system

Abstract

The system according to this embodiment aims to provide users with appropriate advice regarding their legal questions and issues, and to connect them with experts as needed. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a communication unit. The reception unit receives legal questions and issues from users. The analysis unit analyzes the information received by the reception unit to understand the user's situation and background. The provision unit provides legal advice generated by the analysis unit. The communication unit provides connections to experts for serious legal issues.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide appropriate advice for users' legal doubts and problems, and there is a problem that there is a lack of connection to experts especially for serious legal problems.

[0005] The system according to the embodiment aims to provide appropriate advice for users' legal doubts and problems and provide a connection to experts if necessary.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a liaison unit. The reception unit receives legal questions and issues from users. The analysis unit analyzes the information received by the reception unit to understand the user's situation and background. The provision unit provides legal advice generated by the analysis unit. The liaison unit provides connections to experts for serious legal issues. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate advice to users regarding their legal questions and issues, and, if necessary, connect them with experts. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The legal advice generation system according to an embodiment of the present invention is a system that generates appropriate legal advice based on the user's situation and background. This system is designed to guide users to resolve their unique legal issues by utilizing a generating AI. Specifically, it consists of the following steps: First, the user inputs a legal question or issue. Next, the generating AI analyzes the user's situation and background and generates appropriate legal advice. Furthermore, for serious legal issues, it provides a connection to a paid expert to support the resolution of legal troubles. For example, the user inputs a legal question or issue. For example, the user inputs a specific question such as "I don't understand the contents of the contract" or "I want to know about divorce procedures." This information is input to the generating AI. Next, the generating AI analyzes the input information and understands the user's situation and background. Based on the user's input, the generating AI searches for relevant legal knowledge and generates appropriate advice. For example, for a question about the contents of a contract, it provides explanations and points to note for each clause of the contract. For a question about divorce procedures, it guides the user on the necessary procedures and how to prepare the documents. Furthermore, for serious legal issues, it provides a connection to a paid expert. The generating AI assesses the severity of a user's legal problem and recommends consultation with a professional as needed. For example, for complex litigation cases or serious legal disputes, it provides contact information for specialized lawyers and law firms. This system allows users to receive quick and appropriate advice on their legal questions and challenges. Furthermore, for serious legal issues, it supports the resolution of legal disputes by providing connections to professionals. For example, if a user has questions about the contents of a contract, the generating AI explains each clause and points out important details, making it easier for the user to understand the contract. Similarly, for questions about divorce proceedings, the generating AI guides the user through the necessary procedures and how to prepare the required documents, allowing the user to proceed smoothly. Moreover, for serious legal issues, it provides connections to professionals, enabling users to receive appropriate legal support. For example, for complex litigation cases, the generating AI provides contact information for specialized lawyers and law firms, allowing users to receive prompt professional support.This allows the legal advice generation system to provide users with quick and appropriate advice on their legal questions and issues, and to connect them with experts for serious legal problems.

[0029] The legal advice generation system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a communication unit. The reception unit receives legal questions and issues from users. These include, but are not limited to, contract issues, labor issues, and family law. The reception unit receives legal questions and issues entered by users in text format, for example. The reception unit can also accept legal questions and issues using voice input. For example, a user can input a legal question by voice, and the reception unit can convert it into text. Furthermore, the reception unit can estimate the user's emotions and adjust the method of receiving legal questions based on the estimated emotions of the user. For example, if the user is feeling stressed, it can provide a simple interface and minimize the input procedure. The analysis unit analyzes the information received by the reception unit to understand the user's situation and background. The analysis unit uses a generation AI to search for relevant legal knowledge based on the user's input and generate appropriate advice. For example, in response to a question about the content of a contract entered by the user, the generation AI provides explanations and points to note for each clause of the contract. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is feeling anxious, it will provide simple and easy-to-understand analysis results. The service unit provides legal advice generated by the analysis unit. The service unit uses generative AI to provide appropriate legal advice to the user. For example, the service unit provides explanations and points to note for each clause of a contract. It can also guide users on the procedures and document preparation required for divorce proceedings. In addition, the service unit can estimate the user's emotions and adjust the presentation of the advice based on those emotions. For example, if the user is feeling anxious, it will provide simple and easy-to-understand advice. The liaison unit provides connections to experts for serious legal issues. For example, the liaison unit provides contact information for specialized lawyers and law firms for complex litigation cases or serious legal troubles. The liaison unit can also estimate the user's emotions and adjust the method of contacting experts based on those emotions. For example, if the user is feeling anxious, it will provide simple and easy-to-understand contact methods.As a result, the legal advice generation system according to the embodiment can provide appropriate advice to users regarding their legal questions and issues, and connect them with experts in the case of serious legal problems.

[0030] The reception desk receives legal questions and issues from users. These include, but are not limited to, contract issues, labor issues, and family law. The reception desk accepts user-submitted legal questions and issues in text format. It can also accept legal questions and issues using voice input. For example, a user might voice their legal question, and the reception desk converts it to text. Furthermore, the reception desk can estimate the user's emotions and adjust the way legal questions are received based on that estimation. For example, if a user is stressed, it provides a simple interface and minimizes the input steps. The reception desk offers a variety of interfaces for users to submit legal questions. For example, it allows users to submit questions in the most convenient way possible through web forms, mobile apps, chatbots, etc. In the case of voice input, speech recognition technology is used to convert the user's utterance into text with high accuracy, minimizing misrecognition. Additionally, the reception desk has the ability to automatically categorize user input and assign it to the appropriate category. For example, it can automatically categorize it into categories such as contract issues, labor issues, and family law, allowing the analysis department to process it efficiently. Emotion recognition technology is used to estimate user emotions. For example, emotions are estimated from the user's input and tone of voice, and if the user is feeling stressed or tense, the interface is simplified to provide an environment where the user can input information in a relaxed manner. This allows the reception desk to respond to the diverse needs of users and efficiently and accurately receive legal questions and issues.

[0031] The analysis unit analyzes information received by the reception unit to understand the user's situation and background. Using generative AI, the analysis unit searches for relevant legal knowledge based on the user's input and generates appropriate advice. For example, in response to a user's question about the content of a contract, the generative AI provides explanations and points to note for each clause of the contract. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it provides a simple and highly visual analysis result. The analysis unit uses natural language processing technology to analyze the user's input and understand the legal context. For example, if a question about a contract clause is entered, the generative AI analyzes the contract clause, searches for relevant legal knowledge, and generates appropriate advice. The generative AI refers to a large database of legal documents and case law to provide advice based on the latest legal knowledge. Furthermore, the analysis unit uses emotion recognition technology to estimate the user's emotions. For example, it estimates emotions from the user's input and tone of voice, and if the user is feeling nervous or stressed, it provides the analysis result in a simple and highly visual format. This allows users to receive advice in an easily understandable way. The analysis unit can comprehensively understand the user's situation and background, and provide appropriate advice tailored to their individual needs.

[0032] The service provider provides legal advice generated by the analysis unit. Using generative AI, the service provider delivers appropriate legal advice to the user. For example, it provides explanations and points to note for each clause of a contract. It can also guide users through the procedures and document preparation required for divorce proceedings. Furthermore, the service provider can estimate the user's emotions and adjust the presentation of the advice based on those emotions. For example, if the user is nervous, it provides simple and easy-to-understand advice. The service provider delivers advice to the user in various formats. For example, in addition to text-based advice, it uses videos, audio guides, infographics, etc., to provide information in a way that is easy for the user to understand. The generative AI generates optimal advice based on the user's input, and the service provider delivers it to the user. The service provider uses emotion recognition technology to estimate the user's emotions. For example, if the user is nervous, it provides advice in a simple and easy-to-understand format to help the user relax and receive the information. Furthermore, the service provider collects user feedback and continuously improves the content and presentation of the advice. For example, the generative AI learns from the user's feedback on the advice provided, improving the accuracy of future advice. This allows the service provider to offer users appropriate and effective legal advice and assist them in resolving their legal issues.

[0033] The Liaison Department provides connections to experts for serious legal issues. For example, it provides contact information for specialized lawyers and law firms for complex litigation cases and significant legal troubles. The Liaison Department can also estimate the user's emotions and adjust how it contacts experts based on those emotions. For example, if the user is feeling anxious, it will provide a simple and visually clear method of contact. The Liaison Department provides multiple means of contact to make it easier for users to access experts. For example, it will allow users to contact experts using the method most convenient for them, such as phone, email, online chat, or video conferencing. Furthermore, the Liaison Department uses emotion recognition technology to estimate the user's emotions. For example, if the user is feeling anxious, it will provide a simple and visually clear method of contact to create an environment where the user can relax and consult with an expert. The Liaison Department also provides support to ensure smooth contact with experts. For example, it provides guidelines to help users prepare necessary documents and information in advance, and assists in ensuring a smooth initial consultation with an expert. Furthermore, the Liaison Department collects user feedback and continuously improves its contact methods and support. This allows the liaison department to ensure that users receive prompt and appropriate expert assistance for serious legal issues and to help resolve those issues.

[0034] The analysis unit can search for relevant legal knowledge based on user input and generate appropriate advice. For example, in response to a user's question about the contents of a contract, the analysis unit can provide explanations and points to note for each clause of the contract. The analysis unit uses a generative AI to analyze user input and search for relevant legal knowledge. For example, in response to a user's question about divorce proceedings, the generative AI can guide the user through the necessary procedures and how to prepare the necessary documents. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, it will provide a simple and easy-to-understand analysis result. This allows the analysis unit to generate appropriate legal advice based on user input. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit inputs the user's input into the generative AI, which searches for legal knowledge and generates advice.

[0035] The service provider can provide explanations and points to note for each clause of the contract. For example, if a user has questions about the content of the contract, the service provider will explain each clause and provide points to note. The service provider uses generative AI to generate explanations and points to note for each clause of the contract. For example, the generative AI analyzes the clauses of the contract and extracts important points and points to note. The service provider can also estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions of the user. For example, if the user is nervous, it will provide simple and easy-to-understand advice. In this way, the service provider can help the user understand the content of the contract. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider inputs the content of the contract into the generative AI, and the generative AI generates explanations and points to note for each clause.

[0036] The service provider can guide users through the necessary procedures and document preparation for divorce proceedings. For example, if a user has questions about divorce proceedings, the service provider will guide them through the necessary procedures and document preparation. The service provider uses generative AI to generate information on the necessary procedures and document preparation for divorce proceedings. For example, the generative AI analyzes information about divorce proceedings and generates a list of necessary procedures and documents. The service provider can also estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is nervous, it will provide simple and easy-to-understand advice. This allows the service provider to provide information about divorce proceedings and facilitate the process. Some or all of the above-described processes in the service provider may be performed using generative AI or not. For example, the service provider inputs information about divorce proceedings into the generative AI, and the generative AI generates information on the necessary procedures and document preparation.

[0037] The liaison department can provide contact information for specialized lawyers and law firms for complex litigation cases and serious legal troubles. For example, if a user faces a complex litigation case, the liaison department will provide contact information for specialized lawyers and law firms. The liaison department uses generative AI to determine the severity of the user's legal problem and recommend consultation with a professional as needed. For example, the generative AI analyzes the user's input and evaluates the severity of the legal problem. The liaison department can also estimate the user's emotions and adjust the method of contacting a professional based on the estimated emotions. For example, if the user is stressed, it will provide a simple and visually clear method of contact. This allows the liaison department to receive support from professionals for serious legal problems. Some or all of the above processes in the liaison department may be performed using generative AI or not. For example, the liaison department inputs the severity of the user's legal problem into the generative AI, and the generative AI provides contact information for a professional.

[0038] The reception desk can analyze the user's past legal question history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions legal questions that the user has frequently entered in the past. The reception desk uses generative AI to analyze the user's past legal question history. For example, the generative AI analyzes the user's past input data and selects the most suitable reception method. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, it can predict and suggest legal questions to be used during a specific time period based on the user's past legal question history. This allows the reception desk to provide the most suitable reception method based on the user's past history. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's past legal question history into the generative AI, and the generative AI selects the most suitable reception method.

[0039] The reception unit can filter legal inquiries based on the user's current living situation and areas of interest. For example, if the user enters their current living situation, the reception unit will prioritize receiving legal inquiries related to that situation. The reception unit uses generative AI to analyze the user's living situation and areas of interest. For example, the generative AI analyzes the user's input and filters out relevant legal inquiries. The reception unit can also filter and receive relevant legal inquiries based on the user's areas of interest. For example, it can suggest appropriate legal inquiries based on the user's living situation and areas of interest. This allows the reception unit to prioritize receiving legal inquiries that are relevant to the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit inputs the user's living situation and areas of interest into the generative AI, and the generative AI filters out relevant legal inquiries.

[0040] The reception desk can prioritize receiving legal inquiries based on the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize receiving legal inquiries related to that region. The reception desk uses generative AI to analyze the user's geographical location information. For example, the generative AI will prioritize receiving region-specific legal issues based on the user's location information. The reception desk can also prioritize receiving legal inquiries related to the user's current location if the user is on the move. For example, it will suggest highly relevant legal inquiries based on the user's geographical location information. This allows the reception desk to prioritize receiving highly relevant legal inquiries based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk may input the user's geographical location information into the generative AI, which will prioritize receiving highly relevant legal inquiries.

[0041] The reception desk can analyze a user's social media activity and receive relevant legal questions when a legal question is received. For example, the reception desk can analyze a user's social media posts and receive relevant legal questions. The reception desk uses generative AI to analyze a user's social media activity. For example, the generative AI analyzes the content of a user's posts and the number of followers and suggests relevant legal questions. The reception desk can also suggest relevant legal questions based on the user's interests on social media. For example, it can determine the priority of legal questions from the user's social media activity. This allows the reception desk to receive relevant legal questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's social media activity into the generative AI, and the generative AI receives relevant legal questions.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the legal questions during the analysis. For example, the analysis unit performs a detailed analysis for legal questions of high importance. The analysis unit uses a generative AI to evaluate the importance of legal questions. For example, the generative AI analyzes the user's input and evaluates the importance of the legal questions. The analysis unit can also perform a concise analysis for legal questions of low importance. For example, it determines the priority of the analysis according to the importance of the legal questions. This allows the analysis unit to provide analysis results that correspond to the importance of the legal questions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the user's input into a generative AI, the generative AI evaluates the importance of the legal questions, and adjusts the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the legal question during analysis. For example, for legal questions concerning contracts, the analysis unit applies a contract analysis algorithm. The analysis unit uses a generative AI to analyze the category of the legal question. For example, the generative AI analyzes the user's input and identifies the category of the legal question. The analysis unit can also apply a divorce procedure analysis algorithm for legal questions concerning divorce proceedings. For example, for legal questions concerning litigation cases, it applies a litigation analysis algorithm. This allows the analysis unit to apply an appropriate analysis algorithm according to the category of the legal question. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the user's input into a generative AI, the generative AI identifies the category of the legal question, and applies an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on when legal questions were submitted. For example, the analysis unit may prioritize the analysis of legal questions submitted earlier. The analysis unit uses a generative AI to analyze the submission timing of legal questions. For example, the generative AI analyzes the user's input and evaluates the submission timing. The analysis unit can also postpone legal questions submitted later. For example, it may adjust the analysis schedule based on the submission timing. This allows the analysis unit to provide appropriate analysis results based on when legal questions were submitted. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit inputs the user's input into a generative AI, which evaluates the submission timing and determines the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the legal questions during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant legal questions. The analysis unit uses a generative AI to evaluate the relevance of the legal questions. For example, the generative AI analyzes the user's input and evaluates the relevance of the legal questions. The analysis unit can also postpone the analysis of less relevant legal questions. For example, it adjusts the analysis schedule based on the relevance of the legal questions. This allows the analysis unit to provide appropriate analysis results based on the relevance of the legal questions. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit inputs the user's input into a generative AI, which evaluates the relevance of the legal questions and adjusts the order of analysis.

[0046] The service provider can adjust the level of detail of the advice based on the importance of the legal question when providing advice. For example, the service provider will provide detailed advice for high-importance legal questions. The service provider will use generative AI to assess the importance of legal questions. For example, the generative AI will analyze the user's input and assess the importance of the legal question. The service provider can also provide concise advice for low-importance legal questions. For example, it will determine the priority of advice according to the importance of the legal question. This allows the service provider to provide appropriate advice according to the importance of the legal question. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider inputs the user's input into the generative AI, which assesses the importance of the legal question and adjusts the level of detail of the advice.

[0047] The service provider can apply different advice algorithms depending on the category of the legal question when providing advice. For example, for legal questions concerning contracts, the service provider will apply a contract advice algorithm. The service provider uses generative AI to analyze the category of the legal question. For example, the generative AI analyzes the user's input and identifies the category of the legal question. The service provider can also apply a divorce procedure advice algorithm for legal questions concerning divorce proceedings. For example, for legal questions concerning litigation, the service provider will apply a litigation advice algorithm. This allows the service provider to provide appropriate advice according to the category of the legal question. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider inputs the user's input into the generative AI, the generative AI identifies the category of the legal question, and applies an appropriate advice algorithm.

[0048] The service provider can prioritize advice based on when the legal question is submitted. For example, the service provider will prioritize advice for legal questions submitted earlier. The service provider uses generative AI to analyze when legal questions are submitted. For example, the generative AI analyzes the user's input and evaluates the submission timing. The service provider can also postpone legal questions submitted later. For example, it adjusts the advice schedule based on the submission timing. This allows the service provider to provide appropriate advice based on when legal questions are submitted. Some or all of the above processes in the service provider may be performed using generative AI or not. For example, the service provider inputs the user's input into the generative AI, which evaluates the submission timing and determines the priority of advice.

[0049] The service provider can adjust the order of advice based on the relevance of the legal questions when providing advice. For example, the service provider will prioritize advice for highly relevant legal questions. The service provider will use generative AI to evaluate the relevance of legal questions. For example, the generative AI will analyze the user's input and evaluate the relevance of the legal questions. The service provider can also postpone less relevant legal questions. For example, it will adjust the advice schedule based on the relevance of the legal questions. This allows the service provider to provide appropriate advice based on the relevance of the legal questions. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider may input the user's input into the generative AI, which will evaluate the relevance of the legal questions and adjust the order of advice.

[0050] The liaison unit can adjust the level of detail in communications based on the severity of the legal issue. For example, the liaison unit provides detailed communication methods for highly serious legal issues. The liaison unit uses generative AI to assess the severity of legal issues. For example, the generative AI analyzes the user's input and assesses the severity of the legal issue. The liaison unit can also provide concise communication methods for less serious legal issues. For example, it determines the priority of communications according to the severity of the legal issue. This allows the liaison unit to provide appropriate communication methods according to the severity of the legal issue. Some or all of the above processing in the liaison unit may be performed using generative AI or not. For example, the liaison unit inputs the user's input into the generative AI, which assesses the severity of the legal issue and adjusts the level of detail in communications.

[0051] The liaison department can apply different contact algorithms depending on the category of the legal issue at the time of contact. For example, for legal issues related to contracts, the liaison department can provide a method for contacting a lawyer specializing in contracts. The liaison department uses generative AI to analyze the category of the legal issue. For example, the generative AI analyzes the user's input and identifies the category of the legal issue. The liaison department can also provide a method for contacting a lawyer specializing in divorce for legal issues related to divorce proceedings. For example, for legal issues related to litigation, it can provide a method for contacting a lawyer specializing in litigation. In this way, the liaison department can provide an appropriate contact method according to the category of the legal issue. Some or all of the above processing in the liaison department may be performed using generative AI or not. For example, the liaison department inputs the user's input into the generative AI, the generative AI identifies the category of the legal issue, and applies an appropriate contact algorithm.

[0052] The liaison department can prioritize communications based on the timing of legal issues. For example, it may prioritize communications for legal issues that are due sooner. The liaison department uses generative AI to analyze the timing of legal issues. For example, the generative AI analyzes user input and evaluates the timing of submission. The liaison department may also postpone communications for legal issues that are due later. For example, it may adjust the communication schedule based on the timing of submission. This allows the liaison department to provide appropriate communication priorities based on the timing of legal issues. Some or all of the above processes in the liaison department may be performed using generative AI or not. For example, the liaison department inputs user input into the generative AI, which evaluates the timing of submission and determines the communication priority.

[0053] The liaison department can adjust the order of communications based on the relevance of the legal issues. For example, the liaison department will prioritize communications regarding highly relevant legal issues. The liaison department will use generative AI to evaluate the relevance of legal issues. For example, the generative AI will analyze the user's input and evaluate the relevance of the legal issues. The liaison department can also postpone communications regarding less relevant legal issues. For example, it will adjust the communication schedule based on the relevance of the legal issues. This allows the liaison department to provide an appropriate order of communications based on the relevance of the legal issues. Some or all of the above processing in the liaison department may be performed using generative AI or not. For example, the liaison department may input the user's input into the generative AI, which will evaluate the relevance of the legal issues and adjust the order of communications.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The legal advice generation system can provide advice to users regarding their legal questions and issues by referring to their past legal problem-solving history. For example, if a user has had questions about the content of a contract in the past, the system can generate new advice based on the solutions and advice received in that past case. Similarly, if a user has had questions about divorce proceedings in the past, the system can provide more specific advice based on the progress and solutions of those proceedings. Furthermore, if a user has consulted with a professional in the past, the system can refer them to an appropriate professional for similar problems by referring to the content and results of that consultation. In this way, the legal advice generation system can provide more accurate and personalized advice by utilizing the user's past legal problem-solving history.

[0056] The legal advice generation system can provide users with advice that reflects real-time legal changes in response to their legal questions and issues. For example, if a user has questions about the content of a contract, it can provide explanations and points to note for each clause of the contract based on the latest legal changes. Similarly, if a user has questions about divorce proceedings, it can guide them on procedures and how to prepare documents, reflecting the latest legal changes. Furthermore, if a user faces a complex litigation case, it can recommend consulting with the appropriate expert based on the latest legal changes. In this way, the legal advice generation system can always provide advice that reflects the most up-to-date legal changes.

[0057] A legal advice generation system can provide users with advice tailored to their specific profession or industry in response to their legal questions and challenges. For example, if a user has questions about the content of a contract, it can provide explanations and points to note that are specific to that profession or industry. Similarly, if a user has questions about labor issues, it can provide explanations and advice on labor laws specific to that profession or industry. Furthermore, if a user faces legal trouble in a particular industry, it can recommend consulting with an expert specializing in that industry. In this way, the legal advice generation system can provide advice tailored to the user's profession and industry.

[0058] A legal advice generation system can provide users with legal questions and issues tailored to their cultural background and language. For example, if a user has questions about the content of a contract, it can provide explanations and points to note in a way that is appropriate to their cultural background and language. Similarly, if a user has questions about divorce proceedings, it can guide them through the procedures and document preparation methods in a way that is appropriate to their cultural background and language. Furthermore, if a user faces cross-cultural legal disputes, it can recommend consulting with experts in a way that is appropriate to their cultural background and language. In this way, the legal advice generation system can provide advice tailored to the user's cultural background and language.

[0059] The legal advice generation system can provide users with legal questions and challenges tailored to their health condition and physical limitations. For example, if a user has questions about the contents of a contract, it can provide explanations and points to note about the contract that are appropriate for their health condition and physical limitations. Similarly, if a user has questions about divorce proceedings, it can guide them on the procedures and how to prepare the necessary documents, tailored to their health condition and physical limitations. Furthermore, if a user faces legal troubles related to their health condition or physical limitations, the system can recommend consulting with a specialist appropriate to their health condition and physical limitations. In this way, the legal advice generation system can provide advice that is tailored to the user's health condition and physical limitations.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives users' legal questions and issues. These include contract issues, labor issues, family law, etc. The reception desk receives legal questions and issues entered by users in text format. It can also receive legal questions and issues using voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the way legal questions are received based on the estimated emotions of the user. Step 2: The analysis unit analyzes the information received by the reception unit to understand the user's situation and background. Using generative AI, the analysis unit searches for relevant legal knowledge based on the user's input and generates appropriate advice. For example, in response to a question about the content of a contract entered by the user, it provides explanations and points to note for each clause of the contract. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions of the user. Step 3: The service provider provides legal advice generated by the analysis unit. The service provider uses the generation AI to provide appropriate legal advice to the user. For example, it can provide explanations and points to note for each clause of a contract. It can also guide users on the procedures and how to prepare the necessary documents for divorce proceedings. Furthermore, the service provider can estimate the user's emotions and adjust the way the advice is expressed based on those estimated emotions. Step 4: The liaison department provides connections to experts for serious legal issues. The liaison department provides contact information for specialized lawyers and law firms for complex litigation cases and serious legal troubles. The liaison department can also estimate the user's sentiment and adjust how it contacts experts based on the estimated sentiment.

[0062] (Example of form 2) The legal advice generation system according to an embodiment of the present invention is a system that generates appropriate legal advice based on the user's situation and background. This system is designed to guide users to resolve their unique legal issues by utilizing a generating AI. Specifically, it consists of the following steps: First, the user inputs a legal question or issue. Next, the generating AI analyzes the user's situation and background and generates appropriate legal advice. Furthermore, for serious legal issues, it provides a connection to a paid expert to support the resolution of legal troubles. For example, the user inputs a legal question or issue. For example, the user inputs a specific question such as "I don't understand the contents of the contract" or "I want to know about divorce procedures." This information is input to the generating AI. Next, the generating AI analyzes the input information and understands the user's situation and background. Based on the user's input, the generating AI searches for relevant legal knowledge and generates appropriate advice. For example, for a question about the contents of a contract, it provides explanations and points to note for each clause of the contract. For a question about divorce procedures, it guides the user on the necessary procedures and how to prepare the documents. Furthermore, for serious legal issues, it provides a connection to a paid expert. The generating AI assesses the severity of a user's legal problem and recommends consultation with a professional as needed. For example, for complex litigation cases or serious legal disputes, it provides contact information for specialized lawyers and law firms. This system allows users to receive quick and appropriate advice on their legal questions and challenges. Furthermore, for serious legal issues, it supports the resolution of legal disputes by providing connections to professionals. For example, if a user has questions about the contents of a contract, the generating AI explains each clause and points out important details, making it easier for the user to understand the contract. Similarly, for questions about divorce proceedings, the generating AI guides the user through the necessary procedures and how to prepare the required documents, allowing the user to proceed smoothly. Moreover, for serious legal issues, it provides connections to professionals, enabling users to receive appropriate legal support. For example, for complex litigation cases, the generating AI provides contact information for specialized lawyers and law firms, allowing users to receive prompt professional support.This allows the legal advice generation system to provide users with quick and appropriate advice on their legal questions and issues, and to connect them with experts for serious legal problems.

[0063] The legal advice generation system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a communication unit. The reception unit receives legal questions and issues from users. These include, but are not limited to, contract issues, labor issues, and family law. The reception unit receives legal questions and issues entered by users in text format, for example. The reception unit can also accept legal questions and issues using voice input. For example, a user can input a legal question by voice, and the reception unit can convert it into text. Furthermore, the reception unit can estimate the user's emotions and adjust the method of receiving legal questions based on the estimated emotions of the user. For example, if the user is feeling stressed, it can provide a simple interface and minimize the input procedure. The analysis unit analyzes the information received by the reception unit to understand the user's situation and background. The analysis unit uses a generation AI to search for relevant legal knowledge based on the user's input and generate appropriate advice. For example, in response to a question about the content of a contract entered by the user, the generation AI provides explanations and points to note for each clause of the contract. Furthermore, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is feeling anxious, it will provide simple and easy-to-understand analysis results. The service unit provides legal advice generated by the analysis unit. The service unit uses generative AI to provide appropriate legal advice to the user. For example, the service unit provides explanations and points to note for each clause of a contract. It can also guide users on the procedures and document preparation required for divorce proceedings. In addition, the service unit can estimate the user's emotions and adjust the presentation of the advice based on those emotions. For example, if the user is feeling anxious, it will provide simple and easy-to-understand advice. The liaison unit provides connections to experts for serious legal issues. For example, the liaison unit provides contact information for specialized lawyers and law firms for complex litigation cases or serious legal troubles. The liaison unit can also estimate the user's emotions and adjust the method of contacting experts based on those emotions. For example, if the user is feeling anxious, it will provide simple and easy-to-understand contact methods.As a result, the legal advice generation system according to the embodiment can provide appropriate advice to users regarding their legal questions and issues, and connect them with experts in the case of serious legal problems.

[0064] The reception desk receives legal questions and issues from users. These include, but are not limited to, contract issues, labor issues, and family law. The reception desk accepts user-submitted legal questions and issues in text format. It can also accept legal questions and issues using voice input. For example, a user might voice their legal question, and the reception desk converts it to text. Furthermore, the reception desk can estimate the user's emotions and adjust the way legal questions are received based on that estimation. For example, if a user is stressed, it provides a simple interface and minimizes the input steps. The reception desk offers a variety of interfaces for users to submit legal questions. For example, it allows users to submit questions in the most convenient way possible through web forms, mobile apps, chatbots, etc. In the case of voice input, speech recognition technology is used to convert the user's utterance into text with high accuracy, minimizing misrecognition. Additionally, the reception desk has the ability to automatically categorize user input and assign it to the appropriate category. For example, it can automatically categorize it into categories such as contract issues, labor issues, and family law, allowing the analysis department to process it efficiently. Emotion recognition technology is used to estimate user emotions. For example, emotions are estimated from the user's input and tone of voice, and if the user is feeling stressed or tense, the interface is simplified to provide an environment where the user can input information in a relaxed manner. This allows the reception desk to respond to the diverse needs of users and efficiently and accurately receive legal questions and issues.

[0065] The analysis unit analyzes information received by the reception unit to understand the user's situation and background. Using generative AI, the analysis unit searches for relevant legal knowledge based on the user's input and generates appropriate advice. For example, in response to a user's question about the content of a contract, the generative AI provides explanations and points to note for each clause of the contract. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it provides a simple and highly visual analysis result. The analysis unit uses natural language processing technology to analyze the user's input and understand the legal context. For example, if a question about a contract clause is entered, the generative AI analyzes the contract clause, searches for relevant legal knowledge, and generates appropriate advice. The generative AI refers to a large database of legal documents and case law to provide advice based on the latest legal knowledge. Furthermore, the analysis unit uses emotion recognition technology to estimate the user's emotions. For example, it estimates emotions from the user's input and tone of voice, and if the user is feeling nervous or stressed, it provides the analysis result in a simple and highly visual format. This allows users to receive advice in an easily understandable way. The analysis unit can comprehensively understand the user's situation and background, and provide appropriate advice tailored to their individual needs.

[0066] The service provider provides legal advice generated by the analysis unit. Using generative AI, the service provider delivers appropriate legal advice to the user. For example, it provides explanations and points to note for each clause of a contract. It can also guide users through the procedures and document preparation required for divorce proceedings. Furthermore, the service provider can estimate the user's emotions and adjust the presentation of the advice based on those emotions. For example, if the user is nervous, it provides simple and easy-to-understand advice. The service provider delivers advice to the user in various formats. For example, in addition to text-based advice, it uses videos, audio guides, infographics, etc., to provide information in a way that is easy for the user to understand. The generative AI generates optimal advice based on the user's input, and the service provider delivers it to the user. The service provider uses emotion recognition technology to estimate the user's emotions. For example, if the user is nervous, it provides advice in a simple and easy-to-understand format to help the user relax and receive the information. Furthermore, the service provider collects user feedback and continuously improves the content and presentation of the advice. For example, the generative AI learns from the user's feedback on the advice provided, improving the accuracy of future advice. This allows the service provider to offer users appropriate and effective legal advice and assist them in resolving their legal issues.

[0067] The Liaison Department provides connections to experts for serious legal issues. For example, it provides contact information for specialized lawyers and law firms for complex litigation cases and significant legal troubles. The Liaison Department can also estimate the user's emotions and adjust how it contacts experts based on those emotions. For example, if the user is feeling anxious, it will provide a simple and visually clear method of contact. The Liaison Department provides multiple means of contact to make it easier for users to access experts. For example, it will allow users to contact experts using the method most convenient for them, such as phone, email, online chat, or video conferencing. Furthermore, the Liaison Department uses emotion recognition technology to estimate the user's emotions. For example, if the user is feeling anxious, it will provide a simple and visually clear method of contact to create an environment where the user can relax and consult with an expert. The Liaison Department also provides support to ensure smooth contact with experts. For example, it provides guidelines to help users prepare necessary documents and information in advance, and assists in ensuring a smooth initial consultation with an expert. Furthermore, the Liaison Department collects user feedback and continuously improves its contact methods and support. This allows the liaison department to ensure that users receive prompt and appropriate expert assistance for serious legal issues and to help resolve those issues.

[0068] The analysis unit can search for relevant legal knowledge based on user input and generate appropriate advice. For example, in response to a user's question about the contents of a contract, the analysis unit can provide explanations and points to note for each clause of the contract. The analysis unit uses a generative AI to analyze user input and search for relevant legal knowledge. For example, in response to a user's question about divorce proceedings, the generative AI can guide the user through the necessary procedures and how to prepare the necessary documents. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, it will provide a simple and easy-to-understand analysis result. This allows the analysis unit to generate appropriate legal advice based on user input. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit inputs the user's input into the generative AI, which searches for legal knowledge and generates advice.

[0069] The service provider can provide explanations and points to note for each clause of the contract. For example, if a user has questions about the content of the contract, the service provider will explain each clause and provide points to note. The service provider uses generative AI to generate explanations and points to note for each clause of the contract. For example, the generative AI analyzes the clauses of the contract and extracts important points and points to note. The service provider can also estimate the user's emotions and adjust the way the advice is expressed based on the estimated emotions of the user. For example, if the user is nervous, it will provide simple and easy-to-understand advice. In this way, the service provider can help the user understand the content of the contract. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider inputs the content of the contract into the generative AI, and the generative AI generates explanations and points to note for each clause.

[0070] The service provider can guide users through the necessary procedures and document preparation for divorce proceedings. For example, if a user has questions about divorce proceedings, the service provider will guide them through the necessary procedures and document preparation. The service provider uses generative AI to generate information on the necessary procedures and document preparation for divorce proceedings. For example, the generative AI analyzes information about divorce proceedings and generates a list of necessary procedures and documents. The service provider can also estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is nervous, it will provide simple and easy-to-understand advice. This allows the service provider to provide information about divorce proceedings and facilitate the process. Some or all of the above-described processes in the service provider may be performed using generative AI or not. For example, the service provider inputs information about divorce proceedings into the generative AI, and the generative AI generates information on the necessary procedures and document preparation.

[0071] The liaison department can provide contact information for specialized lawyers and law firms for complex litigation cases and serious legal troubles. For example, if a user faces a complex litigation case, the liaison department will provide contact information for specialized lawyers and law firms. The liaison department uses generative AI to determine the severity of the user's legal problem and recommend consultation with a professional as needed. For example, the generative AI analyzes the user's input and evaluates the severity of the legal problem. The liaison department can also estimate the user's emotions and adjust the method of contacting a professional based on the estimated emotions. For example, if the user is stressed, it will provide a simple and visually clear method of contact. This allows the liaison department to receive support from professionals for serious legal problems. Some or all of the above processes in the liaison department may be performed using generative AI or not. For example, the liaison department inputs the severity of the user's legal problem into the generative AI, and the generative AI provides contact information for a professional.

[0072] The reception desk can estimate the user's emotions and adjust the way legal inquiries are handled based on those estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. The reception desk uses generative AI to estimate the user's emotions. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The reception desk can also provide detailed input options and suggest customizable input methods if the user is relaxed. For example, if the user is in a hurry, voice input can be prioritized to allow for quick input of legal inquiries. This allows the reception desk to provide an appropriate handling method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's input into the generative AI, which estimates emotions and adjusts the handling method.

[0073] The reception desk can analyze the user's past legal question history and select the most suitable reception method. For example, the reception desk can automatically display as suggestions legal questions that the user has frequently entered in the past. The reception desk uses generative AI to analyze the user's past legal question history. For example, the generative AI analyzes the user's past input data and selects the most suitable reception method. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, it can predict and suggest legal questions to be used during a specific time period based on the user's past legal question history. This allows the reception desk to provide the most suitable reception method based on the user's past history. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's past legal question history into the generative AI, and the generative AI selects the most suitable reception method.

[0074] The reception unit can filter legal inquiries based on the user's current living situation and areas of interest. For example, if the user enters their current living situation, the reception unit will prioritize receiving legal inquiries related to that situation. The reception unit uses generative AI to analyze the user's living situation and areas of interest. For example, the generative AI analyzes the user's input and filters out relevant legal inquiries. The reception unit can also filter and receive relevant legal inquiries based on the user's areas of interest. For example, it can suggest appropriate legal inquiries based on the user's living situation and areas of interest. This allows the reception unit to prioritize receiving legal inquiries that are relevant to the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit inputs the user's living situation and areas of interest into the generative AI, and the generative AI filters out relevant legal inquiries.

[0075] The reception desk can estimate the user's emotions and prioritize legal inquiries based on those emotions. For example, if the user is tense, the reception desk will prioritize urgent legal inquiries. The reception desk uses generative AI to estimate the user's emotions. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The reception desk can also prioritize detailed legal inquiries if the user is relaxed. For example, if the user is in a hurry, it will prioritize legal inquiries that require a quick resolution. This allows the reception desk to prioritize legal inquiries according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's input into the generative AI, which estimates emotions and determines the priority of legal inquiries.

[0076] The reception desk can prioritize receiving legal inquiries based on the user's geographical location information. For example, if the user is in a specific region, the reception desk will prioritize receiving legal inquiries related to that region. The reception desk uses generative AI to analyze the user's geographical location information. For example, the generative AI will prioritize receiving region-specific legal issues based on the user's location information. The reception desk can also prioritize receiving legal inquiries related to the user's current location if the user is on the move. For example, it will suggest highly relevant legal inquiries based on the user's geographical location information. This allows the reception desk to prioritize receiving highly relevant legal inquiries based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk may input the user's geographical location information into the generative AI, which will prioritize receiving highly relevant legal inquiries.

[0077] The reception desk can analyze a user's social media activity and receive relevant legal questions when a legal question is received. For example, the reception desk can analyze a user's social media posts and receive relevant legal questions. The reception desk uses generative AI to analyze a user's social media activity. For example, the generative AI analyzes the content of a user's posts and the number of followers and suggests relevant legal questions. The reception desk can also suggest relevant legal questions based on the user's interests on social media. For example, it can determine the priority of legal questions from the user's social media activity. This allows the reception desk to receive relevant legal questions based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk inputs the user's social media activity into the generative AI, and the generative AI receives relevant legal questions.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visual analysis result. The analysis unit estimates the user's emotions using a generative AI. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is in a hurry, it provides a concise analysis result. This allows the analysis unit to provide appropriate analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit inputs the user's input into the generative AI, the generative AI estimates emotions, and adjusts the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the legal questions during the analysis. For example, the analysis unit performs a detailed analysis for legal questions of high importance. The analysis unit uses a generative AI to evaluate the importance of legal questions. For example, the generative AI analyzes the user's input and evaluates the importance of the legal questions. The analysis unit can also perform a concise analysis for legal questions of low importance. For example, it determines the priority of the analysis according to the importance of the legal questions. This allows the analysis unit to provide analysis results that correspond to the importance of the legal questions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the user's input into a generative AI, the generative AI evaluates the importance of the legal questions, and adjusts the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the legal question during analysis. For example, for legal questions concerning contracts, the analysis unit applies a contract analysis algorithm. The analysis unit uses a generative AI to analyze the category of the legal question. For example, the generative AI analyzes the user's input and identifies the category of the legal question. The analysis unit can also apply a divorce procedure analysis algorithm for legal questions concerning divorce proceedings. For example, for legal questions concerning litigation cases, it applies a litigation analysis algorithm. This allows the analysis unit to apply an appropriate analysis algorithm according to the category of the legal question. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the user's input into a generative AI, the generative AI identifies the category of the legal question, and applies an appropriate analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. The analysis unit estimates the user's emotions using a generative AI. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is excited, it provides analysis results with visually stimulating effects. This allows the analysis unit to provide appropriate analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit inputs the user's input into the generative AI, the generative AI estimates emotions, and adjusts the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on when legal questions were submitted. For example, the analysis unit may prioritize the analysis of legal questions submitted earlier. The analysis unit uses a generative AI to analyze the submission timing of legal questions. For example, the generative AI analyzes the user's input and evaluates the submission timing. The analysis unit can also postpone legal questions submitted later. For example, it may adjust the analysis schedule based on the submission timing. This allows the analysis unit to provide appropriate analysis results based on when legal questions were submitted. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit inputs the user's input into a generative AI, which evaluates the submission timing and determines the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the legal questions during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant legal questions. The analysis unit uses a generative AI to evaluate the relevance of the legal questions. For example, the generative AI analyzes the user's input and evaluates the relevance of the legal questions. The analysis unit can also postpone the analysis of less relevant legal questions. For example, it adjusts the analysis schedule based on the relevance of the legal questions. This allows the analysis unit to provide appropriate analysis results based on the relevance of the legal questions. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit inputs the user's input into a generative AI, which evaluates the relevance of the legal questions and adjusts the order of analysis.

[0084] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is nervous, the service provider will provide simple and easily understandable advice. The service provider uses generative AI to estimate the user's emotions. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The service provider can also provide detailed advice if the user is relaxed. For example, if the user is in a hurry, it will provide concise advice. This allows the service provider to provide appropriate advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without the generative AI. For example, the service provider inputs the user's input into the generative AI, which estimates emotions and adjusts the way advice is expressed.

[0085] The service provider can adjust the level of detail of the advice based on the importance of the legal question when providing advice. For example, the service provider will provide detailed advice for high-importance legal questions. The service provider will use generative AI to assess the importance of legal questions. For example, the generative AI will analyze the user's input and assess the importance of the legal question. The service provider can also provide concise advice for low-importance legal questions. For example, it will determine the priority of advice according to the importance of the legal question. This allows the service provider to provide appropriate advice according to the importance of the legal question. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider inputs the user's input into the generative AI, which assesses the importance of the legal question and adjusts the level of detail of the advice.

[0086] The service provider can apply different advice algorithms depending on the category of the legal question when providing advice. For example, for legal questions concerning contracts, the service provider will apply a contract advice algorithm. The service provider uses generative AI to analyze the category of the legal question. For example, the generative AI analyzes the user's input and identifies the category of the legal question. The service provider can also apply a divorce procedure advice algorithm for legal questions concerning divorce proceedings. For example, for legal questions concerning litigation, the service provider will apply a litigation advice algorithm. This allows the service provider to provide appropriate advice according to the category of the legal question. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider inputs the user's input into the generative AI, the generative AI identifies the category of the legal question, and applies an appropriate advice algorithm.

[0087] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider will provide short, concise advice. The service provider uses generative AI to estimate the user's emotions. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The service provider can also provide detailed advice if the user is relaxed. For example, if the user is excited, it will provide advice with visually stimulating effects. This allows the service provider to provide appropriate advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without the generative AI. For example, the service provider inputs the user's input into the generative AI, which estimates emotions and adjusts the length of the advice.

[0088] The service provider can prioritize advice based on when the legal question is submitted. For example, the service provider will prioritize advice for legal questions submitted earlier. The service provider uses generative AI to analyze when legal questions are submitted. For example, the generative AI analyzes the user's input and evaluates the submission timing. The service provider can also postpone legal questions submitted later. For example, it adjusts the advice schedule based on the submission timing. This allows the service provider to provide appropriate advice based on when legal questions are submitted. Some or all of the above processes in the service provider may be performed using generative AI or not. For example, the service provider inputs the user's input into the generative AI, which evaluates the submission timing and determines the priority of advice.

[0089] The service provider can adjust the order of advice based on the relevance of the legal questions when providing advice. For example, the service provider will prioritize advice for highly relevant legal questions. The service provider will use generative AI to evaluate the relevance of legal questions. For example, the generative AI will analyze the user's input and evaluate the relevance of the legal questions. The service provider can also postpone less relevant legal questions. For example, it will adjust the advice schedule based on the relevance of the legal questions. This allows the service provider to provide appropriate advice based on the relevance of the legal questions. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider may input the user's input into the generative AI, which will evaluate the relevance of the legal questions and adjust the order of advice.

[0090] The communication unit can estimate the user's emotions and adjust the method of contacting experts based on the estimated emotions. For example, if the user is nervous, the communication unit can provide a simple and highly visible method of contact. The communication unit estimates the user's emotions using generative AI. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The communication unit can also provide a more detailed method of contact if the user is relaxed. For example, if the user is in a hurry, it can provide a way to contact someone quickly. This allows the communication unit to provide an appropriate method of contact according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using or without the generative AI. For example, the communication unit inputs the user's input into the generative AI, which estimates the emotions and adjusts the method of contact.

[0091] The liaison unit can adjust the level of detail in communications based on the severity of the legal issue. For example, the liaison unit provides detailed communication methods for highly serious legal issues. The liaison unit uses generative AI to assess the severity of legal issues. For example, the generative AI analyzes the user's input and assesses the severity of the legal issue. The liaison unit can also provide concise communication methods for less serious legal issues. For example, it determines the priority of communications according to the severity of the legal issue. This allows the liaison unit to provide appropriate communication methods according to the severity of the legal issue. Some or all of the above processing in the liaison unit may be performed using generative AI or not. For example, the liaison unit inputs the user's input into the generative AI, which assesses the severity of the legal issue and adjusts the level of detail in communications.

[0092] The liaison department can apply different contact algorithms depending on the category of the legal issue at the time of contact. For example, for legal issues related to contracts, the liaison department can provide a method for contacting a lawyer specializing in contracts. The liaison department uses generative AI to analyze the category of the legal issue. For example, the generative AI analyzes the user's input and identifies the category of the legal issue. The liaison department can also provide a method for contacting a lawyer specializing in divorce for legal issues related to divorce proceedings. For example, for legal issues related to litigation, it can provide a method for contacting a lawyer specializing in litigation. In this way, the liaison department can provide an appropriate contact method according to the category of the legal issue. Some or all of the above processing in the liaison department may be performed using generative AI or not. For example, the liaison department inputs the user's input into the generative AI, the generative AI identifies the category of the legal issue, and applies an appropriate contact algorithm.

[0093] The communication unit can estimate the user's emotions and determine the priority of communications based on the estimated emotions. For example, if the user is stressed, the communication unit will prioritize urgent communications. The communication unit estimates the user's emotions using generative AI. For example, the generative AI analyzes the user's input and facial expression data to estimate emotions. The communication unit can also prioritize detailed communications if the user is relaxed. For example, if the user is in a hurry, it will prioritize methods that allow for quick communication. In this way, the communication unit can provide appropriate communication priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using generative AI or not. For example, the communication unit inputs the user's input into the generative AI, the generative AI estimates emotions, and determines the priority of communications.

[0094] The liaison department can prioritize communications based on the timing of legal issues. For example, it may prioritize communications for legal issues that are due sooner. The liaison department uses generative AI to analyze the timing of legal issues. For example, the generative AI analyzes user input and evaluates the timing of submission. The liaison department may also postpone communications for legal issues that are due later. For example, it may adjust the communication schedule based on the timing of submission. This allows the liaison department to provide appropriate communication priorities based on the timing of legal issues. Some or all of the above processes in the liaison department may be performed using generative AI or not. For example, the liaison department inputs user input into the generative AI, which evaluates the timing of submission and determines the communication priority.

[0095] The liaison department can adjust the order of communications based on the relevance of the legal issues. For example, the liaison department will prioritize communications regarding highly relevant legal issues. The liaison department will use generative AI to evaluate the relevance of legal issues. For example, the generative AI will analyze the user's input and evaluate the relevance of the legal issues. The liaison department can also postpone communications regarding less relevant legal issues. For example, it will adjust the communication schedule based on the relevance of the legal issues. This allows the liaison department to provide an appropriate order of communications based on the relevance of the legal issues. Some or all of the above processing in the liaison department may be performed using generative AI or not. For example, the liaison department may input the user's input into the generative AI, which will evaluate the relevance of the legal issues and adjust the order of communications.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The legal advice generation system can provide advice to users regarding their legal questions and issues by referring to their past legal problem-solving history. For example, if a user has had questions about the content of a contract in the past, the system can generate new advice based on the solutions and advice received in that past case. Similarly, if a user has had questions about divorce proceedings in the past, the system can provide more specific advice based on the progress and solutions of those proceedings. Furthermore, if a user has consulted with a professional in the past, the system can refer them to an appropriate professional for similar problems by referring to the content and results of that consultation. In this way, the legal advice generation system can provide more accurate and personalized advice by utilizing the user's past legal problem-solving history.

[0098] The legal advice generation system can provide users with advice that reflects real-time legal changes in response to their legal questions and issues. For example, if a user has questions about the content of a contract, it can provide explanations and points to note for each clause of the contract based on the latest legal changes. Similarly, if a user has questions about divorce proceedings, it can guide them on procedures and how to prepare documents, reflecting the latest legal changes. Furthermore, if a user faces a complex litigation case, it can recommend consulting with the appropriate expert based on the latest legal changes. In this way, the legal advice generation system can always provide advice that reflects the most up-to-date legal changes.

[0099] A legal advice generation system can provide users with advice tailored to their specific profession or industry in response to their legal questions and challenges. For example, if a user has questions about the content of a contract, it can provide explanations and points to note that are specific to that profession or industry. Similarly, if a user has questions about labor issues, it can provide explanations and advice on labor laws specific to that profession or industry. Furthermore, if a user faces legal trouble in a particular industry, it can recommend consulting with an expert specializing in that industry. In this way, the legal advice generation system can provide advice tailored to the user's profession and industry.

[0100] A legal advice generation system can provide users with legal questions and issues tailored to their cultural background and language. For example, if a user has questions about the content of a contract, it can provide explanations and points to note in a way that is appropriate to their cultural background and language. Similarly, if a user has questions about divorce proceedings, it can guide them through the procedures and document preparation methods in a way that is appropriate to their cultural background and language. Furthermore, if a user faces cross-cultural legal disputes, it can recommend consulting with experts in a way that is appropriate to their cultural background and language. In this way, the legal advice generation system can provide advice tailored to the user's cultural background and language.

[0101] The legal advice generation system can provide users with legal questions and challenges tailored to their health condition and physical limitations. For example, if a user has questions about the contents of a contract, it can provide explanations and points to note about the contract that are appropriate for their health condition and physical limitations. Similarly, if a user has questions about divorce proceedings, it can guide them on the procedures and how to prepare the necessary documents, tailored to their health condition and physical limitations. Furthermore, if a user faces legal troubles related to their health condition or physical limitations, the system can recommend consulting with a specialist appropriate to their health condition and physical limitations. In this way, the legal advice generation system can provide advice that is tailored to the user's health condition and physical limitations.

[0102] The legal advice generation system can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is stressed, the advice may be temporarily delayed to allow the user to receive it in a relaxed state. Conversely, if the user is tense, the advice may be provided quickly to allow the user to obtain a solution promptly. Furthermore, if the user is relaxed, detailed advice may be provided to ensure the user fully understands it. In this way, the legal advice generation system can provide advice at the appropriate time according to the user's emotions.

[0103] The legal advice generation system can estimate the user's emotions and adjust the format of the advice based on those emotions. For example, if the user is stressed, it can provide simple, visually easy-to-understand advice. If the user is relaxed, it can provide detailed text-based advice. Furthermore, if the user is anxious, it can provide advice in audio or video format, ensuring the advice is presented in a format that is easier for the user to understand. In this way, the legal advice generation system can provide advice in an appropriate format according to the user's emotions.

[0104] The legal advice generation system can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is stressed, it can provide concise and to-the-point advice. If the user is relaxed, it can provide advice that includes detailed background information and relevant legal knowledge. Furthermore, if the user is anxious, it can provide specific action plans and step-by-step guidance to help the user act with confidence. In this way, the legal advice generation system can provide advice that is appropriate to the user's emotions.

[0105] The legal advice generation system can estimate the user's emotions and adjust the tone of the advice based on those emotions. For example, if the user is stressed, the advice can be given in a gentle tone. If the user is relaxed, the advice can be given in a friendly tone. Furthermore, if the user is tense, the advice can be given in a professional and calm tone to help the user feel at ease. In this way, the legal advice generation system can provide advice in an appropriate tone according to the user's emotions.

[0106] The legal advice generation system can estimate the user's emotions and adjust the frequency of advice based on those emotions. For example, if the user is stressed, the frequency of advice can be reduced to allow the user time to relax. Conversely, if the user is relaxed, the frequency of advice can be increased to allow the user to receive more information. Furthermore, if the user is anxious, advice can be provided at a moderate frequency to prevent the user from being overwhelmed with information. In this way, the legal advice generation system can provide advice at an appropriate frequency according to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives users' legal questions and issues. These include contract issues, labor issues, family law, etc. The reception desk receives legal questions and issues entered by users in text format. It can also receive legal questions and issues using voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the way legal questions are received based on the estimated emotions of the user. Step 2: The analysis unit analyzes the information received by the reception unit to understand the user's situation and background. Using generative AI, the analysis unit searches for relevant legal knowledge based on the user's input and generates appropriate advice. For example, in response to a question about the content of a contract entered by the user, it provides explanations and points to note for each clause of the contract. The analysis unit can also estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions of the user. Step 3: The service provider provides legal advice generated by the analysis unit. The service provider uses the generation AI to provide appropriate legal advice to the user. For example, it can provide explanations and points to note for each clause of a contract. It can also guide users on the procedures and how to prepare the necessary documents for divorce proceedings. Furthermore, the service provider can estimate the user's emotions and adjust the way the advice is expressed based on those estimated emotions. Step 4: The liaison department provides connections to experts for serious legal issues. The liaison department provides contact information for specialized lawyers and law firms for complex litigation cases and serious legal troubles. The liaison department can also estimate the user's sentiment and adjust how it contacts experts based on the estimated sentiment.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's legal questions and issues in text format or voice input. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze the user's situation and background and generate appropriate legal advice. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with the legal advice generated by the analysis unit. The communication unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides connections to experts for serious legal issues. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and communication unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's legal questions and issues in text format or voice input. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze the user's situation and background and generate appropriate legal advice. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the user with the legal advice generated by the analysis unit. The communication unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides connections to experts for serious legal issues. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and communication unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's legal questions and issues in text format or voice input. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze the user's situation and background and generate appropriate legal advice. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the user with the legal advice generated by the analysis unit. The communication unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides connections to experts for serious legal issues. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and communication unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's legal questions and issues in text format or voice input. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generating AI to analyze the user's situation and background and generate appropriate legal advice. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the user with the legal advice generated by the analysis unit. The communication unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides connections to experts for serious legal issues. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk that handles users' legal questions and issues, An analysis unit analyzes the information received by the reception unit to understand the user's situation and background, A provisioning unit that provides legal advice generated by the aforementioned analysis unit, It includes a liaison department that provides connections to experts for serious legal issues. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Based on user input, the system searches for relevant legal knowledge and generates appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, This document provides explanations and points to note regarding each clause of the contract. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, This guide explains the procedures and document preparation required for divorce proceedings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned liaison department, We provide contact information for specialized lawyers and law firms for complex litigation cases and serious legal disputes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We estimate user sentiment and adjust how legal inquiries are handled based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past legal inquiry history and select the most appropriate method of submission. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving legal inquiries, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is We estimate the user's sentiment and determine the priority of legal questions to be addressed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving legal inquiries, we prioritize inquiries that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving legal inquiries, we analyze the user's social media activity and receive relevant inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of the legal questions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the legal question. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the legal questions were raised. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the order of analysis will be adjusted based on the relevance of the legal questions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, we adjust the level of detail in the advice based on the importance of the legal question. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, we apply different advice algorithms depending on the category of the legal question. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the legal question was raised. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of the legal questions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned liaison department, It estimates the user's emotions and adjusts how to contact experts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned liaison department, When contacting us, we will adjust the level of detail in our communication based on the severity of the legal issue. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned liaison department, When contacting someone, different contact algorithms are applied depending on the category of the legal issue. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned liaison department, It estimates the user's emotions and determines the priority of communication based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned liaison department, When contacting us, we will prioritize communication based on when the legal issue was filed. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned liaison department, When contacting us, we will adjust the order of communication based on the relevance of the legal issue. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles users' legal questions and issues, An analysis unit analyzes the information received by the reception unit to understand the user's situation and background, A provisioning unit that provides legal advice generated by the aforementioned analysis unit, It includes a liaison department that provides connections to experts for serious legal issues. A system characterized by the following features.

2. The aforementioned analysis unit, Based on user input, the system searches for relevant legal knowledge and generates appropriate advice. The system according to feature 1.

3. The aforementioned supply unit is, This document provides explanations and points to note regarding each clause of the contract. The system according to feature 1.

4. The aforementioned supply unit is, This guide explains the procedures and document preparation required for divorce proceedings. The system according to feature 1.

5. The aforementioned liaison department, We provide contact information for specialized lawyers and law firms for complex litigation cases and serious legal disputes. The system according to feature 1.

6. The aforementioned reception unit is We estimate user sentiment and adjust how legal inquiries are handled based on that estimated sentiment. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past legal inquiry history and select the most appropriate method of submission. The system according to feature 1.

8. The aforementioned reception unit is When receiving legal inquiries, filtering is performed based on the user's current living situation and areas of interest. The system according to feature 1.

Images