System

Abstract

An object of the system according to the embodiment is to reduce the cost and time for the general public to solve legal problems and to improve accessibility.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception unit receives a legal question from a user. The generation unit analyzes the question received by the reception unit and generates legal advice. The providing unit provides the advice generated by the generating 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 technology has made it costly, time-consuming and difficult for ordinary people to access when resolving legal issues.

[0005] The system according to the embodiment aims to reduce the cost and time required for ordinary people to resolve legal issues, and to increase accessibility. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives legal questions from users. The generation unit analyzes the questions received by the reception unit and generates legal advice. The provision unit provides the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the cost and time required for ordinary people to resolve legal issues, and increase accessibility. [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) A legal chatbot system according to an embodiment of the present invention accepts legal questions from users, analyzes them with a generation AI, and provides legal advice. In this system, users input legal questions or problems, and a generation AI analyzes the questions or problems and generates appropriate legal advice, which is then provided to the user. For example, when a user inputs a question such as, "How do I file for divorce?", the generation AI provides detailed explanations of the necessary documents and procedural steps. The generated advice is provided to the user, who can then decide what to do. This reduces the cost and time required for ordinary people to resolve complex legal issues and increases accessibility. For example, this not only saves time and money by consulting a lawyer, but also allows users to obtain legal advice quickly. Furthermore, the generation AI is available 24 hours a day, allowing users to receive legal advice anytime, anywhere. Furthermore, the generation AI continuously learns and reflects the latest legal information. For example, when new laws or precedents are released, the generation AI can learn from them and incorporate them into the advice it provides to users. This allows the legal chatbot system to reduce the cost and time required for ordinary people to resolve complex legal issues and increase accessibility. For example, not only can users save time and money by not consulting a lawyer, but they can also receive legal advice quickly. Generative AI is also available 24 hours a day, allowing users to receive legal advice anytime, anywhere. Generative AI can also continuously learn and reflect the latest legal information. For example, when new laws or precedents are released, generative AI can learn from them and incorporate them into the advice it provides to users.

[0029] A legal chatbot system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives legal questions from a user. Examples of legal questions from a user include, but are not limited to, questions about contracts and litigation. The reception unit, for example, receives text data input by the user. The reception unit can also receive voice input. For example, when a user inputs a question by voice, the voice input can be converted into text data using voice recognition technology. The generation unit uses a generation AI to analyze the question received by the reception unit and generate legal advice. The generation unit analyzes the question using, for example, natural language processing technology or a machine learning algorithm. The generation unit can also generate appropriate legal advice by referring to a legal database or past cases. For example, the generation AI extracts relevant information from a legal database and generates advice in response to the user's question. The provision unit provides the advice generated by the generation unit to the user. The provision unit, for example, displays the generated advice in text format. The provision unit can also provide the generated advice in audio format. For example, the generated advice can be played back as voice using voice synthesis technology, allowing the legal chatbot system according to the embodiment to efficiently receive and analyze legal questions from users and provide advice.

[0030] The generation unit can generate legal advice by referring to a legal database or past cases. Legal databases include, but are not limited to, for example, a case precedent database and a statute database. For example, the generation unit can refer to the case precedent database to generate advice based on past court cases. The generation unit can also refer to a statute database to generate advice based on relevant laws and regulations. For example, the generation unit can analyze past court cases and provide appropriate advice in response to a user's question. The generation unit can also obtain the latest legal information from the statute database and update the advice in response to a user's question. In this way, the generation unit can provide more accurate legal advice by referring to a legal database or past cases.

[0031] The providing unit can provide a document template required by the user. Document templates include, but are not limited to, contracts, application forms, etc. The providing unit can provide, for example, a contract template required by the user. The providing unit can also provide an application template required by the user. For example, the providing unit selects and provides an appropriate document template based on information input by the user. This allows the user to easily obtain the document template required. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input information input by the user into the generation AI and have the generation AI select an appropriate document template.

[0032] The generation unit may include an update unit for continuously acquiring legal information. Examples of legal information include, but are not limited to, new laws and changes in legal precedents. For example, the update unit may periodically check a legal database to acquire new laws and changes in legal precedents. The update unit may also refer to legal news and specialized journals to acquire the latest legal information. For example, the update unit may periodically check legal news sites to acquire the latest legal information. This allows the generation unit to always provide advice that reflects the latest legal information. Some or all of the above-described processing in the update unit may be performed using, or without, the generation AI. For example, the update unit may input information acquired from legal news sites into the generation AI, causing the generation AI to analyze the latest legal information.

[0033] The legal chatbot system according to the embodiment includes a feedback unit that allows a user to provide feedback to the generation AI. The feedback unit has a function for the user to provide feedback to the generation AI. The feedback includes, for example, a user's evaluation and suggestions for improvement, but is not limited to these examples. For example, the feedback unit provides an interface for the user to input an evaluation of the advice. The feedback unit can also provide a function for the user to suggest improvements to the advice. For example, the feedback unit provides a text box for the user to input feedback on the content of the advice. This allows the user to provide feedback to the generation AI, thereby improving the system. Some or all of the above-described processing in the feedback unit may be performed using, or without using, the generation AI. For example, the feedback unit can input feedback entered by the user to the generation AI and have the generation AI analyze the feedback.

[0034] The legal chatbot system according to the embodiment includes a tracking unit that tracks the progress of a procedure. The tracking unit has a function for tracking the progress of the procedure. The progress of the procedure includes, but is not limited to, the progress of a lawsuit, the progress of an application, and the like. For example, the tracking unit provides an interface that allows a user to check the status of an ongoing procedure. The tracking unit can also provide a function for updating the progress of the procedure in real time. For example, the tracking unit periodically checks the progress of a lawsuit and provides the user with the latest information. This allows the user to easily understand the progress of the procedure. Some or all of the above-described processing in the tracking unit may be performed using, or without, a generation AI. For example, the tracking unit can input the progress of the procedure into the generation AI and have the generation AI analyze the progress.

[0035] The legal chatbot system according to the embodiment includes a protection unit that protects the user's privacy. The protection unit has a function for protecting the user's privacy. Privacy includes, but is not limited to, personal information and communication content, for example. The protection unit, for example, provides a function for encrypting the user's personal information. The protection unit can also provide a function for protecting the user's communication content. For example, the protection unit encrypts the user's communication content to prevent access by third parties. This protects the user's privacy. Some or all of the above-described processing in the protection unit may be performed using, or without, a generation AI. For example, the protection unit can input the user's personal information into the generation AI and have the generation AI perform encryption processing.

[0036] The reception unit can analyze the user's past question history and select a reception method. The reception unit has a function for analyzing the user's past question history and selecting an optimal reception method. The question history includes, for example, past question content and question frequency, but is not limited to these examples. The reception unit, for example, automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests a question format to be used in a specific time period based on the user's past question history. This makes it possible to provide an optimal reception method by analyzing the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history into the generation AI and have the generation AI select an optimal reception method.

[0037] The reception unit may filter questions based on the user's current legal situation and areas of interest when receiving the questions. The reception unit has a function for filtering questions based on the user's current legal situation and areas of interest when receiving the questions. Examples of legal situations include, but are not limited to, current litigation status and contract status. The reception unit may, for example, preferentially receive questions related to legal issues currently in progress by the user. The reception unit may also filter and display related questions based on the user's areas of interest. For example, the reception unit may suggest an appropriate question format depending on the user's legal situation. This allows related questions to be preferentially received based on the user's current legal situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's legal situation data into the generation AI and have the generation AI perform filtering.

[0038] The reception unit can select the optimal reception means depending on the user's input method when receiving a question. The reception unit has a function for selecting the optimal reception means depending on the user's input method when receiving a question. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs a question by voice, the reception unit allows the generation AI to receive the question using voice recognition technology. Furthermore, when a user inputs a question by text, the reception unit can also allow the generation AI to receive the question using text analysis technology. Furthermore, when a user inputs a question using an image, the reception unit can also allow the generation AI to receive the question using image analysis technology. This improves user convenience by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's input data into the generation AI and have the generation AI select the optimal reception means.

[0039] The reception unit may prioritize receiving highly relevant questions by taking into consideration the user's geographical location information when receiving a question. The reception unit may include a function for prioritized reception of highly relevant questions by taking into consideration the user's geographical location information when receiving a question. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific region, the reception unit may prioritize receiving legal questions related to that region. The reception unit may also prioritize receiving legal issues specific to the region based on the user's geographical location information. For example, the reception unit may filter and display relevant legal questions based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant questions.

[0040] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit has a function for analyzing the user's social media activity when receiving a question and receiving related questions. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The reception unit can, for example, receive related legal questions based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity and prioritize receiving related legal questions. Furthermore, the reception unit can also refer to the activity of the user's friends on social media to receive related legal questions. In this way, by analyzing the user's social media activity, related questions can be prioritized. Some or all of the above-described processing by the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related questions.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit has a function for customizing the reception method by reflecting the user's past feedback when receiving a question. Feedback includes, but is not limited to, for example, the user's evaluation and suggestions for improvement. The reception unit can propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the reception method. Furthermore, the reception unit can suggest an optimal question format by referring to the user's feedback history. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0042] The generation unit can adjust the level of detail of the advice based on the content of the legal issue when generating the advice. The generation unit has a function for adjusting the level of detail of the advice based on the content of the legal issue when generating the advice. The content of the legal issue includes, but is not limited to, for example, contract issues and litigation issues. The generation unit can provide detailed advice for, for example, legal issues of high importance. The generation unit can also provide concise advice for legal issues of low importance. For example, the generation unit adjusts the level of detail of the advice depending on the importance of the legal issue. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice depending on the importance of the legal issue. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input content data of the legal issue into the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0043] The generation unit can apply different generation algorithms depending on the category of the legal problem when generating advice. The generation unit has a function for applying different generation algorithms depending on the category of the legal problem when generating advice. Examples of generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the generation unit applies a specific algorithm to generate advice for legal issues related to divorce proceedings. The generation unit can also apply a different algorithm to generate advice for legal issues related to labor issues. For example, the generation unit selects an optimal generation algorithm depending on the category of the legal problem to generate advice. This makes it possible to provide more appropriate advice by applying the optimal generation algorithm depending on the category of the legal problem. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input legal issue category data into the generation AI and cause the generation AI to select a generation algorithm.

[0044] The generation unit can improve the accuracy of advice by referring to the user's past advice results when generating advice. The generation unit has a function for improving the accuracy of advice by referring to the user's past advice results when generating advice. Past advice results include, but are not limited to, success cases and failure cases. The generation unit, for example, provides optimal advice by referring to advice the user has received in the past. The generation unit can also analyze the user's past advice results and improve the accuracy of the advice. For example, the generation unit improves the accuracy of the advice based on user feedback. In this way, the accuracy of the advice is improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0045] The generation unit can determine the order of advice based on the submission time of the legal issue when generating the advice. The generation unit has a function for determining the order of advice based on the submission time of the legal issue when generating the advice. The submission time includes, but is not limited to, for example, the submission date, the deadline, etc. The generation unit, for example, provides advice preferentially for legal issues with high urgency. The generation unit can also provide advice preferentially for legal issues with an upcoming submission time. For example, the generation unit determines the priority of advice based on the submission time of the legal issue. This allows for prompt response to issues with high urgency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission time of the legal issue into the generation AI and have the generation AI determine the order of advice.

[0046] The generation unit can adjust the order of advice based on the relevance of legal issues when generating advice. The generation unit has a function for adjusting the order of advice based on the relevance of legal issues when generating advice. Relevance includes, but is not limited to, the same category, similar issues, etc. For example, the generation unit can provide advice preferentially for highly relevant legal issues. The generation unit can also provide advice later for less relevant legal issues. For example, the generation unit adjusts the order of advice based on the relevance of legal issues. In this way, by adjusting the order of advice based on the relevance of legal issues, more relevant advice can be provided preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, or without using, a generation AI. For example, the generation unit can input relevance data of legal issues into the generation AI and cause the generation AI to adjust the order of advice.

[0047] The generation unit can adjust the use of technical terms in the advice according to the user's level of expertise when generating the advice. The generation unit has a function for adjusting the use of technical terms in the advice according to the user's level of expertise when generating the advice. Examples of the level of expertise include, but are not limited to, survey results and past question content. For example, if the user does not have legal knowledge, the generation unit can provide the advice in simple language. Furthermore, if the user has legal knowledge, the generation unit can also provide the advice using technical terms. For example, the generation unit adjusts the use of technical terms in the advice according to the user's level of expertise. This allows the provision of advice that is easier to understand by adjusting the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the advice.

[0048] The providing unit can select the optimal advice providing method by referring to the user's past advice history when providing advice. The providing unit has a function for selecting the optimal advice providing method by referring to the user's past advice history when providing advice. The past advice history includes, for example, past consultation content, advice results, etc., but is not limited to such examples. The providing unit, for example, provides optimal advice by referring to advice the user has received in the past. The providing unit can also analyze the user's past advice history and optimize the advice providing method. For example, the providing unit improves the advice providing method based on user feedback. In this way, the optimal advice providing method can be selected by referring to the user's past advice history. Some or all of the above-described processing in the providing unit may be performed using, or without using, a generation AI. For example, the providing unit can input the user's past advice history data into the generation AI and cause the generation AI to select the optimal advice providing method.

[0049] The providing unit can customize the content of advice provided based on the user's current legal situation when providing advice. The providing unit has a function for customizing the content of advice provided based on the user's current legal situation when providing advice. Legal situations include, but are not limited to, current litigation status, contract status, etc. The providing unit, for example, provides appropriate advice for a legal issue currently in progress for the user. The providing unit can also provide customized advice based on the user's current legal situation. For example, the providing unit provides optimal advice depending on the user's legal situation. This allows for more appropriate advice to be provided by customizing the content of advice based on the user's current legal situation. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's legal situation data into the generation AI and cause the generation AI to customize the content of advice provided.

[0050] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. The providing unit has a function for improving the advice providing method by reflecting user feedback when providing advice. Feedback includes, for example, user evaluations and suggestions for improvement, but is not limited to these examples. The providing unit improves the advice providing method, for example, based on feedback provided by the user. The providing unit can also analyze the user feedback and optimize the advice providing method. Furthermore, the providing unit can suggest an optimal advice providing method by referring to the user's feedback history. In this way, the advice providing method can be improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the advice providing method.

[0051] The providing unit can select the optimal advice delivery method by taking into account the user's geographical location information when providing advice. The providing unit has a function for selecting the optimal advice delivery method by taking into account the user's geographical location information when providing advice. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the providing unit can provide legal advice related to that area. The providing unit can also provide legal advice specific to that area based on the user's geographical location information. Furthermore, the providing unit can provide relevant legal advice based on the user's current location. This allows the optimal advice delivery method to be selected based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal advice delivery method.

[0052] The providing unit can customize the advice content by analyzing the user's social media activity when providing advice. The providing unit has a function for analyzing the user's social media activity when providing advice and customizing the advice content. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The providing unit can provide relevant legal advice, for example, based on the content posted by the user on social media. The providing unit can also analyze the user's social media activity and provide relevant legal advice. Furthermore, the providing unit can provide relevant legal advice by referring to the activity of the user's friends on social media. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to customize the advice content.

[0053] The providing unit can customize the advice providing method by reflecting the user's past feedback when providing advice. The providing unit has a function for customizing the advice providing method by reflecting the user's past feedback when providing advice. Feedback includes, for example, the user's evaluation, suggestions for improvement, etc., but is not limited to these examples. The providing unit, for example, proposes an optimal advice providing method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the advice providing method. Furthermore, the providing unit can propose an optimal advice providing method by referring to the user's feedback history. In this way, the optimal advice providing method can be provided by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's feedback data into the generation AI and cause the generation AI to customize the advice providing method.

[0054] The update unit can optimize the update algorithm by referring to past update data during an update. The update unit has a function for optimizing the update algorithm by referring to past update data during an update. Update data includes, for example, past legal amendment information, changes in precedents, etc., but is not limited to these examples. The update unit, for example, analyzes the past update data and selects an optimal update algorithm. The update unit can also improve the update algorithm by referring to the past update data. For example, the update unit optimizes the update algorithm based on the past update data. In this way, the update algorithm can be optimized by referring to the past update data. Some or all of the above-mentioned processing in the update unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the update unit can input past update data into the generation AI and cause the generation AI to optimize the update algorithm.

[0055] The update unit can update the update data by reflecting user feedback during an update. The update unit has a function for updating the update data by reflecting user feedback during an update. Feedback includes, for example, user evaluations and suggestions for improvement, but is not limited to these examples. The update unit improves the update data, for example, based on feedback provided by the user. The update unit can also analyze user feedback and optimize the update data. For example, the update unit updates the update data by referring to the user's feedback history. In this way, the update data can be optimized by reflecting the user's feedback. Some or all of the above-described processing in the update unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the update unit inputs user feedback data into the generation AI and causes the generation AI to update the update data.

[0056] The update unit can weight the update data based on the submission date of the legal information during the update. The update unit has a function for weighting the update data based on the submission date of the legal information during the update. The submission date includes, but is not limited to, for example, the submission date or the deadline. For example, the update unit weights legal information that is due to be submitted soon and updates it preferentially. The update unit can also weight legal information that is due to be submitted further back and update it later. For example, the update unit weights the update data based on the submission date of the legal information. In this way, by weighting the update data based on the submission date of the legal information, more important information can be updated preferentially. Some or all of the above-mentioned processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input submission date data of the legal information into the generation AI and cause the generation AI to weight the update data.

[0057] The update unit can integrate information from different data sources to enrich the updated data during an update. The update unit has a function for integrating information from different data sources to enrich the updated data during an update. Data sources include, but are not limited to, a statute database, a case law database, etc. The update unit, for example, integrates information from different legal databases to enrich the updated data. The update unit can also integrate information from different legal cases to enrich the updated data. For example, the update unit integrates information from different legal information sources to enrich the updated data. This allows the updated data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the update unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the update unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0058] The feedback unit can select the optimal feedback reception method by referring to the user's past feedback history when receiving feedback. The feedback unit has a function for selecting the optimal feedback reception method by referring to the user's past feedback history when receiving feedback. Past feedback history includes, but is not limited to, past evaluations and suggestions for improvement. For example, the feedback unit suggests the optimal feedback reception method by referring to feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback history and optimize the feedback reception method. For example, the feedback unit selects the optimal feedback reception method based on the user's feedback history. In this way, the optimal feedback reception method can be selected by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI and cause the generation AI to select the optimal reception method.

[0059] The feedback unit can customize the feedback content based on the user's current legal situation when receiving the feedback. The feedback unit has a function for customizing the feedback content based on the user's current legal situation when receiving the feedback. Examples of legal situations include, but are not limited to, current litigation status, contract status, etc. The feedback unit, for example, provides an appropriate feedback reception method for the user's ongoing legal issue. The feedback unit can also provide a customized feedback reception method based on the user's current legal situation. For example, the feedback unit provides an optimal feedback reception method depending on the user's legal situation. This allows the feedback content to be customized based on the user's current legal situation, thereby providing more appropriate feedback. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may input the user's legal situation data into the generation AI and cause the generation AI to customize the feedback content.

[0060] The feedback unit can select the optimal reception method by taking into account the user's geographical location information when receiving feedback. The feedback unit has a function for selecting the optimal reception method by taking into account the user's geographical location information when receiving feedback. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the feedback unit may preferentially receive feedback related to that area. The feedback unit may also preferentially receive region-specific feedback based on the user's geographical location information. Furthermore, the feedback unit may filter and display related feedback based on the user's current location. This allows the optimal feedback reception method to be selected based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may input the user's geographical location data into the generation AI and cause the generation AI to select the optimal reception method.

[0061] The feedback unit may analyze the user's social media activity and customize the feedback content when receiving the feedback. The feedback unit may have a function for analyzing the user's social media activity and customizing the feedback content when receiving the feedback. Social media activity may include, but is not limited to, the content of posts and the number of followers. The feedback unit may, for example, accept relevant feedback based on the content posted by the user on social media. The feedback unit may also analyze the user's social media activity and preferentially accept relevant feedback. Furthermore, the feedback unit may accept relevant feedback based on the activity of the user's friends on social media. This allows relevant feedback to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may input the user's social media data into the generation AI and cause the generation AI to customize the feedback content.

[0062] The tracking unit can select the optimal tracking method by referring to the user's past procedural history when tracking the progress of a procedure. The tracking unit has a function for selecting the optimal tracking method by referring to the user's past procedural history when tracking the progress of a procedure. Past procedural history includes, but is not limited to, past litigation history, application history, etc. The tracking unit can, for example, propose the optimal tracking method based on the procedural history used by the user in the past. The tracking unit can also analyze the user's past procedural history and optimize the tracking method. For example, the tracking unit selects the optimal tracking method by referring to the user's procedural history. In this way, the optimal tracking method can be selected by referring to the user's past procedural history. Some or all of the above-described processing in the tracking unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the tracking unit can input the user's past procedural history data into the generation AI and cause the generation AI to select the optimal tracking method.

[0063] The tracking unit can customize tracking content based on the user's current legal situation when tracking the progress of a procedure. The tracking unit has a function for customizing tracking content based on the user's current legal situation when tracking the progress of a procedure. Legal situations include, but are not limited to, current litigation status, contract status, etc. The tracking unit, for example, provides tracking content appropriate for the user's ongoing legal issues. The tracking unit can also provide customized tracking content based on the user's current legal situation. For example, the tracking unit provides optimal tracking content depending on the user's legal situation. This enables more appropriate tracking by customizing the tracking content based on the user's current legal situation. Some or all of the above-described processing in the tracking unit may be performed using, or without, a generation AI. For example, the tracking unit may input the user's legal situation data into the generation AI and cause the generation AI to customize the tracking content.

[0064] The tracking unit can select the optimal tracking method by taking into account the user's geographical location information when tracking the progress of a procedure. The tracking unit has a function for selecting the optimal tracking method by taking into account the user's geographical location information when tracking the progress of a procedure. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the tracking unit prioritizes tracking of procedures related to that area. The tracking unit can also prioritize tracking of procedures specific to that area based on the user's geographical location information. Furthermore, the tracking unit can filter and display related procedures based on the user's current location. This allows the optimal tracking method to be selected based on the user's geographical location information. Some or all of the above-described processing in the tracking unit may be performed using, or without, a generation AI. For example, the tracking unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal tracking method.

[0065] The tracking unit, when tracking the progress of a procedure, can analyze the user's social media activity to customize the tracking content. The tracking unit has a function for analyzing the user's social media activity to customize the tracking content when tracking the progress of a procedure. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The tracking unit, for example, tracks related procedures based on the content posted by the user on social media. The tracking unit can also analyze the user's social media activity and prioritize tracking related procedures. Furthermore, the tracking unit can track related procedures based on the activity of the user's friends on social media. In this way, related procedures can be tracked by analyzing the user's social media activity. Some or all of the above-described processing in the tracking unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the tracking unit can input the user's social media data into the generation AI and have the generation AI customize the tracking content.

[0066] The protection unit can select the optimal protection method by referring to the user's past privacy setting history during privacy protection. The protection unit has a function for selecting the optimal protection method by referring to the user's past privacy setting history during privacy protection. The past privacy setting history includes, for example, but is not limited to, a history of past privacy setting changes and settings. The protection unit, for example, proposes the optimal protection method by referring to the privacy settings previously set by the user. The protection unit can also analyze the user's past privacy setting history and optimize the protection method. For example, the protection unit selects the optimal protection method based on the user's privacy setting history. In this way, the optimal privacy protection method can be selected by referring to the user's past privacy setting history. Some or all of the above-described processing in the protection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the protection unit can input the user's past privacy setting history data into the generation AI and have the generation AI select the optimal protection method.

[0067] The protection unit can customize the protection content based on the user's current legal situation during privacy protection. The protection unit has a function for customizing the protection content based on the user's current legal situation during privacy protection. Legal situations include, but are not limited to, current litigation status, contract status, etc. The protection unit can provide appropriate privacy protection for the user's ongoing legal issues, for example. The protection unit can also provide customized privacy protection based on the user's current legal situation. For example, the protection unit can provide optimal privacy protection depending on the user's legal situation. This enables more appropriate privacy protection by customizing the protection content based on the user's current legal situation. Some or all of the above-described processing in the protection unit can be performed using, or without, a generation AI. For example, the protection unit can input the user's legal situation data into the generation AI and have the generation AI customize the protection content.

[0068] The protection unit can select an optimal protection method during privacy protection by taking into account the user's geographical location information. The protection unit has a function for selecting an optimal protection method during privacy protection by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific region, the protection unit provides privacy protection related to the region. The protection unit can also provide region-specific privacy protection based on the user's geographical location information. Furthermore, the protection unit can provide relevant privacy protection based on the user's current location. This allows the optimal privacy protection method to be selected based on the user's geographical location information. Some or all of the above-described processing in the protection unit may be performed using, or without, a generation AI. For example, the protection unit can input the user's geographical location data into the generation AI and cause the generation AI to select an optimal protection method.

[0069] The protection unit can customize the protection content by analyzing the user's social media activity during privacy protection. The protection unit has a function for analyzing the user's social media activity and customizing the protection content during privacy protection. Social media activity includes, but is not limited to, post content and the number of followers. The protection unit can provide relevant privacy protection based on, for example, the content posted by the user on social media. The protection unit can also analyze the user's social media activity and provide relevant privacy protection. Furthermore, the protection unit can provide relevant privacy protection by referring to the activity of the user's friends on social media. In this way, relevant privacy protection can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the protection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the protection unit can input the user's social media data into the generation AI and have the generation AI customize the protection content.

[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 reception unit can analyze the user's history of past legal questions and suggest new related questions based on the user's previous questions. For example, if a user has previously asked a question about divorce proceedings, the reception unit can suggest questions about child support and property division based on that history. If a user has previously asked a question about contract creation, the reception unit can suggest questions about contract renewal or termination based on that history. Furthermore, if a user has previously asked a question about litigation, the reception unit can suggest questions about the progress of the litigation or next steps based on that history. This allows users to easily find new questions related to their legal issues and receive legal advice more efficiently.

[0072] When generating advice for a user's legal question, the generator can adjust the content of the advice taking into account the user's level of expertise. For example, if the user does not have legal knowledge, the generator can provide advice in simple language. Alternatively, if the user has legal knowledge, the generator can provide detailed advice using technical terms. Furthermore, if the user is knowledgeable in a particular legal field, the generator can provide advice specialized in that field. This allows the user to receive advice that is appropriate for their level of expertise and is easier to understand.

[0073] When updating legal information, the update unit can determine the priority of update data based on the user's current legal situation. For example, information about a lawsuit currently in progress can be updated with priority. Also, if the user plans to enter into a new contract, legal information related to that contract can be updated with priority. Furthermore, if the user is interested in a specific legal field, the latest legal information related to that field can be updated with priority. This allows the user to quickly obtain the latest information tailored to their legal situation and receive more appropriate legal advice.

[0074] When tracking the progress of a procedure, the tracking unit can select the optimal tracking method by referring to the user's past procedure history. For example, the tracking unit can suggest the optimal tracking method based on the procedure history used by the user in the past. The tracking unit can also analyze the user's past procedure history and optimize the tracking method. Furthermore, the tracking unit can select the optimal tracking method by referring to the user's past procedure history. In this way, the optimal tracking method can be selected by referring to the user's past procedure history, and the progress of the procedure can be efficiently grasped.

[0075] The reception unit can prioritize receiving questions regarding legal issues specific to a region, taking into account the user's geographical location information. For example, if the user is in a specific region, legal questions related to that region can be prioritized. Furthermore, legal issues specific to the region can also be prioritized based on the user's geographical location information. Furthermore, related legal questions can be filtered and displayed based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information, improving user convenience.

[0076] When generating advice, the generation unit can apply different generation algorithms depending on the category of the legal problem. For example, advice can be generated by applying a specific algorithm to a legal problem related to divorce proceedings. Also, advice can be generated by applying a different algorithm to a legal problem related to labor issues. Furthermore, advice can be generated by selecting the optimal generation algorithm for a legal problem related to a contract issue. In this way, more appropriate advice can be provided by applying the optimal generation algorithm depending on the category of the legal problem.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives legal questions from users. Legal questions from users include questions about contracts and lawsuits. The reception unit can receive text data and voice input from the user. In the case of voice input, the voice input is converted into text data using voice recognition technology. Step 2: The generation unit analyzes the question received by the reception unit and generates legal advice. The generation unit analyzes the question using natural language processing technology and machine learning algorithms, and generates appropriate legal advice by referring to legal databases and past cases. The generation AI extracts relevant information from the legal database and generates advice in response to the user's question. Step 3: The providing unit provides the generated advice to the user. The providing unit can display the generated advice in text format or can provide it in voice format using voice synthesis technology.

[0079] (Example 2) A legal chatbot system according to an embodiment of the present invention accepts legal questions from users, analyzes them with a generation AI, and provides legal advice. In this system, users input legal questions or problems, and a generation AI analyzes the questions or problems and generates appropriate legal advice, which is then provided to the user. For example, when a user inputs a question such as, "How do I file for divorce?", the generation AI provides detailed explanations of the necessary documents and procedural steps. The generated advice is provided to the user, who can then decide what to do. This reduces the cost and time required for ordinary people to resolve complex legal issues and increases accessibility. For example, this not only saves time and money by consulting a lawyer, but also allows users to obtain legal advice quickly. Furthermore, the generation AI is available 24 hours a day, allowing users to receive legal advice anytime, anywhere. Furthermore, the generation AI continuously learns and reflects the latest legal information. For example, when new laws or precedents are released, the generation AI can learn from them and incorporate them into the advice it provides to users. This allows the legal chatbot system to reduce the cost and time required for ordinary people to resolve complex legal issues and increase accessibility. For example, not only can users save time and money by not consulting a lawyer, but they can also receive legal advice quickly. Generative AI is also available 24 hours a day, allowing users to receive legal advice anytime, anywhere. Generative AI can also continuously learn and reflect the latest legal information. For example, when new laws or precedents are released, generative AI can learn from them and incorporate them into the advice it provides to users.

[0080] A legal chatbot system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives legal questions from a user. Examples of legal questions from a user include, but are not limited to, questions about contracts and litigation. The reception unit, for example, receives text data input by the user. The reception unit can also receive voice input. For example, when a user inputs a question by voice, the voice input can be converted into text data using voice recognition technology. The generation unit uses a generation AI to analyze the question received by the reception unit and generate legal advice. The generation unit analyzes the question using, for example, natural language processing technology or a machine learning algorithm. The generation unit can also generate appropriate legal advice by referring to a legal database or past cases. For example, the generation AI extracts relevant information from a legal database and generates advice in response to the user's question. The provision unit provides the advice generated by the generation unit to the user. The provision unit, for example, displays the generated advice in text format. The provision unit can also provide the generated advice in audio format. For example, the generated advice can be played back as voice using voice synthesis technology, allowing the legal chatbot system according to the embodiment to efficiently receive and analyze legal questions from users and provide advice.

[0081] The generation unit can generate legal advice by referring to a legal database or past cases. Legal databases include, but are not limited to, for example, a case precedent database and a statute database. For example, the generation unit can refer to the case precedent database to generate advice based on past court cases. The generation unit can also refer to a statute database to generate advice based on relevant laws and regulations. For example, the generation unit can analyze past court cases and provide appropriate advice in response to a user's question. The generation unit can also obtain the latest legal information from the statute database and update the advice in response to a user's question. In this way, the generation unit can provide more accurate legal advice by referring to a legal database or past cases.

[0082] The providing unit can provide a document template required by the user. Document templates include, but are not limited to, contracts, application forms, etc. The providing unit can provide, for example, a contract template required by the user. The providing unit can also provide an application template required by the user. For example, the providing unit selects and provides an appropriate document template based on information input by the user. This allows the user to easily obtain the document template required. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input information input by the user into the generation AI and have the generation AI select an appropriate document template.

[0083] The generation unit may include an update unit for continuously acquiring legal information. Examples of legal information include, but are not limited to, new laws and changes in legal precedents. For example, the update unit may periodically check a legal database to acquire new laws and changes in legal precedents. The update unit may also refer to legal news and specialized journals to acquire the latest legal information. For example, the update unit may periodically check legal news sites to acquire the latest legal information. This allows the generation unit to always provide advice that reflects the latest legal information. Some or all of the above-described processing in the update unit may be performed using, or without, the generation AI. For example, the update unit may input information acquired from legal news sites into the generation AI, causing the generation AI to analyze the latest legal information.

[0084] The legal chatbot system according to the embodiment includes a feedback unit that allows a user to provide feedback to the generation AI. The feedback unit has a function for the user to provide feedback to the generation AI. The feedback includes, for example, a user's evaluation and suggestions for improvement, but is not limited to these examples. For example, the feedback unit provides an interface for the user to input an evaluation of the advice. The feedback unit can also provide a function for the user to suggest improvements to the advice. For example, the feedback unit provides a text box for the user to input feedback on the content of the advice. This allows the user to provide feedback to the generation AI, thereby improving the system. Some or all of the above-described processing in the feedback unit may be performed using, or without using, the generation AI. For example, the feedback unit can input feedback entered by the user to the generation AI and have the generation AI analyze the feedback.

[0085] The legal chatbot system according to the embodiment includes a tracking unit that tracks the progress of a procedure. The tracking unit has a function for tracking the progress of the procedure. The progress of the procedure includes, but is not limited to, the progress of a lawsuit, the progress of an application, and the like. For example, the tracking unit provides an interface that allows a user to check the status of an ongoing procedure. The tracking unit can also provide a function for updating the progress of the procedure in real time. For example, the tracking unit periodically checks the progress of a lawsuit and provides the user with the latest information. This allows the user to easily understand the progress of the procedure. Some or all of the above-described processing in the tracking unit may be performed using, or without, a generation AI. For example, the tracking unit can input the progress of the procedure into the generation AI and have the generation AI analyze the progress.

[0086] The legal chatbot system according to the embodiment includes a protection unit that protects the user's privacy. The protection unit has a function for protecting the user's privacy. Privacy includes, but is not limited to, personal information and communication content, for example. The protection unit, for example, provides a function for encrypting the user's personal information. The protection unit can also provide a function for protecting the user's communication content. For example, the protection unit encrypts the user's communication content to prevent access by third parties. This protects the user's privacy. Some or all of the above-described processing in the protection unit may be performed using, or without, a generation AI. For example, the protection unit can input the user's personal information into the generation AI and have the generation AI perform encryption processing.

[0087] In a legal chatbot system according to an embodiment, a reception unit estimates a user's emotions and adjusts the timing of question reception based on the estimated user emotions. The reception unit includes a function for estimating a user's emotions and adjusting the timing of question reception based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the timing of question reception to be adjusted based on the user's emotions. For example, if the user is stressed, the reception unit can quickly accept questions to reduce the user's burden. Furthermore, if the user is relaxed, the reception unit can accept detailed questions and provide more in-depth advice. Furthermore, if the user is in a hurry, the reception unit can prioritize brief questions and provide prompt advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0088] The reception unit can analyze the user's past question history and select a reception method. The reception unit has a function for analyzing the user's past question history and selecting an optimal reception method. The question history includes, for example, past question content and question frequency, but is not limited to these examples. The reception unit, for example, automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests a question format to be used in a specific time period based on the user's past question history. This makes it possible to provide an optimal reception method by analyzing the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history into the generation AI and have the generation AI select an optimal reception method.

[0089] The reception unit may filter questions based on the user's current legal situation and areas of interest when receiving the questions. The reception unit has a function for filtering questions based on the user's current legal situation and areas of interest when receiving the questions. Examples of legal situations include, but are not limited to, current litigation status and contract status. The reception unit may, for example, preferentially receive questions related to legal issues currently in progress by the user. The reception unit may also filter and display related questions based on the user's areas of interest. For example, the reception unit may suggest an appropriate question format depending on the user's legal situation. This allows related questions to be preferentially received based on the user's current legal situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's legal situation data into the generation AI and have the generation AI perform filtering.

[0090] The reception unit can select the optimal reception means depending on the user's input method when receiving a question. The reception unit has a function for selecting the optimal reception means depending on the user's input method when receiving a question. Input methods include, but are not limited to, voice input, text input, and image input. For example, when a user inputs a question by voice, the reception unit allows the generation AI to receive the question using voice recognition technology. Furthermore, when a user inputs a question by text, the reception unit can also allow the generation AI to receive the question using text analysis technology. Furthermore, when a user inputs a question using an image, the reception unit can also allow the generation AI to receive the question using image analysis technology. This improves user convenience by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's input data into the generation AI and have the generation AI select the optimal reception means.

[0091] The reception unit can estimate the user's emotions and prioritize questions to be received based on the estimated user emotions. The reception unit has a function for estimating the user's emotions and prioritizing the questions to be received based on the estimated user emotions. Emotion estimation methods include, but are not limited to, facial expression recognition and voice analysis. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice and calculates an emotion score. This allows the priority of questions to be determined based on the user's emotions. For example, if the user is stressed, the reception unit can prioritize urgent questions. Also, if the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, if the user is in a hurry, the reception unit can prioritize concise questions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and have the generation AI determine the priority of questions.

[0092] The reception unit may prioritize receiving highly relevant questions by taking into consideration the user's geographical location information when receiving a question. The reception unit may include a function for prioritized reception of highly relevant questions by taking into consideration the user's geographical location information when receiving a question. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific region, the reception unit may prioritize receiving legal questions related to that region. The reception unit may also prioritize receiving legal issues specific to the region based on the user's geographical location information. For example, the reception unit may filter and display relevant legal questions based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant questions.

[0093] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. The reception unit has a function for analyzing the user's social media activity when receiving a question and receiving related questions. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The reception unit can, for example, receive related legal questions based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity and prioritize receiving related legal questions. Furthermore, the reception unit can also refer to the activity of the user's friends on social media to receive related legal questions. In this way, by analyzing the user's social media activity, related questions can be prioritized. Some or all of the above-described processing by the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related questions.

[0094] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit has a function for customizing the reception method by reflecting the user's past feedback when receiving a question. Feedback includes, but is not limited to, for example, the user's evaluation and suggestions for improvement. The reception unit can propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the reception method. Furthermore, the reception unit can suggest an optimal question format by referring to the user's feedback history. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0095] The generation unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user emotions. The generation unit has a function for estimating the user's emotions and adjusting the way the advice is presented based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the way the advice is presented to be adjusted based on the user's emotions. For example, if the user is stressed, the generation unit can provide concise and clear advice. On the other hand, if the user is relaxed, the generation unit can provide detailed and careful advice. Furthermore, if the user is in a hurry, the generation unit can provide quick and to-the-point advice. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0096] The generation unit can adjust the level of detail of the advice based on the content of the legal issue when generating the advice. The generation unit has a function for adjusting the level of detail of the advice based on the content of the legal issue when generating the advice. The content of the legal issue includes, but is not limited to, for example, contract issues and litigation issues. The generation unit can provide detailed advice for, for example, legal issues of high importance. The generation unit can also provide concise advice for legal issues of low importance. For example, the generation unit adjusts the level of detail of the advice depending on the importance of the legal issue. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice depending on the importance of the legal issue. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input content data of the legal issue into the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0097] The generation unit can apply different generation algorithms depending on the category of the legal problem when generating advice. The generation unit has a function for applying different generation algorithms depending on the category of the legal problem when generating advice. Examples of generation algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the generation unit applies a specific algorithm to generate advice for legal issues related to divorce proceedings. The generation unit can also apply a different algorithm to generate advice for legal issues related to labor issues. For example, the generation unit selects an optimal generation algorithm depending on the category of the legal problem to generate advice. This makes it possible to provide more appropriate advice by applying the optimal generation algorithm depending on the category of the legal problem. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input legal issue category data into the generation AI and cause the generation AI to select a generation algorithm.

[0098] The generation unit can improve the accuracy of advice by referring to the user's past advice results when generating advice. The generation unit has a function for improving the accuracy of advice by referring to the user's past advice results when generating advice. Past advice results include, but are not limited to, success cases and failure cases. The generation unit, for example, provides optimal advice by referring to advice the user has received in the past. The generation unit can also analyze the user's past advice results and improve the accuracy of the advice. For example, the generation unit improves the accuracy of the advice based on user feedback. In this way, the accuracy of the advice is improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0099] The generation unit can estimate the user's emotion and adjust the length of the advice based on the estimated user emotion. The generation unit has a function for estimating the user's emotion and adjusting the length of the advice based on the estimated user emotion. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the length of the advice to be adjusted according to the user's emotion. For example, if the user is stressed, the generation unit can provide concise and short advice. On the other hand, if the user is relaxed, the generation unit can provide detailed and long advice. Furthermore, if the user is in a hurry, the generation unit can provide quick and to-the-point advice. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0100] The generation unit can determine the order of advice based on the submission time of the legal issue when generating the advice. The generation unit has a function for determining the order of advice based on the submission time of the legal issue when generating the advice. The submission time includes, but is not limited to, for example, the submission date, the deadline, etc. The generation unit, for example, provides advice preferentially for legal issues with high urgency. The generation unit can also provide advice preferentially for legal issues with an upcoming submission time. For example, the generation unit determines the priority of advice based on the submission time of the legal issue. This allows for prompt response to issues with high urgency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission time of the legal issue into the generation AI and have the generation AI determine the order of advice.

[0101] The generation unit can adjust the order of advice based on the relevance of legal issues when generating advice. The generation unit has a function for adjusting the order of advice based on the relevance of legal issues when generating advice. Relevance includes, but is not limited to, the same category, similar issues, etc. For example, the generation unit can provide advice preferentially for highly relevant legal issues. The generation unit can also provide advice later for less relevant legal issues. For example, the generation unit adjusts the order of advice based on the relevance of legal issues. In this way, by adjusting the order of advice based on the relevance of legal issues, more relevant advice can be provided preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, or without using, a generation AI. For example, the generation unit can input relevance data of legal issues into the generation AI and cause the generation AI to adjust the order of advice.

[0102] The generation unit can adjust the use of technical terms in the advice according to the user's level of expertise when generating the advice. The generation unit has a function for adjusting the use of technical terms in the advice according to the user's level of expertise when generating the advice. Examples of the level of expertise include, but are not limited to, survey results and past question content. For example, if the user does not have legal knowledge, the generation unit can provide the advice in simple language. Furthermore, if the user has legal knowledge, the generation unit can also provide the advice using technical terms. For example, the generation unit adjusts the use of technical terms in the advice according to the user's level of expertise. This allows the provision of advice that is easier to understand by adjusting the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms in the advice.

[0103] The providing unit can estimate the user's emotions and adjust the advice provision method based on the estimated user emotions. The providing unit has a function for estimating the user's emotions and adjusting the advice provision method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the advice provision method to be adjusted according to the user's emotions. For example, if the user is stressed, the providing unit can provide concise and clear advice. On the other hand, if the user is relaxed, the providing unit can provide detailed and careful advice. Furthermore, if the user is in a hurry, the providing unit can provide quick and to-the-point advice. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the method of providing advice.

[0104] The providing unit can select the optimal advice providing method by referring to the user's past advice history when providing advice. The providing unit has a function for selecting the optimal advice providing method by referring to the user's past advice history when providing advice. The past advice history includes, for example, past consultation content, advice results, etc., but is not limited to such examples. The providing unit, for example, provides optimal advice by referring to advice the user has received in the past. The providing unit can also analyze the user's past advice history and optimize the advice providing method. For example, the providing unit improves the advice providing method based on user feedback. In this way, the optimal advice providing method can be selected by referring to the user's past advice history. Some or all of the above-described processing in the providing unit may be performed using, or without using, a generation AI. For example, the providing unit can input the user's past advice history data into the generation AI and cause the generation AI to select the optimal advice providing method.

[0105] The providing unit can customize the content of advice provided based on the user's current legal situation when providing advice. The providing unit has a function for customizing the content of advice provided based on the user's current legal situation when providing advice. Legal situations include, but are not limited to, current litigation status, contract status, etc. The providing unit, for example, provides appropriate advice for a legal issue currently in progress for the user. The providing unit can also provide customized advice based on the user's current legal situation. For example, the providing unit provides optimal advice depending on the user's legal situation. This allows for more appropriate advice to be provided by customizing the content of advice based on the user's current legal situation. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input the user's legal situation data into the generation AI and cause the generation AI to customize the content of advice provided.

[0106] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. The providing unit has a function for improving the advice providing method by reflecting user feedback when providing advice. Feedback includes, for example, user evaluations and suggestions for improvement, but is not limited to these examples. The providing unit improves the advice providing method, for example, based on feedback provided by the user. The providing unit can also analyze the user feedback and optimize the advice providing method. Furthermore, the providing unit can suggest an optimal advice providing method by referring to the user's feedback history. In this way, the advice providing method can be improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the advice providing method.

[0107] The providing unit can estimate the user's emotions and determine the order in which advice should be provided based on the estimated user emotions. The providing unit has a function for estimating the user's emotions and determining the order in which advice should be provided based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the order in which advice should be provided to be determined based on the user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing advice with a high degree of urgency. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed advice. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to determine the order in which advice should be provided.

[0108] The providing unit can select the optimal advice delivery method by taking into account the user's geographical location information when providing advice. The providing unit has a function for selecting the optimal advice delivery method by taking into account the user's geographical location information when providing advice. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the providing unit can provide legal advice related to that area. The providing unit can also provide legal advice specific to that area based on the user's geographical location information. Furthermore, the providing unit can provide relevant legal advice based on the user's current location. This allows the optimal advice delivery method to be selected based on the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal advice delivery method.

[0109] The providing unit can customize the advice content by analyzing the user's social media activity when providing advice. The providing unit has a function for analyzing the user's social media activity when providing advice and customizing the advice content. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The providing unit can provide relevant legal advice, for example, based on the content posted by the user on social media. The providing unit can also analyze the user's social media activity and provide relevant legal advice. Furthermore, the providing unit can provide relevant legal advice by referring to the activity of the user's friends on social media. In this way, relevant advice can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to customize the advice content.

[0110] The providing unit can customize the advice providing method by reflecting the user's past feedback when providing advice. The providing unit has a function for customizing the advice providing method by reflecting the user's past feedback when providing advice. Feedback includes, for example, the user's evaluation, suggestions for improvement, etc., but is not limited to these examples. The providing unit, for example, proposes an optimal advice providing method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and customize the advice providing method. Furthermore, the providing unit can propose an optimal advice providing method by referring to the user's feedback history. In this way, the optimal advice providing method can be provided by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's feedback data into the generation AI and cause the generation AI to customize the advice providing method.

[0111] The update unit can estimate the user's emotions and select update data based on the estimated user emotions. The update unit has a function for estimating the user's emotions and selecting update data based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the update unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The update unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the update unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows update data to be selected based on the user's emotions. For example, if the user is stressed, the update unit can prioritize providing important update data. Also, if the user is relaxed, the update unit can provide detailed update data. Furthermore, if the user is in a hurry, the update unit can provide concise update data. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 update unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the update unit may input user emotion data into the generation AI and cause the generation AI to select update data.

[0112] The update unit can optimize the update algorithm by referring to past update data during an update. The update unit has a function for optimizing the update algorithm by referring to past update data during an update. Update data includes, for example, past legal amendment information, changes in precedents, etc., but is not limited to these examples. The update unit, for example, analyzes the past update data and selects an optimal update algorithm. The update unit can also improve the update algorithm by referring to the past update data. For example, the update unit optimizes the update algorithm based on the past update data. In this way, the update algorithm can be optimized by referring to the past update data. Some or all of the above-mentioned processing in the update unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the update unit can input past update data into the generation AI and cause the generation AI to optimize the update algorithm.

[0113] The update unit can update the update data by reflecting user feedback during an update. The update unit has a function for updating the update data by reflecting user feedback during an update. Feedback includes, for example, user evaluations and suggestions for improvement, but is not limited to these examples. The update unit improves the update data, for example, based on feedback provided by the user. The update unit can also analyze user feedback and optimize the update data. For example, the update unit updates the update data by referring to the user's feedback history. In this way, the update data can be optimized by reflecting the user's feedback. Some or all of the above-described processing in the update unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the update unit inputs user feedback data into the generation AI and causes the generation AI to update the update data.

[0114] The update unit can estimate the user's emotion and adjust the update frequency based on the estimated user's emotion. The update unit has a function for estimating the user's emotion and adjusting the update frequency based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the update unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The update unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the update unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the update frequency to be adjusted according to the user's emotion. For example, if the user is stressed, the update unit can set the update frequency low. Also, if the user is relaxed, the update unit can set the update frequency high. Furthermore, if the user is in a hurry, the update unit can adjust the update frequency. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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 update unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the update unit may input user emotion data into the generation AI and cause the generation AI to adjust the update frequency.

[0115] The update unit can weight the update data based on the submission date of the legal information during the update. The update unit has a function for weighting the update data based on the submission date of the legal information during the update. The submission date includes, but is not limited to, for example, the submission date or the deadline. For example, the update unit weights legal information that is due to be submitted soon and updates it preferentially. The update unit can also weight legal information that is due to be submitted further back and update it later. For example, the update unit weights the update data based on the submission date of the legal information. In this way, by weighting the update data based on the submission date of the legal information, more important information can be updated preferentially. Some or all of the above-mentioned processing in the update unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the update unit can input submission date data of the legal information into the generation AI and cause the generation AI to weight the update data.

[0116] The update unit can integrate information from different data sources to enrich the updated data during an update. The update unit has a function for integrating information from different data sources to enrich the updated data during an update. Data sources include, but are not limited to, a statute database, a case law database, etc. The update unit, for example, integrates information from different legal databases to enrich the updated data. The update unit can also integrate information from different legal cases to enrich the updated data. For example, the update unit integrates information from different legal information sources to enrich the updated data. This allows the updated data to be enriched by integrating information from different data sources. Some or all of the above-described processing in the update unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the update unit can input information from different data sources into the generation AI and cause the generation AI to integrate the information.

[0117] The feedback unit can estimate the user's emotion and adjust the feedback acceptance method based on the estimated user's emotion. The feedback unit has a function for estimating the user's emotion and adjusting the feedback acceptance method based on the estimated user's emotion. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The feedback unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the feedback acceptance method to be adjusted depending on the user's emotion. For example, if the user is stressed, the feedback unit can provide a concise feedback acceptance method. On the other hand, if the user is relaxed, the feedback unit can provide a detailed feedback acceptance method. Furthermore, if the user is in a hurry, the feedback unit can provide a quick feedback acceptance method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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 feedback unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback unit may input user emotion data into the generation AI and cause the generation AI to adjust the method of receiving feedback.

[0118] The feedback unit can select the optimal feedback reception method by referring to the user's past feedback history when receiving feedback. The feedback unit has a function for selecting the optimal feedback reception method by referring to the user's past feedback history when receiving feedback. Past feedback history includes, but is not limited to, past evaluations and suggestions for improvement. For example, the feedback unit suggests the optimal feedback reception method by referring to feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback history and optimize the feedback reception method. For example, the feedback unit selects the optimal feedback reception method based on the user's feedback history. In this way, the optimal feedback reception method can be selected by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI and cause the generation AI to select the optimal reception method.

[0119] The feedback unit can customize the feedback content based on the user's current legal situation when receiving the feedback. The feedback unit has a function for customizing the feedback content based on the user's current legal situation when receiving the feedback. Examples of legal situations include, but are not limited to, current litigation status, contract status, etc. The feedback unit, for example, provides an appropriate feedback reception method for the user's ongoing legal issue. The feedback unit can also provide a customized feedback reception method based on the user's current legal situation. For example, the feedback unit provides an optimal feedback reception method depending on the user's legal situation. This allows the feedback content to be customized based on the user's current legal situation, thereby providing more appropriate feedback. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may input the user's legal situation data into the generation AI and cause the generation AI to customize the feedback content.

[0120] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated user emotions. The feedback unit has a function for estimating the user's emotions and prioritizing feedback based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The feedback unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the priority of feedback to be determined based on the user's emotions. For example, if the user is stressed, the feedback unit can prioritize urgent feedback. Also, if the user is relaxed, the feedback unit can prioritize detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can prioritize concise feedback. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 feedback unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the feedback unit may input user emotion data into the generation AI and have the generation AI determine the priority of the feedback.

[0121] The feedback unit can select the optimal reception method by taking into account the user's geographical location information when receiving feedback. The feedback unit has a function for selecting the optimal reception method by taking into account the user's geographical location information when receiving feedback. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the feedback unit may preferentially receive feedback related to that area. The feedback unit may also preferentially receive region-specific feedback based on the user's geographical location information. Furthermore, the feedback unit may filter and display related feedback based on the user's current location. This allows the optimal feedback reception method to be selected based on the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may input the user's geographical location data into the generation AI and cause the generation AI to select the optimal reception method.

[0122] The feedback unit may analyze the user's social media activity and customize the feedback content when receiving the feedback. The feedback unit may have a function for analyzing the user's social media activity and customizing the feedback content when receiving the feedback. Social media activity may include, but is not limited to, the content of posts and the number of followers. The feedback unit may, for example, accept relevant feedback based on the content posted by the user on social media. The feedback unit may also analyze the user's social media activity and preferentially accept relevant feedback. Furthermore, the feedback unit may accept relevant feedback based on the activity of the user's friends on social media. This allows relevant feedback to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit may input the user's social media data into the generation AI and cause the generation AI to customize the feedback content.

[0123] The tracking unit can estimate the user's emotions and adjust the display method of the procedure progress status based on the estimated user emotions. The tracking unit has a function for estimating the user's emotions and adjusting the display method of the procedure progress status based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the tracking unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The tracking unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the tracking unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the display method of the procedure progress status to be adjusted according to the user's emotions. For example, if the user is stressed, the tracking unit can display a concise and clear progress status. On the other hand, if the user is relaxed, the tracking unit can display a detailed progress status. Furthermore, if the user is in a hurry, the tracking unit can display a quick and concise progress status. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 tracking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the tracking unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method.

[0124] The tracking unit can select the optimal tracking method by referring to the user's past procedural history when tracking the progress of a procedure. The tracking unit has a function for selecting the optimal tracking method by referring to the user's past procedural history when tracking the progress of a procedure. Past procedural history includes, but is not limited to, past litigation history, application history, etc. The tracking unit can, for example, propose the optimal tracking method based on the procedural history used by the user in the past. The tracking unit can also analyze the user's past procedural history and optimize the tracking method. For example, the tracking unit selects the optimal tracking method by referring to the user's procedural history. In this way, the optimal tracking method can be selected by referring to the user's past procedural history. Some or all of the above-described processing in the tracking unit may be performed, for example, using a generation AI or may be performed without using the generation AI. For example, the tracking unit can input the user's past procedural history data into the generation AI and cause the generation AI to select the optimal tracking method.

[0125] The tracking unit can customize tracking content based on the user's current legal situation when tracking the progress of a procedure. The tracking unit has a function for customizing tracking content based on the user's current legal situation when tracking the progress of a procedure. Legal situations include, but are not limited to, current litigation status, contract status, etc. The tracking unit, for example, provides tracking content appropriate for the user's ongoing legal issues. The tracking unit can also provide customized tracking content based on the user's current legal situation. For example, the tracking unit provides optimal tracking content depending on the user's legal situation. This enables more appropriate tracking by customizing the tracking content based on the user's current legal situation. Some or all of the above-described processing in the tracking unit may be performed using, or without, a generation AI. For example, the tracking unit may input the user's legal situation data into the generation AI and cause the generation AI to customize the tracking content.

[0126] The tracking unit can estimate the user's emotions and prioritize the progress of procedures based on the estimated user emotions. The tracking unit has a function for estimating the user's emotions and prioritizing the progress of procedures based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the tracking unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The tracking unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the tracking unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the priority of the progress of procedures to be determined based on the user's emotions. For example, if the user is stressed, the tracking unit can prioritize urgent procedures. Also, if the user is relaxed, the tracking unit can prioritize detailed procedures. Furthermore, if the user is in a hurry, the tracking unit can prioritize simple procedures. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 tracking unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the tracking unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of the progress of procedures.

[0127] The tracking unit can select the optimal tracking method by taking into account the user's geographical location information when tracking the progress of a procedure. The tracking unit has a function for selecting the optimal tracking method by taking into account the user's geographical location information when tracking the progress of a procedure. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the tracking unit prioritizes tracking of procedures related to that area. The tracking unit can also prioritize tracking of procedures specific to that area based on the user's geographical location information. Furthermore, the tracking unit can filter and display related procedures based on the user's current location. This allows the optimal tracking method to be selected based on the user's geographical location information. Some or all of the above-described processing in the tracking unit may be performed using, or without, a generation AI. For example, the tracking unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal tracking method.

[0128] The tracking unit, when tracking the progress of a procedure, can analyze the user's social media activity to customize the tracking content. The tracking unit has a function for analyzing the user's social media activity to customize the tracking content when tracking the progress of a procedure. Social media activity includes, but is not limited to, for example, the content of posts and the number of followers. The tracking unit, for example, tracks related procedures based on the content posted by the user on social media. The tracking unit can also analyze the user's social media activity and prioritize tracking related procedures. Furthermore, the tracking unit can track related procedures based on the activity of the user's friends on social media. In this way, related procedures can be tracked by analyzing the user's social media activity. Some or all of the above-described processing in the tracking unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the tracking unit can input the user's social media data into the generation AI and have the generation AI customize the tracking content.

[0129] The protection unit can estimate a user's emotions and adjust the privacy protection method based on the estimated user emotions. The protection unit has a function for estimating a user's emotions and adjusting the privacy protection method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the protection unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The protection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the protection unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the privacy protection method to be adjusted according to the user's emotions. For example, if the user is stressed, the protection unit can provide strong privacy protection. If the user is relaxed, the protection unit can provide flexible privacy protection. Furthermore, if the user is in a hurry, the protection unit can provide quick privacy protection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 protection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the protection unit may input user emotion data into the generation AI and cause the generation AI to adjust the privacy protection method.

[0130] The protection unit can select the optimal protection method by referring to the user's past privacy setting history during privacy protection. The protection unit has a function for selecting the optimal protection method by referring to the user's past privacy setting history during privacy protection. The past privacy setting history includes, for example, but is not limited to, a history of past privacy setting changes and settings. The protection unit, for example, proposes the optimal protection method by referring to the privacy settings previously set by the user. The protection unit can also analyze the user's past privacy setting history and optimize the protection method. For example, the protection unit selects the optimal protection method based on the user's privacy setting history. In this way, the optimal privacy protection method can be selected by referring to the user's past privacy setting history. Some or all of the above-described processing in the protection unit may be performed, for example, using a generation AI or without using a generation AI. For example, the protection unit can input the user's past privacy setting history data into the generation AI and have the generation AI select the optimal protection method.

[0131] The protection unit can customize the protection content based on the user's current legal situation during privacy protection. The protection unit has a function for customizing the protection content based on the user's current legal situation during privacy protection. Legal situations include, but are not limited to, current litigation status, contract status, etc. The protection unit can provide appropriate privacy protection for the user's ongoing legal issues, for example. The protection unit can also provide customized privacy protection based on the user's current legal situation. For example, the protection unit can provide optimal privacy protection depending on the user's legal situation. This enables more appropriate privacy protection by customizing the protection content based on the user's current legal situation. Some or all of the above-described processing in the protection unit can be performed using, or without, a generation AI. For example, the protection unit can input the user's legal situation data into the generation AI and have the generation AI customize the protection content.

[0132] The protection unit can estimate a user's emotions and determine the priority of privacy protection based on the estimated user emotions. The protection unit has a function for estimating a user's emotions and determining the priority of privacy protection based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition and voice analysis. For example, the protection unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The protection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the protection unit can analyze the tone and speed of the user's voice and calculate an emotion score. This allows the priority of privacy protection to be determined based on the user's emotions. For example, if the user is stressed, the protection unit can prioritize providing privacy protection with a high level of urgency. Also, if the user is relaxed, the protection unit can prioritize providing detailed privacy protection. Furthermore, if the user is in a hurry, the protection unit can prioritize providing concise privacy protection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 protection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the protection unit may input user emotion data into the generation AI and have the generation AI determine the priority of privacy protection.

[0133] The protection unit can select an optimal protection method during privacy protection by taking into account the user's geographical location information. The protection unit has a function for selecting an optimal protection method during privacy protection by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific region, the protection unit provides privacy protection related to the region. The protection unit can also provide region-specific privacy protection based on the user's geographical location information. Furthermore, the protection unit can provide relevant privacy protection based on the user's current location. This allows the optimal privacy protection method to be selected based on the user's geographical location information. Some or all of the above-described processing in the protection unit may be performed using, or without, a generation AI. For example, the protection unit can input the user's geographical location data into the generation AI and cause the generation AI to select an optimal protection method.

[0134] The protection unit can customize the protection content by analyzing the user's social media activity during privacy protection. The protection unit has a function for analyzing the user's social media activity and customizing the protection content during privacy protection. Social media activity includes, but is not limited to, post content and the number of followers. The protection unit can provide relevant privacy protection based on, for example, the content posted by the user on social media. The protection unit can also analyze the user's social media activity and provide relevant privacy protection. Furthermore, the protection unit can provide relevant privacy protection by referring to the activity of the user's friends on social media. In this way, relevant privacy protection can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the protection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the protection unit can input the user's social media data into the generation AI and have the generation AI customize the protection content. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, provision unit, feedback unit, tracking unit, protection unit, and emotion estimation function, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive legal questions from a user using the reception device 38 or microphone 38B of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI to generate legal advice. The provision unit provides the generated advice to the user using the output device 40 of the smart device 14. The feedback unit receives feedback from the user using the touch panel 38A of the smart device 14. The tracking unit tracks the progress of the procedure using the specific processing unit 290 of the data processing device 12 and provides information to the user using the display 40A of the smart device 14. The protection unit encrypts the user's personal information using the specific processing unit 290 of the data processing device 12 and securely communicates via the communication I / F 26. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 38B of the smart device 14, and the reception unit adjusts the timing of receiving questions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, feedback unit, tracking unit, protection unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive legal questions from a user using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI to generate legal advice. The provision unit provides the generated advice to the user using the speaker 240 of the smart glasses 214. The feedback unit receives feedback from the user using the microphone 238 of the smart glasses 214. The tracking unit tracks the progress of the procedure using the specific processing unit 290 of the data processing device 12 and provides information to the user using the display of the smart glasses 214. The protection unit encrypts the user's personal information using the specific processing unit 290 of the data processing device 12 and securely communicates via the communication I / F 26. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the smart glasses 214, and the reception unit adjusts the timing of receiving questions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, feedback unit, tracking unit, protection unit, and emotion estimation function, 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 can receive legal questions from a user using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI to generate legal advice. The provision unit provides the generated advice to the user using the speaker 240 of the headset-type terminal 314. The feedback unit receives feedback from the user using the microphone 238 of the headset-type terminal 314. The tracking unit tracks the progress of the procedure using the specific processing unit 290 of the data processing device 12 and provides information to the user using the display 343 of the headset-type terminal 314. The protection unit encrypts the user's personal information using the specific processing unit 290 of the data processing device 12 and securely communicates via the communication I / F 26. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the headset terminal 314, and the reception unit adjusts the timing of receiving questions. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, feedback unit, tracking unit, protection unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive legal questions from a user using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI to generate legal advice. The provision unit provides the generated advice to the user using the speaker 240 of the robot 414. The feedback unit receives feedback from the user using the microphone 238 of the robot 414. The tracking unit tracks the progress of the procedure using the specific processing unit 290 of the data processing device 12 and provides information to the user using the display of the robot 414. The protection unit encrypts the user's personal information using the specific processing unit 290 of the data processing device 12 and securely communicates via the communication I / F 26. The emotion estimation function estimates the user's emotion using the camera 42 and microphone 238 of the robot 414, and the reception unit adjusts the timing of receiving questions.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The reception unit can analyze the user's history of past legal questions and suggest new related questions based on the user's previous questions. For example, if a user has previously asked a question about divorce proceedings, the reception unit can suggest questions about child support and property division based on that history. If a user has previously asked a question about contract creation, the reception unit can suggest questions about contract renewal or termination based on that history. Furthermore, if a user has previously asked a question about litigation, the reception unit can suggest questions about the progress of the litigation or next steps based on that history. This allows users to easily find new questions related to their legal issues and receive legal advice more efficiently.

[0137] When generating advice for a user's legal question, the generator can adjust the content of the advice taking into account the user's level of expertise. For example, if the user does not have legal knowledge, the generator can provide advice in simple language. Alternatively, if the user has legal knowledge, the generator can provide detailed advice using technical terms. Furthermore, if the user is knowledgeable in a particular legal field, the generator can provide advice specialized in that field. This allows the user to receive advice that is appropriate for their level of expertise and is easier to understand.

[0138] The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide concise and clear advice. If the user is relaxed, the providing unit can provide detailed and careful advice. Furthermore, if the user is in a hurry, the providing unit can provide quick and to-the-point advice. This makes it possible to select the optimal method of providing advice according to the user's emotions, thereby improving user satisfaction.

[0139] When updating legal information, the update unit can determine the priority of update data based on the user's current legal situation. For example, information about a lawsuit currently in progress can be updated with priority. Also, if the user plans to enter into a new contract, legal information related to that contract can be updated with priority. Furthermore, if the user is interested in a specific legal field, the latest legal information related to that field can be updated with priority. This allows the user to quickly obtain the latest information tailored to their legal situation and receive more appropriate legal advice.

[0140] The feedback unit can estimate the user's emotion and adjust the feedback reception method based on the estimated user's emotion. For example, if the user is stressed, the feedback unit can provide a simple feedback reception method. If the user is relaxed, the feedback unit can provide a detailed feedback reception method. If the user is in a hurry, the feedback unit can provide a quick feedback reception method. This makes it possible to select the optimal feedback reception method according to the user's emotion, thereby improving user satisfaction.

[0141] When tracking the progress of a procedure, the tracking unit can select the optimal tracking method by referring to the user's past procedure history. For example, the tracking unit can suggest the optimal tracking method based on the procedure history used by the user in the past. The tracking unit can also analyze the user's past procedure history and optimize the tracking method. Furthermore, the tracking unit can select the optimal tracking method by referring to the user's past procedure history. In this way, the optimal tracking method can be selected by referring to the user's past procedure history, and the progress of the procedure can be efficiently grasped.

[0142] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated user's emotions. For example, if the user is stressed, the protection unit can provide strong privacy protection. If the user is relaxed, the protection unit can provide flexible privacy protection. If the user is in a hurry, the protection unit can provide quick privacy protection. This makes it possible to select the optimal privacy protection method according to the user's emotions, thereby effectively protecting the user's privacy.

[0143] The reception unit can prioritize receiving questions regarding legal issues specific to a region, taking into account the user's geographical location information. For example, if the user is in a specific region, legal questions related to that region can be prioritized. Furthermore, legal issues specific to the region can also be prioritized based on the user's geographical location information. Furthermore, related legal questions can be filtered and displayed based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information, improving user convenience.

[0144] When generating advice, the generation unit can apply different generation algorithms depending on the category of the legal problem. For example, advice can be generated by applying a specific algorithm to a legal problem related to divorce proceedings. Also, advice can be generated by applying a different algorithm to a legal problem related to labor issues. Furthermore, advice can be generated by selecting the optimal generation algorithm for a legal problem related to a contract issue. In this way, more appropriate advice can be provided by applying the optimal generation algorithm depending on the category of the legal problem.

[0145] The providing unit can estimate the user's emotions and determine the order in which advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing advice that is more urgent. Also, if the user is relaxed, the providing unit can prioritize providing detailed advice. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise advice. This makes it possible to select the optimal order in which advice is provided according to the user's emotions, thereby improving user satisfaction.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The reception unit receives legal questions from users. Legal questions from users include questions about contracts and lawsuits. The reception unit can receive text data and voice input from the user. In the case of voice input, the voice input is converted into text data using voice recognition technology. Step 2: The generation unit analyzes the question received by the reception unit and generates legal advice. The generation unit analyzes the question using natural language processing technology and machine learning algorithms, and generates appropriate legal advice by referring to legal databases and past cases. The generation AI extracts relevant information from the legal database and generates advice in response to the user's question. Step 3: The providing unit provides the generated advice to the user. The providing unit can display the generated advice in text format or can provide it in voice format using voice synthesis technology.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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 for receiving legal questions from users; a generation unit that analyzes the question received by the reception unit and generates legal advice; a providing unit that provides the advice generated by the generating unit. A system characterized by:

2. The generation unit Generate legal advice by consulting legal databases and past cases 2. The system of claim 1.

3. The providing unit Provide users with templates for the documents they need 2. The system of claim 1.

4. The generation unit Includes an update section to keep you up to date with legal information 2. The system of claim 1.

5. A feedback section is provided whereby the user provides feedback to the generated AI.

2. The system of claim 1.

6. Equipped with a tracking section to track the progress of procedures 2. The system of claim 1.

7. Equipped with a protection section to protect user privacy 2. The system of claim 1.

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

2. The system of claim 1.

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