System

Abstract

To provide a system for providing a quick and appropriate answer to a user's legal question.SOLUTION: The specific processing unit 290 of the data processing device 12 in the system executes question reception processing of receiving a question of a user, answer generation processing of generating an answer to the question received by the question reception processing, learning processing of learning a question history of the user based on the answer generated by the answer generation processing, and expert cooperation processing of proposing a consultation with an expert as necessary based on information learned by the learning processing.SELECTED DRAWING: Figure 2

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to obtain prompt and appropriate answers to their legal questions.

[0005] The system according to the embodiment aims to provide users with quick and appropriate answers to their legal questions. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, an answer generating unit, a learning unit, and an expert collaboration unit. The question receiving unit receives questions from users. The answer generating unit generates answers to the questions received by the question receiving unit. The learning unit learns the user's question history based on the answers generated by the answer generating unit. The expert collaboration unit suggests consultation with an expert as necessary based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide users with quick and appropriate answers to their legal questions. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 LegalBot system according to an embodiment of the present invention is a system that provides users with quick and easy answers to their legal questions. As a result, the LegalBot system provides users with quick and appropriate answers to their legal questions and allows them to obtain expert advice as needed.

[0029] The LegalBot system according to the embodiment includes a question receiving unit, an answer generating unit, a learning unit, and an expert collaboration unit. The question receiving unit receives user questions. For example, the user inputs a question in text format. The question receiving unit can also receive user questions using voice input. For example, it converts speech into text using speech recognition technology. The answer generating unit generates answers to the questions received by the question receiving unit. For example, the generation AI generates answers to questions using a text generation AI (e.g., LLM). The generation AI can also generate answers to questions using a multimodal generation AI. The generation AI can also generate answers by referencing a database of past questions and answers. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including images and audio, in addition to text. The generation AI references a database of past questions and answers to generate answers to similar questions. The learning unit learns the user's question history based on the answers generated by the answer generating unit. For example, it analyzes the user's question patterns using a machine learning algorithm. The learning unit can also improve the accuracy of answers based on user feedback. For example, it analyzes user feedback and improves the answer generation algorithm. The expert collaboration unit suggests expert consultation as needed based on the information learned by the learning unit. For example, if the generation AI determines that "expert advice is needed for this problem," it suggests expert consultation to the user. Furthermore, when the user seeks expert consultation, the expert collaboration unit can also organize the user's questions in advance and provide them to the expert. This allows the LegalBot system according to the embodiment to provide users with prompt and appropriate answers to their legal questions and, if necessary, to obtain expert advice. For example, the output unit displays the answer results to the user via a web application or a mobile application. If the user desires paper feedback, the results can be printed using a printer.Emailing provides immediate feedback by sending results directly to the user.

[0030] The answer generation unit can cite past legal precedents and legal documents and provide answers with specific examples. For example, if a user asks, "Please tell me about copyrights for online images," the AI ​​can cite past legal precedents and legal documents and provide specific examples, such as, "Permission from the copyright holder is required. For example, in a 2018 court case..." Similarly, if a user asks, "Please tell me about the use of music," the AI ​​can cite relevant legal documents and provide specific examples, such as, "Permission from the copyright holder is required to use music. For example, Article 30 of the Copyright Act states..." Similarly, if a user asks, "Please tell me about the contents of a contract," the AI ​​can cite past legal precedents and legal documents and provide specific examples, such as, "The contents of a contract are interpreted based on the law. For example, in a 2015 court case..." This allows the AI ​​to provide users with detailed answers with specific examples.

[0031] The answer generation unit can automatically attach relevant legal documents and guidelines depending on the content of the question, allowing the user to refer to detailed information. For example, if a user asks, "Please tell me about copyrights for images on the Internet," the generation AI can automatically attach relevant legal documents and guidelines and respond, "Please refer to these guidelines." Also, if a user asks, "Please tell me about how to use music," the generation AI can automatically attach relevant legal documents and guidelines and respond, "Please refer to these legal documents." Also, if a user asks, "Please tell me about the contents of the contract," the generation AI can automatically attach relevant legal documents and guidelines and respond, "Please refer to these precedents." This allows the user to easily refer to detailed legal information.

[0032] The answer generation unit can provide answers to legal questions not only in text but also in audio or video format to help users understand. For example, if a user asks, "Please tell me about the copyright of images on the Internet," the generation AI can explain, in audio or video format, "Permission from the copyright holder is required" in addition to the text answer. Similarly, if a user asks, "Please tell me about how to use music," the generation AI can explain, in audio or video format, "Permission from the copyright holder is required to use music." Similarly, if a user asks, "Please tell me about the contents of the contract," the generation AI can explain, in audio or video format, "The contents of the contract will be interpreted in accordance with the law" in addition to the text answer. This allows answers to be provided in a variety of formats to help users understand.

[0033] The answer generation unit's generation AI supports multiple languages ​​so that it can respond to questions about the laws of different jurisdictions, and can instantly answer international legal questions. For example, if a user asks, "Please tell me about American copyright law," the answer generation unit supports multiple languages ​​and can answer in English, "American copyright law is...." Similarly, if a user asks, "Please tell me about French copyright law," the answer generation unit supports multiple languages ​​and can answer in French, "French copyright law is...." Similarly, if a user asks, "Please tell me about Japanese copyright law," the answer generation unit supports multiple languages ​​and can answer in Japanese, "Japanese copyright law is...." This makes it possible to respond to international legal questions.

[0034] The learning unit analyzes the user's question history, and the generation AI can automatically generate FAQs for frequently asked questions and provide them to the user. For example, if a user repeatedly asks, "Please tell me about the copyright of images on the Internet," the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the copyright of images on the Internet, please refer to this FAQ." If a user repeatedly asks, "Please tell me about how to use music," the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about how to use music, please refer to this FAQ." If a user repeatedly asks, "Please tell me about the contents of the contract," the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the contents of the contract, please refer to this FAQ." This allows users to quickly obtain answers to their frequently asked questions.

[0035] The learning unit can suggest related legal topics based on the content of the question, allowing the user to learn more. For example, if a user asks, "Please tell me about copyrights for online images," the generating AI can suggest related legal topics and provide, "For general information about copyright law, please see here." If a user asks, "Please tell me about how to use music," the generating AI can suggest related legal topics and provide, "For more information about music copyright, please see here." If a user asks, "Please tell me about the contents of contracts," the generating AI can suggest related legal topics and provide, "For general information about contract law, please see here." This allows the user to deepen their legal knowledge.

[0036] The learning unit allows the generation AI to suggest predictive questions to the user based on the learning data of questions, making it easier for the user to input questions. For example, when a user asks, "Please tell me about copyrights for images on the Internet," the generation AI suggests a predictive question, suggesting to the user, "Would you also like to know how to use images?" Similarly, when a user asks, "Please tell me about how to use music," the generation AI suggests a predictive question, suggesting to the user, "Would you also like to know how to distribute music?" Similarly, when a user asks, "Please tell me about the contents of contracts," the generation AI suggests a predictive question, suggesting to the user, "Would you also like to know how to create contracts?" This makes it easier for users to input questions.

[0037] The learning unit can share learning data for questions with other generative AI systems and integrate knowledge from different fields to improve the accuracy of answers. For example, if a user asks, "Please tell me about the copyright of online images," the generative AI can share data with other generative AI systems, integrate knowledge from different fields, and answer, "Permission from the copyright holder is required." Similarly, if a user asks, "Please tell me about how to use music," the generative AI can share data with other generative AI systems, integrate knowledge from different fields, and answer, "Permission from the copyright holder is required to use music." Similarly, if a user asks, "Please tell me about the contents of the contract," the generative AI can share data with other generative AI systems, integrate knowledge from different fields, and answer, "The contents of the contract will be interpreted in accordance with the law." This integrates knowledge from different fields to improve the accuracy of answers.

[0038] The expert collaboration unit can improve the efficiency of consultations by having the generation AI organize the user's questions in advance and provide them to the expert when the user seeks consultation from an expert. For example, if a user requests, "I would like to consult with an expert about the contents of a contract," the expert collaboration unit organizes the user's questions in advance and provides the expert with a message saying, "The user is asking about a specific clause in the contract." Similarly, if a user requests, "I would like to consult with an expert about copyright issues," the expert collaboration unit can organize the user's questions in advance and provide the expert with a message saying, "The user is asking about copyright infringement." Similarly, if a user requests, "I would like to consult with an expert about labor law issues," the expert collaboration unit can organize the user's questions in advance and provide the expert with a message saying, "The user is asking about the interpretation of a labor contract." This improves the efficiency of consultations.

[0039] The expert collaboration unit can have the generation AI automatically record the contents of the consultation so that the user can refer to it later. For example, when a user receives a consultation with an expert, the expert collaboration unit can have the generation AI automatically record the conversation content so that the user can refer to it later if they request to "check the contents of the consultation." In addition, when a user receives a consultation with an expert, the expert collaboration unit can have the generation AI automatically record it and provide the key points if the user requests to "summarize the main points of the consultation." In addition, when a user receives a consultation with an expert, the expert collaboration unit can have the generation AI automatically record it and provide the recording data if the user requests to "listen to the recording of the consultation." This allows the user to refer to the contents of the consultation later.

[0040] The expert collaboration unit can provide expert consultations not only via text chat but also via video calls and voice calls, improving user convenience. For example, when a user receives a consultation with an expert, the generation AI provides a video call, and can accommodate the user's request to "consult via video call." In addition, when a user receives a consultation with an expert, the generation AI can provide a voice call, and can accommodate the user's request to "consult via voice call." In addition, when a user receives a consultation with an expert, the generation AI can provide a text chat, and can accommodate the user's request to "consult via text chat." This improves user convenience.

[0041] The expert collaboration unit allows the generation AI to automate the consultation booking system, enabling users to easily coordinate schedules with experts. For example, when a user books a consultation with an expert, the generation AI automatically adjusts the schedule and notifies the user, "A consultation with an expert has been booked for tomorrow at 3:00 p.m." The expert collaboration unit can also allow the generation AI to automatically adjust the schedule when a user books a consultation with an expert and notify the user, "A consultation with an expert has been booked for next Monday." The expert collaboration unit can also allow the generation AI to automatically adjust the schedule when a user books a consultation with an expert and notify the user, "A consultation with an expert has been booked for this Friday." This allows users to easily coordinate schedules with experts.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] Furthermore, when providing answers to a user's legal questions, the LegalBot system can provide personalized advice based on the user's past question history. For example, if a user has frequently asked questions about copyright in the past, the generating AI can provide information tailored to the user's interests, such as, "We provide additional information about copyright based on your previous questions." Similarly, if a user has frequently asked questions about contracts in the past, the generating AI can provide information tailored to the user's needs, such as, "We provide detailed guidelines for drafting contracts." This allows users to obtain more specific and useful information.

[0044] The LegalBot system can also automatically provide relevant legal news and the latest court cases when answering users' legal questions. For example, if a user asks, "Please tell me about copyrights for images on the Internet," the AI ​​can provide the latest information in the form of, "In recent court cases...." If a user asks, "Please tell me about the use of music," the AI ​​can provide the latest legal information in the form of, "Due to the latest legal changes...." If a user asks, "Please tell me about the contents of contracts," the AI ​​can provide the latest information in the form of, "In recent court cases...." This allows users to obtain the latest legal information.

[0045] When providing answers to users' legal questions, the LegalBot system can also suggest related legal topics based on the content of the user's question, allowing users to learn more. For example, if a user asks, "Please tell me about copyrights for online images," the generated AI can suggest related topics such as, "For general information about copyright law, please see here." If a user asks, "Please tell me about how to use music," the generated AI can suggest related topics such as, "For more information about music copyright, please see here." If a user asks, "Please tell me about the contents of contracts," the generated AI can suggest related topics such as, "For general information about contract law, please see here." This allows users to deepen their legal knowledge.

[0046] Furthermore, when providing answers to users' legal questions, the LegalBot system's generation AI supports multiple languages, allowing it to address questions about laws in different jurisdictions and instantly answer international legal questions. For example, if a user asks, "Please tell me about American copyright law," the generation AI supports multiple languages ​​and can answer in English, "American copyright law is..." If a user asks, "Please tell me about French copyright law," the generation AI supports multiple languages ​​and can answer in French, "French copyright law is..." If a user asks, "Please tell me about Japanese copyright law," the generation AI supports multiple languages ​​and can answer in Japanese, "Japanese copyright law is..." This allows it to respond to international legal questions.

[0047] Furthermore, when providing answers to users' legal questions, the LegalBot system analyzes the user's question history and automatically generates FAQs for frequently asked questions using the generation AI, which can then provide them to the user. For example, if a user repeatedly asks, "What is the copyright of online images?", the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the copyright of online images, please refer to this FAQ." Similarly, if a user repeatedly asks, "What is the use of music?", the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the use of music, please refer to this FAQ." Similarly, if a user repeatedly asks, "What is the content of the contract?", the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the content of the contract, please refer to this FAQ." This allows users to quickly obtain answers to their frequently asked questions.

[0048] When providing answers to users' legal questions, the LegalBot system can also suggest related legal topics based on the content of the user's question, allowing users to learn more. For example, if a user asks, "What is the copyright of online images?", the AI ​​can suggest related legal topics, such as, "For general information on copyright law, please see here." Similarly, if a user asks, "What is the use of music?", the AI ​​can suggest related legal topics, such as, "For more information on music copyright, please see here." Similarly, if a user asks, "What is the content of contracts?", the AI ​​can suggest related legal topics, such as, "For general information on contract law, please see here." This allows users to deepen their legal knowledge.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The question receiving unit receives a question from a user. For example, the user inputs the question in text format. The question receiving unit can also receive a question from a user using voice input. For example, the voice is converted into text using voice recognition technology. Step 2: The answer generation unit generates an answer to the question received by the question reception unit. For example, the generation AI generates an answer to the question using a text generation AI (e.g., LLM). The generation AI can also generate an answer to the question using a multimodal generation AI. The generation AI can also generate an answer by referring to a database of past questions and answers. Step 3: The learning unit learns the user's question history based on the answers generated by the answer generation unit. For example, the learning unit uses a machine learning algorithm to analyze the user's question patterns. The learning unit can also improve the accuracy of the answers based on user feedback. For example, the learning unit analyzes feedback provided by the user and improves the answer generation algorithm. Step 4: The expert collaboration unit suggests consultation with an expert as needed, based on the information learned by the learning unit. For example, if the generation AI determines that "expert advice is needed for this problem," it will suggest consultation with an expert to the user. In addition, when the user seeks expert consultation, the expert collaboration unit can have the generation AI organize the user's questions in advance and provide them to the expert.

[0051] (Example 2) The LegalBot system according to an embodiment of the present invention is a system that provides users with quick and easy answers to their legal questions. As a result, the LegalBot system provides users with quick and appropriate answers to their legal questions and allows them to obtain expert advice as needed.

[0052] The LegalBot system according to the embodiment includes a question receiving unit, an answer generating unit, a learning unit, and an expert collaboration unit. The question receiving unit receives user questions. For example, the user inputs a question in text format. The question receiving unit can also receive user questions using voice input. For example, it converts speech into text using speech recognition technology. The answer generating unit generates answers to the questions received by the question receiving unit. For example, the generation AI generates answers to questions using a text generation AI (e.g., LLM). The generation AI can also generate answers to questions using a multimodal generation AI. The generation AI can also generate answers by referencing a database of past questions and answers. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including images and audio, in addition to text. The generation AI references a database of past questions and answers to generate answers to similar questions. The learning unit learns the user's question history based on the answers generated by the answer generating unit. For example, it analyzes the user's question patterns using a machine learning algorithm. The learning unit can also improve the accuracy of answers based on user feedback. For example, it analyzes user feedback and improves the answer generation algorithm. The expert collaboration unit suggests expert consultation as needed based on the information learned by the learning unit. For example, if the generation AI determines that "expert advice is needed for this problem," it suggests expert consultation to the user. Furthermore, when the user seeks expert consultation, the expert collaboration unit can also organize the user's questions in advance and provide them to the expert. This allows the LegalBot system according to the embodiment to provide users with prompt and appropriate answers to their legal questions and, if necessary, to obtain expert advice. For example, the output unit displays the answer results to the user via a web application or a mobile application. If the user desires paper feedback, the results can be printed using a printer.Emailing provides immediate feedback by sending results directly to the user.

[0053] The answer generation unit can cite past legal precedents and legal documents and provide answers with specific examples. For example, if a user asks, "Please tell me about copyrights for online images," the AI ​​can cite past legal precedents and legal documents and provide specific examples, such as, "Permission from the copyright holder is required. For example, in a 2018 court case..." Similarly, if a user asks, "Please tell me about the use of music," the AI ​​can cite relevant legal documents and provide specific examples, such as, "Permission from the copyright holder is required to use music. For example, Article 30 of the Copyright Act states..." Similarly, if a user asks, "Please tell me about the contents of a contract," the AI ​​can cite past legal precedents and legal documents and provide specific examples, such as, "The contents of a contract are interpreted based on the law. For example, in a 2015 court case..." This allows the AI ​​to provide users with detailed answers with specific examples.

[0054] The answer generation unit can automatically attach relevant legal documents and guidelines depending on the content of the question, allowing the user to refer to detailed information. For example, if a user asks, "Please tell me about copyrights for images on the Internet," the generation AI can automatically attach relevant legal documents and guidelines and respond, "Please refer to these guidelines." Also, if a user asks, "Please tell me about how to use music," the generation AI can automatically attach relevant legal documents and guidelines and respond, "Please refer to these legal documents." Also, if a user asks, "Please tell me about the contents of the contract," the generation AI can automatically attach relevant legal documents and guidelines and respond, "Please refer to these precedents." This allows the user to easily refer to detailed legal information.

[0055] The answer generation unit uses the emotion estimation function to analyze the user's emotions when asking a question and can provide an answer in gentle language to reduce stress and anxiety. For example, when a user asks, "Please tell me about the copyright of images on the Internet," the emotion estimation function detects the user's anxiety, and the generation AI replies in gentle language, "Don't worry, I'll explain the copyright in detail." Similarly, when a user asks, "Please tell me about how to use music," the emotion estimation function detects the user's stress, and the generation AI replies in gentle language, "Don't worry, I'll explain the music usage in detail." Similarly, when a user asks, "Please tell me about the contents of the contract," the emotion estimation function detects the user's anxiety, and the generation AI replies in gentle language, "It's okay, I'll explain the contents of the contract in detail." This reduces the user's stress and anxiety and provides a sense of security.

[0056] The answer generation unit can provide answers to legal questions not only in text but also in audio or video format to help users understand. For example, if a user asks, "Please tell me about the copyright of images on the Internet," the generation AI can explain, in audio or video format, "Permission from the copyright holder is required" in addition to the text answer. Similarly, if a user asks, "Please tell me about how to use music," the generation AI can explain, in audio or video format, "Permission from the copyright holder is required to use music." Similarly, if a user asks, "Please tell me about the contents of the contract," the generation AI can explain, in audio or video format, "The contents of the contract will be interpreted in accordance with the law" in addition to the text answer. This allows answers to be provided in a variety of formats to help users understand.

[0057] The answer generation unit's generation AI supports multiple languages ​​so that it can respond to questions about the laws of different jurisdictions, and can instantly answer international legal questions. For example, if a user asks, "Please tell me about American copyright law," the answer generation unit supports multiple languages ​​and can answer in English, "American copyright law is...." Similarly, if a user asks, "Please tell me about French copyright law," the answer generation unit supports multiple languages ​​and can answer in French, "French copyright law is...." Similarly, if a user asks, "Please tell me about Japanese copyright law," the answer generation unit supports multiple languages ​​and can answer in Japanese, "Japanese copyright law is...." This makes it possible to respond to international legal questions.

[0058] The answer generation unit can use the emotion estimation function to analyze the user's emotions in real time when entering a question and provide an interface that elicits positive emotions. For example, when a user asks, "Please tell me about the copyright of online images," the emotion estimation function detects the user's anxiety, and the generation AI provides an interface that elicits positive emotions by saying, "Don't worry, we'll explain the copyright in detail." Similarly, when a user asks, "Please tell me about how to use music," the emotion estimation function detects the user's stress, and the generation AI provides an interface that elicits positive emotions by saying, "Don't worry, we'll explain the music usage in detail." Similarly, when a user asks, "Please tell me about the contents of the contract," the emotion estimation function detects the user's anxiety, and the generation AI provides an interface that elicits positive emotions by saying, "It's okay, we'll explain the contents of the contract in detail." This elicits positive emotions from the user and encourages them to enter questions.

[0059] The learning unit analyzes the user's question history, and the generation AI can automatically generate FAQs for frequently asked questions and provide them to the user. For example, if a user repeatedly asks, "Please tell me about the copyright of images on the Internet," the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the copyright of images on the Internet, please refer to this FAQ." If a user repeatedly asks, "Please tell me about how to use music," the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about how to use music, please refer to this FAQ." If a user repeatedly asks, "Please tell me about the contents of the contract," the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the contents of the contract, please refer to this FAQ." This allows users to quickly obtain answers to their frequently asked questions.

[0060] The learning unit can suggest related legal topics based on the content of the question, allowing the user to learn more. For example, if a user asks, "Please tell me about copyrights for online images," the generating AI can suggest related legal topics and provide, "For general information about copyright law, please see here." If a user asks, "Please tell me about how to use music," the generating AI can suggest related legal topics and provide, "For more information about music copyright, please see here." If a user asks, "Please tell me about the contents of contracts," the generating AI can suggest related legal topics and provide, "For general information about contract law, please see here." This allows the user to deepen their legal knowledge.

[0061] The learning unit uses the emotion estimation function to analyze the user's emotional response to questions and can provide particularly polite and detailed answers to questions with negative emotions. For example, when a user asks, "Please tell me about the copyright of online images," the emotion estimation function detects the user's anxiety, and the generation AI provides a particularly polite and detailed answer, such as, "Don't worry, I'll explain the copyright in detail." Similarly, when a user asks, "Please tell me about how to use music," the emotion estimation function detects the user's stress, and the generation AI provides a particularly polite and detailed answer, such as, "Don't worry, I'll explain the music in detail." Similarly, when a user asks, "Please tell me about the contents of the contract," the emotion estimation function detects the user's anxiety, and the generation AI provides a particularly polite and detailed answer, such as, "It's okay, I'll explain the contents of the contract in detail." This reduces the user's negative emotions and provides a sense of security.

[0062] The learning unit allows the generation AI to suggest predictive questions to the user based on the learning data of questions, making it easier for the user to input questions. For example, when a user asks, "Please tell me about copyrights for images on the Internet," the generation AI suggests a predictive question, suggesting to the user, "Would you also like to know how to use images?" Similarly, when a user asks, "Please tell me about how to use music," the generation AI suggests a predictive question, suggesting to the user, "Would you also like to know how to distribute music?" Similarly, when a user asks, "Please tell me about the contents of contracts," the generation AI suggests a predictive question, suggesting to the user, "Would you also like to know how to create contracts?" This makes it easier for users to input questions.

[0063] The learning unit can share learning data for questions with other generative AI systems and integrate knowledge from different fields to improve the accuracy of answers. For example, if a user asks, "Please tell me about the copyright of online images," the generative AI can share data with other generative AI systems, integrate knowledge from different fields, and answer, "Permission from the copyright holder is required." Similarly, if a user asks, "Please tell me about how to use music," the generative AI can share data with other generative AI systems, integrate knowledge from different fields, and answer, "Permission from the copyright holder is required to use music." Similarly, if a user asks, "Please tell me about the contents of the contract," the generative AI can share data with other generative AI systems, integrate knowledge from different fields, and answer, "The contents of the contract will be interpreted in accordance with the law." This integrates knowledge from different fields to improve the accuracy of answers.

[0064] The learning unit can use the emotion estimation function to monitor the user's emotional response to questions in real time and provide feedback to elicit positive emotions. For example, when a user asks, "Please tell me about the copyright of online images," the emotion estimation function detects the user's anxiety, and the generation AI provides feedback to elicit positive emotions, such as, "Don't worry, I'll explain the copyright in detail." Similarly, when a user asks, "Please tell me about how to use music," the emotion estimation function detects the user's stress, and the generation AI provides feedback to elicit positive emotions, such as, "Don't worry, I'll explain the music in detail." Similarly, when a user asks, "Please tell me about the contents of the contract," the emotion estimation function detects the user's anxiety, and the generation AI provides feedback to elicit positive emotions, such as, "It's okay, I'll explain the contents of the contract in detail." This elicits positive emotions from the user and encourages them to enter questions.

[0065] The expert collaboration unit can improve the efficiency of consultations by having the generation AI organize the user's questions in advance and provide them to the expert when the user seeks consultation from an expert. For example, if a user requests, "I would like to consult with an expert about the contents of a contract," the expert collaboration unit organizes the user's questions in advance and provides the expert with a message saying, "The user is asking about a specific clause in the contract." Similarly, if a user requests, "I would like to consult with an expert about copyright issues," the expert collaboration unit can organize the user's questions in advance and provide the expert with a message saying, "The user is asking about copyright infringement." Similarly, if a user requests, "I would like to consult with an expert about labor law issues," the expert collaboration unit can organize the user's questions in advance and provide the expert with a message saying, "The user is asking about the interpretation of a labor contract." This improves the efficiency of consultations.

[0066] The expert collaboration unit can have the generation AI automatically record the contents of the consultation so that the user can refer to it later. For example, when a user receives a consultation with an expert, the expert collaboration unit can have the generation AI automatically record the conversation content so that the user can refer to it later if they request to "check the contents of the consultation." In addition, when a user receives a consultation with an expert, the expert collaboration unit can have the generation AI automatically record it and provide the key points if the user requests to "summarize the main points of the consultation." In addition, when a user receives a consultation with an expert, the expert collaboration unit can have the generation AI automatically record it and provide the recording data if the user requests to "listen to the recording of the consultation." This allows the user to refer to the contents of the consultation later.

[0067] The expert collaboration unit can use the emotion estimation function to analyze the user's emotions when receiving a consultation and provide support to reduce stress and anxiety. For example, when a user receives a consultation with an expert, the emotion estimation function can detect the user's anxiety, and the generation AI can provide support by saying, "Relax, an expert will explain things carefully." In addition, when a user receives a consultation with an expert, the emotion estimation function can detect the user's stress, and the generation AI can provide support by saying, "Don't worry, an expert will answer your questions." In addition, when a user receives a consultation with an expert, the expert collaboration unit can detect the user's anxiety, and the generation AI can provide support by saying, "It's okay, an expert will solve your problem." This can reduce the user's stress and anxiety and provide a sense of security.

[0068] The expert collaboration unit can provide expert consultations not only via text chat but also via video calls and voice calls, improving user convenience. For example, when a user receives a consultation with an expert, the generation AI provides a video call, and can accommodate the user's request to "consult via video call." In addition, when a user receives a consultation with an expert, the generation AI can provide a voice call, and can accommodate the user's request to "consult via voice call." In addition, when a user receives a consultation with an expert, the generation AI can provide a text chat, and can accommodate the user's request to "consult via text chat." This improves user convenience.

[0069] The expert collaboration unit allows the generation AI to automate the consultation booking system, enabling users to easily coordinate schedules with experts. For example, when a user books a consultation with an expert, the generation AI automatically adjusts the schedule and notifies the user, "A consultation with an expert has been booked for tomorrow at 3:00 p.m." The expert collaboration unit can also allow the generation AI to automatically adjust the schedule when a user books a consultation with an expert and notify the user, "A consultation with an expert has been booked for next Monday." The expert collaboration unit can also allow the generation AI to automatically adjust the schedule when a user books a consultation with an expert and notify the user, "A consultation with an expert has been booked for this Friday." This allows users to easily coordinate schedules with experts.

[0070] The expert collaboration unit can use the emotion estimation function to monitor the user's emotions in real time when receiving a consultation and provide an interface that elicits positive emotions. For example, when a user receives a consultation with an expert, the emotion estimation function can detect the user's anxiety, and the generation AI can provide an interface that elicits positive emotions by saying, "Don't worry, our experts will explain things to you carefully." The expert collaboration unit can also detect the user's stress when receiving a consultation with an expert, and the generation AI can provide an interface that elicits positive emotions by saying, "Don't worry, our experts will answer your questions." The expert collaboration unit can also detect the user's anxiety when receiving a consultation with an expert, and the generation AI can provide an interface that elicits positive emotions by saying, "Don't worry, our experts will solve your problem." This elicits positive emotions from the user and improves the effectiveness of the consultation.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] Furthermore, when providing answers to a user's legal questions, the LegalBot system can provide personalized advice based on the user's past question history. For example, if a user has frequently asked questions about copyright in the past, the generating AI can provide information tailored to the user's interests, such as, "We provide additional information about copyright based on your previous questions." Similarly, if a user has frequently asked questions about contracts in the past, the generating AI can provide information tailored to the user's needs, such as, "We provide detailed guidelines for drafting contracts." This allows users to obtain more specific and useful information.

[0073] The LegalBot system can also automatically provide relevant legal news and the latest court cases when answering users' legal questions. For example, if a user asks, "Please tell me about copyrights for images on the Internet," the AI ​​can provide the latest information in the form of, "In recent court cases...." If a user asks, "Please tell me about the use of music," the AI ​​can provide the latest legal information in the form of, "Due to the latest legal changes...." If a user asks, "Please tell me about the contents of contracts," the AI ​​can provide the latest information in the form of, "In recent court cases...." This allows users to obtain the latest legal information.

[0074] When providing answers to users' legal questions, the LegalBot system can also suggest related legal topics based on the content of the user's question, allowing users to learn more. For example, if a user asks, "Please tell me about copyrights for online images," the generated AI can suggest related topics such as, "For general information about copyright law, please see here." If a user asks, "Please tell me about how to use music," the generated AI can suggest related topics such as, "For more information about music copyright, please see here." If a user asks, "Please tell me about the contents of contracts," the generated AI can suggest related topics such as, "For general information about contract law, please see here." This allows users to deepen their legal knowledge.

[0075] Furthermore, when providing answers to users' legal questions, the LegalBot system uses its emotion estimation function to analyze the user's emotions at the time of the question and provide answers in gentle language to reduce stress and anxiety. For example, when a user asks, "Please tell me about copyrights for online images," the emotion estimation function detects the user's anxiety, and the generation AI responds in gentle language, "Don't worry, I'll explain copyright in detail." Similarly, when a user asks, "Please tell me about music usage," the emotion estimation function detects the user's stress, and the generation AI responds in gentle language, "Don't worry, I'll explain music usage in detail." Similarly, when a user asks, "Please tell me about the contents of the contract," the emotion estimation function detects the user's anxiety, and the generation AI responds in gentle language, "It's okay, I'll explain the contents of the contract in detail." This reduces users' stress and anxiety and provides a sense of security.

[0076] Furthermore, when providing answers to users' legal questions, the LegalBot system uses its emotion estimation function to analyze the user's emotions in real time as they enter their questions and provide an interface that elicits positive emotions. For example, when a user asks, "Please tell me about the copyright of online images," the emotion estimation function detects the user's anxiety, and the generation AI provides an interface that elicits positive emotions, saying, "Don't worry, we'll explain the copyright in detail." Similarly, when a user asks, "Please tell me about music usage," the emotion estimation function detects the user's stress, and the generation AI provides an interface that elicits positive emotions, saying, "Don't worry, we'll explain the music usage in detail." Similarly, when a user asks, "Please tell me about the contents of the contract," the emotion estimation function detects the user's anxiety, and the generation AI provides an interface that elicits positive emotions, saying, "It's okay, we'll explain the contents of the contract in detail." This elicits positive emotions from users and encourages them to enter questions.

[0077] Furthermore, when providing answers to users' legal questions, the LegalBot system's generation AI supports multiple languages, allowing it to address questions about laws in different jurisdictions and instantly answer international legal questions. For example, if a user asks, "Please tell me about American copyright law," the generation AI supports multiple languages ​​and can answer in English, "American copyright law is..." If a user asks, "Please tell me about French copyright law," the generation AI supports multiple languages ​​and can answer in French, "French copyright law is..." If a user asks, "Please tell me about Japanese copyright law," the generation AI supports multiple languages ​​and can answer in Japanese, "Japanese copyright law is..." This allows it to respond to international legal questions.

[0078] The LegalBot system also uses emotion estimation to analyze the user's emotional response to their legal questions and provide particularly detailed and polite answers to questions with negative emotions. For example, when a user asks, "What about copyrights for online images?", the emotion estimation function detects the user's anxiety, and the AI ​​generator provides a particularly detailed and polite answer, "Don't worry, I'll explain copyright in detail." Similarly, when a user asks, "What about music usage?", the emotion estimation function detects the user's stress, and the AI ​​generator provides a particularly detailed and polite answer, "Don't worry, I'll explain music usage in detail." Similarly, when a user asks, "What about the contents of the contract?", the emotion estimation function detects the user's anxiety, and the AI ​​generator provides a particularly detailed and polite answer, "It's okay, I'll explain the contents of the contract in detail." This reduces the user's negative emotions and provides a sense of security.

[0079] Furthermore, when providing answers to users' legal questions, the LegalBot system analyzes the user's question history and automatically generates FAQs for frequently asked questions using the generation AI, which can then provide them to the user. For example, if a user repeatedly asks, "What is the copyright of online images?", the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the copyright of online images, please refer to this FAQ." Similarly, if a user repeatedly asks, "What is the use of music?", the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the use of music, please refer to this FAQ." Similarly, if a user repeatedly asks, "What is the content of the contract?", the generation AI can automatically generate an FAQ and provide it to the user, saying, "For information about the content of the contract, please refer to this FAQ." This allows users to quickly obtain answers to their frequently asked questions.

[0080] Furthermore, when providing answers to users' legal questions, the LegalBot system uses emotion estimation to monitor the user's emotional response to the question in real time and provide feedback designed to elicit positive emotions. For example, when a user asks, "Please tell me about copyrights for online images," the emotion estimation function detects the user's anxiety, and the generation AI provides feedback that elicits positive emotions, such as, "Don't worry, we'll explain copyright in detail." Similarly, when a user asks, "Please tell me about music usage," the emotion estimation function detects the user's stress, and the generation AI provides feedback that elicits positive emotions, such as, "Don't worry, we'll explain music usage in detail." Similarly, when a user asks, "Please tell me about the contents of the contract," the emotion estimation function detects the user's anxiety, and the generation AI provides feedback that elicits positive emotions, such as, "It's okay, we'll explain the contents of the contract in detail." This elicits positive emotions from users and encourages them to enter questions.

[0081] When providing answers to users' legal questions, the LegalBot system can also suggest related legal topics based on the content of the user's question, allowing users to learn more. For example, if a user asks, "What is the copyright of online images?", the AI ​​can suggest related legal topics, such as, "For general information on copyright law, please see here." Similarly, if a user asks, "What is the use of music?", the AI ​​can suggest related legal topics, such as, "For more information on music copyright, please see here." Similarly, if a user asks, "What is the content of contracts?", the AI ​​can suggest related legal topics, such as, "For general information on contract law, please see here." This allows users to deepen their legal knowledge.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The question receiving unit receives a question from a user. For example, the user inputs the question in text format. The question receiving unit can also receive a question from a user using voice input. For example, the voice is converted into text using voice recognition technology. Step 2: The answer generation unit generates an answer to the question received by the question reception unit. For example, the generation AI generates an answer to the question using a text generation AI (e.g., LLM). The generation AI can also generate an answer to the question using a multimodal generation AI. The generation AI can also generate an answer by referring to a database of past questions and answers. Step 3: The learning unit learns the user's question history based on the answers generated by the answer generation unit. For example, the learning unit uses a machine learning algorithm to analyze the user's question patterns. The learning unit can also improve the accuracy of the answers based on user feedback. For example, the learning unit analyzes feedback provided by the user and improves the answer generation algorithm. Step 4: The expert collaboration unit suggests consultation with an expert as needed, based on the information learned by the learning unit. For example, if the generation AI determines that "expert advice is needed for this problem," it will suggest consultation with an expert to the user. In addition, when the user seeks expert consultation, the expert collaboration unit can have the generation AI organize the user's questions in advance and provide them to the expert.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 question receiving unit that receives questions from users; an answer generation unit that generates an answer to the question received by the question receiving unit; a learning unit that learns a user's question history based on the answers generated by the answer generating unit; and an expert collaboration unit that suggests consultation with an expert as needed based on the information learned by the learning unit. A system characterized by:

2. The answer generation unit Citing past cases and legal documents, providing answers with specific examples 2. The system of claim 1.

3. The answer generation unit Automatically attach relevant legal documents and guidelines based on the question, allowing users to refer to them for further information 2. The system of claim 1.

4. The answer generation unit Analyze the user's emotions when asking a question and provide a gentle response to reduce stress and anxiety 2. The system of claim 1.

5. The answer generation unit Providing answers to legal questions in text, audio and video formats to help users understand 2. The system of claim 1.

Images