System

Abstract

An object of a system according to an embodiment is to quickly provide an appropriate handling method or service for each problem related to insurance.SOLUTION: A system includes a reception unit, an analysis unit, and a guide unit. The reception unit receives a question or a keyword from a user. The analysis unit analyzes the question or the keyword received by the reception unit, and proposes an explanation or a handling method thereof. The guidance unit guides a specific action or service on the basis of the explanation or the handling method proposed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly provide appropriate responses and services to insurance-related problems.

[0005] The system according to the embodiment aims to quickly provide appropriate solutions and services for insurance-related problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a guidance unit. The reception unit receives questions or keywords from a user. The analysis unit analyzes the questions or keywords received by the reception unit and proposes an explanation or a response method. The guidance unit provides guidance on specific actions or services based on the explanation or response method proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide appropriate solutions and services for insurance-related problems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An insurance support system according to an embodiment of the present invention accepts questions and keywords from users, and a generation AI proposes explanations and solutions to the questions and keywords, providing guidance on specific actions and services. When a user inputs simple questions or keywords, the insurance support system proposes explanations and solutions for the keywords. If the user wishes to take specific actions or receive specific services, the system provides guidance on related companies and services. For example, the user inputs specific questions such as "how to purchase earthquake insurance" or "necessary expenses for education insurance." This information is input to the generation AI. The insurance support system then analyzes the input questions and keywords and proposes explanations and solutions. For example, for "how to purchase earthquake insurance," the system provides basic information about earthquake insurance and the process for purchasing insurance. For "necessary expenses for education insurance," the system provides detailed information about the types of education insurance and their respective costs. Furthermore, if the user wishes to take specific actions or receive specific services, the generation AI provides guidance on related companies and services. For example, for "how to purchase earthquake insurance," the system provides a list of insurance companies offering earthquake insurance and their contact information. For "necessary expenses for education insurance," the system provides comparative information on insurance companies and plans offering education insurance. This allows the insurance support system to provide users with appropriate solutions to insurance-related problems, necessary expenses, and introductions to insurance providers simply by inputting a simple question or keyword. This allows the insurance support system to provide efficient insurance support and consulting, quickly resolving users' problems. For example, if a user asks about illness insurance, the generation AI will explain the types of illness insurance, how to apply, and necessary expenses, and will provide information on relevant insurance companies. Similarly, if a user asks about fire insurance, the AI ​​will explain basic information about fire insurance, application procedures, and costs, and will provide information on insurance companies that offer fire insurance.

[0029] The insurance support system according to the embodiment includes a reception unit, an analysis unit, and a guidance unit. The reception unit receives questions or keywords from a user. The questions or keywords from the user may be in text format, audio format, or related to a specific topic, but are not limited to these examples. The reception unit may receive text entered by a user. The reception unit may also receive audio input. The reception unit may also receive image input. For example, the reception unit may analyze the text entered by the user and convert it into an appropriate format. The analysis unit may use a generation AI to analyze the questions or keywords received by the reception unit and propose an explanation or a response. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may use a generation AI to generate an appropriate answer to the user's question. The analysis unit may also use the generation AI to suggest related information based on the user's keywords. The analysis unit may also use the generation AI to suggest a response to the user's question or keywords. For example, the analysis unit may use natural language processing technology to analyze the user's question and generate an appropriate response. The machine learning algorithm learns from past data and suggests optimal answers to the user's questions. The guidance unit suggests specific actions or services based on the explanations or response methods suggested by the analysis unit. The guidance is performed, for example, by providing information on related companies and services, but is not limited to such examples. For example, the guidance unit displays a list of companies that provide the services desired by the user. The guidance unit can also provide contact information for the services desired by the user. The guidance unit can also provide detailed information about the services desired by the user. For example, the guidance unit displays contact information for related companies so that the user can contact them directly. The guidance unit provides detailed information about related services to help the user select a service. As a result, the insurance support system according to the embodiment can suggest appropriate explanations and response methods for the user's questions and keywords, and suggest specific actions and services.Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit may use the generation AI to suggest the optimal service for the user's question.

[0030] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit preferentially suggests question formats that the user has frequently used in the past. The reception unit can also select a reception method appropriate for a specific time period from the user's past question history. The reception unit can also automatically display keywords that the user has frequently used in the past as candidates. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the user's past question history and select the optimal reception method.

[0031] When receiving questions and keywords, the reception unit can filter them based on the user's current insurance contract status and areas of interest. For example, the reception unit preferentially receives questions related to insurance that the user has already signed up for. The reception unit can also automatically filter related keywords based on the user's areas of interest. The reception unit can also suggest an appropriate question format taking into account the user's insurance contract status. This makes it possible to provide highly relevant information by filtering based on the user's insurance contract status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to perform filtering based on the user's insurance contract status and areas of interest.

[0032] When receiving a question or keyword, the reception unit can select an appropriate reception means according to the user's input method. For example, if the user selects voice input, the reception unit causes the generation AI to use voice recognition technology to receive the question. Furthermore, if the user selects text input, the reception unit can also cause the generation AI to use text analysis technology to receive the question. Furthermore, if the user selects image input, the reception unit can also cause the generation AI to use image analysis technology to receive the question. This improves user convenience by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can use the generation AI to select an appropriate reception means according to the user's input method.

[0033] When receiving questions or keywords, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving insurance information related to that area. The reception unit can also prioritize receiving questions about insurance specific to that area based on the user's geographical location information. The reception unit can also prioritize receiving insurance information related to the user's travel destination when the user is traveling. This makes it possible to provide highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to prioritize receiving highly relevant information by taking into account the user's geographical location information.

[0034] The reception unit can analyze the user's social media activity when receiving questions or keywords and receive related information. For example, the reception unit can prioritize receiving questions about insurance mentioned by the user on social media. The reception unit can also automatically filter related insurance information based on the user's social media activity. The reception unit can also receive related insurance information by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's social media activity and receive related information using a generation AI.

[0035] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or keyword. The reception unit, for example, suggests an optimal question format based on feedback provided by the user in the past. The reception unit can also improve the reception method by reflecting the user's past feedback. The reception unit can also preferentially suggest reception methods that the user has previously preferred. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can customize the reception method by using a generation AI to reflect the user's past feedback.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question or keywords. For example, the analysis unit can have the generation AI perform a detailed analysis of a question with a high level of importance. The analysis unit can also have the generation AI perform a concise analysis of a question with a low level of importance. The analysis unit can also have the generation AI adjust the depth of the analysis according to the importance of the question. This makes it possible to provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the question or keywords. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can use the generation AI to adjust the level of detail of the analysis based on the importance of the question or keywords.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question or keywords. In the analysis unit, for example, the generation AI applies different analysis algorithms depending on the type of insurance. The analysis unit can also allow the generation AI to select the optimal analysis algorithm based on the category of the question. The analysis unit can also allow the generation AI to apply an appropriate analysis algorithm depending on the content of the keywords. In this way, by applying different analysis algorithms depending on the category of the question or keywords, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to apply different analysis algorithms depending on the category of the question or keywords.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the user's past analysis results. In addition, the analysis unit can also improve the analysis method by referring to the user's past feedback. In addition, the analysis unit can analyze the user's past analysis history and the generation AI can select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to improve the accuracy of the analysis by referring to the user's past analysis results.

[0039] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the question or keywords. In the analysis unit, for example, the generation AI determines the analysis priority based on the time of submission of the question. The analysis unit can also select the optimal analysis order by taking into account the time of submission of the keywords. The analysis unit can also allow the generation AI to adjust the analysis priority according to the time of submission. In this way, by determining the analysis priority based on the time of submission of the question or keywords, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to determine the analysis priority based on the time of submission of the question or keywords.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the question and keywords. In the analysis unit, for example, the generation AI adjusts the order of analysis based on the relevance of the question. The analysis unit can also select the optimal analysis order by taking into account the relevance of the keywords. The analysis unit can also adjust the order of analysis by the generation AI according to the content of the question. In this way, by adjusting the order of analysis based on the relevance of the question and keywords, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to adjust the order of analysis based on the relevance of the question and keywords.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can cause the generation AI to adjust the use of technical terms according to the user's level of expertise. Furthermore, if the user has specialized knowledge, the analysis unit can cause the generation AI to use detailed technical terms. Furthermore, if the user is a beginner, the analysis unit can cause the generation AI to perform the analysis in simple terms. This allows appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can use the generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0042] When providing guidance, the guidance unit can select the optimal guidance method by analyzing the user's past behavioral history. For example, the guidance unit uses a generation AI to select the optimal guidance method based on the user's past behavioral history. The guidance unit can also use the user's past feedback to improve the guidance method. The guidance unit can also analyze the user's past behavioral patterns and have the generation AI suggest the optimal guidance method. In this way, the optimal guidance method can be provided by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can use the generation AI to analyze the user's past behavioral history and select the optimal guidance method.

[0043] The guidance unit can customize the guidance content based on the user's current living situation and areas of interest when providing guidance. For example, the guidance unit uses a generation AI to propose optimal guidance content taking into account the user's current living situation. The guidance unit can also provide guidance content related to the user's areas of interest by the generation AI. The guidance unit can also propose guidance content customized by the generation AI based on the user's living situation and areas of interest. This enables more appropriate guidance by customizing the guidance content based on the user's living situation and areas of interest. Some or all of the above-described processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can use the generation AI to customize the guidance content based on the user's current living situation and areas of interest.

[0044] When providing guidance, the guidance unit can select the optimal guidance method by taking into account the user's geographical location information. For example, when the user is in a specific area, the guidance unit prioritizes guidance to services related to that area. The guidance unit can also guide the user to services specific to the area based on the user's geographical location information. Furthermore, when the user is traveling, the guidance unit can also guide the user to services related to the travel destination. This makes it possible to provide highly relevant guidance by taking into account the user's geographical location information. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can use a generation AI to select the optimal guidance method by taking into account the user's geographical location information.

[0045] When providing guidance, the guidance unit can analyze the user's social media activity and suggest guidance content. For example, the guidance unit provides guidance related to services mentioned by the user on social media. The guidance unit can also provide guidance on related services based on the user's social media activity. The guidance unit can also provide guidance on related services by referring to the activity of the user's friends on social media. In this way, related guidance content can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can analyze the user's social media activity and suggest guidance content using a generation AI.

[0046] The guidance unit can customize the guidance method by reflecting the user's past feedback when providing guidance. For example, the guidance unit uses a generation AI to propose an optimal guidance method based on the user's past feedback. The guidance unit can also reflect the user's feedback in real time and have the generation AI adjust the guidance method. The guidance unit can also analyze the user's feedback and have the generation AI provide a customized guidance method. In this way, the optimal guidance method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can use the generation AI to customize the guidance method by reflecting the user's past feedback.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception unit can also provide relevant legal advice based on the user's input. For example, if a user asks about "how to file an insurance claim for a traffic accident," the reception unit can use the generation AI to provide information about the legal procedures and necessary documents related to traffic accidents. Also, if a user asks about "procedures for changing the beneficiary of life insurance," the reception unit can use the generation AI to provide information about the legal procedures and necessary documents related to changing the beneficiary. Furthermore, if a user asks about "how to file a fire insurance claim," the reception unit can use the generation AI to provide information about the legal procedures and necessary documents related to fire insurance. This allows users to receive legal advice about insurance, enabling more appropriate responses.

[0049] The reception unit can analyze the user's past question history and provide related educational content. For example, if a user has previously asked about "types of life insurance," the reception unit can use a generation AI to provide detailed educational content about life insurance. Also, if a user asks about "how to choose car insurance," the reception unit can use a generation AI to provide educational content about car insurance. Furthermore, if a user asks about "the need for fire insurance," the reception unit can use a generation AI to provide educational content about fire insurance. This allows users to deepen their knowledge about insurance and make more appropriate choices.

[0050] When receiving questions or keywords, the reception unit can perform filtering based on the user's current health condition. For example, if the user has a specific illness, insurance information related to that illness can be preferentially received. Also, if the user inputs the results of a health check, related insurance information can be filtered based on those results. Furthermore, if the user expresses an interest in health, related insurance information can be filtered based on that interest. In this way, filtering based on the user's health condition can provide highly relevant information.

[0051] When receiving a question or keyword, the reception unit can provide related news and the latest information based on the user's input. For example, if a user asks about "earthquake insurance," the reception unit can use the generation AI to provide the latest news and information about earthquake insurance. Also, if a user asks about "medical insurance," the reception unit can use the generation AI to provide the latest news and information about medical insurance. Furthermore, if a user asks about "automobile insurance," the reception unit can use the generation AI to provide the latest news and information about automobile insurance. This allows users to obtain the latest information and make more appropriate decisions.

[0052] When receiving a question or keyword, the reception unit can provide information about related events and seminars taking into account the user's geographical location information. For example, if the user is in a specific area, information about insurance-related events and seminars held in that area can be received preferentially. Also, if the user is traveling, information about insurance-related events and seminars held at the user's travel destination can be provided. Furthermore, if the user shows interest in a specific insurance, information about events and seminars related to that insurance can be provided. In this way, by taking into account the user's geographical location information, highly relevant event and seminar information can be provided.

[0053] When receiving questions or keywords, the reception unit can analyze the user's social media activity and provide related community and forum information. For example, questions about insurance mentioned by the user on social media can be received with priority. It can also provide related community and forum information based on the user's social media activity. It can also provide related community and forum information by taking into account the activities of the user's friends on social media. This allows the system to provide related community and forum information by analyzing the user's social media activity.

[0054] When receiving a question or keyword, the reception unit can provide relevant customer support information by reflecting the user's past feedback. For example, the reception unit can suggest optimal customer support information based on feedback provided by the user in the past. The reception unit can also improve customer support methods by reflecting the user's past feedback. Furthermore, the reception unit can prioritize customer support methods that the user has previously preferred. In this way, the reception unit can provide optimal customer support information by reflecting the user's past feedback.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit receives a question or keyword from the user. The question or keyword from the user may be in text format, audio format, or related to a specific topic. For example, the reception unit receives text, audio, or images entered by the user and converts them into an appropriate format. Step 2: The analysis unit uses the generation AI to analyze the question or keywords received by the reception unit and propose an explanation or response. The analysis is performed using natural language processing technology and machine learning algorithms. For example, it generates an appropriate answer to the user's question or suggests related information based on keywords. Step 3: The guidance unit guides the user to specific actions or services based on the explanation or response method proposed by the analysis unit. For example, it provides information on related companies and services, and displays a list of companies that provide the service the user desires, as well as contact information and detailed information.

[0057] (Example 2) An insurance support system according to an embodiment of the present invention accepts questions and keywords from users, and a generation AI proposes explanations and solutions to the questions and keywords, providing guidance on specific actions and services. When a user inputs simple questions or keywords, the insurance support system proposes explanations and solutions for the keywords. If the user wishes to take specific actions or receive specific services, the system provides guidance on related companies and services. For example, the user inputs specific questions such as "how to purchase earthquake insurance" or "necessary expenses for education insurance." This information is input to the generation AI. The insurance support system then analyzes the input questions and keywords and proposes explanations and solutions. For example, for "how to purchase earthquake insurance," the system provides basic information about earthquake insurance and the process for purchasing insurance. For "necessary expenses for education insurance," the system provides detailed information about the types of education insurance and their respective costs. Furthermore, if the user wishes to take specific actions or receive specific services, the generation AI provides guidance on related companies and services. For example, for "how to purchase earthquake insurance," the system provides a list of insurance companies offering earthquake insurance and their contact information. For "necessary expenses for education insurance," the system provides comparative information on insurance companies and plans offering education insurance. This allows the insurance support system to provide users with appropriate solutions to insurance-related problems, necessary expenses, and introductions to insurance providers simply by inputting a simple question or keyword. This allows the insurance support system to provide efficient insurance support and consulting, quickly resolving users' problems. For example, if a user asks about illness insurance, the generation AI will explain the types of illness insurance, how to apply, and necessary expenses, and will provide information on relevant insurance companies. Similarly, if a user asks about fire insurance, the AI ​​will explain basic information about fire insurance, application procedures, and costs, and will provide information on insurance companies that offer fire insurance.

[0058] The insurance support system according to the embodiment includes a reception unit, an analysis unit, and a guidance unit. The reception unit receives questions or keywords from a user. The questions or keywords from the user may be in text format, audio format, or related to a specific topic, but are not limited to these examples. The reception unit may receive text entered by a user. The reception unit may also receive audio input. The reception unit may also receive image input. For example, the reception unit may analyze the text entered by the user and convert it into an appropriate format. The analysis unit may use a generation AI to analyze the questions or keywords received by the reception unit and propose an explanation or a response. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may use a generation AI to generate an appropriate answer to the user's question. The analysis unit may also use the generation AI to suggest related information based on the user's keywords. The analysis unit may also use the generation AI to suggest a response to the user's question or keywords. For example, the analysis unit may use natural language processing technology to analyze the user's question and generate an appropriate response. The machine learning algorithm learns from past data and suggests optimal answers to the user's questions. The guidance unit suggests specific actions or services based on the explanations or response methods suggested by the analysis unit. The guidance is performed, for example, by providing information on related companies and services, but is not limited to such examples. For example, the guidance unit displays a list of companies that provide the services desired by the user. The guidance unit can also provide contact information for the services desired by the user. The guidance unit can also provide detailed information about the services desired by the user. For example, the guidance unit displays contact information for related companies so that the user can contact them directly. The guidance unit provides detailed information about related services to help the user select a service. As a result, the insurance support system according to the embodiment can suggest appropriate explanations and response methods for the user's questions and keywords, and suggest specific actions and services.Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the guidance unit may use the generation AI to suggest the optimal service for the user's question.

[0059] The reception unit can estimate the user's emotions and adjust the timing of receiving questions and keywords based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can cause the generation AI to temporarily delay receiving questions, providing time for the user to relax. Furthermore, if the user is relaxed, the reception unit can cause the generation AI to quickly accept questions, promoting smooth dialogue. Furthermore, if the user is in a hurry, the reception unit can cause the generation AI to immediately accept questions and provide a prompt response. This allows for more appropriate responses by adjusting the timing of receiving questions and keywords according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can estimate the user's emotions and adjust the timing of receiving questions using the generation AI.

[0060] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit preferentially suggests question formats that the user has frequently used in the past. The reception unit can also select a reception method appropriate for a specific time period from the user's past question history. The reception unit can also automatically display keywords that the user has frequently used in the past as candidates. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the user's past question history and select the optimal reception method.

[0061] When receiving questions and keywords, the reception unit can filter them based on the user's current insurance contract status and areas of interest. For example, the reception unit preferentially receives questions related to insurance that the user has already signed up for. The reception unit can also automatically filter related keywords based on the user's areas of interest. The reception unit can also suggest an appropriate question format taking into account the user's insurance contract status. This makes it possible to provide highly relevant information by filtering based on the user's insurance contract status and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to perform filtering based on the user's insurance contract status and areas of interest.

[0062] When receiving a question or keyword, the reception unit can select an appropriate reception means according to the user's input method. For example, if the user selects voice input, the reception unit causes the generation AI to use voice recognition technology to receive the question. Furthermore, if the user selects text input, the reception unit can also cause the generation AI to use text analysis technology to receive the question. Furthermore, if the user selects image input, the reception unit can also cause the generation AI to use image analysis technology to receive the question. This improves user convenience by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can use the generation AI to select an appropriate reception means according to the user's input method.

[0063] The reception unit can estimate the user's emotions and determine the priority of questions and keywords to be received based on the estimated user emotions. For example, when the user is feeling anxious, the reception unit allows the generation AI to prioritize important questions. Furthermore, when the user is relaxed, the reception unit can also allow the generation AI to prioritize general questions. Furthermore, when the user is in a hurry, the reception unit can also allow the generation AI to prioritize urgent questions. This enables more appropriate responses by determining the priority of questions and keywords based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can estimate the user's emotions and determine the priority of questions and keywords using the generation AI.

[0064] When receiving questions or keywords, the reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving insurance information related to that area. The reception unit can also prioritize receiving questions about insurance specific to that area based on the user's geographical location information. The reception unit can also prioritize receiving insurance information related to the user's travel destination when the user is traveling. This makes it possible to provide highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to prioritize receiving highly relevant information by taking into account the user's geographical location information.

[0065] The reception unit can analyze the user's social media activity when receiving questions or keywords and receive related information. For example, the reception unit can prioritize receiving questions about insurance mentioned by the user on social media. The reception unit can also automatically filter related insurance information based on the user's social media activity. The reception unit can also receive related insurance information by referring to the activity of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's social media activity and receive related information using a generation AI.

[0066] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or keyword. The reception unit, for example, suggests an optimal question format based on feedback provided by the user in the past. The reception unit can also improve the reception method by reflecting the user's past feedback. The reception unit can also preferentially suggest reception methods that the user has previously preferred. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can customize the reception method by using a generation AI to reflect the user's past feedback.

[0067] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the generation AI can use a concise and reassuring presentation. Alternatively, if the user is relaxed, the generation AI can use a detailed and polite presentation. Alternatively, if the user is in a hurry, the generation AI can use a quick and concise presentation. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented using the generation AI.

[0068] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question or keywords. For example, the analysis unit can have the generation AI perform a detailed analysis of a question with a high level of importance. The analysis unit can also have the generation AI perform a concise analysis of a question with a low level of importance. The analysis unit can also have the generation AI adjust the depth of the analysis according to the importance of the question. This makes it possible to provide appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the question or keywords. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can use the generation AI to adjust the level of detail of the analysis based on the importance of the question or keywords.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the question or keywords. In the analysis unit, for example, the generation AI applies different analysis algorithms depending on the type of insurance. The analysis unit can also allow the generation AI to select the optimal analysis algorithm based on the category of the question. The analysis unit can also allow the generation AI to apply an appropriate analysis algorithm depending on the content of the keywords. In this way, by applying different analysis algorithms depending on the category of the question or keywords, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to apply different analysis algorithms depending on the category of the question or keywords.

[0070] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In the analysis unit, for example, the generation AI improves the accuracy of the analysis based on the user's past analysis results. In addition, the analysis unit can also improve the analysis method by referring to the user's past feedback. In addition, the analysis unit can analyze the user's past analysis history and the generation AI can select the optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to improve the accuracy of the analysis by referring to the user's past analysis results.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the generation AI can perform a short, concise analysis. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is feeling anxious, the generation AI can perform an analysis that provides a sense of security. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis using the generation AI.

[0072] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the question or keywords. In the analysis unit, for example, the generation AI determines the analysis priority based on the time of submission of the question. The analysis unit can also select the optimal analysis order by taking into account the time of submission of the keywords. The analysis unit can also allow the generation AI to adjust the analysis priority according to the time of submission. In this way, by determining the analysis priority based on the time of submission of the question or keywords, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to determine the analysis priority based on the time of submission of the question or keywords.

[0073] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the question and keywords. In the analysis unit, for example, the generation AI adjusts the order of analysis based on the relevance of the question. The analysis unit can also select the optimal analysis order by taking into account the relevance of the keywords. The analysis unit can also adjust the order of analysis by the generation AI according to the content of the question. In this way, by adjusting the order of analysis based on the relevance of the question and keywords, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to adjust the order of analysis based on the relevance of the question and keywords.

[0074] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can cause the generation AI to adjust the use of technical terms according to the user's level of expertise. Furthermore, if the user has specialized knowledge, the analysis unit can cause the generation AI to use detailed technical terms. Furthermore, if the user is a beginner, the analysis unit can cause the generation AI to perform the analysis in simple terms. This allows appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can use the generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0075] The guidance unit can estimate the user's emotions and adjust the guidance method based on the estimated user's emotions. For example, if the user is feeling anxious, the guidance unit uses a guidance method that gives the generation AI a sense of security. Furthermore, if the user is relaxed, the guidance unit can use a detailed and polite guidance method. Furthermore, if the user is in a hurry, the guidance unit can use a quick and concise guidance method. This allows for more appropriate guidance by adjusting the guidance method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can estimate the user's emotions and adjust the guidance method using the generation AI.

[0076] When providing guidance, the guidance unit can select the optimal guidance method by analyzing the user's past behavioral history. For example, the guidance unit uses a generation AI to select the optimal guidance method based on the user's past behavioral history. The guidance unit can also use the user's past feedback to improve the guidance method. The guidance unit can also analyze the user's past behavioral patterns and have the generation AI suggest the optimal guidance method. In this way, the optimal guidance method can be provided by analyzing the user's past behavioral history. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can use the generation AI to analyze the user's past behavioral history and select the optimal guidance method.

[0077] The guidance unit can customize the guidance content based on the user's current living situation and areas of interest when providing guidance. For example, the guidance unit uses a generation AI to propose optimal guidance content taking into account the user's current living situation. The guidance unit can also provide guidance content related to the user's areas of interest by the generation AI. The guidance unit can also propose guidance content customized by the generation AI based on the user's living situation and areas of interest. This enables more appropriate guidance by customizing the guidance content based on the user's living situation and areas of interest. Some or all of the above-described processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can use the generation AI to customize the guidance content based on the user's current living situation and areas of interest.

[0078] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated user emotions. For example, when the user is feeling anxious, the generation AI can prioritize providing important guidance. Furthermore, when the user is relaxed, the generation AI can prioritize general guidance. Furthermore, when the user is in a hurry, the generation AI can prioritize urgent guidance. This enables more appropriate guidance by determining the priority of guidance based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can estimate the user's emotions and determine the priority of guidance using the generation AI.

[0079] When providing guidance, the guidance unit can select the optimal guidance method by taking into account the user's geographical location information. For example, when the user is in a specific area, the guidance unit prioritizes guidance to services related to that area. The guidance unit can also guide the user to services specific to the area based on the user's geographical location information. Furthermore, when the user is traveling, the guidance unit can also guide the user to services related to the travel destination. This makes it possible to provide highly relevant guidance by taking into account the user's geographical location information. Some or all of the above-described processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can use a generation AI to select the optimal guidance method by taking into account the user's geographical location information.

[0080] When providing guidance, the guidance unit can analyze the user's social media activity and suggest guidance content. For example, the guidance unit provides guidance related to services mentioned by the user on social media. The guidance unit can also provide guidance on related services based on the user's social media activity. The guidance unit can also provide guidance on related services by referring to the activity of the user's friends on social media. In this way, related guidance content can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the guidance unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the guidance unit can analyze the user's social media activity and suggest guidance content using a generation AI.

[0081] The guidance unit can customize the guidance method by reflecting the user's past feedback when providing guidance. For example, the guidance unit uses a generation AI to propose an optimal guidance method based on the user's past feedback. The guidance unit can also reflect the user's feedback in real time and have the generation AI adjust the guidance method. The guidance unit can also analyze the user's feedback and have the generation AI provide a customized guidance method. In this way, the optimal guidance method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the guidance unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guidance unit can use the generation AI to customize the guidance method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, and guidance unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text and voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions and keywords using a generation AI and proposes appropriate explanations and responses. The guidance unit is realized, for example, by the output device 40 of the smart device 14 and provides the user with information on related companies and services. The reception unit can estimate the user's emotions using, for example, the camera 42 and microphone 38B of the smart device 14 and adjust the timing of receiving questions. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, and guidance unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions and keywords using a generation AI and proposes appropriate explanations and responses. The guidance unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the user with information on related companies and services. The reception unit can estimate the user's emotions using, for example, the camera 42 and microphone 238 of the smart glasses 214 and adjust the timing of receiving questions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and guidance unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions and keywords using a generation AI and proposes appropriate explanations and responses. The guidance unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the user with information on related companies and services. The reception unit can estimate the user's emotions using, for example, the camera 42 and microphone 238 of the headset-type terminal 314 and adjust the timing of receiving questions. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, and guidance unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's questions and keywords using a generation AI and proposes appropriate explanations and responses. The guidance unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with information about related companies and services. The reception unit can estimate the user's emotions using, for example, the camera 42 and microphone 238 of the robot 414 and adjust the timing of receiving questions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The reception unit can also provide relevant legal advice based on the user's input. For example, if a user asks about "how to file an insurance claim for a traffic accident," the reception unit can use the generation AI to provide information about the legal procedures and necessary documents related to traffic accidents. Also, if a user asks about "procedures for changing the beneficiary of life insurance," the reception unit can use the generation AI to provide information about the legal procedures and necessary documents related to changing the beneficiary. Furthermore, if a user asks about "how to file a fire insurance claim," the reception unit can use the generation AI to provide information about the legal procedures and necessary documents related to fire insurance. This allows users to receive legal advice about insurance, enabling more appropriate responses.

[0084] The reception unit can estimate the user's emotions and send appropriate reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can use the generation AI to send advice and reminders to relax. Also, if the user is relaxed, the reception unit can use the generation AI to send a reminder to proceed to the next step. Furthermore, if the user is in a hurry, the reception unit can use the generation AI to send a reminder to quickly complete an important task. This enables more effective support by providing reminders according to the user's emotions.

[0085] The reception unit can analyze the user's past question history and provide related educational content. For example, if a user has previously asked about "types of life insurance," the reception unit can use a generation AI to provide detailed educational content about life insurance. Also, if a user asks about "how to choose car insurance," the reception unit can use a generation AI to provide educational content about car insurance. Furthermore, if a user asks about "the need for fire insurance," the reception unit can use a generation AI to provide educational content about fire insurance. This allows users to deepen their knowledge about insurance and make more appropriate choices.

[0086] When receiving questions or keywords, the reception unit can perform filtering based on the user's current health condition. For example, if the user has a specific illness, insurance information related to that illness can be preferentially received. Also, if the user inputs the results of a health check, related insurance information can be filtered based on those results. Furthermore, if the user expresses an interest in health, related insurance information can be filtered based on that interest. In this way, filtering based on the user's health condition can provide highly relevant information.

[0087] When receiving a question or keyword, the reception unit can provide related news and the latest information based on the user's input. For example, if a user asks about "earthquake insurance," the reception unit can use the generation AI to provide the latest news and information about earthquake insurance. Also, if a user asks about "medical insurance," the reception unit can use the generation AI to provide the latest news and information about medical insurance. Furthermore, if a user asks about "automobile insurance," the reception unit can use the generation AI to provide the latest news and information about automobile insurance. This allows users to obtain the latest information and make more appropriate decisions.

[0088] The reception unit can estimate the user's emotions and provide appropriate feedback based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can use the generation AI to provide feedback that gives a sense of security. If the user is relaxed, the reception unit can use the generation AI to provide feedback to move on to the next step. Furthermore, if the user is in a hurry, the reception unit can use the generation AI to provide feedback that encourages a quick response. This allows for more effective support by providing feedback that corresponds to the user's emotions.

[0089] When receiving a question or keyword, the reception unit can provide information about related events and seminars taking into account the user's geographical location information. For example, if the user is in a specific area, information about insurance-related events and seminars held in that area can be received preferentially. Also, if the user is traveling, information about insurance-related events and seminars held at the user's travel destination can be provided. Furthermore, if the user shows interest in a specific insurance, information about events and seminars related to that insurance can be provided. In this way, by taking into account the user's geographical location information, highly relevant event and seminar information can be provided.

[0090] When receiving questions or keywords, the reception unit can analyze the user's social media activity and provide related community and forum information. For example, questions about insurance mentioned by the user on social media can be received with priority. It can also provide related community and forum information based on the user's social media activity. It can also provide related community and forum information by taking into account the activities of the user's friends on social media. This allows the system to provide related community and forum information by analyzing the user's social media activity.

[0091] When receiving a question or keyword, the reception unit can provide relevant customer support information by reflecting the user's past feedback. For example, the reception unit can suggest optimal customer support information based on feedback provided by the user in the past. The reception unit can also improve customer support methods by reflecting the user's past feedback. Furthermore, the reception unit can prioritize customer support methods that the user has previously preferred. In this way, the reception unit can provide optimal customer support information by reflecting the user's past feedback.

[0092] The analysis unit can estimate the user's emotions and propose an appropriate action plan based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can use the generation AI to propose an action plan that gives the user a sense of security. If the user is relaxed, the analysis unit can use the generation AI to propose an action plan for moving on to the next step. Furthermore, if the user is in a hurry, the analysis unit can use the generation AI to propose an action plan that encourages a quick response. This allows for more effective support by providing an action plan that matches the user's emotions.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The reception unit receives a question or keyword from the user. The question or keyword from the user may be in text format, audio format, or related to a specific topic. For example, the reception unit receives text, audio, or images entered by the user and converts them into an appropriate format. Step 2: The analysis unit uses the generation AI to analyze the question or keywords received by the reception unit and propose an explanation or response. The analysis is performed using natural language processing technology and machine learning algorithms. For example, it generates an appropriate answer to the user's question or suggests related information based on keywords. Step 3: The guidance unit guides the user to specific actions or services based on the explanation or response method proposed by the analysis unit. For example, it provides information on related companies and services, and displays a list of companies that provide the service the user desires, as well as contact information and detailed information.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions or keywords from users; an analysis unit that analyzes the question or keyword received by the reception unit and proposes an explanation or a response method; a guidance unit that provides guidance on specific actions or services based on the explanation or response method proposed by the analysis unit. A system characterized by:

2. The reception unit Estimates user emotions and adjusts the timing of accepting questions and keywords based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit When accepting questions or keywords, filtering is performed based on the user's current insurance status and areas of interest.

2. The system of claim 1.

5. The reception unit When accepting a question or keyword, select an appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the questions and keywords to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting questions or keywords, the system prioritizes relevant information based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When receiving a question or keyword, analyze the user's social media activity and receive related information.

2. The system of claim 1.

Images