System

Abstract

An object of a system according to an embodiment is to provide quick and appropriate advice when a user makes a legal consultation.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation contents from a user. The analysis unit analyzes the consultation content received by the reception unit. The providing unit provides the legal advice based on the analysis result obtained by the analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to obtain prompt and appropriate advice when seeking legal advice.

[0005] The system according to the embodiment aims to provide prompt and appropriate advice when a user seeks legal advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content from a user. The analysis unit analyzes the consultation content received by the reception unit. The provision unit provides legal advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide prompt and appropriate advice when a user seeks legal advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The legal consultation system according to an embodiment of the present invention uses a generation AI to provide consultation on legal matters in various countries. The system allows users to input their legal consultation details, and the generation AI then examines all legal precedents and provides advice on legal matters. This system allows users to quickly and accurately resolve legal issues. For example, a user may input a question such as, "I want to know the legal procedures required to start a new business in a specific country." This information is then input into the generation AI. The generation AI then analyzes the input consultation details. The generation AI examines all legal precedents and extracts relevant legal information. For example, it may research the legal procedures and regulations required to start a new business in a specific country. Based on the extracted legal information, the generation AI provides specific advice to the user. For example, it may provide advice such as, "To start a new business in a specific country, you must first establish a company and then obtain the necessary permits and licenses." This system allows users to quickly and accurately resolve legal issues. For example, by understanding the procedures required to start a business in advance, users can smoothly launch their business. It also minimizes legal risks. Furthermore, the generation AI can present relevant legal precedents based on the user's consultation details. For example, by providing information such as "what decisions have been made in similar cases in the past," users can receive more specific legal advice. In this way, using generative AI makes it possible to quickly and accurately consult on legal issues in various countries. This allows the legal consultation system to quickly and accurately resolve users' legal problems. For example, by knowing the necessary procedures for starting a business in advance, users can start their business smoothly. It also minimizes legal risks.

[0029] A legal consultation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content from a user. The consultation content includes, but is not limited to, legal consultation, business consultation, and technical consultation. The reception unit receives, for example, text data input by a user. The reception unit can also receive voice input and image input. For example, the reception unit converts voice data into text data using voice recognition technology. The reception unit can also convert image data into text data using image analysis technology. The analysis unit uses a generation AI to analyze the consultation content received by the reception unit. The analysis can be performed using, for example, text analysis, data mining, or a machine learning algorithm, but is not limited to, these examples. For example, the generation AI analyzes the consultation content using a text generation AI (e.g., LLM). The analysis unit can also analyze the consultation content using a multimodal generation AI. The analysis unit can also use the generation AI to extract and analyze important parts of the consultation content. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to extract particularly important information from the consultation content and performs analysis based on that information. The provision unit provides legal advice based on the analysis results obtained by the analysis unit. Legal advice may include, but is not limited to, contract drafting, legal risk assessment, and legal procedure guidance. The provision unit provides specific advice based on the analysis results. The provision unit can also determine the format of the advice according to the user's needs. For example, the provision unit may provide advice in document format, audio format, video format, or the like. This allows the legal consultation system according to the embodiment to efficiently accept and analyze the user's consultation content and provide legal advice. Some or all of the above-described processing by the provision unit may be performed using AI, or may be performed without AI.For example, the providing unit can provide legal advice using an AI model that receives the analysis results obtained by the analyzing unit as input and outputs legal advice.

[0030] The legal consultation system includes an update unit in which a generation AI tracks the latest legal amendments and precedents and updates the database. The update unit uses the generation AI to track the latest legal amendments and precedents and update the database. The generation AI tracks legal amendments and precedents using technologies such as natural language processing, machine learning, and deep learning. For example, the generation AI automatically collects legal amendment information and precedent databases on the Internet and updates the database. The update unit can also determine update priorities based on the importance of legal amendments and precedents. For example, it prioritizes updating the database with legal amendments and precedents of high importance. The update unit can also apply different update algorithms depending on the category of legal amendments and precedents. For example, it applies a specific update algorithm when updating information related to commercial law. This enables the system to provide advice based on the latest legal amendments and precedents. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can update the database using an AI model that uses legal amendment information and precedent data collected by the generation AI as input.

[0031] The legal consultation system includes a format determination unit that determines the format of advice to be received by the user. The format determination unit determines the format of advice to be received by the user. Examples of advice formats include, but are not limited to, document format, audio format, and video format. The format determination unit determines the format of advice according to the user's needs, for example. The format determination unit can also determine the format of advice based on the importance of the legal issue. For example, a detailed format is provided for legal issues with high importance. The format determination unit can also apply different formats depending on the category of the legal issue. For example, a specific format is applied for issues related to commercial law. This allows advice to be provided in a format that meets the user's needs. Some or all of the above-described processing in the format determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the format determination unit can determine the format of advice using an AI model that determines the format of advice based on input of the user's needs and the importance of the legal issue.

[0032] The legal consultation system includes a supervision unit for ensuring the reliability of the advice. The supervision unit supervises the advice provided to ensure the reliability of the advice. Supervision is performed, for example, by methods such as supervision by an expert, evaluation by a third-party organization, or past performance, but is not limited to these examples. For example, the supervision unit performs supervision by a legal expert. The supervision unit can also perform evaluation by a third-party organization. The supervision unit can also evaluate the reliability of the advice based on past performance. This can increase the reliability of the advice provided. Some or all of the above-mentioned processing in the supervision unit may be performed using, for example, AI, or may be performed without using AI. For example, the supervision unit can ensure the reliability of the advice by using an AI model that inputs the advice provided and evaluates its reliability.

[0033] The legal consultation system includes a security unit that protects the user's consultation content and personal information. The security unit protects the user's consultation content and personal information. Personal information includes, but is not limited to, for example, name, address, telephone number, and consultation content. The security unit protects the personal information using methods such as data encryption, access control, and a monitoring system. For example, the security unit encrypts the consultation content and personal information. The security unit can also perform access control to ensure that only specific users can access the personal information. The security unit can also detect unauthorized access using a monitoring system. This allows the user's consultation content and personal information to be safely protected. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can protect the personal information using an AI model that uses personal information as input and performs data encryption and access control.

[0034] The analysis unit can cover all relevant court cases to date and extract relevant legal information. For example, the analysis unit covers all court cases to date and extracts relevant legal information. Court cases include, but are not limited to, Supreme Court cases, district court cases, and cases in specific legal fields. For example, the analysis unit covers Supreme Court cases and extracts relevant legal information. The analysis unit can also cover district court cases and extract relevant legal information. The analysis unit can also cover cases in specific legal fields and extract relevant legal information. This makes it possible to provide accurate legal information based on past court cases. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input court case data into a generation AI and cause the generation AI to extract relevant legal information.

[0035] The providing unit can provide specific advice based on the extracted legal information. The providing unit can provide specific advice based on, for example, the extracted legal information. Specific advice includes, for example, guidance on legal procedures, contract preparation, risk assessment, etc., but is not limited to these examples. The providing unit can, for example, provide guidance on legal procedures. The providing unit can also assist in contract preparation. The providing unit can also evaluate legal risks. This makes it possible to provide specific legal advice. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can provide specific advice using an AI model that receives the extracted legal information as input and outputs specific advice.

[0036] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, analyzes the user's past consultation history and selects the optimal reception method. Optimal reception methods include, but are not limited to, online chat, telephone, and face-to-face consultation. The reception unit, for example, automatically displays as candidates the content of consultations the user has frequently used in the past. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past consultation history. This makes it possible to provide the optimal reception method based on the user's past consultation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI select the optimal reception method.

[0037] The reception unit may filter the consultation content based on the user's current legal issues and areas of interest when receiving the consultation content. For example, the reception unit may filter the consultation content based on the user's current legal issues and areas of interest when receiving the consultation content. Filtering may be performed by, for example, keyword matching, category classification, prioritization, or other methods, but is not limited to these examples. For example, the reception unit may preferentially receive consultation content related to legal areas in which the user is interested. The reception unit may also filter and receive information related to the user's current legal issues. The reception unit may also filter and receive related legal issues based on the user's past consultation content. This allows for preferential reception of consultation content related to the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's current legal issues and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0038] The reception unit can select the optimal reception means according to the user's input method when receiving the consultation content. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving the consultation content. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, the reception unit may use voice recognition to receive the consultation content when the user inputs the consultation content by voice. Furthermore, the reception unit may use text analysis to receive the consultation content when the user inputs the consultation content by text. Furthermore, the reception unit may use image analysis to receive the consultation content when the user inputs the consultation content by image. This makes it possible to provide the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's input data to a generation AI and cause the generation AI to select the optimal reception means.

[0039] The reception unit can prioritize the reception of highly relevant consultation content based on the user's geographical location information when receiving consultation content. For example, the reception unit prioritizes the reception of highly relevant consultation content based on the user's geographical location information when receiving consultation content. Examples of geographical location information include, but are not limited to, GPS data, IP address, and user input information. For example, if the user is in a specific country, the reception unit can prioritize the reception of legal consultation content related to that country. Furthermore, if the user is in a specific region, the reception unit can prioritize the reception of legal consultation content related to that region. Furthermore, if the user is traveling, the reception unit can prioritize the reception of related legal consultation content based on the user's current location. This allows the reception of highly relevant consultation content based on the user's geographical location information to be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI and cause the generation AI to prioritize highly relevant consultation content.

[0040] The reception unit may analyze the user's social media activity when receiving a consultation content and receive related consultation content. For example, the reception unit may analyze the user's social media activity when receiving a consultation content and receive related consultation content. Social media activity may include, but is not limited to, post content, the number of likes, and the number of followers. For example, the reception unit may analyze content posted by the user on social media about legal issues and receive related consultation content. The reception unit may also predict and receive related legal issues from the user's social media activity. The reception unit may also receive related consultation content based on the user's social media activity. In this way, related consultation content can be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without using, AI. For example, the reception unit may input the user's social media activity data into a generation AI and cause the generation AI to receive related consultation content.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the consultation content. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving the consultation content. Feedback includes, but is not limited to, survey results, user comments, and evaluation scores. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the reception method.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the legal issue during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the legal issue during analysis. The level of detail includes, but is not limited to, the depth of information, the specificity of the explanation, and the granularity of the data. For example, the analysis unit performs a detailed analysis of a legal issue with high importance. The analysis unit can also perform a concise analysis of a legal issue with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis depending on the importance. This allows the analysis to be performed at an appropriate level of detail depending on the importance of the legal issue. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the legal issue to the generation AI and cause the generation AI to adjust the level of detail.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the legal problem during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the legal problem during analysis. Examples of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies a specific analysis algorithm to questions related to commercial law. The analysis unit can also apply a different analysis algorithm to questions related to labor law. The analysis unit can also apply a dedicated analysis algorithm to questions related to intellectual property law. This makes it possible to apply an appropriate analysis algorithm depending on the category of the legal problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input legal problem category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Examples of ways to improve the accuracy of the analysis include, but are not limited to, using past data and introducing a feedback loop. For example, the analysis unit complements the current analysis result based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. This can improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0045] The analysis unit can determine the analysis priority based on the time when the legal issue occurred during analysis. The analysis unit, for example, determines the analysis priority based on the time when the legal issue occurred during analysis. The priority can be determined based on criteria such as urgency, importance, and time of occurrence, but is not limited to these examples. For example, the analysis unit prioritizes analysis of recently occurring legal issues. The analysis unit can also lower the priority of legal issues that occurred in the past when analyzing them. The analysis unit can also dynamically adjust the analysis priority based on the time of occurrence. This allows analysis to be performed with an appropriate priority based on the time when the legal issue occurred. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the legal issue occurred to the generation AI and have the generation AI determine the priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of legal issues during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of legal issues during analysis. The order can be determined based on criteria such as, but not limited to, relevance or importance. For example, the analysis unit prioritizes analysis of highly relevant legal issues. The analysis unit can also lower the priority of analysis of less relevant legal issues. The analysis unit can also dynamically adjust the order of analysis based on relevance. This allows analysis to be performed in an appropriate order based on the relevance of legal issues. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of legal issues to a generation AI and cause the generation AI to adjust the order.

[0047] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. Technical terminology includes, but is not limited to, selecting terms according to the user's level of expertise. For example, the analysis unit can provide analysis results that make extensive use of technical terminology when the user has technical expertise. Furthermore, the analysis unit can provide concise and easy-to-understand analysis results when the user does not have technical expertise. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows the analysis results to be provided using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0048] The providing unit can adjust the level of detail of the advice based on the importance of the legal issue when providing the advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the legal issue when providing the advice. The level of detail includes, but is not limited to, the depth of information, the specificity of the explanation, and the granularity of the data. For example, the providing unit provides detailed advice for legal issues of high importance. The providing unit can also provide concise advice for legal issues of low importance. The providing unit can also dynamically adjust the level of detail of the advice according to the importance. This makes it possible to provide advice with an appropriate level of detail according to the importance of the legal issue. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input importance data of the legal issue to a generation AI and cause the generation AI to adjust the level of detail.

[0049] The providing unit can apply different advice algorithms depending on the category of the legal problem when providing advice. For example, the providing unit applies different advice algorithms depending on the category of the legal problem when providing advice. Advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the providing unit applies a specific advice algorithm to issues related to commercial law. The providing unit can also apply a different advice algorithm to issues related to labor law. The providing unit can also apply a dedicated advice algorithm to issues related to intellectual property law. This makes it possible to apply an appropriate advice algorithm depending on the category of the legal problem. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input legal problem category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0050] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. Improvements in the accuracy of advice include, but are not limited to, using past data and introducing a feedback loop. For example, the providing unit complements current advice based on the user's past advice results. The providing unit can also improve the accuracy of advice from the user's past advice results. The providing unit can also analyze the user's past advice results and optimize the advice algorithm. This can improve the accuracy of advice based on the user's past advice results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of advice.

[0051] The providing unit can determine the priority of advice based on the time when the legal issue occurred when providing the advice. For example, the providing unit determines the priority of advice based on the time when the legal issue occurred when providing the advice. The priority can be determined based on criteria such as urgency, importance, and time of occurrence, but is not limited to these examples. For example, the providing unit prioritizes providing advice for legal issues that occurred recently. The providing unit can also provide advice with a lower priority for legal issues that occurred in the past. The providing unit can also dynamically adjust the priority of advice depending on the time of occurrence. This makes it possible to provide advice with an appropriate priority depending on the time when the legal issue occurred. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time when the legal issue occurred to a generation AI and cause the generation AI to determine the priority.

[0052] The providing unit can adjust the order of advice based on the relevance of legal issues when providing advice. For example, the providing unit adjusts the order of advice based on the relevance of legal issues when providing advice. The order can be determined based on criteria such as, for example, relevance or importance, but is not limited to such examples. For example, the providing unit can provide advice with priority for highly relevant legal issues. The providing unit can also provide advice with lower priority for less relevant legal issues. The providing unit can also dynamically adjust the order of advice based on the relevance. This makes it possible to provide advice in an appropriate order based on the relevance of legal issues. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of legal issues to a generation AI and cause the generation AI to adjust the order.

[0053] The providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. For example, the providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. Technical terms include, but are not limited to, selection of terms according to the user's level of expertise. For example, the providing unit may provide advice that uses a lot of technical terms when the user has technical knowledge. Furthermore, the providing unit may provide concise and easy-to-understand advice when the user does not have technical knowledge. Furthermore, the providing unit may dynamically adjust the use of technical terms in the advice according to the user's level of expertise. This allows the advice to be provided using appropriate technical terms according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the user's level of expertise data into a generation AI and cause the generation AI to use technical terms.

[0054] The update unit can determine the update priority based on the importance of the latest legal amendments and precedents during updates. For example, the update unit determines the update priority based on the importance of the latest legal amendments and precedents during updates. Priorities include, but are not limited to, the importance of legal amendments and precedents, the influence of precedents, and the like. For example, the update unit prioritizes updating the database with legal amendments and precedents of high importance. The update unit can also reflect legal amendments and precedents of low importance later. The update unit can also dynamically adjust the update priority based on the importance. This allows the database to be updated with an appropriate priority based on the importance of the latest legal amendments and precedents. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can input importance data of legal amendments and precedents into a generation AI and have the generation AI determine the priority.

[0055] The update unit can apply different update algorithms depending on the category of legal information during updating. For example, the update unit applies different update algorithms depending on the category of legal information during updating. Examples of update algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the update unit applies a specific update algorithm when updating information related to commercial law. The update unit can also apply a different update algorithm when updating information related to labor law. The update unit can also apply a dedicated update algorithm when updating information related to intellectual property law. This makes it possible to apply an appropriate update algorithm depending on the category of legal information. Some or all of the above-mentioned processing in the update unit may be performed using, or without, AI. For example, the update unit can input legal information category data to a generation AI and cause the generation AI to apply an update algorithm.

[0056] The update unit can determine the update priority based on the time of occurrence of the legal information during an update. The update unit, for example, determines the update priority based on the time of occurrence of the legal information during an update. Priorities include, but are not limited to, the time of occurrence and the impact of the legal information. For example, the update unit prioritizes updating recently occurring legal information. The update unit can also postpone updating legal information that occurred in the past. The update unit can also dynamically adjust the update priority based on the time of occurrence. This allows updates to be performed with appropriate priorities based on the time of occurrence of the legal information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the time of occurrence of the legal information into a generation AI and have the generation AI determine the priority.

[0057] The update unit can adjust the update order based on the relevance of the legal information during an update. The update unit, for example, adjusts the update order based on the relevance of the legal information during an update. The order includes, for example, descending order of relevance or descending order of importance, but is not limited to such examples. For example, the update unit prioritizes updating highly relevant legal information. The update unit can also postpone updating less relevant legal information. The update unit can also dynamically adjust the update order based on the relevance. This allows updating to be performed in an appropriate order based on the relevance of the legal information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input relevance data of the legal information to a generation AI and cause the generation AI to adjust the order.

[0058] The format determination unit may determine the format of the advice based on the importance of the legal issue when determining the format. For example, the format determination unit may determine the format of the advice based on the importance of the legal issue when determining the format. Examples of formats include, but are not limited to, text, audio, and video. For example, the format determination unit may provide a detailed format for legal issues of high importance. The format determination unit may also provide a concise format for legal issues of low importance. The format determination unit may also dynamically adjust the format of the advice according to the importance. This allows advice to be provided in an appropriate format according to the importance of the legal issue. Some or all of the above-described processing in the format determination unit may be performed using, or without, AI. For example, the format determination unit may input importance data of the legal issue to a generation AI and cause the generation AI to determine the format.

[0059] The format determination unit can apply different formats depending on the category of the legal problem when determining the format. For example, the format determination unit can apply different formats depending on the category of the legal problem when determining the format. Formats include, but are not limited to, text, audio, and video formats. For example, the format determination unit can apply a specific format to questions related to commercial law. The format determination unit can also apply a different format to questions related to labor law. The format determination unit can also apply a dedicated format to questions related to intellectual property law. This makes it possible to provide advice in an appropriate format depending on the category of the legal problem. Some or all of the above-described processing in the format determination unit can be performed using, or without, AI. For example, the format determination unit can input legal problem category data to a generation AI and cause the generation AI to apply the format.

[0060] The format determination unit may determine the format of the advice based on the time when the legal issue occurred when determining the format. For example, the format determination unit may determine the format of the advice based on the time when the legal issue occurred when determining the format. Examples of formats include, but are not limited to, text format, audio format, and video format. For example, the format determination unit may provide a detailed format for a recently occurred legal issue. The format determination unit may also provide a concise format for a previously occurred legal issue. The format determination unit may also dynamically adjust the format of the advice depending on the time when the legal issue occurred. This makes it possible to provide advice in an appropriate format depending on the time when the legal issue occurred. Some or all of the above-described processing in the format determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the format determination unit may input data on the time when the legal issue occurred to a generation AI and cause the generation AI to determine the format.

[0061] The format determination unit may adjust the format of the advice based on the relevance of the legal issue when determining the format. For example, the format determination unit may adjust the format of the advice based on the relevance of the legal issue when determining the format. Examples of formats include, but are not limited to, text format, audio format, and video format. For example, the format determination unit may provide a detailed format for a highly relevant legal issue. The format determination unit may also provide a concise format for a less relevant legal issue. The format determination unit may also dynamically adjust the format of the advice based on the relevance. This makes it possible to provide advice in an appropriate format based on the relevance of the legal issue. Some or all of the above-described processing in the format determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the format determination unit may input relevance data of the legal issue to a generation AI and cause the generation AI to adjust the format.

[0062] The supervision department can determine the priority of supervision based on the importance of the legal issues during supervision. For example, the supervision department determines the priority of supervision based on the importance of the legal issues during supervision. Priorities include, but are not limited to, the importance and impact of the legal issues. For example, the supervision department prioritizes supervision of legal issues with high importance. The supervision department can also postpone supervision of legal issues with low importance. The supervision department can also dynamically adjust the priority of supervision based on the importance. This allows supervision to be performed with appropriate priorities according to the importance of the legal issues. Some or all of the above-mentioned processing in the supervision department may be performed using, for example, AI, or may be performed without using AI. For example, the supervision department can input importance data of legal issues into the generation AI and have the generation AI determine the priority.

[0063] The supervision department can apply different supervision algorithms depending on the category of the legal issue during supervision. For example, the supervision department can apply different supervision algorithms depending on the category of the legal issue during supervision. Supervision algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the supervision department can apply a specific supervision algorithm when supervising issues related to commercial law. The supervision department can also apply a different supervision algorithm when supervising issues related to labor law. The supervision department can also apply a dedicated supervision algorithm when supervising issues related to intellectual property law. This allows for the application of an appropriate supervision algorithm depending on the category of the legal issue. Some or all of the above-mentioned processing in the supervision department can be performed using, for example, AI, or can be performed without using AI. For example, the supervision department can input legal issue category data into the generation AI and cause the generation AI to apply the supervision algorithm.

[0064] During supervision, the supervision department can determine the priority of supervision based on the timing of the occurrence of legal issues. During supervision, the supervision department, for example, determines the priority of supervision based on the timing of the occurrence of legal issues. Priorities include, but are not limited to, the timing of the occurrence of legal issues and the impact of legal issues. For example, the supervision department prioritizes supervision of recently occurring legal issues. The supervision department can also postpone supervision of legal issues that occurred in the past. The supervision department can also dynamically adjust the priority of supervision based on the timing of the occurrence. This allows supervision to be performed with an appropriate priority based on the timing of the occurrence of legal issues. Some or all of the above-mentioned processing in the supervision department may be performed using, for example, AI, or may be performed without using AI. For example, the supervision department can input data on the timing of the occurrence of legal issues into the generation AI and have the generation AI determine the priority.

[0065] The supervision department can adjust the order of supervision based on the relevance of legal issues during supervision. For example, the supervision department adjusts the order of supervision based on the relevance of legal issues during supervision. The order includes, but is not limited to, for example, descending order of relevance or descending order of importance. For example, the supervision department prioritizes supervision of highly relevant legal issues. The supervision department can also postpone supervision of less relevant legal issues. The supervision department can also dynamically adjust the order of supervision based on the relevance. This allows supervision to be performed in an appropriate order based on the relevance of legal issues. Some or all of the above-described processing in the supervision department may be performed using, for example, AI, or may be performed without using AI. For example, the supervision department can input relevance data of legal issues into a generation AI and have the generation AI adjust the order.

[0066] The security unit can determine security priorities based on the importance of legal issues when implementing security measures. For example, the security unit determines security priorities based on the importance of legal issues when implementing security measures. Priorities include, but are not limited to, the importance and impact of legal issues. For example, the security unit provides enhanced security measures for legal issues of high importance. The security unit can also provide standard security measures for legal issues of low importance. The security unit can also dynamically adjust security priorities based on importance. This allows security measures to be provided with appropriate priorities based on the importance of the legal issues. Some or all of the above-described processing in the security unit may be performed using, or without, AI. For example, the security unit can input importance data of legal issues into a generation AI and have the generation AI determine the priorities.

[0067] The security department can apply different security measures depending on the category of the legal issue during security measures. For example, the security department applies different security measures depending on the category of the legal issue during security measures. Security measures include, but are not limited to, data encryption, access control, and monitoring systems. For example, the security department can apply specific security measures to issues related to commercial law. The security department can also apply different security measures to issues related to labor law. The security department can also apply dedicated security measures to issues related to intellectual property law. This makes it possible to provide appropriate security measures depending on the category of the legal issue. Some or all of the above-mentioned processing in the security department can be performed using, or without, AI. For example, the security department can input legal issue category data into a generation AI and have the generation AI apply the security measures.

[0068] The security unit can determine security priorities based on the timing of legal issue occurrence when implementing security countermeasures. For example, the security unit determines security priorities based on the timing of legal issue occurrence when implementing security countermeasures. Priorities include, but are not limited to, the timing of legal issue occurrence and the impact of the legal issue. For example, the security unit provides enhanced security countermeasures for a recent legal issue. The security unit can also provide standard security countermeasures for a legal issue that occurred in the past. The security unit can also dynamically adjust security priorities based on the timing of the occurrence. This allows security countermeasures to be provided with appropriate priorities based on the timing of the legal issue occurrence. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input data on the timing of legal issue occurrence into a generation AI and have the generation AI determine the priorities.

[0069] The security unit can adjust the order of security measures based on the relevance of legal issues when implementing security measures. For example, the security unit adjusts the order of security measures based on the relevance of legal issues when implementing security measures. Examples of the order include, but are not limited to, descending order of relevance or descending order of importance. For example, the security unit provides enhanced security measures for highly relevant legal issues. The security unit can also provide standard security measures for less relevant legal issues. The security unit can also dynamically adjust the order of security measures based on the relevance. This allows security measures to be provided in an appropriate order based on the relevance of the legal issues. Some or all of the above-described processing in the security unit may be performed using, or without, AI. For example, the security unit can input relevance data of legal issues into a generation AI and have the generation AI adjust the order.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The legal consultation system can analyze a user's past consultation history and provide optimal advice. For example, if the user has had a similar consultation in the past, the advice can be customized based on that history. It can also provide more effective advice by taking into account the results of advice the user has received in the past. Furthermore, it can find specific patterns from the user's past consultation history and provide preventative advice. This makes it possible to provide optimal advice based on the user's past consultation history.

[0072] The legal consultation system can provide relevant legal information based on the user's geographic location information. For example, if the user is in a specific country, legal information related to that country is provided preferentially. Also, if the user is in a specific region, legal information related to that region can be provided. Furthermore, if the user is traveling, relevant legal information can be provided based on the user's current location. This makes it possible to provide appropriate legal information based on the user's geographic location information.

[0073] The legal consultation system can analyze a user's social media activity and provide relevant legal information. For example, it can analyze content posted by a user on social media about legal issues and provide relevant legal information. It can also predict relevant legal issues based on the user's social media activity and provide advice. It can also provide relevant legal information by taking into account the activity of the user's friends on social media. This makes it possible to provide appropriate legal information based on the user's social media activity.

[0074] The legal consultation system can adjust the content of advice according to the user's level of expertise. For example, if the user has specialized knowledge, it can provide advice that uses a lot of technical terminology. On the other hand, if the user does not have specialized knowledge, it can provide concise and easy-to-understand advice. Furthermore, it can dynamically adjust the content of advice according to the user's level of expertise. This makes it possible to provide appropriate advice according to the user's level of expertise.

[0075] The legal consultation system can customize the content of advice by reflecting the user's past feedback. For example, it can propose optimal advice based on feedback provided by the user in the past. It can also prioritize and propose specific advice based on the user's past feedback. Furthermore, it can analyze the user's past feedback and customize the content of advice. This makes it possible to provide optimal advice based on the user's past feedback.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The reception unit receives the consultation content from the user. Consultation content can include legal consultation, business consultation, technical consultation, etc. The reception unit can accept text data entered by the user as well as voice input and image input. It is also possible to convert voice data into text data using voice recognition technology and image analysis technology into text data. Step 2: The analysis unit uses the generation AI to analyze the consultation content received by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. The generation AI analyzes the consultation content using text generation AI (e.g., LLM) or multimodal generation AI, and extracts and analyzes important parts. Step 3: The provision unit provides legal advice based on the analysis results obtained by the analysis unit. Legal advice may include drafting contracts, assessing legal risks, and guiding users through legal procedures. The provision unit provides specific advice based on the analysis results, and can provide advice in written, audio, video, or other formats depending on the user's needs. The processing in the provision unit may be performed with or without the use of AI.

[0078] (Example 2) The legal consultation system according to an embodiment of the present invention uses a generation AI to provide consultation on legal matters in various countries. The system allows users to input their legal consultation details, and the generation AI then examines all legal precedents and provides advice on legal matters. This system allows users to quickly and accurately resolve legal issues. For example, a user may input a question such as, "I want to know the legal procedures required to start a new business in a specific country." This information is then input into the generation AI. The generation AI then analyzes the input consultation details. The generation AI examines all legal precedents and extracts relevant legal information. For example, it may research the legal procedures and regulations required to start a new business in a specific country. Based on the extracted legal information, the generation AI provides specific advice to the user. For example, it may provide advice such as, "To start a new business in a specific country, you must first establish a company and then obtain the necessary permits and licenses." This system allows users to quickly and accurately resolve legal issues. For example, by understanding the procedures required to start a business in advance, users can smoothly launch their business. It also minimizes legal risks. Furthermore, the generation AI can present relevant legal precedents based on the user's consultation details. For example, by providing information such as "what decisions have been made in similar cases in the past," users can receive more specific legal advice. In this way, using generative AI makes it possible to quickly and accurately consult on legal issues in various countries. This allows the legal consultation system to quickly and accurately resolve users' legal problems. For example, by knowing the necessary procedures for starting a business in advance, users can start their business smoothly. It also minimizes legal risks.

[0079] A legal consultation system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives consultation content from a user. The consultation content includes, but is not limited to, legal consultation, business consultation, and technical consultation. The reception unit receives, for example, text data input by a user. The reception unit can also receive voice input and image input. For example, the reception unit converts voice data into text data using voice recognition technology. The reception unit can also convert image data into text data using image analysis technology. The analysis unit uses a generation AI to analyze the consultation content received by the reception unit. The analysis can be performed using, for example, text analysis, data mining, or a machine learning algorithm, but is not limited to, these examples. For example, the generation AI analyzes the consultation content using a text generation AI (e.g., LLM). The analysis unit can also analyze the consultation content using a multimodal generation AI. The analysis unit can also use the generation AI to extract and analyze important parts of the consultation content. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to extract particularly important information from the consultation content and performs analysis based on that information. The provision unit provides legal advice based on the analysis results obtained by the analysis unit. Legal advice may include, but is not limited to, contract drafting, legal risk assessment, and legal procedure guidance. The provision unit provides specific advice based on the analysis results. The provision unit can also determine the format of the advice according to the user's needs. For example, the provision unit may provide advice in document format, audio format, video format, or the like. This allows the legal consultation system according to the embodiment to efficiently accept and analyze the user's consultation content and provide legal advice. Some or all of the above-described processing by the provision unit may be performed using AI, or may be performed without AI.For example, the providing unit can provide legal advice using an AI model that receives the analysis results obtained by the analyzing unit as input and outputs legal advice.

[0080] The legal consultation system includes an update unit in which a generation AI tracks the latest legal amendments and precedents and updates the database. The update unit uses the generation AI to track the latest legal amendments and precedents and update the database. The generation AI tracks legal amendments and precedents using technologies such as natural language processing, machine learning, and deep learning. For example, the generation AI automatically collects legal amendment information and precedent databases on the Internet and updates the database. The update unit can also determine update priorities based on the importance of legal amendments and precedents. For example, it prioritizes updating the database with legal amendments and precedents of high importance. The update unit can also apply different update algorithms depending on the category of legal amendments and precedents. For example, it applies a specific update algorithm when updating information related to commercial law. This enables the system to provide advice based on the latest legal amendments and precedents. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can update the database using an AI model that uses legal amendment information and precedent data collected by the generation AI as input.

[0081] The legal consultation system includes a format determination unit that determines the format of advice to be received by the user. The format determination unit determines the format of advice to be received by the user. Examples of advice formats include, but are not limited to, document format, audio format, and video format. The format determination unit determines the format of advice according to the user's needs, for example. The format determination unit can also determine the format of advice based on the importance of the legal issue. For example, a detailed format is provided for legal issues with high importance. The format determination unit can also apply different formats depending on the category of the legal issue. For example, a specific format is applied for issues related to commercial law. This allows advice to be provided in a format that meets the user's needs. Some or all of the above-described processing in the format determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the format determination unit can determine the format of advice using an AI model that determines the format of advice based on input of the user's needs and the importance of the legal issue.

[0082] The legal consultation system includes a supervision unit for ensuring the reliability of the advice. The supervision unit supervises the advice provided to ensure the reliability of the advice. Supervision is performed, for example, by methods such as supervision by an expert, evaluation by a third-party organization, or past performance, but is not limited to these examples. For example, the supervision unit performs supervision by a legal expert. The supervision unit can also perform evaluation by a third-party organization. The supervision unit can also evaluate the reliability of the advice based on past performance. This can increase the reliability of the advice provided. Some or all of the above-mentioned processing in the supervision unit may be performed using, for example, AI, or may be performed without using AI. For example, the supervision unit can ensure the reliability of the advice by using an AI model that inputs the advice provided and evaluates its reliability.

[0083] The legal consultation system includes a security unit that protects the user's consultation content and personal information. The security unit protects the user's consultation content and personal information. Personal information includes, but is not limited to, for example, name, address, telephone number, and consultation content. The security unit protects the personal information using methods such as data encryption, access control, and a monitoring system. For example, the security unit encrypts the consultation content and personal information. The security unit can also perform access control to ensure that only specific users can access the personal information. The security unit can also detect unauthorized access using a monitoring system. This allows the user's consultation content and personal information to be safely protected. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can protect the personal information using an AI model that uses personal information as input and performs data encryption and access control.

[0084] The analysis unit can cover all relevant court cases to date and extract relevant legal information. For example, the analysis unit covers all court cases to date and extracts relevant legal information. Court cases include, but are not limited to, Supreme Court cases, district court cases, and cases in specific legal fields. For example, the analysis unit covers Supreme Court cases and extracts relevant legal information. The analysis unit can also cover district court cases and extract relevant legal information. The analysis unit can also cover cases in specific legal fields and extract relevant legal information. This makes it possible to provide accurate legal information based on past court cases. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input court case data into a generation AI and cause the generation AI to extract relevant legal information.

[0085] The providing unit can provide specific advice based on the extracted legal information. The providing unit can provide specific advice based on, for example, the extracted legal information. Specific advice includes, for example, guidance on legal procedures, contract preparation, risk assessment, etc., but is not limited to these examples. The providing unit can, for example, provide guidance on legal procedures. The providing unit can also assist in contract preparation. The providing unit can also evaluate legal risks. This makes it possible to provide specific legal advice. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can provide specific advice using an AI model that receives the extracted legal information as input and outputs specific advice.

[0086] The reception unit can estimate the user's emotions and adjust the timing of receiving the consultation content based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of receiving the consultation content based on the estimated user emotions. Emotion estimation is performed using, for example, facial expression recognition, voice analysis, text analysis, or other technologies, but is not limited to these examples. For example, if the user is feeling stressed, the reception unit can immediately receive the consultation content. Furthermore, if the user is relaxed, the reception unit can also preferentially receive a concise consultation content if the user is in a hurry. This allows the consultation content to be received at an appropriate time according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0087] The reception unit can analyze the user's past consultation history and select the optimal reception method. The reception unit, for example, analyzes the user's past consultation history and selects the optimal reception method. Optimal reception methods include, but are not limited to, online chat, telephone, and face-to-face consultation. The reception unit, for example, automatically displays as candidates the content of consultations the user has frequently used in the past. The reception unit can also preferentially suggest reception methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the reception method to be used during a specific time period based on the user's past consultation history. This makes it possible to provide the optimal reception method based on the user's past consultation history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI select the optimal reception method.

[0088] The reception unit may filter the consultation content based on the user's current legal issues and areas of interest when receiving the consultation content. For example, the reception unit may filter the consultation content based on the user's current legal issues and areas of interest when receiving the consultation content. Filtering may be performed by, for example, keyword matching, category classification, prioritization, or other methods, but is not limited to these examples. For example, the reception unit may preferentially receive consultation content related to legal areas in which the user is interested. The reception unit may also filter and receive information related to the user's current legal issues. The reception unit may also filter and receive related legal issues based on the user's past consultation content. This allows for preferential reception of consultation content related to the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's current legal issues and area of ​​interest data into a generation AI and have the generation AI perform filtering.

[0089] The reception unit can select the optimal reception means according to the user's input method when receiving the consultation content. For example, the reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.) when receiving the consultation content. Input methods include, but are not limited to, voice input, text input, image input, etc. For example, the reception unit may use voice recognition to receive the consultation content when the user inputs the consultation content by voice. Furthermore, the reception unit may use text analysis to receive the consultation content when the user inputs the consultation content by text. Furthermore, the reception unit may use image analysis to receive the consultation content when the user inputs the consultation content by image. This makes it possible to provide the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's input data to a generation AI and cause the generation AI to select the optimal reception means.

[0090] The reception unit can estimate the user's emotions and determine the priority of the consultation contents to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the consultation contents to be received based on the estimated user emotions. The priority is determined based on criteria such as urgency, importance, and the user's emotional state, but is not limited to these examples. For example, if the user is feeling stressed, the reception unit can prioritize receiving consultation contents with high urgency. Furthermore, if the user is relaxed, the reception unit can prioritize receiving consultation contents with detailed content. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving consultation contents with concise content. In this way, the consultation contents can be received in priority order according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0091] The reception unit can prioritize the reception of highly relevant consultation content based on the user's geographical location information when receiving consultation content. For example, the reception unit prioritizes the reception of highly relevant consultation content based on the user's geographical location information when receiving consultation content. Examples of geographical location information include, but are not limited to, GPS data, IP address, and user input information. For example, if the user is in a specific country, the reception unit can prioritize the reception of legal consultation content related to that country. Furthermore, if the user is in a specific region, the reception unit can prioritize the reception of legal consultation content related to that region. Furthermore, if the user is traveling, the reception unit can prioritize the reception of related legal consultation content based on the user's current location. This allows the reception of highly relevant consultation content based on the user's geographical location information to be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information data into a generation AI and cause the generation AI to prioritize highly relevant consultation content.

[0092] The reception unit may analyze the user's social media activity when receiving a consultation content and receive related consultation content. For example, the reception unit may analyze the user's social media activity when receiving a consultation content and receive related consultation content. Social media activity may include, but is not limited to, post content, the number of likes, and the number of followers. For example, the reception unit may analyze content posted by the user on social media about legal issues and receive related consultation content. The reception unit may also predict and receive related legal issues from the user's social media activity. The reception unit may also receive related consultation content based on the user's social media activity. In this way, related consultation content can be received based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without using, AI. For example, the reception unit may input the user's social media activity data into a generation AI and cause the generation AI to receive related consultation content.

[0093] The reception unit can customize the reception method by reflecting the user's past feedback when receiving the consultation content. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving the consultation content. Feedback includes, but is not limited to, survey results, user comments, and evaluation scores. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the reception method.

[0094] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the expression method of the analysis based on the estimated user's emotion. Expression methods include, but are not limited to, text format, graphical format, and audio format. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. This allows the analysis result to be provided in an appropriate expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the legal issue during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the legal issue during analysis. The level of detail includes, but is not limited to, the depth of information, the specificity of the explanation, and the granularity of the data. For example, the analysis unit performs a detailed analysis of a legal issue with high importance. The analysis unit can also perform a concise analysis of a legal issue with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis depending on the importance. This allows the analysis to be performed at an appropriate level of detail depending on the importance of the legal issue. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the legal issue to the generation AI and cause the generation AI to adjust the level of detail.

[0096] The analysis unit can apply different analysis algorithms depending on the category of the legal problem during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the legal problem during analysis. Examples of analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies a specific analysis algorithm to questions related to commercial law. The analysis unit can also apply a different analysis algorithm to questions related to labor law. The analysis unit can also apply a dedicated analysis algorithm to questions related to intellectual property law. This makes it possible to apply an appropriate analysis algorithm depending on the category of the legal problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input legal problem category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0097] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Examples of ways to improve the accuracy of the analysis include, but are not limited to, using past data and introducing a feedback loop. For example, the analysis unit complements the current analysis result based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. This can improve the accuracy of the analysis based on the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. Examples of the length of the analysis include, but are not limited to, a summary of the analysis results or a detailed report. For example, the analysis unit can provide a short, concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide an analysis result with a visually stimulating effect when the user is excited. This allows the analysis result to be provided at an appropriate length depending on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0099] The analysis unit can determine the analysis priority based on the time when the legal issue occurred during analysis. The analysis unit, for example, determines the analysis priority based on the time when the legal issue occurred during analysis. The priority can be determined based on criteria such as urgency, importance, and time of occurrence, but is not limited to these examples. For example, the analysis unit prioritizes analysis of recently occurring legal issues. The analysis unit can also lower the priority of legal issues that occurred in the past when analyzing them. The analysis unit can also dynamically adjust the analysis priority based on the time of occurrence. This allows analysis to be performed with an appropriate priority based on the time when the legal issue occurred. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the legal issue occurred to the generation AI and have the generation AI determine the priority.

[0100] The analysis unit can adjust the order of analysis based on the relevance of legal issues during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of legal issues during analysis. The order can be determined based on criteria such as, but not limited to, relevance or importance. For example, the analysis unit prioritizes analysis of highly relevant legal issues. The analysis unit can also lower the priority of analysis of less relevant legal issues. The analysis unit can also dynamically adjust the order of analysis based on relevance. This allows analysis to be performed in an appropriate order based on the relevance of legal issues. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of legal issues to a generation AI and cause the generation AI to adjust the order.

[0101] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. Technical terminology includes, but is not limited to, selecting terms according to the user's level of expertise. For example, the analysis unit can provide analysis results that make extensive use of technical terminology when the user has technical expertise. Furthermore, the analysis unit can provide concise and easy-to-understand analysis results when the user does not have technical expertise. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows the analysis results to be provided using appropriate technical terminology according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terminology.

[0102] The providing unit can estimate the user's emotion and adjust the advice providing method based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the advice providing method based on the estimated user's emotion. Examples of the providing method include, but are not limited to, text, audio, and video formats. For example, if the user is nervous, the providing unit can provide simple, highly visible advice. If the user is relaxed, the providing unit can also provide detailed advice. If the user is in a hurry, the providing unit can also provide advice that focuses on the main points. This allows the advice to be provided in an appropriate manner according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the advice providing method.

[0103] The providing unit can adjust the level of detail of the advice based on the importance of the legal issue when providing the advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the legal issue when providing the advice. The level of detail includes, but is not limited to, the depth of information, the specificity of the explanation, and the granularity of the data. For example, the providing unit provides detailed advice for legal issues of high importance. The providing unit can also provide concise advice for legal issues of low importance. The providing unit can also dynamically adjust the level of detail of the advice according to the importance. This makes it possible to provide advice with an appropriate level of detail according to the importance of the legal issue. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input importance data of the legal issue to a generation AI and cause the generation AI to adjust the level of detail.

[0104] The providing unit can apply different advice algorithms depending on the category of the legal problem when providing advice. For example, the providing unit applies different advice algorithms depending on the category of the legal problem when providing advice. Advice algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the providing unit applies a specific advice algorithm to issues related to commercial law. The providing unit can also apply a different advice algorithm to issues related to labor law. The providing unit can also apply a dedicated advice algorithm to issues related to intellectual property law. This makes it possible to apply an appropriate advice algorithm depending on the category of the legal problem. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input legal problem category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0105] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. Improvements in the accuracy of advice include, but are not limited to, using past data and introducing a feedback loop. For example, the providing unit complements current advice based on the user's past advice results. The providing unit can also improve the accuracy of advice from the user's past advice results. The providing unit can also analyze the user's past advice results and optimize the advice algorithm. This can improve the accuracy of advice based on the user's past advice results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of advice.

[0106] The providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of the advice based on the estimated user's emotion. The length of the advice can include, but is not limited to, a summary of the advice content or a detailed explanation. For example, if the user is in a hurry, the providing unit can provide short, to-the-point advice. If the user is relaxed, the providing unit can also provide detailed advice. If the user is excited, the providing unit can also provide advice with visually stimulating effects. This allows advice to be provided at an appropriate length depending on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-mentioned processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0107] The providing unit can determine the priority of advice based on the time when the legal issue occurred when providing the advice. For example, the providing unit determines the priority of advice based on the time when the legal issue occurred when providing the advice. The priority can be determined based on criteria such as urgency, importance, and time of occurrence, but is not limited to these examples. For example, the providing unit prioritizes providing advice for legal issues that occurred recently. The providing unit can also provide advice with a lower priority for legal issues that occurred in the past. The providing unit can also dynamically adjust the priority of advice depending on the time of occurrence. This makes it possible to provide advice with an appropriate priority depending on the time when the legal issue occurred. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time when the legal issue occurred to a generation AI and cause the generation AI to determine the priority.

[0108] The providing unit can adjust the order of advice based on the relevance of legal issues when providing advice. For example, the providing unit adjusts the order of advice based on the relevance of legal issues when providing advice. The order can be determined based on criteria such as, for example, relevance or importance, but is not limited to such examples. For example, the providing unit can provide advice with priority for highly relevant legal issues. The providing unit can also provide advice with lower priority for less relevant legal issues. The providing unit can also dynamically adjust the order of advice based on the relevance. This makes it possible to provide advice in an appropriate order based on the relevance of legal issues. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of legal issues to a generation AI and cause the generation AI to adjust the order.

[0109] The providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. For example, the providing unit may adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. Technical terms include, but are not limited to, selection of terms according to the user's level of expertise. For example, the providing unit may provide advice that uses a lot of technical terms when the user has technical knowledge. Furthermore, the providing unit may provide concise and easy-to-understand advice when the user does not have technical knowledge. Furthermore, the providing unit may dynamically adjust the use of technical terms in the advice according to the user's level of expertise. This allows the advice to be provided using appropriate technical terms according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit may input the user's level of expertise data into a generation AI and cause the generation AI to use technical terms.

[0110] The update unit can estimate the user's emotion and adjust the update frequency of the database based on the estimated user's emotion. The update unit, for example, estimates the user's emotion and adjusts the update frequency of the database based on the estimated user's emotion. The update frequency includes, but is not limited to, periodic updates and event-driven updates. For example, if the user is nervous, the update unit frequently updates the database to provide the latest information. If the user is relaxed, the update unit can also update the database at a normal update frequency. If the user is in a hurry, the update unit can also prioritize updating important information. This allows the database to be updated at an appropriate frequency according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-mentioned processing in the update unit may be performed using, for example, an AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the update frequency.

[0111] The update unit can determine the update priority based on the importance of the latest legal amendments and precedents during updates. For example, the update unit determines the update priority based on the importance of the latest legal amendments and precedents during updates. Priorities include, but are not limited to, the importance of legal amendments and precedents, the influence of precedents, and the like. For example, the update unit prioritizes updating the database with legal amendments and precedents of high importance. The update unit can also reflect legal amendments and precedents of low importance later. The update unit can also dynamically adjust the update priority based on the importance. This allows the database to be updated with an appropriate priority based on the importance of the latest legal amendments and precedents. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without AI. For example, the update unit can input importance data of legal amendments and precedents into a generation AI and have the generation AI determine the priority.

[0112] The update unit can apply different update algorithms depending on the category of legal information during updating. For example, the update unit applies different update algorithms depending on the category of legal information during updating. Examples of update algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the update unit applies a specific update algorithm when updating information related to commercial law. The update unit can also apply a different update algorithm when updating information related to labor law. The update unit can also apply a dedicated update algorithm when updating information related to intellectual property law. This makes it possible to apply an appropriate update algorithm depending on the category of legal information. Some or all of the above-mentioned processing in the update unit may be performed using, or without, AI. For example, the update unit can input legal information category data to a generation AI and cause the generation AI to apply an update algorithm.

[0113] The update unit can estimate the user's emotions and determine the priority of the legal information to be updated based on the estimated user emotions. The update unit, for example, estimates the user's emotions and determines the priority of the legal information to be updated based on the estimated user emotions. Priorities include, but are not limited to, the user's emotional state and the importance of the legal information. For example, if the user is nervous, the update unit prioritizes updating important legal information. If the user is relaxed, the update unit can also update the legal information with normal priority. If the user is in a hurry, the update unit can also quickly update important legal information. This allows the legal information to be updated with appropriate priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-mentioned processing in the update unit may be performed using, for example, an AI. For example, the update unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0114] The update unit can determine the update priority based on the time of occurrence of the legal information during an update. The update unit, for example, determines the update priority based on the time of occurrence of the legal information during an update. Priorities include, but are not limited to, the time of occurrence and the impact of the legal information. For example, the update unit prioritizes updating recently occurring legal information. The update unit can also postpone updating legal information that occurred in the past. The update unit can also dynamically adjust the update priority based on the time of occurrence. This allows updates to be performed with appropriate priorities based on the time of occurrence of the legal information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input data on the time of occurrence of the legal information into a generation AI and have the generation AI determine the priority.

[0115] The update unit can adjust the update order based on the relevance of the legal information during an update. The update unit, for example, adjusts the update order based on the relevance of the legal information during an update. The order includes, for example, descending order of relevance or descending order of importance, but is not limited to such examples. For example, the update unit prioritizes updating highly relevant legal information. The update unit can also postpone updating less relevant legal information. The update unit can also dynamically adjust the update order based on the relevance. This allows updating to be performed in an appropriate order based on the relevance of the legal information. Some or all of the above-described processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input relevance data of the legal information to a generation AI and cause the generation AI to adjust the order.

[0116] The format determination unit can estimate the user's emotion and adjust the format of the advice based on the estimated user's emotion. The format determination unit, for example, estimates the user's emotion and adjusts the format of the advice based on the estimated user's emotion. Examples of formats include, but are not limited to, text, audio, and video. For example, if the user is nervous, the format determination unit provides a simple, highly visible format. If the user is relaxed, the format determination unit can provide a detailed format. If the user is in a hurry, the format determination unit can provide a format that focuses on the main points. This allows the advice to be provided in an appropriate format according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the format determination unit may be performed using, for example, an AI. For example, the format determination unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the format.

[0117] The format determination unit may determine the format of the advice based on the importance of the legal issue when determining the format. For example, the format determination unit may determine the format of the advice based on the importance of the legal issue when determining the format. Examples of formats include, but are not limited to, text, audio, and video. For example, the format determination unit may provide a detailed format for legal issues of high importance. The format determination unit may also provide a concise format for legal issues of low importance. The format determination unit may also dynamically adjust the format of the advice according to the importance. This allows advice to be provided in an appropriate format according to the importance of the legal issue. Some or all of the above-described processing in the format determination unit may be performed using, or without, AI. For example, the format determination unit may input importance data of the legal issue to a generation AI and cause the generation AI to determine the format.

[0118] The format determination unit can apply different formats depending on the category of the legal problem when determining the format. For example, the format determination unit can apply different formats depending on the category of the legal problem when determining the format. Formats include, but are not limited to, text, audio, and video formats. For example, the format determination unit can apply a specific format to questions related to commercial law. The format determination unit can also apply a different format to questions related to labor law. The format determination unit can also apply a dedicated format to questions related to intellectual property law. This makes it possible to provide advice in an appropriate format depending on the category of the legal problem. Some or all of the above-described processing in the format determination unit can be performed using, or without, AI. For example, the format determination unit can input legal problem category data to a generation AI and cause the generation AI to apply the format.

[0119] The format determination unit can estimate the user's emotion and determine the format of the advice based on the estimated user's emotion. The format determination unit, for example, estimates the user's emotion and determines the format of the advice based on the estimated user's emotion. Formats include, but are not limited to, text, audio, and video formats. For example, if the user is nervous, the format determination unit can provide a simple, highly visible format. If the user is relaxed, the format determination unit can also provide a detailed format. If the user is in a hurry, the format determination unit can also provide a format that focuses on the main points. This allows the advice to be provided in an appropriate format according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the format determination unit may be performed using, for example, an AI. For example, the format determination unit can input the user's emotion data into the generation AI and have the generation AI determine the format.

[0120] The format determination unit may determine the format of the advice based on the time when the legal issue occurred when determining the format. For example, the format determination unit may determine the format of the advice based on the time when the legal issue occurred when determining the format. Examples of formats include, but are not limited to, text format, audio format, and video format. For example, the format determination unit may provide a detailed format for a recently occurred legal issue. The format determination unit may also provide a concise format for a previously occurred legal issue. The format determination unit may also dynamically adjust the format of the advice depending on the time when the legal issue occurred. This makes it possible to provide advice in an appropriate format depending on the time when the legal issue occurred. Some or all of the above-described processing in the format determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the format determination unit may input data on the time when the legal issue occurred to a generation AI and cause the generation AI to determine the format.

[0121] The format determination unit may adjust the format of the advice based on the relevance of the legal issue when determining the format. For example, the format determination unit may adjust the format of the advice based on the relevance of the legal issue when determining the format. Examples of formats include, but are not limited to, text format, audio format, and video format. For example, the format determination unit may provide a detailed format for a highly relevant legal issue. The format determination unit may also provide a concise format for a less relevant legal issue. The format determination unit may also dynamically adjust the format of the advice based on the relevance. This makes it possible to provide advice in an appropriate format based on the relevance of the legal issue. Some or all of the above-described processing in the format determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the format determination unit may input relevance data of the legal issue to a generation AI and cause the generation AI to adjust the format.

[0122] The supervision unit can estimate the user's emotions and adjust the frequency of supervision based on the estimated user emotions. The supervision unit, for example, estimates the user's emotions and adjusts the frequency of supervision based on the estimated user emotions. The frequency of supervision includes, but is not limited to, periodic supervision and event-driven supervision. For example, if the user is nervous, the supervision unit performs supervision frequently to increase reliability. Furthermore, if the user is relaxed, the supervision unit can perform supervision at a normal supervision frequency. Furthermore, if the user is in a hurry, the supervision unit can prioritize supervision of important parts. This allows supervision to be performed at an appropriate frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the supervision unit may be performed using, for example, an AI, or without using an AI. For example, the supervision department can input the user's emotional data into the generation AI and have the generation AI adjust the frequency of supervision.

[0123] The supervision department can determine the priority of supervision based on the importance of the legal issues during supervision. For example, the supervision department determines the priority of supervision based on the importance of the legal issues during supervision. Priorities include, but are not limited to, the importance and impact of the legal issues. For example, the supervision department prioritizes supervision of legal issues with high importance. The supervision department can also postpone supervision of legal issues with low importance. The supervision department can also dynamically adjust the priority of supervision based on the importance. This allows supervision to be performed with appropriate priorities according to the importance of the legal issues. Some or all of the above-mentioned processing in the supervision department may be performed using, for example, AI, or may be performed without using AI. For example, the supervision department can input importance data of legal issues into the generation AI and have the generation AI determine the priority.

[0124] The supervision department can apply different supervision algorithms depending on the category of the legal issue during supervision. For example, the supervision department can apply different supervision algorithms depending on the category of the legal issue during supervision. Supervision algorithms include, but are not limited to, machine learning algorithms and rule-based algorithms. For example, the supervision department can apply a specific supervision algorithm when supervising issues related to commercial law. The supervision department can also apply a different supervision algorithm when supervising issues related to labor law. The supervision department can also apply a dedicated supervision algorithm when supervising issues related to intellectual property law. This allows for the application of an appropriate supervision algorithm depending on the category of the legal issue. Some or all of the above-mentioned processing in the supervision department can be performed using, for example, AI, or can be performed without using AI. For example, the supervision department can input legal issue category data into the generation AI and cause the generation AI to apply the supervision algorithm.

[0125] The supervision unit can estimate the user's emotions and determine the priority of supervision based on the estimated user emotions. The supervision unit, for example, estimates the user's emotions and determines the priority of supervision based on the estimated user emotions. Priorities include, but are not limited to, the user's emotional state and the importance of the legal issue. For example, if the user is nervous, the supervision unit can prioritize supervision of important parts. Also, if the user is relaxed, the supervision unit can perform supervision with normal priority. Also, if the user is in a hurry, the supervision unit can quickly supervise important parts. This allows supervision to be performed with appropriate priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the supervision unit may be performed using, for example, an AI, or without using an AI. For example, the supervision department can input user emotion data into the generation AI and have the generation AI determine priorities.

[0126] During supervision, the supervision department can determine the priority of supervision based on the timing of the occurrence of legal issues. During supervision, the supervision department, for example, determines the priority of supervision based on the timing of the occurrence of legal issues. Priorities include, but are not limited to, the timing of the occurrence of legal issues and the impact of legal issues. For example, the supervision department prioritizes supervision of recently occurring legal issues. The supervision department can also postpone supervision of legal issues that occurred in the past. The supervision department can also dynamically adjust the priority of supervision based on the timing of the occurrence. This allows supervision to be performed with an appropriate priority based on the timing of the occurrence of legal issues. Some or all of the above-mentioned processing in the supervision department may be performed using, for example, AI, or may be performed without using AI. For example, the supervision department can input data on the timing of the occurrence of legal issues into the generation AI and have the generation AI determine the priority.

[0127] The supervision department can adjust the order of supervision based on the relevance of legal issues during supervision. For example, the supervision department adjusts the order of supervision based on the relevance of legal issues during supervision. The order includes, but is not limited to, for example, descending order of relevance or descending order of importance. For example, the supervision department prioritizes supervision of highly relevant legal issues. The supervision department can also postpone supervision of less relevant legal issues. The supervision department can also dynamically adjust the order of supervision based on the relevance. This allows supervision to be performed in an appropriate order based on the relevance of legal issues. Some or all of the above-described processing in the supervision department may be performed using, for example, AI, or may be performed without using AI. For example, the supervision department can input relevance data of legal issues into a generation AI and have the generation AI adjust the order.

[0128] The security unit can estimate a user's emotions and adjust security measures based on the estimated user emotions. The security unit can estimate a user's emotions and adjust security measures based on the estimated user emotions. Security measures include, but are not limited to, data encryption, access control, and monitoring systems. For example, the security unit can provide enhanced security measures when the user is nervous. The security unit can also provide standard security measures when the user is relaxed. The security unit can also provide quick security measures when the user is in a hurry. This allows appropriate security measures to be provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the security unit can be performed using, for example, AI, or without AI. For example, the security unit can input user emotion data into the generation AI and have the generation AI adjust the security measures.

[0129] The security unit can determine security priorities based on the importance of legal issues when implementing security measures. For example, the security unit determines security priorities based on the importance of legal issues when implementing security measures. Priorities include, but are not limited to, the importance and impact of legal issues. For example, the security unit provides enhanced security measures for legal issues of high importance. The security unit can also provide standard security measures for legal issues of low importance. The security unit can also dynamically adjust security priorities based on importance. This allows security measures to be provided with appropriate priorities based on the importance of the legal issues. Some or all of the above-described processing in the security unit may be performed using, or without, AI. For example, the security unit can input importance data of legal issues into a generation AI and have the generation AI determine the priorities.

[0130] The security department can apply different security measures depending on the category of the legal issue during security measures. For example, the security department applies different security measures depending on the category of the legal issue during security measures. Security measures include, but are not limited to, data encryption, access control, and monitoring systems. For example, the security department can apply specific security measures to issues related to commercial law. The security department can also apply different security measures to issues related to labor law. The security department can also apply dedicated security measures to issues related to intellectual property law. This makes it possible to provide appropriate security measures depending on the category of the legal issue. Some or all of the above-mentioned processing in the security department can be performed using, or without, AI. For example, the security department can input legal issue category data into a generation AI and have the generation AI apply the security measures.

[0131] The security unit can estimate the user's emotions and prioritize security measures based on the estimated user emotions. The security unit, for example, estimates the user's emotions and prioritizes the security measures based on the estimated user emotions. Priorities include, but are not limited to, the user's emotional state and the importance of the legal issue. For example, the security unit can prioritize enhanced security measures when the user is nervous. The security unit can also prioritize standard security measures when the user is relaxed. The security unit can also prioritize quick security measures when the user is in a hurry. This allows security measures to be provided with appropriate priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the security unit may be performed using AI, or without AI. For example, the security unit can input the user's emotional data into the generation AI and have the generation AI determine the priorities.

[0132] The security unit can determine security priorities based on the timing of legal issue occurrence when implementing security countermeasures. For example, the security unit determines security priorities based on the timing of legal issue occurrence when implementing security countermeasures. Priorities include, but are not limited to, the timing of legal issue occurrence and the impact of the legal issue. For example, the security unit provides enhanced security countermeasures for a recent legal issue. The security unit can also provide standard security countermeasures for a legal issue that occurred in the past. The security unit can also dynamically adjust security priorities based on the timing of the occurrence. This allows security countermeasures to be provided with appropriate priorities based on the timing of the legal issue occurrence. Some or all of the above-described processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input data on the timing of legal issue occurrence into a generation AI and have the generation AI determine the priorities.

[0133] The security unit can adjust the order of security measures based on the relevance of legal issues when implementing security measures. For example, the security unit adjusts the order of security measures based on the relevance of legal issues when implementing security measures. Examples of the order include, but are not limited to, descending order of relevance or descending order of importance. For example, the security unit provides enhanced security measures for highly relevant legal issues. The security unit can also provide standard security measures for less relevant legal issues. The security unit can also dynamically adjust the order of security measures based on the relevance. This allows security measures to be provided in an appropriate order based on the relevance of the legal issues. Some or all of the above-described processing in the security unit may be performed using, or without, AI. For example, the security unit can input relevance data of legal issues into a generation AI and have the generation AI adjust the order. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, update unit, format determination unit, supervision unit, and security unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives consultation content from a user. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides legal advice based on the analysis results. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and tracks the latest legal amendments and precedents and updates the database. The format determination unit is implemented by the control unit 46A of the smart device 14 and determines the format of advice according to the user's needs. The supervision unit is implemented by the specific processing unit 290 of the data processing device 12 and ensures the reliability of the advice provided. The security unit is implemented by the control unit 46A of the smart device 14 and protects the user's consultation content and personal information. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, update unit, format determination unit, supervision unit, and security unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives the consultation content from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides legal advice based on the analysis results. The update unit is realized by the specific processing unit 290 of the data processing device 12 and tracks the latest legal amendments and precedents and updates the database. The format determination unit is realized by the control unit 46A of the smart glasses 214 and determines the format of the advice according to the user's needs. The supervision unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the reliability of the advice provided. The security unit is realized by the control unit 46A of the smart glasses 214 and protects the consultation content and personal information of the user. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, update unit, format determination unit, supervision unit, and security unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives consultation content from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides legal advice based on the analysis results. The update unit is realized by the specific processing unit 290 of the data processing device 12 and tracks the latest legal amendments and precedents and updates the database. The format determination unit is realized by the control unit 46A of the headset-type terminal 314 and determines the format of advice according to the user's needs. The supervision unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the reliability of the advice provided. The security unit is realized by the control unit 46A of the headset type terminal 314, and protects the consultation contents and personal information of the user. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, update unit, format determination unit, supervision unit, and security unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives consultation content from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the consultation content using a generation AI. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides legal advice based on the analysis results. The update unit is realized by the specific processing unit 290 of the data processing device 12 and tracks the latest legal amendments and precedents and updates the database. The format determination unit is realized by the control unit 46A of the robot 414 and determines the format of advice according to the user's needs. The supervision unit is realized by the specific processing unit 290 of the data processing device 12 and ensures the reliability of the advice provided. The security unit is realized by the control unit 46A of the robot 414 and protects the user's consultation content and personal information.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The legal consultation system can estimate the user's emotions and adjust the content of advice based on the estimated emotions. For example, if the user is feeling anxious, the advice can be made more detailed to provide a sense of security. If the user is in a hurry, the system can provide concise advice that gets to the point. Furthermore, if the user is relaxed, the advice can be made more comprehensive and take future risks into consideration. This makes it possible to provide appropriate advice according to the user's emotions.

[0136] The legal consultation system can analyze a user's past consultation history and provide optimal advice. For example, if the user has had a similar consultation in the past, the advice can be customized based on that history. It can also provide more effective advice by taking into account the results of advice the user has received in the past. Furthermore, it can find specific patterns from the user's past consultation history and provide preventative advice. This makes it possible to provide optimal advice based on the user's past consultation history.

[0137] The legal consultation system can estimate the user's emotions and adjust the timing of providing advice based on the estimated emotions. For example, if the user is feeling stressed, the system can provide immediate advice. If the user is relaxed, the system can provide detailed advice. Furthermore, if the user is in a hurry, the system can prioritize providing concise advice. This allows the system to provide advice at an appropriate time according to the user's emotions.

[0138] The legal consultation system can provide relevant legal information based on the user's geographic location information. For example, if the user is in a specific country, legal information related to that country is provided preferentially. Also, if the user is in a specific region, legal information related to that region can be provided. Furthermore, if the user is traveling, relevant legal information can be provided based on the user's current location. This makes it possible to provide appropriate legal information based on the user's geographic location information.

[0139] The legal consultation system can estimate the user's emotions and adjust the format of advice based on the estimated emotions. For example, if the user is nervous, a simple, highly visible format can be provided. If the user is relaxed, a detailed format can be provided. Furthermore, if the user is in a hurry, a format that focuses on the main points can be provided. This makes it possible to provide advice in an appropriate format according to the user's emotions.

[0140] The legal consultation system can analyze a user's social media activity and provide relevant legal information. For example, it can analyze content posted by a user on social media about legal issues and provide relevant legal information. It can also predict relevant legal issues based on the user's social media activity and provide advice. It can also provide relevant legal information by taking into account the activity of the user's friends on social media. This makes it possible to provide appropriate legal information based on the user's social media activity.

[0141] The legal consultation system can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize providing advice that is highly urgent. Also, if the user is relaxed, it can prioritize providing detailed advice. Furthermore, if the user is in a hurry, it can prioritize providing concise advice. In this way, it is possible to provide advice with appropriate priorities according to the user's emotions.

[0142] The legal consultation system can adjust the content of advice according to the user's level of expertise. For example, if the user has specialized knowledge, it can provide advice that uses a lot of technical terminology. On the other hand, if the user does not have specialized knowledge, it can provide concise and easy-to-understand advice. Furthermore, it can dynamically adjust the content of advice according to the user's level of expertise. This makes it possible to provide appropriate advice according to the user's level of expertise.

[0143] The legal consultation system can estimate the user's emotions and adjust the length of advice based on the estimated emotions. For example, if the user is in a hurry, it can provide short, to-the-point advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is excited, it can provide advice with visually stimulating effects. This makes it possible to provide advice of an appropriate length according to the user's emotions.

[0144] The legal consultation system can customize the content of advice by reflecting the user's past feedback. For example, it can propose optimal advice based on feedback provided by the user in the past. It can also prioritize and propose specific advice based on the user's past feedback. Furthermore, it can analyze the user's past feedback and customize the content of advice. This makes it possible to provide optimal advice based on the user's past feedback.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit receives the consultation content from the user. Consultation content can include legal consultation, business consultation, technical consultation, etc. The reception unit can accept text data entered by the user as well as voice input and image input. It is also possible to convert voice data into text data using voice recognition technology and image analysis technology into text data. Step 2: The analysis unit uses the generation AI to analyze the consultation content received by the reception unit. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. The generation AI analyzes the consultation content using text generation AI (e.g., LLM) or multimodal generation AI, and extracts and analyzes important parts. Step 3: The provision unit provides legal advice based on the analysis results obtained by the analysis unit. Legal advice may include drafting contracts, assessing legal risks, and guiding users through legal procedures. The provision unit provides specific advice based on the analysis results, and can provide advice in written, audio, video, or other formats depending on the user's needs. The processing in the provision unit may be performed with or without the use of AI.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 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.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0191] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0192] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0193] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0194] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0195] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0196] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0201] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0202] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0203] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0204] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0205] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0206] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0207] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0208] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0209] 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.

[0210] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0211] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0212] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0213] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0214] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0215] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0216] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0217] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives inquiries from users; an analysis unit that analyzes the consultation content received by the reception unit; a providing unit that provides legal advice based on the analysis results obtained by the analyzing unit. A system characterized by:

2. The generation AI tracks the latest legal changes and precedents and has an update section that updates the database.

2. The system of claim 1.

3. A format determination unit is provided to determine the format of the advice the user receives.

2. The system of claim 1.

4. We have a supervisory department to ensure the reliability of our advice.

2. The system of claim 1.

5. Equipped with a security department to protect users' consultation details and personal information 2. The system of claim 1.

6. The analysis unit Covers all relevant court cases to date and extracts relevant legal information 2. The system of claim 1.

7. The providing unit Providing specific advice based on extracted legal information 2. The system of claim 1.

8. The reception unit Estimates the user's emotions and adjusts the timing of accepting consultations based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit Analyze the user's past consultation history and select the optimal reception method 2. The system of claim 1.

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