System

Abstract

An object of a system according to an embodiment is to enable a user to perform a professional consultation quickly and easily.SOLUTION: A system includes a consultation content inputting part, a generation AI, and a question answering part. The consultation content input unit inputs the content that the user wishes to consult in the form of a message or an image. The generating AI analyzes the message or image inputted by the consultation content inputting section, and generates an appropriate answer. The question answering unit transmits the answer generated by the generation AI to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technologies, when a user seeks professional advice, the process for obtaining an appropriate answer is complicated and time-consuming.

[0005] The system according to the embodiment aims to enable users to quickly and easily receive professional consultation. [Means for solving the problem]

[0006] The system according to the embodiment includes a consultation content input unit, a generation AI, and a question and answer unit. The consultation content input unit allows a user to input the content they wish to discuss as a message or image. The generation AI analyzes the message or image input by the consultation content input unit and generates an appropriate answer. The question and answer unit sends the answer generated by the generation AI to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows users to quickly and easily receive professional consultation. [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 teacher substitution system according to an embodiment of the present invention is a system in which a user inputs the content of their consultation via a message or image, and a generation AI generates an appropriate answer and sends it to the user. This allows the teacher substitution system to act as a "teacher" in various fields, allowing the user to repeatedly ask questions until they are satisfied.

[0029] The teacher substitution system according to the embodiment includes a consultation content input unit, a generation AI, and a question and answer unit. The consultation content input unit inputs the content the user wants to consult about using a message or an image. For example, the user can send the message or image through the LINE app. The generation AI analyzes the message or image input by the consultation content input unit and generates an appropriate answer. For example, the generation AI generates an answer to the user's question using a text generation AI (e.g., LLM). The generation AI can also analyze an image using image recognition technology and generate an appropriate answer. The question and answer unit sends the answer generated by the generation AI to the user. For example, the question and answer unit sends the answer to the user through the LINE app. In this way, the teacher substitution system according to the embodiment acts as a "teacher" in various fields by allowing the user to input the content the user wants to consult about, and the generation AI generates an appropriate answer and sends it to the user. For example, when a user consults a tutor about a math problem, the generation AI analyzes the content of the problem and generates a solution and an answer. When a user consults a legal consultant about the content of a contract, the generation AI analyzes the content of the contract and generates an appropriate explanation.

[0030] The generation AI can analyze the content entered by the user and automatically refer to related past consultations and answers to generate more accurate answers. For example, the generation AI can analyze the message entered by the user and search a database for similar past consultations. For example, it can refer to answers from users who have previously consulted on a similar math problem and provide the optimal answer. The generation AI can also analyze the content entered by the user and automatically refer to related past legal consultation answers. For example, it can generate a detailed explanation based on past answers regarding a specific clause in a contract. Furthermore, the generation AI can analyze images submitted by the user and search for consultations in which similar images have been submitted in the past. For example, if an image of the same math problem has been submitted in the past, it can provide an explanation based on that answer. This allows the generation AI to provide more accurate answers by referring to past consultations and answers.

[0031] The generative AI analyzes the user's input and automatically suggests additional information or questions as needed, allowing it to elicit more detailed consultation details. For example, the generative AI can analyze messages entered by the user and automatically suggest additional questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from images sent by the user and suggest additional questions. For example, if a specific part of a contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, the generative AI can analyze the content of the user's message and suggest related additional information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask additional questions such as, "Please tell me the specific problem." This allows it to elicit more detailed consultation details by suggesting additional information and questions.

[0032] The generation AI can automatically translate messages and images entered by users into different languages, building a multilingual consultation system. For example, the generation AI can automatically translate messages entered by users and generate answers in different languages. For example, it can translate a message entered in Japanese into English and provide an answer in English. The generation AI can also use image analysis technology to extract text contained in images sent by users and translate it into different languages. For example, it can translate an image of a Japanese contract into English and provide an explanation in English. Furthermore, the generation AI can register messages entered by users in a multilingual database, making it possible to search for consultation content in different languages. For example, it can translate a message entered in English into Japanese and provide an answer in Japanese. This allows the creation of a multilingual consultation system that can accommodate users who speak different languages.

[0033] Generative AI can provide answers that utilize visual and auditory senses by analyzing user input and automatically suggesting related video and audio content. For example, generative AI can analyze messages entered by users and automatically suggest related video content. For example, it can provide explanatory videos for math problems. Generative AI can also use image analysis technology to suggest audio content related to images sent by users. For example, it can provide audio commentary on specific clauses in a contract. Furthermore, generative AI can analyze the content of users' messages and suggest related visual content. For example, it can provide an explanatory diagram in response to a question such as, "Please tell me how to solve this problem." This allows users to deepen their understanding by providing answers that utilize visual and auditory senses.

[0034] When generating answers to user questions, generative AI can refer to relevant expertise and databases to provide more detailed and accurate answers. For example, generative AI can analyze questions entered by users and refer to relevant expertise and databases. For example, it can provide expert explanations for math problems. Generative AI can also use image analysis technology to search databases related to images submitted by users and provide detailed answers. For example, it can provide expert explanations for specific clauses in a contract. Furthermore, generative AI can analyze the content of users' questions and refer to relevant expertise. For example, it can provide expert explanations for questions such as "Please tell me how to solve this problem." This allows it to provide more detailed and accurate answers by referring to expertise and databases.

[0035] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions and information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze a question entered by a user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from images submitted by the user and suggest follow-up questions. For example, if a specific part of a contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of a user's question and suggest related follow-up information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask follow-up questions such as, "Please tell me the specific problem." This can streamline interactions until the user is satisfied by suggesting follow-up questions and information.

[0036] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format according to their preferences. For example, the generation AI can provide a text-format answer to a question entered by the user. For example, it can provide a text explanation for a math problem. The generation AI can also use image analysis technology to provide an audio answer related to an image submitted by the user. For example, it can provide an audio explanation for a specific clause in a contract. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, it can provide an explanatory video in response to a question such as "Please tell me how to solve this problem." This allows the user to select the answer format according to their preferences by providing answers in different formats.

[0037] Generative AI can promote community-based knowledge sharing by analyzing user questions and automatically suggesting related questions and answers from other users. For example, generative AI can analyze a question entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users to the same math problem. Generative AI can also use image analysis technology to suggest questions and answers from other users related to an image submitted by a user. For example, it can provide questions and answers from other users related to the same contract clause. Generative AI can also analyze a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please tell me how to solve this problem," it can provide questions and answers from other users to the same problem. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0038] Generative AI can analyze math problems entered by users and generate solutions and answers. For example, it can analyze math problems entered by users in the LINE app and automatically generate solutions. For example, in response to a question like, "Please tell me how to solve this quadratic equation," it can provide detailed explanations of the factorization process. Generative AI can also use image analysis technology to analyze images of math problems submitted by users and generate solutions. For example, it can analyze handwritten images of problems and provide answers in text format. Furthermore, generative AI can analyze the content of users' messages using natural language processing technology and suggest solutions. For example, in response to a question like, "Please tell me how to solve this problem," it can provide step-by-step explanations. This allows it to act as a tutor by analyzing math problems entered by users and generating solutions and answers.

[0039] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze a math problem entered by a user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of a math problem submitted by the user and suggest follow-up questions. For example, if a specific part of the problem is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of a user's message and suggest related follow-up information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask follow-up questions such as, "Please tell me the specific details of the problem." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0040] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format that best suits their preferences. For example, the generation AI can provide a text-format answer to a math problem entered by the user. For example, the generation AI can provide a detailed text explanation of the factorization procedure. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the math problem submitted by the user. For example, the generation AI can provide an audio explanation of the factorization procedure. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, in response to a question such as "Please tell me how to solve this problem," it can provide an explanatory video. This allows the user to select the answer format that best suits their preferences by providing answers in different formats.

[0041] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze a math problem entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users to the same math problem. Generative AI can also use image analysis technology to suggest questions and answers from other users related to an image of a math problem submitted by a user. For example, it can provide questions and answers from other users to the same problem. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please tell me how to solve this problem," it can provide questions and answers from other users to the same problem. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0042] Generative AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, generative AI can analyze a math problem entered by a user and refer to related expertise and databases. For example, it can provide a detailed explanation based on mathematical formulas and theorems. Generative AI can also use image analysis technology to search databases related to the image of a math problem submitted by a user and provide a detailed answer. For example, it can provide an explanation based on past answers to the same problem. Furthermore, generative AI can analyze the content of a user's question and refer to related expertise. For example, it can provide an expert explanation in response to a question such as, "Please tell me how to solve this problem." This allows it to provide a more detailed and accurate answer by referring to expertise and databases.

[0043] The generation AI can analyze the contents of a contract entered by a user and generate an appropriate explanation. For example, the generation AI can analyze the contents of a contract entered by a user into the LINE app and automatically generate an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. The generation AI can also use image analysis technology to analyze images of contracts sent by users and generate an appropriate explanation. For example, it can provide a detailed explanation of specific terms of the contract. Furthermore, the generation AI can analyze the content of the user's message using natural language processing technology and suggest an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. This allows the generation AI to act as a legal advisor by analyzing the contents of a contract and generating an appropriate explanation.

[0044] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze the content of a contract entered by the user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this clause don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of the contract submitted by the user and suggest follow-up questions. For example, if a specific part of the contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of the user's message and suggest related follow-up information. For example, in response to a question such as, "Please explain the clauses of this contract in more detail," it can ask follow-up questions such as, "Please tell me the specific clauses." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0045] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format according to their preferences. For example, the generation AI can provide a text-format answer to the contents of a contract entered by the user. For example, it can provide a detailed explanation of the contract's terms in text. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the contract sent by the user. For example, it can provide a detailed explanation of the contract's terms in audio. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, it can provide an explanatory video in response to a question such as, "Please explain the terms of this contract in detail." This allows the user to select the answer format according to their preferences by providing answers in different formats.

[0046] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze the content of a contract entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users about the same contract clauses. Generative AI can also use image analysis technology to suggest questions and answers from other users related to images of contracts submitted by a user. For example, it can provide questions and answers from other users about the same contract clauses. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please explain the clauses in this contract in more detail," it can provide questions and answers from other users about the same clauses. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0047] The generation AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, the generation AI can analyze the content of a contract entered by a user and refer to related expertise and databases. For example, it can provide expert explanations about the clauses in the contract. The generation AI can also use image analysis technology to search databases related to the image of the contract submitted by the user and provide a detailed answer. For example, it can provide an explanation based on past answers about the same clause. The generation AI can also analyze the content of a user's question and refer to related expertise. For example, it can provide expert explanations for questions such as, "Please explain the clauses in this contract in detail." This allows the generation AI to provide more detailed and accurate answers by referring to expertise and databases.

[0048] Generative AI can analyze math problems entered by users and generate solutions and answers. For example, it can analyze math problems entered by users in the LINE app and automatically generate solutions. For example, in response to a question like, "Please tell me how to solve this quadratic equation," it can provide detailed explanations of the factorization process. Generative AI can also use image analysis technology to analyze images of math problems submitted by users and generate solutions. For example, it can analyze handwritten images of problems and provide answers in text format. Furthermore, generative AI can analyze the content of users' messages using natural language processing technology and suggest solutions. For example, in response to a question like, "Please tell me how to solve this problem," it can provide step-by-step explanations. This allows it to act as a tutor by analyzing math problems entered by users and generating solutions and answers.

[0049] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze a math problem entered by a user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of a math problem submitted by the user and suggest follow-up questions. For example, if a specific part of the problem is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of a user's message and suggest related follow-up information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask follow-up questions such as, "Please tell me the specific details of the problem." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0050] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format that best suits their preferences. For example, the generation AI can provide a text-format answer to a math problem entered by the user. For example, the generation AI can provide a detailed text explanation of the factorization procedure. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the math problem submitted by the user. For example, the generation AI can provide an audio explanation of the factorization procedure. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, in response to a question such as "Please tell me how to solve this problem," it can provide an explanatory video. This allows the user to select the answer format that best suits their preferences by providing answers in different formats.

[0051] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze a math problem entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users to the same math problem. Generative AI can also use image analysis technology to suggest questions and answers from other users related to an image of a math problem submitted by a user. For example, it can provide questions and answers from other users to the same problem. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please tell me how to solve this problem," it can provide questions and answers from other users to the same problem. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0052] Generative AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, generative AI can analyze a math problem entered by a user and refer to related expertise and databases. For example, it can provide a detailed explanation based on mathematical formulas and theorems. Generative AI can also use image analysis technology to search databases related to the image of a math problem submitted by a user and provide a detailed answer. For example, it can provide an explanation based on past answers to the same problem. Furthermore, generative AI can analyze the content of a user's question and refer to related expertise. For example, it can provide an expert explanation in response to a question such as, "Please tell me how to solve this problem." This allows it to provide a more detailed and accurate answer by referring to expertise and databases.

[0053] The generation AI can analyze the contents of a contract entered by a user and generate an appropriate explanation. For example, the generation AI can analyze the contents of a contract entered by a user into the LINE app and automatically generate an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. The generation AI can also use image analysis technology to analyze images of contracts sent by users and generate an appropriate explanation. For example, it can provide a detailed explanation of specific terms of the contract. Furthermore, the generation AI can analyze the content of the user's message using natural language processing technology and suggest an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. This allows the generation AI to act as a legal advisor by analyzing the contents of a contract and generating an appropriate explanation.

[0054] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze the content of a contract entered by the user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this clause don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of the contract submitted by the user and suggest follow-up questions. For example, if a specific part of the contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of the user's message and suggest related follow-up information. For example, in response to a question such as, "Please explain the clauses of this contract in more detail," it can ask follow-up questions such as, "Please tell me the specific clauses." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0055] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format according to their preferences. For example, the generation AI can provide a text-format answer to the contents of a contract entered by the user. For example, it can provide a detailed explanation of the contract's terms in text. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the contract sent by the user. For example, it can provide a detailed explanation of the contract's terms in audio. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, it can provide an explanatory video in response to a question such as, "Please explain the terms of this contract in detail." This allows the user to select the answer format according to their preferences by providing answers in different formats.

[0056] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze the content of a contract entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users about the same contract clauses. Generative AI can also use image analysis technology to suggest questions and answers from other users related to images of contracts submitted by a user. For example, it can provide questions and answers from other users about the same contract clauses. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please explain the clauses in this contract in more detail," it can provide questions and answers from other users about the same clauses. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0057] The generation AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, the generation AI can analyze the content of a contract entered by a user and refer to related expertise and databases. For example, it can provide expert explanations about the clauses in the contract. The generation AI can also use image analysis technology to search databases related to the image of the contract submitted by the user and provide a detailed answer. For example, it can provide an explanation based on past answers about the same clause. The generation AI can also analyze the content of a user's question and refer to related expertise. For example, it can provide expert explanations for questions such as, "Please explain the clauses in this contract in detail." This allows the generation AI to provide more detailed and accurate answers by referring to expertise and databases.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The generation AI can also analyze the user's input and generate more personalized answers by taking into account the user's past behavioral history and preferences. For example, if a user has frequently consulted on math problems in the past, the generation AI can provide the user with detailed explanations of those math problems. Similarly, if a user has frequently consulted on legal matters in the past, the generation AI can provide the user with specialized legal explanations. Furthermore, the generation AI can suggest related additional information or questions based on the user's past behavioral history. For example, if a user has previously consulted on a specific contract clause, the generation AI can provide additional information related to that clause. This can improve user satisfaction by providing personalized answers that take into account the user's past behavioral history and preferences.

[0060] The generation AI can also analyze the user's input and generate appropriate answers based on the user's current situation and environment. For example, if the user is consulting while out and about, the generation AI can provide a concise answer that suits the situation. If the user is relaxing at home, the generation AI can provide a detailed explanation. Furthermore, it can suggest additional related information or questions based on the user's environmental information. For example, if the user is studying in a library, it can suggest reference materials appropriate for that environment. This improves user convenience by providing appropriate answers based on the user's current situation and environment.

[0061] Generative AI can also analyze user input and automatically suggest related news and the latest information. For example, if a user is seeking legal advice, generative AI can provide information on the latest legal changes. If a user is seeking advice on a math problem, generative AI can introduce the latest mathematical research results. It can also suggest news articles and blog posts related to the user's input. For example, if a user is consulting about a contract clause, generative AI can provide the latest news articles related to that clause. This helps deepen the user's knowledge by providing related news and the latest information.

[0062] The generative AI can also analyze the user's input and automatically introduce relevant experts and consultants. For example, if the user is seeking legal advice, the generative AI can introduce experts in that field. Similarly, if the user is consulting about a math problem, the generative AI can introduce math experts. Furthermore, it can provide contact information and profiles of relevant experts based on the user's input. For example, if the user is consulting about the terms of a contract, the generative AI can provide contact information for experts who are familiar with those terms. This allows the user to receive more specialized advice by introducing relevant experts and consultants.

[0063] Generative AI can also analyze user input and automatically suggest related books and reference materials. For example, if a user is seeking legal advice, the generative AI can suggest reference books in that field. Similarly, if a user is seeking advice on a math problem, the generative AI can suggest math textbooks and reference books. Furthermore, it can suggest related online courses and learning resources based on the user's input. For example, if a user is consulting about the terms of a contract, the generative AI can suggest online courses related to those terms. This can support the user's learning by providing related books and reference materials.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The consultation content input unit inputs the content the user wants to consult about as a message or image. For example, the user can send a message or image via the LINE app. Step 2: The generation AI analyzes the message or image input by the consultation content input unit and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to the user's question. The generation AI can also analyze images using image recognition technology and generate an appropriate answer. Step 3: The question answering unit sends the answer generated by the generation AI to the user. For example, the question answering unit sends the answer to the user via the LINE app.

[0066] (Example 2) The teacher substitution system according to an embodiment of the present invention is a system in which a user inputs the content of their consultation via a message or image, and a generation AI generates an appropriate answer and sends it to the user. This allows the teacher substitution system to act as a "teacher" in various fields, allowing the user to repeatedly ask questions until they are satisfied.

[0067] The teacher substitution system according to the embodiment includes a consultation content input unit, a generation AI, and a question and answer unit. The consultation content input unit inputs the content the user wants to consult about using a message or an image. For example, the user can send the message or image through the LINE app. The generation AI analyzes the message or image input by the consultation content input unit and generates an appropriate answer. For example, the generation AI generates an answer to the user's question using a text generation AI (e.g., LLM). The generation AI can also analyze an image using image recognition technology and generate an appropriate answer. The question and answer unit sends the answer generated by the generation AI to the user. For example, the question and answer unit sends the answer to the user through the LINE app. In this way, the teacher substitution system according to the embodiment acts as a "teacher" in various fields by allowing the user to input the content the user wants to consult about, and the generation AI generates an appropriate answer and sends it to the user. For example, when a user consults a tutor about a math problem, the generation AI analyzes the content of the problem and generates a solution and an answer. When a user consults a legal consultant about the content of a contract, the generation AI analyzes the content of the contract and generates an appropriate explanation.

[0068] The consultation content input unit can infer emotions from messages and images entered by the user and generate appropriate responses based on those emotions. For example, the consultation content input unit analyzes messages and images entered by the user in the LINE app and identifies emotions using an emotion inference algorithm. For example, if a user enters, "I don't understand this problem at all," the generation AI can sense the user's frustration and provide a gentle explanation. It can also infer emotions from images sent by the user using image analysis technology. For example, it can analyze facial expressions from photos of the user's face and generate responses based on those emotions. Furthermore, it can analyze the content of a user's message using natural language processing technology to infer emotions. For example, it can detect confusion from a message such as, "I don't understand the terms of this contract" and provide a detailed explanation. This allows the system to generate appropriate responses based on the user's emotions, enabling more personalized support.

[0069] The generation AI can analyze the content entered by the user and automatically refer to related past consultations and answers to generate more accurate answers. For example, the generation AI can analyze the message entered by the user and search a database for similar past consultations. For example, it can refer to answers from users who have previously consulted on a similar math problem and provide the optimal answer. The generation AI can also analyze the content entered by the user and automatically refer to related past legal consultation answers. For example, it can generate a detailed explanation based on past answers regarding a specific clause in a contract. Furthermore, the generation AI can analyze images submitted by the user and search for consultations in which similar images have been submitted in the past. For example, if an image of the same math problem has been submitted in the past, it can provide an explanation based on that answer. This allows the generation AI to provide more accurate answers by referring to past consultations and answers.

[0070] The generative AI analyzes the user's input and automatically suggests additional information or questions as needed, allowing it to elicit more detailed consultation details. For example, the generative AI can analyze messages entered by the user and automatically suggest additional questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from images sent by the user and suggest additional questions. For example, if a specific part of a contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, the generative AI can analyze the content of the user's message and suggest related additional information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask additional questions such as, "Please tell me the specific problem." This allows it to elicit more detailed consultation details by suggesting additional information and questions.

[0071] The generation AI can automatically translate messages and images entered by users into different languages, building a multilingual consultation system. For example, the generation AI can automatically translate messages entered by users and generate answers in different languages. For example, it can translate a message entered in Japanese into English and provide an answer in English. The generation AI can also use image analysis technology to extract text contained in images sent by users and translate it into different languages. For example, it can translate an image of a Japanese contract into English and provide an explanation in English. Furthermore, the generation AI can register messages entered by users in a multilingual database, making it possible to search for consultation content in different languages. For example, it can translate a message entered in English into Japanese and provide an answer in Japanese. This allows the creation of a multilingual consultation system that can accommodate users who speak different languages.

[0072] Generative AI can provide answers that utilize visual and auditory senses by analyzing user input and automatically suggesting related video and audio content. For example, generative AI can analyze messages entered by users and automatically suggest related video content. For example, it can provide explanatory videos for math problems. Generative AI can also use image analysis technology to suggest audio content related to images sent by users. For example, it can provide audio commentary on specific clauses in a contract. Furthermore, generative AI can analyze the content of users' messages and suggest related visual content. For example, it can provide an explanatory diagram in response to a question such as, "Please tell me how to solve this problem." This allows users to deepen their understanding by providing answers that utilize visual and auditory senses.

[0073] Generative AI can analyze the user's emotions in real time and ask follow-up questions or make suggestions to elicit positive emotions. For example, generative AI can analyze messages entered by users and identify their emotions using an emotion estimation algorithm. For example, if a user enters, "I have no idea about this problem," generative AI can sense the user's frustration and send an encouraging message. It can also use image analysis technology to infer emotions from images submitted by users and make suggestions to elicit positive emotions. For example, it can analyze facial expressions from photos submitted by users and provide positive feedback. Furthermore, generative AI can analyze the content of users' messages using natural language processing technology to infer emotions. For example, it can detect confusion from a message such as, "I don't understand the terms of this contract" and send an encouraging message with a detailed explanation. This can improve user satisfaction by asking follow-up questions or making suggestions to elicit positive emotions.

[0074] When generating answers to user questions, the generation AI can use emotion estimation to consider the user's emotions and generate appropriate answers based on their emotions. For example, the generation AI can analyze questions entered by the user into the LINE app and identify their emotions using an emotion estimation algorithm. For example, if the user enters, "Please tell me again how to solve this problem," the generation AI can detect the user's anxiety and provide a gentle explanation. It can also use image analysis technology to infer emotions from images submitted by the user and generate answers based on their emotions. For example, it can analyze facial expressions from photos submitted by the user and provide answers based on their emotions. Furthermore, the generation AI can analyze the content of the user's question using natural language processing technology to infer emotions. For example, it can detect confusion in a question such as, "Please explain the terms of this contract in detail" and provide a detailed explanation. This can improve user satisfaction by generating appropriate answers that take the user's emotions into account.

[0075] When generating answers to user questions, generative AI can refer to relevant expertise and databases to provide more detailed and accurate answers. For example, generative AI can analyze questions entered by users and refer to relevant expertise and databases. For example, it can provide expert explanations for math problems. Generative AI can also use image analysis technology to search databases related to images submitted by users and provide detailed answers. For example, it can provide expert explanations for specific clauses in a contract. Furthermore, generative AI can analyze the content of users' questions and refer to relevant expertise. For example, it can provide expert explanations for questions such as "Please tell me how to solve this problem." This allows it to provide more detailed and accurate answers by referring to expertise and databases.

[0076] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions and information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze a question entered by a user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from images submitted by the user and suggest follow-up questions. For example, if a specific part of a contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of a user's question and suggest related follow-up information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask follow-up questions such as, "Please tell me the specific problem." This can streamline interactions until the user is satisfied by suggesting follow-up questions and information.

[0077] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format according to their preferences. For example, the generation AI can provide a text-format answer to a question entered by the user. For example, it can provide a text explanation for a math problem. The generation AI can also use image analysis technology to provide an audio answer related to an image submitted by the user. For example, it can provide an audio explanation for a specific clause in a contract. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, it can provide an explanatory video in response to a question such as "Please tell me how to solve this problem." This allows the user to select the answer format according to their preferences by providing answers in different formats.

[0078] Generative AI can promote community-based knowledge sharing by analyzing user questions and automatically suggesting related questions and answers from other users. For example, generative AI can analyze a question entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users to the same math problem. Generative AI can also use image analysis technology to suggest questions and answers from other users related to an image submitted by a user. For example, it can provide questions and answers from other users related to the same contract clause. Generative AI can also analyze a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please tell me how to solve this problem," it can provide questions and answers from other users to the same problem. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0079] When generating answers to user questions, the generative AI can use emotion estimation to analyze the user's emotions in real time and provide additional information or suggestions to elicit positive emotions. For example, the generative AI analyzes the question entered by the user and identifies the emotion using an emotion estimation algorithm. For example, if the user enters, "Please tell me again how to solve this problem," the generative AI can detect the user's anxiety and send an encouraging message. It can also use image analysis technology to infer emotions from images submitted by the user and provide suggestions to elicit positive emotions. For example, it can analyze facial expressions from facial photos submitted by the user and provide positive feedback. Furthermore, the generative AI can analyze the content of the user's question using natural language processing technology to infer emotions. For example, it can detect confusion from a question such as, "Please explain the terms of this contract in detail," and send an encouraging message with a detailed explanation. This can improve user satisfaction by providing additional information and suggestions to elicit positive emotions.

[0080] Generative AI can analyze math problems entered by users and generate solutions and answers. For example, it can analyze math problems entered by users in the LINE app and automatically generate solutions. For example, in response to a question like, "Please tell me how to solve this quadratic equation," it can provide detailed explanations of the factorization process. Generative AI can also use image analysis technology to analyze images of math problems submitted by users and generate solutions. For example, it can analyze handwritten images of problems and provide answers in text format. Furthermore, generative AI can analyze the content of users' messages using natural language processing technology and suggest solutions. For example, in response to a question like, "Please tell me how to solve this problem," it can provide step-by-step explanations. This allows it to act as a tutor by analyzing math problems entered by users and generating solutions and answers.

[0081] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze a math problem entered by a user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of a math problem submitted by the user and suggest follow-up questions. For example, if a specific part of the problem is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of a user's message and suggest related follow-up information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask follow-up questions such as, "Please tell me the specific details of the problem." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0082] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format that best suits their preferences. For example, the generation AI can provide a text-format answer to a math problem entered by the user. For example, the generation AI can provide a detailed text explanation of the factorization procedure. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the math problem submitted by the user. For example, the generation AI can provide an audio explanation of the factorization procedure. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, in response to a question such as "Please tell me how to solve this problem," it can provide an explanatory video. This allows the user to select the answer format that best suits their preferences by providing answers in different formats.

[0083] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze a math problem entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users to the same math problem. Generative AI can also use image analysis technology to suggest questions and answers from other users related to an image of a math problem submitted by a user. For example, it can provide questions and answers from other users to the same problem. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please tell me how to solve this problem," it can provide questions and answers from other users to the same problem. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0084] When generating answers to user questions, the generative AI can use emotion estimation to analyze the user's emotions in real time and provide additional information or suggestions to elicit positive emotions. For example, the generative AI can analyze a math problem entered by the user and identify the emotion using an emotion estimation algorithm. For example, if the user enters, "Please tell me again how to solve this problem," the generative AI can detect the user's anxiety and send an encouraging message. It can also use image analysis technology to infer emotions from the image of the math problem submitted by the user and provide suggestions to elicit positive emotions. For example, it can analyze facial expressions from a facial photo submitted by the user and provide positive feedback. Furthermore, the generative AI can analyze the content of the user's question using natural language processing technology to infer emotions. For example, it can detect confusion in a question such as, "Please tell me how to solve this problem," and send an encouraging message with a detailed explanation. This can improve user satisfaction by providing additional information and suggestions to elicit positive emotions.

[0085] Generative AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, generative AI can analyze a math problem entered by a user and refer to related expertise and databases. For example, it can provide a detailed explanation based on mathematical formulas and theorems. Generative AI can also use image analysis technology to search databases related to the image of a math problem submitted by a user and provide a detailed answer. For example, it can provide an explanation based on past answers to the same problem. Furthermore, generative AI can analyze the content of a user's question and refer to related expertise. For example, it can provide an expert explanation in response to a question such as, "Please tell me how to solve this problem." This allows it to provide a more detailed and accurate answer by referring to expertise and databases.

[0086] The generation AI can analyze the contents of a contract entered by a user and generate an appropriate explanation. For example, the generation AI can analyze the contents of a contract entered by a user into the LINE app and automatically generate an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. The generation AI can also use image analysis technology to analyze images of contracts sent by users and generate an appropriate explanation. For example, it can provide a detailed explanation of specific terms of the contract. Furthermore, the generation AI can analyze the content of the user's message using natural language processing technology and suggest an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. This allows the generation AI to act as a legal advisor by analyzing the contents of a contract and generating an appropriate explanation.

[0087] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze the content of a contract entered by the user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this clause don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of the contract submitted by the user and suggest follow-up questions. For example, if a specific part of the contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of the user's message and suggest related follow-up information. For example, in response to a question such as, "Please explain the clauses of this contract in more detail," it can ask follow-up questions such as, "Please tell me the specific clauses." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0088] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format according to their preferences. For example, the generation AI can provide a text-format answer to the contents of a contract entered by the user. For example, it can provide a detailed explanation of the contract's terms in text. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the contract sent by the user. For example, it can provide a detailed explanation of the contract's terms in audio. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, it can provide an explanatory video in response to a question such as, "Please explain the terms of this contract in detail." This allows the user to select the answer format according to their preferences by providing answers in different formats.

[0089] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze the content of a contract entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users about the same contract clauses. Generative AI can also use image analysis technology to suggest questions and answers from other users related to images of contracts submitted by a user. For example, it can provide questions and answers from other users about the same contract clauses. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please explain the clauses in this contract in more detail," it can provide questions and answers from other users about the same clauses. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0090] When generating answers to user questions, the generative AI can use emotion estimation to analyze the user's emotions in real time and provide additional information or suggestions to elicit positive emotions. For example, the generative AI can analyze the content of a contract entered by the user and identify the emotion using an emotion estimation algorithm. For example, if the user enters, "Please explain the terms of this contract in detail," the generative AI can detect the user's confusion and send an encouraging message. It can also use image analysis technology to infer emotions from the image of the contract submitted by the user and provide suggestions to elicit positive emotions. For example, it can analyze facial expressions from a facial photo submitted by the user and provide positive feedback. Furthermore, the generative AI can analyze the content of the user's question using natural language processing technology to infer emotions. For example, it can detect confusion from a question such as, "Please explain the terms of this contract in detail," and send an encouraging message with a detailed explanation. This can improve user satisfaction by providing additional information and suggestions to elicit positive emotions.

[0091] The generation AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, the generation AI can analyze the content of a contract entered by a user and refer to related expertise and databases. For example, it can provide expert explanations about the clauses in the contract. The generation AI can also use image analysis technology to search databases related to the image of the contract submitted by the user and provide a detailed answer. For example, it can provide an explanation based on past answers about the same clause. The generation AI can also analyze the content of a user's question and refer to related expertise. For example, it can provide expert explanations for questions such as, "Please explain the clauses in this contract in detail." This allows the generation AI to provide more detailed and accurate answers by referring to expertise and databases.

[0092] Generative AI can analyze math problems entered by users and generate solutions and answers. For example, it can analyze math problems entered by users in the LINE app and automatically generate solutions. For example, in response to a question like, "Please tell me how to solve this quadratic equation," it can provide detailed explanations of the factorization process. Generative AI can also use image analysis technology to analyze images of math problems submitted by users and generate solutions. For example, it can analyze handwritten images of problems and provide answers in text format. Furthermore, generative AI can analyze the content of users' messages using natural language processing technology and suggest solutions. For example, in response to a question like, "Please tell me how to solve this problem," it can provide step-by-step explanations. This allows it to act as a tutor by analyzing math problems entered by users and generating solutions and answers.

[0093] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze a math problem entered by a user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this problem don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of a math problem submitted by the user and suggest follow-up questions. For example, if a specific part of the problem is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of a user's message and suggest related follow-up information. For example, in response to a question such as, "Please tell me how to solve this problem," it can ask follow-up questions such as, "Please tell me the specific details of the problem." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0094] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format that best suits their preferences. For example, the generation AI can provide a text-format answer to a math problem entered by the user. For example, the generation AI can provide a detailed text explanation of the factorization procedure. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the math problem submitted by the user. For example, the generation AI can provide an audio explanation of the factorization procedure. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, in response to a question such as "Please tell me how to solve this problem," it can provide an explanatory video. This allows the user to select the answer format that best suits their preferences by providing answers in different formats.

[0095] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze a math problem entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users to the same math problem. Generative AI can also use image analysis technology to suggest questions and answers from other users related to an image of a math problem submitted by a user. For example, it can provide questions and answers from other users to the same problem. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please tell me how to solve this problem," it can provide questions and answers from other users to the same problem. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0096] When generating answers to user questions, the generative AI can use emotion estimation to analyze the user's emotions in real time and provide additional information or suggestions to elicit positive emotions. For example, the generative AI can analyze a math problem entered by the user and identify the emotion using an emotion estimation algorithm. For example, if the user enters, "Please tell me again how to solve this problem," the generative AI can detect the user's anxiety and send an encouraging message. It can also use image analysis technology to infer emotions from the image of the math problem submitted by the user and provide suggestions to elicit positive emotions. For example, it can analyze facial expressions from a facial photo submitted by the user and provide positive feedback. Furthermore, the generative AI can analyze the content of the user's question using natural language processing technology to infer emotions. For example, it can detect confusion in a question such as, "Please tell me how to solve this problem," and send an encouraging message with a detailed explanation. This can improve user satisfaction by providing additional information and suggestions to elicit positive emotions.

[0097] Generative AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, generative AI can analyze a math problem entered by a user and refer to related expertise and databases. For example, it can provide a detailed explanation based on mathematical formulas and theorems. Generative AI can also use image analysis technology to search databases related to the image of a math problem submitted by a user and provide a detailed answer. For example, it can provide an explanation based on past answers to the same problem. Furthermore, generative AI can analyze the content of a user's question and refer to related expertise. For example, it can provide an expert explanation in response to a question such as, "Please tell me how to solve this problem." This allows it to provide a more detailed and accurate answer by referring to expertise and databases.

[0098] The generation AI can analyze the contents of a contract entered by a user and generate an appropriate explanation. For example, the generation AI can analyze the contents of a contract entered by a user into the LINE app and automatically generate an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. The generation AI can also use image analysis technology to analyze images of contracts sent by users and generate an appropriate explanation. For example, it can provide a detailed explanation of specific terms of the contract. Furthermore, the generation AI can analyze the content of the user's message using natural language processing technology and suggest an appropriate explanation. For example, in response to a question such as, "Please explain the terms of this contract in more detail," it can provide a detailed explanation of the terms. This allows the generation AI to act as a legal advisor by analyzing the contents of a contract and generating an appropriate explanation.

[0099] Generative AI can analyze the content of a user's question and automatically suggest related follow-up questions or information, thereby streamlining interactions until the user is satisfied. For example, generative AI can analyze the content of a contract entered by the user and automatically suggest follow-up questions. For example, it can ask questions such as, "Which part of this clause don't you understand?" to elicit more detailed information. It can also use image analysis technology to identify missing information from the image of the contract submitted by the user and suggest follow-up questions. For example, if a specific part of the contract is unclear, it can ask questions such as, "Please explain this part in more detail." Furthermore, generative AI can analyze the content of the user's message and suggest related follow-up information. For example, in response to a question such as, "Please explain the clauses of this contract in more detail," it can ask follow-up questions such as, "Please tell me the specific clauses." This can streamline interactions until the user is satisfied by suggesting follow-up questions or information.

[0100] The generation AI can provide answers to user questions in different formats, allowing the user to select the answer format according to their preferences. For example, the generation AI can provide a text-format answer to the contents of a contract entered by the user. For example, it can provide a detailed explanation of the contract's terms in text. The generation AI can also use image analysis technology to provide an audio-format answer related to the image of the contract sent by the user. For example, it can provide a detailed explanation of the contract's terms in audio. Furthermore, the generation AI can analyze the content of the user's question and provide an answer in video format. For example, it can provide an explanatory video in response to a question such as, "Please explain the terms of this contract in detail." This allows the user to select the answer format according to their preferences by providing answers in different formats.

[0101] Generative AI can promote community-based knowledge sharing by analyzing the content of a user's question and automatically suggesting related questions and answers from other users. For example, generative AI can analyze the content of a contract entered by a user and automatically suggest related questions and answers from other users. For example, it can provide questions and answers from other users about the same contract clauses. Generative AI can also use image analysis technology to suggest questions and answers from other users related to images of contracts submitted by a user. For example, it can provide questions and answers from other users about the same contract clauses. Furthermore, generative AI can analyze the content of a user's question and suggest related questions and answers from other users. For example, in response to a question such as "Please explain the clauses in this contract in more detail," it can provide questions and answers from other users about the same clauses. This promotes community-based knowledge sharing and encourages active information exchange between users.

[0102] When generating answers to user questions, the generative AI can use emotion estimation to analyze the user's emotions in real time and provide additional information or suggestions to elicit positive emotions. For example, the generative AI can analyze the content of a contract entered by the user and identify the emotion using an emotion estimation algorithm. For example, if the user enters, "Please explain the terms of this contract in detail," the generative AI can detect the user's confusion and send an encouraging message. It can also use image analysis technology to infer emotions from the image of the contract submitted by the user and provide suggestions to elicit positive emotions. For example, it can analyze facial expressions from a facial photo submitted by the user and provide positive feedback. Furthermore, the generative AI can analyze the content of the user's question using natural language processing technology to infer emotions. For example, it can detect confusion from a question such as, "Please explain the terms of this contract in detail," and send an encouraging message with a detailed explanation. This can improve user satisfaction by providing additional information and suggestions to elicit positive emotions.

[0103] The generation AI can analyze the content of a user's question and refer to related expertise and databases to provide more detailed and accurate answers. For example, the generation AI can analyze the content of a contract entered by a user and refer to related expertise and databases. For example, it can provide expert explanations about the clauses in the contract. The generation AI can also use image analysis technology to search databases related to the image of the contract submitted by the user and provide a detailed answer. For example, it can provide an explanation based on past answers about the same clause. The generation AI can also analyze the content of a user's question and refer to related expertise. For example, it can provide expert explanations for questions such as, "Please explain the clauses in this contract in detail." This allows the generation AI to provide more detailed and accurate answers by referring to expertise and databases.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The generation AI can also analyze the user's input and generate more personalized answers by taking into account the user's past behavioral history and preferences. For example, if a user has frequently consulted on math problems in the past, the generation AI can provide the user with detailed explanations of those math problems. Similarly, if a user has frequently consulted on legal matters in the past, the generation AI can provide the user with specialized legal explanations. Furthermore, the generation AI can suggest related additional information or questions based on the user's past behavioral history. For example, if a user has previously consulted on a specific contract clause, the generation AI can provide additional information related to that clause. This can improve user satisfaction by providing personalized answers that take into account the user's past behavioral history and preferences.

[0106] The generation AI can also analyze the user's input and generate appropriate answers based on the user's current situation and environment. For example, if the user is consulting while out and about, the generation AI can provide a concise answer that suits the situation. If the user is relaxing at home, the generation AI can provide a detailed explanation. Furthermore, it can suggest additional related information or questions based on the user's environmental information. For example, if the user is studying in a library, it can suggest reference materials appropriate for that environment. This improves user convenience by providing appropriate answers based on the user's current situation and environment.

[0107] Generative AI can also analyze user input and automatically suggest related news and the latest information. For example, if a user is seeking legal advice, generative AI can provide information on the latest legal changes. If a user is seeking advice on a math problem, generative AI can introduce the latest mathematical research results. It can also suggest news articles and blog posts related to the user's input. For example, if a user is consulting about a contract clause, generative AI can provide the latest news articles related to that clause. This helps deepen the user's knowledge by providing related news and the latest information.

[0108] The generative AI can also analyze the user's input and automatically introduce relevant experts and consultants. For example, if the user is seeking legal advice, the generative AI can introduce experts in that field. Similarly, if the user is consulting about a math problem, the generative AI can introduce math experts. Furthermore, it can provide contact information and profiles of relevant experts based on the user's input. For example, if the user is consulting about the terms of a contract, the generative AI can provide contact information for experts who are familiar with those terms. This allows the user to receive more specialized advice by introducing relevant experts and consultants.

[0109] Generative AI can also analyze user input and automatically suggest related books and reference materials. For example, if a user is seeking legal advice, the generative AI can suggest reference books in that field. Similarly, if a user is seeking advice on a math problem, the generative AI can suggest math textbooks and reference books. Furthermore, it can suggest related online courses and learning resources based on the user's input. For example, if a user is consulting about the terms of a contract, the generative AI can suggest online courses related to those terms. This can support the user's learning by providing related books and reference materials.

[0110] Generative AI can analyze user input, infer the user's emotions, and provide appropriate feedback based on those emotions. For example, if a user types, "I don't understand this problem at all," the generative AI can sense the user's frustration and send an encouraging message. Alternatively, if a user types, "I don't understand the terms of this contract," the generative AI can sense the user's confusion and provide a detailed explanation. Furthermore, it can suggest relevant resources and support based on the user's emotions. For example, if a user types, "This problem is too difficult," the generative AI can sense the user's anxiety and suggest starting with an easier problem. This can improve user satisfaction by providing appropriate feedback based on the user's emotions.

[0111] Generative AI can analyze user input, infer the user's emotions, and suggest appropriate actions based on those emotions. For example, if a user types, "I don't understand this problem at all," the generative AI can sense the user's frustration and suggest taking a break. Similarly, if a user types, "I don't understand the terms of this contract," the generative AI can sense the user's confusion and suggest consulting an expert. Furthermore, based on the user's emotions, it can suggest ways to relax or activities to relieve stress. For example, if a user types, "This problem is too difficult," the generative AI can sense the user's anxiety and suggest deep breathing or stretching to relax. This allows the user to reduce stress by suggesting appropriate actions based on their emotions.

[0112] The generative AI can analyze user input, infer the user's emotions, and suggest an appropriate learning plan based on those emotions. For example, if a user inputs, "I don't understand this problem at all," the generative AI can sense the user's frustration and suggest a step-by-step learning plan. Similarly, if a user inputs, "I don't understand the terms of this contract," the generative AI can sense the user's confusion and suggest a plan for learning from the basics. Furthermore, it can manage learning progress based on the user's emotions and provide feedback at the appropriate time. For example, if a user inputs, "This problem is too difficult," the generative AI can sense the user's anxiety and provide feedback according to the user's progress. This can improve the user's learning effectiveness by suggesting an appropriate learning plan based on the user's emotions.

[0113] The generative AI can analyze user input, infer the user's emotions, and select an appropriate communication style based on the user's emotions. For example, if a user types, "I don't understand this problem at all," the generative AI can sense the user's frustration and provide a gentle explanation. Alternatively, if a user types, "I don't understand the terms of this contract," the generative AI can sense the user's confusion and provide a polite explanation. Furthermore, the generative AI can adjust the frequency and timing of communication based on the user's emotions. For example, if a user types, "This problem is too difficult," the generative AI can sense the user's anxiety and provide frequent feedback. This allows the system to improve user satisfaction by selecting an appropriate communication style based on the user's emotions.

[0114] Generative AI can analyze user input, infer the user's emotions, and provide appropriate motivation based on those emotions. For example, if a user types, "I don't understand this problem at all," the generative AI can sense the user's frustration and send an encouraging message. Alternatively, if a user types, "I don't understand the terms of this contract," the generative AI can sense the user's confusion and share their success story. Furthermore, based on the user's emotions, it can suggest ways to set goals and feel a sense of accomplishment. For example, if a user types, "This problem is too difficult," the generative AI can sense the user's anxiety and suggest setting smaller goals. This can motivate the user by providing appropriate motivation based on their emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The consultation content input unit inputs the content the user wants to consult about as a message or image. For example, the user can send a message or image via the LINE app. Step 2: The generation AI analyzes the message or image input by the consultation content input unit and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to the user's question. The generation AI can also analyze images using image recognition technology and generate an appropriate answer. Step 3: The question answering unit sends the answer generated by the generation AI to the user. For example, the question answering unit sends the answer to the user via the LINE app.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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]

[0184] 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 consultation content input section for inputting the content of the consultation the user wants to have by message or image; A generation AI that analyzes the message or the image input by the consultation content input unit and generates an appropriate answer; a question answering unit that sends the answer generated by the generation AI to the user; A system characterized by:

2. The generated AI is The message or image input by the user is automatically translated into different languages, and a multilingual consultation system is constructed.

2. The system of claim 1.

3. The generated AI is Analyzing the mathematical problem entered by the user and generating the solution or answer 2. The system of claim 1.

4. The generated AI is Analyze the contents of the contract entered by the user and generate an appropriate explanation 2. The system of claim 1.

5. The generated AI is When generating the answer to the question of the user, the answer is generated based on the emotion of the user.

2. The system of claim 1.

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