system

Abstract

The system according to the embodiment aims to respond promptly and appropriately to inquiries from users and to transfer the case to an expert as necessary. [Solution] A system according to an embodiment includes a reception unit, a response unit, a handover unit, and an expert unit. The reception unit receives a consultation from a user. The response unit provides an automatic response based on the consultation content received by the reception unit. The handover unit hands over the consultation to an expert based on the content of the response provided by the response unit. The expert unit continues the consultation based on the content handed over by the handover unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to respond quickly and appropriately to inquiries from users, and of not being able to smoothly transfer the case to an expert.

[0005] The system according to the embodiment aims to respond promptly and appropriately to inquiries from users and to transfer the case to an expert as necessary. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a response unit, a handover unit, and an expert unit. The reception unit receives a consultation from a user. The response unit provides an automatic response based on the consultation content received by the reception unit. The handover unit hands over the consultation to an expert based on the content of the response provided by the response unit. The expert unit allows the expert to continue the consultation based on the content handed over by the handover unit. [Effects of the Invention]

[0007] The system according to the embodiment can respond quickly and appropriately to inquiries from users and transfer the case to an expert as necessary. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A legal service system according to an embodiment of the present invention utilizes a generation AI to provide legal services. The system accepts user inquiries, automatically responds to them, and, if necessary, transfers them to a legal expert. It also provides functions such as a legal dictionary, checking for problems and important points in contracts, AI mediation, and introducing legal news. For example, when a user inquires about a general issue, the generation AI automatically responds. For more complex issues, the generation AI can transfer the matter to a legal expert for further consultation. Next, in the legal dictionary function, the generation AI automatically recognizes technical terms that appear in specific legal consultations and explains them in general terms. Furthermore, in the issue and important point checking function, when a user scans or uploads a contract, the generation AI analyzes its contents and checks for problems and important points. Furthermore, in the AI ​​mediation function, the generation AI mediates to resolve interpersonal disputes and proposes fair solutions. Finally, in the legal news introduction function, the generation AI introduces the latest legal news, such as legal reforms and precedents, to users and explains their significance in simple, easy-to-understand terms. This allows users of the legal service system to receive legal advice 24 hours a day, making it easier to understand technical terms, check contracts, resolve disputes, and keep up with the latest legal news. For example, users' written inquiries can be processed quickly and accurately, reducing the burden on legal experts. Users can also learn specific areas for improvement in their inquiries, improving their legal knowledge.

[0029] A legal-related service system according to an embodiment includes a reception unit, a response unit, a handover unit, and an expert unit. The reception unit receives consultations from users. Consultations from users include, but are not limited to, legal consultations, technical consultations, and business consultations. The reception unit can receive consultations via, for example, a web form, a telephone call, a chatbot, or the like. The response unit uses a generation AI to automatically respond based on the consultation content received by the reception unit. The generation AI generates an appropriate response to the user's consultation content using, for example, a text generation AI (e.g., GPT-4 (registered trademark)) or a multimodal generation AI. For example, when a user says, "I don't understand the contents of a contract," the response unit asks, "Please tell me which part of the contract you don't understand," and when the user specifies a specific part, the response unit provides a basic explanation of that part. The handover unit passes on more complex issues to an expert based on the response provided by the response unit. For example, the generation AI analyzes the consultation content and determines whether an expert should handle it. The expert unit continues the consultation with an expert based on the content handed over by the handover unit. The expert unit has experts such as lawyers, engineers, and consultants provide detailed advice on the content of the user's consultation. This enables the legal-related service system according to the embodiment to efficiently accept user consultations, automatically respond, hand over to an expert, and continue the consultation with an expert.

[0030] The response unit can use a generation AI to provide automated responses to general problems. The generation AI uses, for example, text generation AI (e.g., GPT-4) or multimodal generation AI to generate appropriate responses to general user problems. For example, if a user complains, "I don't understand the contents of a contract," the generation AI can ask, "Please tell me which part of the contract you don't understand." If the user specifies a specific part, the generation AI can provide a basic explanation of that part. The generation AI can also respond to user questions by citing relevant laws and regulations. For example, if a user asks, "What is default on a contract?" the generation AI can respond, "Default on a contract means not keeping a promise." This improves the accuracy of automated responses to general problems by using generation AI.

[0031] The handover unit can use the generation AI to hand over more complex issues to an expert. The generation AI, for example, analyzes the content of the user's consultation and determines whether to hand over the complex issue to an expert. For example, if a user says, "I don't understand a particular clause in a contract," the generation AI analyzes the content of that clause and determines whether an expert should handle it. The generation AI can also organize and provide the necessary information when handing over the user's consultation to an expert. For example, the generation AI summarizes the user's consultation and provides the expert with information such as, "The user is asking about Article 5 of the contract." This improves the accuracy of handovers for complex issues by using the generation AI.

[0032] The response unit can automatically recognize technical terms that appear in specific legal consultations and explain them in general terms. For example, if a user uses the term "default," the AI ​​can explain, "Default refers to not keeping a promise." The AI ​​can also provide detailed explanations of technical terms. For example, it could explain, "Default refers to not fulfilling a contractual obligation, which could result in harm to the other party." Furthermore, the AI ​​can provide examples of how technical terms are used. For example, it could explain, "An example of default is failing to repay a debt." This allows the AI ​​to automatically recognize technical terms and explain them in general terms, deepening the user's understanding.

[0033] The response unit can scan or upload the contract and check for problems or caveats. For example, when a user scans or uploads a contract, the generation AI analyzes its contents and checks for problems or caveats. For example, the generation AI can analyze the contract's clauses and, if any unfavorable clauses are included, point them out and warn the user. The generation AI can also summarize the contents of the contract and highlight key points. For example, it can warn the user by saying, "This contract contains a clause with strict termination conditions." Furthermore, the generation AI can check the contents of the contract against the law. For example, it can point out, "Article 3 of this contract may violate current law." This reduces the user's risk by automatically checking for problems and caveats in the contract.

[0034] The response unit can mediate to resolve interpersonal disputes and propose fair solutions. For example, when a user consults the generation AI about an interpersonal dispute, the generation AI mediates and proposes fair solutions. For example, when a user consults the generation AI about a dispute with a neighbor, the generation AI listens to both parties' opinions and proposes a fair solution. The generation AI can also organize the opinions of both parties during the mediation process and find a common solution. For example, it can make a proposal such as, "A possible solution that is acceptable to both parties would be to revise the rules for use of the shared space." Furthermore, the generation AI can record the results of the mediation for future reference. For example, it can leave a record such as, "It was agreed that the rules for use of the shared space should be revised as a solution to this dispute." This improves user satisfaction by resolving interpersonal disputes fairly.

[0035] The response unit can introduce the latest legal news, such as legal reforms and precedents, to users and explain their significance in simple, easy-to-understand language. The generation AI can automatically collect the latest legal news, such as legal reforms and precedents, and introduce it to users. For example, it might introduce news such as, "A new law has been enacted. This law strengthens consumer protection." The generation AI can also explain the significance of legal news in simple, easy-to-understand language. For example, it might explain, "This legal reform will enable consumers to purchase safer products." Furthermore, the generation AI can specifically explain the impact of legal news. For example, it might explain, "This precedent may result in certain clauses being invalidated in future contract drafting." This allows users to improve their legal knowledge by simply explaining the latest legal news.

[0036] The reception unit can analyze the user's past consultation history and select the optimal reception method. For example, the reception unit automatically displays as candidates the contents of consultations that the user has frequently made in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the contents of consultations that will be used in a specific time period based on the user's past consultation history. In this way, the optimal reception method can be provided by analyzing the past consultation history.

[0037] The reception unit can perform filtering based on the user's current situation and areas of interest. For example, the reception unit preferentially displays consultation contents related to problems the user is currently facing. The reception unit can also filter and display related consultation contents based on the user's areas of interest. Furthermore, the reception unit can also suggest appropriate consultation contents depending on the user's current situation (for example, a problem with high urgency). In this way, by filtering according to the user's situation and areas of interest, more appropriate consultation contents can be provided.

[0038] The reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit receives the consultation content using voice recognition technology. Also, if the user selects text input, the reception unit can also receive the consultation content using text analysis technology. Furthermore, if the user uploads an image, the reception unit can also receive the consultation content using image analysis technology. This allows for smoother consultation reception by selecting the reception means depending on the user's input method.

[0039] The reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving legal consultations related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving legal consultations related to the user's travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving legal consultations related to the user's home. In this way, by taking into account the geographical location information, more relevant consultations can be prioritized.

[0040] The reception unit can analyze the user's social media activity and receive related consultations. For example, the reception unit receives related legal consultations based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related legal consultations. Furthermore, the reception unit can also receive related legal consultations by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, more relevant consultations can be provided.

[0041] The reception unit can customize the reception method by reflecting the user's past feedback. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, a more optimal reception method can be provided by reflecting past feedback.

[0042] The response unit can adjust the level of detail of the response based on the importance of the consultation content. For example, the response unit provides a detailed response to a consultation content with a high level of importance. The response unit can also provide a concise response to a consultation content with a low level of importance. Furthermore, the response unit can also adjust the level of detail of the response in stages according to the level of importance. This allows for a more appropriate response by providing a response according to the importance of the consultation content.

[0043] The response unit can apply different response algorithms depending on the category of the consultation content. For example, the response unit can apply a contract analysis algorithm to a consultation about a contract. The response unit can also apply a legal dictionary algorithm to a consultation about legal terminology. Furthermore, the response unit can apply a mediation algorithm to a consultation about solving a problem. This makes it possible to respond more appropriately by applying a response algorithm according to the consultation content.

[0044] The response unit can improve the accuracy of the response by referring to the user's past response results. For example, the response unit can suggest an optimal response based on the response results the user has received in the past. The response unit can also suggest a response to a similar consultation content based on the user's past response results. Furthermore, the response unit can analyze the user's past response results and improve the accuracy of the response. In this way, the accuracy of the response can be improved by referring to the past response results.

[0045] The response unit can determine the priority of responses based on the time when the consultation content was submitted. The response unit can determine the priority of responses based on, for example, the time when the consultation content was submitted. The response unit can also respond quickly to consultation content with high urgency. Furthermore, the response unit can gradually adjust the priority of responses depending on the time of submission. This allows for faster responses by determining the priority of responses based on the time of submission.

[0046] The response unit can adjust the order of responses based on the relevance of the consultation content. For example, the response unit determines the order of responses based on the relevance of the consultation content. The response unit can also apply the same order of responses to similar consultation contents. Furthermore, the response unit can analyze the relevance of the consultation content and propose an optimal order of responses. This allows for a more appropriate response by adjusting the order of responses based on the relevance of the consultation content.

[0047] The response unit can adjust the use of technical terms in the response depending on the user's level of expertise. For example, if the user has technical knowledge, the response unit can respond using a lot of technical terms. Alternatively, if the user does not have technical knowledge, the response unit can respond using general language. Furthermore, the response unit can gradually adjust the use of technical terms in the response depending on the user's level of expertise. This allows for a more appropriate response by providing a response that is appropriate for the user's level of expertise.

[0048] The handover unit can adjust the level of detail of the handover based on the complexity of the consultation content. For example, the handover unit provides detailed handover information for complex consultation content. The handover unit can also provide concise handover information for simple consultation content. Furthermore, the handover unit can gradually adjust the level of detail of the handover based on the complexity of the consultation content. This allows for a more appropriate response by performing a handover based on the complexity of the consultation content.

[0049] The handover unit can apply different handover algorithms depending on the category of the consultation content. For example, the handover unit applies a contract analysis algorithm to a consultation about a contract. The handover unit can also apply a legal dictionary algorithm to a consultation about legal terminology. Furthermore, the handover unit can also apply a mediation algorithm to a consultation about problem resolution. This makes it possible to respond more appropriately by applying a handover algorithm according to the consultation content.

[0050] The handover unit can improve the accuracy of the handover by referring to the user's past handover results. For example, the handover unit proposes an optimal handover method based on the handover results the user has received in the past. The handover unit can also propose a handover method for similar consultation content based on the user's past handover results. Furthermore, the handover unit can analyze the user's past handover results and improve the accuracy of the handover. In this way, the accuracy of the handover is improved by referring to the past handover results.

[0051] The handover unit can adjust the order of handover based on the time of submission of the consultation content. For example, the handover unit determines the order of handover based on the time of submission of the consultation content. The handover unit can also quickly hand over consultation content with high urgency. Furthermore, the handover unit can gradually adjust the order of handover depending on the time of submission. This allows for a faster response by adjusting the order of handover based on the time of submission.

[0052] The handover unit can adjust the handover order based on the relevance of the consultation contents. For example, the handover unit determines the handover order based on the relevance of the consultation contents. The handover unit can also apply the same handover order to similar consultation contents. Furthermore, the handover unit can analyze the relevance of the consultation contents and propose an optimal handover order. This makes it possible to adjust the handover order based on the relevance of the consultation contents, thereby enabling more appropriate responses.

[0053] The handover unit can adjust the level of detail of the handover depending on the user's level of expertise. For example, if the user has specialized knowledge, the handover unit provides detailed handover information. Also, if the user does not have specialized knowledge, the handover unit can provide concise handover information. Furthermore, the handover unit can gradually adjust the level of detail of the handover depending on the user's level of expertise. This allows for more appropriate handling by performing handover according to the user's level of expertise.

[0054] The expert department can adjust the level of detail of the response based on the importance of the consultation content. For example, the expert department can provide a detailed response to consultation content with a high level of importance. The expert department can also provide a concise response to consultation content with a low level of importance. Furthermore, the expert department can gradually adjust the level of detail of the response depending on the importance. This allows for a more appropriate response by responding according to the importance of the consultation content.

[0055] The expert department can apply different response methods depending on the category of the consultation content. For example, the expert department applies a contract analysis method to consultations regarding contracts. The expert department can also apply a legal dictionary method to consultations regarding legal terminology. Furthermore, the expert department can also apply a mediation method to consultations regarding problem resolution. This allows for a more appropriate response by applying a response method according to the consultation content.

[0056] The expert unit can improve the accuracy of the response by referring to the user's past consultation results. For example, the expert unit can propose the optimal response based on the results of consultations the user has received in the past. The expert unit can also propose responses to similar consultation content based on the user's past consultation results. Furthermore, the expert unit can analyze the user's past consultation results and improve the accuracy of the response. In this way, the accuracy of the response can be improved by referring to the past consultation results.

[0057] The expert department can adjust the order of responses based on the time of submission of the consultation content. The expert department determines the order of responses based on, for example, the time of submission of the consultation content. The expert department can also respond quickly to consultation content with a high degree of urgency. Furthermore, the expert department can gradually adjust the order of responses depending on the time of submission. This allows for faster responses by adjusting the order of responses based on the time of submission.

[0058] The expert department can adjust the order of responses based on the relevance of the consultation contents. For example, the expert department determines the order of responses based on the relevance of the consultation contents. The expert department can also apply the same order of responses to similar consultation contents. Furthermore, the expert department can analyze the relevance of the consultation contents and propose an optimal order of responses. This allows for more appropriate responses by adjusting the order of responses based on the relevance of the consultation contents.

[0059] The expert unit can adjust the use of corresponding technical terms depending on the user's level of expertise. For example, if the user has specialized knowledge, the expert unit can respond using a lot of technical terms. Also, if the user does not have specialized knowledge, the expert unit can respond using general language. Furthermore, the expert unit can gradually adjust the use of corresponding technical terms depending on the user's level of expertise. This allows for a more appropriate response by responding according to the user's level of expertise.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The legal service system can also include a response unit that analyzes the user's past consultation history and provides the most appropriate response. For example, it can suggest responses to similar consultations based on the content of the user's past consultations. It can also identify specific patterns from the user's past consultation history and prepare responses to anticipated questions in advance. In this way, by utilizing the user's past consultation history, the accuracy of responses can be improved, and user satisfaction can be increased.

[0062] The legal-related service system may further include a response unit that provides relevant legal information taking into account the user's geographical location information. For example, if the user is in a specific area, information about laws and regulations related to that area may be provided. Also, if the user is traveling, information about laws and regulations in the travel destination may be provided. In this way, by utilizing the geographical location information, it is possible to provide more relevant information to the user.

[0063] The legal service system may further include a response unit that analyzes a user's social media activity and provides relevant legal information. For example, relevant legal information may be provided based on the content posted by the user on social media. The system may also analyze the user's social media activity history and prepare responses to anticipated questions. This makes it possible to provide more relevant information to the user by utilizing social media activity.

[0064] The legal service system may further include a reception unit that performs filtering based on the user's current situation and areas of interest. For example, it may be possible to prioritize the display of consultation content related to a problem the user is currently facing. It may also be possible to filter and display related consultation content based on the user's areas of interest. This makes it possible to provide appropriate consultation content according to the user's situation and areas of interest.

[0065] The legal service system can further include a reception unit that customizes the reception method by reflecting the user's past feedback. For example, the system can suggest the optimal reception method based on the user's past feedback. It can also suggest specific reception methods with priority based on the user's past feedback. This makes it possible to provide a more optimal reception method by utilizing the user's past feedback.

[0066] The legal service system may further include a response unit that adjusts the use of technical terms in the response depending on the user's level of expertise. For example, if the user has technical expertise, the response may be made using a lot of technical terms. On the other hand, if the user does not have technical expertise, the response may be made in general terms. This allows for an appropriate response depending on the user's level of expertise, thereby improving user satisfaction.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The reception department accepts inquiries from users. Inquiries from users include legal, technical, and business inquiries. The reception department can accept inquiries via a web form, telephone, chatbot, etc. Step 2: The response unit uses a generation AI to provide an automatic response based on the consultation content received by the reception unit. The generation AI uses a text generation AI (e.g., GPT-4) or a multimodal generation AI to generate an appropriate response to the user's consultation content. For example, if a user says, "I don't understand the contents of the contract," the generation AI will ask, "Please tell me which part of the contract you don't understand," and when the user specifies the specific part, it will provide a basic explanation of that part. Step 3: The handover unit transfers more complex issues to an expert based on the response provided by the response unit. In the handover unit, the generation AI analyzes the consultation content and determines whether an expert should respond. Step 4: The Expert Division continues the consultation based on the content handed over by the Handover Division. The Expert Division has experts such as lawyers, engineers, and consultants who provide detailed advice on the user's consultation content.

[0069] (Example 2) A legal service system according to an embodiment of the present invention utilizes a generation AI to provide legal services. The system accepts user inquiries, automatically responds to them, and, if necessary, transfers them to a legal expert. It also provides functions such as a legal dictionary, checking for problems and important points in contracts, AI mediation, and introducing legal news. For example, when a user inquires about a general issue, the generation AI automatically responds. For more complex issues, the generation AI can transfer the matter to a legal expert for further consultation. Next, in the legal dictionary function, the generation AI automatically recognizes technical terms that appear in specific legal consultations and explains them in general terms. Furthermore, in the issue and important point checking function, when a user scans or uploads a contract, the generation AI analyzes its contents and checks for problems and important points. Furthermore, in the AI ​​mediation function, the generation AI mediates to resolve interpersonal disputes and proposes fair solutions. Finally, in the legal news introduction function, the generation AI introduces the latest legal news, such as legal reforms and precedents, to users and explains their significance in simple, easy-to-understand terms. This allows users of the legal service system to receive legal advice 24 hours a day, making it easier to understand technical terms, check contracts, resolve disputes, and keep up with the latest legal news. For example, users' written inquiries can be processed quickly and accurately, reducing the burden on legal experts. Users can also learn specific areas for improvement in their inquiries, improving their legal knowledge.

[0070] A legal-related service system according to an embodiment includes a reception unit, a response unit, a handover unit, and an expert unit. The reception unit receives consultations from users. Consultations from users include, but are not limited to, legal consultations, technical consultations, and business consultations. The reception unit can receive consultations via, for example, a web form, a telephone call, a chatbot, or the like. The response unit uses a generation AI to automatically respond based on the consultation content received by the reception unit. The generation AI generates an appropriate response to the user's consultation content using, for example, a text generation AI (e.g., GPT-4) or a multimodal generation AI. For example, when a user says, "I don't understand the contents of a contract," the response unit asks, "Please tell me which part of the contract you don't understand," and when the user specifies a specific part, the response unit provides a basic explanation of that part. The handover unit passes on more complex issues to an expert based on the response provided by the response unit. For example, the generation AI analyzes the consultation content and determines whether an expert should handle it. The expert unit continues the consultation with an expert based on the content handed over by the handover unit. The expert unit has experts such as lawyers, engineers, and consultants provide detailed advice on the content of the user's consultation. This enables the legal-related service system according to the embodiment to efficiently accept user consultations, automatically respond, hand over to an expert, and continue the consultation with an expert.

[0071] The response unit can use a generation AI to provide automated responses to general problems. The generation AI uses, for example, text generation AI (e.g., GPT-4) or multimodal generation AI to generate appropriate responses to general user problems. For example, if a user complains, "I don't understand the contents of a contract," the generation AI can ask, "Please tell me which part of the contract you don't understand." If the user specifies a specific part, the generation AI can provide a basic explanation of that part. The generation AI can also respond to user questions by citing relevant laws and regulations. For example, if a user asks, "What is default on a contract?" the generation AI can respond, "Default on a contract means not keeping a promise." This improves the accuracy of automated responses to general problems by using generation AI.

[0072] The handover unit can use the generation AI to hand over more complex issues to an expert. The generation AI, for example, analyzes the content of the user's consultation and determines whether to hand over the complex issue to an expert. For example, if a user says, "I don't understand a particular clause in a contract," the generation AI analyzes the content of that clause and determines whether an expert should handle it. The generation AI can also organize and provide the necessary information when handing over the user's consultation to an expert. For example, the generation AI summarizes the user's consultation and provides the expert with information such as, "The user is asking about Article 5 of the contract." This improves the accuracy of handovers for complex issues by using the generation AI.

[0073] The response unit can automatically recognize technical terms that appear in specific legal consultations and explain them in general terms. For example, if a user uses the term "default," the AI ​​can explain, "Default refers to not keeping a promise." The AI ​​can also provide detailed explanations of technical terms. For example, it could explain, "Default refers to not fulfilling a contractual obligation, which could result in harm to the other party." Furthermore, the AI ​​can provide examples of how technical terms are used. For example, it could explain, "An example of default is failing to repay a debt." This allows the AI ​​to automatically recognize technical terms and explain them in general terms, deepening the user's understanding.

[0074] The response unit can scan or upload the contract and check for problems or caveats. For example, when a user scans or uploads a contract, the generation AI analyzes its contents and checks for problems or caveats. For example, the generation AI can analyze the contract's clauses and, if any unfavorable clauses are included, point them out and warn the user. The generation AI can also summarize the contents of the contract and highlight key points. For example, it can warn the user by saying, "This contract contains a clause with strict termination conditions." Furthermore, the generation AI can check the contents of the contract against the law. For example, it can point out, "Article 3 of this contract may violate current law." This reduces the user's risk by automatically checking for problems and caveats in the contract.

[0075] The response unit can mediate to resolve interpersonal disputes and propose fair solutions. For example, when a user consults the generation AI about an interpersonal dispute, the generation AI mediates and proposes fair solutions. For example, when a user consults the generation AI about a dispute with a neighbor, the generation AI listens to both parties' opinions and proposes a fair solution. The generation AI can also organize the opinions of both parties during the mediation process and find a common solution. For example, it can make a proposal such as, "A possible solution that is acceptable to both parties would be to revise the rules for use of the shared space." Furthermore, the generation AI can record the results of the mediation for future reference. For example, it can leave a record such as, "It was agreed that the rules for use of the shared space should be revised as a solution to this dispute." This improves user satisfaction by resolving interpersonal disputes fairly.

[0076] The response unit can introduce the latest legal news, such as legal reforms and precedents, to users and explain their significance in simple, easy-to-understand language. The generation AI can automatically collect the latest legal news, such as legal reforms and precedents, and introduce it to users. For example, it might introduce news such as, "A new law has been enacted. This law strengthens consumer protection." The generation AI can also explain the significance of legal news in simple, easy-to-understand language. For example, it might explain, "This legal reform will enable consumers to purchase safer products." Furthermore, the generation AI can specifically explain the impact of legal news. For example, it might explain, "This precedent may result in certain clauses being invalidated in future contract drafting." This allows users to improve their legal knowledge by simply explaining the latest legal news.

[0077] The reception unit can estimate the user's emotions and adjust the consultation reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly enter the consultation details. This allows for more appropriate consultation reception by adjusting the reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] The reception unit can analyze the user's past consultation history and select the optimal reception method. For example, the reception unit automatically displays as candidates the contents of consultations that the user has frequently made in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the contents of consultations that will be used in a specific time period based on the user's past consultation history. In this way, the optimal reception method can be provided by analyzing the past consultation history.

[0079] The reception unit can perform filtering based on the user's current situation and areas of interest. For example, the reception unit preferentially displays consultation contents related to problems the user is currently facing. The reception unit can also filter and display related consultation contents based on the user's areas of interest. Furthermore, the reception unit can also suggest appropriate consultation contents depending on the user's current situation (for example, a problem with high urgency). In this way, by filtering according to the user's situation and areas of interest, more appropriate consultation contents can be provided.

[0080] The reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit receives the consultation content using voice recognition technology. Also, if the user selects text input, the reception unit can also receive the consultation content using text analysis technology. Furthermore, if the user uploads an image, the reception unit can also receive the consultation content using image analysis technology. This allows for smoother consultation reception by selecting the reception means depending on the user's input method.

[0081] The reception unit can estimate the user's emotions and determine the priority of consultations to be accepted based on the estimated user emotions. For example, when the user is nervous, the reception unit prioritizes urgent consultations. Furthermore, when the user is relaxed, the reception unit can also accept consultations with normal priority. Furthermore, when the user is in a hurry, the reception unit can also prioritize consultations that require a quick response. This enables more appropriate responses by determining the priority of consultations according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The reception unit can prioritize receiving highly relevant consultations by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving legal consultations related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving legal consultations related to the user's travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving legal consultations related to the user's home. In this way, by taking into account the geographical location information, more relevant consultations can be prioritized.

[0083] The reception unit can analyze the user's social media activity and receive related consultations. For example, the reception unit receives related legal consultations based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related legal consultations. Furthermore, the reception unit can also receive related legal consultations by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, more relevant consultations can be provided.

[0084] The reception unit can customize the reception method by reflecting the user's past feedback. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and customize the reception method. In this way, a more optimal reception method can be provided by reflecting past feedback.

[0085] The response unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated user's emotions. For example, if the user is nervous, the response unit can respond with a calm expression. If the user is relaxed, the response unit can also respond with a friendly expression. Furthermore, if the user is in a hurry, the response unit can provide a concise and quick response. This enables a more appropriate response by providing a response according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The response unit can adjust the level of detail of the response based on the importance of the consultation content. For example, the response unit provides a detailed response to a consultation content with a high level of importance. The response unit can also provide a concise response to a consultation content with a low level of importance. Furthermore, the response unit can also adjust the level of detail of the response in stages according to the level of importance. This allows for a more appropriate response by providing a response according to the importance of the consultation content.

[0087] The response unit can apply different response algorithms depending on the category of the consultation content. For example, the response unit can apply a contract analysis algorithm to a consultation about a contract. The response unit can also apply a legal dictionary algorithm to a consultation about legal terminology. Furthermore, the response unit can apply a mediation algorithm to a consultation about solving a problem. This makes it possible to respond more appropriately by applying a response algorithm according to the consultation content.

[0088] The response unit can improve the accuracy of the response by referring to the user's past response results. For example, the response unit can suggest an optimal response based on the response results the user has received in the past. The response unit can also suggest a response to a similar consultation content based on the user's past response results. Furthermore, the response unit can analyze the user's past response results and improve the accuracy of the response. In this way, the accuracy of the response can be improved by referring to the past response results.

[0089] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated user emotions. For example, if the user is nervous, the response unit can provide a short, to-the-point response. If the user is relaxed, the response unit can also provide a longer response with detailed explanations. Furthermore, if the user is in a hurry, the response unit can also provide a quick, concise response. This allows for a more appropriate response by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0090] The response unit can determine the priority of responses based on the time when the consultation content was submitted. The response unit can determine the priority of responses based on, for example, the time when the consultation content was submitted. The response unit can also respond quickly to consultation content with high urgency. Furthermore, the response unit can gradually adjust the priority of responses depending on the time of submission. This allows for faster responses by determining the priority of responses based on the time of submission.

[0091] The response unit can adjust the order of responses based on the relevance of the consultation content. For example, the response unit determines the order of responses based on the relevance of the consultation content. The response unit can also apply the same order of responses to similar consultation contents. Furthermore, the response unit can analyze the relevance of the consultation content and propose an optimal order of responses. This allows for a more appropriate response by adjusting the order of responses based on the relevance of the consultation content.

[0092] The response unit can adjust the use of technical terms in the response depending on the user's level of expertise. For example, if the user has technical knowledge, the response unit can respond using a lot of technical terms. Alternatively, if the user does not have technical knowledge, the response unit can respond using general language. Furthermore, the response unit can gradually adjust the use of technical terms in the response depending on the user's level of expertise. This allows for a more appropriate response by providing a response that is appropriate for the user's level of expertise.

[0093] The handover unit can estimate the user's emotions and adjust the handover method based on the estimated user emotions. For example, if the user is nervous, the handover unit can provide a polite handover method. Furthermore, if the user is relaxed, the handover unit can also provide a smooth handover method. Furthermore, if the user is in a hurry, the handover unit can also provide a quick handover method. This allows for a more appropriate response by providing a handover method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The handover unit can adjust the level of detail of the handover based on the complexity of the consultation content. For example, the handover unit provides detailed handover information for complex consultation content. The handover unit can also provide concise handover information for simple consultation content. Furthermore, the handover unit can gradually adjust the level of detail of the handover based on the complexity of the consultation content. This allows for a more appropriate response by performing a handover based on the complexity of the consultation content.

[0095] The handover unit can apply different handover algorithms depending on the category of the consultation content. For example, the handover unit applies a contract analysis algorithm to a consultation about a contract. The handover unit can also apply a legal dictionary algorithm to a consultation about legal terminology. Furthermore, the handover unit can also apply a mediation algorithm to a consultation about problem resolution. This makes it possible to respond more appropriately by applying a handover algorithm according to the consultation content.

[0096] The handover unit can improve the accuracy of the handover by referring to the user's past handover results. For example, the handover unit proposes an optimal handover method based on the handover results the user has received in the past. The handover unit can also propose a handover method for similar consultation content based on the user's past handover results. Furthermore, the handover unit can analyze the user's past handover results and improve the accuracy of the handover. In this way, the accuracy of the handover is improved by referring to the past handover results.

[0097] The handover unit can estimate the user's emotions and determine the priority of handover based on the estimated user emotions. For example, if the user is nervous, the handover unit prioritizes handover of consultation contents with high urgency. Furthermore, if the user is relaxed, the handover unit can also handover consultation contents with normal priority. Furthermore, if the user is in a hurry, the handover unit can also prioritize handover of consultation contents that require a quick response. This enables a more appropriate response by determining the priority of handover according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The handover unit can adjust the order of handover based on the time of submission of the consultation content. For example, the handover unit determines the order of handover based on the time of submission of the consultation content. The handover unit can also quickly hand over consultation content with high urgency. Furthermore, the handover unit can gradually adjust the order of handover depending on the time of submission. This allows for a faster response by adjusting the order of handover based on the time of submission.

[0099] The handover unit can adjust the handover order based on the relevance of the consultation contents. For example, the handover unit determines the handover order based on the relevance of the consultation contents. The handover unit can also apply the same handover order to similar consultation contents. Furthermore, the handover unit can analyze the relevance of the consultation contents and propose an optimal handover order. This makes it possible to adjust the handover order based on the relevance of the consultation contents, thereby enabling more appropriate responses.

[0100] The handover unit can adjust the level of detail of the handover depending on the user's level of expertise. For example, if the user has specialized knowledge, the handover unit provides detailed handover information. Also, if the user does not have specialized knowledge, the handover unit can provide concise handover information. Furthermore, the handover unit can gradually adjust the level of detail of the handover depending on the user's level of expertise. This allows for more appropriate handling by performing handover according to the user's level of expertise.

[0101] The expert unit can estimate the user's emotions and adjust the expert's response method based on the estimated user's emotions. For example, if the user is nervous, the expert unit can respond politely. Furthermore, if the user is relaxed, the expert unit can also respond friendly. Furthermore, if the user is in a hurry, the expert unit can also respond quickly. This enables a more appropriate response by responding according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] The expert department can adjust the level of detail of the response based on the importance of the consultation content. For example, the expert department can provide a detailed response to consultation content with a high level of importance. The expert department can also provide a concise response to consultation content with a low level of importance. Furthermore, the expert department can gradually adjust the level of detail of the response depending on the importance. This allows for a more appropriate response by responding according to the importance of the consultation content.

[0103] The expert department can apply different response methods depending on the category of the consultation content. For example, the expert department applies a contract analysis method to consultations regarding contracts. The expert department can also apply a legal dictionary method to consultations regarding legal terminology. Furthermore, the expert department can also apply a mediation method to consultations regarding problem resolution. This allows for a more appropriate response by applying a response method according to the consultation content.

[0104] The expert unit can improve the accuracy of the response by referring to the user's past consultation results. For example, the expert unit can propose the optimal response based on the results of consultations the user has received in the past. The expert unit can also propose responses to similar consultation content based on the user's past consultation results. Furthermore, the expert unit can analyze the user's past consultation results and improve the accuracy of the response. In this way, the accuracy of the response can be improved by referring to the past consultation results.

[0105] The expert unit can estimate the user's emotions and determine the priority of responses based on the estimated user emotions. For example, if the user is nervous, the expert unit can prioritize consultations with high urgency. Furthermore, if the user is relaxed, the expert unit can also prioritize consultations that require a quick response if the user is in a hurry. This allows for more appropriate responses by determining the priority of responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0106] The expert department can adjust the order of responses based on the time of submission of the consultation content. The expert department determines the order of responses based on, for example, the time of submission of the consultation content. The expert department can also respond quickly to consultation content with a high degree of urgency. Furthermore, the expert department can gradually adjust the order of responses depending on the time of submission. This allows for faster responses by adjusting the order of responses based on the time of submission.

[0107] The expert department can adjust the order of responses based on the relevance of the consultation contents. For example, the expert department determines the order of responses based on the relevance of the consultation contents. The expert department can also apply the same order of responses to similar consultation contents. Furthermore, the expert department can analyze the relevance of the consultation contents and propose an optimal order of responses. This allows for more appropriate responses by adjusting the order of responses based on the relevance of the consultation contents.

[0108] The expert unit can adjust the use of corresponding technical terms depending on the user's level of expertise. For example, if the user has specialized knowledge, the expert unit can respond using a lot of technical terms. Also, if the user does not have specialized knowledge, the expert unit can respond using general language. Furthermore, the expert unit can gradually adjust the use of corresponding technical terms depending on the user's level of expertise. This allows for a more appropriate response by responding according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, response unit, handover unit, and expert unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a consultation from a user. The response unit is realized by the specific processing unit 290 of the data processing device 12 and performs an automatic response using a generation AI. The handover unit is realized by the specific processing unit 290 of the data processing device 12 and hands over more complex issues to an expert. The expert unit is realized by the control unit 46A of the smart device 14 and allows the expert to continue the consultation. The reception unit can, for example, estimate the user's emotions and adjust the method of accepting the consultation based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, response unit, handover unit, and expert unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a consultation from a user. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs an automatic response using a generation AI. The handover unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and hands over more complex issues to an expert. The expert unit is realized, for example, by the control unit 46A of the smart glasses 214 and allows the expert to continue the consultation. The reception unit can, for example, estimate the user's emotions and adjust the method of accepting the consultation based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, response unit, handover unit, and expert unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a consultation from a user. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs an automatic response using a generation AI. The handover unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and hands over more complex issues to an expert. The expert unit is realized, for example, by the control unit 46A of the headset type terminal 314 and allows the expert to continue the consultation. The reception unit can, for example, estimate the user's emotions and adjust the method of accepting the consultation based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, response unit, handover unit, and expert unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a consultation from a user. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs an automatic response using a generative AI. The handover unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and hands over more complex issues to an expert. The expert unit is realized, for example, by the control unit 46A of the robot 414 and allows the expert to continue the consultation. The reception unit can, for example, estimate the user's emotions and adjust the method of accepting the consultation based on the estimated emotions.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The legal service system may further include a response unit that estimates the user's emotions and adjusts the tone of the response based on the estimated emotions. For example, if the user is stressed, the response unit may respond in a calm tone, and if the user is relaxed, the response unit may respond in a friendly tone. Also, if the user is in a hurry, the response unit may provide a quick and concise response. This allows for an appropriate response according to the user's emotions, thereby improving user satisfaction.

[0111] The legal service system can also include a response unit that analyzes the user's past consultation history and provides the most appropriate response. For example, it can suggest responses to similar consultations based on the content of the user's past consultations. It can also identify specific patterns from the user's past consultation history and prepare responses to anticipated questions in advance. In this way, by utilizing the user's past consultation history, the accuracy of responses can be improved, and user satisfaction can be increased.

[0112] The legal-related service system may further include a response unit that provides relevant legal information taking into account the user's geographical location information. For example, if the user is in a specific area, information about laws and regulations related to that area may be provided. Also, if the user is traveling, information about laws and regulations in the travel destination may be provided. In this way, by utilizing the geographical location information, it is possible to provide more relevant information to the user.

[0113] The legal service system may further include a response unit that analyzes a user's social media activity and provides relevant legal information. For example, relevant legal information may be provided based on the content posted by the user on social media. The system may also analyze the user's social media activity history and prepare responses to anticipated questions. This makes it possible to provide more relevant information to the user by utilizing social media activity.

[0114] The legal service system may further include a reception unit that estimates the user's emotions and determines the priority of consultation requests based on the estimated emotions. For example, if the user is nervous, it may be possible to prioritize consultation requests with a high level of urgency. On the other hand, if the user is relaxed, it may be possible to prioritize consultation requests with a normal level of priority. This allows for an appropriate response according to the user's emotions, thereby improving user satisfaction.

[0115] The legal service system may further include a reception unit that performs filtering based on the user's current situation and areas of interest. For example, it may be possible to prioritize the display of consultation content related to a problem the user is currently facing. It may also be possible to filter and display related consultation content based on the user's areas of interest. This makes it possible to provide appropriate consultation content according to the user's situation and areas of interest.

[0116] The legal service system may further include a handover unit that estimates the user's emotions and adjusts the handover method based on the estimated emotions. For example, if the user is nervous, a polite handover method may be provided. Alternatively, if the user is relaxed, a smooth handover method may be provided. This enables an appropriate handover according to the user's emotions, improving user satisfaction.

[0117] The legal service system can further include a reception unit that customizes the reception method by reflecting the user's past feedback. For example, the system can suggest the optimal reception method based on the user's past feedback. It can also suggest specific reception methods with priority based on the user's past feedback. This makes it possible to provide a more optimal reception method by utilizing the user's past feedback.

[0118] The legal service system may further include a response unit that estimates the user's emotions and adjusts the length of the response based on the estimated emotions. For example, if the user is nervous, a short, to-the-point response may be provided. Alternatively, if the user is relaxed, a longer response including detailed explanations may be provided. This allows for an appropriate response according to the user's emotions, improving user satisfaction.

[0119] The legal service system may further include a response unit that adjusts the use of technical terms in the response depending on the user's level of expertise. For example, if the user has technical expertise, the response may be made using a lot of technical terms. On the other hand, if the user does not have technical expertise, the response may be made in general terms. This allows for an appropriate response depending on the user's level of expertise, thereby improving user satisfaction.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The reception department accepts inquiries from users. Inquiries from users include legal, technical, and business inquiries. The reception department can accept inquiries via a web form, telephone, chatbot, etc. Step 2: The response unit uses a generation AI to provide an automatic response based on the consultation content received by the reception unit. The generation AI uses a text generation AI (e.g., GPT-4) or a multimodal generation AI to generate an appropriate response to the user's consultation content. For example, if a user says, "I don't understand the contents of the contract," the generation AI will ask, "Please tell me which part of the contract you don't understand," and when the user specifies the specific part, it will provide a basic explanation of that part. Step 3: The handover unit transfers more complex issues to an expert based on the response provided by the response unit. In the handover unit, the generation AI analyzes the consultation content and determines whether an expert should respond. Step 4: The Expert Division continues the consultation based on the content handed over by the Handover Division. The Expert Division has experts such as lawyers, engineers, and consultants who provide detailed advice on the user's consultation content.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] 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.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives inquiries from users; a response unit that automatically responds based on the consultation content received by the reception unit; a handover unit that hands over the work to an expert based on the response from the response unit; an expert section in which experts continue to consult based on the content taken over by the takeover section; Equipped with A system characterized by:

2. The response unit Using generative AI to provide automated responses to common problems 2. The system of claim 1.

3. The transition portion is Using generative AI to hand off more complex problems to experts 2. The system of claim 1.

4. The response unit Automatically recognizes technical terms that appear in specific legal consultations and explains them in general terms 2. The system of claim 1.

5. The response unit Scan or upload the contract and check for any issues or caveats 2. The system of claim 1.

6. The response unit Mediation to resolve interpersonal disputes and propose fair solutions 2. The system of claim 1.

7. The response unit Introduce users to the latest legal news, such as law changes and precedents, and explain their significance in simple, easy-to-understand language 2. The system of claim 1.

8. The reception unit Estimates the user's emotions and adjusts the consultation reception method based on the estimated user emotions.

2. The system of claim 1.

Images