System

Abstract

An object of a system according to an embodiment is to propose an optimal insurance plan corresponding to a user's needs.SOLUTION: A system includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects user information. The analysis unit analyzes the user information collected by the information collection unit. The proposal unit proposes an optimum insurance plan on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to provide insurance proposals that meet users' needs with sufficient accuracy.

[0005] The system according to the embodiment aims to propose an optimal insurance plan that meets the needs of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects user information. The analysis unit analyzes the user information collected by the information collection unit. The proposal unit proposes an optimal insurance plan based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal insurance plan that meets the needs of the user. [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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The insurance proposal system according to the embodiment of the present invention is a system that uses Google Gemini to improve the accuracy of insurance proposals that meet the needs of users. As a result, the insurance proposal system can analyze user information and propose optimal insurance plans.

[0029] An insurance proposal system according to an embodiment includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects user information. For example, the information collection unit collects information such as age, gender, occupation, income, family structure, health status, and lifestyle provided by the user. The information collection unit can also collect the user's social media activity and online behavior history. The information collection unit can also collect the user's past insurance contract history and claim history. For example, the information collection unit identifies the user's needs based on the information provided by the user. The analysis unit analyzes the user information collected by the information collection unit. For example, the analysis unit analyzes the user information using a generative AI (e.g., LLM). The analysis unit can also analyze the user information using data mining technology. The analysis unit can also analyze the user information using a machine learning algorithm. For example, the analysis unit identifies the user's needs based on information such as the user's age, gender, occupation, income, family structure, health status, and lifestyle. The proposal unit proposes an optimal insurance plan based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests life insurance, medical insurance, property insurance, etc. that best suits the user's needs. The suggestion unit can also customize insurance plans based on user feedback. The suggestion unit can also compare multiple insurance plans and suggest the optimal plan. For example, the suggestion unit selects the optimal insurance plan based on the user's needs and presents details of the plan. This allows the insurance suggestion system according to the embodiment to improve the accuracy of insurance suggestions that meet the user's needs. For example, the user can select the optimal insurance plan based on their lifestyle and future plans. Furthermore, the insurance plan customization and comparison function allows the user to select the insurance that best suits them. Furthermore, the insurance plan update function according to changes in life stages allows the user to always maintain the optimal insurance plan.

[0030] The information collection unit analyzes the user's social media activity and online behavior history to understand more detailed needs. The information collection unit, for example, analyzes the user's social media activity and extracts interests and concerns from the content of posts and reactions. For example, if the user frequently posts about travel, it can be determined that the user has a high need for travel insurance. The information collection unit also analyzes the user's online behavior history and extracts interests and concerns from website browsing history, click history, and search history. For example, if the user frequently visits health-related websites, it can be determined that the user has a high need for health insurance. This makes it possible to understand the user's needs in more detail.

[0031] The information collection unit can analyze the user's past insurance contract history and claim history to evaluate the risk profile in detail. The information collection unit, for example, analyzes the user's past insurance contract history to determine what kind of insurance the user has subscribed to. For example, if the user has subscribed to many medical insurance plans in the past, it determines that the user has a high health risk. The information collection unit also analyzes the user's claim history to determine what kind of claims have been made. For example, if the user has subscribed to many traffic accidents in the past, it determines that the risk of the user's automobile insurance is high. The information collection unit also comprehensively evaluates the user's risk profile. For example, it combines the insurance contract history and claim history to calculate the user's risk score. This allows the user's risk profile to be evaluated in detail.

[0032] The information collection unit can collect the user's health data and analyze the health condition in real time. The information collection unit, for example, analyzes heart rate and step count data collected from the user's wearable device to evaluate the health condition. For example, if the user maintains a high level of exercise on a daily basis, it determines that the health risk is low. The information collection unit also analyzes the user's sleep data and evaluates the quality of sleep. For example, if the user frequently sleeps deeply, it determines that the health condition is good. The information collection unit also analyzes the user's dietary data and evaluates nutritional balance. For example, if the user eats a balanced diet, it determines that the health risk is low. This allows the user's health condition to be analyzed in real time.

[0033] The information collection unit collects insurance policy information of the user's family and friends and can make suggestions that take social networks into consideration. The information collection unit, for example, collects insurance policy information of the user's family and evaluates the risk profile of the entire family. For example, if all family members have the same insurance, a similar insurance policy is suggested. The information collection unit also collects insurance policy information of the user's friends and makes suggestions based on the friend's insurance policy information. For example, if a friend has a specific insurance policy, a similar insurance policy is suggested. The information collection unit also analyzes the user's social network and makes suggestions based on insurance policy information within the network. For example, if many people in the network have a specific insurance policy, a similar insurance policy is suggested. This makes it possible to make suggestions that take social networks into consideration.

[0034] The analysis unit can predict a user's life events and propose an insurance plan based on them. The analysis unit predicts life events such as marriage and childbirth based on information such as the user's age, occupation, and family structure. For example, it proposes an insurance plan that assumes marriage to a single man in his 30s. The analysis unit also builds a system that predicts a user's life events and proposes an insurance plan based on them. For example, it collects data on the user's life events and predicts the life events using a prediction algorithm. The analysis unit also develops an algorithm for predicting a user's life events and proposing an insurance plan based on them. For example, it proposes an insurance plan based on the life event data. This makes it possible to propose an insurance plan based on the user's life events.

[0035] The analysis unit predicts the user's future income and can propose an optimal insurance plan from a long-term perspective. The analysis unit predicts future income based on, for example, the user's occupation, income, and past income history. For example, it proposes an insurance plan from a long-term perspective to a user who has a stable job. The analysis unit also predicts the user's future income and builds a system that proposes an optimal insurance plan from a long-term perspective. For example, it develops an income prediction algorithm based on past income data and economic indicators. The analysis unit also predicts the user's future income and develops an algorithm for proposing an optimal insurance plan from a long-term perspective. For example, it proposes an insurance plan based on income prediction data. This makes it possible to propose an insurance plan based on the user's future income prediction.

[0036] The proposal unit can compare plans from different insurance companies across the board and propose the most suitable plan. For example, the proposal unit collects plans from different insurance companies and builds a system that compares them across the board based on the user's needs. For example, it compares insurance premiums and coverage. The proposal unit also develops an algorithm for comparing plans from different insurance companies across the board and proposing the most suitable plan. For example, it compares plans based on insurance premiums and coverage. This makes it possible to compare plans from different insurance companies across the board and propose the most suitable plan.

[0037] The suggestion unit can suggest insurance plans with benefits based on the user's hobbies and interests. The suggestion unit, for example, analyzes the user's hobbies and interests and suggests insurance plans with benefits based on them. For example, to a user who likes to travel, it suggests a travel insurance plan with benefits. The suggestion unit also builds a system that suggests insurance plans with benefits based on the user's hobbies and interests. For example, it collects data on hobbies and interests and suggests plans with benefits. The suggestion unit also develops an algorithm for suggesting insurance plans with benefits based on the user's hobbies and interests. For example, it suggests plans with benefits based on data on hobbies and interests. This makes it possible to suggest insurance plans with benefits based on the user's hobbies and interests.

[0038] The suggestion unit can analyze user feedback in real time and instantly reflect customization. For example, the suggestion unit builds a system that analyzes feedback provided by users on insurance plans in real time and instantly reflects customization. For example, if a user requests a change in coverage, the suggestion unit re-proposes a plan on the spot. The suggestion unit also analyzes user feedback in real time and develops an algorithm for instantly reflecting customization. For example, the suggestion unit re-proposes a plan based on feedback data. This makes it possible to analyze user feedback in real time and instantly reflect customization.

[0039] The suggestion unit can analyze the user's past customization history and propose an optimal customization pattern. The suggestion unit, for example, analyzes the user's past customization history and builds a system that proposes an optimal customization pattern. For example, the suggestion unit makes a proposal based on customization patterns that many users have chosen in the past. The suggestion unit also analyzes the user's past customization history and develops an algorithm for proposing an optimal customization pattern. For example, the suggestion unit proposes a pattern based on customization history data. This makes it possible to analyze the user's past customization history and propose an optimal customization pattern.

[0040] The suggestion unit can provide customization options that match the user's lifestyle. For example, the suggestion unit analyzes the user's lifestyle and provides customization options based on that analysis. For example, for a user who loves the outdoors, the suggestion unit can suggest an insurance plan that is suited to outdoor activities. The suggestion unit also builds a system that provides customization options that match the user's lifestyle. For example, the suggestion unit collects lifestyle data and suggests customization options. The suggestion unit also develops an algorithm for providing customization options that match the user's lifestyle. For example, the suggestion unit suggests customization options based on the lifestyle data. This makes it possible to provide customization options that match the user's lifestyle.

[0041] The suggestion unit can provide customization options based on the user's family structure and future plans. For example, the suggestion unit analyzes the user's family structure and provides customization options based on the analysis. For example, for a user with children, the suggestion unit suggests an insurance plan that covers the children's education expenses. The suggestion unit also builds a system that provides customization options based on the user's family structure and future plans. For example, the suggestion unit collects family structure data and future plan data and suggests customization options. The suggestion unit also develops an algorithm for providing customization options based on the user's family structure and future plans. For example, the suggestion unit suggests customization options based on the family structure data and future plan data. This makes it possible to provide customization options based on the user's family structure and future plans.

[0042] The suggestion unit can analyze the user's past selection history and suggest optimal comparison criteria. The suggestion unit, for example, analyzes the user's past insurance plan selection history and builds a system that suggests optimal comparison criteria. For example, the comparison criteria are set based on the features of plans selected in the past. The suggestion unit also analyzes the user's past selection history and develops an algorithm for suggesting optimal comparison criteria. For example, the comparison criteria are set based on selection history data. This makes it possible to analyze the user's past selection history and suggest optimal comparison criteria.

[0043] The suggestion unit can analyze the user's risk profile in detail and make a comparison based on risk. The suggestion unit, for example, builds a system that analyzes the user's risk profile in detail and makes a comparison based on risk. For example, it focuses on comparing medical insurance plans for users with high health risks. The suggestion unit also develops an algorithm for analyzing the user's risk profile in detail and making a comparison based on risk. For example, it makes a comparison based on risk profile data. This makes it possible to analyze the user's risk profile in detail and make a comparison based on risk.

[0044] The suggestion unit can predict the user's future life events and make comparisons based on them. The suggestion unit predicts future life events based on information such as the user's age, occupation, and family structure, and builds a system that makes comparisons based on them. For example, it compares insurance plans that assume marriage and childbirth. The suggestion unit also develops an algorithm for predicting the user's future life events and making comparisons based on them. For example, it makes comparisons based on life event data. This makes it possible to predict the user's future life events and make comparisons based on them.

[0045] The proposal unit can monitor changes in the user's life stage in real time and update the insurance plan immediately. The proposal unit, for example, builds a system that monitors changes in the user's life stage in real time and updates the insurance plan immediately. For example, it detects events such as marriage and childbirth and updates the plan. The proposal unit also develops an algorithm for monitoring changes in the user's life stage and updating the insurance plan immediately. For example, it updates the plan based on life stage data. This makes it possible to monitor changes in the user's life stage in real time and update the insurance plan immediately.

[0046] The suggestion unit can analyze changes in the user's health condition and income and perform optimal updates based on the results. The suggestion unit, for example, analyzes changes in the user's health condition and builds a system that updates the insurance plan based on the results. For example, the plan is updated based on the results of a health check. The suggestion unit also analyzes changes in the user's income and builds a system that updates the insurance plan based on the results. For example, the plan is updated based on increases or decreases in salary or changes in income from a side job. The suggestion unit also analyzes changes in the user's health condition and income and develops an algorithm for optimal updates based on the results. For example, the plan is updated based on health data and income data. This makes it possible to analyze changes in the user's health condition and income and perform optimal updates based on the results.

[0047] The suggestion unit can propose updates to the insurance plan based on changes in the user's family structure. The suggestion unit, for example, analyzes changes in the user's family structure and builds a system that updates the insurance plan based on the changes. For example, it detects events such as marriage or childbirth and updates the plan. The suggestion unit also analyzes changes in the user's family structure and develops an algorithm for updating the insurance plan based on the analysis. For example, it updates the plan based on family structure data. This makes it possible to propose updates to the insurance plan based on changes in the user's family structure.

[0048] The suggestion unit can propose insurance plan updates from a long-term perspective based on the user's future plans. The suggestion unit, for example, analyzes the user's future plans and builds a system that updates the insurance plan based on them. For example, the plan is updated taking into account plans such as home purchase and retirement. The suggestion unit also develops an algorithm for proposing insurance plan updates from a long-term perspective based on the user's future plans. For example, the plan is updated based on future plan data. This makes it possible to propose insurance plan updates from a long-term perspective based on the user's future plans.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The information collection unit can collect data based on the user's hobbies and interests and use it to propose insurance plans. For example, if the user likes outdoor activities, it can propose insurance plans related to outdoor activities. The information collection unit can also propose insurance plans with special benefits based on the user's hobbies. For example, it can propose a travel insurance plan with special benefits to a user who likes to travel. Furthermore, the information collection unit can analyze data based on the user's hobbies and interests and predict future needs. For example, it can propose insurance plans that will be needed in the future, taking into account the possibility that hobbies will change.

[0051] The information collection unit can collect the user's health data and analyze the health condition in real time. For example, it analyzes heart rate and step count data collected from the user's wearable device to evaluate the health condition. For example, if the user maintains a high level of exercise daily, it determines that the health risk is low. The information collection unit also analyzes the user's sleep data and evaluates the quality of sleep. For example, if the user frequently sleeps deeply, it determines that the health condition is good. The information collection unit also analyzes the user's dietary data and evaluates nutritional balance. For example, if the user eats a balanced diet, it determines that the health risk is low. This allows the user's health condition to be analyzed in real time.

[0052] The information collection unit collects insurance policy information of the user's family and friends and can make suggestions that take social networks into consideration. For example, insurance policy information of the user's family is collected and the risk profile of the entire family is evaluated. For example, if all family members have the same insurance, a similar insurance policy is suggested. The information collection unit also collects insurance policy information of the user's friends and makes suggestions based on the friends' insurance policy information. For example, if a friend has a specific insurance policy, a similar insurance policy is suggested. The information collection unit also analyzes the user's social network and makes suggestions based on insurance policy information within the network. For example, if many people in the network have a specific insurance policy, a similar insurance policy is suggested. This makes it possible to make suggestions that take social networks into consideration.

[0053] The information collection unit can predict a user's life events and propose an insurance plan based on them. For example, life events such as marriage and childbirth are predicted based on information such as the user's age, occupation, and family structure. For example, an insurance plan based on marriage is proposed to a single man in his 30s. The information collection unit also builds a system that predicts a user's life events and proposes an insurance plan based on them. For example, it collects data on the user's life events and predicts the life events using a prediction algorithm. The information collection unit also develops an algorithm for predicting a user's life events and proposing an insurance plan based on them. For example, an insurance plan is proposed based on the life event data. This makes it possible to propose an insurance plan based on the user's life events.

[0054] The proposal unit can compare plans from different insurance companies across the board and propose the most suitable plan. For example, the proposal unit collects plans from different insurance companies and builds a system that compares them across the board based on the user's needs. For example, it compares insurance premiums and coverage. The proposal unit also develops an algorithm for comparing plans from different insurance companies across the board and proposing the most suitable plan. For example, it compares plans based on insurance premiums and coverage. This makes it possible to compare plans from different insurance companies across the board and propose the most suitable plan.

[0055] The suggestion unit can analyze user feedback in real time and instantly reflect customization. For example, the suggestion unit builds a system that analyzes feedback provided by users on insurance plans in real time and instantly reflects customization. For example, if a user requests a change in coverage, the suggestion unit re-proposes a plan on the spot. The suggestion unit also analyzes user feedback in real time and develops an algorithm for instantly reflecting customization. For example, the suggestion unit re-proposes a plan based on feedback data. This makes it possible to analyze user feedback in real time and instantly reflect customization.

[0056] The suggestion unit can analyze the user's past customization history and propose an optimal customization pattern. The suggestion unit, for example, analyzes the user's past customization history and builds a system that proposes an optimal customization pattern. For example, the suggestion unit makes a proposal based on customization patterns that many users have chosen in the past. The suggestion unit also analyzes the user's past customization history and develops an algorithm for proposing an optimal customization pattern. For example, the suggestion unit proposes a pattern based on customization history data. This makes it possible to analyze the user's past customization history and propose an optimal customization pattern.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information collection unit collects user information. For example, the information collection unit collects information such as age, gender, occupation, income, family structure, health status, and lifestyle provided by the user. The information collection unit may also collect the user's social media activity and online behavior history. Furthermore, the information collection unit may also collect the user's past insurance contract history and claim history. Step 2: The analysis unit analyzes the user information collected by the information collection unit. For example, the analysis unit analyzes the user information using a generative AI (e.g., LLM). The analysis unit can also analyze the user information using data mining technology or machine learning algorithms. This allows the analysis unit to understand the user's needs based on information such as the user's age, gender, occupation, income, family structure, health condition, and lifestyle. Step 3: The proposal unit proposes the optimal insurance plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes life insurance, medical insurance, property insurance, etc. that best suits the user's needs. The proposal unit can also customize the insurance plan based on user feedback. Furthermore, the proposal unit can compare multiple insurance plans and propose the optimal plan.

[0059] (Example 2) The insurance proposal system according to the embodiment of the present invention is a system that uses Google Gemini to improve the accuracy of insurance proposals that meet the needs of users. As a result, the insurance proposal system can analyze user information and propose optimal insurance plans.

[0060] An insurance proposal system according to an embodiment includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects user information. For example, the information collection unit collects information such as age, gender, occupation, income, family structure, health status, and lifestyle provided by the user. The information collection unit can also collect the user's social media activity and online behavior history. The information collection unit can also collect the user's past insurance contract history and claim history. For example, the information collection unit identifies the user's needs based on the information provided by the user. The analysis unit analyzes the user information collected by the information collection unit. For example, the analysis unit analyzes the user information using a generative AI (e.g., LLM). The analysis unit can also analyze the user information using data mining technology. The analysis unit can also analyze the user information using a machine learning algorithm. For example, the analysis unit identifies the user's needs based on information such as the user's age, gender, occupation, income, family structure, health status, and lifestyle. The proposal unit proposes an optimal insurance plan based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests life insurance, medical insurance, property insurance, etc. that best suits the user's needs. The suggestion unit can also customize insurance plans based on user feedback. The suggestion unit can also compare multiple insurance plans and suggest the optimal plan. For example, the suggestion unit selects the optimal insurance plan based on the user's needs and presents details of the plan. This allows the insurance suggestion system according to the embodiment to improve the accuracy of insurance suggestions that meet the user's needs. For example, the user can select the optimal insurance plan based on their lifestyle and future plans. Furthermore, the insurance plan customization and comparison function allows the user to select the insurance that best suits them. Furthermore, the insurance plan update function according to changes in life stages allows the user to always maintain the optimal insurance plan.

[0061] The information collection unit analyzes the user's social media activity and online behavior history to understand more detailed needs. The information collection unit, for example, analyzes the user's social media activity and extracts interests and concerns from the content of posts and reactions. For example, if the user frequently posts about travel, it can be determined that the user has a high need for travel insurance. The information collection unit also analyzes the user's online behavior history and extracts interests and concerns from website browsing history, click history, and search history. For example, if the user frequently visits health-related websites, it can be determined that the user has a high need for health insurance. This makes it possible to understand the user's needs in more detail.

[0062] The information collection unit can analyze the user's past insurance contract history and claim history to evaluate the risk profile in detail. The information collection unit, for example, analyzes the user's past insurance contract history to determine what kind of insurance the user has subscribed to. For example, if the user has subscribed to many medical insurance plans in the past, it determines that the user has a high health risk. The information collection unit also analyzes the user's claim history to determine what kind of claims have been made. For example, if the user has subscribed to many traffic accidents in the past, it determines that the risk of the user's automobile insurance is high. The information collection unit also comprehensively evaluates the user's risk profile. For example, it combines the insurance contract history and claim history to calculate the user's risk score. This allows the user's risk profile to be evaluated in detail.

[0063] The information collection unit uses the emotion estimation function to analyze the emotions the user has toward insurance and identify needs based on those emotions. For example, the information collection unit analyzes the emotions of the user when entering insurance information in real time, and determines that the user has a high interest in the insurance if the user's positive emotions are strong. For example, it detects smiling or excited facial expressions. The information collection unit also analyzes the voice of the user when entering insurance information and estimates emotions from the tone and speed of the voice. For example, it detects an excited tone of voice or fast speaking. The information collection unit also analyzes biometric data (heart rate and electrodermal activity) when the user enters insurance information and estimates emotions. For example, it estimates emotions based on heart rate fluctuations. This makes it possible to identify needs based on the user's emotions.

[0064] The information collection unit can collect the user's health data and analyze the health condition in real time. The information collection unit, for example, analyzes heart rate and step count data collected from the user's wearable device to evaluate the health condition. For example, if the user maintains a high level of exercise on a daily basis, it determines that the health risk is low. The information collection unit also analyzes the user's sleep data and evaluates the quality of sleep. For example, if the user frequently sleeps deeply, it determines that the health condition is good. The information collection unit also analyzes the user's dietary data and evaluates nutritional balance. For example, if the user eats a balanced diet, it determines that the health risk is low. This allows the user's health condition to be analyzed in real time.

[0065] The information collection unit collects insurance policy information of the user's family and friends and can make suggestions that take social networks into consideration. The information collection unit, for example, collects insurance policy information of the user's family and evaluates the risk profile of the entire family. For example, if all family members have the same insurance, a similar insurance policy is suggested. The information collection unit also collects insurance policy information of the user's friends and makes suggestions based on the friend's insurance policy information. For example, if a friend has a specific insurance policy, a similar insurance policy is suggested. The information collection unit also analyzes the user's social network and makes suggestions based on insurance policy information within the network. For example, if many people in the network have a specific insurance policy, a similar insurance policy is suggested. This makes it possible to make suggestions that take social networks into consideration.

[0066] The information collection unit can use the emotion estimation function to analyze the emotions of a user when providing information in real time and provide an interface for eliciting positive emotions. For example, the information collection unit analyzes the facial expressions and voice of the user when entering information, and displays a positive message if it detects a negative emotion. For example, it presents words of encouragement or success stories. The information collection unit also analyzes biometric data when the user enters information, and displays a positive message if it detects a negative emotion. For example, it estimates emotions based on heart rate fluctuations and displays a positive message. The information collection unit also customizes the interface when the user enters information to elicit positive emotions. For example, it changes the color or design. This makes it possible to provide an interface for eliciting positive emotions.

[0067] The analysis unit can predict a user's life events and propose an insurance plan based on them. The analysis unit predicts life events such as marriage and childbirth based on information such as the user's age, occupation, and family structure. For example, it proposes an insurance plan that assumes marriage to a single man in his 30s. The analysis unit also builds a system that predicts a user's life events and proposes an insurance plan based on them. For example, it collects data on the user's life events and predicts the life events using a prediction algorithm. The analysis unit also develops an algorithm for predicting a user's life events and proposing an insurance plan based on them. For example, it proposes an insurance plan based on the life event data. This makes it possible to propose an insurance plan based on the user's life events.

[0068] The analysis unit predicts the user's future income and can propose an optimal insurance plan from a long-term perspective. The analysis unit predicts future income based on, for example, the user's occupation, income, and past income history. For example, it proposes an insurance plan from a long-term perspective to a user who has a stable job. The analysis unit also predicts the user's future income and builds a system that proposes an optimal insurance plan from a long-term perspective. For example, it develops an income prediction algorithm based on past income data and economic indicators. The analysis unit also predicts the user's future income and develops an algorithm for proposing an optimal insurance plan from a long-term perspective. For example, it proposes an insurance plan based on income prediction data. This makes it possible to propose an insurance plan based on the user's future income prediction.

[0069] The proposal unit can compare plans from different insurance companies across the board and propose the most suitable plan. For example, the proposal unit collects plans from different insurance companies and builds a system that compares them across the board based on the user's needs. For example, it compares insurance premiums and coverage. The proposal unit also develops an algorithm for comparing plans from different insurance companies across the board and proposing the most suitable plan. For example, it compares plans based on insurance premiums and coverage. This makes it possible to compare plans from different insurance companies across the board and propose the most suitable plan.

[0070] The suggestion unit can suggest insurance plans with benefits based on the user's hobbies and interests. The suggestion unit, for example, analyzes the user's hobbies and interests and suggests insurance plans with benefits based on them. For example, to a user who likes to travel, it suggests a travel insurance plan with benefits. The suggestion unit also builds a system that suggests insurance plans with benefits based on the user's hobbies and interests. For example, it collects data on hobbies and interests and suggests plans with benefits. The suggestion unit also develops an algorithm for suggesting insurance plans with benefits based on the user's hobbies and interests. For example, it suggests plans with benefits based on data on hobbies and interests. This makes it possible to suggest insurance plans with benefits based on the user's hobbies and interests.

[0071] The suggestion unit uses the emotion estimation function to analyze the user's emotions in real time when selecting an insurance plan, and can support the optimal selection. For example, the suggestion unit analyzes the user's emotions in real time when selecting an insurance plan, and preferentially suggests plans with strong positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the user's emotions when selecting an insurance plan, and builds a system that supports the optimal selection. For example, it analyzes emotions using an emotion recognition algorithm and suggests plans. The suggestion unit also analyzes the user's emotions when selecting an insurance plan, and develops an algorithm to support the optimal selection. For example, it suggests plans based on emotion data. This makes it possible to analyze the user's emotions in real time when selecting an insurance plan, and support the optimal selection.

[0072] The suggestion unit can analyze user feedback in real time and instantly reflect customization. For example, the suggestion unit builds a system that analyzes feedback provided by users on insurance plans in real time and instantly reflects customization. For example, if a user requests a change in coverage, the suggestion unit re-proposes a plan on the spot. The suggestion unit also analyzes user feedback in real time and develops an algorithm for instantly reflecting customization. For example, the suggestion unit re-proposes a plan based on feedback data. This makes it possible to analyze user feedback in real time and instantly reflect customization.

[0073] The suggestion unit can analyze the user's past customization history and propose an optimal customization pattern. The suggestion unit, for example, analyzes the user's past customization history and builds a system that proposes an optimal customization pattern. For example, the suggestion unit makes a proposal based on customization patterns that many users have chosen in the past. The suggestion unit also analyzes the user's past customization history and develops an algorithm for proposing an optimal customization pattern. For example, the suggestion unit proposes a pattern based on customization history data. This makes it possible to analyze the user's past customization history and propose an optimal customization pattern.

[0074] The suggestion unit can use the emotion estimation function to analyze the emotions the user has regarding customization and suggest optimal customization based on the emotions. The suggestion unit, for example, analyzes the emotions the user has regarding customization in real time and preferentially suggests customization that reflects strong positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the emotions the user has regarding customization and builds a system that suggests optimal customization based on the emotions. For example, it uses an emotion recognition algorithm to analyze emotions and suggests customization. The suggestion unit also analyzes the emotions the user has regarding customization and develops an algorithm for suggesting optimal customization based on emotions. For example, it suggests customization based on emotion data. This makes it possible to analyze the emotions the user has regarding customization and suggest optimal customization based on emotions.

[0075] The suggestion unit can provide customization options that match the user's lifestyle. For example, the suggestion unit analyzes the user's lifestyle and provides customization options based on that analysis. For example, for a user who loves the outdoors, the suggestion unit can suggest an insurance plan that is suited to outdoor activities. The suggestion unit also builds a system that provides customization options that match the user's lifestyle. For example, the suggestion unit collects lifestyle data and suggests customization options. The suggestion unit also develops an algorithm for providing customization options that match the user's lifestyle. For example, the suggestion unit suggests customization options based on the lifestyle data. This makes it possible to provide customization options that match the user's lifestyle.

[0076] The suggestion unit can provide customization options based on the user's family structure and future plans. For example, the suggestion unit analyzes the user's family structure and provides customization options based on the analysis. For example, for a user with children, the suggestion unit suggests an insurance plan that covers the children's education expenses. The suggestion unit also builds a system that provides customization options based on the user's family structure and future plans. For example, the suggestion unit collects family structure data and future plan data and suggests customization options. The suggestion unit also develops an algorithm for providing customization options based on the user's family structure and future plans. For example, the suggestion unit suggests customization options based on the family structure data and future plan data. This makes it possible to provide customization options based on the user's family structure and future plans.

[0077] The suggestion unit uses the emotion estimation function to analyze the emotions of the user when customizing in real time and support optimal customization. For example, the suggestion unit analyzes the emotions of the user when customizing in real time and preferentially suggests customizations that reflect strong positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the emotions of the user when customizing and builds a system that supports optimal customization. For example, it analyzes emotions using an emotion recognition algorithm and suggests customizations. The suggestion unit also analyzes the emotions of the user when customizing and develops an algorithm to support optimal customization. For example, it suggests customizations based on emotion data. This makes it possible to analyze the emotions of the user when customizing in real time and support optimal customization.

[0078] The suggestion unit can analyze the user's past selection history and suggest optimal comparison criteria. The suggestion unit, for example, analyzes the user's past insurance plan selection history and builds a system that suggests optimal comparison criteria. For example, the comparison criteria are set based on the features of plans selected in the past. The suggestion unit also analyzes the user's past selection history and develops an algorithm for suggesting optimal comparison criteria. For example, the comparison criteria are set based on selection history data. This makes it possible to analyze the user's past selection history and suggest optimal comparison criteria.

[0079] The suggestion unit can analyze the user's risk profile in detail and make a comparison based on risk. The suggestion unit, for example, builds a system that analyzes the user's risk profile in detail and makes a comparison based on risk. For example, it focuses on comparing medical insurance plans for users with high health risks. The suggestion unit also develops an algorithm for analyzing the user's risk profile in detail and making a comparison based on risk. For example, it makes a comparison based on risk profile data. This makes it possible to analyze the user's risk profile in detail and make a comparison based on risk.

[0080] The suggestion unit uses the emotion estimation function to analyze the emotions felt by the user regarding the comparison results and can provide optimal comparison results based on the emotions. The suggestion unit, for example, analyzes the emotions felt by the user regarding the comparison results in real time and preferentially provides comparison results with stronger positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the emotions felt by the user regarding the comparison results and builds a system that provides optimal comparison results based on the emotions. For example, it analyzes emotions using an emotion recognition algorithm and provides comparison results. The suggestion unit also analyzes the emotions felt by the user regarding the comparison results and develops an algorithm for providing optimal comparison results based on the emotions. For example, it provides comparison results based on emotion data. This makes it possible to analyze the emotions felt by the user regarding the comparison results and provide optimal comparison results based on the emotions.

[0081] The suggestion unit can predict the user's future life events and make comparisons based on them. The suggestion unit predicts future life events based on information such as the user's age, occupation, and family structure, and builds a system that makes comparisons based on them. For example, it compares insurance plans that assume marriage and childbirth. The suggestion unit also develops an algorithm for predicting the user's future life events and making comparisons based on them. For example, it makes comparisons based on life event data. This makes it possible to predict the user's future life events and make comparisons based on them.

[0082] The suggestion unit uses the emotion estimation function to analyze the emotion a user has when making a comparison in real time, and can support optimal comparison. For example, the suggestion unit analyzes the emotion a user has when making a comparison in real time, and preferentially provides comparison results with a stronger positive emotion. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the emotion a user has when making a comparison, and builds a system that supports optimal comparison. For example, it analyzes the emotion using an emotion recognition algorithm, and provides the comparison results. The suggestion unit also analyzes the emotion a user has when making a comparison, and develops an algorithm to support optimal comparison. For example, it provides the comparison results based on the emotion data. This allows the emotion a user has when making a comparison to be analyzed in real time, and can support optimal comparison.

[0083] The proposal unit can monitor changes in the user's life stage in real time and update the insurance plan immediately. The proposal unit, for example, builds a system that monitors changes in the user's life stage in real time and updates the insurance plan immediately. For example, it detects events such as marriage and childbirth and updates the plan. The proposal unit also develops an algorithm for monitoring changes in the user's life stage and updating the insurance plan immediately. For example, it updates the plan based on life stage data. This makes it possible to monitor changes in the user's life stage in real time and update the insurance plan immediately.

[0084] The suggestion unit can analyze changes in the user's health condition and income and perform optimal updates based on the results. The suggestion unit, for example, analyzes changes in the user's health condition and builds a system that updates the insurance plan based on the results. For example, the plan is updated based on the results of a health check. The suggestion unit also analyzes changes in the user's income and builds a system that updates the insurance plan based on the results. For example, the plan is updated based on increases or decreases in salary or changes in income from a side job. The suggestion unit also analyzes changes in the user's health condition and income and develops an algorithm for optimal updates based on the results. For example, the plan is updated based on health data and income data. This makes it possible to analyze changes in the user's health condition and income and perform optimal updates based on the results.

[0085] The suggestion unit can use the emotion estimation function to analyze the emotions a user has regarding insurance plan renewal and suggest an optimal renewal based on the emotions. The suggestion unit, for example, analyzes the emotions a user has regarding insurance plan renewal in real time and prioritizes renewing plans that reflect strong positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the emotions a user has regarding insurance plan renewal and builds a system that suggests an optimal renewal based on the emotions. For example, it uses an emotion recognition algorithm to analyze emotions and suggests a renewal plan. The suggestion unit also develops an algorithm for analyzing the emotions a user has regarding insurance plan renewal and suggesting an optimal renewal based on the emotions. For example, it suggests a renewal plan based on the emotion data. This makes it possible to analyze the emotions a user has regarding insurance plan renewal and suggest an optimal renewal based on the emotions.

[0086] The suggestion unit can propose updates to the insurance plan based on changes in the user's family structure. The suggestion unit, for example, analyzes changes in the user's family structure and builds a system that updates the insurance plan based on the changes. For example, it detects events such as marriage or childbirth and updates the plan. The suggestion unit also analyzes changes in the user's family structure and develops an algorithm for updating the insurance plan based on the analysis. For example, it updates the plan based on family structure data. This makes it possible to propose updates to the insurance plan based on changes in the user's family structure.

[0087] The suggestion unit can propose insurance plan updates from a long-term perspective based on the user's future plans. The suggestion unit, for example, analyzes the user's future plans and builds a system that updates the insurance plan based on them. For example, the plan is updated taking into account plans such as home purchase and retirement. The suggestion unit also develops an algorithm for proposing insurance plan updates from a long-term perspective based on the user's future plans. For example, the plan is updated based on future plan data. This makes it possible to propose insurance plan updates from a long-term perspective based on the user's future plans.

[0088] The suggestion unit uses the emotion estimation function to analyze the user's emotions in real time when renewing their insurance plan and support optimal renewal. The suggestion unit, for example, analyzes the user's emotions in real time when renewing their insurance plan and prioritizes proposing renewal plans that reflect strong positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the user's emotions when renewing their insurance plan and builds a system that supports optimal renewal. For example, it analyzes emotions using an emotion recognition algorithm and proposes a renewal plan. The suggestion unit also analyzes the user's emotions when renewing their insurance plan and develops an algorithm to support optimal renewal. For example, it proposes a renewal plan based on the emotion data. This makes it possible to analyze the user's emotions in real time when renewing their insurance plan and support optimal renewal.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The information collection unit can collect data based on the user's hobbies and interests and use it to propose insurance plans. For example, if the user likes outdoor activities, it can propose insurance plans related to outdoor activities. The information collection unit can also propose insurance plans with special benefits based on the user's hobbies. For example, it can propose a travel insurance plan with special benefits to a user who likes to travel. Furthermore, the information collection unit can analyze data based on the user's hobbies and interests and predict future needs. For example, it can propose insurance plans that will be needed in the future, taking into account the possibility that hobbies will change.

[0091] The information collection unit can collect the user's health data and analyze the health condition in real time. For example, it analyzes heart rate and step count data collected from the user's wearable device to evaluate the health condition. For example, if the user maintains a high level of exercise daily, it determines that the health risk is low. The information collection unit also analyzes the user's sleep data and evaluates the quality of sleep. For example, if the user frequently sleeps deeply, it determines that the health condition is good. The information collection unit also analyzes the user's dietary data and evaluates nutritional balance. For example, if the user eats a balanced diet, it determines that the health risk is low. This allows the user's health condition to be analyzed in real time.

[0092] The information collection unit collects insurance policy information of the user's family and friends and can make suggestions that take social networks into consideration. For example, insurance policy information of the user's family is collected and the risk profile of the entire family is evaluated. For example, if all family members have the same insurance, a similar insurance policy is suggested. The information collection unit also collects insurance policy information of the user's friends and makes suggestions based on the friends' insurance policy information. For example, if a friend has a specific insurance policy, a similar insurance policy is suggested. The information collection unit also analyzes the user's social network and makes suggestions based on insurance policy information within the network. For example, if many people in the network have a specific insurance policy, a similar insurance policy is suggested. This makes it possible to make suggestions that take social networks into consideration.

[0093] The information collection unit uses the emotion estimation function to analyze the emotions the user has toward insurance and identify needs based on those emotions. For example, the information collection unit analyzes the emotions of the user when entering insurance information in real time, and determines that the user has a high interest in the insurance if the user's positive emotions are strong. For example, it detects smiling or excited facial expressions. The information collection unit also analyzes the voice of the user when entering insurance information and estimates emotions from the tone and speed of the voice. For example, it detects an excited tone of voice or fast speaking. The information collection unit also analyzes biometric data (heart rate and electrodermal activity) when the user enters insurance information and estimates emotions. For example, it estimates emotions based on heart rate fluctuations. This makes it possible to identify needs based on the user's emotions.

[0094] The information collection unit can predict a user's life events and propose an insurance plan based on them. For example, life events such as marriage and childbirth are predicted based on information such as the user's age, occupation, and family structure. For example, an insurance plan based on marriage is proposed to a single man in his 30s. The information collection unit also builds a system that predicts a user's life events and proposes an insurance plan based on them. For example, it collects data on the user's life events and predicts the life events using a prediction algorithm. The information collection unit also develops an algorithm for predicting a user's life events and proposing an insurance plan based on them. For example, an insurance plan is proposed based on the life event data. This makes it possible to propose an insurance plan based on the user's life events.

[0095] The proposal unit can compare plans from different insurance companies across the board and propose the most suitable plan. For example, the proposal unit collects plans from different insurance companies and builds a system that compares them across the board based on the user's needs. For example, it compares insurance premiums and coverage. The proposal unit also develops an algorithm for comparing plans from different insurance companies across the board and proposing the most suitable plan. For example, it compares plans based on insurance premiums and coverage. This makes it possible to compare plans from different insurance companies across the board and propose the most suitable plan.

[0096] The suggestion unit uses the emotion estimation function to analyze the user's emotions in real time when selecting an insurance plan, and can support the optimal selection. For example, the suggestion unit analyzes the user's emotions in real time when selecting an insurance plan, and preferentially suggests plans with strong positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the user's emotions when selecting an insurance plan, and builds a system that supports the optimal selection. For example, it analyzes emotions using an emotion recognition algorithm and suggests plans. The suggestion unit also analyzes the user's emotions when selecting an insurance plan, and develops an algorithm to support the optimal selection. For example, it suggests plans based on emotion data. This makes it possible to analyze the user's emotions in real time when selecting an insurance plan, and support the optimal selection.

[0097] The suggestion unit can analyze user feedback in real time and instantly reflect customization. For example, the suggestion unit builds a system that analyzes feedback provided by users on insurance plans in real time and instantly reflects customization. For example, if a user requests a change in coverage, the suggestion unit re-proposes a plan on the spot. The suggestion unit also analyzes user feedback in real time and develops an algorithm for instantly reflecting customization. For example, the suggestion unit re-proposes a plan based on feedback data. This makes it possible to analyze user feedback in real time and instantly reflect customization.

[0098] The suggestion unit can use the emotion estimation function to analyze the emotions the user has regarding customization and suggest optimal customization based on the emotions. The suggestion unit, for example, analyzes the emotions the user has regarding customization in real time and preferentially suggests customization that reflects strong positive emotions. For example, it detects smiling or excited facial expressions. The suggestion unit also analyzes the emotions the user has regarding customization and builds a system that suggests optimal customization based on the emotions. For example, it uses an emotion recognition algorithm to analyze emotions and suggests customization. The suggestion unit also analyzes the emotions the user has regarding customization and develops an algorithm for suggesting optimal customization based on emotions. For example, it suggests customization based on emotion data. This makes it possible to analyze the emotions the user has regarding customization and suggest optimal customization based on emotions.

[0099] The suggestion unit can analyze the user's past customization history and propose an optimal customization pattern. The suggestion unit, for example, analyzes the user's past customization history and builds a system that proposes an optimal customization pattern. For example, the suggestion unit makes a proposal based on customization patterns that many users have chosen in the past. The suggestion unit also analyzes the user's past customization history and develops an algorithm for proposing an optimal customization pattern. For example, the suggestion unit proposes a pattern based on customization history data. This makes it possible to analyze the user's past customization history and propose an optimal customization pattern.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The information collection unit collects user information. For example, the information collection unit collects information such as age, gender, occupation, income, family structure, health status, and lifestyle provided by the user. The information collection unit may also collect the user's social media activity and online behavior history. Furthermore, the information collection unit may also collect the user's past insurance contract history and claim history. Step 2: The analysis unit analyzes the user information collected by the information collection unit. For example, the analysis unit analyzes the user information using a generative AI (e.g., LLM). The analysis unit can also analyze the user information using data mining technology or machine learning algorithms. This allows the analysis unit to understand the user's needs based on information such as the user's age, gender, occupation, income, family structure, health condition, and lifestyle. Step 3: The proposal unit proposes the optimal insurance plan based on the results of the analysis by the analysis unit. For example, the proposal unit proposes life insurance, medical insurance, property insurance, etc. that best suits the user's needs. The proposal unit can also customize the insurance plan based on user feedback. Furthermore, the proposal unit can compare multiple insurance plans and propose the optimal plan.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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. [Explanation of symbols]

[0169] 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. an information collection unit that collects user information; an analysis unit that analyzes the user information collected by the information collection unit; a proposal unit that proposes an optimal insurance plan based on the results of the analysis by the analysis unit. A system characterized by:

2. The information collecting unit Using emotion estimation, we analyze users' feelings about insurance and identify their needs based on their emotions.

2. The system of claim 1.

3. The information collecting unit Collecting user health data and analyzing health status in real time 2. The system of claim 1.

4. The analysis unit Predicting users' life events and proposing insurance plans based on them 2. The system of claim 1.

5. The proposal unit Compare plans from different insurance companies across the board and propose the most suitable plan 2. The system of claim 1.

6. The proposal unit Analyze user feedback in real time and instantly reflect customizations 2. The system of claim 1.

7. The proposal unit Analyzes the user's past selection history and suggests optimal comparison criteria 2. The system of claim 1.

8. The proposal unit Monitor changes in users' life stages in real time and instantly update insurance plans 2. The system of claim 1.

Images