System

Abstract

An object of a system according to an embodiment is to enable even a user without expert knowledge to easily obtain legal advice.SOLUTION: A system according to an embodiment includes an input unit, a situation analysis unit, a legal advice generation unit, and an advice provision unit. The input unit allows a user to input a specific situation. The situation analysis unit analyzes the situation input by the user input reception unit. The legal advice generation unit generates legal advice based on the situation analyzed by the situation analysis unit. The advice providing unit provides the user with the legal advice generated by the legal advice generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users without specialized knowledge of legal issues to obtain appropriate advice.

[0005] The system according to the embodiment aims to enable even users without specialized knowledge to easily obtain legal advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a situation analysis unit, a legal advice generation unit, and an advice providing unit. The input unit allows a user to input a specific situation. The situation analysis unit analyzes the situation input by the user input receiving unit. The legal advice generation unit generates legal advice based on the situation analyzed by the situation analysis unit. The advice providing unit provides the user with the legal advice generated by the legal advice generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows even users without specialized knowledge to easily obtain legal advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A problem-solving tool according to an embodiment of the present invention is a system that allows users to easily obtain lawyer-level answers simply by inputting a specific situation. This system uses generative AI to analyze the situation input by the user and provide appropriate legal advice and solutions. This allows users to easily obtain lawyer-level advice even without specialized legal knowledge. For example, this enables quick and appropriate responses to various legal issues faced in daily life, helping to prevent and resolve legal troubles.

[0029] A problem-solving tool according to an embodiment includes a user input receiving unit, a situation analysis unit, a legal advice generation unit, and an advice providing unit. The user inputs a specific situation through the user input receiving unit. For example, the user can input a specific problem such as "noise trouble with a neighbor" or "question about the contents of a contract." The situation analysis unit uses a generation AI to analyze the situation input by the user input receiving unit. For example, the generation AI analyzes the input situation using a text generation AI (e.g., LLM) to understand its content. The generation AI can also analyze the input situation using a multimodal generation AI. The generation AI generates appropriate advice by referring to noise-related laws and past court cases, for example. The legal advice generation unit generates legal advice based on the situation analyzed by the situation analysis unit. For example, the generation AI provides specific advice such as, "When dealing with noise problems, it is recommended that you first talk to your neighbor directly, and if that does not resolve the issue, consult your local government's noise consultation center." The advice providing unit provides the legal advice generated by the legal advice generation unit to the user. For example, the legal advice and solutions generated by the generation AI are provided to the user in text format. This allows the user to take specific action toward solving the problem by referring to the advice provided by the generative AI. As a result, the problem-solving tool according to the embodiment allows the user to easily obtain lawyer-level answers simply by inputting a specific situation. For example, this enables a quick and appropriate response to legal issues faced by the user, which is useful for preventing and resolving legal troubles.

[0030] The user input acceptance unit can use voice input to explain a situation in a natural conversational style. For example, the user inputs voice using a smartphone or microphone, and the generation AI converts the voice into text. For example, if the user says, "My neighbor is playing loud music in the middle of the night," this content is entered as text. The user input acceptance unit also uses voice recognition technology to convert what the user says into text in real time, and the generation AI analyzes the text. For example, if the user says, "I don't understand this part of the contract," this part is entered as text. When the user explains the situation using voice input, the generation AI interjects questions at appropriate times to elicit more detailed information. For example, it asks, "Please tell me specifically what kind of sound do you hear?" This allows the user to explain the situation in a natural conversational style using voice input.

[0031] The user input acceptance unit allows the generation AI to automatically ask for additional information related to the input text, thereby understanding the situation in more detail. For example, if the user inputs "My neighbors are noisy," the generation AI automatically asks additional questions such as "What specific time periods are the noise occurring?" If the user inputs "The contents of the contract are unclear," the user input acceptance unit asks questions such as "Which parts are unclear? Please tell me the specific clauses," thereby collecting more detailed information. If the user inputs "Trouble with my neighbors," the user input acceptance unit presents options such as "What kind of trouble is it? Is it noise, trash disposal, or some other issue?" to understand the situation in more detail. This allows the generation AI to automatically ask for additional information related to the input and understand the situation in more detail.

[0032] The user input reception unit adds a function that allows the user to attach an image or video when inputting, and visual information can also be used for analysis. For example, when the user describes the situation of a noise problem, the user input reception unit enables the user to attach a video of the noise source being photographed. The generative AI analyzes the video to grasp the type and occurrence situation of the noise. Also, when the user inputs a question regarding the content of a contract, the user input reception unit enables the user to attach an image of the relevant part of the contract being photographed. The generative AI analyzes the image to understand the content of the contract. Additionally, when the user describes a trouble with a neighbor, the user input reception unit enables the user to attach a photo of the trouble scene being photographed. The generative AI analyzes the photo to grasp the specific situation of the trouble. As a result, the user can attach images and videos, and visual information can also be used for analysis.

[0033] The user input reception unit supports input in different languages and can also accommodate international users. For example, the user input reception unit enables the user to input in different languages such as English, French, Chinese, etc., and the generative AI automatically recognizes and analyzes the language. For example, when the user inputs "My neighbor is making noise at night" in English, the content is analyzed. Also, the user input reception unit automatically translates the text input in different languages, and the generative AI performs analysis based on the translation result. For example, when the user inputs "Mon voisin fait du bruit la nuit" in French, the content is automatically translated and analyzed. Additionally, when the user inputs in different languages, the user input reception unit enables the generative AI to provide legal advice corresponding to that language. For example, when the user inputs "我的?居?上很?" in Chinese, advice based on Chinese law is provided. As a result, it supports input in different languages and can also accommodate international users.

[0034] The situation analysis unit can perform more accurate analysis by referring to a database of similar past cases or precedents. For example, when the generation AI analyzes the user's input, the situation analysis unit searches a database for similar past cases and generates advice based on the results. For example, the situation analysis unit provides advice by referring to past precedents regarding noise disputes. The situation analysis unit also refers to a precedent database to identify the precedent that most closely matches the user's situation and generate advice based on that precedent. For example, the situation analysis unit provides advice by referring to precedents regarding the contents of a contract. The situation analysis unit also uses a database of similar past cases or precedents to allow the generation AI to analyze the user's situation and provide the most appropriate advice. For example, the situation analysis unit provides advice based on past precedents regarding neighbor disputes. This allows more accurate analysis to be performed by referring to a database of similar past cases or precedents.

[0035] The situation analysis unit can automatically take into account the laws of the user's region or country. For example, when the generation AI analyzes the user's input, the situation analysis unit automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Japan would be provided with advice based on Japanese law. The situation analysis unit also searches a database for the laws of the user's region or country, and the generation AI generates advice based on those laws. For example, a user in the United States would be provided with advice based on American law. The situation analysis unit also builds a system in which the generation AI automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Europe would be provided with advice based on European law. This makes it possible to provide more appropriate legal advice by automatically taking into account the laws of the user's region or country.

[0036] The legal advice generation unit can present multiple options, allowing the user to select the most appropriate advice. For example, the legal advice generation unit uses a generation AI to generate multiple pieces of legal advice based on the user's situation and present them to the user as options. For example, it provides options such as "discuss the matter directly with your neighbor" or "consult your local government's consultation center." When the user selects an option, the generation AI explains the advantages and disadvantages of each option. For example, it explains the "advantages and disadvantages of discussing the matter directly." The legal advice generation unit also builds a system in which the generation AI generates multiple pieces of legal advice based on the user's situation, allowing the user to select the most appropriate advice. For example, it provides options such as "consult a lawyer about any questions regarding the contents of the contract" or "do your own research." This allows the user to select the most appropriate advice.

[0037] The legal advice generation unit can include specific action steps or templates of required documents in the advice it generates. For example, the legal advice generation unit includes specific action steps in the legal advice that the generation AI provides to the user. For example, it provides "specific steps for initiating a discussion with a neighbor." The legal advice generation unit also includes templates of required documents in the legal advice that the generation AI provides to the user. For example, it provides "document templates for submission to a local government consultation desk." The legal advice generation unit also builds a system that includes specific action steps and templates of required documents in the legal advice that the generation AI provides to the user. For example, it provides "steps for consulting a lawyer about questions about the contents of a contract" and "templates of documents required for consultation." In this way, by including specific action steps and templates of required documents, it becomes easier for the user to actually take action.

[0038] The advice providing unit can customize the advice provided by the generation AI according to the user's preferences. For example, the advice providing unit enables the generation AI to customize advice according to the user's preferences. For example, if the user prefers detailed explanations, detailed advice is provided. The advice providing unit also enables the user to customize the format and content of the advice. For example, the advice providing unit allows the user to choose whether they prefer advice in text format or advice including illustrations. The advice providing unit also builds a system in which the generation AI provides advice according to the user's preferences. For example, when the user selects an option, advice according to the preference is provided. This makes it possible to customize advice according to the user's preferences.

[0039] The advice providing unit can notify the user of the advice provided by the generation AI as a reminder in accordance with the user's schedule. For example, the advice providing unit causes the generation AI to notify the user of advice as a reminder in accordance with the user's schedule. For example, "set a date to bring up the matter with the neighbor and send a reminder on that day." The advice providing unit also causes the generation AI to notify the user of advice as a reminder based on a schedule set by the user. For example, "set a deadline for confirming a contract and send a reminder on that day." The advice providing unit also builds a system in which the generation AI provides a reminder function in accordance with the user's schedule. For example, "set a deadline for legal procedures and send a reminder on that day." This makes it possible to notify the user of advice as a reminder in accordance with their schedule.

[0040] The advice providing unit can save the advice provided by the generation AI on the user's device so that it can be accessed offline. For example, the advice providing unit saves the advice provided by the generation AI on the user's device so that it can be accessed offline. For example, the advice providing unit saves the advice in PDF format so that it can be viewed at any time. The advice providing unit also saves the advice provided by the generation AI on the user's device so that it can be accessed even when there is no internet connection. For example, the advice providing unit saves the advice as a text file. The advice providing unit also builds a system that saves the advice provided by the generation AI on the user's device so that it can be accessed offline. For example, the advice is saved in an app so that it can be viewed offline. This allows the advice provided by the generation AI to be saved on the user's device so that it can be accessed offline.

[0041] The advice providing unit can add a function that allows the advice provided by the generation AI to be shared with the user's family or friends. The advice providing unit, for example, adds a function that allows the user to share the advice provided by the generation AI with family and friends. For example, the advice can be shared via email or a messaging app. The advice providing unit also enables the user to share the advice provided by the generation AI with family and friends. For example, the advice can be shared on social media. The advice providing unit also builds a system that adds a function that allows the advice provided by the generation AI to be shared with the user's family and friends. For example, it generates a link for sharing the advice. This adds a function that allows the advice provided by the generation AI to be shared with the user's family and friends.

[0042] The additional information request and reanalysis section allows the generation AI to reanalyze the data and provide more detailed advice if the user inputs additional questions or information regarding the advice provided. For example, if the user inputs an additional question such as, "If negotiations with my neighbor don't go well, what legal action should I take next?", the generation AI reanalyzes the data and suggests appropriate legal action. Furthermore, if the user inputs additional information such as, "I still don't understand this part of the contract," the generation AI reanalyzes the data and provides a more detailed explanation. Furthermore, if the user inputs an additional question such as, "If the dispute with my neighbor isn't resolved, which agency should I consult next?", the generation AI reanalyzes the data and suggests an appropriate agency. Thus, if the user inputs additional questions or information regarding the advice provided, the generation AI reanalyzes the data and provides more detailed advice.

[0043] The additional information request and reanalysis unit allows the generation AI to refer again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, when a user inputs additional information, the additional information request and reanalysis unit allows the generation AI to refer again to a database of similar past cases or precedents and provide more accurate advice. For example, it may search past precedents again based on the additional information. The additional information request and reanalysis unit also builds a system in which the generation AI refers again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, it may search the precedents database again based on the additional information. The additional information request and reanalysis unit also builds a system in which the generation AI refers again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, it may search past precedents again based on the additional information. The additional information request and reanalysis unit also allows the generation AI to refer again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, it may search past precedents again based on the additional information. This allows the generation AI to refer again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice.

[0044] The additional information request and reanalysis unit allows the generation AI to reconsider the laws of the user's region or country based on the user's additional information, thereby providing more accurate advice. For example, when a user inputs additional information, the additional information request and reanalysis unit allows the generation AI to reconsider the laws of the user's region or country and provide advice based on that law. For example, Japanese law may be referenced again based on the additional information. The additional information request and reanalysis unit also builds a system in which the generation AI reconsiders the laws of the user's region or country based on the user's additional information and provides advice based on that law. For example, American law may be referenced again based on the additional information. The additional information request and reanalysis unit also allows the generation AI to reconsider the laws of the user's region or country based on the user's additional information and provide advice based on that law. For example, European law may be referenced again based on the additional information. This allows the generation AI to reconsider the laws of the user's region or country based on the user's additional information and provide more accurate advice.

[0045] The additional information request and reanalysis unit enables the generation AI to re-provide legal advice specialized for the user's industry or occupation based on the user's additional information. For example, when a user inputs additional information, the additional information request and reanalysis unit enables the generation AI to re-provide legal advice specialized for the user's industry or occupation. For example, based on the additional information, advice based on medical law is provided to a user in the medical industry. The additional information request and reanalysis unit also builds a system in which the generation AI re-provides legal advice specialized for the user's industry or occupation based on the user's additional information. For example, based on the additional information, advice based on IT law is provided to a user in the IT industry. The additional information request and reanalysis unit also enables the generation AI to re-provide legal advice specialized for the user's industry or occupation based on the user's additional information. For example, based on the additional information, advice based on construction law is provided to a user in the construction industry. This allows the generation AI to re-provide legal advice specialized for the user's industry or occupation based on the user's additional information.

[0046] The system according to the embodiment is not limited to the above-described examples, and various modifications are possible, for example, as follows.

[0047] The user input reception unit adds a function that allows the user to attach an image or video when inputting, and visual information can also be used for analysis. For example, when the user explains the situation of a noise trouble, the user can attach a video of the noise source being taken. The generation AI analyzes the video to grasp the type and occurrence situation of the noise. Also, when the user inputs a question regarding the content of a contract, the user input reception unit allows the user to attach an image of the relevant part of the contract. The generation AI analyzes the image to understand the content of the contract. Also, when the user explains a trouble with a neighbor, the user input reception unit allows the user to attach a photo of the trouble scene. The generation AI analyzes the photo to grasp the specific situation of the trouble. As a result, the user can attach images and videos and use visual information for analysis.

[0048] The user input reception unit supports input in different languages and can also handle international users. For example, the user can input in different languages such as English, French, Chinese, etc., and the generation AI automatically recognizes and analyzes the language. For example, when the user inputs "My neighbor is making noise at night" in English, the content is analyzed. Also, the user input reception unit automatically translates the text input in different languages, and the generation AI performs analysis based on the translation result. For example, when the user inputs "Mon voisin fait du bruit la nuit" in French, the content is automatically translated and analyzed. Also, when the user inputs in different languages, the user input reception unit enables the generation AI to provide legal advice corresponding to that language. For example, when the user inputs "我的邻居很吵" in Chinese, advice based on Chinese law is provided. As a result, input in different languages is supported and international users can also be handled.

[0049] The situation analysis unit can perform more accurate analysis by referring to a database of similar past cases or precedents. For example, when the generation AI analyzes the user's input, it searches the database for similar past cases and generates advice based on the results. For example, it provides advice by referring to past precedents for noise disputes. The situation analysis unit also refers to a precedent database to identify the precedent that most closely matches the user's situation and generates advice based on that precedent. For example, it provides advice by referring to precedents regarding the contents of a contract. The situation analysis unit also uses a database of similar past cases and precedents to allow the generation AI to analyze the user's situation and provide the most appropriate advice. For example, it provides advice based on past precedents regarding neighbor disputes. This allows for more accurate analysis by referring to a database of similar past cases and precedents.

[0050] The situation analysis unit can automatically take into account the laws of the user's region or country. For example, when the generation AI analyzes the user's input, it automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Japan would be provided with advice based on Japanese law. The situation analysis unit can also search a database for the laws of the user's region or country, and the generation AI generates advice based on those laws. For example, a user in the United States would be provided with advice based on American law. The situation analysis unit can also build a system in which the generation AI automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Europe would be provided with advice based on European law. This makes it possible to provide more appropriate legal advice by automatically taking into account the laws of the user's region or country.

[0051] The legal advice generation unit can present multiple options, allowing the user to select the most appropriate advice. For example, the generation AI generates multiple pieces of legal advice based on the user's situation and presents them to the user as options. For example, it provides options such as "discuss directly with your neighbor" or "consult with your local government's consultation center." When the user selects an option, the generation AI explains the advantages and disadvantages of each option. For example, it explains the "advantages and disadvantages of discussing directly." The legal advice generation unit also builds a system in which the generation AI generates multiple pieces of legal advice based on the user's situation, allowing the user to select the most appropriate advice. For example, it provides options such as "consult a lawyer about questions about the contents of the contract" or "do your own research." This allows the user to select the most appropriate advice.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The user input unit receives a user input of a specific situation. For example, the user can input a specific problem such as "noise trouble with neighbors" or "question about the contents of a contract." Step 2: The situation analysis unit uses the generation AI to analyze the situation input by the user input reception unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the input situation and understand its content. The generation AI may also use a multimodal generation AI to analyze the input situation. Step 3: The legal advice generator generates legal advice based on the situation analyzed by the situation analyzer. For example, the generator might provide specific advice such as, "When dealing with noise problems, we recommend first talking to your neighbor directly, and if that doesn't resolve the issue, we recommend consulting your local government's noise consultation center." Step 4: The advice providing unit provides the user with the legal advice generated by the legal advice generating unit. For example, the legal advice and solutions generated by the generation AI are provided to the user in text format. This allows the user to take specific action toward resolving the problem by referring to the advice provided by the generation AI.

[0054] (Example 2) A problem-solving tool according to an embodiment of the present invention is a system that allows users to easily obtain lawyer-level answers simply by inputting a specific situation. This system uses generative AI to analyze the situation input by the user and provide appropriate legal advice and solutions. This allows users to easily obtain lawyer-level advice even without specialized legal knowledge. For example, this enables quick and appropriate responses to various legal issues faced in daily life, helping to prevent and resolve legal troubles.

[0055] A problem-solving tool according to an embodiment includes a user input receiving unit, a situation analysis unit, a legal advice generation unit, and an advice providing unit. The user inputs a specific situation through the user input receiving unit. For example, the user can input a specific problem such as "noise trouble with a neighbor" or "question about the contents of a contract." The situation analysis unit uses a generation AI to analyze the situation input by the user input receiving unit. For example, the generation AI analyzes the input situation using a text generation AI (e.g., LLM) to understand its content. The generation AI can also analyze the input situation using a multimodal generation AI. The generation AI generates appropriate advice by referring to noise-related laws and past court cases, for example. The legal advice generation unit generates legal advice based on the situation analyzed by the situation analysis unit. For example, the generation AI provides specific advice such as, "When dealing with noise problems, it is recommended that you first talk to your neighbor directly, and if that does not resolve the issue, consult your local government's noise consultation center." The advice providing unit provides the legal advice generated by the legal advice generation unit to the user. For example, the legal advice and solutions generated by the generation AI are provided to the user in text format. This allows the user to take specific action toward solving the problem by referring to the advice provided by the generative AI. As a result, the problem-solving tool according to the embodiment allows the user to easily obtain lawyer-level answers simply by inputting a specific situation. For example, this enables a quick and appropriate response to legal issues faced by the user, which is useful for preventing and resolving legal troubles.

[0056] The user input acceptance unit can use voice input to explain a situation in a natural conversational style. For example, the user inputs voice using a smartphone or microphone, and the generation AI converts the voice into text. For example, if the user says, "My neighbor is playing loud music in the middle of the night," this content is entered as text. The user input acceptance unit also uses voice recognition technology to convert what the user says into text in real time, and the generation AI analyzes the text. For example, if the user says, "I don't understand this part of the contract," this part is entered as text. When the user explains the situation using voice input, the generation AI interjects questions at appropriate times to elicit more detailed information. For example, it asks, "Please tell me specifically what kind of sound do you hear?" This allows the user to explain the situation in a natural conversational style using voice input.

[0057] The user input acceptance unit allows the generation AI to automatically ask for additional information related to the input text, thereby understanding the situation in more detail. For example, if the user inputs "My neighbors are noisy," the generation AI automatically asks additional questions such as "What specific time periods are the noise occurring?" If the user inputs "The contents of the contract are unclear," the user input acceptance unit asks questions such as "Which parts are unclear? Please tell me the specific clauses," thereby collecting more detailed information. If the user inputs "Trouble with my neighbors," the user input acceptance unit presents options such as "What kind of trouble is it? Is it noise, trash disposal, or some other issue?" to understand the situation in more detail. This allows the generation AI to automatically ask for additional information related to the input and understand the situation in more detail.

[0058] The user input accepting unit can use the emotion estimation function to analyze the user's emotions when entering text and provide an interface for reducing stress or anxiety. The user input accepting unit, for example, analyzes the user's facial expressions and voice tone when entering text to estimate the user's emotional state. For example, if the user looks anxious, the generation AI displays a message such as "Please relax and enter text." The user input accepting unit also uses the emotion estimation function to provide an interface according to the user's emotions when entering text. For example, if the user is feeling stressed, the generation AI displays a message such as "We are always here to help you if you need help." The user input accepting unit also analyzes the user's emotions in real time when entering text and provides advice according to the user's emotional state. For example, if the user is feeling angry, the generation AI displays a message such as "Please calmly explain the situation." This allows the user's emotions to be analyzed and an interface for reducing stress and anxiety to be provided.

[0059] The user input acceptance unit adds a function that allows the user to attach images or videos when entering information, allowing visual information to be used in analysis. For example, when a user explains the circumstances of a noise problem, the user input acceptance unit allows the user to attach a video of the source of the noise. The generation AI analyzes the video and determines the type of noise and the circumstances under which it is occurring. Furthermore, when a user enters a question about the contents of a contract, the user input acceptance unit allows the user to attach an image of the relevant part of the contract. The generation AI analyzes the image and understands the contents of the contract. Furthermore, when a user explains a problem with a neighbor, the user input acceptance unit allows the user to attach a photo of the scene of the problem. The generation AI analyzes the photo and determines the specific circumstances of the problem. This allows the user to attach images or videos, allowing visual information to be used in analysis.

[0060] The user input reception unit supports input in different languages and can accommodate international users. For example, the user input reception unit enables the user to input in different languages such as English, French, Chinese, etc., and the generative AI automatically recognizes and analyzes that language. For example, when the user inputs "My neighbor is making noise at night" in English, the content is analyzed. Also, the user input reception unit automatically translates the text input in different languages, and the generative AI performs analysis based on the translation result. For example, when the user inputs "Mon voisin fait du bruit la nuit" in French, the content is automatically translated and analyzed. Additionally, when the user inputs in different languages, the user input reception unit enables the generative AI to provide legal advice corresponding to that language. For example, when the user inputs "我的?居?上很?" in Chinese, advice based on Chinese law is provided. This enables support for input in different languages and accommodation of international users.

[0061] The situation analysis unit can perform more accurate analysis by referring to past similar cases or case databases. For example, when the generative AI analyzes the user's input content, the situation analysis unit searches for past similar cases from the database and generates advice based on the results. For example, it provides advice by referring to past cases of noise troubles. Also, the situation analysis unit refers to the case database, identifies the case closest to the user's situation, and generates advice based on that case. For example, it provides advice by referring to cases regarding the content of a contract. Additionally, the situation analysis unit uses past similar cases and case databases for the generative AI to analyze the user's situation and provide the most appropriate advice. For example, it provides advice based on past cases regarding neighbor troubles. This enables more accurate analysis by referring to past similar cases and case databases.

[0062] The situation analysis unit can automatically take into account the laws of the user's region or country. For example, when the generation AI analyzes the user's input, the situation analysis unit automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Japan would be provided with advice based on Japanese law. The situation analysis unit also searches a database for the laws of the user's region or country, and the generation AI generates advice based on those laws. For example, a user in the United States would be provided with advice based on American law. The situation analysis unit also builds a system in which the generation AI automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Europe would be provided with advice based on European law. This makes it possible to provide more appropriate legal advice by automatically taking into account the laws of the user's region or country.

[0063] The situation analysis unit can use the emotion estimation function to take the user's emotional state into consideration and provide analysis results that take the user's emotions into account. For example, when the generation AI analyzes the user's input content, the situation analysis unit uses the emotion estimation function to take the user's emotional state into consideration and provide advice that takes the user's emotions into account. For example, if the user is feeling anxious, advice that gives a sense of security is provided. The situation analysis unit also uses the emotion estimation function to analyze the user's emotional state and provide analysis results that take the user's emotions into consideration. For example, if the user is feeling angry, advice on how to respond calmly is provided. The situation analysis unit also builds a system that analyzes the user's emotional state in real time and provides advice that takes the user's emotions into consideration. For example, if the user is feeling stressed, advice on how to relax is provided. This makes it possible to provide analysis results that take the user's emotional state into consideration and provide analysis results that take the user's emotions into consideration.

[0064] The legal advice generation unit can present multiple options, allowing the user to select the most appropriate advice. For example, the legal advice generation unit uses a generation AI to generate multiple pieces of legal advice based on the user's situation and present them to the user as options. For example, it provides options such as "discuss the matter directly with your neighbor" or "consult your local government's consultation center." When the user selects an option, the generation AI explains the advantages and disadvantages of each option. For example, it explains the "advantages and disadvantages of discussing the matter directly." The legal advice generation unit also builds a system in which the generation AI generates multiple pieces of legal advice based on the user's situation, allowing the user to select the most appropriate advice. For example, it provides options such as "consult a lawyer about any questions regarding the contents of the contract" or "do your own research." This allows the user to select the most appropriate advice.

[0065] The legal advice generation unit can include specific action steps or templates of required documents in the advice it generates. For example, the legal advice generation unit includes specific action steps in the legal advice that the generation AI provides to the user. For example, it provides "specific steps for initiating a discussion with a neighbor." The legal advice generation unit also includes templates of required documents in the legal advice that the generation AI provides to the user. For example, it provides "document templates for submission to a local government consultation desk." The legal advice generation unit also builds a system that includes specific action steps and templates of required documents in the legal advice that the generation AI provides to the user. For example, it provides "steps for consulting a lawyer about questions about the contents of a contract" and "templates of documents required for consultation." In this way, by including specific action steps and templates of required documents, it becomes easier for the user to actually take action.

[0066] The legal advice generation unit can use the emotion estimation function to adjust the expression or content of advice taking into consideration the user's emotions. For example, the legal advice generation unit uses a generation AI to analyze the user's emotional state and adjust the expression and content of advice taking into consideration those emotions. For example, if the user is feeling anxious, the legal advice generation unit provides advice using expression that gives a sense of security. The legal advice generation unit also uses the emotion estimation function to adjust the expression and content of advice based on the user's emotions. For example, if the user is feeling angry, the legal advice generation unit provides advice on how to respond calmly. The legal advice generation unit also builds a system that analyzes the user's emotional state in real time and dynamically adjusts the expression and content of advice taking into consideration those emotions. For example, if the user is feeling stressed, the legal advice generation unit provides advice on how to relax. In this way, by adjusting the expression and content of advice taking into consideration the user's emotions, it is possible to provide advice that is easy for the user to accept.

[0067] The advice providing unit can customize the advice provided by the generation AI according to the user's preferences. For example, the advice providing unit enables the generation AI to customize advice according to the user's preferences. For example, if the user prefers detailed explanations, detailed advice is provided. The advice providing unit also enables the user to customize the format and content of the advice. For example, the advice providing unit allows the user to choose whether they prefer advice in text format or advice including illustrations. The advice providing unit also builds a system in which the generation AI provides advice according to the user's preferences. For example, when the user selects an option, advice according to the preference is provided. This makes it possible to customize advice according to the user's preferences.

[0068] The advice providing unit can notify the user of the advice provided by the generation AI as a reminder in accordance with the user's schedule. For example, the advice providing unit causes the generation AI to notify the user of advice as a reminder in accordance with the user's schedule. For example, "set a date to bring up the matter with the neighbor and send a reminder on that day." The advice providing unit also causes the generation AI to notify the user of advice as a reminder based on a schedule set by the user. For example, "set a deadline for confirming a contract and send a reminder on that day." The advice providing unit also builds a system in which the generation AI provides a reminder function in accordance with the user's schedule. For example, "set a deadline for legal procedures and send a reminder on that day." This makes it possible to notify the user of advice as a reminder in accordance with their schedule.

[0069] The advice providing unit can use the emotion estimation function to provide follow-up advice according to the user's emotions. For example, the generation AI analyzes the user's emotional state and provides follow-up advice according to that emotion. For example, if the user is feeling anxious, a follow-up that gives a sense of security is provided. The advice providing unit also uses the emotion estimation function to provide follow-up advice based on the user's emotions. For example, if the user is feeling angry, a follow-up to respond calmly is provided. The advice providing unit also builds a system that analyzes the user's emotional state in real time and provides follow-up advice according to that emotion. For example, if the user is feeling stressed, a follow-up to relax is provided. This makes it possible to provide follow-up advice according to the user's emotions.

[0070] The advice providing unit can save the advice provided by the generation AI on the user's device so that it can be accessed offline. For example, the advice providing unit saves the advice provided by the generation AI on the user's device so that it can be accessed offline. For example, the advice providing unit saves the advice in PDF format so that it can be viewed at any time. The advice providing unit also saves the advice provided by the generation AI on the user's device so that it can be accessed even when there is no internet connection. For example, the advice providing unit saves the advice as a text file. The advice providing unit also builds a system that saves the advice provided by the generation AI on the user's device so that it can be accessed offline. For example, the advice is saved in an app so that it can be viewed offline. This allows the advice provided by the generation AI to be saved on the user's device so that it can be accessed offline.

[0071] The advice providing unit can add a function that allows the advice provided by the generation AI to be shared with the user's family or friends. The advice providing unit, for example, adds a function that allows the user to share the advice provided by the generation AI with family and friends. For example, the advice can be shared via email or a messaging app. The advice providing unit also enables the user to share the advice provided by the generation AI with family and friends. For example, the advice can be shared on social media. The advice providing unit also builds a system that adds a function that allows the advice provided by the generation AI to be shared with the user's family and friends. For example, it generates a link for sharing the advice. This adds a function that allows the advice provided by the generation AI to be shared with the user's family and friends.

[0072] The advice providing unit can use the emotion estimation function to provide follow-up advice based on the user's emotions. For example, the generation AI in the advice providing unit analyzes the user's emotional state and provides follow-up advice according to that emotion. For example, if the user is feeling anxious, a follow-up that gives a sense of security is provided. The advice providing unit also uses the emotion estimation function to provide follow-up advice based on the user's emotions. For example, if the user is feeling angry, a follow-up to respond calmly is provided. The advice providing unit also builds a system that analyzes the user's emotional state in real time and provides follow-up advice according to that emotion. For example, if the user is feeling stressed, a follow-up to relax is provided. This makes it possible to provide follow-up advice based on the user's emotions.

[0073] The additional information request and reanalysis section allows the generation AI to reanalyze the data and provide more detailed advice if the user inputs additional questions or information regarding the advice provided. For example, if the user inputs an additional question such as, "If negotiations with my neighbor don't go well, what legal action should I take next?", the generation AI reanalyzes the data and suggests appropriate legal action. Furthermore, if the user inputs additional information such as, "I still don't understand this part of the contract," the generation AI reanalyzes the data and provides a more detailed explanation. Furthermore, if the user inputs an additional question such as, "If the dispute with my neighbor isn't resolved, which agency should I consult next?", the generation AI reanalyzes the data and suggests an appropriate agency. Thus, if the user inputs additional questions or information regarding the advice provided, the generation AI reanalyzes the data and provides more detailed advice.

[0074] The additional information request and reanalysis unit allows the generation AI to refer again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, when a user inputs additional information, the additional information request and reanalysis unit allows the generation AI to refer again to a database of similar past cases or precedents and provide more accurate advice. For example, it may search past precedents again based on the additional information. The additional information request and reanalysis unit also builds a system in which the generation AI refers again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, it may search the precedents database again based on the additional information. The additional information request and reanalysis unit also builds a system in which the generation AI refers again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, it may search past precedents again based on the additional information. The additional information request and reanalysis unit also allows the generation AI to refer again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice. For example, it may search past precedents again based on the additional information. This allows the generation AI to refer again to a database of similar past cases or precedents based on the user's additional information, thereby providing more accurate advice.

[0075] The additional information request and reanalysis unit can use the emotion estimation function to provide additional advice based on the user's emotion. The additional information request and reanalysis unit, for example, analyzes the emotion of the user when inputting additional information and provides additional advice based on that emotion. For example, if the user is feeling anxious, additional advice that gives a sense of security is provided. The additional information request and reanalysis unit also uses the emotion estimation function to provide additional advice based on the user's emotion. For example, if the user is feeling angry, additional advice to respond calmly is provided. The additional information request and reanalysis unit also analyzes the emotion of the user when inputting additional information in real time, and builds a system that provides additional advice based on that emotion. For example, if the user is feeling stressed, additional advice to relax is provided. In this way, the emotion estimation function can be used to provide additional advice based on the user's emotion.

[0076] The additional information request and reanalysis unit allows the generation AI to reconsider the laws of the user's region or country based on the user's additional information, thereby providing more accurate advice. For example, when a user inputs additional information, the additional information request and reanalysis unit allows the generation AI to reconsider the laws of the user's region or country and provide advice based on that law. For example, Japanese law may be referenced again based on the additional information. The additional information request and reanalysis unit also builds a system in which the generation AI reconsiders the laws of the user's region or country based on the user's additional information and provides advice based on that law. For example, American law may be referenced again based on the additional information. The additional information request and reanalysis unit also allows the generation AI to reconsider the laws of the user's region or country based on the user's additional information and provide advice based on that law. For example, European law may be referenced again based on the additional information. This allows the generation AI to reconsider the laws of the user's region or country based on the user's additional information and provide more accurate advice.

[0077] The additional information request and reanalysis unit enables the generation AI to re-provide legal advice specialized for the user's industry or occupation based on the user's additional information. For example, when a user inputs additional information, the additional information request and reanalysis unit enables the generation AI to re-provide legal advice specialized for the user's industry or occupation. For example, based on the additional information, advice based on medical law is provided to a user in the medical industry. The additional information request and reanalysis unit also builds a system in which the generation AI re-provides legal advice specialized for the user's industry or occupation based on the user's additional information. For example, based on the additional information, advice based on IT law is provided to a user in the IT industry. The additional information request and reanalysis unit also enables the generation AI to re-provide legal advice specialized for the user's industry or occupation based on the user's additional information. For example, based on the additional information, advice based on construction law is provided to a user in the construction industry. This allows the generation AI to re-provide legal advice specialized for the user's industry or occupation based on the user's additional information.

[0078] The additional information request and reanalysis unit can use the emotion estimation function to provide additional advice based on the user's emotion. The additional information request and reanalysis unit, for example, analyzes the emotion of the user when inputting additional information and provides additional advice based on that emotion. For example, if the user is feeling anxious, additional advice that gives a sense of security is provided. The additional information request and reanalysis unit also uses the emotion estimation function to provide additional advice based on the user's emotion. For example, if the user is feeling angry, additional advice to respond calmly is provided. The additional information request and reanalysis unit also analyzes the emotion of the user when inputting additional information in real time, and builds a system that provides additional advice based on that emotion. For example, if the user is feeling stressed, additional advice to relax is provided. In this way, the emotion estimation function can be used to provide additional advice based on the user's emotion.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The user input acceptance unit adds a function that allows users to attach images or videos when entering information, allowing visual information to be used in analysis. For example, when a user explains the circumstances of a noise problem, they can attach a video of the source of the noise. The generation AI analyzes the video and determines the type of noise and the circumstances under which it is occurring. Furthermore, when a user enters a question about the contents of a contract, the user input acceptance unit allows them to attach an image of the relevant part of the contract. The generation AI analyzes the image and understands the contents of the contract. Furthermore, when a user explains a problem with a neighbor, the user input acceptance unit allows them to attach a photo of the scene of the problem. The generation AI analyzes the photo and determines the specific circumstances of the problem. This allows users to attach images or videos, allowing visual information to be used in analysis.

[0081] The user input reception unit supports input in different languages and can accommodate international users. For example, it enables users to input in different languages such as English, French, Chinese, etc., and the generative AI automatically recognizes and analyzes that language. For instance, when a user inputs "My neighbor is making noise at night" in English, the content is analyzed. Also, the user input reception unit automatically translates the text input in different languages, and the generative AI conducts analysis based on the translation result. For example, when a user inputs "Mon voisin fait du bruit la nuit" in French, the content is automatically translated and analyzed. Additionally, when the user inputs in different languages, the user input reception unit enables the generative AI to provide legal advice corresponding to that language. For example, when a user inputs "我的?居?上很?" in Chinese, advice based on Chinese law is provided. This enables support for input in different languages and accommodation of international users.

[0082] The situation analysis unit can refer to past similar cases or case databases to perform more accurate analysis. For example, when the generative AI analyzes the user's input content, it searches for past similar cases from the database and generates advice based on the results. For example, it provides advice by referring to past cases of noise troubles. Also, the situation analysis unit refers to the case database, identifies the case closest to the user's situation, and generates advice based on that case. For example, it provides advice by referring to cases regarding the content of a contract. Additionally, the situation analysis unit uses past similar cases and case databases to enable the generative AI to analyze the user's situation and provide the most appropriate advice. For example, it provides advice based on past cases related to neighbor troubles. This enables reference to past similar cases and case databases to perform more accurate analysis.

[0083] The situation analysis unit can automatically take into account the laws of the user's region or country. For example, when the generation AI analyzes the user's input, it automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Japan would be provided with advice based on Japanese law. The situation analysis unit can also search a database for the laws of the user's region or country, and the generation AI generates advice based on those laws. For example, a user in the United States would be provided with advice based on American law. The situation analysis unit can also build a system in which the generation AI automatically takes into account the laws of the user's region or country and provides advice based on those laws. For example, a user in Europe would be provided with advice based on European law. This makes it possible to provide more appropriate legal advice by automatically taking into account the laws of the user's region or country.

[0084] The legal advice generation unit can present multiple options, allowing the user to select the most appropriate advice. For example, the generation AI generates multiple pieces of legal advice based on the user's situation and presents them to the user as options. For example, it provides options such as "discuss directly with your neighbor" or "consult with your local government's consultation center." When the user selects an option, the generation AI explains the advantages and disadvantages of each option. For example, it explains the "advantages and disadvantages of discussing directly." The legal advice generation unit also builds a system in which the generation AI generates multiple pieces of legal advice based on the user's situation, allowing the user to select the most appropriate advice. For example, it provides options such as "consult a lawyer about questions about the contents of the contract" or "do your own research." This allows the user to select the most appropriate advice.

[0085] The user input accepting unit can use the emotion estimation function to analyze the user's emotions when entering text and provide an interface for reducing stress or anxiety. For example, the unit can analyze the user's facial expressions and tone of voice when entering text to estimate their emotional state. For example, if the user looks anxious, the generation AI displays a message such as "Please relax when entering text." The user input accepting unit also uses the emotion estimation function to provide an interface that corresponds to the user's emotions when entering text. For example, if the user is feeling stressed, the generation AI displays a message such as "We are always here to help you if you need help." The user input accepting unit can also analyze the user's emotions in real time when entering text and provide advice according to their emotional state. For example, if the user is feeling angry, the generation AI displays a message such as "Please calmly explain the situation." This allows the unit to analyze the user's emotions and provide an interface that reduces stress and anxiety.

[0086] The situation analysis unit can use the emotion estimation function to take the user's emotional state into consideration and provide analysis results that take the user's emotions into account. For example, when the generation AI analyzes the user's input content, it uses the emotion estimation function to take the user's emotional state into consideration and provide emotionally considerate advice. For example, if the user is feeling anxious, it can provide advice that gives a sense of security. The situation analysis unit can also use the emotion estimation function to analyze the user's emotional state and provide analysis results that take the user's emotions into consideration. For example, if the user is feeling angry, it can provide advice on how to respond calmly. The situation analysis unit can also build a system that analyzes the user's emotional state in real time and provides advice that takes the user's emotions into consideration. For example, if the user is feeling stressed, it can provide advice on how to relax. This makes it possible to provide analysis results that take the user's emotional state into consideration and provide emotionally considerate advice.

[0087] The legal advice generation unit can use the emotion estimation function to adjust the expression or content of advice to take the user's emotions into consideration. For example, the generation AI analyzes the user's emotional state and adjusts the expression and content of advice to take those emotions into consideration. For example, if the user is feeling anxious, it provides advice in an expression that gives a sense of security. The legal advice generation unit also uses the emotion estimation function to adjust the expression and content of advice based on the user's emotions. For example, if the user is feeling angry, it provides advice on how to respond calmly. The legal advice generation unit also builds a system that analyzes the user's emotional state in real time and dynamically adjusts the expression and content of advice to take those emotions into consideration. For example, if the user is feeling stressed, it provides advice on how to relax. In this way, by adjusting the expression and content of advice to take the user's emotions into consideration, it is possible to provide advice that is easy for the user to accept.

[0088] The advice providing unit can use the emotion estimation function to provide follow-up advice according to the user's emotions. For example, the generation AI analyzes the user's emotional state and provides follow-up advice according to that emotion. For example, if the user is feeling anxious, follow-up advice that gives a sense of security is provided. The advice providing unit also uses the emotion estimation function to provide follow-up advice based on the user's emotions. For example, if the user is feeling angry, follow-up advice to respond calmly is provided. The advice providing unit also builds a system that analyzes the user's emotional state in real time and provides follow-up advice according to that emotion. For example, if the user is feeling stressed, follow-up advice to relax is provided. This makes it possible to provide follow-up advice according to the user's emotions.

[0089] The additional information request and reanalysis unit can use the emotion estimation function to provide additional advice based on the user's emotion. For example, the emotion when the user inputs additional information can be analyzed, and additional advice based on that emotion can be provided. For example, if the user is feeling anxious, additional advice that provides a sense of security can be provided. The additional information request and reanalysis unit can also use the emotion estimation function to provide additional advice based on the user's emotion. For example, if the user is feeling angry, additional advice on how to respond calmly can be provided. The additional information request and reanalysis unit can also analyze the emotion when the user inputs additional information in real time, and build a system that provides additional advice based on that emotion. For example, if the user is feeling stressed, additional advice on how to relax can be provided. In this way, the emotion estimation function can be used to provide additional advice based on the user's emotion.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The user input unit receives a user input of a specific situation. For example, the user can input a specific problem such as "noise trouble with neighbors" or "question about the contents of a contract." Step 2: The situation analysis unit uses the generation AI to analyze the situation input by the user input reception unit. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the input situation and understand its content. The generation AI may also use a multimodal generation AI to analyze the input situation. Step 3: The legal advice generator generates legal advice based on the situation analyzed by the situation analyzer. For example, the generator might provide specific advice such as, "When dealing with noise problems, we recommend first talking to your neighbor directly, and if that doesn't resolve the issue, we recommend consulting your local government's noise consultation center." Step 4: The advice providing unit provides the user with the legal advice generated by the legal advice generating unit. For example, the legal advice and solutions generated by the generation AI are provided to the user in text format. This allows the user to take specific action toward resolving the problem by referring to the advice provided by the generation AI.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 user input receiving unit for receiving a user input of a specific situation; a situation analysis unit that analyzes the situation input by the user input acceptance unit; a legal advice generation unit that generates legal advice based on the situation analyzed by the situation analysis unit; an advice providing unit that provides the legal advice generated by the legal advice generating unit to the user; A system characterized by:

2. The user input receiving unit Use voice input to explain the situation in a natural conversational way 2. The system of claim 1.

3. The situation analysis unit Refer to a database of similar cases or precedents to perform more accurate analysis 2. The system of claim 1.

4. The legal advice generation unit Present multiple options and allow users to choose the most appropriate advice 2. The system of claim 1.

5. The advice providing unit The advice provided by generative AI can be customized to suit the user's preferences.

2. The system of claim 1.

6. The user input receiving unit Analyzes emotions as users type and provides an interface to reduce stress or anxiety 2. The system of claim 1.

7. The situation analysis unit To provide emotionally sensitive analysis results by taking into account the emotional state of the user.

2. The system of claim 1.

8. The legal advice generation unit Adjusting the expression or content of advice to take into account the user's feelings 2. The system of claim 1.

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