system

Abstract

The system according to this embodiment aims to enable users to easily understand their legal responsibilities. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit receives information about the user's events. The analysis unit analyzes the information entered by the reception unit. The search unit searches for laws and precedents based on the information analyzed by the analysis unit. The proposal unit proposes legal responses based on the information retrieved by the search unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to understand laws and precedents, and it is difficult to grasp legal responses before consulting a lawyer.

[0005] The system according to the embodiment aims to enable a user to easily grasp legal responses.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit inputs a user event. The analysis unit analyzes the information input by the reception unit. The search unit searches for laws and precedents based on the information analyzed by the analysis unit. The proposal unit proposes a legal response based on the information searched by the search unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to easily understand their legal responsibilities. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The legal information provision system according to an embodiment of the present invention is a system that allows users to find out how they can legally respond to their own events. This legal information provision system can be used by users to understand their situation in advance before hiring a lawyer, or to decide whether or not to hire a lawyer. It can also be used as a second opinion even after hiring a lawyer. For example, a user inputs their event. For example, they might input something like, "I was in a traffic accident, how should I respond?" This information is input into the generating AI. Next, the generating AI analyzes the input information and searches for relevant laws and precedents. The generating AI refers to legal texts and related legal literature and proposes an appropriate legal response to the user's event. For example, it proposes what procedures are necessary based on laws and past precedents regarding traffic accidents. Furthermore, the generating AI provides information to help the user decide whether or not to hire a lawyer. For example, in the case of a traffic accident, it provides information about the benefits and costs of hiring a lawyer. Finally, the generating AI provides information that can be used as a second opinion even after hiring a lawyer. For example, it provides information about other possible responses and risks in relation to the response proposed by the lawyer. This system allows users to easily access legal information and understand it before consulting a lawyer. Furthermore, even after consulting a lawyer, they can use it for a second opinion, allowing them to proceed with legal matters with greater confidence. Thus, the legal information provision system allows users to easily access legal information and understand it before consulting a lawyer. Furthermore, even after consulting a lawyer, they can use it for a second opinion, allowing them to proceed with legal matters with greater confidence.

[0029] The legal information provision system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit receives input from the user about an event. Such events include, but are not limited to, legal issues, contract disputes, and traffic accidents. The reception unit allows the user to input the event in text format, for example. It also allows the user to input the event using voice input. For example, speech recognition technology can be used to convert the user's voice into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided to minimize the input procedure. The analysis unit analyzes the information input by the reception unit. The analysis unit can analyze the content of the user's event using, for example, text analysis technology. It can also extract information related to the user's event using data mining technology. Furthermore, the analysis unit can analyze the meaning of the user's event using natural language processing technology. The search unit searches for laws and precedents based on the information analyzed by the analysis unit. The search unit can, for example, use keyword search technology to search for relevant laws and precedents. It can also use full-text search technology to search for relevant legal documents. Furthermore, it can use filtering technology to refine the search results. The proposal unit proposes legal responses based on the information retrieved by the search unit. The proposal unit can, for example, provide legal advice. It can also provide guidelines for action. Furthermore, it can provide risk assessments. Thus, the legal information provision system according to this embodiment can propose appropriate legal responses to the user's situation.

[0030] The search function can access legal texts and legal literature. These texts include, but are not limited to, the Civil Code, Criminal Code, and Commercial Code. For example, the search function can access legal texts in digital format to search for relevant laws. It can also access legal literature to search for relevant precedents. For example, it can access case law collections in digital format to search for relevant precedents. Furthermore, it can access legal commentaries and academic papers to search for relevant legal information. For example, it can access legal commentaries in digital format to search for relevant legal interpretations. This allows for the proposal of more accurate legal responses by referencing legal texts and legal literature.

[0031] The proposal section can provide information to help users decide whether or not to hire a lawyer. For example, the proposal section can provide information about the benefits of hiring a lawyer. For instance, it can explain that hiring a lawyer provides legal protection. It can also explain that hiring a lawyer is cost-effective. Furthermore, it can explain that hiring a lawyer can save time. For example, it can explain that hiring a lawyer can resolve the problem quickly. This provides users with the information they need to decide whether or not to hire a lawyer.

[0032] The proposal department can provide information that can be used as a second opinion even after you have engaged a lawyer. For example, the proposal department can provide information about alternative possible solutions to the solutions proposed by the lawyer. For example, the proposal department can also provide information about the risks of the solutions proposed by the lawyer. Furthermore, the proposal department can also provide opinions from other experts. For example, the proposal department can provide opinions from other lawyers or legal professionals. This allows the department to provide information that can be used as a second opinion even after you have engaged a lawyer.

[0033] The analysis unit can search for laws and precedents to propose appropriate legal responses to user events. For example, the analysis unit can search for laws related to user events. For instance, it can search for laws concerning traffic accidents. Furthermore, the analysis unit can search for precedents related to user events. For example, it can search for precedents concerning past traffic accidents. In addition, the analysis unit can search for relevant laws and precedents to propose appropriate legal responses to user events. For example, it can search for relevant laws and precedents to propose legal responses such as lawsuits, settlements, and arbitrations. This allows the analysis unit to search for relevant laws and precedents to propose appropriate legal responses to user events.

[0034] The proposal department can provide information about the benefits and costs of hiring a lawyer. For example, the proposal department can provide information about the specific benefits of hiring a lawyer. For instance, it can explain that hiring a lawyer provides legal protection. It can also explain that hiring a lawyer is cost-effective. Furthermore, it can explain that hiring a lawyer saves time. For example, it can explain that hiring a lawyer leads to a quicker resolution of the problem. This allows the proposal department to provide information about the benefits and costs of hiring a lawyer.

[0035] The reception desk can analyze the user's past input history and suggest the most suitable input method. For example, the reception desk can automatically display as suggestions the types of events the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest the types of events the user will enter during a specific time period based on their past input history. For example, the reception desk can analyze the types of events the user has entered during a specific time period in the past and suggest input content related to that time period. This allows the system to analyze the user's past input history and suggest the optimal input method.

[0036] The input system can filter input based on the user's current situation and areas of interest. For example, it can prioritize displaying legal fields related to the user's current problems. It can also filter and display relevant laws and precedents based on the user's areas of interest. Furthermore, it can suggest appropriate input based on the user's current situation (e.g., traffic accident, labor issues). For example, if the user has been in a traffic accident, the input system can prioritize displaying laws and precedents related to traffic accidents. This allows the input to be filtered based on the user's current situation and areas of interest.

[0037] The reception system can prioritize retrieving highly relevant input content by considering the user's geographical location. For example, if the user is in a specific region, the reception system can prioritize displaying laws and precedents related to that region. Furthermore, the reception system can filter and display relevant laws and precedents based on the user's geographical location. Additionally, if the user is in a specific region, the reception system can prioritize displaying information on law firms and lawyers in that region. For instance, the reception system can acquire the user's geographical location and use that information to search for relevant laws and precedents using an algorithm. This allows for the priority retrieval of highly relevant input content by considering the user's geographical location.

[0038] The reception desk can analyze a user's social media activity and retrieve relevant input. For example, it can identify a user's current legal interests from their social media activity and suggest relevant input. It can also identify a user's current problems from their social media activity and display relevant laws and precedents. Furthermore, it can retrieve and display information about relevant law firms and lawyers from the user's social media activity. For instance, the reception desk can use social media analytics technology to analyze a user's posts, follower count, engagement, etc., and use algorithms to retrieve relevant input. This allows it to analyze a user's social media activity and retrieve relevant input.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content. For example, the analysis unit can provide detailed analysis results for high-importance input content. It can also provide concise analysis results for low-importance input content. Furthermore, it can provide analysis results with an appropriate level of detail for moderately important input content. For instance, the analysis unit can use an algorithm that evaluates the importance of the input content and adjusts the level of detail of the analysis based on that evaluation. This allows the level of detail of the analysis to be adjusted based on the importance of the input content.

[0040] The analysis unit can apply different analysis algorithms depending on the classification of the input content. For example, the analysis unit can apply a specialized analysis algorithm to input content related to traffic accidents. It can also apply a specialized analysis algorithm to input content related to labor issues. Furthermore, it can apply a specialized analysis algorithm to input content related to family issues. For example, the analysis unit can select and apply an appropriate analysis algorithm based on the classification of the input content. This allows for the application of different analysis algorithms depending on the category of the input content.

[0041] The analysis unit can determine the priority of analysis based on when the input content was submitted. For example, the analysis unit can prioritize the analysis of input content submitted earlier. It can also postpone the analysis of input content submitted later. Furthermore, it can perform analysis with an appropriate priority for input content submitted at a moderate time. For instance, the analysis unit can use an algorithm that evaluates the submission timing of the input content and determines the analysis priority based on the evaluation results. This allows the analysis priority to be determined based on the submission timing of the input content.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the input content. For example, the analysis unit can prioritize the analysis of highly relevant input content. It can also postpone the analysis of less relevant input content. Furthermore, it can analyze moderately relevant input content in an appropriate order. For instance, the analysis unit can use an algorithm that evaluates the relevance of the input content and adjusts the order of analysis based on the evaluation results. This allows the order of analysis to be adjusted based on the relevance of the input content.

[0043] The search unit can improve search accuracy by considering the interrelationships between laws and precedents. For example, the search unit can analyze the interrelationships between relevant laws and precedents and prioritize displaying the most relevant information. Furthermore, the search unit can filter search results by considering the interrelationships between relevant laws and precedents. In addition, the search unit can adjust the display order of search results based on the interrelationships between relevant laws and precedents. For instance, the search unit can use an algorithm that analyzes the interrelationships between laws and precedents and improves search accuracy based on the analysis results. This allows for improved search accuracy by considering the interrelationships between relevant laws and precedents.

[0044] The search function can perform searches while considering the user's attribute data. For example, the search function can filter relevant laws and precedents based on the user's age and gender. It can also prioritize the display of relevant laws and precedents based on the user's occupation and position. Furthermore, the search function can suggest relevant laws and precedents based on the user's past search history. For instance, the search function can use an algorithm that retrieves user attribute data and performs searches based on that data. This allows the search to take user attribute information into consideration.

[0045] The search unit can perform searches while considering the geographical distribution of laws and precedents. For example, the search unit can analyze the geographical distribution of relevant laws and precedents and prioritize displaying the most relevant information. Furthermore, the search unit can filter search results while considering the geographical distribution of relevant laws and precedents. In addition, the search unit can adjust the display order of search results based on the geographical distribution of relevant laws and precedents. For example, the search unit can use an algorithm that analyzes the geographical distribution of laws and precedents and performs searches based on the analysis results. This allows searches to be performed while considering the geographical distribution of relevant laws and precedents.

[0046] The search unit can improve search accuracy by referencing relevant legal and case law literature. For example, it can analyze relevant legal and case law literature and prioritize displaying the most relevant information. It can also filter search results by considering relevant legal and case law literature. Furthermore, it can adjust the display order of search results based on relevant legal and case law literature. For instance, the search unit can analyze relevant legal and case law literature and use algorithms to improve search accuracy based on the analysis results. This allows for improved search accuracy by referencing relevant legal and case law literature.

[0047] The proposal function can adjust the level of detail in its proposals based on the importance of the legal response. For example, it can provide detailed proposals for highly important legal responses, concise proposals for less important ones, and proposals with a moderate level of detail for moderately important ones. For instance, the proposal function can use an algorithm that evaluates the importance of a legal response and adjusts the level of detail in its proposals based on that evaluation. This allows for adjustment of proposal detail based on the importance of the legal response.

[0048] The proposal unit can apply different proposal algorithms depending on the classification of the legal response. For example, for legal responses related to traffic accidents, the proposal unit can apply a proposal algorithm specialized for traffic accidents. Similarly, for legal responses related to labor issues, the proposal unit can apply a proposal algorithm specialized for labor issues. Furthermore, for legal responses related to family issues, the proposal unit can apply a proposal algorithm specialized for family issues. For example, the proposal unit can select and apply an appropriate proposal algorithm based on the classification of the legal response. This allows for the application of different proposal algorithms depending on the category of legal response.

[0049] The proposal committee can prioritize proposals based on when legal responses are submitted. For example, the proposal committee can prioritize proposals for legal responses submitted early. Conversely, it can postpone proposals for legal responses submitted late. Furthermore, it can give a moderate priority to legal responses submitted at a moderate time. For example, the proposal committee can use an algorithm that evaluates the submission timing of legal responses and determines proposal priority based on the evaluation results. This allows for the prioritization of proposals based on the submission timing of legal responses.

[0050] The proposal system can adjust the order of proposals based on the relevance of the legal responses. For example, the proposal system can prioritize proposals for highly relevant legal responses. Conversely, it can postpone proposals for less relevant legal responses. Furthermore, it can propose legal responses of moderate relevance in an appropriate order. For instance, the proposal system can use an algorithm that evaluates the relevance of legal responses and adjusts the order of proposals based on the evaluation results. This allows the order of proposals to be adjusted based on the relevance of the legal responses.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can also provide relevant legal information in real time based on the user's input. For example, if a user enters a question about a traffic accident, the reception desk can immediately display an overview of relevant laws and precedents. Furthermore, the reception desk can guide the user through the steps of relevant legal procedures based on their input. In addition, the reception desk can provide templates for relevant legal documents based on the user's input. This allows users to quickly obtain the necessary information and proceed smoothly with legal procedures.

[0053] The search function can also prioritize displaying relevant laws and precedents by considering the user's past search history. For example, if a user has previously searched for information related to traffic accidents, subsequent searches can prioritize information related to traffic accidents. The search function can also suggest relevant legal literature and commentaries based on the user's search history. Furthermore, the search function can analyze the user's search history and notify them of updates to relevant laws and precedents. This ensures that users always have access to the latest legal information.

[0054] The proposal department can also predict future legal risks and suggest preventative measures based on the user's past legal issues. For example, if a user has experienced contract disputes in the past, the proposal department can suggest points to be aware of and risk avoidance measures for future contracts. Similarly, if a user has experienced labor issues in the past, the proposal department can suggest revisions to employment contracts and improvements to the work environment. Furthermore, if a user has experienced family problems in the past, the proposal department can provide advice to prevent legal risks within the family. This allows users to proactively prevent future legal risks.

[0055] The reception desk can automatically categorize and display relevant legal information based on user input. For example, if a user enters a question about a traffic accident, the reception desk can display laws, precedents, procedures, and document templates related to traffic accidents, categorized accordingly. The reception desk can also prioritize and display relevant legal information based on user input. Furthermore, the reception desk can filter and display relevant legal information based on user input. This allows users to quickly find the information they need.

[0056] The proposal department can also predict future legal risks and suggest preventative measures based on the user's past legal issues. For example, if a user has experienced contract disputes in the past, the proposal department can suggest points to be aware of and risk avoidance measures for future contracts. Similarly, if a user has experienced labor issues in the past, the proposal department can suggest revisions to employment contracts and improvements to the work environment. Furthermore, if a user has experienced family problems in the past, the proposal department can provide advice to prevent legal risks within the family. This allows users to proactively prevent future legal risks.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives the user's event. This event may include legal issues, contract disputes, or traffic accidents. The reception desk allows users to input events using text or voice input. In the case of voice input, speech recognition technology is used to convert the voice into text data. Furthermore, the reception desk can estimate the user's emotions and adjust the display of the input interface based on those emotions. For example, if the user is feeling stressed, a simpler interface is provided, minimizing the input steps. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses text analysis technology, data mining technology, and natural language processing technology to analyze the content and meaning of the user's events and extract relevant information. Step 3: The search unit searches for laws and precedents based on the information analyzed by the analysis unit. The search unit uses keyword search technology, full-text search technology, and filtering technology to search for relevant laws, precedents, and legal literature, and then refines the search results. Step 4: The Proposal Department proposes legal responses based on the information retrieved by the Search Department. The Proposal Department provides legal advice, guidelines, and risk assessments, and proposes appropriate legal responses to the user's incident.

[0059] (Example of form 2) The legal information provision system according to an embodiment of the present invention is a system that allows users to find out how they can legally respond to their own events. This legal information provision system can be used by users to understand their situation in advance before hiring a lawyer, or to decide whether or not to hire a lawyer. It can also be used as a second opinion even after hiring a lawyer. For example, a user inputs their event. For example, they might input something like, "I was in a traffic accident, how should I respond?" This information is input into the generating AI. Next, the generating AI analyzes the input information and searches for relevant laws and precedents. The generating AI refers to legal texts and related legal literature and proposes an appropriate legal response to the user's event. For example, it proposes what procedures are necessary based on laws and past precedents regarding traffic accidents. Furthermore, the generating AI provides information to help the user decide whether or not to hire a lawyer. For example, in the case of a traffic accident, it provides information about the benefits and costs of hiring a lawyer. Finally, the generating AI provides information that can be used as a second opinion even after hiring a lawyer. For example, it provides information about other possible responses and risks in relation to the response proposed by the lawyer. This system allows users to easily access legal information and understand it before consulting a lawyer. Furthermore, even after consulting a lawyer, they can use it for a second opinion, allowing them to proceed with legal matters with greater confidence. Thus, the legal information provision system allows users to easily access legal information and understand it before consulting a lawyer. Furthermore, even after consulting a lawyer, they can use it for a second opinion, allowing them to proceed with legal matters with greater confidence.

[0060] The legal information provision system according to this embodiment comprises a reception unit, an analysis unit, a search unit, and a proposal unit. The reception unit receives input from the user about an event. Such events include, but are not limited to, legal issues, contract disputes, and traffic accidents. The reception unit allows the user to input the event in text format, for example. It also allows the user to input the event using voice input. For example, speech recognition technology can be used to convert the user's voice into text data. Furthermore, the reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided to minimize the input procedure. The analysis unit analyzes the information input by the reception unit. The analysis unit can analyze the content of the user's event using, for example, text analysis technology. It can also extract information related to the user's event using data mining technology. Furthermore, the analysis unit can analyze the meaning of the user's event using natural language processing technology. The search unit searches for laws and precedents based on the information analyzed by the analysis unit. The search unit can, for example, use keyword search technology to search for relevant laws and precedents. It can also use full-text search technology to search for relevant legal documents. Furthermore, it can use filtering technology to refine the search results. The proposal unit proposes legal responses based on the information retrieved by the search unit. The proposal unit can, for example, provide legal advice. It can also provide guidelines for action. Furthermore, it can provide risk assessments. Thus, the legal information provision system according to this embodiment can propose appropriate legal responses to the user's situation.

[0061] The search function can access legal texts and legal literature. These texts include, but are not limited to, the Civil Code, Criminal Code, and Commercial Code. For example, the search function can access legal texts in digital format to search for relevant laws. It can also access legal literature to search for relevant precedents. For example, it can access case law collections in digital format to search for relevant precedents. Furthermore, it can access legal commentaries and academic papers to search for relevant legal information. For example, it can access legal commentaries in digital format to search for relevant legal interpretations. This allows for the proposal of more accurate legal responses by referencing legal texts and legal literature.

[0062] The proposal section can provide information to help users decide whether or not to hire a lawyer. For example, the proposal section can provide information about the benefits of hiring a lawyer. For instance, it can explain that hiring a lawyer provides legal protection. It can also explain that hiring a lawyer is cost-effective. Furthermore, it can explain that hiring a lawyer can save time. For example, it can explain that hiring a lawyer can resolve the problem quickly. This provides users with the information they need to decide whether or not to hire a lawyer.

[0063] The proposal department can provide information that can be used as a second opinion even after you have engaged a lawyer. For example, the proposal department can provide information about alternative possible solutions to the solutions proposed by the lawyer. For example, the proposal department can also provide information about the risks of the solutions proposed by the lawyer. Furthermore, the proposal department can also provide opinions from other experts. For example, the proposal department can provide opinions from other lawyers or legal professionals. This allows the department to provide information that can be used as a second opinion even after you have engaged a lawyer.

[0064] The analysis unit can search for laws and precedents to propose appropriate legal responses to user events. For example, the analysis unit can search for laws related to user events. For instance, it can search for laws concerning traffic accidents. Furthermore, the analysis unit can search for precedents related to user events. For example, it can search for precedents concerning past traffic accidents. In addition, the analysis unit can search for relevant laws and precedents to propose appropriate legal responses to user events. For example, it can search for relevant laws and precedents to propose legal responses such as lawsuits, settlements, and arbitrations. This allows the analysis unit to search for relevant laws and precedents to propose appropriate legal responses to user events.

[0065] The proposal department can provide information about the benefits and costs of hiring a lawyer. For example, the proposal department can provide information about the specific benefits of hiring a lawyer. For instance, it can explain that hiring a lawyer provides legal protection. It can also explain that hiring a lawyer is cost-effective. Furthermore, it can explain that hiring a lawyer saves time. For example, it can explain that hiring a lawyer leads to a quicker resolution of the problem. This allows the proposal department to provide information about the benefits and costs of hiring a lawyer.

[0066] The reception system can estimate the user's emotions and adjust how the input interface is displayed based on the estimated emotional state. For example, if the user is stressed, the reception system can provide a simple interface and minimize the input steps. If the user is relaxed, the reception system can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception system can prioritize voice input to allow for quick entry of events. For example, the reception system can use speech recognition technology to convert the user's voice into text data. This allows the system to adjust how the input interface is displayed according to the user's emotions.

[0067] The reception desk can analyze the user's past input history and suggest the most suitable input method. For example, the reception desk can automatically display as suggestions the types of events the user has frequently entered in the past. It can also prioritize suggesting input methods the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest the types of events the user will enter during a specific time period based on their past input history. For example, the reception desk can analyze the types of events the user has entered during a specific time period in the past and suggest input content related to that time period. This allows the system to analyze the user's past input history and suggest the optimal input method.

[0068] The input system can filter input based on the user's current situation and areas of interest. For example, it can prioritize displaying legal fields related to the user's current problems. It can also filter and display relevant laws and precedents based on the user's areas of interest. Furthermore, it can suggest appropriate input based on the user's current situation (e.g., traffic accident, labor issues). For example, if the user has been in a traffic accident, the input system can prioritize displaying laws and precedents related to traffic accidents. This allows the input to be filtered based on the user's current situation and areas of interest.

[0069] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotional state. For example, if the user is nervous, the reception desk can prioritize displaying important input fields to allow for quick input. If the user is relaxed, the reception desk can display detailed input fields and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize displaying the most important input fields to allow for quick input. For instance, the reception desk can use an algorithm to estimate the user's emotions and prioritize input content based on those emotions. This allows the priority of input content to be determined according to the user's emotions.

[0070] The reception system can prioritize retrieving highly relevant input content by considering the user's geographical location. For example, if the user is in a specific region, the reception system can prioritize displaying laws and precedents related to that region. Furthermore, the reception system can filter and display relevant laws and precedents based on the user's geographical location. Additionally, if the user is in a specific region, the reception system can prioritize displaying information on law firms and lawyers in that region. For instance, the reception system can acquire the user's geographical location and use that information to search for relevant laws and precedents using an algorithm. This allows for the priority retrieval of highly relevant input content by considering the user's geographical location.

[0071] The reception desk can analyze a user's social media activity and retrieve relevant input. For example, it can identify a user's current legal interests from their social media activity and suggest relevant input. It can also identify a user's current problems from their social media activity and display relevant laws and precedents. Furthermore, it can retrieve and display information about relevant law firms and lawyers from the user's social media activity. For instance, the reception desk can use social media analytics technology to analyze a user's posts, follower count, engagement, etc., and use algorithms to retrieve relevant input. This allows it to analyze a user's social media activity and retrieve relevant input.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotional state of the user. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For instance, the analysis unit can use an algorithm that estimates the user's emotions and adjusts the presentation of the analysis based on those emotions. This allows the presentation of the analysis to be adjusted according to the user's emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content. For example, the analysis unit can provide detailed analysis results for high-importance input content. It can also provide concise analysis results for low-importance input content. Furthermore, it can provide analysis results with an appropriate level of detail for moderately important input content. For instance, the analysis unit can use an algorithm that evaluates the importance of the input content and adjusts the level of detail of the analysis based on that evaluation. This allows the level of detail of the analysis to be adjusted based on the importance of the input content.

[0074] The analysis unit can apply different analysis algorithms depending on the classification of the input content. For example, the analysis unit can apply a specialized analysis algorithm to input content related to traffic accidents. It can also apply a specialized analysis algorithm to input content related to labor issues. Furthermore, it can apply a specialized analysis algorithm to input content related to family issues. For example, the analysis unit can select and apply an appropriate analysis algorithm based on the classification of the input content. This allows for the application of different analysis algorithms depending on the category of the input content.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotional state. For example, if the user is tense, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can provide a brief and rapid analysis. For instance, the analysis unit can use an algorithm that estimates the user's emotions and adjusts the length of the analysis based on those emotions. This allows the analysis length to be adjusted according to the user's emotions.

[0076] The analysis unit can determine the priority of analysis based on when the input content was submitted. For example, the analysis unit can prioritize the analysis of input content submitted earlier. It can also postpone the analysis of input content submitted later. Furthermore, it can perform analysis with an appropriate priority for input content submitted at a moderate time. For instance, the analysis unit can use an algorithm that evaluates the submission timing of the input content and determines the analysis priority based on the evaluation results. This allows the analysis priority to be determined based on the submission timing of the input content.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the input content. For example, the analysis unit can prioritize the analysis of highly relevant input content. It can also postpone the analysis of less relevant input content. Furthermore, it can analyze moderately relevant input content in an appropriate order. For instance, the analysis unit can use an algorithm that evaluates the relevance of the input content and adjusts the order of analysis based on the evaluation results. This allows the order of analysis to be adjusted based on the relevance of the input content.

[0078] The search engine can estimate the user's emotions and adjust how search results are displayed based on that estimated emotional state. For example, if the user is stressed, the search engine can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a concise display. For instance, the search engine can use an algorithm that estimates the user's emotions and adjusts how search results are displayed based on those emotions. This allows the display of search results to be adjusted according to the user's emotions.

[0079] The search unit can improve search accuracy by considering the interrelationships between laws and precedents. For example, the search unit can analyze the interrelationships between relevant laws and precedents and prioritize displaying the most relevant information. Furthermore, the search unit can filter search results by considering the interrelationships between relevant laws and precedents. In addition, the search unit can adjust the display order of search results based on the interrelationships between relevant laws and precedents. For instance, the search unit can use an algorithm that analyzes the interrelationships between laws and precedents and improves search accuracy based on the analysis results. This allows for improved search accuracy by considering the interrelationships between relevant laws and precedents.

[0080] The search function can perform searches while considering the user's attribute data. For example, the search function can filter relevant laws and precedents based on the user's age and gender. It can also prioritize the display of relevant laws and precedents based on the user's occupation and position. Furthermore, the search function can suggest relevant laws and precedents based on the user's past search history. For instance, the search function can use an algorithm that retrieves user attribute data and performs searches based on that data. This allows the search to take user attribute information into consideration.

[0081] The search engine can estimate the user's emotions and adjust the display order of search results based on the estimated emotional state. For example, if the user is stressed, the search engine can prioritize displaying important information. If the user is relaxed, it can also display search results containing more detailed information. Furthermore, if the user is in a hurry, it can prioritize displaying concise search results. For instance, the search engine can use an algorithm that estimates the user's emotions and adjusts the display order of search results based on those emotions. This allows the display order of search results to be adjusted according to the user's emotions.

[0082] The search unit can perform searches while considering the geographical distribution of laws and precedents. For example, the search unit can analyze the geographical distribution of relevant laws and precedents and prioritize displaying the most relevant information. Furthermore, the search unit can filter search results while considering the geographical distribution of relevant laws and precedents. In addition, the search unit can adjust the display order of search results based on the geographical distribution of relevant laws and precedents. For example, the search unit can use an algorithm that analyzes the geographical distribution of laws and precedents and performs searches based on the analysis results. This allows searches to be performed while considering the geographical distribution of relevant laws and precedents.

[0083] The search unit can improve search accuracy by referencing relevant legal and case law literature. For example, it can analyze relevant legal and case law literature and prioritize displaying the most relevant information. It can also filter search results by considering relevant legal and case law literature. Furthermore, it can adjust the display order of search results based on relevant legal and case law literature. For instance, the search unit can analyze relevant legal and case law literature and use algorithms to improve search accuracy based on the analysis results. This allows for improved search accuracy by referencing relevant legal and case law literature.

[0084] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on the estimated emotional state. For example, if the user is stressed, the suggestion function can provide simple and easily understandable suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. For instance, the suggestion function can use an algorithm that estimates the user's emotions and adjusts the way it presents suggestions based on those emotions. This allows the suggestion function to adapt to the user's emotions.

[0085] The proposal function can adjust the level of detail in its proposals based on the importance of the legal response. For example, it can provide detailed proposals for highly important legal responses, concise proposals for less important ones, and proposals with a moderate level of detail for moderately important ones. For instance, the proposal function can use an algorithm that evaluates the importance of a legal response and adjusts the level of detail in its proposals based on that evaluation. This allows for adjustment of proposal detail based on the importance of the legal response.

[0086] The proposal unit can apply different proposal algorithms depending on the classification of the legal response. For example, for legal responses related to traffic accidents, the proposal unit can apply a proposal algorithm specialized for traffic accidents. Similarly, for legal responses related to labor issues, the proposal unit can apply a proposal algorithm specialized for labor issues. Furthermore, for legal responses related to family issues, the proposal unit can apply a proposal algorithm specialized for family issues. For example, the proposal unit can select and apply an appropriate proposal algorithm based on the classification of the legal response. This allows for the application of different proposal algorithms depending on the category of legal response.

[0087] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotional state. For example, if the user is stressed, the suggestion function can provide a short, concise suggestion. If the user is relaxed, it can provide a more detailed suggestion. Furthermore, if the user is in a hurry, it can provide a brief and quick suggestion. For instance, the suggestion function can use an algorithm that estimates the user's emotions and adjusts the length of the suggestion based on those emotions. This allows the suggestion length to be adjusted according to the user's emotions.

[0088] The proposal committee can prioritize proposals based on when legal responses are submitted. For example, the proposal committee can prioritize proposals for legal responses submitted early. Conversely, it can postpone proposals for legal responses submitted late. Furthermore, it can give a moderate priority to legal responses submitted at a moderate time. For example, the proposal committee can use an algorithm that evaluates the submission timing of legal responses and determines proposal priority based on the evaluation results. This allows for the prioritization of proposals based on the submission timing of legal responses.

[0089] The proposal system can adjust the order of proposals based on the relevance of the legal responses. For example, the proposal system can prioritize proposals for highly relevant legal responses. Conversely, it can postpone proposals for less relevant legal responses. Furthermore, it can propose legal responses of moderate relevance in an appropriate order. For instance, the proposal system can use an algorithm that evaluates the relevance of legal responses and adjusts the order of proposals based on the evaluation results. This allows the order of proposals to be adjusted based on the relevance of the legal responses. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input events in text format or voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the user's events using text analysis technology and natural language processing technology. The search unit is implemented by the specific processing unit 290 of the data processing unit 12, and searches for relevant laws and precedents using keyword search technology and full-text search technology. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, and provides legal advice, action guidelines, and risk assessments. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input events by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the user's events using text analysis technology and natural language processing technology. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and searches for relevant laws and precedents using keyword search technology and full-text search technology. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides legal advice, action guidelines, and risk assessments. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input events by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the user's events using text analysis technology and natural language processing technology. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and searches for relevant laws and precedents using keyword search technology and full-text search technology. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides legal advice, action guidelines, and risk assessments. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, search unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input events via voice input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the user's events using text analysis technology and natural language processing technology. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and searches for relevant laws and precedents using keyword search technology and full-text search technology. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides legal advice, action guidelines, and risk assessments.

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

[0091] The reception desk can also provide relevant legal information in real time based on the user's input. For example, if a user enters a question about a traffic accident, the reception desk can immediately display an overview of relevant laws and precedents. Furthermore, the reception desk can guide the user through the steps of relevant legal procedures based on their input. In addition, the reception desk can provide templates for relevant legal documents based on the user's input. This allows users to quickly obtain the necessary information and proceed smoothly with legal procedures.

[0092] The search function can also prioritize displaying relevant laws and precedents by considering the user's past search history. For example, if a user has previously searched for information related to traffic accidents, subsequent searches can prioritize information related to traffic accidents. The search function can also suggest relevant legal literature and commentaries based on the user's search history. Furthermore, the search function can analyze the user's search history and notify them of updates to relevant laws and precedents. This ensures that users always have access to the latest legal information.

[0093] The proposal team can also estimate the user's emotions and adjust the content of the proposals based on those estimates. For example, if the user is feeling anxious, the proposal team can provide reassuring legal advice. If the user is angry, it can offer suggestions that encourage a calm response. Furthermore, if the user is confused, the proposal team can offer simple and easy-to-understand suggestions. This allows for the provision of appropriate legal advice tailored to the user's emotions.

[0094] The proposal department can also predict future legal risks and suggest preventative measures based on the user's past legal issues. For example, if a user has experienced contract disputes in the past, the proposal department can suggest points to be aware of and risk avoidance measures for future contracts. Similarly, if a user has experienced labor issues in the past, the proposal department can suggest revisions to employment contracts and improvements to the work environment. Furthermore, if a user has experienced family problems in the past, the proposal department can provide advice to prevent legal risks within the family. This allows users to proactively prevent future legal risks.

[0095] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand results. If the user is relaxed, it can provide detailed results. Furthermore, if the user is in a hurry, it can provide concise and to-the-point results. This allows the system to provide appropriate analysis results tailored to the user's emotions.

[0096] The reception desk can automatically categorize and display relevant legal information based on user input. For example, if a user enters a question about a traffic accident, the reception desk can display laws, precedents, procedures, and document templates related to traffic accidents, categorized accordingly. The reception desk can also prioritize and display relevant legal information based on user input. Furthermore, the reception desk can filter and display relevant legal information based on user input. This allows users to quickly find the information they need.

[0097] The search engine can also estimate the user's emotions and adjust how search results are displayed based on those estimates. For example, if the user is stressed, the search engine can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display. This allows the system to provide appropriate search results that match the user's emotions.

[0098] The proposal department can also predict future legal risks and suggest preventative measures based on the user's past legal issues. For example, if a user has experienced contract disputes in the past, the proposal department can suggest points to be aware of and risk avoidance measures for future contracts. Similarly, if a user has experienced labor issues in the past, the proposal department can suggest revisions to employment contracts and improvements to the work environment. Furthermore, if a user has experienced family problems in the past, the proposal department can provide advice to prevent legal risks within the family. This allows users to proactively prevent future legal risks.

[0099] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand results. If the user is relaxed, it can provide detailed results. Furthermore, if the user is in a hurry, it can provide concise and to-the-point results. This allows the system to provide appropriate analysis results tailored to the user's emotions.

[0100] The proposal team can also estimate the user's emotions and adjust the content of the proposals based on those estimates. For example, if the user is feeling anxious, the proposal team can provide reassuring legal advice. If the user is angry, it can offer suggestions that encourage a calm response. Furthermore, if the user is confused, the proposal team can offer simple and easy-to-understand suggestions. This allows for the provision of appropriate legal advice tailored to the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives the user's event. This event may include legal issues, contract disputes, or traffic accidents. The reception desk allows users to input events using text or voice input. In the case of voice input, speech recognition technology is used to convert the voice into text data. Furthermore, the reception desk can estimate the user's emotions and adjust the display of the input interface based on those emotions. For example, if the user is feeling stressed, a simpler interface is provided, minimizing the input steps. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses text analysis technology, data mining technology, and natural language processing technology to analyze the content and meaning of the user's events and extract relevant information. Step 3: The search unit searches for laws and precedents based on the information analyzed by the analysis unit. The search unit uses keyword search technology, full-text search technology, and filtering technology to search for relevant laws, precedents, and legal literature, and then refines the search results. Step 4: The Proposal Department proposes legal responses based on the information retrieved by the Search Department. The Proposal Department provides legal advice, guidelines, and risk assessments, and proposes appropriate legal responses to the user's incident.

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

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

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

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their events, An analysis unit analyzes the information input by the reception unit, A search unit that searches for laws and precedents based on the information analyzed by the aforementioned analysis unit, A proposal unit proposes legal responses based on the information retrieved by the aforementioned search unit, Equipped with A system characterized by the following features.

2. The aforementioned search unit, Refer to legal texts and legal documents. The system according to feature 1.

3. The aforementioned proposal section is, Provides information to help users decide whether or not to hire a lawyer. The system according to feature 1.

4. The aforementioned proposal section is, We provide information that can be used as a second opinion even after you have hired a lawyer. The system according to feature 1.

5. The aforementioned analysis unit, To propose an appropriate legal response to a user's incident, we search for laws and precedents. The system according to feature 1.

6. The aforementioned proposal section is, We provide information about the benefits and costs of hiring a lawyer. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the display method of the input interface based on the estimated emotional state of the user. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the most suitable input method. The system according to feature 1.

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