output is a dialogue log and a list of commonalities between each agent.

[0510] Step 5:

[0511] The server analyzes the results of conversations between virtual agents and calculates a compatibility rating between users. The input consists of conversation logs and a list of commonalities. Data calculations are performed to obtain a numerical compatibility score. The output is the compatibility score between users.

[0512] Step 6:

[0513] The server suggests suitable content and interaction options for the user based on their compatibility score and interest profile. The input is the compatibility score and interest profile, and it generates a list of content and interactions to provide to the user. The output is the suggested content for the user.

[0514] 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.

[0515] This invention is a system that analyzes a user's communication history and combines it with an emotion engine to comprehensively understand their personality traits, interests, and emotional state. This system enables user-centric matching and provides more accurate suggestions for potential interactions.

[0516] First, users register with the system using messaging apps such as LINE. Registration includes basic profile information, hobby categories, and answers to a personality assessment. Once registration is complete, the server periodically collects the user's communication history. This history data contains detailed records of how the user exchanges messages and the content of those messages.

[0517] The server activates an emotion engine along with a generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to identify the user's personality traits and interests. Meanwhile, the emotion engine recognizes the user's emotional state from the text data and estimates how the user felt during the period of the messages.

[0518] Next, the terminal generates a user-specific virtual agent based on the analysis results obtained from the server. This agent simulates the user's personality traits, interests, and emotional state, and reflects this information when interacting with other virtual agents. It also adjusts the content of the conversation as appropriate according to the emotional state recognized by the emotion engine.

[0519] The server matches the generated virtual agents with other users' virtual agents and automatically initiates conversations. These conversations involve dynamic communication based on emotional states. The server collects the conversation results and calculates a compatibility score. This score considers not only shared interests but also emotional compatibility.

[0520] Users are presented with optimal interaction candidates and can initiate interactions with those they are interested in. Once an interaction takes place, feedback is sent back to the server to help refine the virtual agent and emotion engine.

[0521] For example, if user C is interested in music and technology and is generally in a calm emotional state, and user D has similar interests and emotional characteristics, the virtual agent will lead the conversation through relaxed topics and news about the latest technology. In this way, interactions based on the users' actual states are formed, fostering connections that are both trustworthy and enjoyable.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] Users add official bots for LINE and other messaging platforms and register an account. Registration involves providing basic profile information, selecting hobby categories, and answering personality assessment questions.

[0525] Step 2:

[0526] The server receives user registration information and stores it in the database. Once registration is complete, it prepares to periodically monitor user communication records.

[0527] Step 3:

[0528] Every time a user communicates via LINE or other messaging apps, their chat history is collected on the server. This history includes information such as the content of the messages and the time they were sent and received.

[0529] Step 4:

[0530] The server passes the collected communication history to a generating AI to identify the user's personality traits and interests. Using natural language processing technology, the AI ​​extracts the user's hobbies and personality from frequently occurring words and topics.

[0531] Step 5:

[0532] Simultaneously, the emotion engine activates and analyzes the user's emotional state from the message content. This analysis helps recognize the user's emotions during the conversation.

[0533] Step 6:

[0534] The terminal generates a virtual agent that simulates the user's characteristics based on analysis results from the server. This agent incorporates the user's personality, interests, and emotional state.

[0535] Step 7:

[0536] The server automatically matches the generated virtual agent with another user's agent and initiates a conversation. The conversation consists of questions to explore common interests and includes adjustments that respond to emotional states.

[0537] Step 8:

[0538] The server aggregates the results of conversations between virtual agents and calculates a compatibility score. This score is calculated based on factors such as shared hobbies, the activity level of the conversation, and emotional compatibility.

[0539] Step 9:

[0540] The server presents the user with a list of highly compatible interaction candidates. The user can then select an interaction with someone they are interested in from the suggested candidates and begin actual communication.

[0541] Step 10:

[0542] The server analyzes feedback collected from users and uses it to adjust the behavior of the virtual agent and the emotion engine. This allows for improvements so that the next match will be even more accurate.

[0543] (Example 2)

[0544] 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."

[0545] In modern society, communication between users takes place through a variety of means, but selecting the most suitable interaction partners for each user based on their communication history is a challenging task. In particular, conventional systems lack the ability to suggest interaction partners that take into account not only personality and interests, but also emotional states, thus requiring matching based on a deeper understanding.

[0546] 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.

[0547] In this invention, the server includes means for analyzing the user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common topics based on their emotional state. This makes it possible to propose interaction candidates to the user that take into account a more accurate emotional compatibility.

[0548] "Communication history" refers to the history of messages sent and received by a user through messaging applications or other communication methods, and includes information such as text content, date and time of sending, and recipient.

[0549] "Personality traits" are indicators that show a user's personality and behavioral tendencies, and are based on data extracted from their past actions and statements.

[0550] "Interest tendencies" refer to the preferences and directions of interest that a user shows towards specific topics or activities, and are analyzed from communication history and other sources.

[0551] "Emotional state" refers to the state of a user's emotions during communication, and is the mood or emotional state inferred from the content and expression of the message.

[0552] A "virtual agent" is a computer program that simulates the characteristics of a user and is a pseudo-entity created to interact with other virtual agents.

[0553] The "compatibility score" is a numerical value calculated based on the results of conversations between virtual agents, and it is an indicator of the compatibility of shared interests and emotions.

[0554] The term "emotion engine" refers to a set of functions that encompass algorithms and technologies for analyzing a user's emotional state from their messages.

[0555] This invention provides a system that analyzes a user's communication history to comprehensively understand the user's personality traits, interests, and emotional state. The embodiments thereof are described in detail below.

[0556] First, users register for the system using a messaging application. During this process, their profile information, hobby-related information, and personality assessment responses are stored in a database. The user interface is implemented using a standard smartphone or computer.

[0557] The server periodically collects the communication history of registered users. This history includes detailed data such as message content, sending date and time, and recipient user. This data is transferred to the server via an API.

[0558] The server uses a generative AI model and an emotion engine to analyze the collected data. The generative AI model uses natural language processing techniques (e.g., BERT and GPT) to identify the user's personality traits and interests. Meanwhile, the emotion engine performs sentiment analysis on the text of the communication history to understand the user's emotional state.

[0559] Based on the analysis results, the terminal generates a virtual agent specifically for the user. This agent follows prompts created by the generated AI model and simulates conversations that reflect the user's characteristics. The virtual agent interacts with other users' agents and searches for appropriate topics based on their emotional state.

[0560] For example, if a user's past communication history reveals a high level of interest in "music" and "latest technology," and that they are in a "calm" emotional state, this user's virtual agent will engage in "relaxed conversations about music" and "topics about new technologies" with agents possessing similar characteristics. An example of a prompt given to the generative AI model would be: "Analyze the user's personality traits from their communication history and derive their interest nodes. Consider the user's emotional state and output the results."

[0561] This embodiment makes it possible to propose the optimal interaction candidate to the user, taking into account emotional compatibility.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] Users perform initial registration using a messaging application. Input includes user profile information, information about hobbies, and responses to a personality assessment. Based on this input data, the system saves the user's initial profile to a database. Specifically, the user fills in information in an input form and presses the submit button, at which point the data is transferred to the server.

[0565] Step 2:

[0566] The server periodically collects the communication history of registered users. Input includes the user's past message content, sending date and time, and recipient. The server extracts this data using an API and automatically stores it in a database. The output is a communication history dataset prepared for analysis. Specifically, the server periodically makes API calls to collect and store the necessary data.

[0567] Step 3:

[0568] The server activates a generative AI model and an emotion engine to analyze the collected communication history data. The input data is the communication history obtained in step 2. The generative AI model uses natural language processing techniques to analyze the user's personality traits and interests. The emotion engine evaluates the emotional state from the text data. The output is the analysis results, including each user's personality traits, interests, and emotional state. Specifically, the server sends prompt messages to the AI ​​model and receives the analysis results.

[0569] Step 4:

[0570] The terminal generates a user-specific virtual agent based on the analysis results obtained from the server. The input is the analysis results obtained in step 3. From these results, the virtual agent incorporates data to simulate the user's characteristics. The output is a virtual agent customized for each user. Specifically, the terminal sets the metadata of the virtual agent based on the analysis results and prepares a dialogue scenario based on it.

[0571] Step 5:

[0572] The server matches multiple virtual agents and initiates dialogue. The input includes characteristic information of the generated virtual agents. Based on this information, the server calculates a compatibility score and searches for common topics based on emotional states. The output is the optimal interaction candidates and dialogue content. Specifically, the server schedules dialogues between virtual agents, taking emotional compatibility into consideration, and monitors the communication.

[0573] Step 6:

[0574] The system presents the user with the most suitable interaction candidates. The input is the compatibility score calculated in step 5 and the conversation results. The user can then start an actual interaction with someone they are interested in from the presented candidates. The output is a list of interaction candidates provided to the user. Specifically, the terminal displays the list via the user interface and presents the user with choices.

[0575] (Application Example 2)

[0576] 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."

[0577] In today's information-saturated society, users often find it difficult to quickly find content that suits their interests and emotional state. Furthermore, in user interactions, it is desirable that not only shared interests but also emotional compatibility be considered. This invention aims to solve these problems and provide users with a personalized and enriching content experience and optimal matching with other users.

[0578] 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.

[0579] In this invention, the server includes means for analyzing a user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common interests and emotional compatibility. This enables the suggestion of more appropriate and personalized content to the user and the selection of emotionally appropriate interaction candidates.

[0580] "User communication history" refers to a collection of information including records of messages sent and received by individual users in the past, as well as the content of those conversations.

[0581] "Personality traits" refer to a set of specific psychological characteristics that characterize the behavior and thought patterns of individual users.

[0582] "Interest tendencies" refer to information that indicates the subjects and activities that an individual user is particularly interested in.

[0583] "Emotional state" refers to information that describes the characteristics of the emotions and moods a user is experiencing at a particular point in time.

[0584] A "virtual agent" is a software entity generated to simulate a user's personality traits, interests, and emotional state.

[0585] "Emotional compatibility" is an indicator that shows the emotional compatibility between different users and is a criterion for promoting good communication.

[0586] "Content recommendation" refers to the act of determining the content of information and entertainment that users should watch or read, based on their characteristics and circumstances.

[0587] "Potential interaction partners" are other users who may be of interest to the user, and who are people with whom the user should attempt to communicate.

[0588] The system realizing this invention includes a function to analyze the user's communication history and identify personality traits, interest tendencies, and emotional states. The server uses this information to generate a virtual agent. The virtual agent can represent the user's characteristics and interact with other virtual agents. Based on the results of this interaction, the system suggests optimal interaction candidates and content to the user based on common interests and emotional compatibility.

[0589] This system uses devices such as smartphones and tablets as hardware, and utilizes libraries for natural language processing (e.g., Transformers by Hugging Face) and SQL or NoSQL (such as MongoDB) for database management as software. Furthermore, by using machine learning engines (such as Scikit-learn or TensorFlow), content recommendations based on user interests are realized.

[0590] As a concrete example, if user A, who is interested in sports, wants to relax, the system can automatically recommend relaxing music or video content based on their past communication history and current emotional state. This allows the user to easily enjoy appropriate content.

[0591] Because it utilizes a generative AI model, it can use prompts such as, "Based on user A's historical data, generate content that suggests the best music playlist for sports that corresponds to a relaxed emotional state. Consider past behavioral patterns and the results of sentiment analysis, and present recommendations that are updated in real time."

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The server periodically collects communication history data from the user's device. The input is text data obtained from messaging applications. This data includes the content of messages sent and received by the user. The output is the raw data of the collected text.

[0595] Step 2:

[0596] The server analyzes the collected communication history using a generative AI model. The input is the text data obtained in step 1. Natural language processing is performed on this data to identify the user's personality traits, interests, and emotional state. The output is data on the identified personality traits and interests.

[0597] Step 3:

[0598] The server generates a virtual agent based on identified personality traits, interests, and emotional states. The input is the trait data, which is the output of step 2. This data is used to create a virtual agent that simulates the user's characteristics. The output is a profile of the generated virtual agent.

[0599] Step 4:

[0600] The server initiates a process in which each generated virtual agent interacts with one another. The input consists of profiles of multiple virtual agents. These profiles communicate with each other to explore common interests and emotional compatibility. The output is the collection of relevant information derived from these interactions.

[0601] Step 5:

[0602] The server analyzes the results of conversations between virtual agents and suggests the most suitable interaction candidates and content for the user. The input is the conversation result data obtained from step 4. Using this, it calculates indicators of common interests and emotional compatibility and generates suggestions. The output presents recommendations for the most suitable interactions and content for the user.

[0603] Step 6:

[0604] The user's device displays the most suitable content and interaction suggestions sent from the server. The input is the suggestion data provided by the server. The output is a list of suggested content and interaction suggestions presented to the user.

[0605] This process allows users to quickly find content and the most suitable people to interact with that match their interests and emotional state.

[0606] 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.

[0607] 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.

[0608] 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.

[0609] [Fourth Embodiment]

[0610] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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).

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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".

[0623] This invention is a system that uses a communication platform to enable matching users based on their hobbies and interests. This system analyzes the user's conversation history using messaging apps such as LINE to understand their personality traits and interests. Based on this analysis, a user-specific virtual agent is generated, which automatically interacts with other users' virtual agents. Finally, based on the results of the virtual agent interactions, the system presents suitable interaction candidates to the user.

[0624] Specifically, users first add the system's LINE bot as a friend and enter basic settings, hobby categories, and personality assessment results to begin using the service. As the user's daily messages accumulate, the server collects them, and a generating AI analyzes them. This analysis reveals the user's unique personality traits and interests, and a virtual agent is generated based on this data.

[0625] The generated virtual agents simulate interactions with other virtual agents, exploring common interests and characteristics. The interaction information obtained during this process is analyzed by the server, and a compatibility score is calculated. This prepares the server to suggest the most suitable partner to the user.

[0626] For example, suppose user A is interested in cooking, and user B is also interested in cooking and travel. Their virtual agents will converse through topics related to cooking, exchanging information about common recipes and favorite dishes. Based on this information exchange, the server will determine whether the two users are suitable as hobby friends and make suggestions based on the high compatibility score.

[0627] Thus, this invention aims to provide more appropriate user matching and natural interaction based on realistic communication data. User feedback after interaction is sent to the server and used to improve the virtual agent and recommendation algorithm. This continuously improves the overall effectiveness of the system.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] Users become friends with a bot on messaging apps like LINE and complete the initial registration. This registration includes entering basic profile information, hobby categories, and answering personality tests.

[0631] Step 2:

[0632] The server periodically collects the user's communication history. This history data includes the user's everyday conversations and messaging activity.

[0633] Step 3:

[0634] The server applies generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to automatically extract the user's personality traits and interests.

[0635] Step 4:

[0636] The terminal generates a user-specific virtual agent based on the analysis results received from the server. This virtual agent is designed to simulate the user's characteristics and preferences.

[0637] Step 5:

[0638] The server automatically matches the generated virtual agents with the virtual agents of other users and allows them to interact. The interaction is programmatically controlled, with questions and information exchange taking place to explore common interests.

[0639] Step 6:

[0640] The server calculates a compatibility score based on the content of the conversations between virtual agents. The compatibility score is determined by multiple indicators, such as the number of common interests and the activity level of the conversation.

[0641] Step 7:

[0642] The server presents users with potential partners who have high compatibility scores. These suggestions include shared hobbies and related information.

[0643] Step 8:

[0644] Users can review the suggested candidates and begin interacting with those they are interested in. They can also send feedback about this interaction experience to the server.

[0645] Step 9:

[0646] Based on feedback received from users, the server improves the operation of virtual agents and matching algorithms, and uses this information for future matching.

[0647] (Example 1)

[0648] 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".

[0649] Traditional communication platforms lack the technology to efficiently and effectively match users based on their hobbies and interests. This prevents users from fully utilizing opportunities to interact with other users who potentially share similar interests. Furthermore, there are insufficient means to improve the overall system accuracy by leveraging feedback from interactions.

[0650] 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.

[0651] In this invention, the server includes means for analyzing the communication history and identifying the characteristics and interests of the person who initiated the communication; means for generating a virtual entity that simulates the characteristics of the person who initiated the communication based on the identified characteristics and interests; and means for each generated virtual entity to exchange information with each other and explore common interests. This enables the effective suggestion of optimal interaction candidates based on common hobbies and interests among users, and further allows for continuous improvement of the system based on feedback.

[0652] "Communication history" refers to the recorded data of messages and conversations that took place between users.

[0653] "Characteristic traits" refer to an individual's personality, behavioral patterns, and psychological characteristics.

[0654] "Interest" refers to the areas or topics that an individual is particularly interested in.

[0655] A "virtual entity" is a digital agent simulated based on the user's characteristics and interests.

[0656] "Information exchange" refers to the act of virtual entities sharing data and messages with each other.

[0657] A "compatibility index" is an evaluation criterion that quantifies the commonalities and interesting aspects between two parties.

[0658] A "potential interaction partner" refers to a user who is deemed to be a good match for another user and is recommended for interaction.

[0659] This system facilitates matching users based on their hobbies and interests through a communication platform.

[0660] First, the user accesses a messaging app on their device and adds the system's bot as a friend. The user then enters basic information, such as their hobby categories and personality test results, and sends this information from their device to the server.

[0661] The server collects the user's daily message history via a messaging API and stores it in a database. The collected data is analyzed using a generative AI model to extract the user's characteristics and interests. The prompt used for this is "Analyze the user's personality and interests."

[0662] Based on the analysis results, the server creates a virtual entity specifically for the user. This virtual entity simulates the user's personality and interests and exchanges information through interaction with other virtual entities.

[0663] Specifically, for example, if a user is interested in cooking, the server connects them with virtual representations of other users who are also interested in cooking, and these virtual representations interact about common recipes and favorite dishes. By analyzing this information exchange, the server suggests suitable interaction candidates for the user. Based on the generated compatibility index, it connects the user with users who have a high compatibility, thereby supporting more natural and effective interactions.

[0664] Furthermore, users send feedback to the server after their interactions, and the system continuously improves its virtual entities and recommendation algorithms based on this feedback. This promotes mutual understanding among users and improves the overall accuracy and effectiveness of the system.

[0665] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0666] Step 1:

[0667] The user uses their device to add the system's bot as a friend and enters their hobby categories and personality test results. This basic information is sent from the device to the server, thus completing the user's initial profile data.

[0668] Step 2:

[0669] The server collects the user's daily message history through a messaging API. The entered message data is stored in a database in text format. This allows the user's communication history to be accumulated.

[0670] Step 3:

[0671] The server uses a generative AI model to analyze the accumulated message data. Based on the specified prompt "Analyze the user's personality and interests," it analyzes the data using natural language processing techniques to extract the user's characteristics and interests. The analysis results are obtained as output.

[0672] Step 4:

[0673] Based on the analysis results, the server generates a virtual entity specifically for the user. This virtual entity reflects the user's characteristics and interests. This virtual entity is then ready for simulation within the system.

[0674] Step 5:

[0675] The server connects the generated virtual entities and performs dialogue simulations. The virtual entities exchange information about common interests and personality traits. The dialogue results are obtained as output and are used to select potential partners for subsequent interactions.

[0676] Step 6:

[0677] The server analyzes the conversation results and calculates a compatibility index using a generative AI model. This creates a compatibility score between users. Users with the best compatibility can then be suggested as potential interaction partners.

[0678] Step 7:

[0679] Users interact with the suggested interaction candidates and send feedback to the server. Based on this feedback, the server uses it to improve virtual entities and recommendation algorithms, and to enhance the overall accuracy of the system.

[0680] (Application Example 1)

[0681] 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".

[0682] In today's world, where users are exposed to vast amounts of information online, finding content that best suits their interests and personality is not easy. Furthermore, naturally initiating interactions with other users who share similar interests is also difficult. In this context, there is a need to provide content tailored to each individual user and to facilitate interaction.

[0683] 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.

[0684] In this invention, the server includes means for analyzing a user's communication history and identifying the user's characteristics and interests; means for generating a virtual agent that simulates the user's personality based on the identified characteristics and interests; means for each generated virtual agent to interact with each other and explore common interests; and data processing means for providing content based on the user's interests. This allows the user to receive suggestions for content that matches their interests and facilitates natural interaction with other users.

[0685] A "user" refers to an individual whose communication history is analyzed using the system.

[0686] "Communication history" refers to the record of conversations and messages that a user has exchanged online.

[0687] "Characteristics" refer to features that indicate a user's personality and behavioral tendencies.

[0688] "Interest" refers to themes or topics that a user is interested in.

[0689] A "virtual agent" refers to a simulated entity that is generated based on the user's characteristics and interests and interacts with other agents.

[0690] "Hobbies" refer to activities or areas of interest that users enjoy personally.

[0691] "Data processing means" refers to technologies and methods for collecting and analyzing user-related information.

[0692] "Content" refers to media materials such as information, videos, and news articles provided to users.

[0693] "Compatibility rating" refers to a numerical value that represents the degree of compatibility in the relationship between users, calculated based on the results of conversations between virtual agents.

[0694] The system that realizes this invention includes a communication terminal and a server. First, the user uses the communication terminal to communicate daily through a messaging application. At this time, the communication terminal automatically sends the user's communication history to the server. The server analyzes the user's characteristics and interests based on the received communication history. A generative AI model is used for this analysis, making it possible to understand the user's personality traits and interest tendencies. As a specific example, the analysis is performed by inputting a prompt sentence such as "Please give me information on the latest technology trends" into the model.

[0695] Based on the analysis results, the server generates a virtual agent optimized for the user. This virtual agent engages in simulated conversations with other users' virtual agents, searching for agents with shared hobbies and interests. The server analyzes the results of these conversations and calculates a compatibility rating between each agent. Based on this, the server suggests new content and interactions that the user might be interested in.

[0696] For example, if a user enjoys cooking and traveling, and these topics frequently appear in their conversation history, the server will recommend media content to the user, including the latest cooking recipes and travel information. It will also suggest opportunities to interact with other users who share similar interests, enabling the user to build new relationships. This personalizes the user's online experience and supports the creation of richer relationship networks.

[0697] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0698] Step 1:

[0699] The server receives the user's communication history sent from the terminal. The input is a record of the user's interactions in the messaging application. The server saves this data and prepares it for analysis. The output is a set of saved communication history data.

[0700] Step 2:

[0701] The server analyzes user characteristics and interests based on stored communication history data. This analysis utilizes a generative AI model. The input is communication history data, which the AI ​​model analyzes to generate a profile of characteristics and interests. The output is a profile of the user's specific personality traits and interest tendencies.

[0702] Step 3:

[0703] The server generates virtual agents based on the generated user characteristics and interest profiles. The input is the characteristics and interest profiles. Based on these characteristics and interests, the server defines the virtual agent's behavioral parameters and interaction style. The output is an individual virtual agent.

[0704] Step 4:

[0705] The server simulates interaction between other users' virtual agents using the generated virtual agents. The input consists of multiple virtual agents, which explore common interests and hobbies through virtual dialogue. The output is a dialogue log and a list of commonalities between each agent.

[0706] Step 5:

[0707] The server analyzes the results of conversations between virtual agents and calculates a compatibility rating between users. The input consists of conversation logs and a list of commonalities. Data calculations are performed to obtain a numerical compatibility score. The output is the compatibility score between users.

[0708] Step 6:

[0709] The server suggests suitable content and interaction options for the user based on their compatibility score and interest profile. The input is the compatibility score and interest profile, and it generates a list of content and interactions to provide to the user. The output is the suggested content for the user.

[0710] 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.

[0711] This invention is a system that analyzes a user's communication history and combines it with an emotion engine to comprehensively understand their personality traits, interests, and emotional state. This system enables user-centric matching and provides more accurate suggestions for potential interactions.

[0712] First, users register with the system using messaging apps such as LINE. Registration includes basic profile information, hobby categories, and answers to a personality assessment. Once registration is complete, the server periodically collects the user's communication history. This history data contains detailed records of how the user exchanges messages and the content of those messages.

[0713] The server activates an emotion engine along with a generative AI to analyze the collected communication history. The generative AI uses natural language processing techniques to identify the user's personality traits and interests. Meanwhile, the emotion engine recognizes the user's emotional state from the text data and estimates how the user felt during the period of the messages.

[0714] Next, the terminal generates a user-specific virtual agent based on the analysis results obtained from the server. This agent simulates the user's personality traits, interests, and emotional state, and reflects this information when interacting with other virtual agents. It also adjusts the content of the conversation as appropriate according to the emotional state recognized by the emotion engine.

[0715] The server matches the generated virtual agents with other users' virtual agents and automatically initiates conversations. These conversations involve dynamic communication based on emotional states. The server collects the conversation results and calculates a compatibility score. This score considers not only shared interests but also emotional compatibility.

[0716] Users are presented with optimal interaction candidates and can initiate interactions with those they are interested in. Once an interaction takes place, feedback is sent back to the server to help refine the virtual agent and emotion engine.

[0717] For example, if user C is interested in music and technology and is generally in a calm emotional state, and user D has similar interests and emotional characteristics, the virtual agent will lead the conversation through relaxed topics and news about the latest technology. In this way, interactions based on the users' actual states are formed, fostering connections that are both trustworthy and enjoyable.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] Users add official bots for LINE and other messaging platforms and register an account. Registration involves providing basic profile information, selecting hobby categories, and answering personality assessment questions.

[0721] Step 2:

[0722] The server receives user registration information and stores it in the database. Once registration is complete, it prepares to periodically monitor user communication records.

[0723] Step 3:

[0724] Every time a user communicates via LINE or other messaging apps, their chat history is collected on the server. This history includes information such as the content of the messages and the time they were sent and received.

[0725] Step 4:

[0726] The server passes the collected communication history to a generating AI to identify the user's personality traits and interests. Using natural language processing technology, the AI ​​extracts the user's hobbies and personality from frequently occurring words and topics.

[0727] Step 5:

[0728] Simultaneously, the emotion engine activates and analyzes the user's emotional state from the message content. This analysis helps recognize the user's emotions during the conversation.

[0729] Step 6:

[0730] The terminal generates a virtual agent that simulates the user's characteristics based on analysis results from the server. This agent incorporates the user's personality, interests, and emotional state.

[0731] Step 7:

[0732] The server automatically matches the generated virtual agent with another user's agent and initiates a conversation. The conversation consists of questions to explore common interests and includes adjustments that respond to emotional states.

[0733] Step 8:

[0734] The server aggregates the results of conversations between virtual agents and calculates a compatibility score. This score is calculated based on factors such as shared hobbies, the activity level of the conversation, and emotional compatibility.

[0735] Step 9:

[0736] The server presents the user with a list of highly compatible interaction candidates. The user can then select an interaction with someone they are interested in from the suggested candidates and begin actual communication.

[0737] Step 10:

[0738] The server analyzes feedback collected from users and uses it to adjust the behavior of the virtual agent and the emotion engine. This allows for improvements so that the next match will be even more accurate.

[0739] (Example 2)

[0740] 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".

[0741] In modern society, communication between users takes place through a variety of means, but selecting the most suitable interaction partners for each user based on their communication history is a challenging task. In particular, conventional systems lack the ability to suggest interaction partners that take into account not only personality and interests, but also emotional states, thus requiring matching based on a deeper understanding.

[0742] 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.

[0743] In this invention, the server includes means for analyzing the user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common topics based on their emotional state. This makes it possible to propose interaction candidates to the user that take into account a more accurate emotional compatibility.

[0744] "Communication history" refers to the history of messages sent and received by a user through messaging applications or other communication methods, and includes information such as text content, date and time of sending, and recipient.

[0745] "Personality traits" are indicators that show a user's personality and behavioral tendencies, and are based on data extracted from their past actions and statements.

[0746] "Interest tendencies" refer to the preferences and directions of interest that a user shows towards specific topics or activities, and are analyzed from communication history and other sources.

[0747] "Emotional state" refers to the state of a user's emotions during communication, and is the mood or emotional state inferred from the content and expression of the message.

[0748] A "virtual agent" is a computer program that simulates the characteristics of a user and is a pseudo-entity created to interact with other virtual agents.

[0749] The "compatibility score" is a numerical value calculated based on the results of conversations between virtual agents, and it is an indicator of the compatibility of shared interests and emotions.

[0750] The term "emotion engine" refers to a set of functions that encompass algorithms and technologies for analyzing a user's emotional state from their messages.

[0751] This invention provides a system that analyzes a user's communication history to comprehensively understand the user's personality traits, interests, and emotional state. The embodiments thereof are described in detail below.

[0752] First, users register for the system using a messaging application. During this process, their profile information, hobby-related information, and personality assessment responses are stored in a database. The user interface is implemented using a standard smartphone or computer.

[0753] The server periodically collects the communication history of registered users. This history includes detailed data such as message content, sending date and time, and recipient user. This data is transferred to the server via an API.

[0754] The server uses a generative AI model and an emotion engine to analyze the collected data. The generative AI model uses natural language processing techniques (e.g., BERT and GPT) to identify the user's personality traits and interests. Meanwhile, the emotion engine performs sentiment analysis on the text of the communication history to understand the user's emotional state.

[0755] Based on the analysis results, the terminal generates a virtual agent specifically for the user. This agent follows prompts created by the generated AI model and simulates conversations that reflect the user's characteristics. The virtual agent interacts with other users' agents and searches for appropriate topics based on their emotional state.

[0756] For example, if a user's past communication history reveals a high level of interest in "music" and "latest technology," and that they are in a "calm" emotional state, this user's virtual agent will engage in "relaxed conversations about music" and "topics about new technologies" with agents possessing similar characteristics. An example of a prompt given to the generative AI model would be: "Analyze the user's personality traits from their communication history and derive their interest nodes. Consider the user's emotional state and output the results."

[0757] This embodiment makes it possible to propose the optimal interaction candidate to the user, taking into account emotional compatibility.

[0758] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0759] Step 1:

[0760] Users perform initial registration using a messaging application. Input includes user profile information, information about hobbies, and responses to a personality assessment. Based on this input data, the system saves the user's initial profile to a database. Specifically, the user fills in information in an input form and presses the submit button, at which point the data is transferred to the server.

[0761] Step 2:

[0762] The server periodically collects the communication history of registered users. Input includes the user's past message content, sending date and time, and recipient. The server extracts this data using an API and automatically stores it in a database. The output is a communication history dataset prepared for analysis. Specifically, the server periodically makes API calls to collect and store the necessary data.

[0763] Step 3:

[0764] The server activates a generative AI model and an emotion engine to analyze the collected communication history data. The input data is the communication history obtained in step 2. The generative AI model uses natural language processing techniques to analyze the user's personality traits and interests. The emotion engine evaluates the emotional state from the text data. The output is the analysis results, including each user's personality traits, interests, and emotional state. Specifically, the server sends prompt messages to the AI ​​model and receives the analysis results.

[0765] Step 4:

[0766] The terminal generates a user-specific virtual agent based on the analysis results obtained from the server. The input is the analysis results obtained in step 3. From these results, the virtual agent incorporates data to simulate the user's characteristics. The output is a virtual agent customized for each user. Specifically, the terminal sets the metadata of the virtual agent based on the analysis results and prepares a dialogue scenario based on it.

[0767] Step 5:

[0768] The server matches multiple virtual agents and initiates dialogue. The input includes characteristic information of the generated virtual agents. Based on this information, the server calculates a compatibility score and searches for common topics based on emotional states. The output is the optimal interaction candidates and dialogue content. Specifically, the server schedules dialogues between virtual agents, taking emotional compatibility into consideration, and monitors the communication.

[0769] Step 6:

[0770] The system presents the user with the most suitable interaction candidates. The input is the compatibility score calculated in step 5 and the conversation results. The user can then start an actual interaction with someone they are interested in from the presented candidates. The output is a list of interaction candidates provided to the user. Specifically, the terminal displays the list via the user interface and presents the user with choices.

[0771] (Application Example 2)

[0772] 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".

[0773] In today's information-saturated society, users often find it difficult to quickly find content that suits their interests and emotional state. Furthermore, in user interactions, it is desirable that not only shared interests but also emotional compatibility be considered. This invention aims to solve these problems and provide users with a personalized and enriching content experience and optimal matching with other users.

[0774] 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.

[0775] In this invention, the server includes means for analyzing a user's communication history and identifying the user's personality traits, interests, and emotional state; means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state; and means for each generated virtual agent to interact with each other and explore common interests and emotional compatibility. This enables the suggestion of more appropriate and personalized content to the user and the selection of emotionally appropriate interaction candidates.

[0776] "User communication history" refers to a collection of information including records of messages sent and received by individual users in the past, as well as the content of those conversations.

[0777] "Personality traits" refer to a set of specific psychological characteristics that characterize the behavior and thought patterns of individual users.

[0778] "Interest tendencies" refer to information that indicates the subjects and activities that an individual user is particularly interested in.

[0779] "Emotional state" refers to information that describes the characteristics of the emotions and moods a user is experiencing at a particular point in time.

[0780] A "virtual agent" is a software entity generated to simulate a user's personality traits, interests, and emotional state.

[0781] "Emotional compatibility" is an indicator that shows the emotional compatibility between different users and is a criterion for promoting good communication.

[0782] "Content recommendation" refers to the act of determining the content of information and entertainment that users should watch or read, based on their characteristics and circumstances.

[0783] "Potential interaction partners" are other users who may be of interest to the user, and who are people with whom the user should attempt to communicate.

[0784] The system realizing this invention includes a function to analyze the user's communication history and identify personality traits, interest tendencies, and emotional states. The server uses this information to generate a virtual agent. The virtual agent can represent the user's characteristics and interact with other virtual agents. Based on the results of this interaction, the system suggests optimal interaction candidates and content to the user based on common interests and emotional compatibility.

[0785] This system uses devices such as smartphones and tablets as hardware, and utilizes libraries for natural language processing (e.g., Transformers by Hugging Face) and SQL or NoSQL (such as MongoDB) for database management as software. Furthermore, by using machine learning engines (such as Scikit-learn or TensorFlow), content recommendations based on user interests are realized.

[0786] As a concrete example, if user A, who is interested in sports, wants to relax, the system can automatically recommend relaxing music or video content based on their past communication history and current emotional state. This allows the user to easily enjoy appropriate content.

[0787] Because it utilizes a generative AI model, it can use prompts such as, "Based on user A's historical data, generate content that suggests the best music playlist for sports that corresponds to a relaxed emotional state. Consider past behavioral patterns and the results of sentiment analysis, and present recommendations that are updated in real time."

[0788] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0789] Step 1:

[0790] The server periodically collects communication history data from the user's device. The input is text data obtained from messaging applications. This data includes the content of messages sent and received by the user. The output is the raw data of the collected text.

[0791] Step 2:

[0792] The server analyzes the collected communication history using a generative AI model. The input is the text data obtained in step 1. Natural language processing is performed on this data to identify the user's personality traits, interests, and emotional state. The output is data on the identified personality traits and interests.

[0793] Step 3:

[0794] The server generates a virtual agent based on identified personality traits, interests, and emotional states. The input is the trait data, which is the output of step 2. This data is used to create a virtual agent that simulates the user's characteristics. The output is a profile of the generated virtual agent.

[0795] Step 4:

[0796] The server initiates a process in which each generated virtual agent interacts with one another. The input consists of profiles of multiple virtual agents. These profiles communicate with each other to explore common interests and emotional compatibility. The output is the collection of relevant information derived from these interactions.

[0797] Step 5:

[0798] The server analyzes the results of conversations between virtual agents and suggests the most suitable interaction candidates and content for the user. The input is the conversation result data obtained from step 4. Using this, it calculates indicators of common interests and emotional compatibility and generates suggestions. The output presents recommendations for the most suitable interactions and content for the user.

[0799] Step 6:

[0800] The user's device displays the most suitable content and interaction suggestions sent from the server. The input is the suggestion data provided by the server. The output is a list of suggested content and interaction suggestions presented to the user.

[0801] This process allows users to quickly find content and the most suitable people to interact with that match their interests and emotional state.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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 as being incorporated by reference.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A means for analyzing a user's communication history and identifying the user's personality traits and interests,

[0826] A means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits and interest tendencies,

[0827] Each generated virtual agent interacts with one another and explores common interests,

[0828] A means of suggesting the most suitable interaction candidates to the user based on the results of conversations between virtual agents,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, comprising means for analyzing information obtained through dialogue between virtual agents and calculating a compatibility score.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising means for improving the operation and suggestions of the generated virtual agent based on feedback obtained from the user.

[0834] "Example 1"

[0835] (Claim 1)

[0836] A means for analyzing the communication history and identifying the characteristics and interests of the person who initiated the communication,

[0837] A means for generating a virtual entity that simulates the characteristics of the person who initiated the communication, based on the identified characteristics and interests,

[0838] Each generated virtual entity has a means of exchanging information with one another and exploring common interests,

[0839] A means of proposing the most suitable communication candidate to the person who initiated the communication, based on the results of information exchange between virtual entities,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising means for analyzing information obtained through information exchange between virtual entities and calculating a compatibility index.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising means for improving the behavior and proposed content of a generated virtual entity based on evaluations obtained from the person who initiated the communication.

[0845] "Application Example 1"

[0846] (Claim 1)

[0847] A means for analyzing a user's communication history and identifying the user's characteristics and interests,

[0848] A means for generating a virtual agent that simulates the user's personality based on the identified characteristics and interests,

[0849] Each generated virtual agent interacts with one another, providing a means to explore common interests,

[0850] A means of suggesting the most suitable interaction candidates to the user based on the results of conversations between virtual agents,

[0851] A data processing means for providing content based on user interests,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising means for analyzing information obtained through dialogue between virtual agents and calculating a compatibility evaluation.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for improving the operation and content provided by the generated virtual agent based on evaluations obtained from users.

[0857] "Example 2 of combining an emotion engine"

[0858] (Claim 1)

[0859] A means for analyzing a user's communication history to identify the user's personality traits, interests, and emotional state,

[0860] A means for generating a virtual agent that simulates the user's characteristics based on identified personality traits, interests, and emotional states,

[0861] A means by which each generated virtual agent interacts with one another and explores common topics based on emotional states,

[0862] A means of suggesting the most suitable interaction candidate to the user based on the results of conversations between virtual agents and emotional compatibility,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, comprising means for analyzing information obtained through dialogue between virtual agents, calculating a compatibility score, and considering emotional compatibility in the score.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising means for adjusting the behavior, suggestions, and emotion engine of a generated virtual agent based on feedback obtained from the user.

[0868] "Application example 2 when combining with an emotional engine"

[0869] (Claim 1)

[0870] A means for analyzing a user's communication history to identify the user's personality traits, interests, and emotional state,

[0871] A means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits, interests, and emotional state,

[0872] A means by which each generated virtual agent interacts with one another and explores common interests and emotional compatibility,

[0873] A means of suggesting optimal interaction candidates and content to the user based on the results of conversations between virtual agents,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising means for analyzing information obtained through dialogue between virtual agents and calculating a compatibility score and a content suggestion score.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for improving the behavior, suggestions, and content recommendations of the generated virtual agent based on feedback obtained from users. [Explanation of Symbols]

[0879] 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 for analyzing a user's communication history and identifying the user's personality traits and interests, A means for generating a virtual agent that simulates the user's characteristics based on the identified personality traits and interest tendencies, Each generated virtual agent interacts with one another and explores common interests, A means of suggesting the most suitable interaction candidates to the user based on the results of conversations between virtual agents, A system that includes this.

2. The system according to claim 1, comprising means for analyzing information obtained through dialogue between virtual agents and calculating a compatibility score.

3. The system according to claim 1, further comprising means for improving the operation and suggestions of the generated virtual agent based on feedback obtained from the user.

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