system

Abstract

An object of a system according to an embodiment is to enable an ordinary person to understand and execute an appropriate coping method related to a law.SOLUTION: A system according to an embodiment includes a situation understanding unit, a handling method proposal unit, a reminder unit, a data accumulation unit, and a cooperation unit. The situation understanding unit understands the situation of the user. The handling method proposing section proposes an appropriate handling method based on the situation understood by the situation understanding section. The reminder unit reminds the user of the procedure proposed by the handling method proposal unit. The data storage unit stores data of legal precedents or FAQs. The cooperation department collects the latest legal information in cooperation with lawyers.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the legal system is complex, making it difficult for ordinary people to understand and implement appropriate countermeasures.

[0005] The system according to the embodiment aims to enable ordinary people to understand and take appropriate legal action. [Means for solving the problem]

[0006] The system according to the embodiment includes a situation understanding unit, a solution proposal unit, a reminder unit, a data accumulation unit, and a collaboration unit. The situation understanding unit understands the user's situation. The solution proposal unit proposes an appropriate solution based on the situation understood by the situation understanding unit. The reminder unit reminds the user of the procedure proposed by the solution proposal unit. The data accumulation unit accumulates case law or FAQ data. The collaboration unit cooperates with lawyers to collect the latest legal information. [Effects of the Invention]

[0007] The system according to the embodiment can enable the public to understand and take appropriate legal action. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention is a system that suggests solutions and reminds users to take procedures by having them describe the situation they are in. This allows the AI ​​system to understand the user's situation, suggest appropriate solutions, and remind users to take procedures.

[0029] The AI ​​system according to the embodiment includes a situation understanding unit, a solution proposal unit, a reminder unit, a data storage unit, and a collaboration unit. The situation understanding unit understands the user's situation. For example, when the user describes their situation, the generation AI analyzes and understands the content. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input. The generation AI can also analyze data such as audio and images using a multimodal generation AI. The solution proposal unit proposes an appropriate solution based on the situation understood by the situation understanding unit. For example, the generation AI proposes procedures such as reporting to the police, contacting an insurance company, and visiting a medical institution, depending on the user's situation. The generation AI also provides a list of necessary documents and procedures to help the user proceed smoothly with the procedures. The reminder unit reminds the user of the procedures proposed by the solution proposal unit. For example, the generation AI sends notifications when a procedure with a specific deadline approaches. This helps the user remember important procedures and respond appropriately. The data storage unit accumulates data on legal precedents and FAQs. For example, the generation AI works with lawyers to accumulate data on legal precedents and FAQs, providing users with information to make more accurate decisions based on past legal precedents and frequently asked questions. The collaboration unit works with lawyers to collect the latest legal information. For example, the generation AI works with lawyers to collect the latest legal information and legal precedents and reflects them in the system. This allows the system to always provide appropriate advice to users based on the latest information. As a result, the AI ​​system according to the embodiment can understand the user's situation, propose appropriate solutions, remind users of procedures, accumulate data, and work with lawyers to provide the latest legal information. For example, even if a user is involved in a traffic accident, the generation AI can assist the user in responding quickly and appropriately. Furthermore, by working with lawyers, the system can always provide the latest information, allowing users to use the system with peace of mind.

[0030] The situation understanding unit can refer to the user's past consultation history and understand the situation more accurately based on similar cases. For example, the situation understanding unit retrieves the user's past consultation history from a database and understands the current situation based on similar cases. For example, if there has been a past consultation about a traffic accident, the situation understanding unit refers to that information and analyzes the content of the current consultation. The situation understanding unit also analyzes the past consultation history and develops an algorithm to identify similar cases. For example, it compares the content of the past consultation with the content of the current consultation and finds common points. The situation understanding unit also references the user's past consultation history in real time and builds a system that understands the situation based on similar cases. For example, it automatically searches the content of past consultations and provides information that is useful for the current consultation. This allows the situation to be understood more accurately based on the past consultation history.

[0031] The situation understanding unit analyzes not only the text entered by the user but also image or audio data, enabling a more multifaceted understanding of the situation. The situation understanding unit, for example, builds a system that analyzes image and audio data in addition to the text entered by the user. For example, it analyzes photos of the accident scene and audio messages uploaded by the user to understand the situation. The situation understanding unit also uses image recognition technology to understand the situation from image data provided by the user. For example, it analyzes photos of the accident scene to determine the extent of the damage. The situation understanding unit also uses audio analysis technology to understand the situation from audio data provided by the user. For example, it converts what the user says into text and analyzes it. This allows the system to analyze text, images, and audio data and understand the situation from more facets.

[0032] The situation understanding unit can automatically translate inputs in different languages ​​to achieve multilingual support. The situation understanding unit, for example, builds a system that automatically translates and analyzes text input in different languages. For example, it automatically translates consultation content input in a language other than Japanese into Japanese. The situation understanding unit also develops a multilingual natural language processing system to analyze inputs in different languages. For example, it analyzes consultation content input in English or Chinese and proposes an appropriate solution. The situation understanding unit also builds a system that uses an automatic translation function to translate and analyze inputs in different languages ​​in real time. For example, it translates and analyzes content spoken by a user in English. This enables automatic translation of inputs in different languages ​​and multilingual support.

[0033] The solution proposal unit can refer to a database of past legal precedents and propose solutions to similar cases. The solution proposal unit, for example, builds a system that refers to a database of past legal precedents and proposes solutions to similar cases. For example, it proposes an appropriate solution based on past legal precedents related to traffic accidents. The solution proposal unit also analyzes the legal precedent database and develops an algorithm that automatically extracts solutions to similar cases. For example, it finds commonalities in past legal precedents and proposes solutions. The solution proposal unit also builds a system that refers to the database of past legal precedents in real time and proposes solutions to similar cases. For example, when a user enters the content of their consultation, it instantly searches for related legal precedents and proposes solutions. This makes it possible to refer to the database of past legal precedents and propose solutions to similar cases.

[0034] The solution proposal unit can generate a solution customized for the user's individual situation. The solution proposal unit, for example, builds a system that generates a solution customized for the user's individual situation. For example, it proposes an optimal solution based on the user's specific situation and needs. The solution proposal unit also analyzes data input by the user and develops an algorithm that automatically generates a solution tailored to the individual situation. For example, it proposes necessary procedures and documents based on the user's situation. The solution proposal unit also builds a system that analyzes the user's individual situation in real time and generates a customized solution. For example, it proposes an optimal solution based on information input by the user. This makes it possible to generate a solution customized for the user's individual situation.

[0035] The solution proposal unit can refer to legal information from different jurisdictions and propose solutions from an international perspective. The solution proposal unit, for example, builds a system that references legal information from different jurisdictions and proposes solutions from an international perspective. For example, when a user faces a legal issue overseas, it proposes an appropriate solution. The solution proposal unit also integrates international legal information into a database and develops an algorithm that automatically proposes solutions from different jurisdictions. For example, when a user consults about international business, it proposes an appropriate solution. The solution proposal unit also builds a system that references legal information from different jurisdictions in real time and proposes solutions from an international perspective. For example, when a user consults about a multinational corporation, it proposes an appropriate solution. This makes it possible to refer to legal information from different jurisdictions and propose solutions from an international perspective.

[0036] The solution proposal unit can provide a step-by-step guide for a specific procedure in addition to proposing a solution. The solution proposal unit, for example, builds a system that provides a step-by-step guide for a specific procedure in addition to proposing a solution. For example, a detailed guide is provided so that a user can proceed through the necessary procedures in order. The solution proposal unit also develops an algorithm that automatically generates a step-by-step guide for a procedure. For example, the steps of the necessary procedure are explained in detail based on information entered by the user. The solution proposal unit also builds a system that provides a step-by-step guide for a specific procedure in real time along with proposing a solution. For example, the guide is displayed sequentially as the user proceeds with the procedure. This makes it possible to provide a step-by-step guide for a specific procedure in addition to proposing a solution.

[0037] The reminding unit can analyze the user's schedule and suggest the optimal timing for reminding. The reminding unit, for example, builds a system that analyzes the user's schedule and suggests the optimal timing for reminding. For example, it references the user's calendar or planner and reminds them of important procedures. The reminding unit also analyzes the user's schedule data and develops an algorithm that automatically calculates the optimal timing for reminding. For example, it sets reminders to avoid times when the user is busy. The reminding unit also builds a system that analyzes the user's schedule in real time and suggests the optimal timing for reminding. For example, it sets reminders to times when the user is free. This makes it possible to analyze the user's schedule and suggest the optimal timing for reminding.

[0038] The reminder unit can customize reminder content to suit the user's preferences. For example, the reminder unit builds a system that customizes reminder content to suit the user's preferences. For example, it allows the user to select the reminder method they prefer (email, SMS, app notification, etc.). The reminder unit also analyzes the user's past reminder history and develops an algorithm that automatically generates reminder content that suits the user's preferences. For example, it learns the user's preferred reminder frequency and timing. The reminder unit also builds a system that customizes reminder content in real time to suit the user's preferences. For example, if the user wants to change the reminder content, it responds immediately. This allows reminder content to be customized to suit the user's preferences.

[0039] The reminder unit can send reminders not only via email or SMS, but also via social media or messaging apps. For example, the reminder unit builds a system that sends reminders not only via email or SMS, but also via social media or messaging apps. For example, it sends reminders via the social media or messaging apps that the user normally uses. The reminder unit also integrates multiple reminder methods so that the user can receive reminders in the way that is most convenient for them. For example, it allows the user to select from email, SMS, social media, and messaging apps. The reminder unit also builds a system that sends reminders simultaneously across multiple platforms. For example, for important procedures, it sends reminders via email, SMS, social media, and messaging apps. This allows reminders to be sent across multiple platforms.

[0040] The reminding unit can generate infographics for visually displaying the reminder content. The reminding unit, for example, builds a system for generating infographics for visually displaying the reminder content. For example, it graphically displays procedure steps and deadlines. The reminding unit also develops an algorithm for automatically generating infographics to display the reminder content in a visually easy-to-understand manner. For example, it shows the progress of the procedure in graphs and charts. The reminding unit also builds a system for generating infographics in real time for visually displaying the reminder content. For example, it updates the infographics as the user progresses with the procedure. In this way, it is possible to generate infographics for visually displaying the reminder content.

[0041] The data storage unit can automatically classify case law data and store it in an easy-to-search format. The data storage unit, for example, builds a system that automatically classifies case law data and stores it in an easy-to-search format. For example, it classifies it based on the type and content of the case law, allowing users to easily search it. The data storage unit also develops an automatic classification algorithm to efficiently classify case law data. For example, it analyzes the keywords and topics of the case law and classifies it into appropriate categories. The data storage unit also builds a system that classifies case law data in real time and stores it in an easy-to-search format. For example, it automatically classifies each time a new case law is added and stores it in a database. This makes it possible to automatically classify case law data and store it in an easy-to-search format.

[0042] The data accumulation unit can continuously update the FAQ data based on user feedback. The data accumulation unit, for example, builds a system that continuously updates the FAQ data based on user feedback. For example, it modifies the content of the FAQ based on feedback provided by the user. The data accumulation unit also analyzes user feedback and develops an algorithm that automatically updates the FAQ data. For example, it adds FAQ items based on user questions and opinions. The data accumulation unit also builds a system that updates the FAQ data in real time and improves the content based on user feedback. For example, when a user posts a new question, it is immediately reflected in the FAQ. This allows the FAQ data to be continuously updated based on user feedback.

[0043] The data accumulation unit can integrate case law data from different jurisdictions and provide information from an international perspective. For example, the data accumulation unit builds a system that integrates case law data from different jurisdictions and provides information from an international perspective. For example, it integrates case law data from multiple countries into a single database. The data accumulation unit also develops algorithms that automatically collect and integrate international case law data. For example, it obtains and integrates data from each country's case law database. The data accumulation unit also builds a system that integrates case law data from different jurisdictions in real time and provides information from an international perspective. For example, it automatically integrates new case law data whenever it is added and stores it in a database. This makes it possible to integrate case law data from different jurisdictions and provide information from an international perspective.

[0044] The data accumulation unit can create a dashboard for visually displaying case law or FAQ data. The data accumulation unit, for example, builds a system for creating a dashboard for visually displaying case law or FAQ data. For example, the type and content of case law are displayed graphically. The data accumulation unit also develops an algorithm for automatically generating a dashboard, and displays case law or FAQ data in a visually easy-to-understand manner. For example, the progress of case law or the update history of FAQs is displayed in graphs or charts. The data accumulation unit also builds a system for creating a dashboard in real time for visually displaying case law or FAQ data. For example, the dashboard is updated every time new data is added. In this way, a dashboard for visually displaying case law or FAQ data can be created.

[0045] The Cooperation Department can reflect feedback from lawyers in the system in real time. The Cooperation Department, for example, builds a system that reflects feedback from lawyers in the system in real time. For example, the information provided by lawyers is immediately reflected in a database. The Cooperation Department also develops an algorithm that automatically collects feedback from lawyers and reflects it in the system in real time. For example, the information entered by lawyers is automatically analyzed and reflected in the system. The Cooperation Department also builds a system that collects feedback from lawyers in real time and reflects it in the system. For example, the latest legal information provided by lawyers is immediately reflected in the system. This allows feedback from lawyers to be reflected in the system in real time.

[0046] The Cooperation Department can automatically update the latest legal information in cooperation with lawyers. For example, the Cooperation Department may work with lawyers to build a system that automatically updates the latest legal information. For example, when a new law comes into effect, the information provided by lawyers is immediately reflected in the system. The Cooperation Department may also develop an algorithm that automatically collects the latest legal information provided by lawyers and reflects it in the system. For example, the information entered by lawyers is automatically analyzed and reflected in the system. The Cooperation Department may also work with lawyers to build a system that updates the latest legal information in real time. For example, the latest case law and legal amendment information provided by lawyers is immediately reflected in the system. In this way, the Cooperation Department can automatically update the latest legal information in cooperation with lawyers.

[0047] The Cooperation Department will be able to cooperate with lawyers through an online platform, eliminating geographical constraints. For example, the Cooperation Department will build a system that will allow cooperation with lawyers through an online platform, eliminating geographical constraints. For example, it will use video conferencing and chat functions to cooperate with lawyers in remote locations. The Cooperation Department will also develop algorithms that will make cooperation with lawyers more efficient through the online platform. For example, it will share information provided by lawyers in real time and work together. The Cooperation Department will also build a system that will allow cooperation with lawyers through an online platform, eliminating geographical constraints. For example, it will use a cloud-based data sharing system to share information with lawyers. This will allow cooperation with lawyers through an online platform, eliminating geographical constraints.

[0048] The cooperation department can work with lawyers to develop training programs to improve the quality of legal advice provided to users. For example, the cooperation department works with lawyers to develop training programs to improve the quality of legal advice provided to users. For example, the cooperation department creates training programs based on specialized knowledge provided by lawyers. The cooperation department also develops an algorithm to automatically generate training programs to improve the quality of legal advice based on feedback from lawyers. For example, the cooperation department updates the training content based on the latest legal information provided by lawyers. The cooperation department also works with lawyers to build a system to update training programs in real time to improve the quality of legal advice provided to users. For example, new precedents and legal amendments provided by lawyers are instantly reflected in the training programs. This allows the cooperation department to develop training programs to improve the quality of legal advice provided to users in collaboration with lawyers.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The situation understanding unit can refer to the user's past consultation history and understand the situation more accurately based on similar cases. For example, it can retrieve the user's past consultation history from a database and understand the current situation based on similar cases. For example, if there has been a past consultation about a traffic accident, it can refer to that information and analyze the content of the current consultation. The situation understanding unit can also analyze the past consultation history and develop an algorithm to identify similar cases. For example, it can compare the content of past consultations with the content of the current consultation and find commonalities. The situation understanding unit can also refer to the user's past consultation history in real time and build a system to understand the situation based on similar cases. For example, it can automatically search the content of past consultations and provide information that is useful for the current consultation. This allows for a more accurate understanding of the situation based on the past consultation history.

[0051] The situation understanding unit analyzes not only the text entered by the user but also image or audio data, enabling a more multifaceted understanding of the situation. For example, a system can be constructed that analyzes image and audio data in addition to the text entered by the user. For example, it analyzes photos of the accident scene and audio messages uploaded by the user to understand the situation. The situation understanding unit also uses image recognition technology to understand the situation from image data provided by the user. For example, it analyzes photos of the accident scene to determine the extent of the damage. The situation understanding unit also uses audio analysis technology to understand the situation from audio data provided by the user. For example, it converts what the user says into text and analyzes it. This allows for the analysis of text, images, and audio data to understand the situation from more multifaceted perspectives.

[0052] The situation understanding unit can automatically translate inputs in different languages, achieving multilingual support. For example, a system is built that automatically translates and analyzes text input in different languages. For example, consultation content input in a language other than Japanese is automatically translated into Japanese. The situation understanding unit also develops a multilingual natural language processing system and analyzes inputs in different languages. For example, consultation content input in English or Chinese is analyzed and an appropriate response is proposed. The situation understanding unit also uses an automatic translation function to build a system that translates and analyzes inputs in different languages ​​in real time. For example, what a user says in English is translated into Japanese and analyzed. This enables automatic translation of inputs in different languages, enabling multilingual support.

[0053] The solution proposal unit can refer to a database of past legal precedents and propose solutions to similar cases. For example, a system can be constructed that refers to a database of past legal precedents and proposes solutions to similar cases. For example, an appropriate solution can be proposed based on past legal precedents related to traffic accidents. The solution proposal unit can also analyze the legal precedent database and develop an algorithm that automatically extracts solutions to similar cases. For example, it can find commonalities in past legal precedents and propose solutions. The solution proposal unit can also construct a system that refers to a database of past legal precedents in real time and proposes solutions to similar cases. For example, when a user enters the content of their consultation, it can instantly search for related legal precedents and propose solutions. This makes it possible to refer to a database of past legal precedents and propose solutions to similar cases.

[0054] The solution proposal unit can generate a customized solution according to the individual situation of the user. For example, a system is constructed that generates a customized solution according to the individual situation of the user. For example, the optimal solution is proposed based on the specific situation and needs of the user. The solution proposal unit also analyzes data input by the user and develops an algorithm that automatically generates a solution according to the individual situation. For example, the necessary procedures and documents are proposed according to the user's situation. The solution proposal unit also analyzes the individual situation of the user in real time and constructs a system that generates a customized solution. For example, the optimal solution is proposed based on information input by the user. This makes it possible to generate a customized solution according to the individual situation of the user.

[0055] The solution proposal unit can refer to legal information from different jurisdictions and propose solutions from an international perspective. For example, a system can be built that refers to legal information from different jurisdictions and proposes solutions from an international perspective. For example, when a user faces a legal issue overseas, an appropriate solution can be proposed. The solution proposal unit can also integrate international legal information into a database and develop an algorithm that automatically proposes solutions from different jurisdictions. For example, when a user consults about international business, an appropriate solution can be proposed. The solution proposal unit can also build a system that refers to legal information from different jurisdictions in real time and proposes solutions from an international perspective. For example, when a user consults about a multinational corporation, an appropriate solution can be proposed. This makes it possible to refer to legal information from different jurisdictions and propose solutions from an international perspective.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The situation understanding unit understands the user's situation. For example, when the user describes their situation, the generation AI analyzes and understands the content. The generation AI uses text generation AI (e.g., LLM) to analyze the user's input. The generation AI can also use multimodal generation AI to analyze data such as audio and images. Step 2: The solution proposal unit proposes an appropriate solution based on the situation understood by the situation understanding unit. For example, depending on the user's situation, the generation AI may suggest procedures such as reporting to the police, contacting an insurance company, or visiting a medical institution. The generation AI also provides a list of necessary documents and procedures, helping the user to proceed smoothly. Step 3: The reminder unit reminds the user of the procedures suggested by the solution suggestion unit. For example, the generation AI sends a notification when a procedure with a specific deadline approaches. This helps the user remember important procedures and take appropriate action. Step 4: The data storage unit accumulates data on legal precedents and FAQs. For example, the generation AI works with lawyers to accumulate data on legal precedents and FAQs, providing users with information to help them make more accurate decisions based on past legal precedents and frequently asked questions. Step 5: The Collaboration Department works with lawyers to collect the latest legal information. For example, the Generative AI works with lawyers to collect the latest legal information and precedents, and reflects this in the system. This allows the system to always provide appropriate advice to users based on the latest information.

[0058] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that suggests solutions and reminds users to take procedures by having them describe the situation they are in. This allows the AI ​​system to understand the user's situation, suggest appropriate solutions, and remind users to take procedures.

[0059] The AI ​​system according to the embodiment includes a situation understanding unit, a solution proposal unit, a reminder unit, a data storage unit, and a collaboration unit. The situation understanding unit understands the user's situation. For example, when the user describes their situation, the generation AI analyzes and understands the content. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's input. The generation AI can also analyze data such as audio and images using a multimodal generation AI. The solution proposal unit proposes an appropriate solution based on the situation understood by the situation understanding unit. For example, the generation AI proposes procedures such as reporting to the police, contacting an insurance company, and visiting a medical institution, depending on the user's situation. The generation AI also provides a list of necessary documents and procedures to help the user proceed smoothly with the procedures. The reminder unit reminds the user of the procedures proposed by the solution proposal unit. For example, the generation AI sends notifications when a procedure with a specific deadline approaches. This helps the user remember important procedures and respond appropriately. The data storage unit accumulates data on legal precedents and FAQs. For example, the generation AI works with lawyers to accumulate data on legal precedents and FAQs, providing users with information to make more accurate decisions based on past legal precedents and frequently asked questions. The collaboration unit works with lawyers to collect the latest legal information. For example, the generation AI works with lawyers to collect the latest legal information and legal precedents and reflects them in the system. This allows the system to always provide appropriate advice to users based on the latest information. As a result, the AI ​​system according to the embodiment can understand the user's situation, propose appropriate solutions, remind users of procedures, accumulate data, and work with lawyers to provide the latest legal information. For example, even if a user is involved in a traffic accident, the generation AI can assist the user in responding quickly and appropriately. Furthermore, by working with lawyers, the system can always provide the latest information, allowing users to use the system with peace of mind.

[0060] The situation understanding unit can analyze the user's tone of voice or speaking pattern, infer the user's emotional state, and reflect this in situation understanding. The situation understanding unit, for example, analyzes the user's tone of voice and speaking pattern when speaking to infer the emotional state. For example, it analyzes the pitch, speed, and strength of the voice to determine whether the user is nervous or relaxed. The situation understanding unit also analyzes the user's speaking pattern to infer the emotional state. For example, it analyzes repeated phrases and word choice in a conversation to determine whether the user is feeling anxious. The situation understanding unit also analyzes the user's tone of voice and speaking pattern in real time to infer the emotional state. For example, if the user suddenly raises their voice, it is determined that the user is feeling anger or stress. This allows the situation to be understood more accurately by taking the user's emotional state into consideration.

[0061] The situation understanding unit can refer to the user's past consultation history and understand the situation more accurately based on similar cases. For example, the situation understanding unit retrieves the user's past consultation history from a database and understands the current situation based on similar cases. For example, if there has been a past consultation about a traffic accident, the situation understanding unit refers to that information and analyzes the content of the current consultation. The situation understanding unit also analyzes the past consultation history and develops an algorithm to identify similar cases. For example, it compares the content of the past consultation with the content of the current consultation and finds common points. The situation understanding unit also references the user's past consultation history in real time and builds a system that understands the situation based on similar cases. For example, it automatically searches the content of past consultations and provides information that is useful for the current consultation. This allows the situation to be understood more accurately based on the past consultation history.

[0062] The situation understanding unit can use the emotion estimation function to analyze the user's emotions in real time and respond according to the emotions. The situation understanding unit, for example, uses the emotion estimation function to analyze the user's emotions in real time. For example, it analyzes the tone of voice and facial expressions when the user speaks and calculates an emotion score. The situation understanding unit also builds a system that responds according to the user's emotional state. For example, if the user is feeling anxious, it provides advice to reassure the user. The situation understanding unit also uses the emotion estimation function to analyze the user's emotions in real time and adjusts the response based on the results. For example, if the user is feeling angry, it presents guidelines for responding calmly. This makes it possible to respond according to the user's emotions.

[0063] The situation understanding unit analyzes not only the text entered by the user but also image or audio data, enabling a more multifaceted understanding of the situation. The situation understanding unit, for example, builds a system that analyzes image and audio data in addition to the text entered by the user. For example, it analyzes photos of the accident scene and audio messages uploaded by the user to understand the situation. The situation understanding unit also uses image recognition technology to understand the situation from image data provided by the user. For example, it analyzes photos of the accident scene to determine the extent of the damage. The situation understanding unit also uses audio analysis technology to understand the situation from audio data provided by the user. For example, it converts what the user says into text and analyzes it. This allows the system to analyze text, images, and audio data and understand the situation from more facets.

[0064] The situation understanding unit can automatically translate inputs in different languages ​​to achieve multilingual support. The situation understanding unit, for example, builds a system that automatically translates and analyzes text input in different languages. For example, it automatically translates consultation content input in a language other than Japanese into Japanese. The situation understanding unit also develops a multilingual natural language processing system to analyze inputs in different languages. For example, it analyzes consultation content input in English or Chinese and proposes an appropriate solution. The situation understanding unit also builds a system that uses an automatic translation function to translate and analyze inputs in different languages ​​in real time. For example, it translates and analyzes content spoken by a user in English. This enables automatic translation of inputs in different languages ​​and multilingual support.

[0065] The situation understanding unit can use the emotion estimation function to provide customized advice based on the user's emotions. The situation understanding unit, for example, uses the emotion estimation function to build a system that provides customized advice based on the user's emotions. For example, if the user is feeling anxious, specific advice to reassure the user is provided. The situation understanding unit also analyzes the user's emotional state in real time and provides customized advice based on the results. For example, if the user is feeling stressed, it suggests ways to relax. The situation understanding unit also uses the emotion estimation function to develop a system that provides advice based on the user's emotions. For example, if the user is feeling angry, it provides advice to stay calm. This makes it possible to provide customized advice based on the user's emotions.

[0066] The solution proposal unit can refer to a database of past legal precedents and propose solutions to similar cases. The solution proposal unit, for example, builds a system that refers to a database of past legal precedents and proposes solutions to similar cases. For example, it proposes an appropriate solution based on past legal precedents related to traffic accidents. The solution proposal unit also analyzes the legal precedent database and develops an algorithm that automatically extracts solutions to similar cases. For example, it finds commonalities in past legal precedents and proposes solutions. The solution proposal unit also builds a system that refers to the database of past legal precedents in real time and proposes solutions to similar cases. For example, when a user enters the content of their consultation, it instantly searches for related legal precedents and proposes solutions. This makes it possible to refer to the database of past legal precedents and propose solutions to similar cases.

[0067] The solution proposal unit can generate a solution customized for the user's individual situation. The solution proposal unit, for example, builds a system that generates a solution customized for the user's individual situation. For example, it proposes an optimal solution based on the user's specific situation and needs. The solution proposal unit also analyzes data input by the user and develops an algorithm that automatically generates a solution tailored to the individual situation. For example, it proposes necessary procedures and documents based on the user's situation. The solution proposal unit also builds a system that analyzes the user's individual situation in real time and generates a customized solution. For example, it proposes an optimal solution based on information input by the user. This makes it possible to generate a solution customized for the user's individual situation.

[0068] The coping method suggestion unit can use the emotion estimation function to suggest coping methods that take the user's emotions into consideration. The coping method suggestion unit, for example, uses the emotion estimation function to build a system that suggests coping methods that take the user's emotions into consideration. For example, if the user is feeling anxious, the coping method suggestion unit will suggest specific coping methods to reassure the user. The coping method suggestion unit also analyzes the user's emotional state in real time and suggests coping methods that take the emotions into consideration based on the results. For example, if the user is feeling stressed, the coping method suggestion unit will suggest ways to relax. The coping method suggestion unit also uses the emotion estimation function to develop a system that suggests coping methods that take the user's emotions into consideration. For example, if the user is feeling angry, the coping method suggestion unit will suggest coping methods to calm the user down. This makes it possible to suggest coping methods that take the user's emotions into consideration.

[0069] The solution proposal unit can refer to legal information from different jurisdictions and propose solutions from an international perspective. The solution proposal unit, for example, builds a system that references legal information from different jurisdictions and proposes solutions from an international perspective. For example, when a user faces a legal issue overseas, it proposes an appropriate solution. The solution proposal unit also integrates international legal information into a database and develops an algorithm that automatically proposes solutions from different jurisdictions. For example, when a user consults about international business, it proposes an appropriate solution. The solution proposal unit also builds a system that references legal information from different jurisdictions in real time and proposes solutions from an international perspective. For example, when a user consults about a multinational corporation, it proposes an appropriate solution. This makes it possible to refer to legal information from different jurisdictions and propose solutions from an international perspective.

[0070] The solution proposal unit can provide a step-by-step guide for a specific procedure in addition to proposing a solution. The solution proposal unit, for example, builds a system that provides a step-by-step guide for a specific procedure in addition to proposing a solution. For example, a detailed guide is provided so that a user can proceed through the necessary procedures in order. The solution proposal unit also develops an algorithm that automatically generates a step-by-step guide for a procedure. For example, the steps of the necessary procedure are explained in detail based on information entered by the user. The solution proposal unit also builds a system that provides a step-by-step guide for a specific procedure in real time along with proposing a solution. For example, the guide is displayed sequentially as the user proceeds with the procedure. This makes it possible to provide a step-by-step guide for a specific procedure in addition to proposing a solution.

[0071] The coping method suggestion unit can use the emotion estimation function to set a priority order for coping methods based on the user's emotions. The coping method suggestion unit, for example, uses the emotion estimation function to build a system that sets a priority order for coping methods based on the user's emotions. For example, if the user is feeling strong anxiety, it preferentially suggests coping methods that will reassure the user. The coping method suggestion unit also analyzes the user's emotional state in real time and sets a priority order for coping methods based on the results. For example, if the user is feeling stressed, it preferentially suggests coping methods for relaxation. The coping method suggestion unit also uses the emotion estimation function to develop a system that sets a priority order for coping methods based on the user's emotions. For example, if the user is feeling angry, it preferentially suggests coping methods for calming down. This makes it possible to set a priority order for coping methods based on the user's emotions.

[0072] The reminding unit can analyze the user's schedule and suggest the optimal timing for reminding. The reminding unit, for example, builds a system that analyzes the user's schedule and suggests the optimal timing for reminding. For example, it references the user's calendar or planner and reminds them of important procedures. The reminding unit also analyzes the user's schedule data and develops an algorithm that automatically calculates the optimal timing for reminding. For example, it sets reminders to avoid times when the user is busy. The reminding unit also builds a system that analyzes the user's schedule in real time and suggests the optimal timing for reminding. For example, it sets reminders to times when the user is free. This makes it possible to analyze the user's schedule and suggest the optimal timing for reminding.

[0073] The reminder unit can customize reminder content to suit the user's preferences. For example, the reminder unit builds a system that customizes reminder content to suit the user's preferences. For example, it allows the user to select the reminder method they prefer (email, SMS, app notification, etc.). The reminder unit also analyzes the user's past reminder history and develops an algorithm that automatically generates reminder content that suits the user's preferences. For example, it learns the user's preferred reminder frequency and timing. The reminder unit also builds a system that customizes reminder content in real time to suit the user's preferences. For example, if the user wants to change the reminder content, it responds immediately. This allows reminder content to be customized to suit the user's preferences.

[0074] The reminding unit can use the emotion estimation function to select a reminding method according to the emotional state of the user. The reminding unit, for example, uses the emotion estimation function to build a system that selects a reminding method according to the emotional state of the user. For example, if the user is feeling stressed, a gentle reminding method is selected. The reminding unit also analyzes the user's emotional state in real time and selects a reminding method based on the results. For example, if the user is relaxed, a light reminder is performed. The reminding unit also uses the emotion estimation function to develop a system that selects a reminding method according to the emotional state of the user. For example, if the user is nervous, a gentle reminder method is selected. This makes it possible to select a reminding method according to the user's emotional state.

[0075] The reminder unit can send reminders not only via email or SMS, but also via social media or messaging apps. For example, the reminder unit builds a system that sends reminders not only via email or SMS, but also via social media or messaging apps. For example, it sends reminders via the social media or messaging apps that the user normally uses. The reminder unit also integrates multiple reminder methods so that the user can receive reminders in the way that is most convenient for them. For example, it allows the user to select from email, SMS, social media, and messaging apps. The reminder unit also builds a system that sends reminders simultaneously across multiple platforms. For example, for important procedures, it sends reminders via email, SMS, social media, and messaging apps. This allows reminders to be sent across multiple platforms.

[0076] The reminding unit can generate infographics for visually displaying the reminder content. The reminding unit, for example, builds a system for generating infographics for visually displaying the reminder content. For example, it graphically displays procedure steps and deadlines. The reminding unit also develops an algorithm for automatically generating infographics to display the reminder content in a visually easy-to-understand manner. For example, it shows the progress of the procedure in graphs and charts. The reminding unit also builds a system for generating infographics in real time for visually displaying the reminder content. For example, it updates the infographics as the user progresses with the procedure. In this way, it is possible to generate infographics for visually displaying the reminder content.

[0077] The reminding unit can use the emotion estimation function to adjust the frequency of reminders based on the user's emotions. The reminding unit, for example, uses the emotion estimation function to build a system that adjusts the frequency of reminders based on the user's emotions. For example, if the user is feeling stressed, the frequency of reminders is reduced. The reminding unit also analyzes the user's emotional state in real time and adjusts the frequency of reminders based on the results. For example, if the user is relaxed, the frequency of reminders is increased. The reminding unit also uses the emotion estimation function to develop a system that adjusts the frequency of reminders based on the user's emotions. For example, if the user is nervous, the frequency of reminders is appropriately adjusted. This makes it possible to adjust the frequency of reminders based on the user's emotions.

[0078] The data storage unit can automatically classify case law data and store it in an easy-to-search format. The data storage unit, for example, builds a system that automatically classifies case law data and stores it in an easy-to-search format. For example, it classifies it based on the type and content of the case law, allowing users to easily search it. The data storage unit also develops an automatic classification algorithm to efficiently classify case law data. For example, it analyzes the keywords and topics of the case law and classifies it into appropriate categories. The data storage unit also builds a system that classifies case law data in real time and stores it in an easy-to-search format. For example, it automatically classifies each time a new case law is added and stores it in a database. This makes it possible to automatically classify case law data and store it in an easy-to-search format.

[0079] The data accumulation unit can continuously update the FAQ data based on user feedback. The data accumulation unit, for example, builds a system that continuously updates the FAQ data based on user feedback. For example, it modifies the content of the FAQ based on feedback provided by the user. The data accumulation unit also analyzes user feedback and develops an algorithm that automatically updates the FAQ data. For example, it adds FAQ items based on user questions and opinions. The data accumulation unit also builds a system that updates the FAQ data in real time and improves the content based on user feedback. For example, when a user posts a new question, it is immediately reflected in the FAQ. This allows the FAQ data to be continuously updated based on user feedback.

[0080] The data accumulation unit can use the emotion estimation function to set the priority of FAQs based on the user's emotions. The data accumulation unit, for example, uses the emotion estimation function to build a system that sets the priority of FAQs based on the user's emotions. For example, if the user is feeling anxious, FAQs that will reassure the user are displayed preferentially. The data accumulation unit also analyzes the user's emotional state in real time and sets the priority of FAQs based on the results. For example, if the user is feeling stressed, FAQs that will help the user relax are displayed preferentially. The data accumulation unit also uses the emotion estimation function to develop a system that sets the priority of FAQs based on the user's emotions. For example, if the user is feeling angry, FAQs that will help the user stay calm are displayed preferentially. This makes it possible to set the priority of FAQs based on the user's emotions.

[0081] The data accumulation unit can integrate case law data from different jurisdictions and provide information from an international perspective. For example, the data accumulation unit builds a system that integrates case law data from different jurisdictions and provides information from an international perspective. For example, it integrates case law data from multiple countries into a single database. The data accumulation unit also develops algorithms that automatically collect and integrate international case law data. For example, it obtains and integrates data from each country's case law database. The data accumulation unit also builds a system that integrates case law data from different jurisdictions in real time and provides information from an international perspective. For example, it automatically integrates new case law data whenever it is added and stores it in a database. This makes it possible to integrate case law data from different jurisdictions and provide information from an international perspective.

[0082] The data accumulation unit can create a dashboard for visually displaying case law or FAQ data. The data accumulation unit, for example, builds a system for creating a dashboard for visually displaying case law or FAQ data. For example, the type and content of case law are displayed graphically. The data accumulation unit also develops an algorithm for automatically generating a dashboard, and displays case law or FAQ data in a visually easy-to-understand manner. For example, the progress of case law or the update history of FAQs is displayed in graphs or charts. The data accumulation unit also builds a system for creating a dashboard in real time for visually displaying case law or FAQ data. For example, the dashboard is updated every time new data is added. In this way, a dashboard for visually displaying case law or FAQ data can be created.

[0083] The data accumulation unit can use the emotion estimation function to recommend case law or FAQs based on the user's emotions. The data accumulation unit, for example, uses the emotion estimation function to build a system that recommends case law or FAQs based on the user's emotions. For example, if the user is feeling anxious, it recommends case law or FAQs that will reassure the user. The data accumulation unit also analyzes the user's emotional state in real time and recommends case law or FAQs based on the results. For example, if the user is feeling stressed, it recommends case law or FAQs that will help the user relax. The data accumulation unit also uses the emotion estimation function to develop a system that recommends case law or FAQs based on the user's emotions. For example, if the user is feeling angry, it recommends case law or FAQs that will help the user stay calm. This makes it possible to recommend case law or FAQs based on the user's emotions.

[0084] The Cooperation Department can reflect feedback from lawyers in the system in real time. The Cooperation Department, for example, builds a system that reflects feedback from lawyers in the system in real time. For example, the information provided by lawyers is immediately reflected in a database. The Cooperation Department also develops an algorithm that automatically collects feedback from lawyers and reflects it in the system in real time. For example, the information entered by lawyers is automatically analyzed and reflected in the system. The Cooperation Department also builds a system that collects feedback from lawyers in real time and reflects it in the system. For example, the latest legal information provided by lawyers is immediately reflected in the system. This allows feedback from lawyers to be reflected in the system in real time.

[0085] The Cooperation Department can automatically update the latest legal information in cooperation with lawyers. For example, the Cooperation Department may work with lawyers to build a system that automatically updates the latest legal information. For example, when a new law comes into effect, the information provided by lawyers is immediately reflected in the system. The Cooperation Department may also develop an algorithm that automatically collects the latest legal information provided by lawyers and reflects it in the system. For example, the information entered by lawyers is automatically analyzed and reflected in the system. The Cooperation Department may also work with lawyers to build a system that updates the latest legal information in real time. For example, the latest case law and legal amendment information provided by lawyers is immediately reflected in the system. In this way, the Cooperation Department can automatically update the latest legal information in cooperation with lawyers.

[0086] The cooperation department can use the emotion estimation function to select a cooperation method according to the emotional state of the lawyer. For example, the cooperation department uses the emotion estimation function to build a system that selects a cooperation method according to the emotional state of the lawyer. For example, if the lawyer is feeling stressed, it selects a cooperation method that will reduce the lawyer's burden. The cooperation department also analyzes the emotional state of the lawyer in real time and selects a cooperation method based on the results. For example, if the lawyer is relaxed, it encourages active cooperation. The cooperation department also uses the emotion estimation function to develop a system that selects a cooperation method according to the emotional state of the lawyer. For example, if the lawyer is nervous, it adjusts the frequency and content of cooperation. This makes it possible to select a cooperation method according to the emotional state of the lawyer.

[0087] The Cooperation Department will be able to cooperate with lawyers through an online platform, eliminating geographical constraints. For example, the Cooperation Department will build a system that will allow cooperation with lawyers through an online platform, eliminating geographical constraints. For example, it will use video conferencing and chat functions to cooperate with lawyers in remote locations. The Cooperation Department will also develop algorithms that will make cooperation with lawyers more efficient through the online platform. For example, it will share information provided by lawyers in real time and work together. The Cooperation Department will also build a system that will allow cooperation with lawyers through an online platform, eliminating geographical constraints. For example, it will use a cloud-based data sharing system to share information with lawyers. This will allow cooperation with lawyers through an online platform, eliminating geographical constraints.

[0088] The cooperation department can work with lawyers to develop training programs to improve the quality of legal advice provided to users. For example, the cooperation department works with lawyers to develop training programs to improve the quality of legal advice provided to users. For example, the cooperation department creates training programs based on specialized knowledge provided by lawyers. The cooperation department also develops an algorithm to automatically generate training programs to improve the quality of legal advice based on feedback from lawyers. For example, the cooperation department updates the training content based on the latest legal information provided by lawyers. The cooperation department also works with lawyers to build a system to update training programs in real time to improve the quality of legal advice provided to users. For example, new precedents and legal amendments provided by lawyers are instantly reflected in the training programs. This allows the cooperation department to develop training programs to improve the quality of legal advice provided to users in collaboration with lawyers.

[0089] The cooperation department can use the emotion estimation function to set priorities for cooperation based on the emotions of the lawyer. For example, the cooperation department uses the emotion estimation function to build a system that sets priorities for cooperation based on the emotions of the lawyer. For example, if the lawyer is feeling stressed, cooperation to reduce the burden is prioritized. The cooperation department also analyzes the emotional state of the lawyer in real time and sets priorities for cooperation based on the results. For example, if the lawyer is relaxed, active cooperation is prioritized. The cooperation department also uses the emotion estimation function to develop a system that sets priorities for cooperation based on the emotions of the lawyer. For example, if the lawyer is nervous, the frequency and content of cooperation is adjusted. In this way, it is possible to set priorities for cooperation based on the emotions of the lawyer.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The situation understanding unit can analyze the user's tone of voice or speaking pattern to infer their emotional state and reflect this in situation understanding. For example, the situation understanding unit can analyze the user's tone of voice and speaking pattern when they speak to infer their emotional state. For example, it can analyze the pitch, speed, and strength of their voice to determine whether the user is nervous or relaxed. The situation understanding unit can also analyze the user's speaking pattern to infer their emotional state. For example, it can analyze repeated phrases and word choice in a conversation to determine whether the user is feeling anxious. The situation understanding unit can also analyze the user's tone of voice and speaking pattern in real time to infer their emotional state. For example, if the user suddenly raises their voice, it can determine that the user is feeling angry or stressed. This allows the situation to be understood more accurately by taking the user's emotional state into consideration.

[0092] The situation understanding unit can refer to the user's past consultation history and understand the situation more accurately based on similar cases. For example, it can retrieve the user's past consultation history from a database and understand the current situation based on similar cases. For example, if there has been a past consultation about a traffic accident, it can refer to that information and analyze the content of the current consultation. The situation understanding unit can also analyze the past consultation history and develop an algorithm to identify similar cases. For example, it can compare the content of past consultations with the content of the current consultation and find commonalities. The situation understanding unit can also refer to the user's past consultation history in real time and build a system to understand the situation based on similar cases. For example, it can automatically search the content of past consultations and provide information that is useful for the current consultation. This allows for a more accurate understanding of the situation based on the past consultation history.

[0093] The situation understanding unit can use the emotion estimation function to analyze the user's emotions in real time and respond according to the emotions. For example, the emotion estimation function is used to analyze the user's emotions in real time. For example, the emotion estimation function is used to analyze the user's tone of voice and facial expressions when the user speaks, and an emotion score is calculated. The situation understanding unit also builds a system that responds according to the user's emotional state. For example, if the user is feeling anxious, the system provides advice to reassure the user. The situation understanding unit also uses the emotion estimation function to analyze the user's emotions in real time and adjusts the response based on the results. For example, if the user is feeling angry, the system presents guidelines for responding calmly. This makes it possible to respond according to the user's emotions.

[0094] The situation understanding unit analyzes not only the text entered by the user but also image or audio data, enabling a more multifaceted understanding of the situation. For example, a system can be constructed that analyzes image and audio data in addition to the text entered by the user. For example, it analyzes photos of the accident scene and audio messages uploaded by the user to understand the situation. The situation understanding unit also uses image recognition technology to understand the situation from image data provided by the user. For example, it analyzes photos of the accident scene to determine the extent of the damage. The situation understanding unit also uses audio analysis technology to understand the situation from audio data provided by the user. For example, it converts what the user says into text and analyzes it. This allows for the analysis of text, images, and audio data to understand the situation from more multifaceted perspectives.

[0095] The situation understanding unit can automatically translate inputs in different languages, achieving multilingual support. For example, a system is built that automatically translates and analyzes text input in different languages. For example, consultation content input in a language other than Japanese is automatically translated into Japanese. The situation understanding unit also develops a multilingual natural language processing system and analyzes inputs in different languages. For example, consultation content input in English or Chinese is analyzed and an appropriate response is proposed. The situation understanding unit also uses an automatic translation function to build a system that translates and analyzes inputs in different languages ​​in real time. For example, what a user says in English is translated into Japanese and analyzed. This enables automatic translation of inputs in different languages, enabling multilingual support.

[0096] The situation understanding unit can use the emotion estimation function to provide customized advice based on the user's emotions. For example, a system is constructed using the emotion estimation function to provide customized advice based on the user's emotions. For example, if the user is feeling anxious, specific advice to reassure the user is provided. The situation understanding unit also analyzes the user's emotional state in real time and provides customized advice based on the results. For example, if the user is feeling stressed, a method for relaxing is suggested. The situation understanding unit also uses the emotion estimation function to develop a system that provides advice based on the user's emotions. For example, if the user is feeling angry, advice to stay calm is provided. This makes it possible to provide customized advice based on the user's emotions.

[0097] The solution proposal unit can refer to a database of past legal precedents and propose solutions to similar cases. For example, a system can be constructed that refers to a database of past legal precedents and proposes solutions to similar cases. For example, an appropriate solution can be proposed based on past legal precedents related to traffic accidents. The solution proposal unit can also analyze the legal precedent database and develop an algorithm that automatically extracts solutions to similar cases. For example, it can find commonalities in past legal precedents and propose solutions. The solution proposal unit can also construct a system that refers to a database of past legal precedents in real time and proposes solutions to similar cases. For example, when a user enters the content of their consultation, it can instantly search for related legal precedents and propose solutions. This makes it possible to refer to a database of past legal precedents and propose solutions to similar cases.

[0098] The solution proposal unit can generate a customized solution according to the individual situation of the user. For example, a system is constructed that generates a customized solution according to the individual situation of the user. For example, the optimal solution is proposed based on the specific situation and needs of the user. The solution proposal unit also analyzes data input by the user and develops an algorithm that automatically generates a solution according to the individual situation. For example, the necessary procedures and documents are proposed according to the user's situation. The solution proposal unit also analyzes the individual situation of the user in real time and constructs a system that generates a customized solution. For example, the optimal solution is proposed based on information input by the user. This makes it possible to generate a customized solution according to the individual situation of the user.

[0099] The coping method suggestion unit can use the emotion estimation function to suggest coping methods that take the user's emotions into consideration. For example, a system is constructed using the emotion estimation function to suggest coping methods that take the user's emotions into consideration. For example, if the user is feeling anxious, a specific coping method to reassure the user is suggested. The coping method suggestion unit also analyzes the user's emotional state in real time and suggests coping methods that take the emotions into consideration based on the results. For example, if the user is feeling stressed, a method for relaxation is suggested. The coping method suggestion unit also uses the emotion estimation function to develop a system that suggests coping methods that take the user's emotions into consideration. For example, if the user is feeling angry, a coping method to calm the user is suggested. This makes it possible to suggest coping methods that take the user's emotions into consideration.

[0100] The solution proposal unit can refer to legal information from different jurisdictions and propose solutions from an international perspective. For example, a system can be built that refers to legal information from different jurisdictions and proposes solutions from an international perspective. For example, when a user faces a legal issue overseas, an appropriate solution can be proposed. The solution proposal unit can also integrate international legal information into a database and develop an algorithm that automatically proposes solutions from different jurisdictions. For example, when a user consults about international business, an appropriate solution can be proposed. The solution proposal unit can also build a system that refers to legal information from different jurisdictions in real time and proposes solutions from an international perspective. For example, when a user consults about a multinational corporation, an appropriate solution can be proposed. This makes it possible to refer to legal information from different jurisdictions and propose solutions from an international perspective.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The situation understanding unit understands the user's situation. For example, when the user describes their situation, the generation AI analyzes and understands the content. The generation AI uses text generation AI (e.g., LLM) to analyze the user's input. The generation AI can also use multimodal generation AI to analyze data such as audio and images. Step 2: The solution proposal unit proposes an appropriate solution based on the situation understood by the situation understanding unit. For example, depending on the user's situation, the generation AI may suggest procedures such as reporting to the police, contacting an insurance company, or visiting a medical institution. The generation AI also provides a list of necessary documents and procedures, helping the user to proceed smoothly. Step 3: The reminder unit reminds the user of the procedures suggested by the solution suggestion unit. For example, the generation AI sends a notification when a procedure with a specific deadline approaches. This helps the user remember important procedures and take appropriate action. Step 4: The data storage unit accumulates data on legal precedents and FAQs. For example, the generation AI works with lawyers to accumulate data on legal precedents and FAQs, providing users with information to help them make more accurate decisions based on past legal precedents and frequently asked questions. Step 5: The Collaboration Department works with lawyers to collect the latest legal information. For example, the Generative AI works with lawyers to collect the latest legal information and precedents, and reflects this in the system. This allows the system to always provide appropriate advice to users based on the latest information.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, a situation understanding unit that understands a user's situation; a countermeasure suggestion unit that suggests an appropriate countermeasure based on the situation understood by the situation understanding unit; a reminding unit that reminds the user of the procedure proposed by the solution proposal unit; a data storage unit that stores data on legal precedents or FAQs; A cooperation department that cooperates with lawyers to collect the latest legal information. A system characterized by:

2. The situation understanding unit Analyzing the user's tone of voice or speaking patterns to estimate their emotional state and reflect it in situation understanding 2. The system of claim 1.

3. The situation understanding unit Refer to the user's past consultation history and understand the situation more accurately based on similar cases 2. The system of claim 1.

4. The situation understanding unit Analyzing the user's emotions in real time and responding in accordance with the emotions 2. The system of claim 1.

5. The situation understanding unit Analyze not only the text entered by the user but also image or audio data to understand the situation from multiple angles 2. The system of claim 1.

6. The situation understanding unit Automatically translates input in different languages ​​to achieve multilingual support 2. The system of claim 1.

7. The situation understanding unit Providing customized advice based on the user's emotions 2. The system of claim 1.

8. The solution proposal unit Refer to a database of past cases and propose the above-mentioned measures for similar cases 2. The system of claim 1.

Images