system

Abstract

The system according to the embodiment aims to provide personalized insurance products based on an individual's everyday risks. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a discount unit, and a point granting unit. The collection unit collects data on an individual's daily risks. The analysis unit analyzes the data collected by the collection unit and predicts the risk for each individual. The provision unit offers insurance products based on the risk predicted by the analysis unit. The discount unit discounts insurance premiums for insurance products offered by the provision unit. The point granting unit grants points for insurance products offered by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide personalized insurance products based on an individual's everyday risks, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized insurance products based on an individual's everyday risks. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a discount unit, and a point granting unit. The collection unit collects data on an individual's daily risks. The analysis unit analyzes the data collected by the collection unit and predicts the risk for each individual. The provision unit provides insurance products based on the risks predicted by the analysis unit. The discount unit discounts insurance premiums for insurance products provided by the provision unit. The point granting unit grants points for insurance products provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized insurance products based on an individual's everyday risks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An insurance system according to an embodiment of the present invention collects data on an individual's daily risks, analyzes it using a generation AI, and provides insurance products tailored to each individual's risk. The insurance system collects data on an individual's daily risks, analyzes it using a generation AI, and predicts each individual's risk, thereby providing personalized insurance products at appropriate premiums. The insurance system also provides a mechanism for rewarding individuals with insurance premium discounts or points in exchange for providing sensitive data they possess. For example, the insurance system uses a wearable device to collect data on an individual's daily risks, including heart rate, number of steps, sleep patterns, and exercise volume. This data is input into a generation AI. The insurance system then analyzes the collected data using the generation AI to predict each individual's risk. The generation AI then determines each individual's health risk and accident risk based on past data and statistical information. For example, if heart rate fluctuations or a decrease in exercise volume are observed, an increase in health risk is predicted. The insurance system then provides insurance products tailored to each individual's risk based on the analysis results of the generation AI. For example, if a high health risk is predicted, an insurance product specialized in health management is proposed. Furthermore, if a high accident risk is predicted, an insurance product specialized for accident compensation is proposed. Furthermore, the insurance system provides a mechanism for rewarding individuals for providing their sensitive data in the form of discounts on insurance premiums or points. For example, by providing data via a wearable device, individuals can receive discounts on insurance premiums. Points are also awarded in accordance with the data provided, which can be used to pay insurance premiums. This allows the insurance system to provide personalized insurance products based on an individual's everyday risks, and by providing discounts on insurance premiums and awarding points, it is possible to collect more data and make more accurate risk predictions. This allows the insurance system to provide personalized insurance products based on an individual's everyday risks, and by providing discounts on insurance premiums and awarding points, it is possible to collect more data and make more accurate risk predictions. For example, by providing data via a wearable device, individuals can receive discounts on insurance premiums.In addition, points are awarded in accordance with the data provided, and can be used to pay insurance premiums. This increases the incentive for individuals to provide data by receiving discounts on insurance premiums and point rewards, leading to the collection of more data. The collected data is used for analysis by generative AI, improving the accuracy of individual risk predictions. This creates a cycle in which personalized insurance products are provided at reasonable premiums.

[0029] The insurance system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a discount unit, and a point-granting unit. The collection unit collects data related to an individual's daily risks. Examples of the data related to an individual's daily risks include, but are not limited to, heart rate, step count, sleep patterns, and exercise volume. For example, the collection unit measures heart rate using a wearable device. The collection unit can also count steps using a smartphone app. The collection unit can also record sleep patterns using a sleep tracker. For example, the collection unit monitors heart rate in real time and issues an alert if an abnormality is detected. The collection unit can also record daily step counts using a pedometer and evaluate exercise volume. The collection unit can also evaluate sleep quality using the sleep tracker and suggest improvements. The analysis unit uses a generation AI to analyze the data collected by the collection unit and predict individual risks. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the generation AI determines an individual's health risk and accident risk based on past data and statistical information. The analysis unit can also analyze data in real time and immediately grasp changes in risk. The analysis unit can also use the generation AI to analyze individual behavioral patterns and improve the accuracy of risk prediction. For example, the generation AI predicts an increase in health risk when heart rate fluctuations or a decrease in exercise volume are observed. The generation AI can also predict accident risk based on past accident data. The provision unit offers insurance products based on the risks predicted by the analysis unit. Insurance products include, but are not limited to, insurance products specialized in health management and insurance products specialized in accident compensation. For example, if the provision unit predicts a high health risk, it can offer an insurance product specialized in health management. If the provision unit predicts a high accident risk, it can offer an insurance product specialized in accident compensation. The provision unit can also offer customized insurance products according to individual needs. For example, the provision unit can offer discounts on health checkups and fitness programs to individuals with high health risks.The providing unit may also provide, to individuals with a high risk of accidents, compensation for medical expenses in the event of an accident and rehabilitation support after an accident. The discounting unit discounts insurance premiums for insurance products provided by the providing unit. Discounts may be based, for example, on the frequency of data provision and the degree of risk reduction, but are not limited to such examples. For example, the discounting unit discounts insurance premiums when an individual provides data through a wearable device. The discounting unit may also discount insurance premiums when an individual's health risk is reduced. The discounting unit may also adjust the discount rate depending on the frequency of data provision. For example, the discounting unit may apply a higher discount rate to individuals who provide data daily. The discounting unit may also apply a lower discount rate to individuals who provide data once a week. The point assigning unit assigns points for insurance products provided by the providing unit. Points may be assigned, for example, based on the quantity and quality of data provided, but are not limited to such examples. For example, the point assigning unit assigns points when an individual provides data through a wearable device. The point assigning unit may also assign points according to the quality of data provided. Furthermore, the point allocating unit can use the allocated points to pay insurance premiums. For example, the point allocating unit allows an individual to use the allocated points to pay part of an insurance premium. The point allocating unit can also allow an individual to use the points to purchase insurance products or services. As a result, the insurance system according to the embodiment provides personalized insurance products based on an individual's daily risks, and by discounting insurance premiums and allocating points, it is possible to collect more data and make highly accurate risk predictions.

[0030] The collection unit can collect data on heart rate, number of steps, sleep patterns, and amount of exercise. The collection unit, for example, uses a wearable device to measure heart rate. For example, the collection unit monitors heart rate in real time using a smartwatch. The collection unit can also use a smartphone app to count steps. For example, the collection unit counts steps using an acceleration sensor on the smartphone. The collection unit can also use a sleep tracker to record sleep patterns. For example, the collection unit evaluates sleep quality using the sleep tracker and suggests improvements. The collection unit can also use a fitness tracker to measure exercise volume. For example, the collection unit uses the fitness tracker to record exercise volume and evaluate the effects of exercise. This allows for the collection of detailed data on an individual's daily risks, thereby improving the accuracy of risk prediction. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from the wearable device into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and predict individual health risks and accident risks. The analysis unit, for example, analyzes the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can use a generation AI to predict individual health risks based on past data and statistical information. The analysis unit can also use the generation AI to analyze the collected data in real time and immediately grasp risk fluctuations. The analysis unit can also use the generation AI to analyze individual behavioral patterns and improve the accuracy of risk predictions. For example, the analysis unit predicts increased health risks when heart rate fluctuations or decreased exercise volume are observed. The analysis unit can also predict accident risk based on past accident data. This allows for accurate prediction of individual risks and the provision of appropriate insurance products. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI perform risk predictions.

[0032] The provision unit can provide an insurance product specialized for health management when a high health risk is predicted. For example, the provision unit can provide an insurance product specialized for health management when a high health risk is predicted. For example, the provision unit can offer discounts on health checkups and fitness programs. The provision unit can also provide advice and support regarding health management to individuals with high health risks. The provision unit can also provide information regarding health management to individuals with high health risks. For example, the provision unit can provide news and articles related to health management to individuals with high health risks. The provision unit can also provide information about events and seminars related to health management to individuals with high health risks. This allows appropriate insurance coverage to be achieved by providing insurance products according to individuals' health risks. Some or all of the above-mentioned processing in the provision unit can be performed, for example, using AI or without AI. For example, the provision unit can use generation AI to propose insurance products specialized for health management to individuals with high health risks.

[0033] The provision unit can provide an insurance product specialized for accident compensation when a high accident risk is predicted. For example, the provision unit provides an insurance product specialized for accident compensation when a high accident risk is predicted. For example, the provision unit provides medical expense compensation in the event of an accident and rehabilitation support after an accident. The provision unit can also provide advice and support regarding accident prevention to individuals with a high accident risk. The provision unit can also provide information regarding accident prevention to individuals with a high accident risk. For example, the provision unit can provide news and articles regarding accident prevention to individuals with a high accident risk. The provision unit can also provide information about events and seminars related to accident prevention to individuals with a high accident risk. This allows appropriate insurance coverage to be achieved by providing insurance products according to each individual's accident risk. Some or all of the above-described processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can use generation AI to propose an insurance product specialized for accident compensation to individuals with a high accident risk.

[0034] The discount unit can provide a discount on insurance premiums when an individual provides data through a wearable device. The discount unit, for example, provides a discount on insurance premiums when an individual provides data through a wearable device. For example, the discount unit provides a discount on insurance premiums when an individual provides data daily. The discount unit can also provide a discount on insurance premiums when an individual's health risk is reduced. The discount unit can also adjust the discount rate depending on the frequency of data provision. For example, the discount unit can apply a higher discount rate to individuals who provide data daily. The discount unit can also apply a lower discount rate to individuals who provide data once a week. This allows for discounts on insurance premiums as an incentive for data provision, thereby collecting more data. Some or all of the above-described processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can use generation AI to provide discounts on insurance premiums based on the frequency and quality of data provision.

[0035] The point assigning unit assigns points in accordance with data provided, and the points can be used to pay insurance premiums. The point assigning unit assigns points, for example, when an individual provides data through a wearable device. For example, the point assigning unit assigns points when an individual provides data daily. The point assigning unit can also assign points based on the quality of data provided. The point assigning unit can also use the assigned points to pay insurance premiums. For example, the point assigning unit allows an individual to use the assigned points to pay part of insurance premiums. The point assigning unit can also allow an individual to use the points to purchase insurance products or services. This allows more data to be collected by assigning points as an incentive for data provision. Some or all of the above-described processing in the point assigning unit may be performed using, for example, AI, or may be performed without AI. For example, the point assigning unit can assign points based on the frequency and quality of data provided using a generation AI.

[0036] The system may be equipped with a mechanism for protecting privacy at each stage of data collection, analysis, and provision. The system may be equipped with a mechanism for protecting privacy at each stage of data collection, analysis, and provision. For example, the system may encrypt data when collecting it. The system may also perform access control when analyzing it. The system may also anonymize data when providing it. For example, the system may encrypt data when collecting it to prevent third parties from accessing it. The system may also perform access control when analyzing it to ensure that only specific users can access the data. The system may also anonymize data when providing it to prevent individuals from being identified. This protects privacy at each stage of data collection, analysis, and provision, thereby improving users' sense of security. Some or all of the above-mentioned processing in the system may be performed using, or without, AI. For example, the system may use generative AI to encrypt and anonymize data.

[0037] The collection unit can analyze the user's past health data and select an appropriate data collection method. The collection unit, for example, uses statistical analysis techniques to analyze the user's past health data. For example, the collection unit can analyze past heart rate data and, if an abnormality is detected, increase the frequency of heart rate collection. The collection unit can also analyze past sleep patterns and, if sleep quality is low, strengthen sleep data collection. The collection unit can also analyze past exercise amount data and, if insufficient exercise is detected, increase exercise data collection. In this way, by analyzing the user's past health data, the optimal data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past health data into a generation AI and have the generation AI select a data collection method.

[0038] The collection unit can filter data based on the user's current living situation and activity level when collecting data. The collection unit, for example, uses a sensor to understand the user's current living situation and activity level. For example, when the user is at work, the collection unit can moderate data collection and concentrate data collection during breaks. Furthermore, when the user is exercising, the collection unit can strengthen collection of exercise data and refrain from collecting other data. Furthermore, when the user is sleeping, the collection unit can prioritize collection of sleep data and refrain from collecting other data. This allows for more accurate data collection by adjusting data collection according to the user's living situation and activity level. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by a sensor into a generation AI and have the generation AI perform data collection filtering.

[0039] The collection unit can select the optimal collection means depending on the user's input method when collecting data. The collection unit, for example, uses technology to detect the user's input method in order to select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Also, if the user prefers text input, the collection unit can prioritize collecting text data. Also, if the user prefers image input, the collection unit can prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can detect the user's input method, input that information to the generation AI, and have the generation AI select the optimal collection means.

[0040] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, uses GPS technology to acquire the user's geographical location information. For example, when the user is at home, the collection unit can prioritize collecting indoor environment data. Furthermore, when the user is out, the collection unit can prioritize collecting exercise data. Furthermore, when the user is traveling, the collection unit can prioritize collecting environmental data of the travel destination. This improves the usefulness of the data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI determine the priority of highly relevant data.

[0041] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, uses text analysis technology to analyze the user's social media activity. For example, if the user posts about exercise on social media, the collection unit can prioritize collecting exercise data. Furthermore, if the user posts about food on social media, the collection unit can prioritize collecting food data. Furthermore, if the user posts about travel on social media, the collection unit can prioritize collecting travel data. This improves the usefulness of the data by collecting related data based on the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the content of the social media posts into a generation AI and cause the generation AI to collect related data.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, uses a questionnaire technique to collect the user's past feedback. For example, if the user has requested collection of heart rate data in the past, the collection unit can enhance collection of heart rate data. Furthermore, if the user has requested collection of sleep data in the past, the collection unit can enhance collection of sleep data. Furthermore, if the user has requested collection of exercise data in the past, the collection unit can enhance collection of exercise data. This improves the accuracy of data collection by customizing the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the survey results into the generation AI and cause the generation AI to customize the collection method.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, uses statistical analysis techniques to evaluate the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, uses clustering technology to classify data categories. For example, the analysis unit can apply a health risk analysis algorithm to health data. The analysis unit can also apply an accident risk analysis algorithm to accident data. The analysis unit can also apply an environmental risk analysis algorithm to environmental data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and apply an appropriate analysis algorithm to the generation AI.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses database technology to collect the user's past analysis results. For example, the analysis unit analyzes the user's current health risk by referring to the user's past health risk analysis results. The analysis unit can also analyze the current accident risk by referring to the user's past accident risk analysis results. The analysis unit can also analyze the current environmental risk by referring to the user's past environmental risk analysis results. In this way, the accuracy of the current analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the accuracy of the current analysis.

[0046] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, uses timestamp technology to evaluate the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also refer to past data while placing emphasis on the most recent data. The analysis unit can also prioritize analyzing data from a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, uses correlation analysis technology to evaluate the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0048] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a questionnaire technique to evaluate the user's level of expertise. For example, the analysis unit uses a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also dynamically adjust the use of technical terms according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the survey results into a generation AI and have the generation AI adjust the use of technical terms.

[0049] The provision unit can adjust the level of detail of the insurance product to be provided based on the importance of the risk when providing the insurance product. The provision unit, for example, uses statistical analysis techniques to evaluate the importance of the risk. For example, the provision unit can provide a detailed insurance product for a risk with high importance. The provision unit can also provide a simple insurance product for a risk with low importance. The provision unit can also provide an insurance product with an appropriate level of detail for a risk with medium importance. This enables efficient provision of insurance products by adjusting the level of detail of the insurance product according to the importance of the risk. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the importance of the risk to the generation AI and cause the generation AI to adjust the level of detail of the insurance product.

[0050] The provision unit can propose different insurance products according to the risk category at the time of provision. The provision unit, for example, uses clustering technology to classify risk categories. For example, the provision unit proposes an insurance product specialized in health management for health risks. The provision unit can also propose an insurance product specialized in accident compensation for accident risks. The provision unit can also propose an insurance product specialized in environmental risks for environmental risks. In this way, by proposing an appropriate insurance product according to the risk category, it is possible to provide optimal insurance coverage for the user. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the risk category into the generation AI and cause the generation AI to propose an appropriate insurance product.

[0051] The provision unit can improve the accuracy of the insurance product provided by referring to the user's past insurance usage history when providing the insurance product. The provision unit, for example, uses database technology to collect the user's past insurance usage history. For example, the provision unit can propose a current health insurance product by referring to the user's past health insurance usage history. The provision unit can also propose a current accident insurance product by referring to the user's past accident insurance usage history. The provision unit can also propose a current environmental insurance product by referring to the user's past environmental insurance usage history. This improves the accuracy of the current insurance product by referring to the user's past insurance usage history. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can input the past insurance usage history into the generation AI to improve the accuracy of the current insurance product.

[0052] The provision unit can determine the priority of insurance products based on the timing of risk occurrence at the time of provision. The provision unit, for example, uses timestamp technology to evaluate the timing of risk occurrence. For example, the provision unit prioritizes providing insurance products for risks that will occur soon. The provision unit can also postpone providing insurance products for risks that will occur in the medium to long term. The provision unit can also dynamically adjust the order in which insurance products are provided depending on the timing of risk occurrence. In this way, by determining the priority of insurance products based on the timing of risk occurrence, insurance products can be provided preferentially for risks that will occur soon. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the timing of risk occurrence to a generation AI and have the generation AI determine the priority of insurance products.

[0053] The provision unit can adjust the order of insurance products based on risk relevance when providing them. The provision unit, for example, uses correlation analysis technology to evaluate risk relevance. For example, the provision unit can provide insurance products preferentially for highly relevant risks. The provision unit can also provide insurance products later for less relevant risks. The provision unit can also dynamically adjust the order of providing insurance products according to risk relevance. This enables efficient provision of insurance products by adjusting the order of insurance products based on risk relevance. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input risk relevance to a generation AI and have the generation AI adjust the order of insurance products.

[0054] The providing unit can adjust the description of the insurance product according to the user's level of expertise when providing the insurance product. The providing unit, for example, uses a questionnaire technique to evaluate the user's level of expertise. For example, if the user has specialized knowledge, the providing unit can provide a description of the insurance product that uses a lot of technical jargon. Furthermore, if the user does not have specialized knowledge, the providing unit can also provide a description of the insurance product that avoids technical jargon. The providing unit can also dynamically adjust the description of the insurance product according to the user's level of expertise. This allows the user to be provided with an insurance product that is easy to understand by adjusting the description of the insurance product according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the survey results into a generation AI and have the generation AI adjust the description of the insurance product.

[0055] When applying a discount, the discount unit can analyze the user's past data provision history and select the optimal discount method. The discount unit, for example, uses database technology to collect the user's past data provision history. For example, the discount unit can set a high discount rate if the user has provided a large amount of data in the past. The discount unit can also set a low discount rate if the user has provided a small amount of data in the past. The discount unit can also dynamically adjust the discount method based on the user's past data provision history. This allows the optimal discount to be provided to the user by selecting the optimal discount method based on the user's past data provision history. Some or all of the above-mentioned processing in the discount unit can be performed, for example, using AI or without AI. For example, the discount unit can input the past data provision history into the generation AI and have the generation AI select the optimal discount method.

[0056] The discount unit can customize the discount method based on the user's current living situation when applying a discount. The discount unit, for example, uses a questionnaire technique to evaluate the user's current living situation. For example, the discount unit can set a high discount rate if the user is in a financially difficult situation. The discount unit can also set a low discount rate if the user is financially stable. The discount unit can also dynamically adjust the discount method based on the user's current living situation. This allows the user to receive the optimal discount by customizing the discount method based on the user's current living situation. Some or all of the above-described processing in the discount unit can be performed using, for example, AI, or can be performed without using AI. For example, the discount unit can input the survey results into a generation AI and have the generation AI customize the discount method.

[0057] The discount unit can improve the discount method by reflecting user feedback when applying a discount. The discount unit, for example, uses survey technology to collect user feedback. For example, the discount unit maintains the current discount method if the user is satisfied with the discount method. The discount unit can also adjust the discount rate if the user is dissatisfied with the discount method. The discount unit can also dynamically improve the discount method based on user feedback. This makes it possible to provide the optimal discount for the user by improving the discount method based on user feedback. Some or all of the above-mentioned processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can input the feedback results to a generation AI and cause the generation AI to improve the discount method.

[0058] The discount unit can select the optimal discount method by taking into account the user's geographical location information when applying a discount. The discount unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user lives in an urban area, the discount unit can provide a discount method for urban areas. Also, if the user lives in a rural area, the discount unit can provide a discount method for rural areas. The discount unit can also dynamically adjust the discount method based on the user's geographical location information. This allows the optimal discount to be provided to the user by selecting the optimal discount method based on the user's geographical location information. Some or all of the above-described processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can input GPS data into the generation AI and have the generation AI select the optimal discount method.

[0059] The discount unit can analyze the user's social media activity and suggest discount methods when applying a discount. The discount unit, for example, uses text analysis technology to analyze the user's social media activity. For example, if the user posts about discounts on social media, the discount unit can provide relevant discount methods. Also, if the user posts about a specific product on social media, the discount unit can provide discount methods related to the product. The discount unit can also dynamically suggest discount methods based on the user's social media activity. This makes it possible to provide the optimal discount for the user by suggesting discount methods based on the user's social media activity. Some or all of the above-mentioned processing in the discount unit may be performed, for example, using AI or without AI. For example, the discount unit can input the content of the social media post into a generation AI and have the generation AI suggest discount methods.

[0060] The discount unit can customize the discount method by reflecting the user's past feedback when applying a discount. The discount unit, for example, uses survey technology to collect the user's past feedback. For example, the discount unit maintains the current discount method if the user has been satisfied with the discount method in the past. The discount unit can also adjust the discount rate if the user has been dissatisfied with the discount method in the past. The discount unit can also dynamically customize the discount method based on the user's past feedback. This makes it possible to provide the optimal discount for the user by customizing the discount method based on the user's past feedback. Some or all of the above-described processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can input the feedback results to a generation AI and have the generation AI customize the discount method.

[0061] When awarding points, the point awarding unit can analyze the user's past data provision history and select the optimal point awarding method. The point awarding unit, for example, uses database technology to collect the user's past data provision history. For example, the point awarding unit can set a high point award rate if the user has provided a large amount of data in the past. The point awarding unit can also set a low point award rate if the user has provided a small amount of data in the past. The point awarding unit can also dynamically adjust the point awarding method based on the user's past data provision history. This allows the optimal point awarding method to be selected based on the user's past data provision history, thereby providing optimal points for the user. Some or all of the above-described processing in the point awarding unit may be performed, for example, using AI or without AI. For example, the point awarding unit can input the past data provision history into a generation AI and have the generation AI select the optimal point awarding method.

[0062] The point assigning unit can customize the point assigning means based on the user's current living situation when assigning points. The point assigning unit, for example, uses a questionnaire technique to evaluate the user's current living situation. For example, the point assigning unit can set a high point assigning rate if the user is in a financially difficult situation. The point assigning unit can also set a low point assigning rate if the user is financially stable. The point assigning unit can also dynamically adjust the point assigning means based on the user's current living situation. This allows the point assigning means to be customized based on the user's current living situation, thereby providing optimal point assignment for the user. Some or all of the above-mentioned processing in the point assigning unit may be performed using, for example, AI, or may be performed without using AI. For example, the point assigning unit can input the survey results into a generation AI and have the generation AI customize the point assigning means.

[0063] The point assigning unit can improve the point assigning method by reflecting user feedback when assigning points. The point assigning unit, for example, uses a survey technique to collect user feedback. For example, if the user is satisfied with the point assigning method, the point assigning unit maintains the current point assigning method. Alternatively, if the user is dissatisfied with the point assigning method, the point assigning unit can adjust the point assigning rate. Alternatively, the point assigning unit can dynamically improve the point assigning method based on user feedback. This allows the point assigning method to be improved based on user feedback, thereby providing optimal point assignment for the user. Some or all of the above-described processing in the point assigning unit may be performed using, for example, AI, or may be performed without using AI. For example, the point assigning unit can input the feedback results to a generation AI and cause the generation AI to improve the point assigning method.

[0064] When awarding points, the point awarding unit can select the optimal point awarding method by taking into account the user's geographical location information. The point awarding unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user lives in an urban area, the point awarding unit can provide a point awarding method tailored to the urban area. Furthermore, if the user lives in a rural area, the point awarding unit can also provide a point awarding method tailored to the rural area. The point awarding unit can also dynamically adjust the point awarding method based on the user's geographical location information. This allows the optimal point awarding method to be selected based on the user's geographical location information, thereby providing the optimal point award for the user. Some or all of the above-described processing in the point awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the point awarding unit can input GPS data into a generation AI and have the generation AI select the optimal point awarding method.

[0065] When awarding points, the point awarding unit can analyze the user's social media activity and suggest a method for awarding points. The point awarding unit, for example, uses text analysis technology to analyze the user's social media activity. For example, if the user posts about points on social media, the point awarding unit can provide a related point awarding method. Furthermore, if the user posts about a specific product on social media, the point awarding unit can also provide a point awarding method related to the product. Furthermore, the point awarding unit can dynamically suggest a method for awarding points based on the user's social media activity. This allows the point awarding unit to provide optimal points for the user by suggesting a method for awarding points based on the user's social media activity. Some or all of the above-described processing in the point awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the point awarding unit can input the content of the social media post into a generation AI and have the generation AI execute the suggestion of a method for awarding points.

[0066] The point assigning unit can customize the point assigning method by reflecting the user's past feedback when assigning points. The point assigning unit, for example, uses a survey technique to collect the user's past feedback. For example, if the user has been satisfied with the point assigning method in the past, the point assigning unit maintains the current point assigning method. The point assigning unit can also adjust the point assigning rate if the user has been dissatisfied with the point assigning method in the past. The point assigning unit can also dynamically customize the point assigning method based on the user's past feedback. This allows the point assigning method to be customized based on the user's past feedback, thereby providing optimal point assignment for the user. Some or all of the above-described processing in the point assigning unit may be performed using, for example, AI, or may be performed without AI. For example, the point assigning unit can input the feedback results to a generation AI and cause the generation AI to customize the point assigning method.

[0067] The privacy protection function can analyze a user's past data provision history to select the optimal protection method during privacy protection. The privacy protection function, for example, uses database technology to collect a user's past data provision history. For example, the privacy protection function can set a high level of privacy protection if the user has provided a large amount of data in the past. The privacy protection function can also set a standard level of privacy protection if the user has provided a small amount of data in the past. The privacy protection function can also dynamically adjust the level of privacy protection based on the user's past data provision history. This allows optimal privacy protection for the user to be provided by selecting the optimal privacy protection method based on the user's past data provision history. Some or all of the above-described processing in the privacy protection function can be performed, for example, using AI or without AI. For example, the privacy protection function can input past data provision history into a generation AI and have the generation AI select the optimal privacy protection method.

[0068] The privacy protection function can customize privacy protection measures based on the user's current living situation during privacy protection. For example, the privacy protection function uses questionnaire technology to evaluate the user's current living situation. For example, the privacy protection function can set a high level of privacy protection if the user is in financial difficulty. Alternatively, the privacy protection function can set a standard level of privacy protection if the user is financially stable. The privacy protection function can also dynamically adjust the privacy protection measures based on the user's current living situation. This allows the privacy protection measures to be customized based on the user's current living situation, thereby providing optimal privacy protection for the user. Some or all of the above-mentioned processing in the privacy protection function may be performed using AI, for example, or may be performed without AI. For example, the privacy protection function can input questionnaire results into a generation AI and have the generation AI customize the privacy protection measures.

[0069] The privacy protection function can improve the privacy protection method by reflecting user feedback. For example, the privacy protection function uses survey technology to collect user feedback. For example, the privacy protection function maintains the current privacy protection method if the user is satisfied with the privacy protection method. The privacy protection function can also adjust the protection level if the user is dissatisfied with the privacy protection method. The privacy protection function can also dynamically improve the privacy protection method based on user feedback. This makes it possible to provide optimal privacy protection for the user by improving the privacy protection method based on user feedback. Some or all of the above-mentioned processing in the privacy protection function may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection function can input the feedback results into a generation AI and have the generation AI improve the privacy protection method.

[0070] The privacy protection function can select the optimal privacy protection method by taking into account the user's geographical location information during privacy protection. For example, the privacy protection function uses GPS technology to acquire the user's geographical location information. For example, if the user lives in an urban area, the privacy protection function can provide a privacy protection method for urban areas. Also, if the user lives in a rural area, the privacy protection function can provide a privacy protection method for rural areas. The privacy protection function can also dynamically adjust the privacy protection method based on the user's geographical location information. This allows optimal privacy protection for the user to be provided by selecting the optimal privacy protection method based on the user's geographical location information. Some or all of the above-described processing in the privacy protection function may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection function can input GPS data into a generation AI and have the generation AI select the optimal privacy protection method.

[0071] The privacy protection function can analyze a user's social media activity and suggest protection measures during privacy protection. For example, the privacy protection function uses text analysis technology to analyze a user's social media activity. For example, if a user posts about privacy on social media, the privacy protection function can provide relevant privacy protection methods. Furthermore, if a user posts about specific information on social media, the privacy protection function can also provide privacy protection methods related to that information. Furthermore, the privacy protection function can dynamically suggest privacy protection measures based on the user's social media activity. This allows optimal privacy protection for the user by suggesting privacy protection measures based on the user's social media activity. Some or all of the above-described processing in the privacy protection function can be performed, for example, using AI or without AI. For example, the privacy protection function can input the content of social media posts into a generation AI and have the generation AI suggest privacy protection measures.

[0072] The privacy protection function can customize the privacy protection method by reflecting the user's past feedback. The privacy protection function, for example, uses survey technology to collect the user's past feedback. For example, the privacy protection function maintains the current privacy protection method if the user has been satisfied with the privacy protection method in the past. The privacy protection function can also adjust the protection level if the user has been dissatisfied with the privacy protection method in the past. The privacy protection function can also dynamically customize the privacy protection method based on the user's past feedback. This allows the privacy protection method to be customized based on the user's past feedback, thereby providing optimal privacy protection for the user. Some or all of the above-mentioned processing in the privacy protection function may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection function can input the feedback results into a generation AI and have the generation AI customize the privacy protection method.

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

[0074] The insurance system can further include a behavior analysis unit that analyzes the user's past behavioral data and optimizes insurance product proposals based on the user's behavioral patterns. For example, if the user has traveled frequently in the past, travel insurance proposals can be strengthened. Also, if the user regularly undergoes health checkups, insurance products specialized for health management can be proposed. Furthermore, if the user actively participates in sports, sports-related insurance products can be proposed. This makes it possible to propose insurance products based on the user's behavioral patterns, and to provide insurance products that meet the user's needs.

[0075] The insurance system can also be equipped with a health monitoring unit that monitors the user's health data in real time and issues an alert if an abnormality is detected. For example, if the heart rate suddenly increases, an alert can be issued urging the user to visit a medical institution. Also, if the quality of sleep declines, an alert can be issued suggesting measures to improve the situation. Furthermore, if the amount of exercise decreases, an alert can be issued urging the user to exercise. This makes it possible to monitor the user's health condition in real time and prompt appropriate measures to be taken.

[0076] The insurance system can further include an environment adaptation unit that collects data on the user's living environment and proposes insurance products tailored to the environment. For example, if the user lives in an urban area, insurance products tailored to risks specific to the city can be proposed. If the user lives in a rural area, insurance products tailored to agricultural risks can be proposed. Furthermore, if the user lives by the sea, insurance products tailored to risks specific to the seaside can be proposed. This makes it possible to propose insurance products tailored to the user's living environment, thereby providing more appropriate insurance coverage.

[0077] The insurance system can further include a social analysis unit that analyzes a user's social media activity and suggests insurance products based on their social media activity. For example, if a user frequently posts about health on social media, insurance products specialized in health management can be suggested. If a user frequently posts about travel, travel insurance can be suggested. Furthermore, if a user frequently posts about sports, sports-related insurance products can be suggested. This makes it possible to suggest insurance products based on a user's social media activity, and to provide insurance products that match the user's interests.

[0078] The insurance system can also be equipped with a preventive medicine section that provides information on preventive medicine based on the user's health data. For example, if the user's health data indicates an increased risk of a particular condition, preventive measures can be suggested. Also, if the user's health data indicates a high risk of lifestyle-related diseases, lifestyle improvement measures can be suggested. Furthermore, if the user's health data indicates a lack of exercise, an exercise program can be suggested. This makes it possible to provide preventive medicine information based on the user's health data, contributing to the user's health maintenance.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection unit collects data on the individual's daily risks. The data on the individual's daily risks includes, for example, heart rate, number of steps, sleep patterns, and amount of exercise. The collection unit can measure the heart rate using a wearable device, count steps using a smartphone app, and record sleep patterns using a sleep tracker. For example, the collection unit can monitor the heart rate in real time and issue an alert if an abnormality is detected. The collection unit can also record the number of steps taken daily using a pedometer and evaluate the amount of exercise. Furthermore, the collection unit can evaluate the quality of sleep using a sleep tracker and suggest areas for improvement. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and predict risks for each individual. The analysis is performed using statistical analysis and machine learning algorithms. For example, the generation AI determines an individual's health risk and accident risk based on past data and statistical information. The analysis unit can also analyze data in real time and immediately grasp changes in risk. Furthermore, the analysis unit can use the generation AI to analyze an individual's behavioral patterns and improve the accuracy of risk predictions. For example, the generation AI predicts an increase in health risk if there is a change in heart rate or a decrease in exercise volume. The generation AI can also predict accident risk based on past accident data. Step 3: The provision unit provides insurance products based on the risks predicted by the analysis unit. Insurance products include insurance products specialized for health management and insurance products specialized for accident compensation. For example, if a high health risk is predicted, the provision unit provides an insurance product specialized for health management. Also, if a high accident risk is predicted, the provision unit can provide an insurance product specialized for accident compensation. Furthermore, the provision unit can provide customized insurance products according to individual needs. For example, the provision unit can provide discounts on health checkups and fitness programs to individuals with high health risks. Also, the provision unit can provide medical expense compensation in the event of an accident and rehabilitation support after an accident to individuals with high accident risks. Step 4: The discount unit discounts the insurance premiums for the insurance products provided by the provider unit. The discount is based on the frequency of data provision and the degree of risk reduction. For example, the discount unit discounts the insurance premiums if an individual provides data through a wearable device. The discount unit can also discount the insurance premiums if an individual's health risk is reduced. Furthermore, the discount unit can adjust the discount rate depending on the frequency of data provision. For example, the discount unit can apply a higher discount rate to an individual who provides data every day. The discount unit can also apply a lower discount rate to an individual who provides data once a week. Step 5: The point allocating unit allocates points for the insurance product provided by the providing unit. The points are allocated based on the quantity and quality of the data provided. For example, the point allocating unit allocates points when an individual provides data through a wearable device. The point allocating unit can also allocate points according to the quality of the data provided. Furthermore, the point allocating unit can use the allocated points to pay insurance premiums. For example, the point allocating unit can enable an individual to use the allocated points to pay part of the insurance premium. The point allocating unit can also enable an individual to use the allocated points to purchase insurance products or services.

[0081] (Example 2) An insurance system according to an embodiment of the present invention collects data on an individual's daily risks, analyzes it using a generation AI, and provides insurance products tailored to each individual's risk. The insurance system collects data on an individual's daily risks, analyzes it using a generation AI, and predicts each individual's risk, thereby providing personalized insurance products at appropriate premiums. The insurance system also provides a mechanism for rewarding individuals with insurance premium discounts or points in exchange for providing sensitive data they possess. For example, the insurance system uses a wearable device to collect data on an individual's daily risks, including heart rate, number of steps, sleep patterns, and exercise volume. This data is input into a generation AI. The insurance system then analyzes the collected data using the generation AI to predict each individual's risk. The generation AI then determines each individual's health risk and accident risk based on past data and statistical information. For example, if heart rate fluctuations or a decrease in exercise volume are observed, an increase in health risk is predicted. The insurance system then provides insurance products tailored to each individual's risk based on the analysis results of the generation AI. For example, if a high health risk is predicted, an insurance product specialized in health management is proposed. Furthermore, if a high accident risk is predicted, an insurance product specialized for accident compensation is proposed. Furthermore, the insurance system provides a mechanism for rewarding individuals for providing their sensitive data in the form of discounts on insurance premiums or points. For example, by providing data via a wearable device, individuals can receive discounts on insurance premiums. Points are also awarded in accordance with the data provided, which can be used to pay insurance premiums. This allows the insurance system to provide personalized insurance products based on an individual's everyday risks, and by providing discounts on insurance premiums and awarding points, it is possible to collect more data and make more accurate risk predictions. This allows the insurance system to provide personalized insurance products based on an individual's everyday risks, and by providing discounts on insurance premiums and awarding points, it is possible to collect more data and make more accurate risk predictions. For example, by providing data via a wearable device, individuals can receive discounts on insurance premiums.In addition, points are awarded in accordance with the data provided, and can be used to pay insurance premiums. This increases the incentive for individuals to provide data by receiving discounts on insurance premiums and point rewards, leading to the collection of more data. The collected data is used for analysis by generative AI, improving the accuracy of individual risk predictions. This creates a cycle in which personalized insurance products are provided at reasonable premiums.

[0082] The insurance system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a discount unit, and a point-granting unit. The collection unit collects data related to an individual's daily risks. Examples of the data related to an individual's daily risks include, but are not limited to, heart rate, step count, sleep patterns, and exercise volume. For example, the collection unit measures heart rate using a wearable device. The collection unit can also count steps using a smartphone app. The collection unit can also record sleep patterns using a sleep tracker. For example, the collection unit monitors heart rate in real time and issues an alert if an abnormality is detected. The collection unit can also record daily step counts using a pedometer and evaluate exercise volume. The collection unit can also evaluate sleep quality using the sleep tracker and suggest improvements. The analysis unit uses a generation AI to analyze the data collected by the collection unit and predict individual risks. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the generation AI determines an individual's health risk and accident risk based on past data and statistical information. The analysis unit can also analyze data in real time and immediately grasp changes in risk. The analysis unit can also use the generation AI to analyze individual behavioral patterns and improve the accuracy of risk prediction. For example, the generation AI predicts an increase in health risk when heart rate fluctuations or a decrease in exercise volume are observed. The generation AI can also predict accident risk based on past accident data. The provision unit offers insurance products based on the risks predicted by the analysis unit. Insurance products include, but are not limited to, insurance products specialized in health management and insurance products specialized in accident compensation. For example, if the provision unit predicts a high health risk, it can offer an insurance product specialized in health management. If the provision unit predicts a high accident risk, it can offer an insurance product specialized in accident compensation. The provision unit can also offer customized insurance products according to individual needs. For example, the provision unit can offer discounts on health checkups and fitness programs to individuals with high health risks.The providing unit may also provide, to individuals with a high risk of accidents, compensation for medical expenses in the event of an accident and rehabilitation support after an accident. The discounting unit discounts insurance premiums for insurance products provided by the providing unit. Discounts may be based, for example, on the frequency of data provision and the degree of risk reduction, but are not limited to such examples. For example, the discounting unit discounts insurance premiums when an individual provides data through a wearable device. The discounting unit may also discount insurance premiums when an individual's health risk is reduced. The discounting unit may also adjust the discount rate depending on the frequency of data provision. For example, the discounting unit may apply a higher discount rate to individuals who provide data daily. The discounting unit may also apply a lower discount rate to individuals who provide data once a week. The point assigning unit assigns points for insurance products provided by the providing unit. Points may be assigned, for example, based on the quantity and quality of data provided, but are not limited to such examples. For example, the point assigning unit assigns points when an individual provides data through a wearable device. The point assigning unit may also assign points according to the quality of data provided. Furthermore, the point allocating unit can use the allocated points to pay insurance premiums. For example, the point allocating unit allows an individual to use the allocated points to pay part of an insurance premium. The point allocating unit can also allow an individual to use the points to purchase insurance products or services. As a result, the insurance system according to the embodiment provides personalized insurance products based on an individual's daily risks, and by discounting insurance premiums and allocating points, it is possible to collect more data and make highly accurate risk predictions.

[0083] The collection unit can collect data on heart rate, number of steps, sleep patterns, and amount of exercise. The collection unit, for example, uses a wearable device to measure heart rate. For example, the collection unit monitors heart rate in real time using a smartwatch. The collection unit can also use a smartphone app to count steps. For example, the collection unit counts steps using an acceleration sensor on the smartphone. The collection unit can also use a sleep tracker to record sleep patterns. For example, the collection unit evaluates sleep quality using the sleep tracker and suggests improvements. The collection unit can also use a fitness tracker to measure exercise volume. For example, the collection unit uses the fitness tracker to record exercise volume and evaluate the effects of exercise. This allows for the collection of detailed data on an individual's daily risks, thereby improving the accuracy of risk prediction. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from the wearable device into a generation AI and have the generation AI analyze the data.

[0084] The analysis unit can analyze the collected data and predict individual health risks and accident risks. The analysis unit, for example, analyzes the collected data using statistical analysis or machine learning algorithms. For example, the analysis unit can use a generation AI to predict individual health risks based on past data and statistical information. The analysis unit can also use the generation AI to analyze the collected data in real time and immediately grasp risk fluctuations. The analysis unit can also use the generation AI to analyze individual behavioral patterns and improve the accuracy of risk predictions. For example, the analysis unit predicts increased health risks when heart rate fluctuations or decreased exercise volume are observed. The analysis unit can also predict accident risk based on past accident data. This allows for accurate prediction of individual risks and the provision of appropriate insurance products. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI perform risk predictions.

[0085] The provision unit can provide an insurance product specialized for health management when a high health risk is predicted. For example, the provision unit can provide an insurance product specialized for health management when a high health risk is predicted. For example, the provision unit can offer discounts on health checkups and fitness programs. The provision unit can also provide advice and support regarding health management to individuals with high health risks. The provision unit can also provide information regarding health management to individuals with high health risks. For example, the provision unit can provide news and articles related to health management to individuals with high health risks. The provision unit can also provide information about events and seminars related to health management to individuals with high health risks. This allows appropriate insurance coverage to be achieved by providing insurance products according to individuals' health risks. Some or all of the above-mentioned processing in the provision unit can be performed, for example, using AI or without AI. For example, the provision unit can use generation AI to propose insurance products specialized for health management to individuals with high health risks.

[0086] The provision unit can provide an insurance product specialized for accident compensation when a high accident risk is predicted. For example, the provision unit provides an insurance product specialized for accident compensation when a high accident risk is predicted. For example, the provision unit provides medical expense compensation in the event of an accident and rehabilitation support after an accident. The provision unit can also provide advice and support regarding accident prevention to individuals with a high accident risk. The provision unit can also provide information regarding accident prevention to individuals with a high accident risk. For example, the provision unit can provide news and articles regarding accident prevention to individuals with a high accident risk. The provision unit can also provide information about events and seminars related to accident prevention to individuals with a high accident risk. This allows appropriate insurance coverage to be achieved by providing insurance products according to each individual's accident risk. Some or all of the above-described processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can use generation AI to propose an insurance product specialized for accident compensation to individuals with a high accident risk.

[0087] The discount unit can provide a discount on insurance premiums when an individual provides data through a wearable device. The discount unit, for example, provides a discount on insurance premiums when an individual provides data through a wearable device. For example, the discount unit provides a discount on insurance premiums when an individual provides data daily. The discount unit can also provide a discount on insurance premiums when an individual's health risk is reduced. The discount unit can also adjust the discount rate depending on the frequency of data provision. For example, the discount unit can apply a higher discount rate to individuals who provide data daily. The discount unit can also apply a lower discount rate to individuals who provide data once a week. This allows for discounts on insurance premiums as an incentive for data provision, thereby collecting more data. Some or all of the above-described processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can use generation AI to provide discounts on insurance premiums based on the frequency and quality of data provision.

[0088] The point assigning unit assigns points in accordance with data provided, and the points can be used to pay insurance premiums. The point assigning unit assigns points, for example, when an individual provides data through a wearable device. For example, the point assigning unit assigns points when an individual provides data daily. The point assigning unit can also assign points based on the quality of data provided. The point assigning unit can also use the assigned points to pay insurance premiums. For example, the point assigning unit allows an individual to use the assigned points to pay part of insurance premiums. The point assigning unit can also allow an individual to use the points to purchase insurance products or services. This allows more data to be collected by assigning points as an incentive for data provision. Some or all of the above-described processing in the point assigning unit may be performed using, for example, AI, or may be performed without AI. For example, the point assigning unit can assign points based on the frequency and quality of data provided using a generation AI.

[0089] The system may be equipped with a mechanism for protecting privacy at each stage of data collection, analysis, and provision. The system may be equipped with a mechanism for protecting privacy at each stage of data collection, analysis, and provision. For example, the system may encrypt data when collecting it. The system may also perform access control when analyzing it. The system may also anonymize data when providing it. For example, the system may encrypt data when collecting it to prevent third parties from accessing it. The system may also perform access control when analyzing it to ensure that only specific users can access the data. The system may also anonymize data when providing it to prevent individuals from being identified. This protects privacy at each stage of data collection, analysis, and provision, thereby improving users' sense of security. Some or all of the above-mentioned processing in the system may be performed using, or without, AI. For example, the system may use generative AI to encrypt and anonymize data.

[0090] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit records the user's voice using a microphone and estimates the emotions using a voice analysis algorithm. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text entered by the user and estimates the emotions. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0091] The collection unit can analyze the user's past health data and select an appropriate data collection method. The collection unit, for example, uses statistical analysis techniques to analyze the user's past health data. For example, the collection unit can analyze past heart rate data and, if an abnormality is detected, increase the frequency of heart rate collection. The collection unit can also analyze past sleep patterns and, if sleep quality is low, strengthen sleep data collection. The collection unit can also analyze past exercise amount data and, if insufficient exercise is detected, increase exercise data collection. In this way, by analyzing the user's past health data, the optimal data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past health data into a generation AI and have the generation AI select a data collection method.

[0092] The collection unit can filter data based on the user's current living situation and activity level when collecting data. The collection unit, for example, uses a sensor to understand the user's current living situation and activity level. For example, when the user is at work, the collection unit can moderate data collection and concentrate data collection during breaks. Furthermore, when the user is exercising, the collection unit can strengthen collection of exercise data and refrain from collecting other data. Furthermore, when the user is sleeping, the collection unit can prioritize collection of sleep data and refrain from collecting other data. This allows for more accurate data collection by adjusting data collection according to the user's living situation and activity level. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by a sensor into a generation AI and have the generation AI perform data collection filtering.

[0093] The collection unit can select the optimal collection means depending on the user's input method when collecting data. The collection unit, for example, uses technology to detect the user's input method in order to select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Also, if the user prefers text input, the collection unit can prioritize collecting text data. Also, if the user prefers image input, the collection unit can prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can detect the user's input method, input that information to the generation AI, and have the generation AI select the optimal collection means.

[0094] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the collection unit captures the user's facial expressions using a camera and estimates the emotions using an emotion estimation algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit records the user's voice using a microphone and estimates the emotions using a voice analysis algorithm. The collection unit can also estimate the user's emotions using text analysis technology. For example, the collection unit analyzes text entered by the user and estimates the emotions. This allows the priority of data to be collected based on the user's emotions, thereby enabling prioritized collection of important data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0095] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit, for example, uses GPS technology to acquire the user's geographical location information. For example, when the user is at home, the collection unit can prioritize collecting indoor environment data. Furthermore, when the user is out, the collection unit can prioritize collecting exercise data. Furthermore, when the user is traveling, the collection unit can prioritize collecting environmental data of the travel destination. This improves the usefulness of the data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input GPS data to a generation AI and have the generation AI determine the priority of highly relevant data.

[0096] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit, for example, uses text analysis technology to analyze the user's social media activity. For example, if the user posts about exercise on social media, the collection unit can prioritize collecting exercise data. Furthermore, if the user posts about food on social media, the collection unit can prioritize collecting food data. Furthermore, if the user posts about travel on social media, the collection unit can prioritize collecting travel data. This improves the usefulness of the data by collecting related data based on the user's social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the content of the social media posts into a generation AI and cause the generation AI to collect related data.

[0097] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit, for example, uses a questionnaire technique to collect the user's past feedback. For example, if the user has requested collection of heart rate data in the past, the collection unit can enhance collection of heart rate data. Furthermore, if the user has requested collection of sleep data in the past, the collection unit can enhance collection of sleep data. Furthermore, if the user has requested collection of exercise data in the past, the collection unit can enhance collection of exercise data. This improves the accuracy of data collection by customizing the collection method based on the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the survey results into the generation AI and cause the generation AI to customize the collection method.

[0098] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the analysis unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit analyzes text entered by the user and estimates the emotion. This allows the analysis result to be easily understood by adjusting the expression method of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0099] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, uses statistical analysis techniques to evaluate the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0100] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, uses clustering technology to classify data categories. For example, the analysis unit can apply a health risk analysis algorithm to health data. The analysis unit can also apply an accident risk analysis algorithm to accident data. The analysis unit can also apply an environmental risk analysis algorithm to environmental data. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category into the generation AI and apply an appropriate analysis algorithm to the generation AI.

[0101] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses database technology to collect the user's past analysis results. For example, the analysis unit analyzes the user's current health risk by referring to the user's past health risk analysis results. The analysis unit can also analyze the current accident risk by referring to the user's past accident risk analysis results. The analysis unit can also analyze the current environmental risk by referring to the user's past environmental risk analysis results. In this way, the accuracy of the current analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and have the generation AI improve the accuracy of the current analysis.

[0102] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the analysis unit uses a camera to capture the user's facial expression and estimate the emotion using an emotion estimation algorithm. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit uses a microphone to record the user's voice and estimate the emotion using a voice analysis algorithm. The analysis unit can also estimate the user's emotion using text analysis technology. For example, the analysis unit analyzes text entered by the user and estimates the emotion. This allows the analysis length to be adjusted according to the user's emotion, thereby providing an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0103] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, uses timestamp technology to evaluate the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also refer to past data while placing emphasis on the most recent data. The analysis unit can also prioritize analyzing data from a specific period. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0104] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, uses correlation analysis technology to evaluate the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0105] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a questionnaire technique to evaluate the user's level of expertise. For example, the analysis unit uses a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also dynamically adjust the use of technical terms according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the survey results into a generation AI and have the generation AI adjust the use of technical terms.

[0106] The providing unit can estimate the user's emotion and adjust the method of providing the insurance product based on the estimated user's emotion. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the providing unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. The providing unit can also estimate the user's emotion using text analysis technology. For example, the providing unit analyzes text entered by the user and estimates the emotion. This allows the insurance product to be optimally provided to the user by adjusting the method of providing the insurance product based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of a user taken with a camera into the generating AI and have the generating AI estimate emotions.

[0107] The provision unit can adjust the level of detail of the insurance product to be provided based on the importance of the risk when providing the insurance product. The provision unit, for example, uses statistical analysis techniques to evaluate the importance of the risk. For example, the provision unit can provide a detailed insurance product for a risk with high importance. The provision unit can also provide a simple insurance product for a risk with low importance. The provision unit can also provide an insurance product with an appropriate level of detail for a risk with medium importance. This enables efficient provision of insurance products by adjusting the level of detail of the insurance product according to the importance of the risk. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the importance of the risk to the generation AI and cause the generation AI to adjust the level of detail of the insurance product.

[0108] The provision unit can propose different insurance products according to the risk category at the time of provision. The provision unit, for example, uses clustering technology to classify risk categories. For example, the provision unit proposes an insurance product specialized in health management for health risks. The provision unit can also propose an insurance product specialized in accident compensation for accident risks. The provision unit can also propose an insurance product specialized in environmental risks for environmental risks. In this way, by proposing an appropriate insurance product according to the risk category, it is possible to provide optimal insurance coverage for the user. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the risk category into the generation AI and cause the generation AI to propose an appropriate insurance product.

[0109] The provision unit can improve the accuracy of the insurance product provided by referring to the user's past insurance usage history when providing the insurance product. The provision unit, for example, uses database technology to collect the user's past insurance usage history. For example, the provision unit can propose a current health insurance product by referring to the user's past health insurance usage history. The provision unit can also propose a current accident insurance product by referring to the user's past accident insurance usage history. The provision unit can also propose a current environmental insurance product by referring to the user's past environmental insurance usage history. This improves the accuracy of the current insurance product by referring to the user's past insurance usage history. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or without AI. For example, the provision unit can input the past insurance usage history into the generation AI to improve the accuracy of the current insurance product.

[0110] The providing unit can estimate the user's emotion and adjust the order in which insurance products are provided based on the estimated user's emotion. The providing unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the providing unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. The providing unit can also estimate the user's emotion using text analysis technology. For example, the providing unit analyzes text entered by the user and estimates the emotion. This allows the insurance products to be provided in an order optimal for the user by adjusting the order in which the insurance products are provided based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of a user taken with a camera into the generating AI and have the generating AI estimate emotions.

[0111] The provision unit can determine the priority of insurance products based on the timing of risk occurrence at the time of provision. The provision unit, for example, uses timestamp technology to evaluate the timing of risk occurrence. For example, the provision unit prioritizes providing insurance products for risks that will occur soon. The provision unit can also postpone providing insurance products for risks that will occur in the medium to long term. The provision unit can also dynamically adjust the order in which insurance products are provided depending on the timing of risk occurrence. In this way, by determining the priority of insurance products based on the timing of risk occurrence, insurance products can be provided preferentially for risks that will occur soon. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the timing of risk occurrence to a generation AI and have the generation AI determine the priority of insurance products.

[0112] The provision unit can adjust the order of insurance products based on risk relevance when providing them. The provision unit, for example, uses correlation analysis technology to evaluate risk relevance. For example, the provision unit can provide insurance products preferentially for highly relevant risks. The provision unit can also provide insurance products later for less relevant risks. The provision unit can also dynamically adjust the order of providing insurance products according to risk relevance. This enables efficient provision of insurance products by adjusting the order of insurance products based on risk relevance. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input risk relevance to a generation AI and have the generation AI adjust the order of insurance products.

[0113] The providing unit can adjust the description of the insurance product according to the user's level of expertise when providing the insurance product. The providing unit, for example, uses a questionnaire technique to evaluate the user's level of expertise. For example, if the user has specialized knowledge, the providing unit can provide a description of the insurance product that uses a lot of technical jargon. Furthermore, if the user does not have specialized knowledge, the providing unit can also provide a description of the insurance product that avoids technical jargon. The providing unit can also dynamically adjust the description of the insurance product according to the user's level of expertise. This allows the user to be provided with an insurance product that is easy to understand by adjusting the description of the insurance product according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the survey results into a generation AI and have the generation AI adjust the description of the insurance product.

[0114] The discount unit can estimate the user's emotion and adjust the discount application method based on the estimated user's emotion. The discount unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the discount unit uses a camera to capture the user's facial expression and estimate the emotion using an emotion estimation algorithm. The discount unit can also estimate the user's emotion using voice analysis technology. For example, the discount unit uses a microphone to record the user's voice and estimate the emotion using a voice analysis algorithm. The discount unit can also estimate the user's emotion using text analysis technology. For example, the discount unit analyzes text entered by the user and estimates the emotion. This allows the discount application method to be adjusted according to the user's emotion, thereby providing optimal discount information for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the discount unit can be performed, for example, using AI or without AI. For example, the discount unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0115] When applying a discount, the discount unit can analyze the user's past data provision history and select the optimal discount method. The discount unit, for example, uses database technology to collect the user's past data provision history. For example, the discount unit can set a high discount rate if the user has provided a large amount of data in the past. The discount unit can also set a low discount rate if the user has provided a small amount of data in the past. The discount unit can also dynamically adjust the discount method based on the user's past data provision history. This allows the optimal discount to be provided to the user by selecting the optimal discount method based on the user's past data provision history. Some or all of the above-mentioned processing in the discount unit can be performed, for example, using AI or without AI. For example, the discount unit can input the past data provision history into the generation AI and have the generation AI select the optimal discount method.

[0116] The discount unit can customize the discount method based on the user's current living situation when applying a discount. The discount unit, for example, uses a questionnaire technique to evaluate the user's current living situation. For example, the discount unit can set a high discount rate if the user is in a financially difficult situation. The discount unit can also set a low discount rate if the user is financially stable. The discount unit can also dynamically adjust the discount method based on the user's current living situation. This allows the user to receive the optimal discount by customizing the discount method based on the user's current living situation. Some or all of the above-described processing in the discount unit can be performed using, for example, AI, or can be performed without using AI. For example, the discount unit can input the survey results into a generation AI and have the generation AI customize the discount method.

[0117] The discount unit can improve the discount method by reflecting user feedback when applying a discount. The discount unit, for example, uses survey technology to collect user feedback. For example, the discount unit maintains the current discount method if the user is satisfied with the discount method. The discount unit can also adjust the discount rate if the user is dissatisfied with the discount method. The discount unit can also dynamically improve the discount method based on user feedback. This makes it possible to provide the optimal discount for the user by improving the discount method based on user feedback. Some or all of the above-mentioned processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can input the feedback results to a generation AI and cause the generation AI to improve the discount method.

[0118] The discount unit can estimate the user's emotions and determine the priority of discounts based on the estimated user emotions. The discount unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the discount unit uses a camera to capture the user's facial expression and estimate the emotion using an emotion estimation algorithm. The discount unit can also estimate the user's emotions using voice analysis technology. For example, the discount unit uses a microphone to record the user's voice and estimate the emotion using a voice analysis algorithm. The discount unit can also estimate the user's emotions using text analysis technology. For example, the discount unit analyzes text entered by the user and estimates the emotion. This allows the user to be provided with optimal discount information by determining the priority of discounts according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the discount unit can be performed using, for example, AI, or without AI. For example, the discount unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0119] The discount unit can select the optimal discount method by taking into account the user's geographical location information when applying a discount. The discount unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user lives in an urban area, the discount unit can provide a discount method for urban areas. Also, if the user lives in a rural area, the discount unit can provide a discount method for rural areas. The discount unit can also dynamically adjust the discount method based on the user's geographical location information. This allows the optimal discount to be provided to the user by selecting the optimal discount method based on the user's geographical location information. Some or all of the above-described processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can input GPS data into the generation AI and have the generation AI select the optimal discount method.

[0120] The discount unit can analyze the user's social media activity and suggest discount methods when applying a discount. The discount unit, for example, uses text analysis technology to analyze the user's social media activity. For example, if the user posts about discounts on social media, the discount unit can provide relevant discount methods. Also, if the user posts about a specific product on social media, the discount unit can provide discount methods related to the product. The discount unit can also dynamically suggest discount methods based on the user's social media activity. This makes it possible to provide the optimal discount for the user by suggesting discount methods based on the user's social media activity. Some or all of the above-mentioned processing in the discount unit may be performed, for example, using AI or without AI. For example, the discount unit can input the content of the social media post into a generation AI and have the generation AI suggest discount methods.

[0121] The discount unit can customize the discount method by reflecting the user's past feedback when applying a discount. The discount unit, for example, uses survey technology to collect the user's past feedback. For example, the discount unit maintains the current discount method if the user has been satisfied with the discount method in the past. The discount unit can also adjust the discount rate if the user has been dissatisfied with the discount method in the past. The discount unit can also dynamically customize the discount method based on the user's past feedback. This makes it possible to provide the optimal discount for the user by customizing the discount method based on the user's past feedback. Some or all of the above-described processing in the discount unit may be performed using, for example, AI, or may be performed without using AI. For example, the discount unit can input the feedback results to a generation AI and have the generation AI customize the discount method.

[0122] The point assigning unit can estimate the user's emotion and adjust the point awarding method based on the estimated user's emotion. The point assigning unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the point assigning unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The point assigning unit can also estimate the user's emotion using voice analysis technology. For example, the point assigning unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. The point assigning unit can also estimate the user's emotion using text analysis technology. For example, the point assigning unit analyzes text entered by the user and estimates the emotion. This allows the point awarding method to be adjusted according to the user's emotion, thereby providing optimal point award information for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the point assigning unit can be performed using, for example, AI, or without AI. For example, the point-granting unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0123] When awarding points, the point awarding unit can analyze the user's past data provision history and select the optimal point awarding method. The point awarding unit, for example, uses database technology to collect the user's past data provision history. For example, the point awarding unit can set a high point award rate if the user has provided a large amount of data in the past. The point awarding unit can also set a low point award rate if the user has provided a small amount of data in the past. The point awarding unit can also dynamically adjust the point awarding method based on the user's past data provision history. This allows the optimal point awarding method to be selected based on the user's past data provision history, thereby providing optimal points for the user. Some or all of the above-described processing in the point awarding unit may be performed, for example, using AI or without AI. For example, the point awarding unit can input the past data provision history into a generation AI and have the generation AI select the optimal point awarding method.

[0124] The point assigning unit can customize the point assigning means based on the user's current living situation when assigning points. The point assigning unit, for example, uses a questionnaire technique to evaluate the user's current living situation. For example, the point assigning unit can set a high point assigning rate if the user is in a financially difficult situation. The point assigning unit can also set a low point assigning rate if the user is financially stable. The point assigning unit can also dynamically adjust the point assigning means based on the user's current living situation. This allows the point assigning means to be customized based on the user's current living situation, thereby providing optimal point assignment for the user. Some or all of the above-mentioned processing in the point assigning unit may be performed using, for example, AI, or may be performed without using AI. For example, the point assigning unit can input the survey results into a generation AI and have the generation AI customize the point assigning means.

[0125] The point assigning unit can improve the point assigning method by reflecting user feedback when assigning points. The point assigning unit, for example, uses a survey technique to collect user feedback. For example, if the user is satisfied with the point assigning method, the point assigning unit maintains the current point assigning method. Alternatively, if the user is dissatisfied with the point assigning method, the point assigning unit can adjust the point assigning rate. Alternatively, the point assigning unit can dynamically improve the point assigning method based on user feedback. This allows the point assigning method to be improved based on user feedback, thereby providing optimal point assignment for the user. Some or all of the above-described processing in the point assigning unit may be performed using, for example, AI, or may be performed without using AI. For example, the point assigning unit can input the feedback results to a generation AI and cause the generation AI to improve the point assigning method.

[0126] The point assigning unit can estimate the user's emotions and determine the priority of point assignment based on the estimated user's emotions. The point assigning unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the point assigning unit captures the user's facial expression using a camera and estimates the emotion using an emotion estimation algorithm. The point assigning unit can also estimate the user's emotions using voice analysis technology. For example, the point assigning unit records the user's voice using a microphone and estimates the emotion using a voice analysis algorithm. The point assigning unit can also estimate the user's emotions using text analysis technology. For example, the point assigning unit analyzes text entered by the user and estimates the emotion. This allows the point assigning unit to determine the priority of point assignment according to the user's emotions, thereby providing the user with optimal point assignment information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the point assigning unit can be performed using, for example, AI, or without AI. For example, the point-granting unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate emotions.

[0127] When awarding points, the point awarding unit can select the optimal point awarding method by taking into account the user's geographical location information. The point awarding unit, for example, uses GPS technology to acquire the user's geographical location information. For example, if the user lives in an urban area, the point awarding unit can provide a point awarding method tailored to the urban area. Furthermore, if the user lives in a rural area, the point awarding unit can also provide a point awarding method tailored to the rural area. The point awarding unit can also dynamically adjust the point awarding method based on the user's geographical location information. This allows the optimal point awarding method to be selected based on the user's geographical location information, thereby providing the optimal point award for the user. Some or all of the above-described processing in the point awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the point awarding unit can input GPS data into a generation AI and have the generation AI select the optimal point awarding method.

[0128] When awarding points, the point awarding unit can analyze the user's social media activity and suggest a method for awarding points. The point awarding unit, for example, uses text analysis technology to analyze the user's social media activity. For example, if the user posts about points on social media, the point awarding unit can provide a related point awarding method. Furthermore, if the user posts about a specific product on social media, the point awarding unit can also provide a point awarding method related to the product. Furthermore, the point awarding unit can dynamically suggest a method for awarding points based on the user's social media activity. This allows the point awarding unit to provide optimal points for the user by suggesting a method for awarding points based on the user's social media activity. Some or all of the above-described processing in the point awarding unit may be performed using, for example, AI, or may be performed without using AI. For example, the point awarding unit can input the content of the social media post into a generation AI and have the generation AI execute the suggestion of a method for awarding points.

[0129] The point assigning unit can customize the point assigning method by reflecting the user's past feedback when assigning points. The point assigning unit, for example, uses a survey technique to collect the user's past feedback. For example, if the user has been satisfied with the point assigning method in the past, the point assigning unit maintains the current point assigning method. The point assigning unit can also adjust the point assigning rate if the user has been dissatisfied with the point assigning method in the past. The point assigning unit can also dynamically customize the point assigning method based on the user's past feedback. This allows the point assigning method to be customized based on the user's past feedback, thereby providing optimal point assignment for the user. Some or all of the above-described processing in the point assigning unit may be performed using, for example, AI, or may be performed without AI. For example, the point assigning unit can input the feedback results to a generation AI and cause the generation AI to customize the point assigning method.

[0130] The privacy protection function can estimate a user's emotions and adjust the level of privacy protection based on the estimated user's emotions. The privacy protection function, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the privacy protection function uses a camera to capture the user's facial expressions and estimate the emotions using an emotion estimation algorithm. The privacy protection function can also estimate the user's emotions using voice analysis technology. For example, the privacy protection function uses a microphone to record the user's voice and estimate the emotions using a voice analysis algorithm. The privacy protection function can also estimate the user's emotions using text analysis technology. For example, the privacy protection function analyzes text entered by the user and estimates the emotions. This allows the level of privacy protection to be adjusted according to the user's emotions, thereby providing optimal privacy protection for the user. Emotion estimation is achieved using the emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the privacy protection function may be performed using, for example, AI, or without AI. For example, the privacy protection function can input image data of the user taken with a camera into the generation AI and have the generation AI perform emotion estimation.

[0131] The privacy protection function can analyze a user's past data provision history to select the optimal protection method during privacy protection. The privacy protection function, for example, uses database technology to collect a user's past data provision history. For example, the privacy protection function can set a high level of privacy protection if the user has provided a large amount of data in the past. The privacy protection function can also set a standard level of privacy protection if the user has provided a small amount of data in the past. The privacy protection function can also dynamically adjust the level of privacy protection based on the user's past data provision history. This allows optimal privacy protection for the user to be provided by selecting the optimal privacy protection method based on the user's past data provision history. Some or all of the above-described processing in the privacy protection function can be performed, for example, using AI or without AI. For example, the privacy protection function can input past data provision history into a generation AI and have the generation AI select the optimal privacy protection method.

[0132] The privacy protection function can customize privacy protection measures based on the user's current living situation during privacy protection. For example, the privacy protection function uses questionnaire technology to evaluate the user's current living situation. For example, the privacy protection function can set a high level of privacy protection if the user is in financial difficulty. Alternatively, the privacy protection function can set a standard level of privacy protection if the user is financially stable. The privacy protection function can also dynamically adjust the privacy protection measures based on the user's current living situation. This allows the privacy protection measures to be customized based on the user's current living situation, thereby providing optimal privacy protection for the user. Some or all of the above-mentioned processing in the privacy protection function may be performed using AI, for example, or may be performed without AI. For example, the privacy protection function can input questionnaire results into a generation AI and have the generation AI customize the privacy protection measures.

[0133] The privacy protection function can improve the privacy protection method by reflecting user feedback. For example, the privacy protection function uses survey technology to collect user feedback. For example, the privacy protection function maintains the current privacy protection method if the user is satisfied with the privacy protection method. The privacy protection function can also adjust the protection level if the user is dissatisfied with the privacy protection method. The privacy protection function can also dynamically improve the privacy protection method based on user feedback. This makes it possible to provide optimal privacy protection for the user by improving the privacy protection method based on user feedback. Some or all of the above-mentioned processing in the privacy protection function may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection function can input the feedback results into a generation AI and have the generation AI improve the privacy protection method.

[0134] The privacy protection function can estimate a user's emotions and determine the priority of privacy protection based on the estimated user emotions. The privacy protection function, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the privacy protection function uses a camera to capture the user's facial expressions and estimate the emotions using an emotion estimation algorithm. The privacy protection function can also estimate the user's emotions using voice analysis technology. For example, the privacy protection function uses a microphone to record the user's voice and estimate the emotions using a voice analysis algorithm. The privacy protection function can also estimate the user's emotions using text analysis technology. For example, the privacy protection function analyzes text entered by the user and estimates the emotions. This allows the system to determine the priority of privacy protection based on the user's emotions, thereby providing optimal privacy protection for the user. Emotion estimation is achieved using the emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the privacy protection function can be performed using, for example, AI, or without AI. For example, the privacy protection function can input image data of the user taken with a camera into the generation AI and have the generation AI perform emotion estimation.

[0135] The privacy protection function can select the optimal privacy protection method by taking into account the user's geographical location information during privacy protection. For example, the privacy protection function uses GPS technology to acquire the user's geographical location information. For example, if the user lives in an urban area, the privacy protection function can provide a privacy protection method for urban areas. Also, if the user lives in a rural area, the privacy protection function can provide a privacy protection method for rural areas. The privacy protection function can also dynamically adjust the privacy protection method based on the user's geographical location information. This allows optimal privacy protection for the user to be provided by selecting the optimal privacy protection method based on the user's geographical location information. Some or all of the above-described processing in the privacy protection function may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection function can input GPS data into a generation AI and have the generation AI select the optimal privacy protection method.

[0136] The privacy protection function can analyze a user's social media activity and suggest protection measures during privacy protection. For example, the privacy protection function uses text analysis technology to analyze a user's social media activity. For example, if a user posts about privacy on social media, the privacy protection function can provide relevant privacy protection methods. Furthermore, if a user posts about specific information on social media, the privacy protection function can also provide privacy protection methods related to that information. Furthermore, the privacy protection function can dynamically suggest privacy protection measures based on the user's social media activity. This allows optimal privacy protection for the user by suggesting privacy protection measures based on the user's social media activity. Some or all of the above-described processing in the privacy protection function can be performed, for example, using AI or without AI. For example, the privacy protection function can input the content of social media posts into a generation AI and have the generation AI suggest privacy protection measures.

[0137] The privacy protection function can customize the privacy protection method by reflecting the user's past feedback. The privacy protection function, for example, uses survey technology to collect the user's past feedback. For example, the privacy protection function maintains the current privacy protection method if the user has been satisfied with the privacy protection method in the past. The privacy protection function can also adjust the protection level if the user has been dissatisfied with the privacy protection method in the past. The privacy protection function can also dynamically customize the privacy protection method based on the user's past feedback. This allows the privacy protection method to be customized based on the user's past feedback, thereby providing optimal privacy protection for the user. Some or all of the above-mentioned processing in the privacy protection function may be performed using AI, for example, or may be performed without using AI. For example, the privacy protection function can input the feedback results into a generation AI and have the generation AI customize the privacy protection method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, discount unit, and point granting unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on an individual's daily risks using the camera 42 and microphone 38B of the smart device 14. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generative AI and predicts the individual's risk. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides insurance products based on the analysis results. The discount unit and point granting unit, realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, provide discounts on insurance premiums and grant points in exchange for providing data. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, discount unit, and point granting unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on an individual's daily risks using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generative AI and predicts the individual's risk. The provision unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides insurance products based on the analysis results. The discount unit and point granting unit, realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, provide discounts on insurance premiums and grant points in exchange for providing data. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, discount unit, and point granting unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data on an individual's daily risks using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generative AI to predict the individual's risk. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides insurance products based on the analysis results. The discount unit and point granting unit are realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and provide discounts on insurance premiums or grant points in exchange for providing data. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, discount unit, and point granting unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on an individual's daily risks using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generative AI to predict the individual's risk. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides insurance products based on the analysis results. The discount unit and point granting unit are realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provide discounts on insurance premiums and grant points in exchange for providing data.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The insurance system may further include an emotion response unit that estimates the user's emotions and adjusts the insurance product proposals based on the estimated emotions. For example, if the user is feeling stressed, a health management program with a relaxing effect may be proposed. If the user is seeking peace of mind, an insurance product with comprehensive coverage may be proposed. Furthermore, if the user is excited, an insurance product suitable for an active lifestyle may be proposed. This makes it possible to propose insurance products according to the user's emotions, thereby improving user satisfaction.

[0140] The insurance system can further include a behavior analysis unit that analyzes the user's past behavioral data and optimizes insurance product proposals based on the user's behavioral patterns. For example, if the user has traveled frequently in the past, travel insurance proposals can be strengthened. Also, if the user regularly undergoes health checkups, insurance products specialized for health management can be proposed. Furthermore, if the user actively participates in sports, sports-related insurance products can be proposed. This makes it possible to propose insurance products based on the user's behavioral patterns, and to provide insurance products that meet the user's needs.

[0141] The insurance system can also be equipped with a health monitoring unit that monitors the user's health data in real time and issues an alert if an abnormality is detected. For example, if the heart rate suddenly increases, an alert can be issued urging the user to visit a medical institution. Also, if the quality of sleep declines, an alert can be issued suggesting measures to improve the situation. Furthermore, if the amount of exercise decreases, an alert can be issued urging the user to exercise. This makes it possible to monitor the user's health condition in real time and prompt appropriate measures to be taken.

[0142] The insurance system may further include an explanation adjustment unit that estimates the user's emotions and adjusts the explanation method for the insurance product based on the estimated emotions. For example, if the user feels anxious, a detailed explanation can be provided to reassure the user. If the user feels excited, a concise and easy-to-understand explanation can be provided. Furthermore, if the user feels relaxed, the explanation can be given in a relaxed atmosphere. In this way, by providing an explanation method that corresponds to the user's emotions, the user's understanding can be improved and the selection of an insurance product can be made easier.

[0143] The insurance system can further include an environment adaptation unit that collects data on the user's living environment and proposes insurance products tailored to the environment. For example, if the user lives in an urban area, insurance products tailored to risks specific to the city can be proposed. If the user lives in a rural area, insurance products tailored to agricultural risks can be proposed. Furthermore, if the user lives by the sea, insurance products tailored to risks specific to the seaside can be proposed. This makes it possible to propose insurance products tailored to the user's living environment, thereby providing more appropriate insurance coverage.

[0144] The insurance system may further include an emotion discount unit that estimates the user's emotion and adjusts the discount rate of the insurance premium based on the estimated emotion. For example, if the user is feeling stressed, the discount rate can be set high to reduce the financial burden. Also, if the user is seeking a sense of security, the discount rate can be adjusted to provide a sense of security. Furthermore, if the user is excited, the discount rate can be adjusted to increase purchasing motivation. This makes it possible to adjust the discount rate according to the user's emotion, thereby improving user satisfaction.

[0145] The insurance system can further include a social analysis unit that analyzes a user's social media activity and suggests insurance products based on their social media activity. For example, if a user frequently posts about health on social media, insurance products specialized in health management can be suggested. If a user frequently posts about travel, travel insurance can be suggested. Furthermore, if a user frequently posts about sports, sports-related insurance products can be suggested. This makes it possible to suggest insurance products based on a user's social media activity, and to provide insurance products that match the user's interests.

[0146] The insurance system can further include a timing adjustment unit that estimates the user's emotions and adjusts the timing of offering insurance products based on the estimated emotions. For example, if the user is relaxed, the timing of offering insurance products can be selected. Also, if the user is feeling stressed, the timing of the offer can be adjusted to reduce the burden on the user. Furthermore, if the user is excited, the timing of the offer can be adjusted to increase the user's desire to purchase. This makes it possible to adjust the offering timing according to the user's emotions, and to offer insurance products at a timing that is easily acceptable to the user.

[0147] The insurance system can also be equipped with a preventive medicine section that provides information on preventive medicine based on the user's health data. For example, if the user's health data indicates an increased risk of a particular condition, preventive measures can be suggested. Also, if the user's health data indicates a high risk of lifestyle-related diseases, lifestyle improvement measures can be suggested. Furthermore, if the user's health data indicates a lack of exercise, an exercise program can be suggested. This makes it possible to provide preventive medicine information based on the user's health data, contributing to the user's health maintenance.

[0148] The insurance system may further include a customization unit that estimates the user's emotions and customizes the insurance product based on the estimated emotions. For example, if the user desires peace of mind, an insurance product with comprehensive coverage may be customized. If the user is relaxed, benefits that have a relaxing effect may be added. If the user is excited, benefits suitable for an active lifestyle may be added. This makes it possible to customize insurance products according to the user's emotions, and to provide insurance products that meet the user's needs.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The collection unit collects data on the individual's daily risks. The data on the individual's daily risks includes, for example, heart rate, number of steps, sleep patterns, and amount of exercise. The collection unit can measure the heart rate using a wearable device, count steps using a smartphone app, and record sleep patterns using a sleep tracker. For example, the collection unit can monitor the heart rate in real time and issue an alert if an abnormality is detected. The collection unit can also record the number of steps taken daily using a pedometer and evaluate the amount of exercise. Furthermore, the collection unit can evaluate the quality of sleep using a sleep tracker and suggest areas for improvement. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and predict risks for each individual. The analysis is performed using statistical analysis and machine learning algorithms. For example, the generation AI determines an individual's health risk and accident risk based on past data and statistical information. The analysis unit can also analyze data in real time and immediately grasp changes in risk. Furthermore, the analysis unit can use the generation AI to analyze an individual's behavioral patterns and improve the accuracy of risk predictions. For example, the generation AI predicts an increase in health risk if there is a change in heart rate or a decrease in exercise volume. The generation AI can also predict accident risk based on past accident data. Step 3: The provision unit provides insurance products based on the risks predicted by the analysis unit. Insurance products include insurance products specialized for health management and insurance products specialized for accident compensation. For example, if a high health risk is predicted, the provision unit provides an insurance product specialized for health management. Also, if a high accident risk is predicted, the provision unit can provide an insurance product specialized for accident compensation. Furthermore, the provision unit can provide customized insurance products according to individual needs. For example, the provision unit can provide discounts on health checkups and fitness programs to individuals with high health risks. Also, the provision unit can provide medical expense compensation in the event of an accident and rehabilitation support after an accident to individuals with high accident risks. Step 4: The discount unit discounts the insurance premiums for the insurance products provided by the provider unit. The discount is based on the frequency of data provision and the degree of risk reduction. For example, the discount unit discounts the insurance premiums if an individual provides data through a wearable device. The discount unit can also discount the insurance premiums if an individual's health risk is reduced. Furthermore, the discount unit can adjust the discount rate depending on the frequency of data provision. For example, the discount unit can apply a higher discount rate to an individual who provides data every day. The discount unit can also apply a lower discount rate to an individual who provides data once a week. Step 5: The point allocating unit allocates points for the insurance product provided by the providing unit. The points are allocated based on the quantity and quality of the data provided. For example, the point allocating unit allocates points when an individual provides data through a wearable device. The point allocating unit can also allocate points according to the quality of the data provided. Furthermore, the point allocating unit can use the allocated points to pay insurance premiums. For example, the point allocating unit can enable an individual to use the allocated points to pay part of the insurance premium. The point allocating unit can also enable an individual to use the allocated points to purchase insurance products or services.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0198] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects data on individuals' daily risks; an analysis unit that analyzes the data collected by the collection unit and predicts risks for each individual; a provision unit that provides insurance products based on the risks predicted by the analysis unit; a discount unit that discounts insurance premiums for the insurance products provided by the provision unit; a point granting unit that grants points for the insurance product provided by the providing unit; Equipped with A system characterized by:

2. The collecting unit Collects data on heart rate, steps, sleep patterns, and activity 2. The system of claim 1.

3. The analysis unit Analyzing collected data to predict individual health and accident risks 2. The system of claim 1.

4. The providing unit If a person is predicted to have a high health risk, we will provide insurance products that specialize in health management.

2. The system of claim 1.

5. The providing unit If a high risk of accidents is predicted, insurance products specializing in accident compensation will be offered.

2. The system of claim 1.

6. The discount unit Individuals can receive discounts on their insurance premiums by providing data via wearable devices.

2. The system of claim 1.

7. The point giving unit Points are awarded in accordance with the data provided, and can be used to pay insurance premiums.

2. The system of claim 1.

8. The system comprises: Establish a system to protect privacy at each stage of data collection, analysis, and provision 2. The system of claim 1.

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