system

Abstract

The system according to this embodiment aims to streamline the selection of insurance products and the review of contract details, thereby reducing the burden on users. [Solution] The system according to the embodiment comprises a response unit, a comparison unit, a proposal unit, and a provision unit. The response unit responds to the user's questions. The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. The proposal unit proposes changes to the contract based on the information generated by the comparison unit. The provision unit provides the user with the changes proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the selection of insurance products and the review of contract contents are complicated and time-consuming for users.

[0005] The system according to the embodiment aims to streamline the selection of insurance products and the review of contract contents and reduce the burden on users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a response unit, a comparison unit, a proposal unit, and a provision unit. The response unit responds to user questions. The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. The proposal unit proposes changes to the contract based on the information generated by the comparison unit. The provision unit provides the user with the changes proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the selection of insurance products and the review of contract details, thereby reducing the burden on the user. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The insurance agent system according to an embodiment of the present invention is an innovative system that streamlines each step from insurance selection to contract management by utilizing generative AI. This insurance agent system allows even busy users to easily receive support through a 24-hour chatbot. Using generative AI, it generates highly accurate responses in real time using natural language processing (NLP) to immediately respond to simple questions and consultations from users. For example, if a user asks, "Do I need medical insurance?", the generative AI will explain the high-cost medical care benefit system and the advantages and disadvantages of medical insurance. Next, there is an interactive insurance suggestion function. The generative AI analyzes user information and automatically generates comparative information on insurance products and explanations of their respective advantages and disadvantages. It automatically compares products that match the user's needs and presents them visually and in an easy-to-understand manner. For example, if a user inputs, "I recently got married, so I'd like to consider life insurance," the generative AI will suggest appropriate life insurance products and explain their features. Furthermore, there is an automated contract review function. The generative AI suggests changes to the contract content according to the user's life stage and market changes, and the automation and simplification of procedures significantly reduces the effort involved. For example, if a user enters "I want to review my insurance because I've had a child," the generating AI will analyze the current contract details and suggest necessary changes. This system solves the problems faced by users who find face-to-face insurance procedures cumbersome. Specifically, it solves the following problems: Time constraints: Busy weekdays and weekends limit the time available to freely consult with insurance agents. Difficulty in selection: Lack of knowledge about insurance products makes it difficult to compare each product and understand its merits and demerits. The hassle of reviewing contract details: Users need to review whether their current insurance is appropriate for their life stage, but they don't know where to start. They want to simplify the troublesome procedures as much as possible. One way to utilize the generating AI is for it to analyze user information and automatically generate comparative information on insurance products and explanations of their respective merits and demerits. In addition, it automatically generates responses from a whitelist to avoid violating the Insurance Business Act. This allows the insurance agent system to streamline each step from insurance selection to contract management.

[0029] The insurance agent system according to this embodiment comprises a response unit, a comparison unit, a proposal unit, and a provision unit. The response unit responds to user questions. The response unit uses natural language processing technology, for example, with a generation AI, to respond to user questions in real time. For example, if a user asks, "Do I need medical insurance?", the response unit uses the generation AI to explain the high-cost medical care system and the advantages and disadvantages of medical insurance. The response unit can also use the generation AI to analyze the user's question and generate an optimal answer in order to provide appropriate information to the user. The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. The comparison unit uses, for example, a generation AI to analyze user information and generate comparative information on insurance products and explanations of their respective advantages and disadvantages. For example, if a user inputs, "I recently got married, so I'd like to consider life insurance," the comparison unit uses the generation AI to propose appropriate life insurance products and explain their features. The comparison unit can also automatically compare products that meet the user's needs and present them visually and in an easy-to-understand manner. The proposal unit proposes changes to the contract based on the information generated by the comparison unit. The proposal unit uses, for example, generation AI to propose changes to the contract in accordance with the user's life stage and market changes. For example, if a user inputs "I want to review my insurance because I have a child," the proposal unit uses generation AI to analyze the current contract and propose necessary changes. The proposal unit can also propose appropriate changes to the contract in accordance with the user's life stage and market changes. The delivery unit provides the user with the changes proposed by the proposal unit. The delivery unit uses, for example, generation AI to present the proposed changes to the user in a visual and easy-to-understand manner. For example, the delivery unit uses graphs and charts to visually display the proposed changes. The delivery unit can also provide the user with the proposed changes via email or website. As a result, the insurance agent system according to the embodiment can streamline each step from insurance selection to contract management.

[0030] The response unit responds to user questions. The response unit uses natural language processing technology, such as generative AI, to respond to user questions in real time. Specifically, the generative AI analyzes the user's question, searches for relevant information, and generates an appropriate answer. For example, if a user asks, "Is health insurance necessary?", the response unit uses generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. The generative AI extracts relevant information from a vast database and provides answers in a format that is easy for the user to understand. Furthermore, to provide appropriate information in response to user questions, the generative AI can analyze the user's question and generate the optimal answer. For example, if a user asks, "Please tell me about the necessity of cancer insurance," the generative AI will explain in detail the coverage, cost-effectiveness, and differences from other types of insurance. In addition, the response unit can provide more personalized answers by considering the user's past question history and profile information. This allows users to quickly and accurately obtain information to choose the insurance product best suited to them. The response unit can interact with the user through a user interface in the form of text chat or voice assistant. This allows users to ask questions and receive answers in a way that suits their preferences. The response unit can also collect user feedback and continuously improve the accuracy of the generating AI's answers. For example, by having users rate the answers provided, the generating AI learns from that rating and improves the quality of subsequent answers. In this way, the response unit can always provide the latest and most appropriate information, increasing user satisfaction.

[0031] The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. For example, using a generation AI, the comparison unit analyzes user information and generates comparative information on insurance products along with explanations of their respective advantages and disadvantages. Specifically, the generation AI considers information such as the user's age, family structure, income, and health status to select the most suitable insurance product. For example, if a user inputs, "I recently got married, so I'd like to consider life insurance," the comparison unit uses the generation AI to suggest appropriate life insurance products and explain their features. The generation AI compares detailed information such as premiums, coverage, riders, and surrender values ​​for each insurance product, presenting the most suitable product for the user. Furthermore, the comparison unit can automatically compare products tailored to the user's needs and present them in a visually and easily understandable way. For example, it can use graphs and charts to display the features and differences of each insurance product so that they can be understood at a glance. In addition, the comparison unit supports the selection of insurance products from a long-term perspective, according to the user's life stage and future plans. For example, it can suggest insurance products that take into account future expenses such as children's education costs and retirement living expenses. The comparison section can continuously improve the accuracy and presentation of comparison information based on user feedback. This allows users to efficiently obtain information to select the insurance product best suited to them. The comparison section is designed to allow users to obtain comparison information on insurance products with simple operations through the user interface. This enables users to compare multiple insurance products and make the best choice without hassle.

[0032] The proposal department proposes changes to the contract based on information generated by the comparison department. For example, the proposal department uses generative AI to propose changes to the contract in accordance with the user's life stage and market changes. Specifically, the generative AI analyzes the user's current contract and identifies the necessary changes. For example, if a user inputs, "I want to review my insurance because I've had a child," the proposal department uses generative AI to analyze the current contract and propose the necessary changes. The generative AI proposes adding coverage or reviewing premiums to match the user's new life stage. The proposal department can also propose appropriate changes to the contract in accordance with the user's life stage and market changes. For example, if a user changes jobs, the proposal department will propose a review of insurance products to match the benefits and income of the new workplace. Furthermore, the proposal department can propose changes to the contract from a long-term perspective based on the user's future plans and goals. For example, if a user is planning to buy a house in the future, the proposal department will propose a review of insurance products to match that plan. The proposal department can also continuously improve the accuracy and presentation method of its proposals based on user feedback. This allows users to maintain the optimal insurance contract in accordance with their life stage and market changes. The proposal section is designed to allow users to receive contract change proposals with simple operations through the user interface. This enables users to review their contract details without hassle and maintain the optimal insurance policy.

[0033] The service provider provides users with the proposed changes. The service provider uses, for example, generative AI to present the proposed changes to users in a visually understandable way. Specifically, the service provider uses graphs and charts to visually display the proposed changes. For example, it displays changes in insurance premiums and coverage details in a way that can be understood at a glance. The service provider can also provide users with the proposed changes via email or website. This allows users to review the proposed changes and take necessary actions at their convenience. Furthermore, the service provider can continuously improve its presentation methods based on user feedback. For example, users can evaluate the information provided, allowing the generative AI to learn from that evaluation and improve future presentation methods. The service provider is designed to allow users to easily review proposed changes and take necessary actions through a user interface. This enables users to review proposed changes without hassle and maintain optimal insurance coverage. Considering user convenience, the service provider can provide information using multiple communication methods. For example, it can use a combination of email, SMS, and website notifications to ensure important information reaches users. This allows the service provider to deliver information to users quickly and reliably, and to support them in maintaining optimal insurance contracts.

[0034] The response unit can respond to user questions in real time using natural language processing. For example, the response unit can use generative AI to respond quickly and accurately to user questions. For instance, if a user asks, "Is health insurance necessary?", the response unit will use generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. Furthermore, in order to provide appropriate information to the user's question, the response unit can use generative AI to analyze the content of the user's question and generate the optimal answer. This enables the response unit to respond quickly and accurately to user questions.

[0035] The comparison unit can analyze user information and generate comparative information on insurance products, along with explanations of their respective advantages and disadvantages. For example, using a generation AI, the comparison unit analyzes user information and generates comparative information on insurance products, along with explanations of their respective advantages and disadvantages. For instance, if a user inputs "I recently got married and would like to consider life insurance," the comparison unit uses its generation AI to suggest appropriate life insurance products and explain their features. The comparison unit can also automatically compare products tailored to the user's needs and present them visually and in an easy-to-understand manner. This allows the system to provide users with appropriate comparative information on insurance products.

[0036] The proposal department can suggest changes to contract terms in accordance with the user's life stage and market changes. For example, the proposal department uses generative AI to suggest changes to contract terms in accordance with the user's life stage and market changes. For example, if a user inputs "I want to review my insurance because I have a child," the proposal department will use generative AI to analyze the current contract terms and suggest necessary changes. The proposal department can also suggest appropriate changes to contract terms in accordance with the user's life stage and market changes. This allows for the suggestion of appropriate changes to contract terms that are tailored to the user's situation.

[0037] The service provider can present the proposed changes to the user in a visually and easily understandable manner. For example, the service provider can use generative AI to present the proposed changes to the user in a visually and easily understandable manner. For example, the service provider can use graphs and charts to visually display the proposed changes. Furthermore, the service provider can also provide the proposed changes to the user via email or website. This allows the changes to be presented to the user in an easily understandable manner.

[0038] The response unit can analyze the user's past question history and select the optimal response method. For example, the response unit uses generative AI to analyze the user's past question history and select the optimal response method. For instance, based on the content of questions the user has frequently asked in the past, the response unit uses generative AI to prioritize providing relevant information. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and provide information relevant to a specific time period based on the user's past question history. This allows the system to provide the optimal response based on the user's past question history.

[0039] The response unit can customize its response based on the user's current situation and areas of interest. For example, it can use generative AI to customize the response based on the user's current situation and areas of interest. If the user is asking about their current insurance policy, the response unit can use generative AI to provide information related to the policy. If the user is interested in a specific insurance product, the response unit can also use generative AI to provide detailed information about that product. Furthermore, if the user is asking about changes in their life stage, the response unit can use generative AI to suggest insurance products that are appropriate for those changes. This allows the system to provide appropriate responses tailored to the user's current situation and areas of interest.

[0040] The response unit can prioritize providing highly relevant information when responding, taking into account the user's geographical location. For example, the response unit can use generative AI to prioritize providing highly relevant information when responding, taking into account the user's geographical location. For instance, if the user lives in a specific region, the response unit can use generative AI to provide insurance information relevant to that region. If the user is traveling, the response unit can also use generative AI to provide insurance information relevant to the travel destination. Furthermore, if the user is planning to move, the response unit can use generative AI to provide insurance information relevant to the new region. This allows the system to provide highly relevant information based on the user's geographical location.

[0041] The response unit can analyze the user's social media activity and provide relevant information when responding. For example, the response unit can use generative AI to analyze the user's social media activity and provide relevant information when responding. For example, if a user mentions a specific insurance product on social media, the response unit can use generative AI to provide information related to that product. Also, if a user shares a life event on social media, the response unit can use generative AI to provide insurance information related to that event. Furthermore, if a user shows a specific interest on social media, the response unit can use generative AI to provide insurance information related to that interest. In this way, relevant information can be provided based on the user's social media activity.

[0042] The comparison unit can adjust the level of detail based on the importance of insurance products when generating comparison information. For example, the comparison unit uses a generation AI to adjust the level of detail based on the importance of insurance products when generating comparison information. For example, for insurance products of high importance, the comparison unit uses the generation AI to provide detailed information. For insurance products of low importance, the comparison unit can also use the generation AI to provide concise information. Furthermore, for insurance products that the user shows particular interest in, the comparison unit can use the generation AI to provide detailed information. This makes it possible to provide comparison information with an appropriate level of detail according to the importance of insurance products.

[0043] The comparison unit can apply different comparison algorithms depending on the insurance product category when generating comparison information. For example, the comparison unit can use a generation AI to apply different comparison algorithms depending on the insurance product category when generating comparison information. For example, for life insurance, the comparison unit can use a generation AI to apply a comparison algorithm that emphasizes risk and return. For medical insurance, the comparison unit can also use a generation AI to apply a comparison algorithm that emphasizes coverage and cost. Furthermore, for automobile insurance, the comparison unit can use a generation AI to apply a comparison algorithm that emphasizes accident rate and premiums. This allows for the application of an appropriate comparison algorithm according to the insurance product category.

[0044] The comparison unit can determine priorities based on the submission timing of insurance products when generating comparison information. For example, the comparison unit uses a generation AI to determine priorities based on the submission timing of insurance products when generating comparison information. For example, for insurance products with an upcoming submission date, the comparison unit uses the generation AI to provide comparison information preferentially. For insurance products with a distant submission date, the comparison unit can also use the generation AI to postpone providing comparison information. Furthermore, for insurance products with an unknown submission date, the comparison unit can use the generation AI to determine priorities based on the user's level of interest. This allows for the provision of comparison information with appropriate priorities according to the submission timing of insurance products.

[0045] The comparison unit can adjust the order of insurance products based on their relevance when generating comparison information. For example, the comparison unit uses a generation AI to adjust the order of insurance products based on their relevance when generating comparison information. For instance, for insurance products that the user is particularly interested in, the comparison unit uses the generation AI to provide comparison information first. Conversely, for insurance products that the user is not interested in, the comparison unit can use the generation AI to provide comparison information later. Furthermore, for insurance products related to the user's life stage, the comparison unit can use the generation AI to prioritize providing comparison information. This allows for the provision of comparison information in an appropriate order according to the relevance of the insurance products.

[0046] The proposal department can analyze the user's past contract history and select the optimal proposal method at the time of proposal. For example, the proposal department can use generative AI to analyze the user's past contract history and select the optimal proposal method at the time of proposal. For example, based on the insurance products the user has contracted in the past, the proposal department can use generative AI to provide relevant proposal content. The proposal department can also use generative AI to select the optimal proposal method from the user's past contract history. Furthermore, by analyzing the user's past contract history, the proposal department can use generative AI to provide the most efficient proposal method. This allows the proposal department to provide the optimal proposal method based on the user's past contract history.

[0047] The proposal function can customize the proposed content based on the user's current living situation. For example, the proposal function uses generative AI to customize the proposed content based on the user's current living situation. If the user gets married, the proposal function uses generative AI to propose insurance products related to marriage. If the user has children, the proposal function can also use generative AI to propose insurance products related to children. Furthermore, if the user is planning to move, the proposal function can use generative AI to propose insurance products related to the new area. This allows the system to provide appropriate proposals tailored to the user's current living situation.

[0048] The proposal unit can select the optimal proposal method when making a proposal, taking into account the user's geographical location information. For example, the proposal unit can use generative AI to select the optimal proposal method when making a proposal, taking into account the user's geographical location information. For example, if the user lives in a specific region, the proposal unit can use generative AI to propose insurance products related to that region. Also, if the user is traveling, the proposal unit can use generative AI to propose insurance products related to the travel destination. Furthermore, if the user is planning to move, the proposal unit can use generative AI to propose insurance products related to the new region. In this way, the optimal proposal method can be provided based on the user's geographical location information.

[0049] The proposal department can analyze the user's social media activity and adjust the proposal content when making a proposal. For example, the proposal department can use generative AI to analyze the user's social media activity and adjust the proposal content when making a proposal. For example, if a user mentions a specific insurance product on social media, the proposal department can use generative AI to make a proposal related to that product. Also, if a user shares a life event on social media, the proposal department can use generative AI to make a proposal for an insurance product related to that event. Furthermore, if a user shows a specific interest on social media, the proposal department can use generative AI to make a proposal for an insurance product related to that interest. This allows the department to provide appropriate proposal content based on the user's social media activity.

[0050] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can use a generation AI to select the optimal display method by referring to the user's past operation history at the time of service provision. For example, based on the display methods the user has used in the past, the service provider can use a generation AI to provide the optimal display method. Furthermore, the service provider can use a generation AI to select the most efficient display method from the user's past operation history. In addition, by analyzing the user's past operation history, the service provider can use a generation AI to provide the display method with the highest visibility. This makes it possible to provide the optimal display method based on the user's past operation history.

[0051] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, the service provider can use a generation AI to select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can use a generation AI to provide a display method that is appropriate for the screen size. If the user is using a tablet, the service provider can also use a generation AI to provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can use a generation AI to provide a simple and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The response unit can respond to user questions in real time using generative AI. For example, if a user asks, "Is health insurance necessary?", the response unit will use generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. The response unit can also analyze the content of the user's question and generate the most appropriate answer. Furthermore, the response unit can analyze the user's past question history and select the most appropriate response method. For example, based on the content of questions the user has frequently asked in the past, the response unit will use generative AI to prioritize providing relevant information. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. In addition, it can predict and provide information relevant to a specific time period based on the user's past question history. This allows the system to provide the most appropriate response based on the user's past question history.

[0054] The comparison unit can analyze user information and generate comparative information on insurance products, along with explanations of their respective advantages and disadvantages. For example, if a user inputs "I recently got married and would like to consider life insurance," the comparison unit uses its AI generation capabilities to suggest appropriate life insurance products and explain their features. The comparison unit can also automatically compare products tailored to the user's needs and present them visually and in an easy-to-understand manner. Furthermore, when generating comparative information, the comparison unit can adjust the level of detail based on the importance of each insurance product. For example, for highly important insurance products, the comparison unit uses its AI generation capabilities to provide detailed information. For less important insurance products, the comparison unit can provide concise information using its AI generation capabilities. Additionally, for insurance products that the user shows particular interest in, the comparison unit can use its AI generation capabilities to provide detailed information. This allows the system to provide comparative information with an appropriate level of detail according to the importance of each insurance product.

[0055] The proposal department can suggest changes to contract details in accordance with the user's life stage and market changes. For example, if a user inputs "I want to review my insurance because I have a child," the proposal department will use generative AI to analyze the current contract details and suggest necessary changes. The proposal department can also suggest appropriate changes to contract details in accordance with the user's life stage and market changes. Furthermore, when making a proposal, the proposal department can analyze the user's past contract history to select the optimal proposal method. For example, based on the insurance products the user has contracted in the past, the proposal department will use generative AI to provide relevant proposal content. The proposal department can also use generative AI to select the optimal proposal method from the user's past contract history. Furthermore, by analyzing the user's past contract history, the proposal department can use generative AI to provide the most efficient proposal method. This allows the proposal department to provide the optimal proposal method based on the user's past contract history.

[0056] The service provider can present proposed changes to users in a visually and easily understandable manner. For example, the service provider can use generative AI to present proposed changes to users in a visually and easily understandable manner. For example, the service provider can use graphs and charts to visually display proposed changes. The service provider can also provide proposed changes to users via email or website. Furthermore, the service provider can select the optimal display method by referring to the user's past operation history when providing the changes. For example, based on the display methods the user has used in the past, the service provider can use generative AI to provide the optimal display method. The service provider can also use generative AI to select the most efficient display method from the user's past operation history. Furthermore, by analyzing the user's past operation history, the service provider can use generative AI to provide the most visually appealing display method. This allows the service provider to provide the optimal display method based on the user's past operation history.

[0057] The comparison unit can apply different comparison algorithms depending on the insurance product category when generating comparison information. For example, for life insurance, the comparison unit uses a generation AI to apply a comparison algorithm that emphasizes risk and return. For medical insurance, the comparison unit can also use a generation AI to apply a comparison algorithm that emphasizes coverage and cost. Furthermore, for automobile insurance, the comparison unit can use a generation AI to apply a comparison algorithm that emphasizes accident rate and premiums. This allows for the application of an appropriate comparison algorithm according to the insurance product category.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The response unit responds to the user's questions. The response unit uses generative AI and natural language processing technology to respond to user questions in real time. For example, if a user asks, "Is health insurance necessary?", the response unit uses generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. In addition, the response unit can use generative AI to analyze the user's question and generate the optimal answer in order to provide appropriate information to the user. Step 2: The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. The comparison unit uses generation AI to analyze user information and generates comparative information on insurance products and explanations of their respective advantages and disadvantages. For example, if a user inputs "I recently got married, so I'd like to consider life insurance," the comparison unit uses generation AI to suggest appropriate life insurance products and explain their features. The comparison unit can also automatically compare products tailored to the user's needs and present them in a visually and easily understandable way. Step 3: The proposal unit proposes changes to the contract based on the information generated by the comparison unit. The proposal unit uses generative AI to propose changes to the contract in accordance with the user's life stage and market changes. For example, if the user inputs "I want to review my insurance because I had a child," the proposal unit uses generative AI to analyze the current contract and propose necessary changes. The proposal unit can also propose appropriate changes to the contract in accordance with the user's life stage and market changes. Step 4: The Provider team provides the user with the changes proposed by the Recommendation team. The Provider team uses generative AI to present the proposed changes to the user in a visually and easily understandable way. For example, the Provider team may use graphs and charts to visually display the proposed changes. The Provider team can also provide the proposed changes to the user via email or website.

[0060] (Example of form 2) The insurance agent system according to an embodiment of the present invention is an innovative system that streamlines each step from insurance selection to contract management by utilizing generative AI. This insurance agent system allows even busy users to easily receive support through a 24-hour chatbot. Using generative AI, it generates highly accurate responses in real time using natural language processing (NLP) to immediately respond to simple questions and consultations from users. For example, if a user asks, "Do I need medical insurance?", the generative AI will explain the high-cost medical care benefit system and the advantages and disadvantages of medical insurance. Next, there is an interactive insurance suggestion function. The generative AI analyzes user information and automatically generates comparative information on insurance products and explanations of their respective advantages and disadvantages. It automatically compares products that match the user's needs and presents them visually and in an easy-to-understand manner. For example, if a user inputs, "I recently got married, so I'd like to consider life insurance," the generative AI will suggest appropriate life insurance products and explain their features. Furthermore, there is an automated contract review function. The generative AI suggests changes to the contract content according to the user's life stage and market changes, and the automation and simplification of procedures significantly reduces the effort involved. For example, if a user enters "I want to review my insurance because I've had a child," the generating AI will analyze the current contract details and suggest necessary changes. This system solves the problems faced by users who find face-to-face insurance procedures cumbersome. Specifically, it solves the following problems: Time constraints: Busy weekdays and weekends limit the time available to freely consult with insurance agents. Difficulty in selection: Lack of knowledge about insurance products makes it difficult to compare each product and understand its merits and demerits. The hassle of reviewing contract details: Users need to review whether their current insurance is appropriate for their life stage, but they don't know where to start. They want to simplify the troublesome procedures as much as possible. One way to utilize the generating AI is for it to analyze user information and automatically generate comparative information on insurance products and explanations of their respective merits and demerits. In addition, it automatically generates responses from a whitelist to avoid violating the Insurance Business Act. This allows the insurance agent system to streamline each step from insurance selection to contract management.

[0061] The insurance agent system according to this embodiment comprises a response unit, a comparison unit, a proposal unit, and a provision unit. The response unit responds to user questions. The response unit uses natural language processing technology, for example, with a generation AI, to respond to user questions in real time. For example, if a user asks, "Do I need medical insurance?", the response unit uses the generation AI to explain the high-cost medical care system and the advantages and disadvantages of medical insurance. The response unit can also use the generation AI to analyze the user's question and generate an optimal answer in order to provide appropriate information to the user. The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. The comparison unit uses, for example, a generation AI to analyze user information and generate comparative information on insurance products and explanations of their respective advantages and disadvantages. For example, if a user inputs, "I recently got married, so I'd like to consider life insurance," the comparison unit uses the generation AI to propose appropriate life insurance products and explain their features. The comparison unit can also automatically compare products that meet the user's needs and present them visually and in an easy-to-understand manner. The proposal unit proposes changes to the contract based on the information generated by the comparison unit. The proposal unit uses, for example, generation AI to propose changes to the contract in accordance with the user's life stage and market changes. For example, if a user inputs "I want to review my insurance because I have a child," the proposal unit uses generation AI to analyze the current contract and propose necessary changes. The proposal unit can also propose appropriate changes to the contract in accordance with the user's life stage and market changes. The delivery unit provides the user with the changes proposed by the proposal unit. The delivery unit uses, for example, generation AI to present the proposed changes to the user in a visual and easy-to-understand manner. For example, the delivery unit uses graphs and charts to visually display the proposed changes. The delivery unit can also provide the user with the proposed changes via email or website. As a result, the insurance agent system according to the embodiment can streamline each step from insurance selection to contract management.

[0062] The response unit responds to user questions. The response unit uses natural language processing technology, such as generative AI, to respond to user questions in real time. Specifically, the generative AI analyzes the user's question, searches for relevant information, and generates an appropriate answer. For example, if a user asks, "Is health insurance necessary?", the response unit uses generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. The generative AI extracts relevant information from a vast database and provides answers in a format that is easy for the user to understand. Furthermore, to provide appropriate information in response to user questions, the generative AI can analyze the user's question and generate the optimal answer. For example, if a user asks, "Please tell me about the necessity of cancer insurance," the generative AI will explain in detail the coverage, cost-effectiveness, and differences from other types of insurance. In addition, the response unit can provide more personalized answers by considering the user's past question history and profile information. This allows users to quickly and accurately obtain information to choose the insurance product best suited to them. The response unit can interact with the user through a user interface in the form of text chat or voice assistant. This allows users to ask questions and receive answers in a way that suits their preferences. The response unit can also collect user feedback and continuously improve the accuracy of the generating AI's answers. For example, by having users rate the answers provided, the generating AI learns from that rating and improves the quality of subsequent answers. In this way, the response unit can always provide the latest and most appropriate information, increasing user satisfaction.

[0063] The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. For example, using a generation AI, the comparison unit analyzes user information and generates comparative information on insurance products along with explanations of their respective advantages and disadvantages. Specifically, the generation AI considers information such as the user's age, family structure, income, and health status to select the most suitable insurance product. For example, if a user inputs, "I recently got married, so I'd like to consider life insurance," the comparison unit uses the generation AI to suggest appropriate life insurance products and explain their features. The generation AI compares detailed information such as premiums, coverage, riders, and surrender values ​​for each insurance product, presenting the most suitable product for the user. Furthermore, the comparison unit can automatically compare products tailored to the user's needs and present them in a visually and easily understandable way. For example, it can use graphs and charts to display the features and differences of each insurance product so that they can be understood at a glance. In addition, the comparison unit supports the selection of insurance products from a long-term perspective, according to the user's life stage and future plans. For example, it can suggest insurance products that take into account future expenses such as children's education costs and retirement living expenses. The comparison section can continuously improve the accuracy and presentation of comparison information based on user feedback. This allows users to efficiently obtain information to select the insurance product best suited to them. The comparison section is designed to allow users to obtain comparison information on insurance products with simple operations through the user interface. This enables users to compare multiple insurance products and make the best choice without hassle.

[0064] The proposal department proposes changes to the contract based on information generated by the comparison department. For example, the proposal department uses generative AI to propose changes to the contract in accordance with the user's life stage and market changes. Specifically, the generative AI analyzes the user's current contract and identifies the necessary changes. For example, if a user inputs, "I want to review my insurance because I've had a child," the proposal department uses generative AI to analyze the current contract and propose the necessary changes. The generative AI proposes adding coverage or reviewing premiums to match the user's new life stage. The proposal department can also propose appropriate changes to the contract in accordance with the user's life stage and market changes. For example, if a user changes jobs, the proposal department will propose a review of insurance products to match the benefits and income of the new workplace. Furthermore, the proposal department can propose changes to the contract from a long-term perspective based on the user's future plans and goals. For example, if a user is planning to buy a house in the future, the proposal department will propose a review of insurance products to match that plan. The proposal department can also continuously improve the accuracy and presentation method of its proposals based on user feedback. This allows users to maintain the optimal insurance contract in accordance with their life stage and market changes. The proposal section is designed to allow users to receive contract change proposals with simple operations through the user interface. This enables users to review their contract details without hassle and maintain the optimal insurance policy.

[0065] The service provider provides users with the proposed changes. The service provider uses, for example, generative AI to present the proposed changes to users in a visually understandable way. Specifically, the service provider uses graphs and charts to visually display the proposed changes. For example, it displays changes in insurance premiums and coverage details in a way that can be understood at a glance. The service provider can also provide users with the proposed changes via email or website. This allows users to review the proposed changes and take necessary actions at their convenience. Furthermore, the service provider can continuously improve its presentation methods based on user feedback. For example, users can evaluate the information provided, allowing the generative AI to learn from that evaluation and improve future presentation methods. The service provider is designed to allow users to easily review proposed changes and take necessary actions through a user interface. This enables users to review proposed changes without hassle and maintain optimal insurance coverage. Considering user convenience, the service provider can provide information using multiple communication methods. For example, it can use a combination of email, SMS, and website notifications to ensure important information reaches users. This allows the service provider to deliver information to users quickly and reliably, and to support them in maintaining optimal insurance contracts.

[0066] The response unit can respond to user questions in real time using natural language processing. For example, the response unit can use generative AI to respond quickly and accurately to user questions. For instance, if a user asks, "Is health insurance necessary?", the response unit will use generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. Furthermore, in order to provide appropriate information to the user's question, the response unit can use generative AI to analyze the content of the user's question and generate the optimal answer. This enables the response unit to respond quickly and accurately to user questions.

[0067] The comparison unit can analyze user information and generate comparative information on insurance products, along with explanations of their respective advantages and disadvantages. For example, using a generation AI, the comparison unit analyzes user information and generates comparative information on insurance products, along with explanations of their respective advantages and disadvantages. For instance, if a user inputs "I recently got married and would like to consider life insurance," the comparison unit uses its generation AI to suggest appropriate life insurance products and explain their features. The comparison unit can also automatically compare products tailored to the user's needs and present them visually and in an easy-to-understand manner. This allows the system to provide users with appropriate comparative information on insurance products.

[0068] The proposal department can suggest changes to contract terms in accordance with the user's life stage and market changes. For example, the proposal department uses generative AI to suggest changes to contract terms in accordance with the user's life stage and market changes. For example, if a user inputs "I want to review my insurance because I have a child," the proposal department will use generative AI to analyze the current contract terms and suggest necessary changes. The proposal department can also suggest appropriate changes to contract terms in accordance with the user's life stage and market changes. This allows for the suggestion of appropriate changes to contract terms that are tailored to the user's situation.

[0069] The service provider can present the proposed changes to the user in a visually and easily understandable manner. For example, the service provider can use generative AI to present the proposed changes to the user in a visually and easily understandable manner. For example, the service provider can use graphs and charts to visually display the proposed changes. Furthermore, the service provider can also provide the proposed changes to the user via email or website. This allows the changes to be presented to the user in an easily understandable manner.

[0070] The response unit can estimate the user's emotions and adjust the tone and content of its response based on those emotions. For example, it can use generative AI to estimate the user's emotions and adjust the tone and content of its response based on those emotions. For instance, if the user is feeling anxious, the response unit can use generative AI to generate a reassuring response in a gentle tone. If the user is excited, the response unit can also use generative AI to generate a calm response that provides information in a calm tone and soothes the user. Furthermore, if the user is relaxed, the response unit can use generative AI to generate a friendly response in a friendly tone. This allows for the provision of appropriate responses tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The response unit can analyze the user's past question history and select the optimal response method. For example, the response unit uses generative AI to analyze the user's past question history and select the optimal response method. For instance, based on the content of questions the user has frequently asked in the past, the response unit uses generative AI to prioritize providing relevant information. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and provide information relevant to a specific time period based on the user's past question history. This allows the system to provide the optimal response based on the user's past question history.

[0072] The response unit can customize its response based on the user's current situation and areas of interest. For example, it can use generative AI to customize the response based on the user's current situation and areas of interest. If the user is asking about their current insurance policy, the response unit can use generative AI to provide information related to the policy. If the user is interested in a specific insurance product, the response unit can also use generative AI to provide detailed information about that product. Furthermore, if the user is asking about changes in their life stage, the response unit can use generative AI to suggest insurance products that are appropriate for those changes. This allows the system to provide appropriate responses tailored to the user's current situation and areas of interest.

[0073] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, the response unit can use generative AI to estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user asks an urgent question, the response unit can use generative AI to respond to that question with the highest priority. Also, if the user is relaxed, the response unit can use generative AI to respond to that question with the same priority as other questions. Furthermore, if the user is feeling anxious, the response unit can use generative AI to prioritize that question and provide a sense of security. This allows for the provision of responses with appropriate priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The response unit can prioritize providing highly relevant information when responding, taking into account the user's geographical location. For example, the response unit can use generative AI to prioritize providing highly relevant information when responding, taking into account the user's geographical location. For instance, if the user lives in a specific region, the response unit can use generative AI to provide insurance information relevant to that region. If the user is traveling, the response unit can also use generative AI to provide insurance information relevant to the travel destination. Furthermore, if the user is planning to move, the response unit can use generative AI to provide insurance information relevant to the new region. This allows the system to provide highly relevant information based on the user's geographical location.

[0075] The response unit can analyze the user's social media activity and provide relevant information when responding. For example, the response unit can use generative AI to analyze the user's social media activity and provide relevant information when responding. For example, if a user mentions a specific insurance product on social media, the response unit can use generative AI to provide information related to that product. Also, if a user shares a life event on social media, the response unit can use generative AI to provide insurance information related to that event. Furthermore, if a user shows a specific interest on social media, the response unit can use generative AI to provide insurance information related to that interest. In this way, relevant information can be provided based on the user's social media activity.

[0076] The comparison unit can estimate the user's emotions and adjust the way the comparison information is presented based on the estimated emotions. For example, the comparison unit can use generative AI to estimate the user's emotions and adjust the way the comparison information is presented based on the estimated emotions. For example, if the user is feeling anxious, the comparison unit can use generative AI to provide simple and easy-to-understand comparison information. If the user is excited, the comparison unit can also use generative AI to provide detailed comparison information. Furthermore, if the user is relaxed, the comparison unit can use generative AI to provide comparison information in a friendly tone. This allows for the provision of comparison information in an appropriate presentation style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The comparison unit can adjust the level of detail based on the importance of insurance products when generating comparison information. For example, the comparison unit uses a generation AI to adjust the level of detail based on the importance of insurance products when generating comparison information. For example, for insurance products of high importance, the comparison unit uses the generation AI to provide detailed information. For insurance products of low importance, the comparison unit can also use the generation AI to provide concise information. Furthermore, for insurance products that the user shows particular interest in, the comparison unit can use the generation AI to provide detailed information. This makes it possible to provide comparison information with an appropriate level of detail according to the importance of insurance products.

[0078] The comparison unit can apply different comparison algorithms depending on the insurance product category when generating comparison information. For example, the comparison unit can use a generation AI to apply different comparison algorithms depending on the insurance product category when generating comparison information. For example, for life insurance, the comparison unit can use a generation AI to apply a comparison algorithm that emphasizes risk and return. For medical insurance, the comparison unit can also use a generation AI to apply a comparison algorithm that emphasizes coverage and cost. Furthermore, for automobile insurance, the comparison unit can use a generation AI to apply a comparison algorithm that emphasizes accident rate and premiums. This allows for the application of an appropriate comparison algorithm according to the insurance product category.

[0079] The comparison unit can estimate the user's emotions and adjust the length of the comparison information based on the estimated emotions. For example, the comparison unit can use generative AI to estimate the user's emotions and adjust the length of the comparison information based on the estimated emotions. For example, if the user is in a hurry, the comparison unit can use generative AI to provide short, concise comparison information. If the user is relaxed, the comparison unit can also use generative AI to provide detailed comparison information. Furthermore, if the user is excited, the comparison unit can use generative AI to provide visually stimulating comparison information. This allows for the provision of comparison information of an appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The comparison unit can determine priorities based on the submission timing of insurance products when generating comparison information. For example, the comparison unit uses a generation AI to determine priorities based on the submission timing of insurance products when generating comparison information. For example, for insurance products with an upcoming submission date, the comparison unit uses the generation AI to provide comparison information preferentially. For insurance products with a distant submission date, the comparison unit can also use the generation AI to postpone providing comparison information. Furthermore, for insurance products with an unknown submission date, the comparison unit can use the generation AI to determine priorities based on the user's level of interest. This allows for the provision of comparison information with appropriate priorities according to the submission timing of insurance products.

[0081] The comparison unit can adjust the order of insurance products based on their relevance when generating comparison information. For example, the comparison unit uses a generation AI to adjust the order of insurance products based on their relevance when generating comparison information. For instance, for insurance products that the user is particularly interested in, the comparison unit uses the generation AI to provide comparison information first. Conversely, for insurance products that the user is not interested in, the comparison unit can use the generation AI to provide comparison information later. Furthermore, for insurance products related to the user's life stage, the comparison unit can use the generation AI to prioritize providing comparison information. This allows for the provision of comparison information in an appropriate order according to the relevance of the insurance products.

[0082] The suggestion unit can estimate the user's emotions and adjust the suggested content based on those emotions. For example, the suggestion unit can use generative AI to estimate the user's emotions and adjust the suggested content based on those emotions. For example, if the user is feeling anxious, the suggestion unit can use generative AI to provide reassuring suggestions. If the user is excited, the suggestion unit can also use generative AI to provide suggestions in a calm tone. Furthermore, if the user is relaxed, the suggestion unit can use generative AI to provide suggestions in a friendly tone. This allows for the provision of appropriate suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The proposal department can analyze the user's past contract history and select the optimal proposal method at the time of proposal. For example, the proposal department can use generative AI to analyze the user's past contract history and select the optimal proposal method at the time of proposal. For example, based on the insurance products the user has contracted in the past, the proposal department can use generative AI to provide relevant proposal content. The proposal department can also use generative AI to select the optimal proposal method from the user's past contract history. Furthermore, by analyzing the user's past contract history, the proposal department can use generative AI to provide the most efficient proposal method. This allows the proposal department to provide the optimal proposal method based on the user's past contract history.

[0084] The proposal function can customize the proposed content based on the user's current living situation. For example, the proposal function uses generative AI to customize the proposed content based on the user's current living situation. If the user gets married, the proposal function uses generative AI to propose insurance products related to marriage. If the user has children, the proposal function can also use generative AI to propose insurance products related to children. Furthermore, if the user is planning to move, the proposal function can use generative AI to propose insurance products related to the new area. This allows the system to provide appropriate proposals tailored to the user's current living situation.

[0085] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, the suggestion unit can use generative AI to estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is seeking an urgent suggestion, the suggestion unit can use generative AI to provide that suggestion with the highest priority. If the user is relaxed, the suggestion unit can also use generative AI to provide that suggestion with the same priority as other suggestions. Furthermore, if the user is feeling anxious, the suggestion unit can use generative AI to prioritize that suggestion and provide a sense of security. This allows for the provision of suggestions with appropriate priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The proposal unit can select the optimal proposal method when making a proposal, taking into account the user's geographical location information. For example, the proposal unit can use generative AI to select the optimal proposal method when making a proposal, taking into account the user's geographical location information. For example, if the user lives in a specific region, the proposal unit can use generative AI to propose insurance products related to that region. Also, if the user is traveling, the proposal unit can use generative AI to propose insurance products related to the travel destination. Furthermore, if the user is planning to move, the proposal unit can use generative AI to propose insurance products related to the new region. In this way, the optimal proposal method can be provided based on the user's geographical location information.

[0087] The proposal department can analyze the user's social media activity and adjust the proposal content when making a proposal. For example, the proposal department can use generative AI to analyze the user's social media activity and adjust the proposal content when making a proposal. For example, if a user mentions a specific insurance product on social media, the proposal department can use generative AI to make a proposal related to that product. Also, if a user shares a life event on social media, the proposal department can use generative AI to make a proposal for an insurance product related to that event. Furthermore, if a user shows a specific interest on social media, the proposal department can use generative AI to make a proposal for an insurance product related to that interest. This allows the department to provide appropriate proposal content based on the user's social media activity.

[0088] The service provider can estimate the user's emotions and adjust the display method of the content based on the estimated emotions. For example, the service provider can use generative AI to estimate the user's emotions and adjust the display method of the content based on the estimated emotions. For example, if the user is feeling anxious, the service provider can use generative AI to provide a simple and highly visible display method. If the user is relaxed, the service provider can also use generative AI to provide a display method that includes detailed information. Furthermore, if the user is excited, the service provider can use generative AI to provide a visually stimulating display method. This allows the service provider to display the content in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can use a generation AI to select the optimal display method by referring to the user's past operation history at the time of service provision. For example, based on the display methods the user has used in the past, the service provider can use a generation AI to provide the optimal display method. Furthermore, the service provider can use a generation AI to select the most efficient display method from the user's past operation history. In addition, by analyzing the user's past operation history, the service provider can use a generation AI to provide the display method with the highest visibility. This makes it possible to provide the optimal display method based on the user's past operation history.

[0090] The service provider can estimate the user's emotions and determine the priority of the content offered based on those emotions. For example, the service provider can use generative AI to estimate the user's emotions and determine the priority of the content offered based on those emotions. For example, if the user is seeking urgent information, the service provider can use generative AI to provide that information with the highest priority. If the user is relaxed, the service provider can also use generative AI to provide that information with the same priority as other information. Furthermore, if the user is feeling anxious, the service provider can use generative AI to prioritize that information and provide a sense of security. This allows the service provider to offer content with appropriate priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, the service provider can use a generation AI to select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can use a generation AI to provide a display method that is appropriate for the screen size. If the user is using a tablet, the service provider can also use a generation AI to provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can use a generation AI to provide a simple and highly visible display method. This allows the service provider to provide the optimal display method based on the user's device information.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The response unit can respond to user questions in real time using generative AI. For example, if a user asks, "Is health insurance necessary?", the response unit will use generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. The response unit can also analyze the content of the user's question and generate the most appropriate answer. Furthermore, the response unit can analyze the user's past question history and select the most appropriate response method. For example, based on the content of questions the user has frequently asked in the past, the response unit will use generative AI to prioritize providing relevant information. It can also prioritize suggesting question formats (voice, text, etc.) that the user has used in the past. In addition, it can predict and provide information relevant to a specific time period based on the user's past question history. This allows the system to provide the most appropriate response based on the user's past question history.

[0094] The comparison unit can analyze user information and generate comparative information on insurance products, along with explanations of their respective advantages and disadvantages. For example, if a user inputs "I recently got married and would like to consider life insurance," the comparison unit uses its AI generation capabilities to suggest appropriate life insurance products and explain their features. The comparison unit can also automatically compare products tailored to the user's needs and present them visually and in an easy-to-understand manner. Furthermore, when generating comparative information, the comparison unit can adjust the level of detail based on the importance of each insurance product. For example, for highly important insurance products, the comparison unit uses its AI generation capabilities to provide detailed information. For less important insurance products, the comparison unit can provide concise information using its AI generation capabilities. Additionally, for insurance products that the user shows particular interest in, the comparison unit can use its AI generation capabilities to provide detailed information. This allows the system to provide comparative information with an appropriate level of detail according to the importance of each insurance product.

[0095] The proposal department can suggest changes to contract details in accordance with the user's life stage and market changes. For example, if a user inputs "I want to review my insurance because I have a child," the proposal department will use generative AI to analyze the current contract details and suggest necessary changes. The proposal department can also suggest appropriate changes to contract details in accordance with the user's life stage and market changes. Furthermore, when making a proposal, the proposal department can analyze the user's past contract history to select the optimal proposal method. For example, based on the insurance products the user has contracted in the past, the proposal department will use generative AI to provide relevant proposal content. The proposal department can also use generative AI to select the optimal proposal method from the user's past contract history. Furthermore, by analyzing the user's past contract history, the proposal department can use generative AI to provide the most efficient proposal method. This allows the proposal department to provide the optimal proposal method based on the user's past contract history.

[0096] The service provider can present proposed changes to users in a visually and easily understandable manner. For example, the service provider can use generative AI to present proposed changes to users in a visually and easily understandable manner. For example, the service provider can use graphs and charts to visually display proposed changes. The service provider can also provide proposed changes to users via email or website. Furthermore, the service provider can select the optimal display method by referring to the user's past operation history when providing the changes. For example, based on the display methods the user has used in the past, the service provider can use generative AI to provide the optimal display method. The service provider can also use generative AI to select the most efficient display method from the user's past operation history. Furthermore, by analyzing the user's past operation history, the service provider can use generative AI to provide the most visually appealing display method. This allows the service provider to provide the optimal display method based on the user's past operation history.

[0097] The response unit can estimate the user's emotions and adjust the tone and content of its response based on those emotions. For example, if the user is feeling anxious, the response unit can use generative AI to generate a reassuring response in a gentle tone. If the user is excited, the response unit can also use generative AI to generate a calm response that provides information in a calm tone and soothes the user. Furthermore, if the user is relaxed, the response unit can use generative AI to generate a friendly response in a friendly tone. This allows for the provision of appropriate responses tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The comparison unit can estimate the user's emotions and adjust the way the comparison information is presented based on the estimated emotions. For example, if the user is feeling anxious, the comparison unit can use generative AI to provide simple and easy-to-understand comparison information. If the user is excited, the comparison unit can also use generative AI to provide detailed comparison information. Furthermore, if the user is relaxed, the comparison unit can use generative AI to provide comparison information in a friendly tone. This allows for the provision of comparison information in an appropriate manner according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The suggestion function can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion function can use generative AI to provide reassuring suggestions. If the user is excited, the suggestion function can also use generative AI to provide suggestions in a calm tone. Furthermore, if the user is relaxed, the suggestion function can use generative AI to provide suggestions in a friendly tone. This allows for the provision of appropriate suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The service provider can estimate the user's emotions and adjust the display method of the content based on the estimated emotions. For example, if the user is feeling anxious, the service provider can use generative AI to provide a simple and highly visible display method. If the user is relaxed, the service provider can also use generative AI to provide a display method that includes detailed information. Furthermore, if the user is excited, the service provider can use generative AI to provide a visually stimulating display method. This allows the service provider to display the content in an appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated emotions. For example, if the user asks an urgent question, the response unit will use generative AI to prioritize that question. If the user is relaxed, the response unit can also use generative AI to respond to the question with the same priority as other questions. Furthermore, if the user is feeling anxious, the response unit can use generative AI to prioritize that question and provide reassurance. This allows for the provision of responses with appropriate priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The comparison unit can apply different comparison algorithms depending on the insurance product category when generating comparison information. For example, for life insurance, the comparison unit uses a generation AI to apply a comparison algorithm that emphasizes risk and return. For medical insurance, the comparison unit can also use a generation AI to apply a comparison algorithm that emphasizes coverage and cost. Furthermore, for automobile insurance, the comparison unit can use a generation AI to apply a comparison algorithm that emphasizes accident rate and premiums. This allows for the application of an appropriate comparison algorithm according to the insurance product category.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The response unit responds to the user's questions. The response unit uses generative AI and natural language processing technology to respond to user questions in real time. For example, if a user asks, "Is health insurance necessary?", the response unit uses generative AI to explain the high-cost medical care system and the advantages and disadvantages of health insurance. In addition, the response unit can use generative AI to analyze the user's question and generate the optimal answer in order to provide appropriate information to the user. Step 2: The comparison unit generates comparative information on insurance products based on the information obtained by the response unit. The comparison unit uses generation AI to analyze user information and generates comparative information on insurance products and explanations of their respective advantages and disadvantages. For example, if a user inputs "I recently got married, so I'd like to consider life insurance," the comparison unit uses generation AI to suggest appropriate life insurance products and explain their features. The comparison unit can also automatically compare products tailored to the user's needs and present them in a visually and easily understandable way. Step 3: The proposal unit proposes changes to the contract based on the information generated by the comparison unit. The proposal unit uses generative AI to propose changes to the contract in accordance with the user's life stage and market changes. For example, if the user inputs "I want to review my insurance because I had a child," the proposal unit uses generative AI to analyze the current contract and propose necessary changes. The proposal unit can also propose appropriate changes to the contract in accordance with the user's life stage and market changes. Step 4: The Provider team provides the user with the changes proposed by the Recommendation team. The Provider team uses generative AI to present the proposed changes to the user in a visually and easily understandable way. For example, the Provider team may use graphs and charts to visually display the proposed changes. The Provider team can also provide the proposed changes to the user via email or website.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the response unit, comparison unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the smart device 14 and responds to user questions in real time using generating AI. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates comparative information on insurance products by analyzing user information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes changes to the contract content according to the user's life stage and market changes. The provision unit is implemented by the control unit 46A of the smart device 14 and presents the proposed changes to the user in a visual and easy-to-understand manner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the response unit, comparison unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the smart glasses 214 and responds to user questions in real time using generating AI. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates comparative information on insurance products by analyzing user information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes changes to the contract content according to the user's life stage and market changes. The provision unit is implemented by the control unit 46A of the smart glasses 214 and presents the proposed changes to the user in a visual and easy-to-understand manner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the response unit, comparison unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the headset terminal 314 and responds to user questions in real time using generating AI. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates comparative information on insurance products by analyzing user information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes changes to the contract content according to the user's life stage and market changes. The provision unit is implemented by the control unit 46A of the headset terminal 314 and presents the proposed changes to the user in a visual and easy-to-understand manner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the response unit, comparison unit, proposal unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the response unit is implemented by the control unit 46A of the robot 414 and responds to user questions in real time using generating AI. The comparison unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates comparative information on insurance products by analyzing user information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes changes to the contract content according to the user's life stage and market changes. The provision unit is implemented by the control unit 46A of the robot 414 and presents the proposed changes to the user in a visual and easy-to-understand manner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A response unit that answers user questions, A comparison unit generates comparative information on insurance products based on the information obtained by the response unit, A proposal unit proposes changes to the contract terms based on the information generated by the comparison unit, The system comprises a provisioning unit that provides the user with the changes proposed by the proposal unit. A system characterized by the following features. (Note 2) The response unit is Responding to user questions in real time using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The comparison unit is, Analyze user information to generate comparative information on insurance products and explanations of their respective advantages and disadvantages. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose changes to the contract terms in accordance with the user's life stage and market changes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Present the proposed changes to the user in a visually and easily understandable manner. The system described in Appendix 1, characterized by the features described herein. (Note 6) The response unit is It estimates the user's emotions and adjusts the tone and content of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The response unit is Analyze the user's past question history and select the optimal response method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The response unit is When responding, the response content is customized based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The response unit is It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The response unit is When responding, the system prioritizes providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The response unit is When responding, the system analyzes the user's social media activity and provides relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The comparison unit is, It estimates the user's emotions and adjusts how comparative information is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The comparison unit is, When generating comparative information, adjust the level of detail based on the importance of the insurance products. The system described in Appendix 1, characterized by the features described herein. (Note 14) The comparison unit is, When generating comparative information, different comparison algorithms are applied depending on the category of insurance product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The comparison unit is, It estimates the user's emotions and adjusts the length of the comparison information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The comparison unit is, When generating comparative information, prioritization is determined based on the submission date of insurance products. The system described in Appendix 1, characterized by the features described herein. (Note 17) The comparison unit is, When generating comparative information, the order is adjusted based on the relevance of insurance products. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, we analyze the user's past contract history to select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, customize the proposal based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, the optimal proposal method will be selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and adjust the proposal accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A response unit that answers user questions, A comparison unit generates comparative information on insurance products based on the information obtained by the response unit, A proposal unit proposes changes to the contract terms based on the information generated by the comparison unit, The system comprises a provisioning unit that provides the user with the changes proposed by the proposal unit. A system characterized by the following features.

2. The response unit is Responding to user questions in real time using natural language processing. The system according to feature 1.

3. The comparison unit is, Analyze user information to generate comparative information on insurance products and explanations of their respective advantages and disadvantages. The system according to feature 1.

4. The aforementioned proposal section is, We propose changes to the contract terms in accordance with the user's life stage and market changes. The system according to feature 1.

5. The aforementioned supply unit is, Present the proposed changes to the user in a visually and easily understandable manner. The system according to feature 1.

6. The response unit is It estimates the user's emotions and adjusts the tone and content of the response based on the estimated emotions. The system according to feature 1.

7. The response unit is Analyze the user's past question history and select the optimal response method. The system according to feature 1.

8. The response unit is When responding, the response content is customized based on the user's current situation and areas of interest. The system according to feature 1.

9. The response unit is It estimates the user's emotions and determines the priority of responses based on the estimated user emotions. The system according to feature 1.

10. The response unit is When responding, the system prioritizes providing highly relevant information, taking into account the user's geographical location. The system according to feature 1.

Images