system

Abstract

The system according to this embodiment aims to provide prompt and appropriate answers to legal and specialized questions. [Solution] The system according to this embodiment comprises 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 appropriate answers. The provision unit provides the answers generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to quickly and appropriately answer specialized legal questions, and there is a problem that the burden on the legal department is large.

[0005] The system according to the embodiment aims to quickly and appropriately answer specialized legal questions.

Means for Solving the Problems

[0006] The system according to the embodiment 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 an appropriate answer. The provision unit provides the answer generated by the generation unit to the user.

Effects of the Invention

[0007] The system according to this embodiment can quickly and appropriately answer specialized legal questions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The legal support system according to an embodiment of the present invention is an AI chatbot for quickly and appropriately answering specialized legal questions. This legal support system allows users to input legal questions, and the generating AI analyzes the questions based on a comprehensive legal database and the latest legal case databases to generate appropriate answers. This AI chatbot is available 24 hours a day, contributing to reducing the burden on the legal department and simultaneously improving overall corporate risk management. It also collects specific legal themes and matters for each company, contributing to improved risk management for each employee. For example, a user inputs a legal question, such as, "Please explain a specific clause in a contract." This question is input to the generating AI. Next, the generating AI analyzes the input question. The generating AI analyzes the question based on a comprehensive legal database and the latest legal case databases to generate appropriate answers. For example, in response to a question about a specific clause in a contract, it generates an answer based on relevant laws and cases. The generated answer is provided to the user. For example, in response to a question about a specific clause in a contract, the answer generated by the generating AI is displayed to the user. This allows the user to obtain quick and appropriate answers. This system reduces the burden on the legal department. The legal department will no longer need to handle routine legal consultations, allowing them to focus on more important tasks. It also contributes to improving overall corporate risk management. For example, by having each employee input legal questions into the AI ​​chatbot, specific legal themes and matters are collected, improving overall corporate risk management. Furthermore, because the AI ​​chatbot is available 24 hours a day, it streamlines legal consultations and enables rapid responses to legal issues. For example, users can get quick answers to legal questions even late at night or on holidays. In this way, a legal support chatbot utilizing generative AI reduces the burden on the legal department, improves overall corporate risk management, streamlines legal consultations, and enables rapid responses to legal issues. As a result, the legal support system can answer users' legal questions quickly and appropriately.

[0029] The legal support system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives legal questions from users. For example, the reception unit can receive questions entered by users in text format. The reception unit can also accept voice input. For example, users can input questions by voice using a microphone. Furthermore, the reception unit can also accept image input. For example, users can upload an image of a contract to input a question. The generation unit analyzes the questions received by the reception unit and generates appropriate answers. The generation unit analyzes the questions using generation AI. For example, the generation unit analyzes the questions based on the complete collection of laws and the latest legal case databases and generates appropriate answers. The generation unit can generate answers using text generation AI (e.g., LLM). The generation unit can also generate answers using multimodal generation AI. For example, the generation unit can generate answers that combine text and images. The provision unit provides the answers generated by the generation unit to the user. The provision unit can provide the generated answers in text format. For example, the provision unit displays the answers on the user's screen. Furthermore, the system can provide answers in audio format. For example, it can use speech synthesis technology to play back the answer as audio. In addition, the system can provide answers in image format. For example, it can display the answer as an image. As a result, the legal support system according to this embodiment can quickly and appropriately answer the user's legal questions.

[0030] The reception desk receives legal inquiries from users. For example, the reception desk can receive user-submitted questions in text format. Specifically, when a user enters a question into a web form or chat box, that text data is sent to the reception desk. The reception desk can also accept voice input. For example, a user can use a microphone to input a question by voice. In this case, voice recognition technology is used to convert the voice data into text data and send it to the reception desk. Furthermore, the reception desk can also accept image input. For example, a user can upload an image of a contract and input a question. In this case, image recognition technology is used to analyze the image data and convert it into text data. This allows the reception desk to accept questions in various formats from users. In addition, the reception desk has a function to automatically classify user input and distribute it to the appropriate processing department. For example, questions about contracts can be distributed to the contract law department, and questions about labor issues can be distributed to the labor law department. This allows the reception desk to process user inquiries quickly and accurately. The reception desk can also refer to a user's past question history and refer to past answers if similar questions have been asked. This allows the reception desk to provide users with consistent answers.

[0031] The generation unit analyzes questions received by the reception unit and generates appropriate answers. The generation unit analyzes questions using generation AI. Specifically, the generation unit analyzes questions based on the complete legal code and the latest legal case databases and generates appropriate answers. The generation unit can generate answers using text generation AI (e.g., LLM). LLM has learned from a large amount of legal documents and case data and has the ability to generate appropriate answers to user questions. For example, if a user asks, "Please tell me how to terminate a contract," the generation unit will refer to relevant legal articles and past cases and generate an answer with a specific termination method. The generation unit can also generate answers using multimodal generation AI. For example, the generation unit can generate answers that combine text and images. If a user asks about a specific clause in a contract, it can generate an answer that includes an image of that clause. Furthermore, the generation unit can adjust the format and level of detail of the answer according to the content of the user's question. For example, it can provide detailed legal articles and cases to users with specialized legal knowledge and provide easy-to-understand explanations to general users. This allows the generation unit to quickly generate appropriate responses that meet the user's needs.

[0032] The providing unit provides users with answers generated by the generating unit. The providing unit can provide the generated answers in text format. For example, the providing unit displays the answers on the user's screen. Specifically, it displays the answers to the user's questions in text format via a web browser or mobile app. The providing unit can also provide answers in audio format. For example, the providing unit plays the answers aloud using speech synthesis technology. This allows for the use of users with visual impairments or those whose hands are occupied. Furthermore, the providing unit can provide answers in image format. For example, the providing unit displays the answers as images. For questions about specific clauses in a contract, it can provide answers that include images of those clauses. This allows users to visually confirm the information. In addition, the providing unit can collect user feedback and continuously improve the accuracy and quality of the answers. For example, users can input ratings and comments on the provided answers, and the generating unit's algorithm is adjusted based on that feedback. The providing unit can also reliably transmit information using multiple communication methods. For example, it is possible to send answers via email or SMS. This allows the service provider to provide users with quick and reliable answers, thereby improving the usability of the legal support system.

[0033] The legal support system includes a data collection unit that collects specific legal themes and matters for each company. The data collection unit can, for example, collect information provided by the company's legal department. It can also collect information provided by various departments within the company. For example, it can collect information on contracts provided by the sales department. Furthermore, it can collect information provided by external legal experts. For example, it can collect the latest legal cases provided by law firms. This contributes to improving the risk management of each employee by collecting specific legal themes and matters for each company. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information provided by the company's legal department into the AI ​​and have the AI ​​perform the information collection.

[0034] The data collection unit can collect information that contributes to improving each employee's risk management. For example, the data collection unit can collect information on legal risks. For example, it can collect information on legal risks provided by the company's legal department. The data collection unit can also collect compliance information. For example, it can collect information provided by the company's compliance department. Furthermore, the data collection unit can collect information provided by external legal experts. For example, it can collect the latest compliance information provided by law firms. This improves the overall risk management of the company by collecting information that contributes to improving each employee's risk management. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information provided by the company's legal department into AI and have the AI ​​perform the information collection.

[0035] The generation unit can analyze questions based on the complete collection of Japanese laws and the latest legal case databases and generate appropriate answers. For example, the generation unit can analyze questions based on the complete collection of Japanese laws. For example, the generation unit can analyze questions based on the contents of the complete collection of Japanese laws such as the Civil Code, Criminal Code, and Commercial Code. The generation unit can also analyze questions based on the latest legal case databases. For example, the generation unit can analyze questions based on the contents of case law collections and legal databases. Furthermore, the generation unit can analyze questions using generation AI and generate appropriate answers. For example, the generation unit can analyze questions and generate answers using text generation AI (e.g., LLM). This allows for the generation of appropriate answers by analyzing questions based on the complete collection of Japanese laws and the latest legal case databases. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the contents of the complete collection of Japanese laws and the latest legal case databases into the AI ​​and have the AI ​​perform the question analysis.

[0036] The providing unit can provide the generated answers to the user. For example, the providing unit can provide the generated answers in text format. For example, the providing unit can display the answers on the user's screen. The providing unit can also provide answers in audio format. For example, the providing unit can play the answers aloud using speech synthesis technology. Furthermore, the providing unit can also provide answers in image format. For example, the providing unit can display the answers as images. By providing the generated answers to the user, the user can obtain quick and appropriate answers. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated answers into AI and have the AI ​​perform the task of providing the answers.

[0037] The reception department can accept legal inquiries 24 hours a day. For example, the reception department can accept questions entered by users on a 24-hour basis. For example, the reception department can accept questions even late at night or on holidays. In addition, the reception department can implement a shift system to enable 24-hour question acceptance. For example, the reception department can adopt a shift system in which multiple operators take turns accepting questions. This will facilitate legal consultations and enable a quick response to legal issues by accepting legal inquiries 24 hours a day. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input questions entered by users into AI and have the AI ​​handle the question acceptance.

[0038] The reception desk can select the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception desk can automatically display as suggestions the types of questions the user has frequently asked in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest the types of questions the user will use at specific times based on the user's past question history. For example, the reception desk can predict and suggest similar questions based on the types of questions the user has asked at specific times in the past. This allows the reception desk to select the optimal question reception method by referring to the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into AI and have the AI ​​select the optimal reception method.

[0039] The reception desk can filter questions based on the user's current work situation and areas of interest. For example, the reception desk can prioritize questions related to projects the user is currently working on. For example, the reception desk can automatically filter relevant questions based on the user's areas of interest. The reception desk can also suggest appropriate question categories based on the user's work situation. For example, the reception desk can suggest relevant questions based on the user's current work situation. This allows for the reception of more relevant questions by filtering them based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's work situation and areas of interest into the AI ​​and have the AI ​​perform the question filtering.

[0040] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving legal questions related to that region. For example, based on the user's geographical location, the reception desk can suggest region-specific legal issues. Furthermore, if the user is on the move, the reception desk can prioritize receiving legal questions related to their current location. For example, if the user is on the move, the reception desk will prioritize receiving legal questions related to their current location. This allows the reception desk to prioritize receiving questions that are highly relevant by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into AI and have AI perform the question reception.

[0041] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can identify legal issues the user is currently interested in from their social media posts and accept relevant questions. For example, the reception desk can prioritize questions on specific legal topics based on the user's social media activity. The reception desk can also suggest the optimal time to accept questions based on the user's social media activity. For example, the reception desk suggests the optimal time to accept questions based on the user's social media activity. This allows the reception desk to accept relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity data into an AI and have the AI ​​handle the question acceptance.

[0042] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate a detailed answer for important legal issues. For example, the generation unit can generate a concise answer for general legal questions. The generation unit can also generate answers quickly for urgent questions. For example, the generation unit can quickly generate answers for urgent questions. By adjusting the level of detail in the answer based on the importance of the question, it is possible to provide a more appropriate answer. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input question importance data into AI and have the AI ​​perform the adjustment of the level of detail in the answer.

[0043] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply a generation algorithm specifically for contract law to questions concerning contract law. For example, the generation unit can apply a generation algorithm specifically for labor law to questions concerning labor law. Furthermore, the generation unit can apply a generation algorithm specifically for intellectual property law to questions concerning intellectual property law. For example, the generation unit can apply a generation algorithm specifically for intellectual property law to questions concerning intellectual property law. By applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input question category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit can generate answers quickly for urgent questions. For example, the generation unit can generate answers to regular questions with a normal priority. The generation unit can also postpone the generation of answers for questions that were submitted in the past. For example, the generation unit postpones the generation of answers for questions that were submitted in the past. This allows for faster responses by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question submission date data into AI and have the AI ​​perform the determination of answer priority.

[0045] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, if a question is highly relevant, the generation unit will prioritize generating answers for that question. For example, if a question is less relevant, the generation unit can postpone generating answers for that question. The generation unit can also automatically adjust the order of answers based on the relevance of the questions. For example, the generation unit can automatically adjust the order of answers based on the relevance of the questions. This allows for the provision of more appropriate answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into AI and have AI perform the adjustment of the order of answers.

[0046] The service provider can select the optimal delivery method by referring to the user's past question history when providing answers. For example, the service provider can automatically display as candidates the content of questions the user has frequently asked in the past. For example, the service provider can prioritize suggesting delivery methods (voice, text, etc.) that the user has used in the past. The service provider can also predict and suggest delivery methods to be used at specific times based on the user's past question history. For example, the service provider can predict and suggest similar delivery methods based on the content of questions the user has asked at specific times in the past. This allows the service provider to select the optimal delivery method by referring to the user's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past question history into AI and have the AI ​​select the optimal delivery method.

[0047] The service provider can customize the means of providing answers based on the user's current work situation. For example, the service provider may prioritize providing answers related to projects the user is currently working on. For example, the service provider may suggest an appropriate means of delivery (email, chat, etc.) depending on the user's work situation. The service provider can also adjust the level of detail in the answers based on the user's work situation. For example, the service provider may adjust the level of detail in the answers based on the user's work situation. This allows for the provision of more appropriate answers by customizing the means of delivery based on the user's current work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input user work situation data into AI and have the AI ​​perform the customization of the means of delivery.

[0048] The service provider can select the optimal delivery method by considering the user's geographical location when providing answers. For example, if the user is in a specific region, the service provider can prioritize providing legal answers relevant to that region. For example, the service provider can suggest region-specific legal issues based on the user's geographical location. The service provider can also prioritize providing legal answers relevant to the user's current location if the user is on the move. For example, if the service provider is on the move, the service provider can prioritize providing legal answers relevant to the user's current location. This allows the service provider to select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location into AI and have AI select the delivery method.

[0049] The service provider can analyze the user's social media activity and suggest a method of providing answers when providing responses. For example, the service provider can identify legal issues the user is currently interested in from their social media posts and provide relevant answers. For example, the service provider can prioritize providing answers on specific legal topics based on the user's social media activity. The service provider can also suggest the optimal time to provide answers based on the user's social media activity. For example, the service provider can suggest the optimal time to provide answers based on the user's social media activity. In this way, by analyzing the user's social media activity, the service provider can suggest the optimal method of providing answers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI and have the AI ​​suggest a method of providing answers.

[0050] The data collection unit can select the optimal data collection method by referring to a company's past legal data when collecting information. For example, the data collection unit can propose the optimal data collection method based on a company's past legal data. For example, the data collection unit can prioritize the collection of information related to a specific legal issue from a company's past legal data. The data collection unit can also analyze a company's past legal data and select the most efficient data collection method. For example, the data collection unit analyzes a company's past legal data and selects the optimal data collection method. This allows the optimal data collection method to be selected by referring to a company's past legal data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input a company's past legal data into AI and have AI perform the selection of the data collection method.

[0051] The data collection unit can select the information to collect while considering the company's current legal situation. For example, the data collection unit can prioritize the collection of relevant information based on the company's current legal situation. For example, the data collection unit can analyze the company's current legal situation and propose the optimal information collection method. The data collection unit can also determine the priority of the information to collect according to the company's current legal situation. For example, the data collection unit determines the priority of the information to collect based on the company's current legal situation. This makes it possible to collect more appropriate information by considering the company's current legal situation. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the company's current legal situation data into AI and have the AI ​​perform the information selection.

[0052] The data collection unit can select the optimal data collection method by considering the geographical location of the company when collecting information. For example, the data collection unit can prioritize the collection of region-specific legal information based on the company's geographical location. For example, the data collection unit can propose the optimal data collection method by considering the company's geographical location. The data collection unit can also determine the priority of the information to be collected according to the company's geographical location. For example, the data collection unit determines the priority of the information to be collected based on the company's geographical location. This allows the optimal data collection method to be selected by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location into AI and have AI select the data collection method.

[0053] The data collection unit can analyze a company's social media activities and select the information to collect during the data collection process. For example, the data collection unit can identify legal issues of current interest from a company's social media activities and collect relevant information. For example, the data collection unit can prioritize the collection of information on specific legal topics from a company's social media activities. The data collection unit can also suggest the optimal time for data collection based on the time of day a company is active on social media. For example, the data collection unit suggests the optimal time for data collection based on a company's social media activities. This allows for more appropriate data collection by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input company social media activity data into AI and have the AI ​​perform the information selection.

[0054] The data collection unit can select the information to collect while considering the company's operational status. For example, the data collection unit can prioritize the collection of relevant information based on the company's current operational status. For example, the data collection unit can analyze the company's operational status and propose the optimal information collection method. The data collection unit can also determine the priority of the information to be collected according to the company's operational status. For example, the data collection unit determines the priority of the information to be collected based on the company's operational status. This makes it possible to collect more appropriate information by considering the company's operational status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's operational status data into AI and have the AI ​​perform the information selection.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The legal support system may further include a reception unit that selects the optimal reception method by referring to the user's past question history. The reception unit can, for example, automatically display as suggestions the types of questions the user has frequently asked in the past. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the types of questions the user will use at specific times of the day based on the user's past question history. This allows the system to select the optimal question reception method by referring to the user's past question history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past question history into the AI ​​and have the AI ​​select the optimal reception method.

[0057] The legal support system may further include a reception unit that filters questions based on the user's current work situation and areas of interest. For example, the reception unit may prioritize receiving questions related to projects the user is currently working on. For example, it may automatically filter relevant questions based on the user's areas of interest. It may also suggest appropriate question categories according to the user's work situation. This allows the system to receive more relevant questions by filtering them based on the user's current work situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit may input data on the user's work situation and areas of interest into the AI ​​and have the AI ​​perform the question filtering.

[0058] The legal support system may also include a reception unit that prioritizes receiving highly relevant questions by considering the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving legal questions related to that region. For instance, it can suggest region-specific legal issues based on the user's geographical location. Furthermore, if the user is on the move, it can prioritize receiving legal questions related to their current location. This ensures that highly relevant questions are prioritized by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location into the AI ​​and have the AI ​​handle the question reception.

[0059] The legal support system may further include a reception unit that analyzes the user's social media activity and receives relevant questions. The reception unit can, for example, identify legal issues the user is currently interested in from their social media posts and receive relevant questions. For example, it can prioritize receiving questions on specific legal topics based on the user's social media activity. It can also suggest optimal question reception times based on the user's social media activity schedule. This allows for the reception of relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's social media activity data into an AI and have the AI ​​handle question reception.

[0060] The legal support system may also include a data collection unit that selects the optimal data collection method by referring to the company's past legal data during information gathering. For example, the data collection unit may propose the optimal data collection method based on the company's past legal data. For instance, it may prioritize the collection of information related to specific legal issues from the company's past legal data. It may also analyze the company's past legal data and select the most efficient data collection method. This allows for the selection of the optimal data collection method by referring to the company's past legal data. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit may input the company's past legal data into an AI and have the AI ​​select the data collection method.

[0061] The legal support system may also include a data collection unit that selects information to collect while considering the company's current legal situation. For example, the data collection unit prioritizes the collection of relevant information based on the company's current legal situation. For instance, it can analyze the company's current legal situation and propose the optimal information collection method. It can also determine the priority of information to collect according to the company's current legal situation. This allows for more appropriate information collection by considering the company's current legal situation. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the company's current legal situation into an AI and have the AI ​​perform the information selection.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives legal questions from users. For example, the reception desk can accept questions entered by users in text format. The reception desk can also accept voice input. For example, users can use a microphone to input their questions by voice. Furthermore, the reception desk can also accept image input. For example, users can upload an image of a contract and input their questions that way. Step 2: The generation unit analyzes the question received by the reception unit and generates an appropriate answer. The generation unit analyzes the question using generation AI. For example, the generation unit analyzes the question based on the complete collection of laws and the latest legal case databases and generates an appropriate answer. The generation unit can generate answers using text generation AI (e.g., LLM). The generation unit can also generate answers using multimodal generation AI. For example, the generation unit can generate answers that combine text and images. Step 3: The provider unit provides the user with the answer generated by the generator unit. The provider unit can provide the generated answer in text format. For example, the provider unit displays the answer on the user's screen. The provider unit can also provide the answer in audio format. For example, the provider unit plays the answer as audio using speech synthesis technology. Furthermore, the provider unit can also provide the answer in image format. For example, the provider unit displays the answer as an image.

[0064] (Example of form 2) The legal support system according to an embodiment of the present invention is an AI chatbot for quickly and appropriately answering specialized legal questions. This legal support system allows users to input legal questions, and the generating AI analyzes the questions based on a comprehensive legal database and the latest legal case databases to generate appropriate answers. This AI chatbot is available 24 hours a day, contributing to reducing the burden on the legal department and simultaneously improving overall corporate risk management. It also collects specific legal themes and matters for each company, contributing to improved risk management for each employee. For example, a user inputs a legal question, such as, "Please explain a specific clause in a contract." This question is input to the generating AI. Next, the generating AI analyzes the input question. The generating AI analyzes the question based on a comprehensive legal database and the latest legal case databases to generate appropriate answers. For example, in response to a question about a specific clause in a contract, it generates an answer based on relevant laws and cases. The generated answer is provided to the user. For example, in response to a question about a specific clause in a contract, the answer generated by the generating AI is displayed to the user. This allows the user to obtain quick and appropriate answers. This system reduces the burden on the legal department. The legal department will no longer need to handle routine legal consultations, allowing them to focus on more important tasks. It also contributes to improving overall corporate risk management. For example, by having each employee input legal questions into the AI ​​chatbot, specific legal themes and matters are collected, improving overall corporate risk management. Furthermore, because the AI ​​chatbot is available 24 hours a day, it streamlines legal consultations and enables rapid responses to legal issues. For example, users can get quick answers to legal questions even late at night or on holidays. In this way, a legal support chatbot utilizing generative AI reduces the burden on the legal department, improves overall corporate risk management, streamlines legal consultations, and enables rapid responses to legal issues. As a result, the legal support system can answer users' legal questions quickly and appropriately.

[0065] The legal support system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives legal questions from users. For example, the reception unit can receive questions entered by users in text format. The reception unit can also accept voice input. For example, users can input questions by voice using a microphone. Furthermore, the reception unit can also accept image input. For example, users can upload an image of a contract to input a question. The generation unit analyzes the questions received by the reception unit and generates appropriate answers. The generation unit analyzes the questions using generation AI. For example, the generation unit analyzes the questions based on the complete collection of laws and the latest legal case databases and generates appropriate answers. The generation unit can generate answers using text generation AI (e.g., LLM). The generation unit can also generate answers using multimodal generation AI. For example, the generation unit can generate answers that combine text and images. The provision unit provides the answers generated by the generation unit to the user. The provision unit can provide the generated answers in text format. For example, the provision unit displays the answers on the user's screen. Furthermore, the system can provide answers in audio format. For example, it can use speech synthesis technology to play back the answer as audio. In addition, the system can provide answers in image format. For example, it can display the answer as an image. As a result, the legal support system according to this embodiment can quickly and appropriately answer the user's legal questions.

[0066] The reception desk receives legal inquiries from users. For example, the reception desk can receive user-submitted questions in text format. Specifically, when a user enters a question into a web form or chat box, that text data is sent to the reception desk. The reception desk can also accept voice input. For example, a user can use a microphone to input a question by voice. In this case, voice recognition technology is used to convert the voice data into text data and send it to the reception desk. Furthermore, the reception desk can also accept image input. For example, a user can upload an image of a contract and input a question. In this case, image recognition technology is used to analyze the image data and convert it into text data. This allows the reception desk to accept questions in various formats from users. In addition, the reception desk has a function to automatically classify user input and distribute it to the appropriate processing department. For example, questions about contracts can be distributed to the contract law department, and questions about labor issues can be distributed to the labor law department. This allows the reception desk to process user inquiries quickly and accurately. The reception desk can also refer to a user's past question history and refer to past answers if similar questions have been asked. This allows the reception desk to provide users with consistent answers.

[0067] The generation unit analyzes questions received by the reception unit and generates appropriate answers. The generation unit analyzes questions using generation AI. Specifically, the generation unit analyzes questions based on the complete legal code and the latest legal case databases and generates appropriate answers. The generation unit can generate answers using text generation AI (e.g., LLM). LLM has learned from a large amount of legal documents and case data and has the ability to generate appropriate answers to user questions. For example, if a user asks, "Please tell me how to terminate a contract," the generation unit will refer to relevant legal articles and past cases and generate an answer with a specific termination method. The generation unit can also generate answers using multimodal generation AI. For example, the generation unit can generate answers that combine text and images. If a user asks about a specific clause in a contract, it can generate an answer that includes an image of that clause. Furthermore, the generation unit can adjust the format and level of detail of the answer according to the content of the user's question. For example, it can provide detailed legal articles and cases to users with specialized legal knowledge and provide easy-to-understand explanations to general users. This allows the generation unit to quickly generate appropriate responses that meet the user's needs.

[0068] The providing unit provides users with answers generated by the generating unit. The providing unit can provide the generated answers in text format. For example, the providing unit displays the answers on the user's screen. Specifically, it displays the answers to the user's questions in text format via a web browser or mobile app. The providing unit can also provide answers in audio format. For example, the providing unit plays the answers aloud using speech synthesis technology. This allows for the use of users with visual impairments or those whose hands are occupied. Furthermore, the providing unit can provide answers in image format. For example, the providing unit displays the answers as images. For questions about specific clauses in a contract, it can provide answers that include images of those clauses. This allows users to visually confirm the information. In addition, the providing unit can collect user feedback and continuously improve the accuracy and quality of the answers. For example, users can input ratings and comments on the provided answers, and the generating unit's algorithm is adjusted based on that feedback. The providing unit can also reliably transmit information using multiple communication methods. For example, it is possible to send answers via email or SMS. This allows the service provider to provide users with quick and reliable answers, thereby improving the usability of the legal support system.

[0069] The legal support system includes a data collection unit that collects specific legal themes and matters for each company. The data collection unit can, for example, collect information provided by the company's legal department. It can also collect information provided by various departments within the company. For example, it can collect information on contracts provided by the sales department. Furthermore, it can collect information provided by external legal experts. For example, it can collect the latest legal cases provided by law firms. This contributes to improving the risk management of each employee by collecting specific legal themes and matters for each company. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information provided by the company's legal department into the AI ​​and have the AI ​​perform the information collection.

[0070] The data collection unit can collect information that contributes to improving each employee's risk management. For example, the data collection unit can collect information on legal risks. For example, it can collect information on legal risks provided by the company's legal department. The data collection unit can also collect compliance information. For example, it can collect information provided by the company's compliance department. Furthermore, the data collection unit can collect information provided by external legal experts. For example, it can collect the latest compliance information provided by law firms. This improves the overall risk management of the company by collecting information that contributes to improving each employee's risk management. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input information provided by the company's legal department into AI and have the AI ​​perform the information collection.

[0071] The generation unit can analyze questions based on the complete collection of Japanese laws and the latest legal case databases and generate appropriate answers. For example, the generation unit can analyze questions based on the complete collection of Japanese laws. For example, the generation unit can analyze questions based on the contents of the complete collection of Japanese laws such as the Civil Code, Criminal Code, and Commercial Code. The generation unit can also analyze questions based on the latest legal case databases. For example, the generation unit can analyze questions based on the contents of case law collections and legal databases. Furthermore, the generation unit can analyze questions using generation AI and generate appropriate answers. For example, the generation unit can analyze questions and generate answers using text generation AI (e.g., LLM). This allows for the generation of appropriate answers by analyzing questions based on the complete collection of Japanese laws and the latest legal case databases. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the contents of the complete collection of Japanese laws and the latest legal case databases into the AI ​​and have the AI ​​perform the question analysis.

[0072] The providing unit can provide the generated answers to the user. For example, the providing unit can provide the generated answers in text format. For example, the providing unit can display the answers on the user's screen. The providing unit can also provide answers in audio format. For example, the providing unit can play the answers aloud using speech synthesis technology. Furthermore, the providing unit can also provide answers in image format. For example, the providing unit can display the answers as images. By providing the generated answers to the user, the user can obtain quick and appropriate answers. Some or all of the above processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the generated answers into AI and have the AI ​​perform the task of providing the answers.

[0073] The reception department can accept legal inquiries 24 hours a day. For example, the reception department can accept questions entered by users on a 24-hour basis. For example, the reception department can accept questions even late at night or on holidays. In addition, the reception department can implement a shift system to enable 24-hour question acceptance. For example, the reception department can adopt a shift system in which multiple operators take turns accepting questions. This will facilitate legal consultations and enable a quick response to legal issues by accepting legal inquiries 24 hours a day. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input questions entered by users into AI and have the AI ​​handle the question acceptance.

[0074] The reception desk can estimate the user's emotions and adjust the way questions are answered based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if the user is in a hurry, allowing for quick question entry. For example, when the user enters a question by voice, the reception desk can use speech recognition technology to quickly convert it to text. This allows for more appropriate question answering by adjusting the way questions are answered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0075] The reception desk can select the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception desk can automatically display as suggestions the types of questions the user has frequently asked in the past. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest the types of questions the user will use at specific times based on the user's past question history. For example, the reception desk can predict and suggest similar questions based on the types of questions the user has asked at specific times in the past. This allows the reception desk to select the optimal question reception method by referring to the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past question history into AI and have the AI ​​select the optimal reception method.

[0076] The reception desk can filter questions based on the user's current work situation and areas of interest. For example, the reception desk can prioritize questions related to projects the user is currently working on. For example, the reception desk can automatically filter relevant questions based on the user's areas of interest. The reception desk can also suggest appropriate question categories based on the user's work situation. For example, the reception desk can suggest relevant questions based on the user's current work situation. This allows for the reception of more relevant questions by filtering them based on the user's current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's work situation and areas of interest into the AI ​​and have the AI ​​perform the question filtering.

[0077] The reception desk can estimate the user's emotions and determine the priority of questions to be answered based on the estimated emotions. For example, if the user feels urgent, the reception desk will prioritize that question. For example, if the user is relaxed, the reception desk can answer questions with normal priority. The reception desk can also raise the priority of questions to respond quickly if the user is feeling anxious. For example, if the reception desk is feeling anxious, the reception desk will prioritize that question. This allows for more appropriate question answering by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0078] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving legal questions related to that region. For example, based on the user's geographical location, the reception desk can suggest region-specific legal issues. Furthermore, if the user is on the move, the reception desk can prioritize receiving legal questions related to their current location. For example, if the user is on the move, the reception desk will prioritize receiving legal questions related to their current location. This allows the reception desk to prioritize receiving questions that are highly relevant by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into AI and have AI perform the question reception.

[0079] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, the reception desk can identify legal issues the user is currently interested in from their social media posts and accept relevant questions. For example, the reception desk can prioritize questions on specific legal topics based on the user's social media activity. The reception desk can also suggest the optimal time to accept questions based on the user's social media activity. For example, the reception desk suggests the optimal time to accept questions based on the user's social media activity. This allows the reception desk to accept relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity data into an AI and have the AI ​​handle the question acceptance.

[0080] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a concise and clear response. For example, if the user is relaxed, the generation unit can generate a response that includes detailed explanations. Also, if the user is in a hurry, the generation unit can generate a short, to-the-point response. For example, if the user is in a hurry, the generation unit will generate a short, to-the-point response to that question. This allows for the provision of more appropriate responses by adjusting the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the way the response is expressed.

[0081] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate a detailed answer for important legal issues. For example, the generation unit can generate a concise answer for general legal questions. The generation unit can also generate answers quickly for urgent questions. For example, the generation unit can quickly generate answers for urgent questions. By adjusting the level of detail in the answer based on the importance of the question, it is possible to provide a more appropriate answer. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input question importance data into AI and have the AI ​​perform the adjustment of the level of detail in the answer.

[0082] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply a generation algorithm specifically for contract law to questions concerning contract law. For example, the generation unit can apply a generation algorithm specifically for labor law to questions concerning labor law. Furthermore, the generation unit can apply a generation algorithm specifically for intellectual property law to questions concerning intellectual property law. For example, the generation unit can apply a generation algorithm specifically for intellectual property law to questions concerning intellectual property law. By applying different generation algorithms depending on the question category, more appropriate answers can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input question category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise response. For example, if the user is relaxed, the generation unit can generate a longer response that includes detailed explanations. The generation unit can also generate a response with visually stimulating effects if the user is excited. For example, if the user is excited, the generation unit can generate a response to that question with visually stimulating effects. This allows for more appropriate responses to be provided by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the response.

[0084] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit can generate answers quickly for urgent questions. For example, the generation unit can generate answers to regular questions with a normal priority. The generation unit can also postpone the generation of answers for questions that were submitted in the past. For example, the generation unit postpones the generation of answers for questions that were submitted in the past. This allows for faster responses by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question submission date data into AI and have the AI ​​perform the determination of answer priority.

[0085] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, if a question is highly relevant, the generation unit will prioritize generating answers for that question. For example, if a question is less relevant, the generation unit can postpone generating answers for that question. The generation unit can also automatically adjust the order of answers based on the relevance of the questions. For example, the generation unit can automatically adjust the order of answers based on the relevance of the questions. This allows for the provision of more appropriate answers by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into AI and have AI perform the adjustment of the order of answers.

[0086] The service provider can estimate the user's emotions and adjust the way it provides answers based on the estimated emotions. For example, if the user is stressed, the service provider can provide a concise and clear answer. For example, if the user is relaxed, the service provider can provide an answer that includes a detailed explanation. Also, if the user is in a hurry, the service provider can provide a short, to-the-point answer. For example, if the user is in a hurry, the service provider can provide a short, to-the-point answer to that question. By adjusting the way it provides answers according to the user's emotions, it is possible to provide more appropriate answers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the way it provides answers.

[0087] The service provider can select the optimal delivery method by referring to the user's past question history when providing answers. For example, the service provider can automatically display as candidates the content of questions the user has frequently asked in the past. For example, the service provider can prioritize suggesting delivery methods (voice, text, etc.) that the user has used in the past. The service provider can also predict and suggest delivery methods to be used at specific times based on the user's past question history. For example, the service provider can predict and suggest similar delivery methods based on the content of questions the user has asked at specific times in the past. This allows the service provider to select the optimal delivery method by referring to the user's past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past question history into AI and have the AI ​​select the optimal delivery method.

[0088] The service provider can customize the means of providing answers based on the user's current work situation. For example, the service provider may prioritize providing answers related to projects the user is currently working on. For example, the service provider may suggest an appropriate means of delivery (email, chat, etc.) depending on the user's work situation. The service provider can also adjust the level of detail in the answers based on the user's work situation. For example, the service provider may adjust the level of detail in the answers based on the user's work situation. This allows for the provision of more appropriate answers by customizing the means of delivery based on the user's current work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input user work situation data into AI and have the AI ​​perform the customization of the means of delivery.

[0089] The service provider can estimate the user's emotions and determine the order in which responses are provided based on the estimated emotions. For example, if the user feels urgent, the service provider will provide that response with the highest priority. For example, if the user is relaxed, the service provider can provide responses with the normal priority. The service provider can also raise the priority of responses to respond quickly if the user is feeling anxious. For example, if the service provider is feeling anxious, the service provider will provide that response with priority. In this way, by determining the order in which responses are provided according to the user's emotions, more appropriate responses can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​determine the order in which responses are provided.

[0090] The service provider can select the optimal delivery method by considering the user's geographical location when providing answers. For example, if the user is in a specific region, the service provider can prioritize providing legal answers relevant to that region. For example, the service provider can suggest region-specific legal issues based on the user's geographical location. The service provider can also prioritize providing legal answers relevant to the user's current location if the user is on the move. For example, if the service provider is on the move, the service provider can prioritize providing legal answers relevant to the user's current location. This allows the service provider to select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location into AI and have AI select the delivery method.

[0091] The service provider can analyze the user's social media activity and suggest a method of providing answers when providing responses. For example, the service provider can identify legal issues the user is currently interested in from their social media posts and provide relevant answers. For example, the service provider can prioritize providing answers on specific legal topics based on the user's social media activity. The service provider can also suggest the optimal time to provide answers based on the user's social media activity. For example, the service provider can suggest the optimal time to provide answers based on the user's social media activity. In this way, by analyzing the user's social media activity, the service provider can suggest the optimal method of providing answers. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI and have the AI ​​suggest a method of providing answers.

[0092] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user feels a sense of urgency, the data collection unit will prioritize collecting that information. For example, if the user is relaxed, the data collection unit can collect information with normal priority. The data collection unit can also raise the priority of information to respond quickly if the user is feeling anxious. For example, if the user is feeling anxious, the data collection unit will prioritize collecting that information. This allows for more appropriate information collection by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of information.

[0093] The data collection unit can select the optimal data collection method by referring to a company's past legal data when collecting information. For example, the data collection unit can propose the optimal data collection method based on a company's past legal data. For example, the data collection unit can prioritize the collection of information related to a specific legal issue from a company's past legal data. The data collection unit can also analyze a company's past legal data and select the most efficient data collection method. For example, the data collection unit analyzes a company's past legal data and selects the optimal data collection method. This allows the optimal data collection method to be selected by referring to a company's past legal data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input a company's past legal data into AI and have AI perform the selection of the data collection method.

[0094] The data collection unit can select the information to collect while considering the company's current legal situation. For example, the data collection unit can prioritize the collection of relevant information based on the company's current legal situation. For example, the data collection unit can analyze the company's current legal situation and propose the optimal information collection method. The data collection unit can also determine the priority of the information to collect according to the company's current legal situation. For example, the data collection unit determines the priority of the information to collect based on the company's current legal situation. This makes it possible to collect more appropriate information by considering the company's current legal situation. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the company's current legal situation data into AI and have the AI ​​perform the information selection.

[0095] The data collection unit can estimate the user's emotions and adjust how the collected information is displayed based on the estimated emotions. For example, if the user is tense, the data collection unit can provide a simple and highly visible display method. For example, if the user is relaxed, the data collection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the data collection unit can provide a concise display method. For example, if the user is in a hurry, the data collection unit can provide the information in a concise display method. By adjusting how information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust how information is displayed.

[0096] The data collection unit can select the optimal data collection method by considering the geographical location of the company when collecting information. For example, the data collection unit can prioritize the collection of region-specific legal information based on the company's geographical location. For example, the data collection unit can propose the optimal data collection method by considering the company's geographical location. The data collection unit can also determine the priority of the information to be collected according to the company's geographical location. For example, the data collection unit determines the priority of the information to be collected based on the company's geographical location. This allows the optimal data collection method to be selected by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location into AI and have AI select the data collection method.

[0097] The data collection unit can analyze a company's social media activities and select the information to collect during the data collection process. For example, the data collection unit can identify legal issues of current interest from a company's social media activities and collect relevant information. For example, the data collection unit can prioritize the collection of information on specific legal topics from a company's social media activities. The data collection unit can also suggest the optimal time for data collection based on the time of day a company is active on social media. For example, the data collection unit suggests the optimal time for data collection based on a company's social media activities. This allows for more appropriate data collection by analyzing a company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input company social media activity data into AI and have the AI ​​perform the information selection.

[0098] The data collection unit can select the information to collect while considering the company's operational status. For example, the data collection unit can prioritize the collection of relevant information based on the company's current operational status. For example, the data collection unit can analyze the company's operational status and propose the optimal information collection method. The data collection unit can also determine the priority of the information to be collected according to the company's operational status. For example, the data collection unit determines the priority of the information to be collected based on the company's operational status. This makes it possible to collect more appropriate information by considering the company's operational status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's operational status data into AI and have the AI ​​perform the information selection.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The legal support system may further include a generation unit that estimates the user's emotions and adjusts the way the response is expressed based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a concise and clear response. For example, if the user is relaxed, it can generate a response that includes detailed explanations. If the user is in a hurry, it can also generate a short, to-the-point response. This allows for the provision of more appropriate responses by adjusting the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into the AI ​​and have the AI ​​adjust the way the response is expressed.

[0101] The legal support system may further include a reception unit that selects the optimal reception method by referring to the user's past question history. The reception unit can, for example, automatically display as suggestions the types of questions the user has frequently asked in the past. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the types of questions the user will use at specific times of the day based on the user's past question history. This allows the system to select the optimal question reception method by referring to the user's past question history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past question history into the AI ​​and have the AI ​​select the optimal reception method.

[0102] The legal support system may further include a reception unit that filters questions based on the user's current work situation and areas of interest. For example, the reception unit may prioritize receiving questions related to projects the user is currently working on. For example, it may automatically filter relevant questions based on the user's areas of interest. It may also suggest appropriate question categories according to the user's work situation. This allows the system to receive more relevant questions by filtering them based on the user's current work situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit may input data on the user's work situation and areas of interest into the AI ​​and have the AI ​​perform the question filtering.

[0103] The legal support system may further include a reception unit that estimates the user's emotions and determines the priority of questions to be received based on the estimated emotions. For example, if the user feels urgent, the reception unit will receive that question with the highest priority. For example, if the user is relaxed, the question can be received with the normal priority. Also, if the user is anxious, the priority of the question can be increased in order to respond quickly. This makes it possible to receive questions more appropriately by determining the priority of questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input the user's emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0104] The legal support system may also include a reception unit that prioritizes receiving highly relevant questions by considering the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving legal questions related to that region. For instance, it can suggest region-specific legal issues based on the user's geographical location. Furthermore, if the user is on the move, it can prioritize receiving legal questions related to their current location. This ensures that highly relevant questions are prioritized by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location into the AI ​​and have the AI ​​handle the question reception.

[0105] The legal support system may further include a reception unit that analyzes the user's social media activity and receives relevant questions. The reception unit can, for example, identify legal issues the user is currently interested in from their social media posts and receive relevant questions. For example, it can prioritize receiving questions on specific legal topics based on the user's social media activity. It can also suggest optimal question reception times based on the user's social media activity schedule. This allows for the reception of relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's social media activity data into an AI and have the AI ​​handle question reception.

[0106] The legal support system may further include a data collection unit that estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user feels a sense of urgency, the data collection unit will prioritize collecting that information. For example, if the user is relaxed, information can be collected with normal priority. Also, if the user is feeling anxious, the priority of information can be increased to allow for a quicker response. This makes it possible to collect more appropriate information by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the determination of information priorities.

[0107] The legal support system may also include a data collection unit that selects the optimal data collection method by referring to the company's past legal data during information gathering. For example, the data collection unit may propose the optimal data collection method based on the company's past legal data. For instance, it may prioritize the collection of information related to specific legal issues from the company's past legal data. It may also analyze the company's past legal data and select the most efficient data collection method. This allows for the selection of the optimal data collection method by referring to the company's past legal data. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit may input the company's past legal data into an AI and have the AI ​​select the data collection method.

[0108] The legal support system may also include a data collection unit that selects information to collect while considering the company's current legal situation. For example, the data collection unit prioritizes the collection of relevant information based on the company's current legal situation. For instance, it can analyze the company's current legal situation and propose the optimal information collection method. It can also determine the priority of information to collect according to the company's current legal situation. This allows for more appropriate information collection by considering the company's current legal situation. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data on the company's current legal situation into an AI and have the AI ​​perform the information selection.

[0109] The legal support system may further include a data collection unit that estimates the user's emotions and adjusts the way information is displayed based on the estimated emotions. For example, if the user is tense, the data collection unit can provide a simple and highly visible display method. For example, if the user is relaxed, it can provide a display method that includes detailed information. It can also provide a concise display method if the user is in a hurry. This allows for more appropriate information to be provided by adjusting the way information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the way information is displayed.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives legal questions from users. For example, the reception desk can accept questions entered by users in text format. The reception desk can also accept voice input. For example, users can use a microphone to input their questions by voice. Furthermore, the reception desk can also accept image input. For example, users can upload an image of a contract and input their questions that way. Step 2: The generation unit analyzes the question received by the reception unit and generates an appropriate answer. The generation unit analyzes the question using generation AI. For example, the generation unit analyzes the question based on the complete collection of laws and the latest legal case databases and generates an appropriate answer. The generation unit can generate answers using text generation AI (e.g., LLM). The generation unit can also generate answers using multimodal generation AI. For example, the generation unit can generate answers that combine text and images. Step 3: The provider unit provides the user with the answer generated by the generator unit. The provider unit can provide the generated answer in text format. For example, the provider unit displays the answer on the user's screen. The provider unit can also provide the answer in audio format. For example, the provider unit plays the answer as audio using speech synthesis technology. Furthermore, the provider unit can also provide the answer in image format. For example, the provider unit displays the answer as an image.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text or voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question based on the complete legal code and the latest legal case database and generates an appropriate answer. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated answer to the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information provided by the legal department and other departments of the company. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives text or voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the question based on the complete legal code and the latest legal case database and generates an appropriate answer. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated answer to the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information provided by the legal department and other departments of the company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and collection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives text or voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the question based on the complete legal code and the latest legal case database and generates an appropriate answer. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the generated answer to the user. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information provided by the legal department and other departments of the company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and collection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives text or voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the question based on the complete legal code and the latest legal case database and generates an appropriate answer. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated answer to the user. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information provided by the legal department and other departments of the company. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A reception desk that handles legal inquiries from users, A generation unit analyzes the questions received by the reception unit and generates appropriate answers, The system includes a providing unit that provides the answer generated by the generation unit to the user. A system characterized by the following features. (Note 2) Each company has a dedicated department for collecting information on specific legal themes and matters. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is We collect information that contributes to improving risk management for each employee. The system described in Appendix 2, characterized by the features described herein. (Note 4) The generating unit is The system analyzes questions based on the complete collection of Japanese laws and the latest legal case databases, and generates appropriate answers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated response to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We accept legal inquiries 24 hours a day. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a question is submitted, the system will refer to the user's past question history to select the most appropriate submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving questions, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and selects relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating answers, different generation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing an answer, the system will refer to the user's past question history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing responses, the method of delivery will be customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which responses are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing responses, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing responses, we analyze the user's social media activity and suggest methods for providing the responses. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned collection unit is When gathering information, refer to the company's past legal data to select the most suitable collection method. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned collection unit is When gathering information, select the information to collect while considering the company's current legal situation. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned collection unit is It estimates the user's emotions and adjusts how the collected information is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned collection unit is When gathering information, select the optimal collection method considering the company's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned collection unit is When gathering information, we analyze companies' social media activities to select the information to collect. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned collection unit is When gathering information, select the information to collect while considering the company's operational situation. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles legal inquiries from users, A generation unit analyzes the questions received by the reception unit and generates appropriate answers, The system includes a providing unit that provides the answer generated by the generation unit to the user. A system characterized by the following features.

2. Each company has a dedicated department for collecting information on specific legal themes and matters. The system according to feature 1.

3. The aforementioned collection unit is We collect information that contributes to improving risk management for each employee. The system according to feature 2.

4. The generating unit is The system analyzes questions based on the complete collection of Japanese laws and the latest legal case databases, and generates appropriate answers. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated response to the user. The system according to feature 1.

6. The aforementioned reception unit is We accept legal inquiries 24 hours a day. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When a question is submitted, the system will refer to the user's past question history to select the most appropriate submission method. The system according to feature 1.

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

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system according to feature 1.

Images