system

Abstract

The system according to this embodiment aims to provide expert consultation in an efficient and cost-effective manner. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a confirmation unit, and a provision unit. The reception unit receives inquiries from users. The generation unit analyzes the inquiries received by the reception unit and generates an initial response. The confirmation unit confirms the initial response generated by the generation unit and provides corrections or additional advice as necessary. The provision unit provides the final advice confirmed by the confirmation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the consultation fee for experts is high and it is difficult for ordinary people.

[0005] The system according to the embodiment aims to provide consultation with experts in an efficient and cost-effective manner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a confirmation unit, and a provision unit. The reception unit receives inquiries from users. The generation unit analyzes the inquiries received by the reception unit and generates an initial response. The confirmation unit confirms the initial response generated by the generation unit and provides corrections or additional advice as needed. The provision unit provides the final advice confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide expert consultation in an efficient and cost-effective manner. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor z8, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The consulting system according to an embodiment of the present invention is a hybrid model combining a generative AI and human legal professionals, providing efficient and cost-effective consultations on matters such as inheritance issues, divorce mediation, tax saving strategies, business succession, and subsidy applications. This consulting system works by having the user input their consultation details, the generative AI generating an initial response, and then a human legal professional reviewing and revising it to provide final advice. For example, the user inputs specific consultation details such as inheritance issues, divorce mediation, or tax saving strategies. This information is input into the generative AI, which provides an initial response based on past cases and legal knowledge. For instance, the generative AI might present basic procedures and points to note regarding inheritance issues. Subsequently, a human legal professional reviews the generative AI's response and provides revisions or additional advice as needed. For example, regarding the inheritance procedure flow presented by the generative AI, the human legal professional might supplement it with specific document preparation methods and submission locations. Finally, the user receives final advice from the legal professional and takes the necessary steps and actions. This system allows users to receive consulting that combines the efficiency of generative AI with the expertise of human legal professionals. This hybrid model allows individuals to receive accurate and specific advice while keeping consultation costs down, making it accessible and reassuring for everyone. This enables the consulting system to efficiently receive, analyze, and review user inquiries, and then provide final advice.

[0029] The consulting system according to this embodiment comprises a reception unit, a generation unit, a confirmation unit, and a provision unit. The reception unit receives inquiries from users. Inquiries from users include, for example, inheritance issues, divorce mediation, and tax saving strategies, but are not limited to such examples. The reception unit receives inquiries entered by users in digital format, for example. The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses a generation AI to analyze the inquiries received by the reception unit and generate an initial response. The generation unit provides an initial response to the user's inquiry, for example, based on past cases and legal knowledge. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the inquiries and generate appropriate responses. For example, the generation AI presents basic procedures and points to note regarding inheritance issues. The generation unit can also generate the initial responses provided by the generation AI on a template basis. The confirmation unit confirms the initial responses generated by the generation unit and provides corrections or additional advice as needed. The verification unit supplements the inheritance procedure flow provided by the generating AI with specific information such as how to prepare and submit documents. The verification unit can also conduct expert reviews to confirm the accuracy of the initial response. The provision unit provides the user with the final advice confirmed by the verification unit. The provision unit provides the user with specific procedural instructions and additional information. The provision unit may also include a support unit to assist the user in carrying out specific procedures and actions. For example, the provision unit may provide support for the user in preparing necessary documents and a function to monitor the progress of the procedure. As a result, the consulting system according to this embodiment can efficiently receive, analyze, verify, and provide final advice on the user's inquiries.

[0030] The generation unit can generate initial answers based on past cases or legal knowledge. For example, the generation unit can generate initial answers based on past legal precedents or medical case studies. For example, the generation unit can refer to past precedents regarding inheritance issues to generate initial answers. The generation unit can also generate initial answers based on legal knowledge. For example, the generation unit can provide initial answers to the user's inquiries based on legal knowledge such as civil law, criminal law, and commercial law. In this way, the generation unit can provide more accurate initial answers by generating initial answers based on past cases and legal knowledge. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past cases and legal knowledge into a generation AI and have the generation AI perform the generation of initial answers.

[0031] The verification unit can review the initial response provided by the generating AI and make corrections or provide additional advice as needed. For example, the verification unit can have experts review the initial response provided by the generating AI to verify its accuracy. For example, the verification unit can supplement the inheritance procedure flow provided by the generating AI with specific information on how to prepare and submit documents. The verification unit can also make corrections to the initial response provided by the generating AI as needed. For example, if there is an error in the response provided by the generating AI, the verification unit will correct the error and provide an accurate response. The verification unit can also provide additional advice to the initial response provided by the generating AI. For example, the verification unit can supplement the inheritance procedure flow provided by the generating AI with specific information on how to prepare and submit documents. In this way, the verification unit can provide more accurate final advice by reviewing the initial response from the generating AI and making corrections or providing additional advice as needed. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the initial response provided by the generating AI into the AI ​​and have the AI ​​perform the verification process.

[0032] The service provider can provide the user with final advice. For example, the service provider can provide the user with specific procedural instructions or additional information. For example, the service provider can provide the user with a detailed outline of the inheritance procedure and a list of necessary documents. The service provider can also have a support unit that assists the user in carrying out specific procedures and actions. For example, the service provider can provide support for the user in preparing necessary documents and a function to monitor the progress of the procedure. In this way, the service provider can support the user in carrying out specific procedures and actions by providing final advice. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the advice to be provided to the user into AI and have AI perform the provision of advice.

[0033] The reception desk can receive specific inquiries regarding inheritance issues, divorce mediation, and tax-saving strategies. For example, the reception desk can receive specific inquiries such as inheritance issues, divorce mediation, and tax-saving strategies entered by users in digital format. For example, the reception desk can receive inquiries entered by users in text format. The reception desk can also support multiple input methods, such as voice input and text input. This allows the reception desk to respond to a wide range of user inquiries by receiving specific inquiries. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the inquiries entered by users into AI and have the AI ​​perform the task of receiving the inquiries.

[0034] The generation unit can present basic procedures and points to note regarding inheritance issues. For example, the generation unit can present the flow of inheritance procedures and a list of necessary documents. The generation unit can also present points to note regarding inheritance issues. For example, the generation unit can present points to be aware of and strict adherence to deadlines during inheritance procedures. In this way, the generation unit enables users to carry out appropriate procedures by presenting basic procedures and points to note regarding inheritance issues. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input basic procedures and points to note regarding inheritance issues into a generation AI and have the generation AI execute the presentation process.

[0035] The verification unit can provide supplementary information on specific document preparation methods and submission locations. For example, the verification unit can provide examples of how to fill out the necessary documents for inheritance procedures and the required attachments. The verification unit can also provide specific information on submission locations. For example, the verification unit can provide information on the address and department of the submission location. In this way, the verification unit helps users proceed with the procedures smoothly by supplementing information on specific document preparation methods and submission locations. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input information on specific document preparation methods and submission locations into AI and have the AI ​​perform the supplementary processing.

[0036] The service provider may include a support unit that assists users in carrying out specific procedures and actions. For example, the service provider may provide support for users in preparing necessary documents and a function to monitor the progress of procedures. The service provider may also provide specific advice to users when they carry out procedures. For example, the service provider may present points to note and procedures for users when they carry out procedures. In this way, the service provider helps users proceed with procedures with peace of mind by supporting them when they carry out specific procedures and actions. Some or all of the above-mentioned processes in the service provider may be carried out using AI, for example, or not using AI. For example, the service provider may input support information for users when they carry out procedures into the AI ​​and have the AI ​​execute the support processes.

[0037] The reception desk can select the most suitable reception method when receiving a consultation request by referring to the user's past consultation history. For example, the reception desk can automatically display relevant questions based on the content the user has previously consulted. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions related to specific fields based on the user's past consultation history. In this way, the reception desk can provide the most suitable reception method by referring to the user's past consultation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past consultation history into AI and have the AI ​​select the reception method.

[0038] The reception desk can filter incoming inquiries based on the user's current living situation and areas of interest. For example, the reception desk can prioritize displaying relevant inquiries based on the user's current occupation and family structure. The reception desk can also suggest appropriate questions based on the user's areas of interest (e.g., inheritance, divorce, tax saving). Furthermore, the reception desk can filter the most relevant inquiries based on the user's living situation (e.g., income, housing situation). This allows the reception desk to receive more appropriate inquiries by filtering based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's living situation and areas of interest into an AI and have the AI ​​perform the filtering process.

[0039] The reception desk can prioritize receiving inquiries that are highly relevant to the user's geographical location, taking into account the user's location information. For example, if the user lives in a specific area, the reception desk will prioritize inquiries that are based on laws and regulations relevant to that area. The reception desk can also suggest collaboration with nearby experts based on the user's geographical location information. Furthermore, the reception desk can prioritize receiving inquiries related to region-specific issues based on the user's location information. In this way, the reception desk can prioritize receiving inquiries related to region-specific issues by taking into account the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into AI and have AI select the appropriate reception method.

[0040] The reception desk can analyze the user's social media activity when receiving a consultation request and accept relevant consultation requests. For example, the reception desk can analyze the user's social media activity when receiving a consultation request and accept relevant consultation requests. For example, the reception desk can analyze the user's social media posts and suggest consultation requests related to areas of interest. The reception desk can also prioritize accepting consultation requests on specific issues based on the user's social media activity. Furthermore, the reception desk can suggest relevant consultation requests by referring to the activities of the user's social media followers and friends. In this way, the reception desk can accept consultation requests related to areas of interest by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's social media activity into AI and have the AI ​​perform the analysis.

[0041] The generation unit can adjust the level of detail in the initial response based on the importance of the consultation. For example, the generation unit can generate a response that includes detailed procedures and points to note for high-importance consultations. The generation unit can also generate a concise response for low-importance consultations. Furthermore, the generation unit can generate a response that appropriately includes the necessary information according to the importance level. In this way, the generation unit can provide appropriate responses to important consultations by adjusting the level of detail in the response based on the importance of the consultation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance data of the consultation into the generation AI and have the generation AI perform the adjustment of the level of detail.

[0042] The generation unit can apply different generation algorithms depending on the category of the consultation content when generating initial responses. For example, the generation unit can apply a generation algorithm based on inheritance law for inheritance issues. It can also apply a generation algorithm based on family law for divorce mediation. Furthermore, it can apply a generation algorithm based on tax law for tax saving strategies. This allows the generation unit to provide more appropriate initial responses by applying different generation algorithms depending on the category of the consultation content. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input consultation category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0043] The generation unit can determine the priority of responses based on the submission date of the consultation content during the initial response generation. For example, the generation unit can prioritize generating responses for consultation content submitted most recently. It can also postpone generating responses for older consultation content. Furthermore, the generation unit can dynamically adjust the priority of responses according to the submission date. This allows the generation unit to respond quickly to most recently submitted consultation content by determining the priority of responses based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input consultation content submission date data into a generation AI and have the generation AI perform the priority determination.

[0044] The generation unit can adjust the order of responses based on the relevance of the consultation content during the initial response generation. For example, the generation unit can generate responses in order of relevance if the consultation content is related. Also, if the consultation content is different, the generation unit can generate responses in order of increasing relevance. Furthermore, the generation unit can dynamically adjust the order of responses according to the relevance of the consultation content. In this way, the generation unit can provide responses in order of increasing relevance by adjusting the order of responses based on the relevance of the consultation content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance data of the consultation content into a generation AI and have the generation AI perform the order adjustment.

[0045] The verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses during the verification process. For example, if an initial response relates to multiple consultation topics, the verification unit will consider the interrelationships during the verification process. The verification unit can also improve accuracy by checking whether the initial responses are consistent. Furthermore, if an initial response is contradictory, the verification unit can correct it to improve accuracy. In this way, the verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data on the interrelationships of the initial responses into the AI ​​and have the AI ​​perform the accuracy improvement.

[0046] The verification unit can perform verification while considering the attribute information of the person submitting the consultation. For example, the verification unit can select an appropriate verification method based on the submitter's occupation and age. The verification unit can also improve the accuracy of verification by referring to the submitter's past consultation history. Furthermore, the verification unit can provide appropriate advice based on the submitter's attribute information. In this way, the verification unit can select an appropriate verification method by considering the submitter's attribute information. Some or all of the above processes in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the submitter's attribute information data into AI and have the AI ​​perform the verification process.

[0047] The verification unit can perform verification while considering the geographical distribution of the consultation content. For example, if the consultation content is concentrated in a particular area, the verification unit will perform verification while considering the laws and regulations related to that area. The verification unit can also select an appropriate verification method based on the geographical distribution. Furthermore, the verification unit can perform verification for region-specific issues while considering the geographical distribution. In this way, the verification unit can perform verification for region-specific issues by considering the geographical distribution of the consultation content. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input geographical distribution data of the consultation content into AI and have the AI ​​perform the verification process.

[0048] The verification unit can improve the accuracy of its verification by referring to relevant literature on the consultation content during the verification process. For example, the verification unit can improve the accuracy of its verification by referring to relevant literature on the consultation content during the verification process. For example, the verification unit can improve the accuracy of its verification by referring to legal literature related to the consultation content. The verification unit can also improve the accuracy of its verification by referring to past cases related to the consultation content. Furthermore, the verification unit can improve the accuracy of its verification by referring to specialized books related to the consultation content. In this way, the verification unit can improve the accuracy of its verification by referring to relevant literature on the consultation content. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input relevant literature data into AI and have AI perform the accuracy improvement.

[0049] The service provider can select the optimal delivery method by referring to the user's past consultation history when providing final advice. For example, the service provider can select the optimal delivery method by referring to the user's past consultation history when providing final advice. For example, the service provider can provide relevant advice based on advice the user has received in the past. The service provider can also select an appropriate advice method from the user's past consultation history. Furthermore, the service provider can provide optimal advice by referring to the user's past consultation history. In this way, the service provider can provide the optimal advice method by referring to the user's past consultation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past consultation history data into AI and have the AI ​​select the delivery method.

[0050] The service provider can customize the means of providing advice based on the user's current living situation when delivering the final advice. For example, the service provider can provide concise and quick advice when the user is busy, or provide detailed and attentive advice when the user is relaxed. Furthermore, the service provider can select the appropriate means of advice (email, phone, video call, etc.) according to the user's living situation. In this way, the service provider can provide appropriate advice by customizing the means of advice according to the user's living situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's living situation data into AI and have the AI ​​perform the customization of the means.

[0051] The service provider can select the optimal delivery method when providing final advice, taking into account the user's geographical location. For example, if the user lives in a specific area, the service provider can provide advice based on the laws and regulations relevant to that area. The service provider can also suggest collaboration with nearby experts based on the user's geographical location. Furthermore, the service provider can provide advice on area-specific issues based on the user's location. Thus, by considering the user's geographical location, the service provider can provide advice on area-specific issues. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's geographical location data into AI and have the AI ​​select the delivery method.

[0052] The service provider can analyze the user's social media activity and propose methods for providing final advice. For example, the service provider can analyze the user's social media activity and propose methods for providing final advice. For example, the service provider can analyze the user's social media posts and provide advice related to areas of interest. The service provider can also prioritize providing advice on specific issues based on the user's social media activity. Furthermore, the service provider can propose relevant advice by referring to the activities of the user's social media followers and friends. In this way, the service provider can provide advice related to areas of interest by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI and have the AI ​​execute the proposal of methods.

[0053] The support department can analyze the user's past consultation history to select the optimal support method during support. For example, the support department can analyze the user's past consultation history to select the optimal support method during support. For example, the support department can provide relevant support based on the support the user has received in the past. The support department can also select an appropriate support method from the user's past consultation history. Furthermore, the support department can provide optimal support by referring to the user's past consultation history. In this way, the support department can provide the optimal support method by analyzing the user's past consultation history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's past consultation history data into AI and have the AI ​​select the support method.

[0054] The support unit can customize the means of support based on the user's current living situation during support. For example, the support unit can provide concise and quick support when the user is busy, or provide detailed and attentive support when the user is relaxed. Furthermore, the support unit can select the appropriate means of support (email, phone, video call, etc.) according to the user's living situation. In this way, the support unit can provide appropriate support by customizing the means of support according to the user's living situation. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input user living situation data into AI and have the AI ​​perform the customization of the means of support.

[0055] The support department can select the optimal support method when providing support, taking into account the user's geographical location. For example, if the user lives in a specific area, the support department can provide support based on the laws and regulations relevant to that area. The support department can also suggest collaboration with nearby experts based on the user's geographical location. Furthermore, the support department can provide support regarding region-specific issues based on the user's location. In this way, the support department can provide support regarding region-specific issues by taking into account the user's geographical location. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the user's geographical location data into AI and have the AI ​​select the support method.

[0056] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media posts and provide support related to areas of interest. The support unit can also prioritize providing support for specific issues based on the user's social media activity. Furthermore, the support unit can propose relevant support by referring to the activities of the user's social media followers and friends. In this way, the support unit can provide support related to areas of interest by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media activity data into AI and have the AI ​​propose support measures.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can select the most appropriate reception method when receiving a user's inquiry by referring to the user's past consultation history. For example, if a user has previously consulted about inheritance issues, related questions will be automatically displayed. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions related to specific areas based on the user's past consultation history. In this way, the reception desk can provide the most suitable reception method by referring to the user's past consultation history.

[0059] The generation unit can adjust the level of detail in the initial response based on the importance of the consultation. For example, for highly important consultations, it can generate a response that includes detailed procedures and points to note. Conversely, for less important consultations, it can generate a concise response. Furthermore, it can generate a response that appropriately includes the necessary information according to its importance. In this way, the generation unit can provide appropriate responses to important consultations by adjusting the level of detail in the response based on the importance of the consultation.

[0060] The verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses during the verification process. For example, if the initial response relates to multiple consultation topics, it will perform the verification while considering these interrelationships. It can also improve accuracy by checking whether the initial response is consistent. Furthermore, if the initial response is contradictory, it can correct it to improve accuracy. In this way, the verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses.

[0061] The service provider can select the most appropriate method of providing advice by referring to the user's past consultation history when delivering final advice. For example, it can provide relevant advice based on advice the user has received in the past. It can also select an appropriate advice method from the user's past consultation history. Furthermore, it can provide the most appropriate advice by referring to the user's past consultation history. In this way, the service provider can provide the most appropriate advice method by referring to the user's past consultation history.

[0062] The reception desk can filter incoming inquiries based on the user's current living situation and areas of interest. For example, it can prioritize displaying relevant inquiries based on the user's current occupation and family structure. It can also suggest appropriate questions based on the user's areas of interest (inheritance, divorce, tax saving, etc.). Furthermore, it can filter the most relevant inquiries based on the user's living situation (income, housing situation, etc.). As a result, the reception desk can receive more appropriate inquiries by filtering based on the user's living situation and areas of interest.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives inquiries from users. These inquiries may include, but are not limited to, inheritance issues, divorce mediation, and tax-saving strategies. The reception desk can, for example, receive user-submitted inquiries in digital format. The reception desk can also support multiple input methods, such as voice input and text input. Step 2: The generation unit uses a generation AI to analyze the consultation content received by the reception unit and generate an initial response. The generation unit provides an initial response to the user's consultation content based on, for example, past cases and legal knowledge. The generation AI analyzes the consultation content and generates an appropriate response using text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation AI presents basic procedures and points to note regarding inheritance issues. The generation unit can also generate the initial response provided by the generation AI using a template. Step 3: The verification unit reviews the initial response generated by the generation unit and provides corrections or additional advice as needed. For example, the verification unit supplements the inheritance procedure flow provided by the generation AI with specific information on how to prepare and submit documents. The verification unit can also conduct expert reviews to confirm the accuracy of the initial response. Step 4: The service provider provides the user with the final advice confirmed by the verification service provider. For example, the service provider provides the user with specific procedural instructions and additional information. The service provider may also have a support service provider to assist the user in carrying out specific procedures and actions. For example, the service provider may provide support for the user in preparing necessary documents and a function to monitor the progress of the procedures.

[0065] (Example of form 2) The consulting system according to an embodiment of the present invention is a hybrid model combining a generative AI and human legal professionals, providing efficient and cost-effective consultations on matters such as inheritance issues, divorce mediation, tax saving strategies, business succession, and subsidy applications. This consulting system works by having the user input their consultation details, the generative AI generating an initial response, and then a human legal professional reviewing and revising it to provide final advice. For example, the user inputs specific consultation details such as inheritance issues, divorce mediation, or tax saving strategies. This information is input into the generative AI, which provides an initial response based on past cases and legal knowledge. For instance, the generative AI might present basic procedures and points to note regarding inheritance issues. Subsequently, a human legal professional reviews the generative AI's response and provides revisions or additional advice as needed. For example, regarding the inheritance procedure flow presented by the generative AI, the human legal professional might supplement it with specific document preparation methods and submission locations. Finally, the user receives final advice from the legal professional and takes the necessary steps and actions. This system allows users to receive consulting that combines the efficiency of generative AI with the expertise of human legal professionals. This hybrid model allows individuals to receive accurate and specific advice while keeping consultation costs down, making it accessible and reassuring for everyone. This enables the consulting system to efficiently receive, analyze, and review user inquiries, and then provide final advice.

[0066] The consulting system according to this embodiment comprises a reception unit, a generation unit, a confirmation unit, and a provision unit. The reception unit receives inquiries from users. Inquiries from users include, for example, inheritance issues, divorce mediation, and tax saving strategies, but are not limited to such examples. The reception unit receives inquiries entered by users in digital format, for example. The reception unit can also support multiple input methods, such as voice input and text input. The generation unit uses a generation AI to analyze the inquiries received by the reception unit and generate an initial response. The generation unit provides an initial response to the user's inquiry, for example, based on past cases and legal knowledge. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to analyze the inquiries and generate appropriate responses. For example, the generation AI presents basic procedures and points to note regarding inheritance issues. The generation unit can also generate the initial responses provided by the generation AI on a template basis. The confirmation unit confirms the initial responses generated by the generation unit and provides corrections or additional advice as needed. The verification unit supplements the inheritance procedure flow provided by the generating AI with specific information such as how to prepare and submit documents. The verification unit can also conduct expert reviews to confirm the accuracy of the initial response. The provision unit provides the user with the final advice confirmed by the verification unit. The provision unit provides the user with specific procedural instructions and additional information. The provision unit may also include a support unit to assist the user in carrying out specific procedures and actions. For example, the provision unit may provide support for the user in preparing necessary documents and a function to monitor the progress of the procedure. As a result, the consulting system according to this embodiment can efficiently receive, analyze, verify, and provide final advice on the user's inquiries.

[0067] The generation unit can generate initial answers based on past cases or legal knowledge. For example, the generation unit can generate initial answers based on past legal precedents or medical case studies. For example, the generation unit can refer to past precedents regarding inheritance issues to generate initial answers. The generation unit can also generate initial answers based on legal knowledge. For example, the generation unit can provide initial answers to the user's inquiries based on legal knowledge such as civil law, criminal law, and commercial law. In this way, the generation unit can provide more accurate initial answers by generating initial answers based on past cases and legal knowledge. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past cases and legal knowledge into a generation AI and have the generation AI perform the generation of initial answers.

[0068] The verification unit can review the initial response provided by the generating AI and make corrections or provide additional advice as needed. For example, the verification unit can have experts review the initial response provided by the generating AI to verify its accuracy. For example, the verification unit can supplement the inheritance procedure flow provided by the generating AI with specific information on how to prepare and submit documents. The verification unit can also make corrections to the initial response provided by the generating AI as needed. For example, if there is an error in the response provided by the generating AI, the verification unit will correct the error and provide an accurate response. The verification unit can also provide additional advice to the initial response provided by the generating AI. For example, the verification unit can supplement the inheritance procedure flow provided by the generating AI with specific information on how to prepare and submit documents. In this way, the verification unit can provide more accurate final advice by reviewing the initial response from the generating AI and making corrections or providing additional advice as needed. Some or all of the above processes in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the initial response provided by the generating AI into the AI ​​and have the AI ​​perform the verification process.

[0069] The service provider can provide the user with final advice. For example, the service provider can provide the user with specific procedural instructions or additional information. For example, the service provider can provide the user with a detailed outline of the inheritance procedure and a list of necessary documents. The service provider can also have a support unit that assists the user in carrying out specific procedures and actions. For example, the service provider can provide support for the user in preparing necessary documents and a function to monitor the progress of the procedure. In this way, the service provider can support the user in carrying out specific procedures and actions by providing final advice. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the advice to be provided to the user into AI and have AI perform the provision of advice.

[0070] The reception desk can receive specific inquiries regarding inheritance issues, divorce mediation, and tax-saving strategies. For example, the reception desk can receive specific inquiries such as inheritance issues, divorce mediation, and tax-saving strategies entered by users in digital format. For example, the reception desk can receive inquiries entered by users in text format. The reception desk can also support multiple input methods, such as voice input and text input. This allows the reception desk to respond to a wide range of user inquiries by receiving specific inquiries. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the inquiries entered by users into AI and have the AI ​​perform the task of receiving the inquiries.

[0071] The generation unit can present basic procedures and points to note regarding inheritance issues. For example, the generation unit can present the flow of inheritance procedures and a list of necessary documents. The generation unit can also present points to note regarding inheritance issues. For example, the generation unit can present points to be aware of and strict adherence to deadlines during inheritance procedures. In this way, the generation unit enables users to carry out appropriate procedures by presenting basic procedures and points to note regarding inheritance issues. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input basic procedures and points to note regarding inheritance issues into a generation AI and have the generation AI execute the presentation process.

[0072] The verification unit can provide supplementary information on specific document preparation methods and submission locations. For example, the verification unit can provide examples of how to fill out the necessary documents for inheritance procedures and the required attachments. The verification unit can also provide specific information on submission locations. For example, the verification unit can provide information on the address and department of the submission location. In this way, the verification unit helps users proceed with the procedures smoothly by supplementing information on specific document preparation methods and submission locations. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input information on specific document preparation methods and submission locations into AI and have the AI ​​perform the supplementary processing.

[0073] The service provider may include a support unit that assists users in carrying out specific procedures and actions. For example, the service provider may provide support for users in preparing necessary documents and a function to monitor the progress of procedures. The service provider may also provide specific advice to users when they carry out procedures. For example, the service provider may present points to note and procedures for users when they carry out procedures. In this way, the service provider helps users proceed with procedures with peace of mind by supporting them when they carry out specific procedures and actions. Some or all of the above-mentioned processes in the service provider may be carried out using AI, for example, or not using AI. For example, the service provider may input support information for users when they carry out procedures into the AI ​​and have the AI ​​execute the support processes.

[0074] Furthermore, the consulting system includes a reception unit that estimates the user's emotions and adjusts the method of receiving consultations based on the estimated emotions. For example, if the user is feeling anxious, the reception unit displays a guidance message in a gentle tone to provide reassurance. If the user is in a hurry, it provides a concise and quick input form to smoothly receive the consultation. Furthermore, if the user is relaxed, it can provide detailed input options and encourage the input of customizable consultation details. In this way, the reception unit provides an environment in which the user can consult with peace of mind by adjusting the method of receiving consultations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input the user's emotion data into the AI ​​and have the AI ​​perform the adjustment of the reception method.

[0075] The reception desk can select the most suitable reception method when receiving a consultation request by referring to the user's past consultation history. For example, the reception desk can automatically display relevant questions based on the content the user has previously consulted. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest questions related to specific fields based on the user's past consultation history. In this way, the reception desk can provide the most suitable reception method by referring to the user's past consultation history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past consultation history into AI and have the AI ​​select the reception method.

[0076] The reception desk can filter incoming inquiries based on the user's current living situation and areas of interest. For example, the reception desk can prioritize displaying relevant inquiries based on the user's current occupation and family structure. The reception desk can also suggest appropriate questions based on the user's areas of interest (e.g., inheritance, divorce, tax saving). Furthermore, the reception desk can filter the most relevant inquiries based on the user's living situation (e.g., income, housing situation). This allows the reception desk to receive more appropriate inquiries by filtering based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's living situation and areas of interest into an AI and have the AI ​​perform the filtering process.

[0077] Furthermore, the consulting system includes a reception unit that estimates the user's emotions and determines the priority of consultations to be accepted based on the estimated emotions. For example, if the user is feeling highly anxious, urgent consultations will be given priority. If the user is relaxed, consultations can be accepted with normal priority. Furthermore, if the user is in a hurry, consultations requiring a quick response can be given priority. In this way, the reception unit can prioritize urgent consultations by determining the priority of consultations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into the AI ​​and have the AI ​​perform the priority determination.

[0078] The reception desk can prioritize receiving inquiries that are highly relevant to the user's geographical location, taking into account the user's location information. For example, if the user lives in a specific area, the reception desk will prioritize inquiries that are based on laws and regulations relevant to that area. The reception desk can also suggest collaboration with nearby experts based on the user's geographical location information. Furthermore, the reception desk can prioritize receiving inquiries related to region-specific issues based on the user's location information. In this way, the reception desk can prioritize receiving inquiries related to region-specific issues by taking into account the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into AI and have AI select the appropriate reception method.

[0079] The reception desk can analyze the user's social media activity when receiving a consultation request and accept relevant consultation requests. For example, the reception desk can analyze the user's social media activity when receiving a consultation request and accept relevant consultation requests. For example, the reception desk can analyze the user's social media posts and suggest consultation requests related to areas of interest. The reception desk can also prioritize accepting consultation requests on specific issues based on the user's social media activity. Furthermore, the reception desk can suggest relevant consultation requests by referring to the activities of the user's social media followers and friends. In this way, the reception desk can accept consultation requests related to areas of interest by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's social media activity into AI and have the AI ​​perform the analysis.

[0080] Furthermore, the consulting system includes a generation unit that estimates the user's emotions and adjusts the expression of the initial response based on the estimated emotions. For example, if the user is feeling anxious, the generation unit can generate a response in a gentle tone. If the user is relaxed, it can generate a detailed and polite response. Furthermore, if the user is in a hurry, it can generate a concise and quick response. In this way, the generation unit can provide responses that are easy for the user to understand by adjusting the expression of the initial response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform the adjustment of the expression.

[0081] The generation unit can adjust the level of detail in the initial response based on the importance of the consultation. For example, the generation unit can generate a response that includes detailed procedures and points to note for high-importance consultations. The generation unit can also generate a concise response for low-importance consultations. Furthermore, the generation unit can generate a response that appropriately includes the necessary information according to the importance level. In this way, the generation unit can provide appropriate responses to important consultations by adjusting the level of detail in the response based on the importance of the consultation. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance data of the consultation into the generation AI and have the generation AI perform the adjustment of the level of detail.

[0082] The generation unit can apply different generation algorithms depending on the category of the consultation content when generating initial responses. For example, the generation unit can apply a generation algorithm based on inheritance law for inheritance issues. It can also apply a generation algorithm based on family law for divorce mediation. Furthermore, it can apply a generation algorithm based on tax law for tax saving strategies. This allows the generation unit to provide more appropriate initial responses by applying different generation algorithms depending on the category of the consultation content. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input consultation category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0083] Furthermore, the consulting system includes a generation unit that estimates the user's emotions and adjusts the length of the initial response based on the estimated emotions. For example, if the user is feeling anxious, it may generate a longer response that includes detailed explanations. If the user is relaxed, it may generate a response of moderate length. If the user is in a hurry, it may generate a concise and short response. In this way, the generation unit can provide the user with a response of appropriate length by adjusting the length of the initial response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit may input user emotion data into a generation AI and have the generation AI perform the length adjustment.

[0084] The generation unit can determine the priority of responses based on the submission date of the consultation content during the initial response generation. For example, the generation unit can prioritize generating responses for consultation content submitted most recently. It can also postpone generating responses for older consultation content. Furthermore, the generation unit can dynamically adjust the priority of responses according to the submission date. This allows the generation unit to respond quickly to most recently submitted consultation content by determining the priority of responses based on the submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input consultation content submission date data into a generation AI and have the generation AI perform the priority determination.

[0085] The generation unit can adjust the order of responses based on the relevance of the consultation content during the initial response generation. For example, the generation unit can generate responses in order of relevance if the consultation content is related. Also, if the consultation content is different, the generation unit can generate responses in order of increasing relevance. Furthermore, the generation unit can dynamically adjust the order of responses according to the relevance of the consultation content. In this way, the generation unit can provide responses in order of increasing relevance by adjusting the order of responses based on the relevance of the consultation content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance data of the consultation content into a generation AI and have the generation AI perform the order adjustment.

[0086] Furthermore, the consulting system includes a verification unit that estimates the user's emotions and adjusts the verification criteria based on the estimated emotions. For example, if the user is feeling anxious, the verification unit performs a detailed verification to provide reassurance. If the user is relaxed, the verification unit can respond using normal verification criteria. Furthermore, if the user is in a hurry, the verification unit can perform a quick verification to provide a smooth response. In this way, the verification unit can provide reassurance to the user by adjusting the verification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input user emotion data into the AI ​​and have the AI ​​perform the adjustment of the criteria.

[0087] The verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses during the verification process. For example, if an initial response relates to multiple consultation topics, the verification unit will consider the interrelationships during the verification process. The verification unit can also improve accuracy by checking whether the initial responses are consistent. Furthermore, if an initial response is contradictory, the verification unit can correct it to improve accuracy. In this way, the verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data on the interrelationships of the initial responses into the AI ​​and have the AI ​​perform the accuracy improvement.

[0088] The verification unit can perform verification while considering the attribute information of the person submitting the consultation. For example, the verification unit can select an appropriate verification method based on the submitter's occupation and age. The verification unit can also improve the accuracy of verification by referring to the submitter's past consultation history. Furthermore, the verification unit can provide appropriate advice based on the submitter's attribute information. In this way, the verification unit can select an appropriate verification method by considering the submitter's attribute information. Some or all of the above processes in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input the submitter's attribute information data into AI and have the AI ​​perform the verification process.

[0089] Furthermore, the consulting system includes a verification unit that estimates the user's emotions and adjusts the order in which the verification results are displayed based on the estimated emotions. The verification unit estimates the user's emotions and adjusts the order in which the verification results are displayed based on the estimated emotions. For example, if the user is feeling anxious, important results are displayed preferentially. If the user is relaxed, the results can be displayed in the normal order. Furthermore, if the user is in a hurry, the verification results can be displayed quickly. In this way, the verification unit can prioritize the display of important results by adjusting the order in which the verification results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input user emotion data into AI and have AI perform the adjustment of the display order.

[0090] The verification unit can perform verification while considering the geographical distribution of the consultation content. For example, if the consultation content is concentrated in a particular area, the verification unit will perform verification while considering the laws and regulations related to that area. The verification unit can also select an appropriate verification method based on the geographical distribution. Furthermore, the verification unit can perform verification for region-specific issues while considering the geographical distribution. In this way, the verification unit can perform verification for region-specific issues by considering the geographical distribution of the consultation content. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input geographical distribution data of the consultation content into AI and have the AI ​​perform the verification process.

[0091] The verification unit can improve the accuracy of its verification by referring to relevant literature on the consultation content during the verification process. For example, the verification unit can improve the accuracy of its verification by referring to relevant literature on the consultation content during the verification process. For example, the verification unit can improve the accuracy of its verification by referring to legal literature related to the consultation content. The verification unit can also improve the accuracy of its verification by referring to past cases related to the consultation content. Furthermore, the verification unit can improve the accuracy of its verification by referring to specialized books related to the consultation content. In this way, the verification unit can improve the accuracy of its verification by referring to relevant literature on the consultation content. Some or all of the above processing in the verification unit may be performed using AI, for example, or without using AI. For example, the verification unit can input relevant literature data into AI and have AI perform the accuracy improvement.

[0092] Furthermore, the consulting system includes a service provider that estimates the user's emotions and adjusts the method of delivering the final advice based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide advice in a gentle tone. If the user is relaxed, the service provider can provide detailed and courteous advice. Furthermore, if the user is in a hurry, the service provider can provide concise and quick advice. In this way, the service provider can provide advice that is easy for the user to understand by adjusting the method of delivering the final advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the adjustment of the delivery method.

[0093] The service provider can select the optimal delivery method by referring to the user's past consultation history when providing final advice. For example, the service provider can select the optimal delivery method by referring to the user's past consultation history when providing final advice. For example, the service provider can provide relevant advice based on advice the user has received in the past. The service provider can also select an appropriate advice method from the user's past consultation history. Furthermore, the service provider can provide optimal advice by referring to the user's past consultation history. In this way, the service provider can provide the optimal advice method by referring to the user's past consultation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past consultation history data into AI and have the AI ​​select the delivery method.

[0094] The service provider can customize the means of providing advice based on the user's current living situation when delivering the final advice. For example, the service provider can provide concise and quick advice when the user is busy, or provide detailed and attentive advice when the user is relaxed. Furthermore, the service provider can select the appropriate means of advice (email, phone, video call, etc.) according to the user's living situation. In this way, the service provider can provide appropriate advice by customizing the means of advice according to the user's living situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's living situation data into AI and have the AI ​​perform the customization of the means.

[0095] Furthermore, the consulting system includes a service provider that estimates the user's emotions and determines the priority of final advice based on the estimated emotions. For example, if the user is feeling highly anxious, urgent advice will be given priority. If the user is relaxed, advice can be given with normal priority. Furthermore, if the user is in a hurry, advice requiring immediate attention can be given priority. In this way, the service provider can prioritize urgent advice by determining the priority of final advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the priority determination.

[0096] The service provider can select the optimal delivery method when providing final advice, taking into account the user's geographical location. For example, if the user lives in a specific area, the service provider can provide advice based on the laws and regulations relevant to that area. The service provider can also suggest collaboration with nearby experts based on the user's geographical location. Furthermore, the service provider can provide advice on area-specific issues based on the user's location. Thus, by considering the user's geographical location, the service provider can provide advice on area-specific issues. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's geographical location data into AI and have the AI ​​select the delivery method.

[0097] The service provider can analyze the user's social media activity and propose methods for providing final advice. For example, the service provider can analyze the user's social media activity and propose methods for providing final advice. For example, the service provider can analyze the user's social media posts and provide advice related to areas of interest. The service provider can also prioritize providing advice on specific issues based on the user's social media activity. Furthermore, the service provider can propose relevant advice by referring to the activities of the user's social media followers and friends. In this way, the service provider can provide advice related to areas of interest by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI and have the AI ​​execute the proposal of methods.

[0098] Furthermore, the consulting system includes a support unit that estimates the user's emotions and adjusts the support method based on the estimated emotions. For example, if the user is feeling anxious, the support unit can provide support in a gentle tone. If the user is relaxed, it can provide detailed and attentive support. Furthermore, if the user is in a hurry, it can provide concise and quick support. In this way, the support unit can provide support that is easy for the user to understand by adjusting the support method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into the AI ​​and have the AI ​​perform the adjustment of the method.

[0099] The support department can analyze the user's past consultation history to select the optimal support method during support. For example, the support department can analyze the user's past consultation history to select the optimal support method during support. For example, the support department can provide relevant support based on the support the user has received in the past. The support department can also select an appropriate support method from the user's past consultation history. Furthermore, the support department can provide optimal support by referring to the user's past consultation history. In this way, the support department can provide the optimal support method by analyzing the user's past consultation history. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's past consultation history data into AI and have the AI ​​select the support method.

[0100] The support unit can customize the means of support based on the user's current living situation during support. For example, the support unit can provide concise and quick support when the user is busy, or provide detailed and attentive support when the user is relaxed. Furthermore, the support unit can select the appropriate means of support (email, phone, video call, etc.) according to the user's living situation. In this way, the support unit can provide appropriate support by customizing the means of support according to the user's living situation. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input user living situation data into AI and have the AI ​​perform the customization of the means of support.

[0101] Furthermore, the consulting system includes a support unit that estimates the user's emotions and determines the priority of support based on the estimated emotions. For example, if the user is feeling highly anxious, urgent support will be provided first. If the user is relaxed, support can be provided with normal priority. Furthermore, if the user is in a hurry, support requiring a quick response can be provided first. In this way, the support unit can prioritize providing urgent support by determining the priority of support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into AI and have the AI ​​perform the priority determination.

[0102] The support department can select the optimal support method when providing support, taking into account the user's geographical location. For example, if the user lives in a specific area, the support department can provide support based on the laws and regulations relevant to that area. The support department can also suggest collaboration with nearby experts based on the user's geographical location. Furthermore, the support department can provide support regarding region-specific issues based on the user's location. In this way, the support department can provide support regarding region-specific issues by taking into account the user's geographical location. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input the user's geographical location data into AI and have the AI ​​select the support method.

[0103] The support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media activity and propose support measures during support. For example, the support unit can analyze a user's social media posts and provide support related to areas of interest. The support unit can also prioritize providing support for specific issues based on the user's social media activity. Furthermore, the support unit can propose relevant support by referring to the activities of the user's social media followers and friends. In this way, the support unit can provide support related to areas of interest by analyzing the user's social media activity. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the user's social media activity data into AI and have the AI ​​propose support measures. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, confirmation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives inquiries from the user. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an initial response using generation AI. The confirmation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and confirms the generated response and provides corrections or additional advice as needed. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the final advice to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, confirmation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the content of the inquiry from the user. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates an initial response using generation AI. The confirmation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and confirms the generated response and makes corrections or additional advice as needed. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the final advice to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, confirmation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives inquiries from the user. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an initial response using a generation AI. The confirmation unit is implemented by the identification processing unit 290 of the data processing unit 12 and confirms the generated response and provides corrections or additional advice as needed. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the final advice to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, confirmation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives inquiries from the user. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates an initial response using a generation AI. The confirmation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and confirms the generated response and provides corrections or additional advice as needed. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the final advice to the user.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception desk can select the most appropriate reception method when receiving a user's inquiry by referring to the user's past consultation history. For example, if a user has previously consulted about inheritance issues, related questions will be automatically displayed. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions related to specific areas based on the user's past consultation history. In this way, the reception desk can provide the most suitable reception method by referring to the user's past consultation history.

[0106] The generation unit can adjust the level of detail in the initial response based on the importance of the consultation. For example, for highly important consultations, it can generate a response that includes detailed procedures and points to note. Conversely, for less important consultations, it can generate a concise response. Furthermore, it can generate a response that appropriately includes the necessary information according to its importance. In this way, the generation unit can provide appropriate responses to important consultations by adjusting the level of detail in the response based on the importance of the consultation.

[0107] The verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses during the verification process. For example, if the initial response relates to multiple consultation topics, it will perform the verification while considering these interrelationships. It can also improve accuracy by checking whether the initial response is consistent. Furthermore, if the initial response is contradictory, it can correct it to improve accuracy. In this way, the verification unit can improve the accuracy of its verification by considering the interrelationships of the initial responses.

[0108] The service provider can select the most appropriate method of providing advice by referring to the user's past consultation history when delivering final advice. For example, it can provide relevant advice based on advice the user has received in the past. It can also select an appropriate advice method from the user's past consultation history. Furthermore, it can provide the most appropriate advice by referring to the user's past consultation history. In this way, the service provider can provide the most appropriate advice method by referring to the user's past consultation history.

[0109] The reception desk can filter incoming inquiries based on the user's current living situation and areas of interest. For example, it can prioritize displaying relevant inquiries based on the user's current occupation and family structure. It can also suggest appropriate questions based on the user's areas of interest (inheritance, divorce, tax saving, etc.). Furthermore, it can filter the most relevant inquiries based on the user's living situation (income, housing situation, etc.). As a result, the reception desk can receive more appropriate inquiries by filtering based on the user's living situation and areas of interest.

[0110] The generation unit can estimate the user's emotions and adjust the way the initial response is expressed based on those emotions. For example, if the user is feeling anxious, it can generate a response in a gentle tone. If the user is relaxed, it can generate a detailed and polite response. Furthermore, if the user is in a hurry, it can generate a concise and quick response. In this way, the generation unit can provide responses that are easy for the user to understand by adjusting the way the initial response is expressed according to the user's emotions.

[0111] The verification unit can estimate the user's emotions and adjust the verification criteria based on those emotions. For example, if the user is feeling anxious, it can perform a detailed verification to provide reassurance. If the user is relaxed, it can respond using the normal verification criteria. Furthermore, if the user is in a hurry, it can perform a quick verification to provide a smooth response. In this way, the verification unit can provide reassurance to the user by adjusting the verification criteria according to the user's emotions.

[0112] The service provider can estimate the user's emotions and adjust the way the final advice is delivered based on those emotions. For example, if the user is feeling anxious, the advice can be delivered in a gentle tone. If the user is relaxed, detailed and thoughtful advice can be provided. Furthermore, if the user is in a hurry, concise and quick advice can be provided. In this way, the service provider can deliver advice that is easy for the user to understand by adjusting the way the final advice is delivered according to the user's emotions.

[0113] The support team can estimate the user's emotions and adjust their support methods based on those estimates. For example, if the user is feeling anxious, they can provide support in a gentle tone. If the user is relaxed, they can provide detailed and attentive support. Furthermore, if the user is in a hurry, they can provide concise and quick support. In this way, the support team can provide support that is easy for the user to understand by adjusting their support methods according to the user's emotions.

[0114] The service provider can estimate the user's emotions and determine the priority of final advice based on those emotions. For example, if the user is feeling highly anxious, urgent advice will be given priority. If the user is relaxed, advice can be given with normal priority. Furthermore, if the user is in a hurry, advice requiring immediate attention can be given priority. In this way, the service provider can prioritize urgent advice by determining the final priority of advice according to the user's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives inquiries from users. These inquiries may include, but are not limited to, inheritance issues, divorce mediation, and tax-saving strategies. The reception desk can, for example, receive user-submitted inquiries in digital format. The reception desk can also support multiple input methods, such as voice input and text input. Step 2: The generation unit uses a generation AI to analyze the consultation content received by the reception unit and generate an initial response. The generation unit provides an initial response to the user's consultation content based on, for example, past cases and legal knowledge. The generation AI analyzes the consultation content and generates an appropriate response using text generation AI (e.g., LLM) or multimodal generation AI. For example, the generation AI presents basic procedures and points to note regarding inheritance issues. The generation unit can also generate the initial response provided by the generation AI using a template. Step 3: The verification unit reviews the initial response generated by the generation unit and provides corrections or additional advice as needed. For example, the verification unit supplements the inheritance procedure flow provided by the generation AI with specific information on how to prepare and submit documents. The verification unit can also conduct expert reviews to confirm the accuracy of the initial response. Step 4: The service provider provides the user with the final advice confirmed by the verification service provider. For example, the service provider provides the user with specific procedural instructions and additional information. The service provider may also have a support service provider to assist the user in carrying out specific procedures and actions. For example, the service provider may provide support for the user in preparing necessary documents and a function to monitor the progress of the procedures.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception department that receives inquiries from users, A generation unit analyzes the consultation content received by the reception unit and generates an initial response, A verification unit that checks the initial response generated by the generation unit and makes corrections or provides additional advice as needed, A providing unit that provides the final advice confirmed by the aforementioned verification unit, Equipped with A system characterized by the following features.

2. The generating unit is Generate an initial response based on past cases or legal knowledge. The system according to feature 1.

3. The aforementioned verification unit is Review the initial response provided by the AI ​​generator and make corrections or provide additional advice as needed. The system according to feature 1.

4. The aforementioned supply unit is, Provide the user with final advice. The system according to feature 1.

5. The aforementioned reception unit is We accept consultations on specific matters such as inheritance issues, divorce mediation, and tax-saving strategies. The system according to feature 1.

6. The generating unit is This document outlines basic procedures and points to note regarding inheritance issues. The system according to feature 1.

7. The aforementioned verification unit is This will provide additional information on how to prepare specific documents and where to submit them. The system according to feature 1.

8. The aforementioned supply unit is, The facility includes a support department to assist users with specific procedures and actions. The system according to feature 1.

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