System

Abstract

An object of the system according to the embodiment is to propose and continuously review insurance and compensation contents optimal for a user.SOLUTION: A system according to an embodiment includes a receiving unit, an analyzing unit, a proposing unit, a providing unit, a joining unit, and a monitoring unit. The reception unit receives information from a user. The analysis unit analyzes the information received by the reception unit and creates a risk profile of the user. The proposal unit proposes insurance and compensation contents based on the risk profile created by the analysis unit. The providing unit provides the insurance and the compensation content proposed by the proposing unit to the user. The user subscribes to the insurance based on the proposal provided by the providing unit. The monitoring unit continuously monitors the contents of the insurance subscribed by the subscribing unit and proposes review as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose optimal insurance and coverage to users and continuously review them, so there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal insurance and compensation details to users and to continuously review them. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a provision unit, a subscription unit, and a monitoring unit. The reception unit accepts information from a user. The analysis unit analyzes the information accepted by the reception unit and creates a risk profile for the user. The proposal unit proposes insurance and coverage based on the risk profile created by the analysis unit. The provision unit provides the user with the insurance and coverage proposed by the proposal unit. The subscription unit allows the user to purchase insurance based on the proposal provided by the provision unit. The monitoring unit continuously monitors the content of the insurance purchased by the subscription unit and suggests revisions as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal insurance and compensation details to users and continuously review them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An insurance proposal system according to an embodiment of the present invention accepts and analyzes information from users, proposes optimal insurance and coverage, supports enrollment, and continuously monitors the user. The insurance proposal system allows users to input their personal information, and AI analyzes the information to propose optimal insurance and coverage. The user can easily enroll in the proposed insurance. For example, the insurance proposal system allows users to input information such as their age, occupation, family composition, health status, and hobbies. The insurance proposal system then uses AI to analyze the input information and create a user risk profile. For example, for users in good health, the system proposes enhanced health insurance coverage. The proposed insurance and coverage are provided to the user, who can then enroll online. This allows users to easily find and enroll in the optimal insurance and coverage. The insurance proposal system also continuously monitors the user's information and makes suggestions for reviewing the insurance and coverage as needed. For example, if the user's health condition changes, the AI ​​proposes new insurance and coverage based on that information. This allows the user to always have the optimal insurance and coverage. The insurance proposal system allows users to easily find and enroll in the optimal insurance and coverage, allowing them to live with peace of mind. For example, for users who work in high-risk occupations, the system will make suggestions to enhance the coverage of their workers' compensation insurance, and the users can take out insurance based on these suggestions. The insurance suggestion system also continuously monitors the user's information and makes suggestions to review insurance and coverage as needed. For example, if there is a change in family composition, the AI ​​will use that information to suggest insurance that is suitable for the whole family. This ensures that users always have the insurance and coverage that is best suited to them.

[0029] The insurance proposal system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a provision unit, a subscription unit, and a monitoring unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, age, occupation, family structure, health status, hobbies, and past insurance enrollment history. The reception unit, for example, stores the information entered by the user in a database. The reception unit can also transmit the information entered by the user to an AI. The analysis unit analyzes the information accepted by the reception unit and creates a risk profile for the user. The analysis unit, for example, uses AI to analyze the user information and creates the risk profile. The risk profile includes, but is not limited to, the user's health status, occupational risk, and family structure. The proposal unit proposes optimal insurance and coverage based on the risk profile created by the analysis unit. The proposal unit, for example, uses AI to analyze the user's risk profile and proposes optimal insurance and coverage. The proposal unit, for example, proposes enhanced health insurance coverage for a user in good health. The suggestion unit can also make suggestions to enhance the coverage of workers' compensation insurance for users who are engaged in high-risk occupations. The provision unit provides the user with the insurance and coverage suggested by the suggestion unit. For example, the provision unit provides the user with details of the proposed insurance and supports online enrollment procedures. For example, the provision unit enables the user to check the details of the proposed insurance and complete the enrollment procedures online. The enrollment unit causes the user to enroll in insurance based on the suggestion provided by the provision unit. For example, the enrollment unit enables the user to easily enroll in the proposed insurance. For example, the enrollment unit enables the user to complete the enrollment procedures online. The monitoring unit continuously monitors the content of the insurance enrolled by the enrollment unit and suggests reviewing it as necessary. For example, the monitoring unit continuously monitors user information and suggests reviewing the insurance and coverage as necessary. For example, if the user's health condition changes, the monitoring unit suggests new insurance and coverage based on the information.As a result, the insurance proposal system of the embodiment can accept and analyze user information, propose optimal insurance and coverage, support enrollment, and continuously monitor, thereby providing users with optimal insurance and coverage.

[0030] The reception unit can accept information such as the user's age, occupation, family composition, health condition, hobbies, and past insurance history. The reception unit, for example, stores the information entered by the user, such as age, occupation, family composition, health condition, hobbies, and past insurance history, in a database. The reception unit can also transmit the information entered by the user to an AI. For example, the reception unit can collect data for proposing appropriate insurance based on the age entered by the user. The reception unit can also collect data for assessing occupational risk based on the user's occupation. Furthermore, the reception unit can collect data for proposing insurance suitable for the entire family based on the user's family composition. By accepting detailed information about the user, a more accurate risk profile can be created.

[0031] The analysis unit can analyze the received information to create a risk profile for the user. The analysis unit can, for example, use AI to analyze the user's information and create a risk profile. The analysis unit can create a risk profile based on information such as the user's health condition, occupational risk, and family composition. For example, the analysis unit can evaluate health risks based on the user's health checkup results. The analysis unit can also analyze data related to occupations to evaluate the user's occupational risk. Furthermore, the analysis unit can create a risk profile to propose insurance suitable for the entire family based on the user's family composition. In this way, the user's risk profile can be accurately created by analyzing the received information.

[0032] The suggestion unit can make suggestions to enhance the health insurance coverage for users who are in good health. The suggestion unit, for example, uses AI to analyze the user's health condition and make suggestions to enhance the health insurance coverage. The suggestion unit can make suggestions to enhance the health insurance coverage based on the user's health checkup results, for example. The suggestion unit can also make suggestions to enhance the health insurance coverage if the user's health condition is good. Furthermore, the suggestion unit can also suggest the optimal health insurance depending on the user's health condition. This makes it possible to make optimal insurance suggestions depending on the user's health condition.

[0033] The provision unit can provide the user with details of the proposed insurance and support the online enrollment procedure. For example, the provision unit can provide the user with details of the proposed insurance and support the online enrollment procedure. For example, the provision unit can enable the user to check the details of the proposed insurance and complete the enrollment procedure online. The provision unit can also guide the user through the necessary documents and procedural steps when completing the enrollment procedure online. Furthermore, the provision unit can provide support when the user completes the enrollment procedure online. This supports the user so that they can easily enroll in the proposed insurance.

[0034] The subscription unit can enable the user to easily subscribe to the proposed insurance. For example, the subscription unit enables the user to easily subscribe to the proposed insurance. For example, the subscription unit enables the user to perform the subscription procedure online. Furthermore, the subscription unit can guide the user through the necessary documents and procedural steps when subscribing to the proposed insurance. Furthermore, the subscription unit can provide support when the user performs the subscription procedure online. This enables the user to easily subscribe to the proposed insurance.

[0035] The monitoring unit can continuously monitor the user's information and make suggestions to review the insurance and compensation details. The monitoring unit, for example, continuously monitors the user's information and makes suggestions to review the insurance and compensation details as needed. For example, if the user's health condition changes, the monitoring unit can propose new insurance and compensation details based on that information. Furthermore, if the user's family composition changes, the monitoring unit can also propose insurance that is suitable for the entire family based on that information. Furthermore, the monitoring unit can continuously monitor the user's information and make suggestions to review the insurance and compensation details as needed. In this way, the system can continuously monitor the user's information and continue to provide optimal insurance and compensation details.

[0036] The monitoring unit can propose new insurance and coverage based on changes in the user's health condition. For example, if the user's health condition changes, the monitoring unit proposes new insurance and coverage based on that information. For example, if the user's health check results change, the monitoring unit proposes new health insurance based on that information. Furthermore, if the user's health condition worsens, the monitoring unit can also propose a review of the coverage based on that information. Furthermore, if the user's health condition improves, the monitoring unit can also propose a review of the coverage based on that information. This makes it possible to propose optimal insurance in response to changes in the user's health condition and family composition.

[0037] The reception unit can analyze the user's past insurance enrollment history and select the optimal information input method. The reception unit, for example, analyzes the user's past insurance enrollment history and selects the optimal information input method. The reception unit, for example, prioritizes relevant information input based on the types of insurance the user has previously enrolled in. The reception unit can also automatically complete frequently entered information from the user's past insurance enrollment history. Furthermore, the reception unit can analyze the user's past insurance enrollment history and provide templates to reduce the effort required for input. In this way, by analyzing the user's past insurance enrollment history, the effort required for information input can be reduced and efficient information input becomes possible.

[0038] The reception unit can perform filtering based on the user's current living situation and areas of interest when inputting information. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when inputting information. For example, when the user inputs their current living situation, the reception unit preferentially displays related insurance information. The reception unit can also filter unnecessary information based on the user's areas of interest and display only necessary information. Furthermore, the reception unit can customize input items based on the user's living situation and areas of interest. This allows the user to efficiently input only necessary information by filtering information based on the user's living situation and areas of interest.

[0039] The reception unit can select the optimum input means depending on the user's input method when inputting information. For example, the reception unit selects the optimum input means depending on the user's input method (voice, text, image, etc.) when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. In this way, by selecting the optimum input means depending on the user's input method, the efficiency of information input is improved.

[0040] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, if the user lives in a specific area, the reception unit can prioritize inputting insurance information related to that area. Also, if the user is traveling, the reception unit can also prioritize inputting insurance information related to the user's travel destination. Furthermore, the reception unit can prioritize inputting insurance information for risks specific to the area based on the user's geographical location information. In this way, highly relevant information can be prioritized by considering the user's geographical location information.

[0041] The reception unit can analyze the user's social media activity and input related information when inputting information. For example, the reception unit analyzes the user's social media activity and inputs related information when inputting information. For example, the reception unit inputs related insurance information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and automatically input required information. Furthermore, the reception unit can input related insurance information by referring to the activity of the user's friends on social media. In this way, related information can be input efficiently by analyzing the user's social media activity.

[0042] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting information. For example, the reception unit improves the input method based on feedback provided by the user in the past. The reception unit can also simplify the input procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the input interface based on the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback, enabling efficient information input.

[0043] The analysis unit can adjust the level of detail of the analysis based on the user's importance during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the user's importance during analysis. For example, the analysis unit performs a detailed analysis when the user is an important customer. The analysis unit can also perform a standard analysis when the user is a general customer. Furthermore, the analysis unit can also perform a basic analysis when the user is a new customer. This allows for efficient analysis by adjusting the level of detail of the analysis according to the user's importance.

[0044] The analysis unit can apply different analysis algorithms depending on the user category during analysis. For example, the analysis unit applies different analysis algorithms depending on the user category during analysis. For example, if the user is an individual, the analysis unit applies an analysis algorithm for individuals. Furthermore, if the user is a corporation, the analysis unit can also apply an analysis algorithm for corporations. Furthermore, if the user belongs to a specific industry, the analysis unit can also apply an analysis algorithm specialized for that industry. This allows for more appropriate analysis by applying an analysis algorithm depending on the user category.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the user's past analysis results.

[0046] The analysis unit can determine the priority of analysis based on the time when the user submitted information during analysis. For example, the analysis unit determines the priority of analysis based on the time when the user submitted information during analysis. For example, the analysis unit determines the priority of analysis based on the time when the user submitted information. In addition, if the user is in a hurry, the analysis unit can also prioritize analysis regardless of the time of submission. Furthermore, if the user submitted information within a specific deadline, the analysis unit can also prioritize analysis of that information. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the user submitted information.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the user during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the user's information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the user's information. Furthermore, if the user submits important information, the analysis unit can also prioritize analyzing that information. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the relevance of the user's information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the user's information.

[0048] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that use a lot of technical terms. Also, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terms. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0049] The proposal unit can adjust the level of detail of the proposal based on the importance of the insurance when making a proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the insurance when making a proposal. For example, the proposal unit makes a detailed proposal for important insurance. The proposal unit can also make a standard proposal for general insurance. Furthermore, the proposal unit can also make a basic proposal for simple insurance. In this way, by adjusting the level of detail of the proposal based on the importance of the insurance, efficient proposals can be made.

[0050] The suggestion unit can apply different suggestion algorithms depending on the insurance category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the insurance category when making a suggestion. For example, for health insurance, the suggestion unit applies a suggestion algorithm based on health status. For workers' compensation insurance, the suggestion unit can also apply a suggestion algorithm based on occupational risk. Furthermore, for automobile insurance, the suggestion unit can also apply a suggestion algorithm based on driving history. In this way, by applying a suggestion algorithm depending on the insurance category, more appropriate suggestions can be made.

[0051] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the current suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the user's past suggestion results. In this way, the accuracy of the current suggestion is improved by referring to the user's past suggestion results.

[0052] The proposal unit can determine the priority of proposals based on the time of insurance submission when making a proposal. For example, the proposal unit determines the priority of proposals based on the time of insurance submission when making a proposal. For example, if the time of insurance submission is approaching, the proposal unit prioritizes proposals related to that insurance. Also, if the time of submission is further away, the proposal unit can prioritize proposals related to other insurance. Furthermore, the proposal unit can adjust the level of detail of the proposal based on the time of submission. This enables efficient proposals by determining the priority of proposals based on the time of insurance submission.

[0053] The proposal unit can adjust the order of proposals based on the relevance of the insurance when making a proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of the insurance when making a proposal. For example, if the relevance of an insurance is high, the proposal unit prioritizes proposals related to that insurance. Also, if the relevance is low, the proposal unit can prioritize proposals related to other insurance. Furthermore, the proposal unit can adjust the level of detail of the proposal based on the relevance of the insurance. This enables efficient proposals by adjusting the order of proposals based on the relevance of the insurance.

[0054] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit can make a proposal that uses a lot of technical terms. Also, if the user has general knowledge, the suggestion unit can make a proposal that uses less technical terms. Furthermore, the suggestion unit can adjust the level of detail in the proposal according to the user's level of expertise. This allows for more appropriate suggestions by adjusting the use of technical terms in the proposal according to the user's level of expertise.

[0055] The providing unit can select the optimal providing method by analyzing the user's past providing history at the time of providing. For example, the providing unit selects the optimal providing method by analyzing the user's past providing history at the time of providing. The providing unit selects the optimal providing method based on the user's past providing history, for example. The providing unit can also analyze the user's past providing history and improve the providing method. Furthermore, the providing unit can adjust the level of detail of the providing by referring to the user's past providing history. In this way, the optimal providing method can be selected by analyzing the user's past providing history.

[0056] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. For example, the providing unit customizes the means of provision based on the user's current living situation at the time of provision. For example, the providing unit selects the optimal means of provision based on the user's current living situation. The providing unit can also customize the means of provision according to the user's living situation. Furthermore, the providing unit can adjust the level of detail of the provision taking the user's living situation into consideration. This enables more appropriate information to be provided by customizing the means of provision based on the user's current living situation.

[0057] The providing unit can improve the method of providing information by reflecting user feedback at the time of providing the information. For example, the providing unit improves the method of providing information by reflecting user feedback at the time of providing the information. For example, the providing unit improves the method of providing information based on user feedback. The providing unit can also customize the means of providing information by reflecting user feedback. Furthermore, the providing unit can adjust the level of detail of the information provided by referring to user feedback. In this way, the method of providing information can be improved by reflecting user feedback, making it possible to provide more appropriate information.

[0058] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, the providing unit selects the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, if the user lives in a specific area, the providing unit can preferentially provide information related to that area. Also, if the user is traveling, the providing unit can preferentially provide information related to the travel destination. Furthermore, the providing unit can preferentially provide information regarding risks specific to the area based on the user's geographical location information. In this way, by taking into consideration the user's geographical location information, highly relevant information can be preferentially provided.

[0059] The providing unit can analyze the user's social media activity and suggest a means of providing the information at the time of providing. For example, the providing unit analyzes the user's social media activity and suggests a means of providing the information at the time of providing. The providing unit can provide related information, for example, based on information shared by the user on social media. The providing unit can also analyze the user's social media activity and automatically provide necessary information. Furthermore, the providing unit can provide related information by referring to the activity of the user's friends on social media. In this way, related information can be efficiently provided by analyzing the user's social media activity.

[0060] The providing unit can customize the method of providing information by reflecting the user's past feedback at the time of providing the information. For example, the providing unit customizes the method of providing information by reflecting the user's past feedback at the time of providing the information. For example, the providing unit improves the method of providing information based on feedback provided by the user in the past. The providing unit can also customize the means of providing information by reflecting the user's past feedback. Furthermore, the providing unit can customize the interface of providing information based on the user's past feedback. This allows the method of providing information to be customized by reflecting the user's past feedback, thereby enabling efficient information provision.

[0061] The subscription unit can select the optimal subscription method by analyzing the user's past subscription history at the time of subscription. For example, the subscription unit selects the optimal subscription method by analyzing the user's past subscription history at the time of subscription. For example, the subscription unit selects the optimal subscription method based on the user's past subscription history. The subscription unit can also analyze the user's past subscription history and improve the subscription method. Furthermore, the subscription unit can adjust the level of subscription detail by referring to the user's past subscription history. In this way, the optimal subscription method can be selected by analyzing the user's past subscription history.

[0062] The subscription unit can customize the subscription method based on the user's current living situation at the time of subscription. For example, the subscription unit customizes the subscription method based on the user's current living situation at the time of subscription. For example, the subscription unit selects the optimal subscription method based on the user's current living situation. The subscription unit can also customize the subscription method according to the user's living situation. Furthermore, the subscription unit can adjust the level of subscription detail taking the user's living situation into consideration. This allows for more appropriate subscription by customizing the subscription method based on the user's current living situation.

[0063] The subscription unit can improve the subscription method by reflecting user feedback at the time of subscription. For example, the subscription unit improves the subscription method by reflecting user feedback at the time of subscription. For example, the subscription unit improves the subscription method based on user feedback. The subscription unit can also customize the subscription means by reflecting user feedback. Furthermore, the subscription unit can adjust the level of subscription detail by referring to user feedback. In this way, the subscription method can be improved by reflecting user feedback, enabling more appropriate subscription.

[0064] The subscription unit can select the optimal subscription method by taking into account the user's geographical location information at the time of subscription. For example, the subscription unit selects the optimal subscription method by taking into account the user's geographical location information at the time of subscription. For example, if the user lives in a specific area, the subscription unit can prioritize subscription of insurance related to that area. Also, if the user is traveling, the subscription unit can prioritize subscription of insurance related to the user's travel destination. Furthermore, the subscription unit can prioritize subscription of insurance against risks specific to the area based on the user's geographical location information. In this way, by taking into account the user's geographical location information, it is possible to prioritize subscription of highly relevant insurance.

[0065] The subscription unit can analyze the user's social media activity at the time of subscription and suggest a subscription method. For example, at the time of subscription, the subscription unit analyzes the user's social media activity and suggests a subscription method. For example, the subscription unit can suggest relevant insurance based on information shared by the user on social media. The subscription unit can also analyze the user's social media activity and automatically suggest necessary insurance. Furthermore, the subscription unit can suggest relevant insurance based on the activity of the user's friends on social media. In this way, relevant insurance can be efficiently suggested by analyzing the user's social media activity.

[0066] The subscription unit can customize the subscription method by reflecting the user's past feedback at the time of subscription. For example, the subscription unit customizes the subscription method by reflecting the user's past feedback at the time of subscription. For example, the subscription unit improves the subscription method based on feedback provided by the user in the past. The subscription unit can also customize the subscription means by reflecting the user's past feedback. Furthermore, the subscription unit can customize the subscription interface based on the user's past feedback. In this way, the subscription method can be customized by reflecting the user's past feedback, enabling efficient subscription.

[0067] The monitoring unit can select the optimal monitoring method by analyzing the user's past monitoring history during monitoring. For example, the monitoring unit selects the optimal monitoring method by analyzing the user's past monitoring history during monitoring. The monitoring unit selects the optimal monitoring method based on the user's past monitoring history, for example. The monitoring unit can also analyze the user's past monitoring history and improve the monitoring method. Furthermore, the monitoring unit can adjust the level of monitoring detail by referring to the user's past monitoring history. In this way, the optimal monitoring method can be selected by analyzing the user's past monitoring history.

[0068] The monitoring unit can customize the monitoring means based on the user's current living situation during monitoring. For example, the monitoring unit customizes the monitoring means based on the user's current living situation during monitoring. The monitoring unit selects the optimal monitoring means based on the user's current living situation, for example. The monitoring unit can also customize the monitoring means according to the user's living situation. Furthermore, the monitoring unit can adjust the level of detail of monitoring taking the user's living situation into consideration. This enables more appropriate monitoring by customizing the monitoring means based on the user's current living situation.

[0069] The monitoring unit can improve the monitoring method by reflecting user feedback during monitoring. For example, the monitoring unit improves the monitoring method by reflecting user feedback during monitoring. For example, the monitoring unit improves the monitoring method based on user feedback. The monitoring unit can also customize the monitoring means by reflecting user feedback. Furthermore, the monitoring unit can adjust the level of detail of monitoring by referring to user feedback. In this way, the monitoring method can be improved by reflecting user feedback, making more appropriate monitoring possible.

[0070] The monitoring unit can select an optimal monitoring method during monitoring by taking into consideration the user's geographical location information. For example, the monitoring unit selects an optimal monitoring method during monitoring by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the monitoring unit can prioritize monitoring information related to that area. Also, if the user is traveling, the monitoring unit can prioritize monitoring information related to the user's travel destination. Furthermore, the monitoring unit can prioritize monitoring information related to risks specific to the area based on the user's geographical location information. In this way, highly relevant information can be prioritized by taking into consideration the user's geographical location information.

[0071] The monitoring unit can analyze the user's social media activities during monitoring and suggest monitoring methods. For example, the monitoring unit can analyze the user's social media activities during monitoring and suggest monitoring methods. For example, the monitoring unit monitors related information based on information shared by the user on social media. The monitoring unit can also analyze the user's social media activities and automatically monitor necessary information. Furthermore, the monitoring unit can monitor related information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently monitored by analyzing the user's social media activities.

[0072] The monitoring unit can customize the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit, for example, customizes the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit improves the monitoring method, for example, based on feedback provided by the user in the past. The monitoring unit can also customize the monitoring means by reflecting the user's past feedback. Furthermore, the monitoring unit can customize the monitoring interface based on the user's past feedback. In this way, the monitoring method can be customized by reflecting the user's past feedback, enabling efficient monitoring.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The reception unit can analyze the user's lifestyle and behavioral patterns based on the information input by the user, thereby improving the accuracy of insurance proposals. For example, if the user exercises daily, the health insurance coverage can be adjusted based on that information. Also, if the user travels frequently, travel insurance proposals can be enhanced. Furthermore, if the user has a specific diet, health risks can be evaluated based on that information and appropriate insurance can be proposed. This enables more accurate insurance proposals based on the user's lifestyle and behavioral patterns.

[0075] The analysis unit can take the user's past medical history into consideration when creating a user's risk profile. For example, if the user has had a particular disease in the past, the analysis unit can evaluate their health risk based on that information. It can also predict future medical risks based on information about the treatments and surgeries the user has undergone in the past. Furthermore, it can evaluate the user's current health condition and suggest appropriate insurance based on the results of health checkups the user has undergone in the past. This makes it possible to create a more accurate risk profile based on the user's past medical history.

[0076] The reception unit can analyze the user's hobbies and interests based on the information input by the user, thereby improving the accuracy of insurance proposals. For example, if the user likes outdoor activities, the reception unit can strengthen outdoor insurance proposals based on that information. Also, if the user plays a specific sport, the reception unit can propose sports insurance based on that information. Furthermore, if the user has a specific hobby, the reception unit can propose insurance related to that hobby based on that information. This enables more accurate insurance proposals based on the user's hobbies and interests.

[0077] When creating a user's risk profile, the analysis unit can evaluate in detail the risks associated with the user's occupation. For example, if the user is engaged in a high-risk occupation, the analysis unit can use that information to enhance the coverage of the worker's accident compensation insurance. Also, if the user belongs to a specific industry, the analysis unit can evaluate the risks specific to that industry and suggest appropriate insurance. Furthermore, if the user works as a freelancer, the analysis unit can use that information to suggest insurance for freelancers. This makes it possible to create a risk profile that evaluates in detail the risks associated with the user's occupation.

[0078] The proposal unit can analyze the user's past insurance enrollment history and improve the accuracy of proposals based on the results of past proposals. For example, the current proposal can be adjusted based on the type of insurance and coverage that the user has previously enrolled. The proposal unit can also analyze the reasons why the user rejected proposals in the past and improve the proposal content based on that information. Furthermore, the proposal unit can incorporate elements of proposals that the user was satisfied with in the past and reflect them in the current proposal. This makes it possible to make more accurate proposals based on the user's past insurance enrollment history.

[0079] The providing unit can provide insurance information for risks specific to a region, taking into account the user's geographical location information. For example, if the user lives in an earthquake-prone area, information on earthquake insurance related to that region can be provided. Also, if the user lives in an area with a high risk of flooding, information on flood insurance related to that region can be provided. Furthermore, if the user is traveling to a specific region, information on travel insurance related to that region can be provided. This makes it possible to provide more appropriate insurance information that takes into account the user's geographical location information.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The reception unit receives information from the user. This information includes, for example, age, occupation, family composition, health status, hobbies, and past insurance history. The reception unit can also store the information entered by the user in a database and send it to the AI. Step 2: The analysis unit analyzes the information received by the reception unit and creates a risk profile for the user. The analysis unit uses AI to analyze the user's information and creates a risk profile. The risk profile includes the user's health condition, occupational risks, family composition, etc. Step 3: The proposal unit proposes optimal insurance and compensation based on the risk profile created by the analysis unit. The proposal unit uses AI to analyze the user's risk profile and proposes optimal insurance and compensation. For example, it may propose enhancing health insurance coverage for a user in good health, or it may propose enhancing workers' compensation insurance coverage for a user engaged in a high-risk occupation. Step 4: The provider provides the user with the insurance and coverage proposed by the suggestion unit. The provider provides the user with details of the proposed insurance and supports the user in completing the online enrollment process. The user can confirm the details of the proposed insurance and complete the online enrollment process. Step 5: The subscription unit allows the user to subscribe to the insurance based on the proposal provided by the provider unit. The subscription unit allows the user to easily subscribe to the proposed insurance and complete the subscription procedure online. Step 6: The monitoring unit continuously monitors the contents of the insurance purchased by the subscription unit and proposes revisions as necessary. The monitoring unit continuously monitors the user's information and proposes revisions to the insurance and coverage as necessary. For example, if the user's health condition changes, it will propose new insurance and coverage based on that information.

[0082] (Example 2) An insurance proposal system according to an embodiment of the present invention accepts and analyzes information from users, proposes optimal insurance and coverage, supports enrollment, and continuously monitors the user. The insurance proposal system allows users to input their personal information, and AI analyzes the information to propose optimal insurance and coverage. The user can easily enroll in the proposed insurance. For example, the insurance proposal system allows users to input information such as their age, occupation, family composition, health status, and hobbies. The insurance proposal system then uses AI to analyze the input information and create a user risk profile. For example, for users in good health, the system proposes enhanced health insurance coverage. The proposed insurance and coverage are provided to the user, who can then enroll online. This allows users to easily find and enroll in the optimal insurance and coverage. The insurance proposal system also continuously monitors the user's information and makes suggestions for reviewing the insurance and coverage as needed. For example, if the user's health condition changes, the AI ​​proposes new insurance and coverage based on that information. This allows the user to always have the optimal insurance and coverage. The insurance proposal system allows users to easily find and enroll in the optimal insurance and coverage, allowing them to live with peace of mind. For example, for users who work in high-risk occupations, the system will make suggestions to enhance the coverage of their workers' compensation insurance, and the users can take out insurance based on these suggestions. The insurance suggestion system also continuously monitors the user's information and makes suggestions to review insurance and coverage as needed. For example, if there is a change in family composition, the AI ​​will use that information to suggest insurance that is suitable for the whole family. This ensures that users always have the insurance and coverage that is best suited to them.

[0083] The insurance proposal system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a provision unit, a subscription unit, and a monitoring unit. The reception unit accepts information from a user. The information from the user includes, but is not limited to, age, occupation, family structure, health status, hobbies, and past insurance enrollment history. The reception unit, for example, stores the information entered by the user in a database. The reception unit can also transmit the information entered by the user to an AI. The analysis unit analyzes the information accepted by the reception unit and creates a risk profile for the user. The analysis unit, for example, uses AI to analyze the user information and creates the risk profile. The risk profile includes, but is not limited to, the user's health status, occupational risk, and family structure. The proposal unit proposes optimal insurance and coverage based on the risk profile created by the analysis unit. The proposal unit, for example, uses AI to analyze the user's risk profile and proposes optimal insurance and coverage. The proposal unit, for example, proposes enhanced health insurance coverage for a user in good health. The suggestion unit can also make suggestions to enhance the coverage of workers' compensation insurance for users who are engaged in high-risk occupations. The provision unit provides the user with the insurance and coverage suggested by the suggestion unit. For example, the provision unit provides the user with details of the proposed insurance and supports online enrollment procedures. For example, the provision unit enables the user to check the details of the proposed insurance and complete the enrollment procedures online. The enrollment unit causes the user to enroll in insurance based on the suggestion provided by the provision unit. For example, the enrollment unit enables the user to easily enroll in the proposed insurance. For example, the enrollment unit enables the user to complete the enrollment procedures online. The monitoring unit continuously monitors the content of the insurance enrolled by the enrollment unit and suggests reviewing it as necessary. For example, the monitoring unit continuously monitors user information and suggests reviewing the insurance and coverage as necessary. For example, if the user's health condition changes, the monitoring unit suggests new insurance and coverage based on the information.As a result, the insurance proposal system of the embodiment can accept and analyze user information, propose optimal insurance and coverage, support enrollment, and continuously monitor, thereby providing users with optimal insurance and coverage.

[0084] The reception unit can accept information such as the user's age, occupation, family composition, health condition, hobbies, and past insurance history. The reception unit, for example, stores the information entered by the user, such as age, occupation, family composition, health condition, hobbies, and past insurance history, in a database. The reception unit can also transmit the information entered by the user to an AI. For example, the reception unit can collect data for proposing appropriate insurance based on the age entered by the user. The reception unit can also collect data for assessing occupational risk based on the user's occupation. Furthermore, the reception unit can collect data for proposing insurance suitable for the entire family based on the user's family composition. By accepting detailed information about the user, a more accurate risk profile can be created.

[0085] The analysis unit can analyze the received information to create a risk profile for the user. The analysis unit can, for example, use AI to analyze the user's information and create a risk profile. The analysis unit can create a risk profile based on information such as the user's health condition, occupational risk, and family composition. For example, the analysis unit can evaluate health risks based on the user's health checkup results. The analysis unit can also analyze data related to occupations to evaluate the user's occupational risk. Furthermore, the analysis unit can create a risk profile to propose insurance suitable for the entire family based on the user's family composition. In this way, the user's risk profile can be accurately created by analyzing the received information.

[0086] The suggestion unit can make suggestions to enhance the health insurance coverage for users who are in good health. The suggestion unit, for example, uses AI to analyze the user's health condition and make suggestions to enhance the health insurance coverage. The suggestion unit can make suggestions to enhance the health insurance coverage based on the user's health checkup results, for example. The suggestion unit can also make suggestions to enhance the health insurance coverage if the user's health condition is good. Furthermore, the suggestion unit can also suggest the optimal health insurance depending on the user's health condition. This makes it possible to make optimal insurance suggestions depending on the user's health condition.

[0087] The provision unit can provide the user with details of the proposed insurance and support the online enrollment procedure. For example, the provision unit can provide the user with details of the proposed insurance and support the online enrollment procedure. For example, the provision unit can enable the user to check the details of the proposed insurance and complete the enrollment procedure online. The provision unit can also guide the user through the necessary documents and procedural steps when completing the enrollment procedure online. Furthermore, the provision unit can provide support when the user completes the enrollment procedure online. This supports the user so that they can easily enroll in the proposed insurance.

[0088] The subscription unit can enable the user to easily subscribe to the proposed insurance. For example, the subscription unit enables the user to easily subscribe to the proposed insurance. For example, the subscription unit enables the user to perform the subscription procedure online. Furthermore, the subscription unit can guide the user through the necessary documents and procedural steps when subscribing to the proposed insurance. Furthermore, the subscription unit can provide support when the user performs the subscription procedure online. This enables the user to easily subscribe to the proposed insurance.

[0089] The monitoring unit can continuously monitor the user's information and make suggestions to review the insurance and compensation details. The monitoring unit, for example, continuously monitors the user's information and makes suggestions to review the insurance and compensation details as needed. For example, if the user's health condition changes, the monitoring unit can propose new insurance and compensation details based on that information. Furthermore, if the user's family composition changes, the monitoring unit can also propose insurance that is suitable for the entire family based on that information. Furthermore, the monitoring unit can continuously monitor the user's information and make suggestions to review the insurance and compensation details as needed. In this way, the system can continuously monitor the user's information and continue to provide optimal insurance and compensation details.

[0090] The monitoring unit can propose new insurance and coverage based on changes in the user's health condition. For example, if the user's health condition changes, the monitoring unit proposes new insurance and coverage based on that information. For example, if the user's health check results change, the monitoring unit proposes new health insurance based on that information. Furthermore, if the user's health condition worsens, the monitoring unit can also propose a review of the coverage based on that information. Furthermore, if the user's health condition improves, the monitoring unit can also propose a review of the coverage based on that information. This makes it possible to propose optimal insurance in response to changes in the user's health condition and family composition.

[0091] The reception unit can estimate the user's emotions and adjust the timing of information input based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the timing of information input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input information during a time when the user is able to relax. Furthermore, if the user is relaxed, the reception unit can select the timing to prompt the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit can prioritize simplified information input. This allows for more appropriate information input by adjusting the timing of information input according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The reception unit can analyze the user's past insurance enrollment history and select the optimal information input method. The reception unit, for example, analyzes the user's past insurance enrollment history and selects the optimal information input method. The reception unit, for example, prioritizes relevant information input based on the types of insurance the user has previously enrolled in. The reception unit can also automatically complete frequently entered information from the user's past insurance enrollment history. Furthermore, the reception unit can analyze the user's past insurance enrollment history and provide templates to reduce the effort required for input. In this way, by analyzing the user's past insurance enrollment history, the effort required for information input can be reduced and efficient information input becomes possible.

[0093] The reception unit can perform filtering based on the user's current living situation and areas of interest when inputting information. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when inputting information. For example, when the user inputs their current living situation, the reception unit preferentially displays related insurance information. The reception unit can also filter unnecessary information based on the user's areas of interest and display only necessary information. Furthermore, the reception unit can customize input items based on the user's living situation and areas of interest. This allows the user to efficiently input only necessary information by filtering information based on the user's living situation and areas of interest.

[0094] The reception unit can select the optimum input means depending on the user's input method when inputting information. For example, the reception unit selects the optimum input means depending on the user's input method (voice, text, image, etc.) when inputting information. For example, if the user selects voice input, the reception unit inputs information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input information using a keyboard or touch screen. Furthermore, if the user selects image input, the reception unit can also input information using image recognition technology. In this way, by selecting the optimum input means depending on the user's input method, the efficiency of information input is improved.

[0095] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, the reception unit estimates the user's emotions and determines the priority of information to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit allows the user to input important information with priority. Furthermore, when the user is relaxed, the reception unit can also prioritize information for which detailed information can be left for later. Furthermore, when the user is in a hurry, the reception unit can also prioritize input of only the most important information. In this way, by determining the priority of information according to the user's emotions, important information can be input with priority. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting information. For example, if the user lives in a specific area, the reception unit can prioritize inputting insurance information related to that area. Also, if the user is traveling, the reception unit can also prioritize inputting insurance information related to the user's travel destination. Furthermore, the reception unit can prioritize inputting insurance information for risks specific to the area based on the user's geographical location information. In this way, highly relevant information can be prioritized by considering the user's geographical location information.

[0097] The reception unit can analyze the user's social media activity and input related information when inputting information. For example, the reception unit analyzes the user's social media activity and inputs related information when inputting information. For example, the reception unit inputs related insurance information based on information shared by the user on social media. The reception unit can also analyze the user's social media activity and automatically input required information. Furthermore, the reception unit can input related insurance information by referring to the activity of the user's friends on social media. In this way, related information can be input efficiently by analyzing the user's social media activity.

[0098] The reception unit can customize the input method by reflecting the user's past feedback when inputting information. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting information. For example, the reception unit improves the input method based on feedback provided by the user in the past. The reception unit can also simplify the input procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the input interface based on the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback, enabling efficient information input.

[0099] The analysis unit can estimate the user's emotions and adjust the risk profile creation method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the risk profile creation method based on the estimated user emotions. For example, the analysis unit can create a simplified risk profile when the user is stressed. The analysis unit can also create a detailed risk profile when the user is relaxed. Furthermore, the analysis unit can prioritize only important risks in the profile when the user is in a hurry. This allows for a more appropriate risk profile to be created by adjusting the risk profile creation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The analysis unit can adjust the level of detail of the analysis based on the user's importance during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the user's importance during analysis. For example, the analysis unit performs a detailed analysis when the user is an important customer. The analysis unit can also perform a standard analysis when the user is a general customer. Furthermore, the analysis unit can also perform a basic analysis when the user is a new customer. This allows for efficient analysis by adjusting the level of detail of the analysis according to the user's importance.

[0101] The analysis unit can apply different analysis algorithms depending on the user category during analysis. For example, the analysis unit applies different analysis algorithms depending on the user category during analysis. For example, if the user is an individual, the analysis unit applies an analysis algorithm for individuals. Furthermore, if the user is a corporation, the analysis unit can also apply an analysis algorithm for corporations. Furthermore, if the user belongs to a specific industry, the analysis unit can also apply an analysis algorithm specialized for that industry. This allows for more appropriate analysis by applying an analysis algorithm depending on the user category.

[0102] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the user's past analysis results. In this way, the accuracy of the current analysis is improved by referring to the user's past analysis results.

[0103] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit performs an analysis that is completed in a short time. The analysis unit can also perform a detailed analysis if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can analyze only important information. This allows for more appropriate analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The analysis unit can determine the priority of analysis based on the time when the user submitted information during analysis. For example, the analysis unit determines the priority of analysis based on the time when the user submitted information during analysis. For example, the analysis unit determines the priority of analysis based on the time when the user submitted information. In addition, if the user is in a hurry, the analysis unit can also prioritize analysis regardless of the time of submission. Furthermore, if the user submitted information within a specific deadline, the analysis unit can also prioritize analysis of that information. In this way, efficient analysis is possible by determining the priority of analysis based on the time when the user submitted information.

[0105] The analysis unit can adjust the order of analysis based on the relevance of the user during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the user's information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the user's information. Furthermore, if the user submits important information, the analysis unit can also prioritize analyzing that information. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the relevance of the user's information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the user's information.

[0106] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit provides analysis results that use a lot of technical terms. Also, if the user has general knowledge, the analysis unit can provide analysis results that use less technical terms. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0107] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is feeling stressed, the suggestion unit can make a simple and easy-to-understand suggestion. If the user is relaxed, the suggestion unit can also make a suggestion that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also make a suggestion that focuses on the main points. This allows for more appropriate suggestions by adjusting the way the suggestion is expressed based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] The proposal unit can adjust the level of detail of the proposal based on the importance of the insurance when making a proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the insurance when making a proposal. For example, the proposal unit makes a detailed proposal for important insurance. The proposal unit can also make a standard proposal for general insurance. Furthermore, the proposal unit can also make a basic proposal for simple insurance. In this way, by adjusting the level of detail of the proposal based on the importance of the insurance, efficient proposals can be made.

[0109] The suggestion unit can apply different suggestion algorithms depending on the insurance category when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the insurance category when making a suggestion. For example, for health insurance, the suggestion unit applies a suggestion algorithm based on health status. For workers' compensation insurance, the suggestion unit can also apply a suggestion algorithm based on occupational risk. Furthermore, for automobile insurance, the suggestion unit can also apply a suggestion algorithm based on driving history. In this way, by applying a suggestion algorithm depending on the insurance category, more appropriate suggestions can be made.

[0110] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit improves the accuracy of the current suggestion based on the user's past suggestion results. The suggestion unit can also adjust the suggestion algorithm by referring to the user's past suggestion results. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the user's past suggestion results. In this way, the accuracy of the current suggestion is improved by referring to the user's past suggestion results.

[0111] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is feeling stressed, the suggestion unit can make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can also make a longer suggestion including detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can also make a short suggestion including only important information. This allows for more appropriate suggestions by adjusting the length of the suggestion according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The proposal unit can determine the priority of proposals based on the time of insurance submission when making a proposal. For example, the proposal unit determines the priority of proposals based on the time of insurance submission when making a proposal. For example, if the time of insurance submission is approaching, the proposal unit prioritizes proposals related to that insurance. Also, if the time of submission is further away, the proposal unit can prioritize proposals related to other insurance. Furthermore, the proposal unit can adjust the level of detail of the proposal based on the time of submission. This enables efficient proposals by determining the priority of proposals based on the time of insurance submission.

[0113] The proposal unit can adjust the order of proposals based on the relevance of the insurance when making a proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of the insurance when making a proposal. For example, if the relevance of an insurance is high, the proposal unit prioritizes proposals related to that insurance. Also, if the relevance is low, the proposal unit can prioritize proposals related to other insurance. Furthermore, the proposal unit can adjust the level of detail of the proposal based on the relevance of the insurance. This enables efficient proposals by adjusting the order of proposals based on the relevance of the insurance.

[0114] The suggestion unit can adjust the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit adjusts the use of technical terms in the proposal according to the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit can make a proposal that uses a lot of technical terms. Also, if the user has general knowledge, the suggestion unit can make a proposal that uses less technical terms. Furthermore, the suggestion unit can adjust the level of detail in the proposal according to the user's level of expertise. This allows for more appropriate suggestions by adjusting the use of technical terms in the proposal according to the user's level of expertise.

[0115] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide information in a simple and easy-to-understand manner. Furthermore, if the user is relaxed, the providing unit can provide information in a manner including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information in a manner that focuses on the main points. This allows for more appropriate information provision by adjusting the method of providing information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The providing unit can select the optimal providing method by analyzing the user's past providing history at the time of providing. For example, the providing unit selects the optimal providing method by analyzing the user's past providing history at the time of providing. The providing unit selects the optimal providing method based on the user's past providing history, for example. The providing unit can also analyze the user's past providing history and improve the providing method. Furthermore, the providing unit can adjust the level of detail of the providing by referring to the user's past providing history. In this way, the optimal providing method can be selected by analyzing the user's past providing history.

[0117] The providing unit can customize the means of provision based on the user's current living situation at the time of provision. For example, the providing unit customizes the means of provision based on the user's current living situation at the time of provision. For example, the providing unit selects the optimal means of provision based on the user's current living situation. The providing unit can also customize the means of provision according to the user's living situation. Furthermore, the providing unit can adjust the level of detail of the provision taking the user's living situation into consideration. This enables more appropriate information to be provided by customizing the means of provision based on the user's current living situation.

[0118] The providing unit can improve the method of providing information by reflecting user feedback at the time of providing the information. For example, the providing unit improves the method of providing information by reflecting user feedback at the time of providing the information. For example, the providing unit improves the method of providing information based on user feedback. The providing unit can also customize the means of providing information by reflecting user feedback. Furthermore, the providing unit can adjust the level of detail of the information provided by referring to user feedback. In this way, the method of providing information can be improved by reflecting user feedback, making it possible to provide more appropriate information.

[0119] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize information for which detailed information can be left for later. Furthermore, when the user is in a hurry, the providing unit can prioritize providing only the most important information. In this way, by determining the priority of information provision according to the user's emotions, important information can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] The providing unit can select the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, the providing unit selects the optimal providing method by taking into consideration the user's geographical location information when providing information. For example, if the user lives in a specific area, the providing unit can preferentially provide information related to that area. Also, if the user is traveling, the providing unit can preferentially provide information related to the travel destination. Furthermore, the providing unit can preferentially provide information regarding risks specific to the area based on the user's geographical location information. In this way, by taking into consideration the user's geographical location information, highly relevant information can be preferentially provided.

[0121] The providing unit can analyze the user's social media activity and suggest a means of providing the information at the time of providing. For example, the providing unit analyzes the user's social media activity and suggests a means of providing the information at the time of providing. The providing unit can provide related information, for example, based on information shared by the user on social media. The providing unit can also analyze the user's social media activity and automatically provide necessary information. Furthermore, the providing unit can provide related information by referring to the activity of the user's friends on social media. In this way, related information can be efficiently provided by analyzing the user's social media activity.

[0122] The providing unit can customize the method of providing information by reflecting the user's past feedback at the time of providing the information. For example, the providing unit customizes the method of providing information by reflecting the user's past feedback at the time of providing the information. For example, the providing unit improves the method of providing information based on feedback provided by the user in the past. The providing unit can also customize the means of providing information by reflecting the user's past feedback. Furthermore, the providing unit can customize the interface of providing information based on the user's past feedback. This allows the method of providing information to be customized by reflecting the user's past feedback, thereby enabling efficient information provision.

[0123] The subscription unit can estimate the user's emotions and adjust the subscription method based on the estimated user emotions. For example, the subscription unit can estimate the user's emotions and adjust the subscription method based on the estimated user emotions. For example, if the user is stressed, the subscription unit can provide a simple and easy-to-understand subscription method. If the user is relaxed, the subscription unit can also provide a subscription method that includes detailed information. Furthermore, if the user is in a hurry, the subscription unit can also provide a subscription method that focuses on the main points. This allows for more appropriate subscription by adjusting the subscription method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0124] The subscription unit can select the optimal subscription method by analyzing the user's past subscription history at the time of subscription. For example, the subscription unit selects the optimal subscription method by analyzing the user's past subscription history at the time of subscription. For example, the subscription unit selects the optimal subscription method based on the user's past subscription history. The subscription unit can also analyze the user's past subscription history and improve the subscription method. Furthermore, the subscription unit can adjust the level of subscription detail by referring to the user's past subscription history. In this way, the optimal subscription method can be selected by analyzing the user's past subscription history.

[0125] The subscription unit can customize the subscription method based on the user's current living situation at the time of subscription. For example, the subscription unit customizes the subscription method based on the user's current living situation at the time of subscription. For example, the subscription unit selects the optimal subscription method based on the user's current living situation. The subscription unit can also customize the subscription method according to the user's living situation. Furthermore, the subscription unit can adjust the level of subscription detail taking the user's living situation into consideration. This allows for more appropriate subscription by customizing the subscription method based on the user's current living situation.

[0126] The subscription unit can improve the subscription method by reflecting user feedback at the time of subscription. For example, the subscription unit improves the subscription method by reflecting user feedback at the time of subscription. For example, the subscription unit improves the subscription method based on user feedback. The subscription unit can also customize the subscription means by reflecting user feedback. Furthermore, the subscription unit can adjust the level of subscription detail by referring to user feedback. In this way, the subscription method can be improved by reflecting user feedback, enabling more appropriate subscription.

[0127] The subscription unit can estimate the user's emotions and determine the priority of subscriptions based on the estimated user emotions. For example, the subscription unit can estimate the user's emotions and determine the priority of subscriptions based on the estimated user emotions. For example, when the user is feeling stressed, the subscription unit can prioritize purchasing important insurance. Furthermore, when the user is relaxed, the subscription unit can prioritize purchasing insurance for which detailed information can be postponed. Furthermore, when the user is in a hurry, the subscription unit can prioritize purchasing only the most important insurance. In this way, by determining the priority of subscriptions according to the user's emotions, important insurance can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] The subscription unit can select the optimal subscription method by taking into account the user's geographical location information at the time of subscription. For example, the subscription unit selects the optimal subscription method by taking into account the user's geographical location information at the time of subscription. For example, if the user lives in a specific area, the subscription unit can prioritize subscription of insurance related to that area. Also, if the user is traveling, the subscription unit can prioritize subscription of insurance related to the user's travel destination. Furthermore, the subscription unit can prioritize subscription of insurance against risks specific to the area based on the user's geographical location information. In this way, by taking into account the user's geographical location information, it is possible to prioritize subscription of highly relevant insurance.

[0129] The subscription unit can analyze the user's social media activity at the time of subscription and suggest a subscription method. For example, at the time of subscription, the subscription unit analyzes the user's social media activity and suggests a subscription method. For example, the subscription unit can suggest relevant insurance based on information shared by the user on social media. The subscription unit can also analyze the user's social media activity and automatically suggest necessary insurance. Furthermore, the subscription unit can suggest relevant insurance based on the activity of the user's friends on social media. In this way, relevant insurance can be efficiently suggested by analyzing the user's social media activity.

[0130] The subscription unit can customize the subscription method by reflecting the user's past feedback at the time of subscription. For example, the subscription unit customizes the subscription method by reflecting the user's past feedback at the time of subscription. For example, the subscription unit improves the subscription method based on feedback provided by the user in the past. The subscription unit can also customize the subscription means by reflecting the user's past feedback. Furthermore, the subscription unit can customize the subscription interface based on the user's past feedback. In this way, the subscription method can be customized by reflecting the user's past feedback, enabling efficient subscription.

[0131] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can provide a simple and easy-to-understand monitoring method. If the user is relaxed, the monitoring unit can also provide a monitoring method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can also provide a monitoring method that focuses on the main points. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0132] The monitoring unit can select the optimal monitoring method by analyzing the user's past monitoring history during monitoring. For example, the monitoring unit selects the optimal monitoring method by analyzing the user's past monitoring history during monitoring. The monitoring unit selects the optimal monitoring method based on the user's past monitoring history, for example. The monitoring unit can also analyze the user's past monitoring history and improve the monitoring method. Furthermore, the monitoring unit can adjust the level of monitoring detail by referring to the user's past monitoring history. In this way, the optimal monitoring method can be selected by analyzing the user's past monitoring history.

[0133] The monitoring unit can customize the monitoring means based on the user's current living situation during monitoring. For example, the monitoring unit customizes the monitoring means based on the user's current living situation during monitoring. The monitoring unit selects the optimal monitoring means based on the user's current living situation, for example. The monitoring unit can also customize the monitoring means according to the user's living situation. Furthermore, the monitoring unit can adjust the level of detail of monitoring taking the user's living situation into consideration. This enables more appropriate monitoring by customizing the monitoring means based on the user's current living situation.

[0134] The monitoring unit can improve the monitoring method by reflecting user feedback during monitoring. For example, the monitoring unit improves the monitoring method by reflecting user feedback during monitoring. For example, the monitoring unit improves the monitoring method based on user feedback. The monitoring unit can also customize the monitoring means by reflecting user feedback. Furthermore, the monitoring unit can adjust the level of detail of monitoring by referring to user feedback. In this way, the monitoring method can be improved by reflecting user feedback, making more appropriate monitoring possible.

[0135] The monitoring unit can estimate the user's emotions and determine the monitoring priority based on the estimated user emotions. For example, the monitoring unit estimates the user's emotions and determines the monitoring priority based on the estimated user emotions. For example, when the user is feeling stressed, the monitoring unit prioritizes monitoring important information. Furthermore, when the user is relaxed, the monitoring unit can prioritize information for which detailed information can be postponed. Furthermore, when the user is in a hurry, the monitoring unit can prioritize monitoring only the most important information. In this way, by determining the monitoring priority according to the user's emotions, important information can be prioritized for monitoring. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0136] The monitoring unit can select an optimal monitoring method during monitoring by taking into consideration the user's geographical location information. For example, the monitoring unit selects an optimal monitoring method during monitoring by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the monitoring unit can prioritize monitoring information related to that area. Also, if the user is traveling, the monitoring unit can prioritize monitoring information related to the user's travel destination. Furthermore, the monitoring unit can prioritize monitoring information related to risks specific to the area based on the user's geographical location information. In this way, highly relevant information can be prioritized by taking into consideration the user's geographical location information.

[0137] The monitoring unit can analyze the user's social media activities during monitoring and suggest monitoring methods. For example, the monitoring unit can analyze the user's social media activities during monitoring and suggest monitoring methods. For example, the monitoring unit monitors related information based on information shared by the user on social media. The monitoring unit can also analyze the user's social media activities and automatically monitor necessary information. Furthermore, the monitoring unit can monitor related information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently monitored by analyzing the user's social media activities.

[0138] The monitoring unit can customize the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit, for example, customizes the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit improves the monitoring method, for example, based on feedback provided by the user in the past. The monitoring unit can also customize the monitoring means by reflecting the user's past feedback. Furthermore, the monitoring unit can customize the monitoring interface based on the user's past feedback. In this way, the monitoring method can be customized by reflecting the user's past feedback, enabling efficient monitoring. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, provision unit, subscription unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's information and creates a risk profile. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal insurance and coverage details. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides details of the proposed insurance to the user. The subscription unit is realized, for example, by the control unit 46A of the smart device 14 and supports the user in completing online subscription procedures. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and continuously monitors the user's information and proposes to review the insurance and coverage details as necessary. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, provision unit, subscription unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's information and creates a risk profile. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal insurance and coverage details. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides details of the proposed insurance to the user. The subscription unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports the user in completing online subscription procedures. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and continuously monitors the user's information and makes proposals to review the insurance and coverage details as necessary. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, provision unit, subscription unit, and monitoring unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives information from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's information and creates a risk profile. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes optimal insurance and coverage details. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides details of the proposed insurance to the user. The subscription unit is realized by the control unit 46A of the headset-type terminal 314 and supports the user in completing the subscription procedure online. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and continuously monitors the user's information and proposes to review the insurance and coverage details as necessary. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, provision unit, subscription unit, and monitoring unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives information from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's information and creates a risk profile. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal insurance and coverage details. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the user with details of the proposed insurance. The subscription unit is realized, for example, by the control unit 46A of the robot 414 and supports the user in completing the subscription procedure online. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and continuously monitors the user's information and proposes to review the insurance and coverage details as necessary.

[0139] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0140] The reception unit can analyze the user's lifestyle and behavioral patterns based on the information input by the user, thereby improving the accuracy of insurance proposals. For example, if the user exercises daily, the health insurance coverage can be adjusted based on that information. Also, if the user travels frequently, travel insurance proposals can be enhanced. Furthermore, if the user has a specific diet, health risks can be evaluated based on that information and appropriate insurance can be proposed. This enables more accurate insurance proposals based on the user's lifestyle and behavioral patterns.

[0141] The analysis unit can take the user's past medical history into consideration when creating a user's risk profile. For example, if the user has had a particular disease in the past, the analysis unit can evaluate their health risk based on that information. It can also predict future medical risks based on information about the treatments and surgeries the user has undergone in the past. Furthermore, it can evaluate the user's current health condition and suggest appropriate insurance based on the results of health checkups the user has undergone in the past. This makes it possible to create a more accurate risk profile based on the user's past medical history.

[0142] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, suggestions can be made during a time when the user is able to relax. Also, if the user is relaxed, the timing for making detailed suggestions can be selected. Furthermore, if the user is in a hurry, simplified suggestions can be prioritized. In this way, more appropriate suggestions can be made by adjusting the timing of suggestions according to the user's emotions.

[0143] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, information can be provided using visually easy-to-understand graphs and diagrams. If the user is relaxed, detailed text information can be provided. Furthermore, if the user is in a hurry, information can be provided in a bulleted list format that summarizes the main points. In this way, by adjusting the format of information provision according to the user's emotions, more appropriate information can be provided.

[0144] The enrollment unit can estimate the user's emotions and adjust the steps of the enrollment procedure based on the estimated user's emotions. For example, if the user is feeling stressed, the procedure can be simplified to allow the user to enroll with a minimum number of steps. Also, if the user is relaxed, the procedure including detailed explanations can be provided. Furthermore, if the user is in a hurry, only important information can be provided preferentially to allow the user to complete the procedure quickly. In this way, adjusting the steps of the enrollment procedure according to the user's emotions enables more appropriate enrollment.

[0145] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring frequency can be reduced to reduce the burden on the user. Also, if the user is relaxed, detailed monitoring can be performed. Furthermore, if the user is in a hurry, only important information can be monitored with priority. In this way, more appropriate monitoring can be achieved by adjusting the monitoring frequency according to the user's emotions.

[0146] The reception unit can analyze the user's hobbies and interests based on the information input by the user, thereby improving the accuracy of insurance proposals. For example, if the user likes outdoor activities, the reception unit can strengthen outdoor insurance proposals based on that information. Also, if the user plays a specific sport, the reception unit can propose sports insurance based on that information. Furthermore, if the user has a specific hobby, the reception unit can propose insurance related to that hobby based on that information. This enables more accurate insurance proposals based on the user's hobbies and interests.

[0147] When creating a user's risk profile, the analysis unit can evaluate in detail the risks associated with the user's occupation. For example, if the user is engaged in a high-risk occupation, the analysis unit can use that information to enhance the coverage of the worker's accident compensation insurance. Also, if the user belongs to a specific industry, the analysis unit can evaluate the risks specific to that industry and suggest appropriate insurance. Furthermore, if the user works as a freelancer, the analysis unit can use that information to suggest insurance for freelancers. This makes it possible to create a risk profile that evaluates in detail the risks associated with the user's occupation.

[0148] The proposal unit can analyze the user's past insurance enrollment history and improve the accuracy of proposals based on the results of past proposals. For example, the current proposal can be adjusted based on the type of insurance and coverage that the user has previously enrolled. The proposal unit can also analyze the reasons why the user rejected proposals in the past and improve the proposal content based on that information. Furthermore, the proposal unit can incorporate elements of proposals that the user was satisfied with in the past and reflect them in the current proposal. This makes it possible to make more accurate proposals based on the user's past insurance enrollment history.

[0149] The providing unit can provide insurance information for risks specific to a region, taking into account the user's geographical location information. For example, if the user lives in an earthquake-prone area, information on earthquake insurance related to that region can be provided. Also, if the user lives in an area with a high risk of flooding, information on flood insurance related to that region can be provided. Furthermore, if the user is traveling to a specific region, information on travel insurance related to that region can be provided. This makes it possible to provide more appropriate insurance information that takes into account the user's geographical location information.

[0150] The processing flow of the second embodiment will be briefly explained below.

[0151] Step 1: The reception unit receives information from the user. This information includes, for example, age, occupation, family composition, health status, hobbies, and past insurance history. The reception unit can also store the information entered by the user in a database and send it to the AI. Step 2: The analysis unit analyzes the information received by the reception unit and creates a risk profile for the user. The analysis unit uses AI to analyze the user's information and creates a risk profile. The risk profile includes the user's health condition, occupational risks, family composition, etc. Step 3: The proposal unit proposes optimal insurance and compensation based on the risk profile created by the analysis unit. The proposal unit uses AI to analyze the user's risk profile and proposes optimal insurance and compensation. For example, it may propose enhancing health insurance coverage for a user in good health, or it may propose enhancing workers' compensation insurance coverage for a user engaged in a high-risk occupation. Step 4: The provider provides the user with the insurance and coverage proposed by the suggestion unit. The provider provides the user with details of the proposed insurance and supports the user in completing the online enrollment process. The user can confirm the details of the proposed insurance and complete the online enrollment process. Step 5: The subscription unit allows the user to subscribe to the insurance based on the proposal provided by the provider unit. The subscription unit allows the user to easily subscribe to the proposed insurance and complete the subscription procedure online. Step 6: The monitoring unit continuously monitors the contents of the insurance purchased by the subscription unit and proposes revisions as necessary. The monitoring unit continuously monitors the user's information and proposes revisions to the insurance and coverage as necessary. For example, if the user's health condition changes, it will propose new insurance and coverage based on that information.

[0152] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0156] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0173] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0174] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0175] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0176] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0178] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0179] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0189] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0190] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0191] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0192] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0193] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0194] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0195] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0196] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0197] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0198] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0199] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0200] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0201] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0202] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0203] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0204] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0205] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0206] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0207] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0208] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0209] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0210] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0211] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0212] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0213] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0214] 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.

[0215] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0216] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0217] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0218] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0219] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0220] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0221] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0222] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0223] [Explanation of symbols]

[0224] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from a user; an analysis unit that analyzes the information received by the reception unit and creates a risk profile for the user; a proposal unit that proposes insurance and compensation details based on the risk profile created by the analysis unit; a providing unit that provides the insurance and compensation details proposed by the proposing unit to the user; a subscription unit for allowing a user to purchase insurance based on the proposal provided by the provision unit; a monitoring department that continuously monitors the contents of the insurance subscribed by the subscription department and proposes revisions as necessary; Equipped with A system characterized by:

2. The reception unit Accepts information on the user's age, occupation, family composition, health status, hobbies, and past insurance history 2. The system of claim 1.

3. The analysis unit Analyze the information received to create a risk profile for the user 2. The system of claim 1.

4. The proposal unit Users in good health are offered suggestions to improve their health insurance coverage.

2. The system of claim 1.

5. The providing unit Providing users with details of the proposed insurance and assisting them in the online enrollment process 2. The system of claim 1.

6. The subscription unit: Allowing users to easily purchase the proposed insurance 2. The system of claim 1.

7. The monitoring unit Continuously monitor user information and make recommendations to review insurance and coverage 2. The system of claim 1.

8. The monitoring unit Recommend new insurance and coverage options based on changes in the user's health status 2. The system of claim 1.

Images