System

Abstract

An object of the system according to the embodiment is to learn wording and thought patterns specific to a user and function as a proxy for the user.SOLUTION: A system includes a learning unit, a proxy unit, and a topic providing unit. The learning unit learns wording and thinking patterns specific to the user through a dialogue with the user. The agent unit functions as an agent of the user based on the wording and thinking pattern of the user learned by the learning unit. The topic provider participates in the chat on behalf of the user by the agent unit and provides a fresh topic.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not fully realizing a system that can learn a user's unique language and thought patterns and function as a user's proxy.

[0005] The system according to the embodiment aims to learn the user's unique language and thought patterns and function as a proxy for the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning unit, a proxy unit, and a topic providing unit. The learning unit learns a user's unique language and thought patterns through dialogue with the user. The proxy unit functions as a proxy for the user based on the user's language and thought patterns learned by the learning unit. The topic providing unit participates in chats on behalf of the user through the proxy unit and provides fresh topics. [Effects of the Invention]

[0007] The system according to the embodiment can learn the user's unique language and thought patterns and function as a proxy for the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The LINE Bot system according to an embodiment of the present invention is a system that creates a LINE Bot for each user, learns the user's unique language and thought patterns through dialogue with the user, and functions as a "proxy" for the user over time. This allows the LINE Bot system to function as a "proxy" for the user, facilitating communication with friends and family and enriching their lives.

[0029] The LINE Bot system according to the embodiment includes a learning unit, a proxy unit, and a topic providing unit. The learning unit learns a user's unique language and thought patterns through dialogue with the user. For example, the learning unit analyzes phrases and expressions frequently used by the user, and the Bot evolves to imitate them. The learning unit can also analyze the user's responses to specific topics and allow the Bot to learn from them. The learning unit generates prompts based on the content of the user's dialogue, and the generation AI learns based on the prompts. For example, the generation AI analyzes the content of the user's dialogue and learns based on that content. The proxy unit functions as a proxy for the user based on the user's language and thought patterns learned by the learning unit. For example, the proxy unit can participate in chats with friends and family on behalf of the user when the user is busy, and communicate using the user's unique expressions. The proxy unit can also promote conversational excitement while maintaining the user's presence. The proxy unit can also provide fresh topics on the user's behalf. For example, the proxy unit can suggest topics based on the latest news, trends, and the user's interests. The topic providing unit participates in chats on behalf of the user through the proxy unit and provides fresh topics. For example, the topic providing unit provides the latest news and trends to promote lively conversations. The topic providing unit can also suggest topics based on the user's interests. The topic providing unit can also participate in chats with the user's friends and family and provide fresh topics. In this way, the LINE Bot system according to the embodiment functions as a "proxy" for the user, facilitating communication with friends and family and enriching their lives. For example, even when the user is busy, the Bot can communicate on behalf of the user, allowing them to maintain relationships with friends and family. Furthermore, by providing new ideas and topics, conversations can be livened up, making the user's life more fulfilling.

[0030] The learning unit can analyze a user's past chat history and learn language appropriate for specific situations and contexts. For example, the learning unit stores the user's past chat history in a database and analyzes it using natural language processing technology. For example, the learning unit extracts the user's language in specific topics and situations, and the bot learns to imitate it. The learning unit can also analyze a user's chat history and learn language appropriate for specific contexts. For example, the learning unit can identify the language the user uses while working and the language the user uses during breaks, and learn language appropriate for specific situations. The learning unit can also analyze a user's chat history and learn language appropriate for specific situations. For example, the learning unit can identify the language the user uses in specific events or situations, and learn language appropriate for specific situations. This enables more appropriate communication by analyzing a user's past chat history and learning language appropriate for specific situations and contexts.

[0031] The learning unit can analyze the user's voice data and learn language usage based on the tone and rhythm of the voice. The learning unit, for example, collects the user's voice data and analyzes the tone and rhythm using voice analysis technology. For example, the learning unit identifies the pitch and speed of the user's voice when speaking and learns language usage based on the pitch and speed. The learning unit can also analyze the user's voice data and learn language usage based on a specific tone and rhythm. For example, the learning unit can identify the tone and rhythm when the user speaks with emotion and learn language usage based on the pitch and speed. The learning unit can also analyze the user's voice data and learn language usage based on the rhythm. For example, the learning unit can identify the pauses and rhythm when the user speaks and learn language usage based on the rhythm. In this way, analyzing the user's voice data and learning language usage based on the pitch and speed enables more natural communication.

[0032] The learning unit can also include language used on other platforms, such as the user's social media posts and blog articles, in the learning target. The learning unit, for example, collects the user's social media posts and analyzes them using natural language processing technology. For example, it analyzes the content of posts on Twitter and Facebook to learn the user's language and expressions. The learning unit can also collect the user's blog articles and analyze them using natural language processing technology. For example, it analyzes the content of blog articles written by the user to learn specific language and expressions. The learning unit can also include the user's language used on other platforms in the learning target. For example, it analyzes the content of posts on forums and communities in which the user participates to learn specific language and expressions. This enables a wider range of communication by including the user's language used on other platforms, such as social media posts and blog articles, in the learning target.

[0033] The learning unit can also learn the vocabulary of the user's friends and family, enabling more natural communication. For example, the learning unit analyzes chat history with the user's friends and family and learns their vocabulary. For example, it extracts conversation patterns with specific friends and family and trains the bot to imitate them. The learning unit can also analyze social media posts and blog articles to learn the vocabulary of the user's friends and family. For example, it analyzes the content of posts written by the user's friends and family and learns specific vocabulary and expressions. The learning unit can also analyze voice data to learn the vocabulary of the user's friends and family. For example, it collects voice data of the user's friends and family and learns vocabulary based on specific tones and rhythms. This allows the bot to learn the vocabulary of the user's friends and family, enabling more natural communication.

[0034] The proxy unit can integrate the user's schedule and task management information and participate in chats on behalf of the user at the appropriate time. For example, the proxy unit can obtain the user's schedule information from a calendar app and participate in chats on behalf of the user at the appropriate time. For example, a bot can reply on behalf of the user when the user is in a meeting or out of the office. The proxy unit can also integrate the user's task management information and participate in chats on behalf of the user at the appropriate time. For example, the bot can reply on behalf of the user when the user is busy or concentrating on a task. The proxy unit can also participate in chats on behalf of the user at specific times based on the user's schedule and task management information. For example, the bot can reply on behalf of the user before or after an important event or meeting. In this way, the proxy unit can integrate the user's schedule and task management information and participate in chats on behalf of the user at the appropriate time, thereby reducing the burden on the user.

[0035] The agent unit can communicate with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history. For example, the agent unit analyzes the user's past chat history and communicates with the user taking into consideration the user's relationships with specific friends and family members. For example, the agent unit uses casual language with close friends and formal language with business associates. The agent unit can also communicate with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history. For example, the agent unit uses appropriate language and expressions to help the user maintain their relationships with specific friends and family members. The agent unit can also develop an algorithm for communicating with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history. For example, the agent unit analyzes the user's past chat history, evaluates the user's relationships with specific friends and family members, and communicates based on the evaluation. This enables more appropriate responses by communicating with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history.

[0036] The agent unit can learn the user's preferences and interests and provide topics based on them. For example, the agent unit can analyze the user's past chat history and social media posts to learn the user's preferences and interests. For example, it can provide topics based on specific hobbies or interests. The agent unit can also develop algorithms to learn the user's preferences and interests and provide topics based on them. For example, it can analyze the user's past chat history to identify specific preferences and interests and provide topics based on them. The agent unit can also use generative AI to learn the user's preferences and interests and provide topics based on them. For example, the generative AI can suggest new topics based on the user's preferences and interests. This allows the agent unit to learn the user's preferences and interests and provide topics based on them, enabling more interesting conversations.

[0037] The agent unit can also handle business emails and official communications on behalf of the user. For example, the agent unit learns the user's business email templates and creates business emails on behalf of the user. For example, it automatically generates emails based on standard phrases and formats. The agent unit can also handle official communications on behalf of the user. For example, it can create official announcements and press releases. The agent unit can also develop algorithms for handling business emails and official communications on behalf of the user. For example, it can analyze the user's past business emails and official communications and automatically generate emails and communications based on them. In this way, the agent unit can handle business emails and official communications on behalf of the user, reducing the burden on the user.

[0038] The agent unit can also post and comment on SNS on behalf of the user. For example, the agent unit analyzes the user's past SNS posts and posts and comments on their behalf. For example, it automatically generates posts on specific topics and comments to friends. The agent unit can also develop algorithms for posting and commenting on SNS on behalf of the user. For example, it can analyze the user's past SNS posts and automatically generate posts and comments based on them. The agent unit can also use a generation AI to post and comment on SNS on behalf of the user. For example, the generation AI can suggest new posts and comments based on the user's past posts and comments. In this way, the agent unit can post and comment on SNS on behalf of the user, reducing the burden on the user.

[0039] The topic providing unit can provide the latest news and trends based on the user's interests in real time. The topic providing unit, for example, analyzes the user's past chat history and social media posts to identify interests. For example, it provides news and trends related to a specific topic in real time. The topic providing unit can also develop an algorithm for providing the latest news and trends based on the user's interests. For example, it can analyze the user's past chat history to identify specific interests and provide news and trends based on those interests. The topic providing unit can also use a generation AI to provide the latest news and trends based on the user's interests. For example, the generation AI can suggest new news and trends based on the user's interests. This helps to stimulate conversation by providing the latest news and trends based on the user's interests in real time.

[0040] The topic provision unit can analyze the user's past chat history and re-suggest topics that were popular in the past. The topic provision unit, for example, analyzes the user's past chat history and identifies topics that were particularly popular. For example, it re-suggests conversations about specific topics or events. The topic provision unit can also develop an algorithm for analyzing the user's past chat history and re-suggesting topics that were popular in the past. For example, it can analyze the user's past chat history, identify specific topics that were popular, and re-suggest them. The topic provision unit can also use a generation AI to analyze the user's past chat history and re-suggest topics that were popular in the past. For example, the generation AI can suggest new topics based on the user's past chat history. In this way, by analyzing the user's past chat history and re-suggesting topics that were popular in the past, the continuity of the conversation can be enhanced.

[0041] The topic provision unit can also learn the interests of the user's friends and family and provide topics based on them. For example, the topic provision unit can analyze the social media posts and blog articles of the user's friends and family to identify their interests. For example, it can provide topics that interest specific friends and family. The topic provision unit can also develop an algorithm to learn the interests of the user's friends and family and provide topics based on them. For example, it can analyze the social media posts of the user's friends and family to identify specific interests and provide topics based on them. The topic provision unit can also use a generation AI to learn the interests of the user's friends and family and provide topics based on them. For example, the generation AI can suggest new topics based on the interests of the user's friends and family. In this way, the topic provision unit can learn the interests of the user's friends and family and provide topics based on them, thereby promoting lively conversations.

[0042] The topic providing unit can suggest events and activities based on the user's interests. For example, the topic providing unit can analyze the user's past chat history and SNS posts to suggest events and activities based on the user's interests. For example, it can provide events related to specific hobbies or interests. The topic providing unit can also develop an algorithm for suggesting events and activities based on the user's interests. For example, it can analyze the user's past chat history to identify specific interests and suggest events and activities based on those interests. The topic providing unit can also use a generation AI to suggest events and activities based on the user's interests. For example, the generation AI can suggest new events and activities based on the user's interests. In this way, suggesting events and activities based on the user's interests promotes lively conversations.

[0043] The topic providing unit can recommend books and movies based on the user's interests. For example, the topic providing unit analyzes the user's past chat history and social media posts to recommend books and movies based on the user's interests. For example, it can provide works related to a specific genre or theme. The topic providing unit can also develop an algorithm for recommending books and movies based on the user's interests. For example, it can analyze the user's past chat history to identify specific interests and recommend books and movies based on those interests. The topic providing unit can also use a generative AI to recommend books and movies based on the user's interests. For example, the generative AI can suggest new books and movies based on the user's interests. This can encourage lively conversations by recommending books and movies based on the user's interests.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The LINE Bot system can also include a health management module that monitors the user's health and provides appropriate advice. For example, the health management module can collect data from the user's smartwatch or fitness tracker to analyze their daily exercise and sleep patterns. The health management module can also collect the user's food records and evaluate their nutritional balance. Furthermore, the health management module can monitor the user's stress level and suggest relaxation methods. This allows for comprehensive management of the user's health and improves their quality of life.

[0046] The LINE Bot system can also include a learning support module that recommends relevant online courses and learning resources based on the user's hobbies and interests. For example, the learning support module can suggest online courses in areas of interest to the user. The learning support module can also analyze the user's past chat history to provide learning resources based on specific interests. The learning support module can also monitor the user's learning progress and provide appropriate feedback. This can increase the user's motivation to learn and support their self-improvement.

[0047] The LINE Bot system can also be equipped with a travel support module that supports users in planning their trips. For example, the travel support module can analyze the user's past travel history and interests to suggest the next travel destination. The travel support module can also create an optimal travel plan based on the user's schedule. Furthermore, the travel support module can recommend accommodations and tourist spots based on the user's budget and preferences. This allows the system to efficiently support users in planning their trips and provide a more fulfilling travel experience.

[0048] The LINE Bot system can also include a household management module that supports users in managing their household finances. For example, the household management module can monitor the user's income and expenses and set a budget. The household management module can also analyze the user's spending patterns and provide savings advice. Furthermore, the household management module can suggest savings plans based on the user's goals. This allows the system to efficiently support the user's household finances and provide financial stability.

[0049] The LINE Bot system can also include a community support unit that recommends related communities and events based on the user's hobbies and interests. For example, the community support unit can suggest online communities in areas of interest to the user. The community support unit can also analyze the user's past chat history to provide events based on specific interests. Furthermore, the community support unit can recommend related workshops and seminars based on the user's interests. This can encourage participation in communities and events based on the user's hobbies and interests and strengthen social connections.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The learning module learns the user's unique language and thought patterns through dialogue with the user. For example, the learning module analyzes the user's frequently used phrases and expressions, and the Bot evolves to imitate them. The learning module can also analyze the user's reactions to specific topics, and the Bot can learn from them. Furthermore, the learning module generates prompts based on the user's dialogue, and the generation AI learns based on those prompts. Step 2: The proxy unit acts as a proxy for the user based on the user's language and thought patterns learned by the learning unit. For example, when the user is busy, the proxy unit can participate in chats with friends and family on behalf of the user and communicate using the user's unique expressions. The proxy unit can also promote conversational excitement while maintaining the user's presence. Step 3: The topic provider participates in the chat on behalf of the user through the proxy component and provides fresh topics. For example, the topic provider may provide the latest news and trends to encourage lively conversations. The topic provider may also suggest topics based on the user's interests.

[0052] (Example 2) The LINE Bot system according to an embodiment of the present invention is a system that creates a LINE Bot for each user, learns the user's unique language and thought patterns through dialogue with the user, and functions as a "proxy" for the user over time. This allows the LINE Bot system to function as a "proxy" for the user, facilitating communication with friends and family and enriching their lives.

[0053] The LINE Bot system according to the embodiment includes a learning unit, a proxy unit, and a topic providing unit. The learning unit learns a user's unique language and thought patterns through dialogue with the user. For example, the learning unit analyzes phrases and expressions frequently used by the user, and the Bot evolves to imitate them. The learning unit can also analyze the user's responses to specific topics and allow the Bot to learn from them. The learning unit generates prompts based on the content of the user's dialogue, and the generation AI learns based on the prompts. For example, the generation AI analyzes the content of the user's dialogue and learns based on that content. The proxy unit functions as a proxy for the user based on the user's language and thought patterns learned by the learning unit. For example, the proxy unit can participate in chats with friends and family on behalf of the user when the user is busy, and communicate using the user's unique expressions. The proxy unit can also promote conversational excitement while maintaining the user's presence. The proxy unit can also provide fresh topics on the user's behalf. For example, the proxy unit can suggest topics based on the latest news, trends, and the user's interests. The topic providing unit participates in chats on behalf of the user through the proxy unit and provides fresh topics. For example, the topic providing unit provides the latest news and trends to promote lively conversations. The topic providing unit can also suggest topics based on the user's interests. The topic providing unit can also participate in chats with the user's friends and family and provide fresh topics. In this way, the LINE Bot system according to the embodiment functions as a "proxy" for the user, facilitating communication with friends and family and enriching their lives. For example, even when the user is busy, the Bot can communicate on behalf of the user, allowing them to maintain relationships with friends and family. Furthermore, by providing new ideas and topics, conversations can be livened up, making the user's life more fulfilling.

[0054] The learning unit can estimate the user's emotional state in real time and learn language and expressions that correspond to that emotion. For example, the learning unit uses facial expression recognition technology to estimate the user's emotional state in real time. For example, it can analyze the user's facial expressions through a camera and identify emotions such as joy or sadness. This allows the Bot to learn language and expressions that correspond to the user's emotions. The learning unit can also estimate the user's emotional state using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to identify emotions. The learning unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze emotions using an emotion estimation algorithm. For example, it can identify emotions based on fluctuations in heart rate. This allows the Bot to learn language and expressions that correspond to the user's emotional state, enabling more natural communication.

[0055] The learning unit can analyze a user's past chat history and learn language appropriate for specific situations and contexts. For example, the learning unit stores the user's past chat history in a database and analyzes it using natural language processing technology. For example, the learning unit extracts the user's language in specific topics and situations, and the bot learns to imitate it. The learning unit can also analyze a user's chat history and learn language appropriate for specific contexts. For example, the learning unit can identify the language the user uses while working and the language the user uses during breaks, and learn language appropriate for specific situations. The learning unit can also analyze a user's chat history and learn language appropriate for specific situations. For example, the learning unit can identify the language the user uses in specific events or situations, and learn language appropriate for specific situations. This enables more appropriate communication by analyzing a user's past chat history and learning language appropriate for specific situations and contexts.

[0056] The learning unit can analyze the user's voice data and learn language usage based on the tone and rhythm of the voice. The learning unit, for example, collects the user's voice data and analyzes the tone and rhythm using voice analysis technology. For example, the learning unit identifies the pitch and speed of the user's voice when speaking and learns language usage based on the pitch and speed. The learning unit can also analyze the user's voice data and learn language usage based on a specific tone and rhythm. For example, the learning unit can identify the tone and rhythm when the user speaks with emotion and learn language usage based on the pitch and speed. The learning unit can also analyze the user's voice data and learn language usage based on the rhythm. For example, the learning unit can identify the pauses and rhythm when the user speaks and learn language usage based on the rhythm. In this way, analyzing the user's voice data and learning language usage based on the pitch and speed enables more natural communication.

[0057] The learning unit can also include language used on other platforms, such as the user's social media posts and blog articles, in the learning target. The learning unit, for example, collects the user's social media posts and analyzes them using natural language processing technology. For example, it analyzes the content of posts on Twitter and Facebook to learn the user's language and expressions. The learning unit can also collect the user's blog articles and analyze them using natural language processing technology. For example, it analyzes the content of blog articles written by the user to learn specific language and expressions. The learning unit can also include the user's language used on other platforms in the learning target. For example, it analyzes the content of posts on forums and communities in which the user participates to learn specific language and expressions. This enables a wider range of communication by including the user's language used on other platforms, such as social media posts and blog articles, in the learning target.

[0058] The learning unit can also learn the vocabulary of the user's friends and family, enabling more natural communication. For example, the learning unit analyzes chat history with the user's friends and family and learns their vocabulary. For example, it extracts conversation patterns with specific friends and family and trains the bot to imitate them. The learning unit can also analyze social media posts and blog articles to learn the vocabulary of the user's friends and family. For example, it analyzes the content of posts written by the user's friends and family and learns specific vocabulary and expressions. The learning unit can also analyze voice data to learn the vocabulary of the user's friends and family. For example, it collects voice data of the user's friends and family and learns vocabulary based on specific tones and rhythms. This allows the bot to learn the vocabulary of the user's friends and family, enabling more natural communication.

[0059] The learning unit can use the emotion estimation function to learn language based on the user's emotions and provide appropriate expressions according to the emotions. For example, the learning unit uses the emotion estimation function to analyze the user's emotional state in real time and learns language based on the emotions. For example, the learning unit learns positive expressions when the user is happy and comforting words when the user is sad. The learning unit can also use the emotion estimation function to provide appropriate expressions based on the user's emotions. For example, the learning unit provides calm language when the user is angry and cheerful language when the user is having fun. The learning unit can also use the emotion estimation function to learn language based on the user's emotions and provide appropriate expressions according to specific situations. For example, the learning unit provides relaxing language when the user is stressed and calming language when the user is excited. In this way, by using the emotion estimation function to learn language based on the user's emotions and providing appropriate expressions, more natural communication is possible.

[0060] The proxy unit can integrate the user's schedule and task management information and participate in chats on behalf of the user at the appropriate time. For example, the proxy unit can obtain the user's schedule information from a calendar app and participate in chats on behalf of the user at the appropriate time. For example, a bot can reply on behalf of the user when the user is in a meeting or out of the office. The proxy unit can also integrate the user's task management information and participate in chats on behalf of the user at the appropriate time. For example, the bot can reply on behalf of the user when the user is busy or concentrating on a task. The proxy unit can also participate in chats on behalf of the user at specific times based on the user's schedule and task management information. For example, the bot can reply on behalf of the user before or after an important event or meeting. In this way, the proxy unit can integrate the user's schedule and task management information and participate in chats on behalf of the user at the appropriate time, thereby reducing the burden on the user.

[0061] The agent unit can communicate with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history. For example, the agent unit analyzes the user's past chat history and communicates with the user taking into consideration the user's relationships with specific friends and family members. For example, the agent unit uses casual language with close friends and formal language with business associates. The agent unit can also communicate with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history. For example, the agent unit uses appropriate language and expressions to help the user maintain their relationships with specific friends and family members. The agent unit can also develop an algorithm for communicating with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history. For example, the agent unit analyzes the user's past chat history, evaluates the user's relationships with specific friends and family members, and communicates based on the evaluation. This enables more appropriate responses by communicating with the user taking into consideration the user's relationships with specific friends and family members based on the user's past chat history.

[0062] The agent unit can learn the user's preferences and interests and provide topics based on them. For example, the agent unit can analyze the user's past chat history and social media posts to learn the user's preferences and interests. For example, it can provide topics based on specific hobbies or interests. The agent unit can also develop algorithms to learn the user's preferences and interests and provide topics based on them. For example, it can analyze the user's past chat history to identify specific preferences and interests and provide topics based on them. The agent unit can also use generative AI to learn the user's preferences and interests and provide topics based on them. For example, the generative AI can suggest new topics based on the user's preferences and interests. This allows the agent unit to learn the user's preferences and interests and provide topics based on them, enabling more interesting conversations.

[0063] The agent unit can also handle business emails and official communications on behalf of the user. For example, the agent unit learns the user's business email templates and creates business emails on behalf of the user. For example, it automatically generates emails based on standard phrases and formats. The agent unit can also handle official communications on behalf of the user. For example, it can create official announcements and press releases. The agent unit can also develop algorithms for handling business emails and official communications on behalf of the user. For example, it can analyze the user's past business emails and official communications and automatically generate emails and communications based on them. In this way, the agent unit can handle business emails and official communications on behalf of the user, reducing the burden on the user.

[0064] The agent unit can also post and comment on SNS on behalf of the user. For example, the agent unit analyzes the user's past SNS posts and posts and comments on their behalf. For example, it automatically generates posts on specific topics and comments to friends. The agent unit can also develop algorithms for posting and commenting on SNS on behalf of the user. For example, it can analyze the user's past SNS posts and automatically generate posts and comments based on them. The agent unit can also use a generation AI to post and comment on SNS on behalf of the user. For example, the generation AI can suggest new posts and comments based on the user's past posts and comments. In this way, the agent unit can post and comment on SNS on behalf of the user, reducing the burden on the user.

[0065] The agent unit can use the emotion estimation function to participate in chats as a proxy at appropriate times based on the user's emotions. For example, the agent unit uses the emotion estimation function to analyze the user's emotional state in real time and participate in chats as a proxy at appropriate times. For example, the agent unit replies on behalf of the user when the user is feeling stressed. The agent unit can also use the emotion estimation function to develop an algorithm for participating in chats as a proxy at appropriate times based on the user's emotions. For example, the agent unit analyzes the user's emotional state and participates in chats as a proxy at specific times. The agent unit can also use the emotion estimation function to use a generation AI to participate in chats as a proxy at appropriate times based on the user's emotions. For example, the generation AI identifies appropriate times based on the user's emotional state and participates in chats as a proxy. This enables more natural communication by using the emotion estimation function to participate in chats as a proxy at appropriate times based on the user's emotions.

[0066] The topic providing unit can provide the latest news and trends based on the user's interests in real time. The topic providing unit, for example, analyzes the user's past chat history and social media posts to identify interests. For example, it provides news and trends related to a specific topic in real time. The topic providing unit can also develop an algorithm for providing the latest news and trends based on the user's interests. For example, it can analyze the user's past chat history to identify specific interests and provide news and trends based on those interests. The topic providing unit can also use a generation AI to provide the latest news and trends based on the user's interests. For example, the generation AI can suggest new news and trends based on the user's interests. This helps to stimulate conversation by providing the latest news and trends based on the user's interests in real time.

[0067] The topic provision unit can analyze the user's past chat history and re-suggest topics that were popular in the past. The topic provision unit, for example, analyzes the user's past chat history and identifies topics that were particularly popular. For example, it re-suggests conversations about specific topics or events. The topic provision unit can also develop an algorithm for analyzing the user's past chat history and re-suggesting topics that were popular in the past. For example, it can analyze the user's past chat history, identify specific topics that were popular, and re-suggest them. The topic provision unit can also use a generation AI to analyze the user's past chat history and re-suggest topics that were popular in the past. For example, the generation AI can suggest new topics based on the user's past chat history. In this way, by analyzing the user's past chat history and re-suggesting topics that were popular in the past, the continuity of the conversation can be enhanced.

[0068] The topic provision unit can also learn the interests of the user's friends and family and provide topics based on them. For example, the topic provision unit can analyze the social media posts and blog articles of the user's friends and family to identify their interests. For example, it can provide topics that interest specific friends and family. The topic provision unit can also develop an algorithm to learn the interests of the user's friends and family and provide topics based on them. For example, it can analyze the social media posts of the user's friends and family to identify specific interests and provide topics based on them. The topic provision unit can also use a generation AI to learn the interests of the user's friends and family and provide topics based on them. For example, the generation AI can suggest new topics based on the interests of the user's friends and family. In this way, the topic provision unit can learn the interests of the user's friends and family and provide topics based on them, thereby promoting lively conversations.

[0069] The topic providing unit can suggest events and activities based on the user's interests. For example, the topic providing unit can analyze the user's past chat history and SNS posts to suggest events and activities based on the user's interests. For example, it can provide events related to specific hobbies or interests. The topic providing unit can also develop an algorithm for suggesting events and activities based on the user's interests. For example, it can analyze the user's past chat history to identify specific interests and suggest events and activities based on those interests. The topic providing unit can also use a generation AI to suggest events and activities based on the user's interests. For example, the generation AI can suggest new events and activities based on the user's interests. In this way, suggesting events and activities based on the user's interests promotes lively conversations.

[0070] The topic providing unit can recommend books and movies based on the user's interests. For example, the topic providing unit analyzes the user's past chat history and social media posts to recommend books and movies based on the user's interests. For example, it can provide works related to a specific genre or theme. The topic providing unit can also develop an algorithm for recommending books and movies based on the user's interests. For example, it can analyze the user's past chat history to identify specific interests and recommend books and movies based on those interests. The topic providing unit can also use a generative AI to recommend books and movies based on the user's interests. For example, the generative AI can suggest new books and movies based on the user's interests. This can encourage lively conversations by recommending books and movies based on the user's interests.

[0071] The topic providing unit can use the emotion estimation function to provide fresh topics based on the user's emotions and promote lively conversations. The topic providing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide fresh topics based on the emotions. For example, it provides positive topics when the user is happy and comforting topics when the user is sad. The topic providing unit can also use the emotion estimation function to develop an algorithm for providing fresh topics based on the user's emotions. For example, it can analyze the user's emotional state, identify topics based on specific emotions, and provide them. The topic providing unit can also use the emotion estimation function to use a generation AI to provide fresh topics based on the user's emotions. For example, the generation AI can suggest new topics based on the user's emotional state. This allows the emotion estimation function to provide fresh topics based on the user's emotions and promote lively conversations, enabling more natural communication.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The LINE Bot system can also include a health management module that monitors the user's health and provides appropriate advice. For example, the health management module can collect data from the user's smartwatch or fitness tracker to analyze their daily exercise and sleep patterns. The health management module can also collect the user's food records and evaluate their nutritional balance. Furthermore, the health management module can monitor the user's stress level and suggest relaxation methods. This allows for comprehensive management of the user's health and improves their quality of life.

[0074] The LINE Bot system can also include a music recommendation module that estimates the user's emotions and recommends appropriate music based on those emotions. For example, the music recommendation module can recommend upbeat songs when the user is happy and relaxing songs when the user is sad. The music recommendation module can also analyze the user's emotional state and create a playlist based on a specific emotion. Furthermore, the music recommendation module can suggest music genres and artists based on the user's emotions. This allows the system to provide a music experience that is tailored to the user's emotions.

[0075] The LINE Bot system can also include a learning support module that recommends relevant online courses and learning resources based on the user's hobbies and interests. For example, the learning support module can suggest online courses in areas of interest to the user. The learning support module can also analyze the user's past chat history to provide learning resources based on specific interests. The learning support module can also monitor the user's learning progress and provide appropriate feedback. This can increase the user's motivation to learn and support their self-improvement.

[0076] The LINE Bot system can also include a relaxation support unit that estimates the user's emotions and suggests appropriate relaxation methods based on those emotions. For example, the relaxation support unit can suggest deep breathing or meditation when the user is feeling stressed. The relaxation support unit can also analyze the user's emotional state and offer relaxation methods based on specific emotions. Furthermore, the relaxation support unit can suggest relaxing environmental sounds or aromatherapy depending on the user's emotions. This allows the system to provide a relaxation experience that is in tune with the user's emotions.

[0077] The LINE Bot system can also be equipped with a travel support module that supports users in planning their trips. For example, the travel support module can analyze the user's past travel history and interests to suggest the next travel destination. The travel support module can also create an optimal travel plan based on the user's schedule. Furthermore, the travel support module can recommend accommodations and tourist spots based on the user's budget and preferences. This allows the system to efficiently support users in planning their trips and provide a more fulfilling travel experience.

[0078] The LINE Bot system can also include a fitness support unit that estimates the user's emotions and suggests appropriate exercises based on those emotions. For example, the fitness support unit can suggest relaxing yoga or stretching exercises when the user is feeling stressed. The fitness support unit can also analyze the user's emotional state and provide exercises based on specific emotions. Furthermore, the fitness support unit can adjust the intensity and type of exercise depending on the user's emotions. This allows the system to provide a fitness experience that is in tune with the user's emotions.

[0079] The LINE Bot system can also include a household management module that supports users in managing their household finances. For example, the household management module can monitor the user's income and expenses and set a budget. The household management module can also analyze the user's spending patterns and provide savings advice. Furthermore, the household management module can suggest savings plans based on the user's goals. This allows the system to efficiently support the user's household finances and provide financial stability.

[0080] The LINE Bot system can also include a reading support unit that estimates the user's emotions and suggests an appropriate reading list based on those emotions. For example, the reading support unit can recommend light reading material when the user wants to relax, and books with deeper content when the user wants to concentrate. The reading support unit can also analyze the user's emotional state and provide a reading list based on a specific emotion. Furthermore, the reading support unit can adjust the reading pace and genre according to the user's emotions. This allows the system to provide a reading experience that is attuned to the user's emotions.

[0081] The LINE Bot system can also include a community support unit that recommends related communities and events based on the user's hobbies and interests. For example, the community support unit can suggest online communities in areas of interest to the user. The community support unit can also analyze the user's past chat history to provide events based on specific interests. Furthermore, the community support unit can recommend related workshops and seminars based on the user's interests. This can encourage participation in communities and events based on the user's hobbies and interests and strengthen social connections.

[0082] The LINE Bot system can also include an entertainment support unit that estimates the user's emotions and recommends appropriate movies and dramas based on those emotions. For example, the entertainment support unit can recommend comedy movies when the user wants to relax, and drama movies when the user wants to be moved. The entertainment support unit can also analyze the user's emotional state and provide movies and dramas based on specific emotions. Furthermore, the entertainment support unit can adjust the genre and theme of movies and dramas according to the user's emotions. This allows the system to provide an entertainment experience that is tailored to the user's emotions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The learning module learns the user's unique language and thought patterns through dialogue with the user. For example, the learning module analyzes the user's frequently used phrases and expressions, and the Bot evolves to imitate them. The learning module can also analyze the user's reactions to specific topics, and the Bot can learn from them. Furthermore, the learning module generates prompts based on the user's dialogue, and the generation AI learns based on those prompts. Step 2: The proxy unit acts as a proxy for the user based on the user's language and thought patterns learned by the learning unit. For example, when the user is busy, the proxy unit can participate in chats with friends and family on behalf of the user and communicate using the user's unique expressions. The proxy unit can also promote conversational excitement while maintaining the user's presence. Step 3: The topic provider participates in the chat on behalf of the user through the proxy component and provides fresh topics. For example, the topic provider may provide the latest news and trends to encourage lively conversations. The topic provider may also suggest topics based on the user's interests.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a learning unit that learns a user's unique language and thought patterns through dialogue with the user; an agent unit that functions as a proxy for the user based on the user's language and thought patterns learned by the learning unit; a topic providing unit that participates in chat on behalf of the user by the proxy unit and provides fresh topics. A system characterized by:

2. The learning unit The emotional state of the user is estimated in real time, and the language and expressions corresponding to the emotion are learned.

2. The system of claim 1.

3. The learning unit The learning target also includes the user's use of the language on other platforms, such as social media posts and blog articles.

2. The system of claim 1.

4. The agent unit is Integrates schedule and task management information of the user and participates in the chat at an appropriate time 2. The system of claim 1.

5. The topic providing unit Providing the latest news and trends in real time based on the user's interests 2. The system of claim 1.

6. The learning unit Analyzing the user's past chat history and learning the language used depending on the specific situation or context 2. The system of claim 1.

7. The agent unit is Based on the user's chat history, communication is carried out taking into account relationships with specific friends and family.

2. The system of claim 1.

8. The topic providing unit To provide fresh topics based on the emotions of the user and promote lively conversations 2. The system of claim 1.

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