System

Abstract

A system is provided.SOLUTION: A system comprising: a wearable device for acquiring health data of a user; a server means for receiving the health data from the wearable device and storing the health data in a database; an analysis means for analyzing the health data stored in the server means and calculating a health score of the user; a price calculation means for dynamically calculating an insurance premium based on the health score; a generation means for generating a suggestion for diet and exercise for improving the health score; and a notification means for notifying the user of the generated suggestion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, methods for calculating insurance premiums based on an individual's lifestyle and health status are not common. Furthermore, there are limited means for users to accurately understand their own health status and receive specific suggestions for maintaining and improving their health. This makes it difficult for health-conscious users to improve their lifestyles and receive financial incentives. Another issue is that collected health data is not being fully utilized for medical research and development. To solve these issues, there is a need for a system that uses detailed and accurate lifestyle data to calculate individual insurance premiums and make health suggestions. [Means for solving the problem]

[0005] To solve the above problems, the following solution is proposed. The present invention includes a wearable device for acquiring health data of a user. The wearable device monitors heart rate, electrocardiogram, exercise data, etc. in real time and acquires the data. Next, a server is provided that receives the health data from the wearable device and stores it in a database. An analysis means is provided that analyzes the health data stored in the server and calculates a health score for each user. The system also includes a price calculation means that dynamically calculates insurance premiums based on the health score.

[0006] Furthermore, it includes a generating means for generating specific diet and exercise suggestions to improve health scores, and a notification means for notifying users of the generated suggestions. This allows users to understand their own health status and take action based on the suggestions to maintain their health and enjoy discounts on insurance premiums. In addition, the collected data is integrated with medical big data, and a generative AI model is used to contribute to the development of medical care.

[0007] A "wearable device" is an electronic device that can be worn by a user and has the ability to acquire health data such as heart rate, electrocardiogram, and exercise data in real time.

[0008] "Health data" is a general term for data related to a user's health condition and lifestyle habits, such as heart rate, electrocardiogram, amount of exercise, and dietary content.

[0009] "Server means" refers to a computer system that has the function of receiving health data transmitted from a wearable device and storing it in a database.

[0010] "Analysis Means" refers to an algorithm or software for analyzing the health data stored in the Server Means and calculating the user's health score.

[0011] A "health score" is an evaluation index that quantifies a user's health status and is calculated by integrating data such as heart rate, amount of exercise, and dietary content.

[0012] "Pricing Calculator" refers to the algorithm or software that dynamically calculates premiums based on health scores.

[0013] "Generation means" refers to an algorithm or software that has the function of generating specific diet and exercise suggestions to improve a user's health score based on the user's health score.

[0014] "Notification means" refers to a device or software having a function for notifying a user of a proposal generated by a generation means.

[0015] A "generative AI model" is an artificial intelligence model trained using large datasets, capable of analyzing health data and making individualized suggestions and predictions.

[0016] "Medical big data" refers to large datasets collected in the medical field, including information on diseases, treatments, and patient health status. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] As an embodiment of the present invention, the following specific system and its operation will be described. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[0039] Wearable devices (terminals)

[0040] The wearable device is worn by the user and has the function of monitoring and recording health data such as heart rate, electrocardiogram, and exercise data in real time. The user can also input the details of their diet using a smartphone application.

[0041] server

[0042] The server receives the health data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health information. The server also analyzes this data and generates a score for the user's health condition. Specifically, the health score is calculated by integrating heart rate variability, exercise volume, and dietary nutritional balance.

[0043] Data analysis

[0044] The server compares the collected health data with medical big data and analyzes it using a generative AI model. The result of the analysis is a health score, which is used to dynamically calculate each user's insurance premium. This calculation reflects each user's individual health risks and efforts to improve their lifestyle habits.

[0045] Proposal generation and notification

[0046] Based on the analyzed health data and scores, the server uses a generative AI model to generate specific recommendations for maintaining and improving health tailored to each user. Suggestions might include, for example, "add aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." These recommendations are sent via the user's wearable device or smartphone application.

[0047] Recalculation of insurance premiums

[0048] The server detects if the user implements the suggested health improvement measures and recalculates the next insurance premium. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, the next year's insurance premium will be reduced by an additional amount.

[0049] Utilizing medical big data

[0050] The server integrates collected health data with medical big data and utilizes generative AI models to gain insights for further research and diagnosis, which can contribute to the development of new treatments and preventative measures in the medical field.

[0051] Specific examples

[0052] A specific example is shown below.

[0053] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and records the 1 km walk as exercise data. The smartphone application also records that User A had bread and milk for breakfast.

[0054] The server receives this data and calculates User A's health score as "75 points." Next, using the generative AI model, the server suggests "adding aerobic exercise three times a week and increasing vegetable intake," and notifies User A.

[0055] If User A follows the suggestion to do aerobic exercise three times a week and incorporate more vegetables into his / her diet, his / her insurance premiums for the next year will be reduced by 500 yen.

[0056] In this way, a system will be built that uses interconnected terminal and server technology to collect health data, analyze it, make recommendations, and dynamically adjust insurance premiums. This system will promote the health of each individual user and provide a fair insurance premium system that reflects each individual's efforts.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The wearable device is worn by the user and collects daily heart rate, electrocardiogram, and exercise data in real time. The user also inputs their dietary habits using a smartphone application.

[0060] Step 2:

[0061] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, and dietary data to the server. The transmitted data includes each data item and a corresponding timestamp.

[0062] Step 3:

[0063] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database that includes heart rate, exercise volume, and diet history.

[0064] Step 4:

[0065] The server analyzes the received data and calculates the user's health score, taking into account the heart rate fluctuation pattern, the cumulative amount of exercise, and the nutritional balance of the diet.

[0066] Step 5:

[0067] The server references medical big data and complements the analysis results with generative AI models, which allows for accurate individualized predictions and health status assessments.

[0068] Step 6:

[0069] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account each user's health risk and the degree of lifestyle improvements.

[0070] Step 7:

[0071] The server generates specific diet and exercise recommendations to improve the user's health score, customized based on individual health data and medical big data.

[0072] Step 8:

[0073] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[0074] Step 9:

[0075] Users receive suggestions from the server and act on them to improve their lifestyle, such as doing aerobic exercise three times a week or adding more vegetables to their diet.

[0076] Step 10:

[0077] The server monitors whether the suggestions have been implemented and verifies this by checking against new data recorded in the database.

[0078] Step 11:

[0079] If the proposed plan is achieved, the server recalculates the next insurance premium, and applies an additional discount according to the achievement level.

[0080] Step 12:

[0081] The server integrates the collected health data and medical big data and analyzes them using a generative AI model. Based on the analysis results, the data is shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[0082] In this way, each step works together to establish a system that supports users' health management and enables dynamic adjustment of insurance premiums. The collected data will also be used to advance medical care.

[0083] Example 1

[0084] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0085] In modern society, there is a demand for optimal management of individual health conditions and fair and dynamic calculation of insurance premiums. However, conventional systems lack the means to efficiently collect and analyze users' health data, which makes it difficult to obtain appropriate feedback for calculating insurance premiums. Furthermore, they are also inadequate in providing specific health improvement suggestions based on users' lifestyle habits.

[0086] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0087] In this invention, the server includes a wearable device means for acquiring a user's health data, a computer means for receiving the health data from the wearable device and storing it in a data storage device, a data analysis means for analyzing the health data stored in the computer means and calculating the user's health rating, a price calculation means for dynamically calculating an insurance premium based on the health rating, a proposal generation means for generating diet and exercise proposals to improve the health rating, and a notification means for notifying the user of the generated proposals. This makes it possible to efficiently collect and analyze a user's health data, make specific proposals for improving their health, and dynamically calculate insurance premiums in a fair manner.

[0088] Definitions of important words

[0089] A "wearable device" is a device that a user wears on their body to collect health data such as heart rate, electrocardiogram, and exercise data.

[0090] A "computer device" is a device for receiving health data transmitted from a wearable device and storing the data in a data storage device.

[0091] "Data storage device" means a storage device for storing health data received by a computing device.

[0092] The "data analysis device" is a device for analyzing health data stored in a data storage device and calculating a health evaluation of a user.

[0093] The "price calculation device" is a device for dynamically calculating insurance premiums based on the health assessment calculated by the data analysis device.

[0094] The "suggestion generation device" is a device for automatically generating suggestions regarding diet and exercise to improve health assessment.

[0095] The "notification device" is a device for notifying users of the proposal content generated by the proposal generation device.

[0096] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate health scores and recommendations.

[0097] "Big data" refers to large datasets in fields such as medicine, and its use improves the accuracy of data analysis and model generation.

[0098] MODE FOR CARRYING OUT THE INVENTION

[0099] A specific system for implementing this invention and its operation are described below. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[0100] Wearable devices (terminals)

[0101] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. Specific examples of wearable devices include fitness trackers and smartwatches. Users can also input their dietary information using a smartphone application.

[0102] Data collection and transmission

[0103] The device sends the collected health data to the server via Bluetooth or Wi-Fi. This ensures that the data reaches the server quickly and without loss. For example, if user A inputs into a smartphone application that he or she had bread and milk for breakfast, that data is also sent.

[0104] Receiving and storing data

[0105] The server receives the health data sent from the device and stores it in a data storage device, which uses database management systems such as AWS RDS and Google Cloud Spanner to manage the data by linking it to each user's ID.

[0106] Data analysis and health assessment calculation

[0107] The server analyzes the received health data and calculates the user's health rating. Specifically, it integrates multiple factors such as heart rate variability, exercise volume, and nutritional balance of meals to arrive at a health rating. A generative AI model is used for this analysis. TensorFlow and PyTorch are used as generative AI models. As an example, the server calculates a health score of 75 points based on User A's data.

[0108] Generate health improvement suggestions

[0109] Based on the health assessment, the server generates suggestions on how to improve the user's health. The suggestions are generated using a generative AI model, and specific suggestions include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The generated suggestions are expressed in the form of prompt sentences as follows:

[0110] Generate appropriate health maintenance and improvement suggestions based on the health data of user A. The heart rate is 85 bpm, the exercise data is 1 km of walking, and the diet consists of bread and milk. The health score is 75 points.

[0111] Notification of proposal details

[0112] The server notifies the user of the generated suggestions via a push notification service to the wearable device or smartphone application, allowing the user to receive the notification and start taking action based on the suggestions.

[0113] Recollection of behavioral data and recalculation of insurance premiums

[0114] The server again collects data to detect whether the user acted on the suggestions. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, this is recorded. The server then recalculates the next year's insurance premium to appropriately reflect the user's efforts. The recalculated premium is saved as an updated database.

[0115] Utilizing medical big data

[0116] The server integrates the collected health data with large amounts of medical data and performs further detailed analysis. By utilizing generative AI models, it contributes to the development of new treatments and preventative measures. This analysis is carried out using Apache Spark and Hadoop.

[0117] These processes enable a single flow of processes, from collecting user health data to analyzing it, making recommendations, and dynamically adjusting insurance premiums. As a practical example, a system could be realized in which collected data is analyzed based on a health assessment, specific recommendations are made based on the data, and users act on those recommendations to ensure that their insurance premiums are appropriately adjusted. This system is an effective means of promoting user health and providing a fair insurance premium system.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Program processing flow

[0120] Step 1:

[0121] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. The input is the wearable device, and the output is the health data obtained from the device.

[0122] Step 2:

[0123] The device collects health data and inputs meal details into a smartphone application. Specifically, the device records the user's heart rate and exercise data, and the user inputs meal details into the smartphone application. The input is data entered manually by the wearable device and the user, and the output is comprehensive health data recorded on the device.

[0124] Step 3:

[0125] The device sends the collected data to the server. The device sends data to the server in real time or periodically via Bluetooth or Wi-Fi. The input is the health data recorded on the device, and the output is the data sent to the server.

[0126] Step 4:

[0127] The server receives the data and stores it in a data storage device. Specifically, the server stores the data received from the terminal in a database and links it to each user's ID. The input is the health data sent to the server, and the output is the data stored in the database.

[0128] Step 5:

[0129] The server analyzes the data and calculates a health score. The server uses a generative AI model to analyze the received and stored data and calculates a health assessment score (e.g., 75 points) based on heart rate variability, exercise volume, and dietary nutritional balance. The input is the health data stored in the database, and the output is the calculated health score.

[0130] Step 6:

[0131] The server generates suggestions using a generative AI model. The server generates prompts based on the calculated health score and uses the AI ​​model to create specific health improvement suggestions. Specific examples include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health improvement suggestions.

[0132] Step 7:

[0133] The server notifies the user of the suggestions. The server generates the suggestions in the form of prompts and notifies the user's wearable device or smartphone application via a push notification service. The input is the generated health improvement suggestions, and the output is a notification to the user.

[0134] Step 8:

[0135] The user acts according to the suggestions. Specifically, the user practices health-improving behaviors, such as aerobic exercise three times a week. The input is the notified suggestions, and the output is the user's behavioral data.

[0136] Step 9:

[0137] The server recollects the user's behavioral data and recalculates the insurance premium. The server receives the data again through the terminal and verifies whether the proposal has been implemented. It then recalculates the insurance premium and updates the database. The input is the recollected behavioral data, and the output is the recalculated insurance premium.

[0138] Step 10:

[0139] The server performs further analysis using medical big data. The server compares the collected user health data with large amounts of medical data and utilizes generative AI models to contribute to the development of new treatments and preventative measures. The input is user data and medical big data, and the output is new insights and proposals.

[0140] (Application example 1)

[0141] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] While conventional health management systems can collect and analyze users' health data and adjust insurance premiums, they lack the functionality to calculate more accurate health scores using payment data. This means that they cannot accurately reflect users' lifestyles and consumption behavior, making it difficult to ensure fairness in insurance premiums and generate appropriate health recommendations.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0144] In this invention, the server includes a wearable device means for acquiring the user's health data, a server means for receiving the health data from the wearable device and storing it in a database, an analysis means for analyzing the health data stored in the server means and calculating the user's health score, a price calculation means for dynamically calculating the insurance premium based on the health score, a generation means for generating diet and exercise suggestions to improve the health score, a notification means for notifying the user of the generated suggestions, and a payment data aggregation means for receiving payment data and reflecting it in the calculation of the health score. This enables the calculation of a health score that accurately reflects the user's lifestyle and consumption behavior.

[0145] A "wearable device" is an electronic device worn by a user to collect health data.

[0146] The "server means" is a device that has the function of receiving data sent from a wearable device and storing it in a database.

[0147] The "analysis means" is a system for analyzing the data stored in the server means and calculating the user's health score.

[0148] The "price calculation means" is a tool that has the function of dynamically calculating insurance premiums based on the health score obtained by the analysis means.

[0149] The "generator" is a device or program that generates diet and exercise suggestions to improve the health score.

[0150] The "notification means" is a system having a function of notifying the user of the proposal created by the creation means.

[0151] The "payment data aggregation means" is a mechanism that receives users' payment information and reflects it in the calculation of the health score.

[0152] An embodiment of the present invention will be described below. This system is configured by combining a wearable device, a server, and a smart device (a smartphone or smart glasses).

[0153] System Overview

[0154] This system collects the user's health data using a wearable device and transmits the data to a server. The server then uses analytical means to calculate a health score and dynamically calculates insurance premiums based on the results. The server also generates proposals and notifies the user via their smart device. It also collects payment data and reflects it in the calculation of the health score.

[0155] Health data collection

[0156] First, the user puts on a wearable device that monitors and records heart rate, electrocardiogram, exercise data, and other information in real time. Health data is then sent to a server via the user's smartphone or smart glasses.

[0157] Data analysis by server

[0158] The server stores the received health data in a database, using a database system such as MySQL. The server then analyzes the data using a generative AI model (e.g., TensorFlow or PyTorch). This integrates heart rate variability, exercise volume, and dietary nutritional balance to calculate a health score.

[0159] Payment data aggregation

[0160] Additionally, the server receives users' electronic payment data and incorporates it into the calculation of their health score. For example, it automatically records the food and exercise-related items purchased by users, thereby improving the accuracy of the health score.

[0161] Proposal generation and notification

[0162] The server generates specific suggestions for maintaining and improving health based on the analysis results. These suggestions are then sent to the user via smartphone or smart glasses, making it easier for the user to improve their daily lifestyle habits.

[0163] Insurance premium calculation

[0164] The server dynamically calculates insurance premiums based on the health score, for example, discounts on premiums if the health score is high, and recalculates premiums to take into account any suggested health improvements implemented.

[0165] Hardware and software used

[0166] Hardware: Wearable devices (e.g. fitness trackers), smartphones, smart glasses.

[0167] Software: Health data collection app (smartphone app), database system (MySQL), analysis platform (TensorFlow, PyTorch).

[0168] Specific examples

[0169] A specific example is shown below.

[0170] Example of operation:

[0171] 1. A user purchases a salad and a healthy drink for breakfast on 2023-10-05 08:00.

[0172] 2. The wearable device records a heart rate of 80 bpm at 2023-10-05 07:30.

[0173] 3. The app sends the above data to the server and receives a health score of 85 points as the analysis result.

[0174] 4. The server generates a suggestion to the user: "Add aerobic exercise three times a week."

[0175] 5. Insurance premiums will be calculated dynamically and a 300 yen discount will be applied to the next month's premium.

[0176] Example of an input prompt for a generative AI model:

[0177] "User ID: 12345's health data: Heart rate 80 bpm, Walking 5000 steps, Breakfast: Salad, Healthy drink. Calculate my health score and get suggestions and insurance discounts."

[0178] The above is a specific embodiment for carrying out the present invention, which makes it possible to calculate a health score that accurately reflects the lifestyle habits and consumption behavior of the user.

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data). This data is transmitted in real time to a smartphone or smart glasses. The input is the health data from the wearable device, and the output is transmitted to the smart device.

[0182] Step 2:

[0183] The smart device receives health data from the wearable device and sends it to the server. The input is the health data from the wearable device, and the output is the data sent to the server.

[0184] Step 3:

[0185] The server stores the received health data in a database. Specifically, the data is stored in a MySQL database, for example. The input is the data sent from the smart device, and the output is the health data stored in the database.

[0186] Step 4:

[0187] The server collects the user's electronic payment data using a payment data aggregation mechanism, where the input is the user's payment record and the output is the payment data for use in calculating the health score.

[0188] Step 5:

[0189] The server integrates the health data and payment data and calculates a health score using a generative AI model (such as TensorFlow or PyTorch) as an analytical tool. The input is the health data and payment data, and the output is the calculated health score.

[0190] Step 6:

[0191] The server uses the generation means to generate health suggestions based on the health score, such as "do aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health suggestions.

[0192] Step 7:

[0193] The server notifies the user of the generated health suggestions via a notification mechanism. The input is the health suggestions, and the output is the notified suggestions.

[0194] Step 8:

[0195] If the user implements the health suggestions, the server detects the implementation results and recalculates the next insurance premium. For example, if the user performs aerobic exercise three times a week, the server will discount the insurance premium. The input is the implementation results of the health suggestions, and the output is the recalculated insurance premium.

[0196] These are the specific processing steps of the system that realizes this application example. This makes it possible to calculate a health score that accurately reflects the user's lifestyle and consumption behavior, and to dynamically calculate insurance premiums.

[0197] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0198] As a form for implementing the present invention, a system will be specifically described that acquires health data and emotional data of a user, dynamically calculates insurance premiums based on this data, and makes appropriate suggestions for maintaining and improving health.

[0199] Wearable devices (terminals)

[0200] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application.

[0201] Emotion Engine

[0202] The device's built-in emotion engine analyzes the user's emotional state using skin galvanic response, heart rate variability, facial expression data, etc. This allows the device to grasp the user's current emotions, such as stress or relaxation, in real time.

[0203] server

[0204] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information.

[0205] Data analysis

[0206] The server analyzes the received health and emotional data and calculates the user's health score. Specifically, the calculation takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. For example, if a person is experiencing persistent stress, measures such as a more severe health score assessment may be taken.

[0207] Dynamic calculation of insurance premiums

[0208] The server dynamically calculates insurance premiums based on the analyzed health score, which is designed to reflect individual health risks, emotional state, and lifestyle improvement efforts.

[0209] Proposal generation and notification

[0210] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. These suggestions include, for example, "meditate to reduce stress" or "take a walk in nature on your day off to relax." These suggestions are customized taking into account emotional data generated by the emotion engine. The suggestions are then communicated to the user via their wearable device or smartphone application.

[0211] Recalculation of insurance premiums

[0212] The server monitors whether the user has implemented the suggested health improvements, checking against new data recorded in the database. If the recommendations have been achieved, the server recalculates the next insurance premium and notifies the user of the results.

[0213] Utilizing medical big data

[0214] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[0215] Specific examples

[0216] A specific example is shown below.

[0217] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and collects data on a 1 km walk and breakfast (bread and milk). Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and evaluates that he is in a "stressed state."

[0218] The server receives this data and calculates User A's health score as "70 points." It also generates a stress reduction suggestion of "meditating three times a week" from the emotional data and notifies User A.

[0219] If User A follows the suggested meditation and improves his / her lifestyle, the insurance premium for the next year will be reduced by 500 yen based on that data.

[0220] The server will integrate the new data into medical big data and share the analysis results with medical research institutions, thereby advancing research into new treatments for stress management.

[0221] In this way, by linking wearable devices, servers, and emotion engines and utilizing health and emotion data, a system can be built that provides optimal health management and dynamic adjustment of insurance premiums for each individual user.

[0222] The processing flow will be explained below.

[0223] Step 1:

[0224] The wearable device allows users to monitor their daily heart rate, electrocardiogram, exercise data, and emotional status in real time, and also allows users to input their dietary information using a smartphone application.

[0225] Step 2:

[0226] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, dietary data, and emotion data to the server. The transmitted data includes each data item and a corresponding timestamp.

[0227] Step 3:

[0228] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database, recording their heart rate, exercise volume, dietary habits, and emotional state.

[0229] Step 4:

[0230] The server analyzes the stored data and calculates the user's health score, taking into account heart rate fluctuation patterns, exercise volume, nutritional balance of meals, and emotional state.

[0231] Step 5:

[0232] The server references medical big data and uses generative AI models to complement and refine the health score analysis results, enabling more accurate predictions and assessments.

[0233] Step 6:

[0234] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account the user's health risks, emotional state, and lifestyle improvement efforts.

[0235] Step 7:

[0236] The server generates specific suggestions for maintaining or improving health based on the user's health score and emotional state, and the suggestions are customized taking into account the user's emotional data.

[0237] Step 8:

[0238] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[0239] Step 9:

[0240] Users receive suggestions from the server and use them to improve their lifestyle, such as doing aerobic exercise three times a week or performing meditation sessions to reduce stress.

[0241] Step 10:

[0242] The server monitors whether the proposed health improvement measures have been implemented, checking against new data recorded in the database, and if implementation is confirmed, records the result in the database.

[0243] Step 11:

[0244] If the proposal is achieved, the server recalculates the next premium, applying an additional discount according to the achievement level.

[0245] Step 12:

[0246] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[0247] In this way, each step works in tandem to create a system that comprehensively manages users' health and emotions, providing individually optimized recommendations and dynamically adjusting insurance premiums. The collected data will also be used in medical research, making a wide-ranging contribution to society.

[0248] Example 2

[0249] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0250] In modern society, there is a demand for accurate understanding of individual health conditions and stress levels, and for dynamic calculation of insurance premiums. However, achieving this requires a system that can collect and analyze a variety of health and emotional data in real time and automatically generate optimal health management proposals for each individual user. Conventional systems have had difficulty dynamically calculating insurance premiums that take emotional states into account, or recalculating premiums based on the implementation of proposals. It has also been difficult to integrate collected data with medical big data and analyze it using generative AI models to contribute to new medical research.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0252] In this invention, the server includes a means for receiving health data and emotion data and storing them in a database, a means for analyzing the health data and emotion data to calculate a health score, a means for dynamically calculating insurance premiums based on the health score, and a means for generating health maintenance and improvement proposals. This allows for comprehensive management of the user's health status, dynamic calculation of insurance premiums, and real-time healthcare proposals. Furthermore, insurance premiums can be recalculated by monitoring the implementation status of health improvement measures, and the collected data can be integrated with medical big data to contribute to new medical research.

[0253] A "wearable device" is a device that is worn on the user's body and collects health and emotional data in real time.

[0254] "Health data" refers to physiological data that indicates the user's health condition, such as heart rate, electrocardiogram, and exercise data.

[0255] "Emotional data" refers to data that indicates the emotional state of the user, such as skin galvanic response, heart rate variability, and facial expression data.

[0256] The "server means" refers to a device and software for receiving health data and emotion data transmitted from a wearable device and storing the data in a database.

[0257] The "analysis means" refers to a device and software for analyzing the health data and emotion data stored on the server and calculating the user's health score.

[0258] The "health score" is a numerical value calculated by the analysis means to comprehensively evaluate the health condition of the user.

[0259] A "price calculator" is a device and software for dynamically calculating insurance premiums based on health scores.

[0260] The "generation means" refers to a device and software for generating health maintenance and improvement suggestions to improve the user's health score.

[0261] The "notification means" refers to devices and software for notifying users of generated suggestions.

[0262] "Monitoring means" means the equipment and software used to monitor whether the proposed health improvement measures have been implemented and to recalculate the next premium.

[0263] "Medical big data" refers to a large-scale dataset that compiles a wide range of health and medical data.

[0264] A "generative AI model" is a machine learning model that uses AI technology to generate specific suggestions for maintaining and improving health.

[0265] This invention is a system that collects health and emotional data from users, dynamically calculates insurance premiums based on that data, and makes specific suggestions for maintaining and improving their health. This system is composed of a wearable device (terminal), a server, and a generative AI model.

[0266] Wearable devices (terminals)

[0267] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application. For example, when the user goes for a daily walk or jog, the device uses GPS to record the distance and elapsed time. It is also equipped with a heart rate sensor to monitor the heart rate during exercise.

[0268] Emotion Engine

[0269] The device's built-in emotion engine can analyze the user's emotional state using data on skin electrodermal response, heart rate variability, and facial expressions. For example, if skin electrodermal response increases and heart rate variability increases, it will recognize this as a "stress state" and provide a real-time evaluation.

[0270] server

[0271] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information. Each time the server receives data, it stores it in the database and performs a comparative analysis with the user's past data.

[0272] Data analysis

[0273] The server analyzes the received health and emotional data and calculates the user's health score. The analysis takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, emotional state, and other factors. For example, if the user exercises less and is under stress for a long period of time, the health score will be calculated as low.

[0274] Dynamic calculation of insurance premiums

[0275] The server dynamically calculates insurance premiums based on the analyzed health score. This calculation is designed to reflect individual health risks, emotional state, and efforts to improve lifestyle habits. For example, premiums can be set lower for users with a high health score, and higher for users with a low health score.

[0276] Proposal generation and notification

[0277] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. For example, a specific suggestion such as "Meditate three times a week to reduce stress" may be generated. These suggestions are customized taking into account the user's emotional data and are communicated via a wearable device or smartphone application.

[0278] Example (prompt):

[0279] "If the user is stressed, suggest specific actions to reduce stress."

[0280] Proposal implementation and monitoring

[0281] The server monitors whether the user has implemented the suggested health improvements. This data is compared with the recommendations, and if implementation is confirmed, the next insurance premium is recalculated. For example, if a user meditates three times a week, the insurance premium will be reduced based on that data.

[0282] Utilizing medical big data

[0283] The server integrates the collected health and emotional data with medical big data and analyzes it using a generative AI model. The results of this analysis will be shared with medical research institutions, contributing to the development of new treatments and preventative measures. Specifically, it is expected that research into new treatments for stress management will progress.

[0284] In this way, by linking wearable devices, a server, and an emotion engine, this system effectively utilizes users' health and emotion data to dynamically adjust insurance premiums and provide suggestions for maintaining and improving health.

[0285] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0286] Step 1:

[0287] The device collects heart rate, electrocardiogram, exercise data, and emotional data in real time. Specifically, sensors measure heart rate every second and record exercise data (e.g., walking distance) using GPS. The emotion engine analyzes electrodermal response and heart rate variability to determine emotional state. The input is real-time data from the sensors, and the output is collected health data and emotional data.

[0288] Step 2:

[0289] The terminal sends the collected data to the server at regular intervals. Specifically, the device packets the data every 30 seconds and sends it to the server via the smartphone. The input is the data collected by the terminal, and the output is the data sent to the server.

[0290] Step 3:

[0291] The server receives the health and emotion data sent from the device and stores it in a database. Specifically, the server associates the received data with the user's ID and stores it in the corresponding table in the database. The input is the data sent from the device, and the output is the data stored in the database.

[0292] Step 4:

[0293] The server calculates the user's health score based on the data received. Specifically, the server's analysis engine calculates a comprehensive health score by taking into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. The input is the health and emotional data stored in the database, and the output is the calculated health score.

[0294] Step 5:

[0295] The server dynamically calculates the insurance premium based on the analyzed health score. Specifically, it inputs the health score and calculates a new insurance premium according to the insurance premium setting rules. The input is the health score, and the output is the calculated new insurance premium.

[0296] Step 6:

[0297] The server uses the generative AI model to generate suggestions for maintaining or improving health, such as suggesting "meditation methods to reduce stress." The input is the health score and emotion data, and the output is the generated health suggestions.

[0298] Step 7:

[0299] The server notifies the user of the generated suggestions. Specifically, the suggestions are notified via a wearable device or a smartphone app. The input is the generated health suggestions, and the output is the notified information.

[0300] Step 8:

[0301] The server monitors whether the user has implemented the suggested health improvement measures and recalculates the next insurance premium. Specifically, it compares the new data sent from the device with the proposed measures. The input is the new health data and the proposed measures, and the output is the recalculated insurance premium.

[0302] Step 9:

[0303] The server integrates the collected data with medical big data, analyzes it using a generative AI model, and shares the analysis results with medical research institutions. Specifically, the data is uploaded to a large dataset and the analysis results are provided through a shared API. The input is the collected health data and emotion data, and the output is the analysis results.

[0304] (Application example 2)

[0305] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0306] While modern insurance systems calculate insurance premiums based on users' health data, they do not dynamically calculate premiums that take into account the user's emotional state or provide customized suggestions to improve the health score itself. Furthermore, there is a lack of mechanisms for providing services and promotions based on the user's health status in physical stores. This makes it difficult to implement specific measures for users' health management and improvement of their emotional state, making it difficult to promote the overall health of users.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0308] In this invention, the server includes means for receiving data from a wearable device to acquire a user's health data and emotional data and storing the data in a database, means for analyzing the health data and emotional data stored in the server means and calculating the user's health score, means for generating suggestions for diet, exercise, and relaxation to improve the health score, and means for notifying the user of the generated suggestions as services and promotions in a physical store. This makes it possible not only to precisely analyze a user's health data and emotional data and provide individually optimized suggestions for maintaining and improving health, but also to effectively utilize and notify the suggestions in a physical store.

[0309] A "wearable device" is a device for collecting health and emotional data of a user. It has the function of collecting heart rate, electrocardiogram, exercise data, and emotional state in real time.

[0310] The "server means" is a device that receives the health data and emotion data sent from the wearable device and stores them in a database.

[0311] The "analysis means" is a device that analyzes the health data and emotion data stored on the server and calculates the user's health score.

[0312] The "price calculation means" is a device that dynamically calculates insurance premiums based on the health score calculated by the analysis means.

[0313] The "generation means" is a device that generates diet and exercise suggestions, as well as relaxation suggestions, to improve the health score.

[0314] The "notification means" is a device for notifying the user of the generated proposal, which is provided to the user as a service or promotion in a physical store.

[0315] "Health Data" refers to physiological data such as a user's heart rate, electrocardiogram, and exercise data.

[0316] "Emotion data" refers to data on the user's emotional state that is analyzed based on the user's skin galvanic response, heart rate variability, facial expression data, and the like.

[0317] The "health score" is an index showing the user's health condition calculated by the analysis means by integrating health data and emotional data.

[0318] "Dietary Suggestions" are specific dietary recommendations to improve a user's health score.

[0319] "Exercise Suggestions" are recommendations for specific exercises to improve a user's health score.

[0320] "Relaxation suggestions" are recommendations for specific activities to improve the user's emotional state and reduce stress.

[0321] "Services and Promotions" refers to promotional activities, including discounts and special offers on specific services and products offered in physical stores.

[0322] As an embodiment of the present invention, we will specifically explain a system that acquires health data and emotional data of users, dynamically calculates insurance premiums based on this data, and makes appropriate health maintenance and improvement proposals. This system is composed of the following main components.

[0323] 1. Wearable devices

[0324] The wearable device has the function of acquiring the following data in real time:

[0325] Heart rate

[0326] electro-cardiogram

[0327] Exercise data

[0328] Emotional state (analysis based on skin galvanic response, heart rate variability, and facial expression data)

[0329] When worn by the user, these physiological and emotional data are collected and sent to a server via a smartphone application.

[0330] 2. Server Means

[0331] The server receives the health data and emotion data sent from the wearable device and stores them in a database. The server has the following functions:

[0332] Receiving and storing data

[0333] Health and emotional data analysis

[0334] Calculating your health score

[0335] Dynamic calculation of insurance premiums

[0336] Proposal Generation

[0337] 3. Analysis method

[0338] The analysis method comprehensively analyzes the health data and emotional data stored on the server and calculates the user's health score. The analysis uses medical big data and generative AI models. For example, if a person is experiencing persistent stress, the health score can be set low.

[0339] 4. Generation means

[0340] The generation means generates dietary and exercise suggestions, as well as relaxation suggestions, based on the health score calculated by the analysis means. Using the generative AI model, the following specific suggestions are generated:

[0341] "Meditate three times a week"

[0342] "I walk in nature to relax."

[0343] 5. Means of notification

[0344] The notification means notifies the user of the proposal created by the generation means. In particular, the proposal can be provided as a service or promotion in a physical store. The notification is performed via a smartphone application or smart glasses.

[0345] Specific examples

[0346] A specific example is given below.

[0347] At 2023-10-05 07:30:00, User A's heart rate is recorded as 85 bpm, and data on 1 km of walking and breakfast (bread and milk) is collected. Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and assesses that he is in a stressed state.

[0348] The server receives this data and calculates User A's health score as "70 points." From the emotional data, it generates a stress reduction suggestion of "meditating three times a week" and notifies User A. An example of a specific notification would be "Please relax in the relaxation area."

[0349] An example of a prompt sentence is, "Generate recommended service suggestions if the user's health score is 80 or less. Input example: 'My heart rate is 85 bpm and I'm in a stressed state. What services would you recommend?'" By inputting this prompt into the generation AI model, it is possible to automatically generate appropriate suggestions.

[0350] The embodiment of the present invention is as described above. It is a system that comprehensively manages a user's health and emotional state and provides individualized suggestions to promote health improvement and dynamically adjust insurance premiums.

[0351] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0352] Step 1:

[0353] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data) and emotional data (galvanic skin response, heart rate variability, facial expression data) in real time and transmits it to a smartphone application.

[0354] Input: Health and emotion data

[0355] Output: Send data to a smartphone application

[0356] How it works: The wearable device stores data collected by sensors in its internal memory and periodically transmits it to a smartphone application via Bluetooth or Wi-Fi.

[0357] Step 2:

[0358] The smartphone application transmits the data received from the wearable device to a server.

[0359] Input: Health and emotion data from wearable devices

[0360] Output: Send data to the server

[0361] Operation: The smartphone application sends the received data to the server as an HTTP request, along with the user ID.

[0362] Step 3:

[0363] The server stores the data received from the smartphone application in a database.

[0364] Input: Data from a smartphone application

[0365] Output: Data storage in database

[0366] Operation: The server stores the received data in the appropriate database table based on the user ID.

[0367] Step 4:

[0368] The server analyzes the health data and emotion data stored in the database and calculates the user's health score.

[0369] Input: Health and emotion data stored in a database

[0370] Output: Health score

[0371] How it works: The server uses a generative AI model to analyze the data and calculate a health score based on information such as heart rate and emotional state, with medical big data also being taken into account.

[0372] Step 5:

[0373] The server dynamically calculates the insurance premium based on the calculated health score.

[0374] Input: Health score

[0375] Output: Insurance premium

[0376] Operation: The server recalculates the insurance premium based on the health risk assessment. This calculation is performed by the insurance risk algorithm.

[0377] Step 6:

[0378] The server generates diet, exercise, and relaxation suggestions to improve the health score.

[0379] Input: Health score

[0380] Output: Specific proposals

[0381] How it works: Based on your health score, the server uses a generative AI model to generate personalized health maintenance and improvement suggestions, such as meditation and relaxation exercises to reduce stress.

[0382] Step 7:

[0383] The server notifies the user of the generated proposals as services or promotions in the physical store.

[0384] Input: Specific proposal details

[0385] Output: Notification to user

[0386] Operation: The server notifies the user of the proposed offer via a smartphone application or smart glasses. It also provides promotional information in conjunction with in-store displays and digital signage.

[0387] Examples and prompts

[0388] As a specific example, consider the case where user A is suggested to meditate three times a week and is notified through the application to "relax in the relaxation area."

[0389] Example prompt: "Generate recommended services if the user's health score is below 80. Example input: 'My heart rate is 85 bpm and I'm stressed. What services would you recommend?'"

[0390] Through the above steps, the system of the present invention comprehensively manages the user's health and emotional state, provides personalized recommendations, promotes health improvement, and dynamically adjusts insurance premiums.

[0391] 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.

[0392] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0393] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0394] [Second embodiment]

[0395] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0396] 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.

[0397] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0398] 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.

[0399] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0400] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0401] 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.

[0402] 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.

[0403] 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 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.

[0404] 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.

[0405] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0406] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0407] As an embodiment of the present invention, the following specific system and its operation will be described. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[0408] Wearable devices (terminals)

[0409] The wearable device is worn by the user and has the function of monitoring and recording health data such as heart rate, electrocardiogram, and exercise data in real time. The user can also input the details of their diet using a smartphone application.

[0410] server

[0411] The server receives the health data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health information. The server also analyzes this data and generates a score for the user's health condition. Specifically, the health score is calculated by integrating heart rate variability, exercise volume, and dietary nutritional balance.

[0412] Data analysis

[0413] The server compares the collected health data with medical big data and analyzes it using a generative AI model. The result of the analysis is a health score, which is used to dynamically calculate each user's insurance premium. This calculation reflects each user's individual health risks and efforts to improve their lifestyle habits.

[0414] Proposal generation and notification

[0415] Based on the analyzed health data and scores, the server uses a generative AI model to generate specific recommendations for maintaining and improving health tailored to each user. Suggestions might include, for example, "add aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." These recommendations are sent via the user's wearable device or smartphone application.

[0416] Recalculation of insurance premiums

[0417] The server detects if the user implements the suggested health improvement measures and recalculates the next insurance premium. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, the next year's insurance premium will be reduced by an additional amount.

[0418] Utilizing medical big data

[0419] The server integrates collected health data with medical big data and utilizes generative AI models to gain insights for further research and diagnosis, which can contribute to the development of new treatments and preventative measures in the medical field.

[0420] Specific examples

[0421] A specific example is shown below.

[0422] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and records the 1 km walk as exercise data. The smartphone application also records that User A had bread and milk for breakfast.

[0423] The server receives this data and calculates User A's health score as "75 points." Next, using the generative AI model, the server suggests "adding aerobic exercise three times a week and increasing vegetable intake," and notifies User A.

[0424] If User A follows the suggestion to do aerobic exercise three times a week and incorporate more vegetables into his / her diet, his / her insurance premiums for the next year will be reduced by 500 yen.

[0425] In this way, a system will be built that uses interconnected terminal and server technology to collect health data, analyze it, make recommendations, and dynamically adjust insurance premiums. This system will promote the health of each individual user and provide a fair insurance premium system that reflects each individual's efforts.

[0426] The processing flow will be explained below.

[0427] Step 1:

[0428] The wearable device is worn by the user and collects daily heart rate, electrocardiogram, and exercise data in real time. The user also inputs their dietary habits using a smartphone application.

[0429] Step 2:

[0430] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, and dietary data to the server. The transmitted data includes each data item and a corresponding timestamp.

[0431] Step 3:

[0432] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database. This database includes heart rate, exercise volume, and diet history.

[0433] Step 4:

[0434] The server analyzes the received data and calculates the user's health score, taking into account the heart rate fluctuation pattern, the cumulative amount of exercise, and the nutritional balance of the diet.

[0435] Step 5:

[0436] The server references medical big data and complements the analysis results with generative AI models, which allows for accurate individualized predictions and health status assessments.

[0437] Step 6:

[0438] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account each user's health risk and the degree of lifestyle improvements.

[0439] Step 7:

[0440] The server generates specific diet and exercise recommendations to improve the user's health score, customized based on individual health data and medical big data.

[0441] Step 8:

[0442] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[0443] Step 9:

[0444] Users receive suggestions from the server and act on them to improve their lifestyle, such as doing aerobic exercise three times a week or adding more vegetables to their diet.

[0445] Step 10:

[0446] The server monitors whether the suggestions have been implemented and verifies this by checking against new data recorded in the database.

[0447] Step 11:

[0448] If the proposed plan is achieved, the server recalculates the next insurance premium, and applies an additional discount according to the achievement level.

[0449] Step 12:

[0450] The server integrates the collected health data and medical big data and analyzes them using a generative AI model. Based on the analysis results, the data is shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[0451] In this way, each step works together to establish a system that supports users' health management and enables dynamic adjustment of insurance premiums. The collected data will also be used to advance medical care.

[0452] Example 1

[0453] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0454] In modern society, there is a demand for optimal management of individual health conditions and fair and dynamic calculation of insurance premiums. However, conventional systems lack the means to efficiently collect and analyze users' health data, which makes it difficult to obtain appropriate feedback for calculating insurance premiums. Furthermore, they are also inadequate in providing specific health improvement suggestions based on users' lifestyle habits.

[0455] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0456] In this invention, the server includes a wearable device means for acquiring a user's health data, a computer means for receiving the health data from the wearable device and storing it in a data storage device, a data analysis means for analyzing the health data stored in the computer means and calculating the user's health rating, a price calculation means for dynamically calculating an insurance premium based on the health rating, a proposal generation means for generating diet and exercise proposals to improve the health rating, and a notification means for notifying the user of the generated proposals. This makes it possible to efficiently collect and analyze a user's health data, make specific proposals for improving their health, and dynamically calculate insurance premiums in a fair manner.

[0457] Definitions of important words

[0458] A "wearable device" is a device that a user wears on their body to collect health data such as heart rate, electrocardiogram, and exercise data.

[0459] A "computer device" is a device for receiving health data transmitted from a wearable device and storing the data in a data storage device.

[0460] "Data storage device" means a storage device for storing health data received by a computing device.

[0461] The "data analysis device" is a device for analyzing health data stored in a data storage device and calculating a health evaluation of a user.

[0462] The "price calculation device" is a device for dynamically calculating insurance premiums based on the health assessment calculated by the data analysis device.

[0463] The "suggestion generation device" is a device for automatically generating suggestions regarding diet and exercise to improve health assessment.

[0464] The "notification device" is a device for notifying users of the proposal content generated by the proposal generation device.

[0465] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate health scores and recommendations.

[0466] "Big data" refers to large datasets in fields such as medicine, and its use improves the accuracy of data analysis and model generation.

[0467] MODE FOR CARRYING OUT THE INVENTION

[0468] A specific system for implementing this invention and its operation are described below. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[0469] Wearable devices (terminals)

[0470] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. Specific examples of wearable devices include fitness trackers and smartwatches. Users can also input their dietary information using a smartphone application.

[0471] Data collection and transmission

[0472] The device sends the collected health data to the server via Bluetooth or Wi-Fi. This ensures that the data reaches the server quickly and without loss. For example, if user A inputs into a smartphone application that he or she had bread and milk for breakfast, that data is also sent.

[0473] Receiving and storing data

[0474] The server receives the health data sent from the device and stores it in a data storage device, which uses database management systems such as AWS RDS and Google Cloud Spanner to manage the data by linking it to each user's ID.

[0475] Data analysis and health assessment calculation

[0476] The server analyzes the received health data and calculates the user's health rating. Specifically, it integrates multiple factors such as heart rate variability, exercise volume, and nutritional balance of meals to arrive at a health rating. A generative AI model is used for this analysis. TensorFlow and PyTorch are used as generative AI models. As an example, the server calculates a health score of 75 points based on User A's data.

[0477] Generate health improvement suggestions

[0478] Based on the health assessment, the server generates suggestions on how to improve the user's health. The suggestions are generated using a generative AI model, and specific suggestions include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The generated suggestions are expressed in the form of prompt sentences as follows:

[0479] Generate appropriate health maintenance and improvement suggestions based on the health data of user A. The heart rate is 85 bpm, the exercise data is 1 km of walking, and the diet consists of bread and milk. The health score is 75 points.

[0480] Notification of proposal details

[0481] The server notifies the user of the generated suggestions via a push notification service to the wearable device or smartphone application, allowing the user to receive the notification and start taking action based on the suggestions.

[0482] Recollection of behavioral data and recalculation of insurance premiums

[0483] The server again collects data to detect whether the user acted on the suggestions. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, this is recorded. The server then recalculates the next year's insurance premium to appropriately reflect the user's efforts. The recalculated premium is saved as an updated database.

[0484] Utilizing medical big data

[0485] The server integrates the collected health data with large amounts of medical data and performs further detailed analysis. By utilizing generative AI models, it contributes to the development of new treatments and preventative measures. This analysis is carried out using Apache Spark and Hadoop.

[0486] These processes enable a single flow of processes, from collecting user health data to analyzing it, making recommendations, and dynamically adjusting insurance premiums. As a practical example, a system could be realized in which collected data is analyzed based on a health assessment, specific recommendations are made based on the data, and users act on those recommendations to ensure that their insurance premiums are appropriately adjusted. This system is an effective means of promoting user health and providing a fair insurance premium system.

[0487] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0488] Program processing flow

[0489] Step 1:

[0490] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. The input is the wearable device, and the output is the health data obtained from the device.

[0491] Step 2:

[0492] The device collects health data and inputs meal details into a smartphone application. Specifically, the device records the user's heart rate and exercise data, and the user inputs meal details into the smartphone application. The input is data entered manually by the wearable device and the user, and the output is comprehensive health data recorded on the device.

[0493] Step 3:

[0494] The device sends the collected data to the server. The device sends data to the server in real time or periodically via Bluetooth or Wi-Fi. The input is the health data recorded on the device, and the output is the data sent to the server.

[0495] Step 4:

[0496] The server receives the data and stores it in a data storage device. Specifically, the server stores the data received from the terminal in a database and links it to each user's ID. The input is the health data sent to the server, and the output is the data stored in the database.

[0497] Step 5:

[0498] The server analyzes the data and calculates a health score. The server uses a generative AI model to analyze the received and stored data and calculates a health assessment score (e.g., 75 points) based on heart rate variability, exercise volume, and dietary nutritional balance. The input is the health data stored in the database, and the output is the calculated health score.

[0499] Step 6:

[0500] The server generates suggestions using a generative AI model. The server generates prompts based on the calculated health score and uses the AI ​​model to create specific health improvement suggestions. Specific examples include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health improvement suggestions.

[0501] Step 7:

[0502] The server notifies the user of the suggestions. The server generates the suggestions in the form of prompts and notifies the user's wearable device or smartphone application via a push notification service. The input is the generated health improvement suggestions, and the output is a notification to the user.

[0503] Step 8:

[0504] The user acts according to the suggestions. Specifically, the user practices health-improving behaviors, such as aerobic exercise three times a week. The input is the notified suggestions, and the output is the user's behavioral data.

[0505] Step 9:

[0506] The server recollects the user's behavioral data and recalculates the insurance premium. The server receives the data again through the terminal and verifies whether the proposal has been implemented. It then recalculates the insurance premium and updates the database. The input is the recollected behavioral data, and the output is the recalculated insurance premium.

[0507] Step 10:

[0508] The server performs further analysis using medical big data. The server compares the collected user health data with large amounts of medical data and utilizes generative AI models to contribute to the development of new treatments and preventative measures. The input is user data and medical big data, and the output is new insights and proposals.

[0509] (Application example 1)

[0510] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0511] While conventional health management systems can collect and analyze users' health data and adjust insurance premiums, they lack the functionality to calculate more accurate health scores using payment data. This means that they cannot accurately reflect users' lifestyles and consumption behavior, making it difficult to ensure fairness in insurance premiums and generate appropriate health recommendations.

[0512] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0513] In this invention, the server includes a wearable device means for acquiring the user's health data, a server means for receiving the health data from the wearable device and storing it in a database, an analysis means for analyzing the health data stored in the server means and calculating the user's health score, a price calculation means for dynamically calculating the insurance premium based on the health score, a generation means for generating diet and exercise suggestions to improve the health score, a notification means for notifying the user of the generated suggestions, and a payment data aggregation means for receiving payment data and reflecting it in the calculation of the health score. This enables the calculation of a health score that accurately reflects the user's lifestyle and consumption behavior.

[0514] A "wearable device" is an electronic device worn by a user to collect health data.

[0515] The "server means" is a device that has the function of receiving data sent from a wearable device and storing it in a database.

[0516] The "analysis means" is a system for analyzing the data stored in the server means and calculating the user's health score.

[0517] The "price calculation means" is a tool that has the function of dynamically calculating insurance premiums based on the health score obtained by the analysis means.

[0518] The "generator" is a device or program that generates diet and exercise suggestions to improve the health score.

[0519] The "notification means" is a system having a function of notifying the user of the proposal created by the creation means.

[0520] The "payment data aggregation means" is a mechanism that receives users' payment information and reflects it in the calculation of the health score.

[0521] An embodiment of the present invention will be described below. This system is configured by combining a wearable device, a server, and a smart device (a smartphone or smart glasses).

[0522] System Overview

[0523] This system collects the user's health data using a wearable device and transmits the data to a server. The server then uses analytical means to calculate a health score and dynamically calculates insurance premiums based on the results. The server also generates proposals and notifies the user via their smart device. It also collects payment data and reflects it in the calculation of the health score.

[0524] Health data collection

[0525] First, the user puts on a wearable device that monitors and records heart rate, electrocardiogram, exercise data, and other information in real time. Health data is then sent to a server via the user's smartphone or smart glasses.

[0526] Data analysis by server

[0527] The server stores the received health data in a database, using a database system such as MySQL. The server then analyzes the data using a generative AI model (e.g., TensorFlow or PyTorch). This integrates heart rate variability, exercise volume, and dietary nutritional balance to calculate a health score.

[0528] Payment data aggregation

[0529] Additionally, the server receives users' electronic payment data and incorporates it into the calculation of their health score. For example, it automatically records the food and exercise-related items purchased by users, thereby improving the accuracy of the health score.

[0530] Proposal generation and notification

[0531] The server generates specific suggestions for maintaining and improving health based on the analysis results. These suggestions are then sent to the user via smartphone or smart glasses, making it easier for the user to improve their daily lifestyle habits.

[0532] Insurance premium calculation

[0533] The server dynamically calculates insurance premiums based on the health score, for example, discounts on premiums if the health score is high, and recalculates premiums to take into account any suggested health improvements implemented.

[0534] Hardware and software used

[0535] Hardware: Wearable devices (e.g. fitness trackers), smartphones, smart glasses.

[0536] Software: Health data collection app (smartphone app), database system (MySQL), analysis platform (TensorFlow, PyTorch).

[0537] Specific examples

[0538] A specific example is shown below.

[0539] Example of operation:

[0540] 1. A user purchases a salad and a healthy drink for breakfast on 2023-10-05 08:00.

[0541] 2. The wearable device records a heart rate of 80 bpm at 2023-10-05 07:30.

[0542] 3. The app sends the above data to the server and receives a health score of 85 points as the analysis result.

[0543] 4. The server generates a suggestion to the user: "Add aerobic exercise three times a week."

[0544] 5. Insurance premiums will be calculated dynamically and a 300 yen discount will be applied to the next month's premium.

[0545] Example of an input prompt for a generative AI model:

[0546] "User ID: 12345's health data: Heart rate 80 bpm, Walking 5000 steps, Breakfast: Salad, Healthy drink. Calculate my health score and get suggestions and insurance discounts."

[0547] The above is a specific embodiment for carrying out the present invention, which makes it possible to calculate a health score that accurately reflects the lifestyle habits and consumption behavior of the user.

[0548] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0549] Step 1:

[0550] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data). This data is transmitted in real time to a smartphone or smart glasses. The input is the health data from the wearable device, and the output is transmitted to the smart device.

[0551] Step 2:

[0552] The smart device receives health data from the wearable device and sends it to the server. The input is the health data from the wearable device, and the output is the data sent to the server.

[0553] Step 3:

[0554] The server stores the received health data in a database. Specifically, the data is stored in a MySQL database, for example. The input is the data sent from the smart device, and the output is the health data stored in the database.

[0555] Step 4:

[0556] The server collects the user's electronic payment data using a payment data aggregation mechanism, where the input is the user's payment record and the output is the payment data for use in calculating the health score.

[0557] Step 5:

[0558] The server integrates the health data and payment data and calculates a health score using a generative AI model (such as TensorFlow or PyTorch) as an analytical tool. The input is the health data and payment data, and the output is the calculated health score.

[0559] Step 6:

[0560] The server uses the generation means to generate health suggestions based on the health score, such as "do aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health suggestions.

[0561] Step 7:

[0562] The server notifies the user of the generated health suggestions via a notification mechanism. The input is the health suggestions, and the output is the notified suggestions.

[0563] Step 8:

[0564] If the user implements the health suggestions, the server detects the implementation results and recalculates the next insurance premium. For example, if the user performs aerobic exercise three times a week, the server will discount the insurance premium. The input is the implementation results of the health suggestions, and the output is the recalculated insurance premium.

[0565] These are the specific processing steps of the system that realizes this application example. This makes it possible to calculate a health score that accurately reflects the user's lifestyle and consumption behavior, and to dynamically calculate insurance premiums.

[0566] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0567] As a form for implementing the present invention, a system will be specifically described that acquires health data and emotional data of a user, dynamically calculates insurance premiums based on this data, and makes appropriate suggestions for maintaining and improving health.

[0568] Wearable devices (terminals)

[0569] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application.

[0570] Emotion Engine

[0571] The device's built-in emotion engine analyzes the user's emotional state using skin galvanic response, heart rate variability, facial expression data, etc. This allows the device to grasp the user's current emotions, such as stress or relaxation, in real time.

[0572] server

[0573] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information.

[0574] Data analysis

[0575] The server analyzes the received health and emotional data and calculates the user's health score. Specifically, the calculation takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. For example, if a person is experiencing persistent stress, measures such as a more severe health score assessment may be taken.

[0576] Dynamic calculation of insurance premiums

[0577] The server dynamically calculates insurance premiums based on the analyzed health score, which is designed to reflect individual health risks, emotional state, and lifestyle improvement efforts.

[0578] Proposal generation and notification

[0579] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. These suggestions include, for example, "meditate to reduce stress" or "take a walk in nature on your day off to relax." These suggestions are customized taking into account emotional data generated by the emotion engine. The suggestions are then communicated to the user via their wearable device or smartphone application.

[0580] Recalculation of insurance premiums

[0581] The server monitors whether the user has implemented the suggested health improvements, checking against new data recorded in the database. If the recommendations have been achieved, the server recalculates the next insurance premium and notifies the user of the results.

[0582] Utilizing medical big data

[0583] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions to contribute to the development of new treatments and preventative measures.

[0584] Specific examples

[0585] A specific example is shown below.

[0586] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and collects data on a 1 km walk and breakfast (bread and milk). Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and evaluates that he is in a "stressed state."

[0587] The server receives this data and calculates User A's health score as "70 points." It also generates a stress reduction suggestion of "meditating three times a week" from the emotional data and notifies User A.

[0588] If User A follows the suggested meditation and improves his / her lifestyle, the insurance premium for the next year will be reduced by 500 yen based on that data.

[0589] The server will integrate the new data into medical big data and share the analysis results with medical research institutions, thereby advancing research into new treatments for stress management.

[0590] In this way, by linking wearable devices, servers, and emotion engines and utilizing health and emotion data, a system can be built that provides optimal health management and dynamic adjustment of insurance premiums for each individual user.

[0591] The processing flow will be explained below.

[0592] Step 1:

[0593] The wearable device allows users to monitor their daily heart rate, electrocardiogram, exercise data, and emotional status in real time, and also allows users to input their dietary information using a smartphone application.

[0594] Step 2:

[0595] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, dietary data, and emotion data to the server. The transmitted data includes each data item and a corresponding timestamp.

[0596] Step 3:

[0597] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database, recording their heart rate, exercise volume, dietary habits, and emotional state.

[0598] Step 4:

[0599] The server analyzes the stored data and calculates the user's health score, taking into account heart rate fluctuation patterns, exercise volume, nutritional balance of meals, and emotional state.

[0600] Step 5:

[0601] The server references medical big data and uses generative AI models to complement and refine the health score analysis results, enabling more accurate predictions and assessments.

[0602] Step 6:

[0603] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account the user's health risks, emotional state, and lifestyle improvement efforts.

[0604] Step 7:

[0605] The server generates specific suggestions for maintaining or improving health based on the user's health score and emotional state, and the suggestions are customized taking into account the user's emotional data.

[0606] Step 8:

[0607] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[0608] Step 9:

[0609] Users receive suggestions from the server and use them to improve their lifestyle, such as doing aerobic exercise three times a week or performing meditation sessions to reduce stress.

[0610] Step 10:

[0611] The server monitors whether the proposed health improvement measures have been implemented, checking against new data recorded in the database, and if implementation is confirmed, records the result in the database.

[0612] Step 11:

[0613] If the proposal is achieved, the server recalculates the next premium, applying an additional discount according to the achievement level.

[0614] Step 12:

[0615] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions to contribute to the development of new treatments and preventative measures.

[0616] In this way, each step works in tandem to create a system that comprehensively manages users' health and emotions, providing individually optimized recommendations and dynamically adjusting insurance premiums. The collected data will also be used for medical research, making a wide-ranging contribution to society.

[0617] Example 2

[0618] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0619] In modern society, there is a demand for accurate understanding of individual health conditions and stress levels, and for dynamic calculation of insurance premiums. However, achieving this requires a system that can collect and analyze a variety of health and emotional data in real time and automatically generate optimal health management proposals for each user. Conventional systems have had difficulty dynamically calculating insurance premiums that take emotional states into account, or recalculating premiums based on the implementation of proposals. It has also been difficult to contribute to new medical research by integrating collected data with medical big data and analyzing it using generative AI models.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0621] In this invention, the server includes a means for receiving health data and emotion data and storing them in a database, a means for analyzing the health data and emotion data to calculate a health score, a means for dynamically calculating insurance premiums based on the health score, and a means for generating health maintenance and improvement proposals. This allows for comprehensive management of the user's health status, dynamic calculation of insurance premiums, and real-time healthcare proposals. Furthermore, insurance premiums can be recalculated by monitoring the implementation status of health improvement measures, and the collected data can be integrated with medical big data to contribute to new medical research.

[0622] A "wearable device" is a device that is worn on the user's body and collects health and emotional data in real time.

[0623] "Health data" refers to physiological data that indicates the user's health condition, such as heart rate, electrocardiogram, and exercise data.

[0624] "Emotional data" refers to data that indicates the emotional state of the user, such as skin galvanic response, heart rate variability, and facial expression data.

[0625] The "server means" refers to a device and software for receiving health data and emotion data transmitted from a wearable device and storing the data in a database.

[0626] The "analysis means" refers to a device and software for analyzing the health data and emotion data stored on the server and calculating the user's health score.

[0627] The "health score" is a numerical value calculated by the analysis means to comprehensively evaluate the health condition of the user.

[0628] A "price calculator" is a device and software for dynamically calculating insurance premiums based on health scores.

[0629] The "generation means" refers to a device and software for generating health maintenance and improvement suggestions to improve the user's health score.

[0630] The "notification means" refers to devices and software for notifying users of generated suggestions.

[0631] "Monitoring means" means the equipment and software used to monitor whether the proposed health improvement measures have been implemented and to recalculate the next premium.

[0632] "Medical big data" refers to a large-scale dataset that compiles a wide range of health and medical data.

[0633] A "generative AI model" is a machine learning model that uses AI technology to generate specific suggestions for maintaining and improving health.

[0634] This invention is a system that collects health and emotional data from users, dynamically calculates insurance premiums based on that data, and makes specific suggestions for maintaining and improving their health. This system is composed of a wearable device (terminal), a server, and a generative AI model.

[0635] Wearable devices (terminals)

[0636] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application. For example, when the user goes for a daily walk or jog, the device uses GPS to record the distance and elapsed time. It is also equipped with a heart rate sensor to monitor the heart rate during exercise.

[0637] Emotion Engine

[0638] The device's built-in emotion engine can analyze the user's emotional state using data on skin galvanic response, heart rate variability, and facial expressions. For example, if skin galvanic response increases and heart rate variability increases, it will recognize this as a "stress state" and provide a real-time evaluation.

[0639] server

[0640] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information. Each time the server receives data, it stores it in the database and performs a comparative analysis with the user's past data.

[0641] Data analysis

[0642] The server analyzes the received health and emotional data and calculates the user's health score. The analysis takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, emotional state, and other factors. For example, if the user exercises less and is under stress for a long period of time, the health score will be calculated as low.

[0643] Dynamic calculation of insurance premiums

[0644] The server dynamically calculates insurance premiums based on the analyzed health score. This calculation is designed to reflect individual health risks, emotional state, and efforts to improve lifestyle habits. For example, premiums can be set lower for users with a high health score, and higher for users with a low health score.

[0645] Proposal generation and notification

[0646] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. For example, a specific suggestion such as "Meditate three times a week to reduce stress" may be generated. These suggestions are customized taking into account the user's emotional data and are communicated via a wearable device or smartphone application.

[0647] Example (prompt):

[0648] "If the user is stressed, suggest specific actions to reduce stress."

[0649] Proposal implementation and monitoring

[0650] The server monitors whether the user has implemented the suggested health improvements. This data is compared with the recommendations, and if implementation is confirmed, the next insurance premium is recalculated. For example, if a user meditates three times a week, the insurance premium will be reduced based on that data.

[0651] Utilizing medical big data

[0652] The server integrates the collected health and emotional data with medical big data and analyzes it using a generative AI model. The results of this analysis will be shared with medical research institutions, contributing to the development of new treatments and preventative measures. Specifically, it is expected that research into new treatments for stress management will progress.

[0653] In this way, by linking wearable devices, a server, and an emotion engine, this system effectively utilizes users' health and emotion data to dynamically adjust insurance premiums and provide suggestions for maintaining and improving health.

[0654] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0655] Step 1:

[0656] The device collects heart rate, electrocardiogram, exercise data, and emotional data in real time. Specifically, sensors measure heart rate every second and record exercise data (e.g., walking distance) using GPS. The emotion engine analyzes electrodermal response and heart rate variability to determine emotional state. The input is real-time data from the sensors, and the output is collected health data and emotional data.

[0657] Step 2:

[0658] The terminal sends the collected data to the server at regular intervals. Specifically, the device packets the data every 30 seconds and sends it to the server via the smartphone. The input is the data collected by the terminal, and the output is the data sent to the server.

[0659] Step 3:

[0660] The server receives the health and emotion data sent from the device and stores it in a database. Specifically, the server associates the received data with the user's ID and stores it in the corresponding table in the database. The input is the data sent from the device, and the output is the data stored in the database.

[0661] Step 4:

[0662] The server calculates the user's health score based on the data received. Specifically, the server's analysis engine calculates a comprehensive health score by taking into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. The input is the health and emotional data stored in the database, and the output is the calculated health score.

[0663] Step 5:

[0664] The server dynamically calculates the insurance premium based on the analyzed health score. Specifically, it inputs the health score and calculates a new insurance premium according to the insurance premium setting rules. The input is the health score, and the output is the calculated new insurance premium.

[0665] Step 6:

[0666] The server uses the generative AI model to generate suggestions for maintaining or improving health, such as suggesting "meditation methods to reduce stress." The input is the health score and emotion data, and the output is the generated health suggestions.

[0667] Step 7:

[0668] The server notifies the user of the generated suggestions. Specifically, the suggestions are notified via a wearable device or a smartphone app. The input is the generated health suggestions, and the output is the notified information.

[0669] Step 8:

[0670] The server monitors whether the user has implemented the suggested health improvement measures and recalculates the next insurance premium. Specifically, it compares the new data sent from the device with the proposed measures. The input is the new health data and the proposed measures, and the output is the recalculated insurance premium.

[0671] Step 9:

[0672] The server integrates the collected data with medical big data, analyzes it using a generative AI model, and shares the analysis results with medical research institutions. Specifically, the data is uploaded to a large dataset and the analysis results are provided through a shared API. The input is the collected health data and emotion data, and the output is the analysis results.

[0673] (Application example 2)

[0674] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0675] While modern insurance systems calculate insurance premiums based on users' health data, they do not dynamically calculate premiums that take into account the user's emotional state or provide customized suggestions to improve the health score itself. Furthermore, there is a lack of mechanisms for providing services and promotions based on the user's health status in physical stores. This makes it difficult to implement specific measures for users' health management and improvement of their emotional state, making it difficult to promote the overall health of users.

[0676] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0677] In this invention, the server includes means for receiving data from a wearable device to acquire a user's health data and emotional data and storing the data in a database, means for analyzing the health data and emotional data stored in the server means and calculating the user's health score, means for generating suggestions for diet, exercise, and relaxation to improve the health score, and means for notifying the user of the generated suggestions as services and promotions in a physical store. This makes it possible not only to precisely analyze a user's health data and emotional data and provide individually optimized suggestions for maintaining and improving health, but also to effectively utilize and notify the suggestions in a physical store.

[0678] A "wearable device" is a device for collecting health and emotional data of a user. It has the function of collecting heart rate, electrocardiogram, exercise data, and emotional state in real time.

[0679] The "server means" is a device that receives the health data and emotion data sent from the wearable device and stores them in a database.

[0680] The "analysis means" is a device that analyzes the health data and emotion data stored on the server and calculates the user's health score.

[0681] The "price calculation means" is a device that dynamically calculates insurance premiums based on the health score calculated by the analysis means.

[0682] The "generation means" is a device that generates diet and exercise suggestions, as well as relaxation suggestions, to improve the health score.

[0683] The "notification means" is a device for notifying the user of the generated proposal, which is provided to the user as a service or promotion in a physical store.

[0684] "Health Data" refers to physiological data such as a user's heart rate, electrocardiogram, and exercise data.

[0685] "Emotion data" refers to data on the user's emotional state that is analyzed based on the user's skin galvanic response, heart rate variability, facial expression data, and the like.

[0686] The "health score" is an index showing the user's health condition calculated by the analysis means by integrating health data and emotional data.

[0687] "Dietary Suggestions" are specific dietary recommendations to improve a user's health score.

[0688] "Exercise Suggestions" are recommendations for specific exercises to improve a user's health score.

[0689] "Relaxation suggestions" are recommendations for specific activities to improve the user's emotional state and reduce stress.

[0690] "Services and Promotions" refers to promotional activities, including discounts and special offers on specific services and products offered in physical stores.

[0691] As an embodiment of the present invention, we will specifically explain a system that acquires health data and emotional data of users, dynamically calculates insurance premiums based on this data, and makes appropriate health maintenance and improvement proposals. This system is composed of the following main components.

[0692] 1. Wearable devices

[0693] The wearable device has the function of acquiring the following data in real time:

[0694] Heart rate

[0695] electro-cardiogram

[0696] Exercise data

[0697] Emotional state (analysis based on skin galvanic response, heart rate variability, and facial expression data)

[0698] When worn by the user, these physiological and emotional data are collected and sent to a server via a smartphone application.

[0699] 2. Server Means

[0700] The server receives the health data and emotion data sent from the wearable device and stores them in a database. The server has the following functions:

[0701] Receiving and storing data

[0702] Health and emotional data analysis

[0703] Calculating your health score

[0704] Dynamic calculation of insurance premiums

[0705] Proposal Generation

[0706] 3. Analysis method

[0707] The analysis method comprehensively analyzes the health data and emotional data stored on the server and calculates the user's health score. The analysis uses medical big data and generative AI models. For example, if a person is experiencing persistent stress, the health score can be set low.

[0708] 4. Generation means

[0709] The generation means generates dietary and exercise suggestions, as well as relaxation suggestions, based on the health score calculated by the analysis means. Using the generative AI model, the following specific suggestions are generated:

[0710] "Meditate three times a week"

[0711] "I walk in nature to relax."

[0712] 5. Means of notification

[0713] The notification means notifies the user of the proposal created by the generation means. In particular, the proposal can be provided as a service or promotion in a physical store. The notification is performed via a smartphone application or smart glasses.

[0714] Specific examples

[0715] A specific example is given below.

[0716] At 2023-10-05 07:30:00, User A's heart rate is recorded as 85 bpm, and data on 1 km of walking and breakfast (bread and milk) is collected. Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and assesses that he is in a stressed state.

[0717] The server receives this data and calculates User A's health score as "70 points." From the emotional data, it generates a stress reduction suggestion of "meditating three times a week" and notifies User A. An example of a specific notification would be "Please relax in the relaxation area."

[0718] An example of a prompt sentence is, "Generate recommended service suggestions if the user's health score is 80 or less. Input example: 'My heart rate is 85 bpm and I'm in a stressed state. What services would you recommend?'" By inputting this prompt into the generation AI model, it is possible to automatically generate appropriate suggestions.

[0719] The embodiment of the present invention is as described above. It is a system that comprehensively manages a user's health and emotional state and provides individualized suggestions to promote health improvement and dynamically adjust insurance premiums.

[0720] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0721] Step 1:

[0722] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data) and emotional data (galvanic skin response, heart rate variability, facial expression data) in real time and transmits it to a smartphone application.

[0723] Input: Health and emotion data

[0724] Output: Send data to a smartphone application

[0725] How it works: The wearable device stores data collected by sensors in its internal memory and periodically transmits it to a smartphone application via Bluetooth or Wi-Fi.

[0726] Step 2:

[0727] The smartphone application transmits the data received from the wearable device to a server.

[0728] Input: Health and emotion data from wearable devices

[0729] Output: Send data to the server

[0730] Operation: The smartphone application sends the received data to the server as an HTTP request, along with the user ID.

[0731] Step 3:

[0732] The server stores the data received from the smartphone application in a database.

[0733] Input: Data from a smartphone application

[0734] Output: Data storage in database

[0735] Operation: The server stores the received data in the appropriate database table based on the user ID.

[0736] Step 4:

[0737] The server analyzes the health data and emotion data stored in the database and calculates the user's health score.

[0738] Input: Health and emotion data stored in a database

[0739] Output: Health score

[0740] How it works: The server uses a generative AI model to analyze the data and calculate a health score based on information such as heart rate and emotional state, with medical big data also being taken into account.

[0741] Step 5:

[0742] The server dynamically calculates the insurance premium based on the calculated health score.

[0743] Input: Health score

[0744] Output: Insurance premium

[0745] Operation: The server recalculates the insurance premium based on the health risk assessment. This calculation is performed by the insurance risk algorithm.

[0746] Step 6:

[0747] The server generates diet, exercise, and relaxation suggestions to improve the health score.

[0748] Input: Health score

[0749] Output: Specific proposals

[0750] How it works: Based on your health score, the server uses a generative AI model to generate personalized health maintenance and improvement suggestions, such as meditation and relaxation exercises to reduce stress.

[0751] Step 7:

[0752] The server notifies the user of the generated proposals as services or promotions in the physical store.

[0753] Input: Specific proposal details

[0754] Output: Notification to user

[0755] Operation: The server notifies the user of the proposed offer via a smartphone application or smart glasses. It also provides promotional information in conjunction with in-store displays and digital signage.

[0756] Examples and prompts

[0757] As a specific example, consider the case where user A is suggested to meditate three times a week and is notified through the application to "relax in the relaxation area."

[0758] Example prompt: "Generate recommended services if the user's health score is below 80. Example input: 'My heart rate is 85 bpm and I'm stressed. What services would you recommend?'"

[0759] Through the above steps, the system of the present invention comprehensively manages the user's health and emotional state, provides personalized recommendations, promotes health improvement, and dynamically adjusts insurance premiums.

[0760] 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.

[0761] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0762] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0763] [Third embodiment]

[0764] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0765] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0766] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0767] 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.

[0768] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0769] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0770] 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.

[0771] 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.

[0772] 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 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.

[0773] 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.

[0774] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0775] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0776] As an embodiment of the present invention, the following specific system and its operation will be described. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[0777] Wearable devices (terminals)

[0778] The wearable device is worn by the user and has the function of monitoring and recording health data such as heart rate, electrocardiogram, and exercise data in real time. The user can also input the details of their diet using a smartphone application.

[0779] server

[0780] The server receives the health data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health information. The server also analyzes this data and generates a score for the user's health condition. Specifically, the health score is calculated by integrating heart rate variability, exercise volume, and dietary nutritional balance.

[0781] Data analysis

[0782] The server compares the collected health data with medical big data and analyzes it using a generative AI model. The result of the analysis is a health score, which is used to dynamically calculate each user's insurance premium. This calculation reflects each user's individual health risks and efforts to improve their lifestyle habits.

[0783] Proposal generation and notification

[0784] Based on the analyzed health data and scores, the server uses a generative AI model to generate specific recommendations for maintaining and improving health tailored to each user. Suggestions might include, for example, "add aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." These recommendations are sent via the user's wearable device or smartphone application.

[0785] Recalculation of insurance premiums

[0786] The server detects if the user implements the suggested health improvement measures and recalculates the next insurance premium. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, the next year's insurance premium will be reduced by an additional amount.

[0787] Utilizing medical big data

[0788] The server will integrate collected health data with medical big data and utilize generative AI models to gain insights for further research and diagnosis, which will contribute to the development of new treatments and preventative measures in the medical field.

[0789] Specific examples

[0790] A specific example is shown below.

[0791] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and records the 1 km walk as exercise data. The smartphone application also records that User A had bread and milk for breakfast.

[0792] The server receives this data and calculates User A's health score as "75 points." Next, using the generative AI model, the server suggests "adding aerobic exercise three times a week and increasing vegetable intake," and notifies User A.

[0793] If User A follows the suggestion to do aerobic exercise three times a week and incorporate more vegetables into his / her diet, his / her insurance premiums for the next year will be reduced by 500 yen.

[0794] In this way, a system will be built that uses interconnected terminal and server technology to collect health data, analyze it, make recommendations, and dynamically adjust insurance premiums. This system will promote the health of each individual user and provide a fair insurance premium system that reflects each individual's efforts.

[0795] The processing flow will be explained below.

[0796] Step 1:

[0797] The wearable device is worn by the user and collects daily heart rate, electrocardiogram, and exercise data in real time. The user also inputs their dietary habits using a smartphone application.

[0798] Step 2:

[0799] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, and dietary data to the server. The transmitted data includes each data item and a corresponding timestamp.

[0800] Step 3:

[0801] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database. This database includes heart rate, exercise volume, and diet history.

[0802] Step 4:

[0803] The server analyzes the received data and calculates the user's health score, taking into account the heart rate fluctuation pattern, the cumulative amount of exercise, and the nutritional balance of the diet.

[0804] Step 5:

[0805] The server references medical big data and complements the analysis results with generative AI models, which allows for accurate individualized predictions and health status assessments.

[0806] Step 6:

[0807] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account each user's health risk and the degree of lifestyle improvements.

[0808] Step 7:

[0809] The server generates specific diet and exercise recommendations to improve the user's health score, customized based on individual health data and medical big data.

[0810] Step 8:

[0811] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[0812] Step 9:

[0813] Users receive suggestions from the server and act on them to improve their lifestyle, such as doing aerobic exercise three times a week or adding more vegetables to their diet.

[0814] Step 10:

[0815] The server monitors whether the suggestions have been implemented and verifies this by checking against new data recorded in the database.

[0816] Step 11:

[0817] If the proposed plan is achieved, the server recalculates the next insurance premium, and applies an additional discount according to the achievement level.

[0818] Step 12:

[0819] The server integrates the collected health data and medical big data and analyzes them using a generative AI model. Based on the analysis results, the data is shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[0820] In this way, each step works together to establish a system that supports users' health management and enables dynamic adjustment of insurance premiums. The collected data will also be used to advance medical care.

[0821] Example 1

[0822] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0823] In modern society, there is a demand for optimal management of individual health conditions and fair and dynamic calculation of insurance premiums. However, conventional systems lack the means to efficiently collect and analyze users' health data, which makes it difficult to obtain appropriate feedback for calculating insurance premiums. Furthermore, they are also inadequate in providing specific health improvement suggestions based on users' lifestyle habits.

[0824] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0825] In this invention, the server includes a wearable device means for acquiring a user's health data, a computer means for receiving the health data from the wearable device and storing it in a data storage device, a data analysis means for analyzing the health data stored in the computer means and calculating the user's health rating, a price calculation means for dynamically calculating an insurance premium based on the health rating, a proposal generation means for generating diet and exercise proposals to improve the health rating, and a notification means for notifying the user of the generated proposals. This makes it possible to efficiently collect and analyze a user's health data, make specific proposals for improving their health, and dynamically calculate insurance premiums in a fair manner.

[0826] Definitions of important words

[0827] A "wearable device" is a device that a user wears on their body to collect health data such as heart rate, electrocardiogram, and exercise data.

[0828] A "computer device" is a device for receiving health data transmitted from a wearable device and storing the data in a data storage device.

[0829] "Data storage device" means a storage device for storing health data received by a computing device.

[0830] The "data analysis device" is a device for analyzing health data stored in a data storage device and calculating a health evaluation of a user.

[0831] The "price calculation device" is a device for dynamically calculating insurance premiums based on the health assessment calculated by the data analysis device.

[0832] The "suggestion generation device" is a device for automatically generating suggestions regarding diet and exercise to improve health assessment.

[0833] The "notification device" is a device for notifying users of the proposal content generated by the proposal generation device.

[0834] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate health scores and recommendations.

[0835] "Big data" refers to large datasets in fields such as medicine, and its use improves the accuracy of data analysis and model generation.

[0836] MODE FOR CARRYING OUT THE INVENTION

[0837] A specific system for implementing this invention and its operation are described below. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[0838] Wearable devices (terminals)

[0839] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. Specific examples of wearable devices include fitness trackers and smartwatches. Users can also input their dietary information using a smartphone application.

[0840] Data collection and transmission

[0841] The device sends the collected health data to the server via Bluetooth or Wi-Fi. This ensures that the data reaches the server quickly and without loss. For example, if user A inputs into a smartphone application that he or she had bread and milk for breakfast, that data is also sent.

[0842] Receiving and storing data

[0843] The server receives the health data sent from the device and stores it in a data storage device, which uses database management systems such as AWS RDS and Google Cloud Spanner to manage the data by linking it to each user's ID.

[0844] Data analysis and health assessment calculation

[0845] The server analyzes the received health data and calculates the user's health rating. Specifically, it integrates multiple factors such as heart rate variability, exercise volume, and nutritional balance of meals to arrive at a health rating. A generative AI model is used for this analysis. TensorFlow and PyTorch are used as generative AI models. As an example, the server calculates a health score of 75 points based on User A's data.

[0846] Generate health improvement suggestions

[0847] Based on the health assessment, the server generates suggestions on how to improve the user's health. The suggestions are generated using a generative AI model, and specific suggestions include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The generated suggestions are expressed in the form of prompt sentences as follows:

[0848] Generate appropriate health maintenance and improvement suggestions based on the health data of user A. The heart rate is 85 bpm, the exercise data is 1 km of walking, and the diet consists of bread and milk. The health score is 75 points.

[0849] Notification of proposal details

[0850] The server notifies the user of the generated suggestions via a push notification service to the wearable device or smartphone application, allowing the user to receive the notification and start taking action based on the suggestions.

[0851] Recollection of behavioral data and recalculation of insurance premiums

[0852] The server again collects data to detect whether the user acted on the suggestions. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, this is recorded. The server then recalculates the next year's insurance premium to appropriately reflect the user's efforts. The recalculated premium is saved as an updated database.

[0853] Utilizing medical big data

[0854] The server integrates the collected health data with large amounts of medical data and performs further detailed analysis. By utilizing generative AI models, it contributes to the development of new treatments and preventative measures. This analysis is carried out using Apache Spark and Hadoop.

[0855] These processes enable a single flow of processes, from collecting user health data to analyzing it, making recommendations, and dynamically adjusting insurance premiums. As a practical example, a system could be realized in which collected data is analyzed based on a health assessment, specific recommendations are made based on the data, and users act on those recommendations to ensure that their insurance premiums are appropriately adjusted. This system is an effective means of promoting user health and providing a fair insurance premium system.

[0856] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0857] Program processing flow

[0858] Step 1:

[0859] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. The input is the wearable device, and the output is the health data obtained from the device.

[0860] Step 2:

[0861] The device collects health data and inputs meal details into a smartphone application. Specifically, the device records the user's heart rate and exercise data, and the user inputs meal details into the smartphone application. The input is data entered manually by the wearable device and the user, and the output is comprehensive health data recorded on the device.

[0862] Step 3:

[0863] The device sends the collected data to the server. The device sends data to the server in real time or periodically via Bluetooth or Wi-Fi. The input is the health data recorded on the device, and the output is the data sent to the server.

[0864] Step 4:

[0865] The server receives the data and stores it in a data storage device. Specifically, the server stores the data received from the terminal in a database and links it to each user's ID. The input is the health data sent to the server, and the output is the data stored in the database.

[0866] Step 5:

[0867] The server analyzes the data and calculates a health score. The server uses a generative AI model to analyze the received and stored data and calculates a health assessment score (e.g., 75 points) based on heart rate variability, exercise volume, and dietary nutritional balance. The input is the health data stored in the database, and the output is the calculated health score.

[0868] Step 6:

[0869] The server generates suggestions using a generative AI model. The server generates prompts based on the calculated health score and uses the AI ​​model to create specific health improvement suggestions. Specific examples include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health improvement suggestions.

[0870] Step 7:

[0871] The server notifies the user of the suggestions. The server generates the suggestions in the form of prompts and notifies the user's wearable device or smartphone application via a push notification service. The input is the generated health improvement suggestions, and the output is a notification to the user.

[0872] Step 8:

[0873] The user acts according to the suggestions. Specifically, the user practices health-improving behaviors, such as aerobic exercise three times a week. The input is the notified suggestions, and the output is the user's behavioral data.

[0874] Step 9:

[0875] The server recollects the user's behavioral data and recalculates the insurance premium. The server receives the data again through the terminal and verifies whether the proposal has been implemented. It then recalculates the insurance premium and updates the database. The input is the recollected behavioral data, and the output is the recalculated insurance premium.

[0876] Step 10:

[0877] The server performs further analysis using medical big data. The server compares the collected user health data with large amounts of medical data and utilizes generative AI models to contribute to the development of new treatments and preventative measures. The input is user data and medical big data, and the output is new insights and proposals.

[0878] (Application example 1)

[0879] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0880] While conventional health management systems can collect and analyze users' health data and adjust insurance premiums, they lack the functionality to calculate more accurate health scores using payment data. This means that they cannot accurately reflect users' lifestyles and consumption behavior, making it difficult to ensure fairness in insurance premiums and generate appropriate health recommendations.

[0881] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0882] In this invention, the server includes a wearable device means for acquiring the user's health data, a server means for receiving the health data from the wearable device and storing it in a database, an analysis means for analyzing the health data stored in the server means and calculating the user's health score, a price calculation means for dynamically calculating the insurance premium based on the health score, a generation means for generating diet and exercise suggestions to improve the health score, a notification means for notifying the user of the generated suggestions, and a payment data aggregation means for receiving payment data and reflecting it in the calculation of the health score. This enables the calculation of a health score that accurately reflects the user's lifestyle and consumption behavior.

[0883] A "wearable device" is an electronic device worn by a user to collect health data.

[0884] The "server means" is a device that has the function of receiving data sent from a wearable device and storing it in a database.

[0885] The "analysis means" is a system for analyzing the data stored in the server means and calculating the user's health score.

[0886] The "price calculation means" is a tool that has the function of dynamically calculating insurance premiums based on the health score obtained by the analysis means.

[0887] The "generator" is a device or program that generates diet and exercise suggestions to improve the health score.

[0888] The "notification means" is a system having a function of notifying the user of the proposal created by the creation means.

[0889] The "payment data aggregation means" is a mechanism that receives users' payment information and reflects it in the calculation of the health score.

[0890] An embodiment of the present invention will be described below. This system is configured by combining a wearable device, a server, and a smart device (a smartphone or smart glasses).

[0891] System Overview

[0892] This system collects the user's health data using a wearable device and transmits the data to a server. The server then uses analytical means to calculate a health score and dynamically calculates insurance premiums based on the results. The server also generates proposals and notifies the user via their smart device. It also collects payment data and reflects it in the calculation of the health score.

[0893] Health data collection

[0894] First, the user puts on a wearable device that monitors and records heart rate, electrocardiogram, exercise data, and other information in real time. Health data is then sent to a server via the user's smartphone or smart glasses.

[0895] Data analysis by server

[0896] The server stores the received health data in a database, using a database system such as MySQL. The server then analyzes the data using a generative AI model (e.g., TensorFlow or PyTorch). This integrates heart rate variability, exercise volume, and dietary nutritional balance to calculate a health score.

[0897] Payment data aggregation

[0898] Additionally, the server receives users' electronic payment data and incorporates it into the calculation of their health score. For example, it automatically records the food and exercise-related items purchased by users, thereby improving the accuracy of the health score.

[0899] Proposal generation and notification

[0900] The server generates specific suggestions for maintaining and improving health based on the analysis results. These suggestions are then sent to the user via smartphone or smart glasses, making it easier for the user to improve their daily lifestyle habits.

[0901] Insurance premium calculation

[0902] The server dynamically calculates insurance premiums based on the health score, for example, discounts on premiums if the health score is high, and recalculates premiums to take into account any suggested health improvements implemented.

[0903] Hardware and software used

[0904] Hardware: Wearable devices (e.g. fitness trackers), smartphones, smart glasses.

[0905] Software: Health data collection app (smartphone app), database system (MySQL), analysis platform (TensorFlow, PyTorch).

[0906] Specific examples

[0907] A specific example is shown below.

[0908] Example of operation:

[0909] 1. A user purchases a salad and a healthy drink for breakfast on 2023-10-05 08:00.

[0910] 2. The wearable device records a heart rate of 80 bpm at 2023-10-05 07:30.

[0911] 3. The app sends the above data to the server and receives a health score of 85 points as the analysis result.

[0912] 4. The server generates a suggestion to the user: "Add aerobic exercise three times a week."

[0913] 5. Insurance premiums will be calculated dynamically and a 300 yen discount will be applied to the next month's premium.

[0914] Example of an input prompt for a generative AI model:

[0915] "User ID: 12345's health data: Heart rate 80 bpm, Walking 5000 steps, Breakfast: Salad, Healthy drink. Calculate my health score and get suggestions and insurance discounts."

[0916] The above is a specific embodiment for carrying out the present invention, which makes it possible to calculate a health score that accurately reflects the lifestyle habits and consumption behavior of the user.

[0917] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0918] Step 1:

[0919] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data). This data is transmitted in real time to a smartphone or smart glasses. The input is the health data from the wearable device, and the output is transmitted to the smart device.

[0920] Step 2:

[0921] The smart device receives health data from the wearable device and sends it to the server. The input is the health data from the wearable device, and the output is the data sent to the server.

[0922] Step 3:

[0923] The server stores the received health data in a database. Specifically, the data is stored in a MySQL database, for example. The input is the data sent from the smart device, and the output is the health data stored in the database.

[0924] Step 4:

[0925] The server collects the user's electronic payment data using a payment data aggregation mechanism, where the input is the user's payment record and the output is the payment data for use in calculating the health score.

[0926] Step 5:

[0927] The server integrates the health data and payment data and calculates a health score using a generative AI model (such as TensorFlow or PyTorch) as an analytical tool. The input is the health data and payment data, and the output is the calculated health score.

[0928] Step 6:

[0929] The server uses the generation means to generate health suggestions based on the health score, such as "do aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health suggestions.

[0930] Step 7:

[0931] The server notifies the user of the generated health suggestions via a notification mechanism. The input is the health suggestions, and the output is the notified suggestions.

[0932] Step 8:

[0933] If the user implements the health suggestions, the server detects the implementation results and recalculates the next insurance premium. For example, if the user performs aerobic exercise three times a week, the server will discount the insurance premium. The input is the implementation results of the health suggestions, and the output is the recalculated insurance premium.

[0934] These are the specific processing steps of the system that realizes this application example. This makes it possible to calculate a health score that accurately reflects the user's lifestyle and consumption behavior, and to dynamically calculate insurance premiums.

[0935] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0936] As a form for implementing the present invention, a system will be specifically described that acquires health data and emotional data of a user, dynamically calculates insurance premiums based on this data, and makes appropriate suggestions for maintaining and improving health.

[0937] Wearable devices (terminals)

[0938] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application.

[0939] Emotion Engine

[0940] The device's built-in emotion engine analyzes the user's emotional state using skin galvanic response, heart rate variability, facial expression data, etc. This allows the device to grasp the user's current emotions, such as stress or relaxation, in real time.

[0941] server

[0942] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information.

[0943] Data analysis

[0944] The server analyzes the received health and emotional data and calculates the user's health score. Specifically, the calculation takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. For example, if a person is experiencing persistent stress, measures such as a more severe health score assessment may be taken.

[0945] Dynamic calculation of insurance premiums

[0946] The server dynamically calculates insurance premiums based on the analyzed health score, which is designed to reflect individual health risks, emotional state, and lifestyle improvement efforts.

[0947] Proposal generation and notification

[0948] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. These suggestions include, for example, "meditate to reduce stress" or "take a walk in nature on your day off to relax." These suggestions are customized taking into account emotional data generated by the emotion engine. The suggestions are then communicated to the user via their wearable device or smartphone application.

[0949] Recalculation of insurance premiums

[0950] The server monitors whether the user has implemented the suggested health improvements, checking against new data recorded in the database. If the recommendations have been achieved, the server recalculates the next insurance premium and notifies the user of the results.

[0951] Utilizing medical big data

[0952] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[0953] Specific examples

[0954] A specific example is shown below.

[0955] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and collects data on a 1 km walk and breakfast (bread and milk). Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and evaluates that he is in a "stressed state."

[0956] The server receives this data and calculates User A's health score as "70 points." It also generates a stress reduction suggestion of "meditating three times a week" from the emotional data and notifies User A.

[0957] If User A follows the suggested meditation and improves his / her lifestyle, the insurance premium for the next year will be reduced by 500 yen based on that data.

[0958] The server will integrate the new data into medical big data and share the analysis results with medical research institutions, thereby advancing research into new treatments for stress management.

[0959] In this way, by linking wearable devices, servers, and emotion engines and utilizing health and emotion data, a system can be built that provides optimal health management and dynamic adjustment of insurance premiums for each individual user.

[0960] The processing flow will be explained below.

[0961] Step 1:

[0962] The wearable device allows users to monitor their daily heart rate, electrocardiogram, exercise data, and emotional status in real time, and also allows users to input their dietary information using a smartphone application.

[0963] Step 2:

[0964] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, dietary data, and emotion data to the server. The transmitted data includes each data item and a corresponding timestamp.

[0965] Step 3:

[0966] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database, recording their heart rate, exercise volume, dietary habits, and emotional state.

[0967] Step 4:

[0968] The server analyzes the stored data and calculates the user's health score, taking into account heart rate fluctuation patterns, exercise volume, nutritional balance of meals, and emotional state.

[0969] Step 5:

[0970] The server references medical big data and uses generative AI models to complement and refine the health score analysis results, enabling more accurate predictions and assessments.

[0971] Step 6:

[0972] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account the user's health risks, emotional state, and lifestyle improvement efforts.

[0973] Step 7:

[0974] The server generates specific suggestions for maintaining or improving health based on the user's health score and emotional state, and the suggestions are customized taking into account the user's emotional data.

[0975] Step 8:

[0976] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[0977] Step 9:

[0978] Users receive suggestions from the server and use them to improve their lifestyle, such as doing aerobic exercise three times a week or performing meditation sessions to reduce stress.

[0979] Step 10:

[0980] The server monitors whether the proposed health improvement measures have been implemented, checking against new data recorded in the database, and if implementation is confirmed, records the result in the database.

[0981] Step 11:

[0982] If the proposal is achieved, the server recalculates the next premium, applying an additional discount according to the achievement level.

[0983] Step 12:

[0984] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[0985] In this way, each step works in tandem to create a system that comprehensively manages users' health and emotions, providing individually optimized recommendations and dynamically adjusting insurance premiums. The collected data will also be used in medical research, making a wide-ranging contribution to society.

[0986] Example 2

[0987] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0988] In modern society, there is a demand for accurate understanding of individual health conditions and stress levels, and for dynamic calculation of insurance premiums. However, achieving this requires a system that can collect and analyze a variety of health and emotional data in real time and automatically generate optimal health management proposals for each user. Conventional systems have had difficulty dynamically calculating insurance premiums that take emotional states into account, or recalculating premiums based on the implementation of proposals. It has also been difficult to contribute to new medical research by integrating collected data with medical big data and analyzing it using generative AI models.

[0989] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0990] In this invention, the server includes a means for receiving health data and emotion data and storing them in a database, a means for analyzing the health data and emotion data to calculate a health score, a means for dynamically calculating insurance premiums based on the health score, and a means for generating health maintenance and improvement proposals. This allows for comprehensive management of the user's health status, dynamic calculation of insurance premiums, and real-time healthcare proposals. Furthermore, insurance premiums can be recalculated by monitoring the implementation status of health improvement measures, and the collected data can be integrated with medical big data to contribute to new medical research.

[0991] A "wearable device" is a device that is worn on the user's body and collects health and emotional data in real time.

[0992] "Health data" refers to physiological data that indicates the user's health condition, such as heart rate, electrocardiogram, and exercise data.

[0993] "Emotional data" refers to data that indicates the emotional state of the user, such as skin galvanic response, heart rate variability, and facial expression data.

[0994] The "server means" refers to a device and software for receiving health data and emotion data transmitted from a wearable device and storing the data in a database.

[0995] The "analysis means" refers to a device and software for analyzing the health data and emotion data stored on the server and calculating the user's health score.

[0996] The "health score" is a numerical value calculated by the analysis means to comprehensively evaluate the health condition of the user.

[0997] A "price calculator" is a device and software for dynamically calculating insurance premiums based on health scores.

[0998] The "generation means" refers to a device and software for generating health maintenance and improvement suggestions to improve the user's health score.

[0999] The "notification means" refers to devices and software for notifying users of generated suggestions.

[1000] "Monitoring means" means the equipment and software used to monitor whether the proposed health improvement measures have been implemented and to recalculate the next premium.

[1001] "Medical big data" refers to a large-scale dataset that compiles a wide range of health and medical data.

[1002] A "generative AI model" is a machine learning model that uses AI technology to generate specific suggestions for maintaining and improving health.

[1003] This invention is a system that collects health and emotional data from users, dynamically calculates insurance premiums based on that data, and makes specific suggestions for maintaining and improving their health. This system is composed of a wearable device (terminal), a server, and a generative AI model.

[1004] Wearable devices (terminals)

[1005] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application. For example, when the user goes for a daily walk or jog, the device uses GPS to record the distance and elapsed time. It is also equipped with a heart rate sensor to monitor the heart rate during exercise.

[1006] Emotion Engine

[1007] The device's built-in emotion engine can analyze the user's emotional state using data on skin electrodermal response, heart rate variability, and facial expressions. For example, if skin electrodermal response increases and heart rate variability increases, it will recognize this as a "stress state" and provide a real-time evaluation.

[1008] server

[1009] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information. Each time the server receives data, it stores it in the database and performs a comparative analysis with the user's past data.

[1010] Data analysis

[1011] The server analyzes the received health and emotional data and calculates the user's health score. The analysis takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, emotional state, and other factors. For example, if the user exercises less and is under stress for a long period of time, the health score will be calculated as low.

[1012] Dynamic calculation of insurance premiums

[1013] The server dynamically calculates insurance premiums based on the analyzed health score. This calculation is designed to reflect individual health risks, emotional state, and efforts to improve lifestyle habits. For example, premiums can be set lower for users with a high health score, and higher for users with a low health score.

[1014] Proposal generation and notification

[1015] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. For example, a specific suggestion such as "Meditate three times a week to reduce stress" may be generated. These suggestions are customized taking into account the user's emotional data and are communicated via a wearable device or smartphone application.

[1016] Example (prompt):

[1017] "If the user is stressed, suggest specific actions to reduce stress."

[1018] Proposal implementation and monitoring

[1019] The server monitors whether the user has implemented the suggested health improvements. This data is compared with the recommendations, and if implementation is confirmed, the next insurance premium is recalculated. For example, if a user meditates three times a week, the insurance premium will be reduced based on that data.

[1020] Utilizing medical big data

[1021] The server integrates the collected health and emotional data with medical big data and analyzes it using a generative AI model. The results of this analysis will be shared with medical research institutions, contributing to the development of new treatments and preventative measures. Specifically, it is expected that research into new treatments for stress management will progress.

[1022] In this way, by linking wearable devices, a server, and an emotion engine, this system effectively utilizes users' health and emotion data to dynamically adjust insurance premiums and provide suggestions for maintaining and improving health.

[1023] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1024] Step 1:

[1025] The device collects heart rate, electrocardiogram, exercise data, and emotional data in real time. Specifically, sensors measure heart rate every second and record exercise data (e.g., walking distance) using GPS. The emotion engine analyzes electrodermal response and heart rate variability to determine emotional state. The input is real-time data from the sensors, and the output is collected health data and emotional data.

[1026] Step 2:

[1027] The terminal sends the collected data to the server at regular intervals. Specifically, the device packets the data every 30 seconds and sends it to the server via the smartphone. The input is the data collected by the terminal, and the output is the data sent to the server.

[1028] Step 3:

[1029] The server receives the health and emotion data sent from the device and stores it in a database. Specifically, the server associates the received data with the user's ID and stores it in the corresponding table in the database. The input is the data sent from the device, and the output is the data stored in the database.

[1030] Step 4:

[1031] The server calculates the user's health score based on the data received. Specifically, the server's analysis engine calculates a comprehensive health score by taking into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. The input is the health and emotional data stored in the database, and the output is the calculated health score.

[1032] Step 5:

[1033] The server dynamically calculates the insurance premium based on the analyzed health score. Specifically, it inputs the health score and calculates a new insurance premium according to the insurance premium setting rules. The input is the health score, and the output is the calculated new insurance premium.

[1034] Step 6:

[1035] The server uses the generative AI model to generate suggestions for maintaining or improving health, such as suggesting "meditation methods to reduce stress." The input is the health score and emotion data, and the output is the generated health suggestions.

[1036] Step 7:

[1037] The server notifies the user of the generated suggestions. Specifically, the suggestions are notified via a wearable device or a smartphone app. The input is the generated health suggestions, and the output is the notified information.

[1038] Step 8:

[1039] The server monitors whether the user has implemented the suggested health improvement measures and recalculates the next insurance premium. Specifically, it compares the new data sent from the device with the proposed measures. The input is the new health data and the proposed measures, and the output is the recalculated insurance premium.

[1040] Step 9:

[1041] The server integrates the collected data with medical big data, analyzes it using a generative AI model, and shares the analysis results with medical research institutions. Specifically, the data is uploaded to a large dataset and the analysis results are provided through a shared API. The input is the collected health data and emotion data, and the output is the analysis results.

[1042] (Application example 2)

[1043] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1044] While modern insurance systems calculate insurance premiums based on users' health data, they do not dynamically calculate premiums that take into account the user's emotional state or provide customized suggestions to improve the health score itself. Furthermore, there is a lack of mechanisms for providing services and promotions based on the user's health status in physical stores. This makes it difficult to implement specific measures for users' health management and improvement of their emotional state, making it difficult to promote the overall health of users.

[1045] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1046] In this invention, the server includes means for receiving data from a wearable device to acquire a user's health data and emotional data and storing the data in a database, means for analyzing the health data and emotional data stored in the server means and calculating the user's health score, means for generating suggestions for diet, exercise, and relaxation to improve the health score, and means for notifying the user of the generated suggestions as services and promotions in a physical store. This makes it possible not only to precisely analyze a user's health data and emotional data and provide individually optimized suggestions for maintaining and improving health, but also to effectively utilize and notify the suggestions in a physical store.

[1047] A "wearable device" is a device for collecting health and emotional data of a user. It has the function of collecting heart rate, electrocardiogram, exercise data, and emotional state in real time.

[1048] The "server means" is a device that receives the health data and emotion data sent from the wearable device and stores them in a database.

[1049] The "analysis means" is a device that analyzes the health data and emotion data stored on the server and calculates the user's health score.

[1050] The "price calculation means" is a device that dynamically calculates insurance premiums based on the health score calculated by the analysis means.

[1051] The "generation means" is a device that generates diet and exercise suggestions, as well as relaxation suggestions, to improve the health score.

[1052] The "notification means" is a device for notifying the user of the generated proposal, which is provided to the user as a service or promotion in a physical store.

[1053] "Health Data" refers to physiological data such as a user's heart rate, electrocardiogram, and exercise data.

[1054] "Emotion data" refers to data on the user's emotional state that is analyzed based on the user's skin galvanic response, heart rate variability, facial expression data, and the like.

[1055] The "health score" is an index showing the user's health condition calculated by the analysis means by integrating health data and emotional data.

[1056] "Dietary Suggestions" are specific dietary recommendations to improve a user's health score.

[1057] "Exercise Suggestions" are recommendations for specific exercises to improve a user's health score.

[1058] "Relaxation suggestions" are recommendations for specific activities to improve the user's emotional state and reduce stress.

[1059] "Services and Promotions" refers to promotional activities, including discounts and special offers on specific services and products offered in physical stores.

[1060] As an embodiment of the present invention, we will specifically explain a system that acquires health data and emotional data of users, dynamically calculates insurance premiums based on this data, and makes appropriate health maintenance and improvement proposals. This system is composed of the following main components.

[1061] 1. Wearable devices

[1062] The wearable device has the function of acquiring the following data in real time:

[1063] Heart rate

[1064] electro-cardiogram

[1065] Exercise data

[1066] Emotional state (analysis based on skin galvanic response, heart rate variability, and facial expression data)

[1067] When worn by the user, these physiological and emotional data are collected and sent to a server via a smartphone application.

[1068] 2. Server Means

[1069] The server receives the health data and emotion data sent from the wearable device and stores them in a database. The server has the following functions:

[1070] Receiving and storing data

[1071] Health and emotional data analysis

[1072] Calculating your health score

[1073] Dynamic calculation of insurance premiums

[1074] Proposal Generation

[1075] 3. Analysis method

[1076] The analysis method comprehensively analyzes the health data and emotional data stored on the server and calculates the user's health score. The analysis uses medical big data and generative AI models. For example, if a person is experiencing persistent stress, the health score can be set low.

[1077] 4. Generation means

[1078] The generation means generates dietary and exercise suggestions, as well as relaxation suggestions, based on the health score calculated by the analysis means. Using the generative AI model, the following specific suggestions are generated:

[1079] "Meditate three times a week"

[1080] "I walk in nature to relax."

[1081] 5. Means of notification

[1082] The notification means notifies the user of the proposal created by the generation means. In particular, the proposal can be provided as a service or promotion in a physical store. The notification is performed via a smartphone application or smart glasses.

[1083] Specific examples

[1084] A specific example is given below.

[1085] At 2023-10-05 07:30:00, User A's heart rate is recorded as 85 bpm, and data on 1 km of walking and breakfast (bread and milk) is collected. Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and assesses that he is in a stressed state.

[1086] The server receives this data and calculates User A's health score as "70 points." From the emotional data, it generates a stress reduction suggestion of "meditating three times a week" and notifies User A. An example of a specific notification would be "Please relax in the relaxation area."

[1087] An example of a prompt sentence is, "Generate recommended service suggestions if the user's health score is 80 or less. Input example: 'My heart rate is 85 bpm and I'm in a stressed state. What services would you recommend?'" By inputting this prompt into the generation AI model, it is possible to automatically generate appropriate suggestions.

[1088] The embodiment of the present invention is as described above. It is a system that comprehensively manages a user's health and emotional state and provides individualized suggestions to promote health improvement and dynamically adjust insurance premiums.

[1089] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1090] Step 1:

[1091] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data) and emotional data (galvanic skin response, heart rate variability, facial expression data) in real time and transmits it to a smartphone application.

[1092] Input: Health and emotion data

[1093] Output: Send data to a smartphone application

[1094] How it works: The wearable device stores data collected by sensors in its internal memory and periodically transmits it to a smartphone application via Bluetooth or Wi-Fi.

[1095] Step 2:

[1096] The smartphone application transmits the data received from the wearable device to a server.

[1097] Input: Health and emotion data from wearable devices

[1098] Output: Send data to the server

[1099] Operation: The smartphone application sends the received data to the server as an HTTP request, along with the user ID.

[1100] Step 3:

[1101] The server stores the data received from the smartphone application in a database.

[1102] Input: Data from a smartphone application

[1103] Output: Data storage in database

[1104] Operation: The server stores the received data in the appropriate database table based on the user ID.

[1105] Step 4:

[1106] The server analyzes the health data and emotion data stored in the database and calculates the user's health score.

[1107] Input: Health and emotion data stored in a database

[1108] Output: Health score

[1109] How it works: The server uses a generative AI model to analyze the data and calculate a health score based on information such as heart rate and emotional state, with medical big data also being taken into account.

[1110] Step 5:

[1111] The server dynamically calculates the insurance premium based on the calculated health score.

[1112] Input: Health score

[1113] Output: Insurance premium

[1114] Operation: The server recalculates the insurance premium based on the health risk assessment. This calculation is performed by the insurance risk algorithm.

[1115] Step 6:

[1116] The server generates diet, exercise, and relaxation suggestions to improve the health score.

[1117] Input: Health score

[1118] Output: Specific proposals

[1119] How it works: Based on your health score, the server uses a generative AI model to generate personalized health maintenance and improvement suggestions, such as meditation and relaxation exercises to reduce stress.

[1120] Step 7:

[1121] The server notifies the user of the generated proposals as services or promotions in the physical store.

[1122] Input: Specific proposal details

[1123] Output: Notification to user

[1124] Operation: The server notifies the user of the proposed offer via a smartphone application or smart glasses. It also provides promotional information in conjunction with in-store displays and digital signage.

[1125] Examples and prompts

[1126] As a specific example, consider the case where user A is suggested to meditate three times a week and is notified through the application to "relax in the relaxation area."

[1127] Example prompt: "Generate recommended services if the user's health score is below 80. Example input: 'My heart rate is 85 bpm and I'm stressed. What services would you recommend?'"

[1128] Through the above steps, the system of the present invention comprehensively manages the user's health and emotional state, provides personalized recommendations, promotes health improvement, and dynamically adjusts insurance premiums.

[1129] 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.

[1130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1131] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1132] [Fourth embodiment]

[1133] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1136] 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.

[1137] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1138] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1139] 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.

[1140] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1141] 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.

[1142] 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 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.

[1143] 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.

[1144] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1145] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1146] As an embodiment of the present invention, the following specific system and its operation will be described. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[1147] Wearable devices (terminals)

[1148] The wearable device is worn by the user and has the function of monitoring and recording health data such as heart rate, electrocardiogram, and exercise data in real time. The user can also input the details of their diet using a smartphone application.

[1149] server

[1150] The server receives the health data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health information. The server also analyzes this data and generates a score for the user's health condition. Specifically, the health score is calculated by integrating heart rate variability, exercise volume, and dietary nutritional balance.

[1151] Data analysis

[1152] The server compares the collected health data with medical big data and analyzes it using a generative AI model. The result of the analysis is a health score, which is used to dynamically calculate each user's insurance premium. This calculation reflects each user's individual health risks and efforts to improve their lifestyle habits.

[1153] Proposal generation and notification

[1154] Based on the analyzed health data and scores, the server uses a generative AI model to generate specific recommendations for maintaining and improving health tailored to each user. Suggestions might include, for example, "add aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." These recommendations are sent via the user's wearable device or smartphone application.

[1155] Recalculation of insurance premiums

[1156] The server detects if the user implements the suggested health improvement measures and recalculates the next insurance premium. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, the next year's insurance premium will be reduced by an additional amount.

[1157] Utilizing medical big data

[1158] The server integrates collected health data with medical big data and utilizes generative AI models to gain insights for further research and diagnosis, which can contribute to the development of new treatments and preventative measures in the medical field.

[1159] Specific examples

[1160] A specific example is shown below.

[1161] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and records the 1 km walk as exercise data. The smartphone application also records that User A had bread and milk for breakfast.

[1162] The server receives this data and calculates User A's health score as "75 points." Next, using the generative AI model, the server suggests "adding aerobic exercise three times a week and increasing vegetable intake," and notifies User A.

[1163] If User A follows the suggestion to do aerobic exercise three times a week and incorporate more vegetables into his / her diet, his / her insurance premiums for the next year will be reduced by 500 yen.

[1164] In this way, a system will be built that uses interconnected terminal and server technology to collect health data, analyze it, make recommendations, and dynamically adjust insurance premiums. This system will promote the health of each individual user and provide a fair insurance premium system that reflects each individual's efforts.

[1165] The processing flow will be explained below.

[1166] Step 1:

[1167] The wearable device is worn by the user and collects daily heart rate, electrocardiogram, and exercise data in real time. The user also inputs their dietary habits using a smartphone application.

[1168] Step 2:

[1169] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, and dietary data to the server. The transmitted data includes each data item and a corresponding timestamp.

[1170] Step 3:

[1171] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database. This database includes heart rate, exercise volume, and diet history.

[1172] Step 4:

[1173] The server analyzes the received data and calculates the user's health score, taking into account the heart rate fluctuation pattern, the cumulative amount of exercise, and the nutritional balance of the diet.

[1174] Step 5:

[1175] The server references medical big data and complements the analysis results with generative AI models, which allows for accurate individualized predictions and health status assessments.

[1176] Step 6:

[1177] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account each user's health risk and the degree of lifestyle improvements.

[1178] Step 7:

[1179] The server generates specific diet and exercise recommendations to improve the user's health score, customized based on individual health data and medical big data.

[1180] Step 8:

[1181] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[1182] Step 9:

[1183] Users receive suggestions from the server and act on them to improve their lifestyle, such as doing aerobic exercise three times a week or adding more vegetables to their diet.

[1184] Step 10:

[1185] The server monitors whether the suggestions have been implemented and verifies this by checking against new data recorded in the database.

[1186] Step 11:

[1187] If the proposed plan is achieved, the server recalculates the next insurance premium, and applies an additional discount according to the achievement level.

[1188] Step 12:

[1189] The server integrates the collected health data and medical big data and analyzes them using a generative AI model. Based on the analysis results, the data is shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[1190] In this way, each step works together to establish a system that supports users' health management and enables dynamic adjustment of insurance premiums. The collected data will also be used to advance medical care.

[1191] Example 1

[1192] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1193] In modern society, there is a demand for optimal management of individual health conditions and fair and dynamic calculation of insurance premiums. However, conventional systems lack the means to efficiently collect and analyze users' health data, which makes it difficult to obtain appropriate feedback for calculating insurance premiums. Furthermore, they are also inadequate in providing specific health improvement suggestions based on users' lifestyle habits.

[1194] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1195] In this invention, the server includes a wearable device means for acquiring a user's health data, a computer means for receiving the health data from the wearable device and storing it in a data storage device, a data analysis means for analyzing the health data stored in the computer means and calculating the user's health rating, a price calculation means for dynamically calculating an insurance premium based on the health rating, a proposal generation means for generating diet and exercise proposals to improve the health rating, and a notification means for notifying the user of the generated proposals. This makes it possible to efficiently collect and analyze a user's health data, make specific proposals for improving their health, and dynamically calculate insurance premiums in a fair manner.

[1196] Definitions of important words

[1197] A "wearable device" is a device that a user wears on their body to collect health data such as heart rate, electrocardiogram, and exercise data.

[1198] A "computer device" is a device for receiving health data transmitted from a wearable device and storing the data in a data storage device.

[1199] "Data storage device" means a storage device for storing health data received by a computing device.

[1200] The "data analysis device" is a device for analyzing health data stored in a data storage device and calculating a health evaluation of a user.

[1201] The "price calculation device" is a device for dynamically calculating insurance premiums based on the health assessment calculated by the data analysis device.

[1202] The "suggestion generation device" is a device for automatically generating suggestions regarding diet and exercise to improve health assessment.

[1203] The "notification device" is a device for notifying users of the proposal content generated by the proposal generation device.

[1204] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate health scores and recommendations.

[1205] "Big data" refers to large datasets in fields such as medicine, and its use improves the accuracy of data analysis and model generation.

[1206] MODE FOR CARRYING OUT THE INVENTION

[1207] A specific system for implementing this invention and its operation are described below. In this system, a user wears a wearable device to collect daily health data, which is then analyzed by a server to dynamically calculate insurance premiums.

[1208] Wearable devices (terminals)

[1209] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. Specific examples of wearable devices include fitness trackers and smartwatches. Users can also input their dietary information using a smartphone application.

[1210] Data collection and transmission

[1211] The device sends the collected health data to the server via Bluetooth or Wi-Fi. This ensures that the data reaches the server quickly and without loss. For example, if user A inputs into a smartphone application that he or she had bread and milk for breakfast, that data is also sent.

[1212] Receiving and storing data

[1213] The server receives the health data sent from the device and stores it in a data storage device, which uses database management systems such as AWS RDS and Google Cloud Spanner to manage the data by linking it to each user's ID.

[1214] Data analysis and health assessment calculation

[1215] The server analyzes the received health data and calculates the user's health rating. Specifically, it integrates multiple factors such as heart rate variability, exercise volume, and nutritional balance of meals to arrive at a health rating. A generative AI model is used for this analysis. TensorFlow and PyTorch are used as generative AI models. As an example, the server calculates a health score of 75 points based on User A's data.

[1216] Generate health improvement suggestions

[1217] Based on the health assessment, the server generates suggestions on how to improve the user's health. The suggestions are generated using a generative AI model, and specific suggestions include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The generated suggestions are expressed in the form of prompt sentences as follows:

[1218] Generate appropriate health maintenance and improvement suggestions based on the health data of user A. The heart rate is 85 bpm, the exercise data is 1 km of walking, and the diet consists of bread and milk. The health score is 75 points.

[1219] Notification of proposal details

[1220] The server notifies the user of the generated suggestions via a push notification service to the wearable device or smartphone application, allowing the user to receive the notification and start taking action based on the suggestions.

[1221] Recollection of behavioral data and recalculation of insurance premiums

[1222] The server again collects data to detect whether the user acted on the suggestions. For example, if the user follows the suggestions and engages in aerobic exercise three times a week, this is recorded. The server then recalculates the next year's insurance premium to appropriately reflect the user's efforts. The recalculated premium is saved as an updated database.

[1223] Utilizing medical big data

[1224] The server integrates the collected health data with large amounts of medical data and performs further detailed analysis. By utilizing generative AI models, it contributes to the development of new treatments and preventative measures. This analysis is carried out using Apache Spark and Hadoop.

[1225] These processes enable a single flow of processes, from collecting user health data to analyzing it, making recommendations, and dynamically adjusting insurance premiums. As a practical example, a system could be realized in which collected data is analyzed based on a health assessment, specific recommendations are made based on the data, and users act on those recommendations to ensure that their insurance premiums are appropriately adjusted. This system is an effective means of promoting user health and providing a fair insurance premium system.

[1226] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1227] Program processing flow

[1228] Step 1:

[1229] The user wears a wearable device that monitors and records health data such as heart rate, electrocardiogram, and exercise data in real time. The input is the wearable device, and the output is the health data obtained from the device.

[1230] Step 2:

[1231] The device collects health data and inputs meal details into a smartphone application. Specifically, the device records the user's heart rate and exercise data, and the user inputs meal details into the smartphone application. The input is data entered manually by the wearable device and the user, and the output is comprehensive health data recorded on the device.

[1232] Step 3:

[1233] The device sends the collected data to the server. The device sends data to the server in real time or periodically via Bluetooth or Wi-Fi. The input is the health data recorded on the device, and the output is the data sent to the server.

[1234] Step 4:

[1235] The server receives the data and stores it in a data storage device. Specifically, the server stores the data received from the terminal in a database and links it to each user's ID. The input is the health data sent to the server, and the output is the data stored in the database.

[1236] Step 5:

[1237] The server analyzes the data and calculates a health score. The server uses a generative AI model to analyze the received and stored data and calculates a health assessment score (e.g., 75 points) based on heart rate variability, exercise volume, and dietary nutritional balance. The input is the health data stored in the database, and the output is the calculated health score.

[1238] Step 6:

[1239] The server generates suggestions using a generative AI model. The server generates prompts based on the calculated health score and uses the AI ​​model to create specific health improvement suggestions. Specific examples include "add aerobic exercise three times a week" and "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health improvement suggestions.

[1240] Step 7:

[1241] The server notifies the user of the suggestions. The server generates the suggestions in the form of prompts and notifies the user's wearable device or smartphone application via a push notification service. The input is the generated health improvement suggestions, and the output is a notification to the user.

[1242] Step 8:

[1243] The user acts according to the suggestions. Specifically, the user practices health-improving behaviors, such as aerobic exercise three times a week. The input is the notified suggestions, and the output is the user's behavioral data.

[1244] Step 9:

[1245] The server recollects the user's behavioral data and recalculates the insurance premium. The server receives the data again through the terminal and verifies whether the proposal has been implemented. It then recalculates the insurance premium and updates the database. The input is the recollected behavioral data, and the output is the recalculated insurance premium.

[1246] Step 10:

[1247] The server performs further analysis using medical big data. The server compares the collected user health data with large amounts of medical data and utilizes generative AI models to contribute to the development of new treatments and preventative measures. The input is user data and medical big data, and the output is new insights and proposals.

[1248] (Application example 1)

[1249] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1250] While conventional health management systems can collect and analyze users' health data and adjust insurance premiums, they lack the functionality to calculate more accurate health scores using payment data. This means that they cannot accurately reflect users' lifestyles and consumption behavior, making it difficult to ensure fairness in insurance premiums and generate appropriate health recommendations.

[1251] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1252] In this invention, the server includes a wearable device means for acquiring the user's health data, a server means for receiving the health data from the wearable device and storing it in a database, an analysis means for analyzing the health data stored in the server means and calculating the user's health score, a price calculation means for dynamically calculating the insurance premium based on the health score, a generation means for generating diet and exercise suggestions to improve the health score, a notification means for notifying the user of the generated suggestions, and a payment data aggregation means for receiving payment data and reflecting it in the calculation of the health score. This enables the calculation of a health score that accurately reflects the user's lifestyle and consumption behavior.

[1253] A "wearable device" is an electronic device worn by a user to collect health data.

[1254] The "server means" is a device that has the function of receiving data sent from a wearable device and storing it in a database.

[1255] The "analysis means" is a system for analyzing the data stored in the server means and calculating the user's health score.

[1256] The "price calculation means" is a tool that has the function of dynamically calculating insurance premiums based on the health score obtained by the analysis means.

[1257] The "generator" is a device or program that generates diet and exercise suggestions to improve the health score.

[1258] The "notification means" is a system having a function of notifying the user of the proposal created by the creation means.

[1259] The "payment data aggregation means" is a mechanism that receives users' payment information and reflects it in the calculation of the health score.

[1260] An embodiment of the present invention will be described below. This system is configured by combining a wearable device, a server, and a smart device (a smartphone or smart glasses).

[1261] System Overview

[1262] This system collects the user's health data using a wearable device and transmits the data to a server. The server then uses analytical means to calculate a health score and dynamically calculates insurance premiums based on the results. The server also generates proposals and notifies the user via their smart device. It also collects payment data and reflects it in the calculation of the health score.

[1263] Health data collection

[1264] First, the user puts on a wearable device that monitors and records heart rate, electrocardiogram, exercise data, and other information in real time. Health data is then sent to a server via the user's smartphone or smart glasses.

[1265] Data analysis by server

[1266] The server stores the received health data in a database, using a database system such as MySQL. The server then analyzes the data using a generative AI model (e.g., TensorFlow or PyTorch). This integrates heart rate variability, exercise volume, and dietary nutritional balance to calculate a health score.

[1267] Payment data aggregation

[1268] Additionally, the server receives users' electronic payment data and incorporates it into the calculation of their health score. For example, it automatically records the food and exercise-related items purchased by users, thereby improving the accuracy of the health score.

[1269] Proposal generation and notification

[1270] The server generates specific suggestions for maintaining and improving health based on the analysis results. These suggestions are then sent to the user via smartphone or smart glasses, making it easier for the user to improve their daily lifestyle habits.

[1271] Insurance premium calculation

[1272] The server dynamically calculates insurance premiums based on the health score, for example, discounts on premiums if the health score is high, and recalculates premiums to take into account any suggested health improvements implemented.

[1273] Hardware and software used

[1274] Hardware: Wearable devices (e.g. fitness trackers), smartphones, smart glasses.

[1275] Software: Health data collection app (smartphone app), database system (MySQL), analysis platform (TensorFlow, PyTorch).

[1276] Specific examples

[1277] A specific example is shown below.

[1278] Example of operation:

[1279] 1. A user purchases a salad and a healthy drink for breakfast on 2023-10-05 08:00.

[1280] 2. The wearable device records a heart rate of 80 bpm at 2023-10-05 07:30.

[1281] 3. The app sends the above data to the server and receives a health score of 85 points as the analysis result.

[1282] 4. The server generates a suggestion to the user: "Add aerobic exercise three times a week."

[1283] 5. Insurance premiums will be calculated dynamically and a 300 yen discount will be applied to the next month's premium.

[1284] Example of an input prompt for a generative AI model:

[1285] "User ID: 12345's health data: Heart rate 80 bpm, Walking 5000 steps, Breakfast: Salad, Healthy drink. Calculate my health score and get suggestions and insurance discounts."

[1286] The above is a specific embodiment for carrying out the present invention, which makes it possible to calculate a health score that accurately reflects the lifestyle habits and consumption behavior of the user.

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data). This data is transmitted in real time to a smartphone or smart glasses. The input is the health data from the wearable device, and the output is transmitted to the smart device.

[1290] Step 2:

[1291] The smart device receives health data from the wearable device and sends it to the server. The input is the health data from the wearable device, and the output is the data sent to the server.

[1292] Step 3:

[1293] The server stores the received health data in a database. Specifically, the data is stored in a MySQL database, for example. The input is the data sent from the smart device, and the output is the health data stored in the database.

[1294] Step 4:

[1295] The server collects the user's electronic payment data using a payment data aggregation mechanism, where the input is the user's payment record and the output is the payment data for use in calculating the health score.

[1296] Step 5:

[1297] The server integrates the health data and payment data and calculates a health score using a generative AI model (such as TensorFlow or PyTorch) as an analytical tool. The input is the health data and payment data, and the output is the calculated health score.

[1298] Step 6:

[1299] The server uses the generation means to generate health suggestions based on the health score, such as "do aerobic exercise three times a week" or "add more green and yellow vegetables to your diet." The input is the health score, and the output is the generated health suggestions.

[1300] Step 7:

[1301] The server notifies the user of the generated health suggestions via a notification mechanism. The input is the health suggestions, and the output is the notified suggestions.

[1302] Step 8:

[1303] If the user implements the health suggestions, the server detects the implementation results and recalculates the next insurance premium. For example, if the user performs aerobic exercise three times a week, the server will discount the insurance premium. The input is the implementation results of the health suggestions, and the output is the recalculated insurance premium.

[1304] These are the specific processing steps of the system that realizes this application example. This makes it possible to calculate a health score that accurately reflects the user's lifestyle and consumption behavior, and to dynamically calculate insurance premiums.

[1305] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1306] As a form for implementing the present invention, a system will be specifically described that acquires health data and emotional data of a user, dynamically calculates insurance premiums based on this data, and makes appropriate suggestions for maintaining and improving health.

[1307] Wearable devices (terminals)

[1308] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application.

[1309] Emotion Engine

[1310] The device's built-in emotion engine analyzes the user's emotional state using skin galvanic response, heart rate variability, facial expression data, etc. This allows the device to grasp the user's current emotions, such as stress or relaxation, in real time.

[1311] server

[1312] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information.

[1313] Data analysis

[1314] The server analyzes the received health and emotional data and calculates the user's health score. Specifically, the calculation takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. For example, if a person is experiencing persistent stress, measures such as a more severe health score assessment may be taken.

[1315] Dynamic calculation of insurance premiums

[1316] The server dynamically calculates insurance premiums based on the analyzed health score, which is designed to reflect individual health risks, emotional state, and lifestyle improvement efforts.

[1317] Proposal generation and notification

[1318] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. These suggestions include, for example, "meditate to reduce stress" or "take a walk in nature on your day off to relax." These suggestions are customized taking into account emotional data generated by the emotion engine. The suggestions are then communicated to the user via their wearable device or smartphone application.

[1319] Recalculation of insurance premiums

[1320] The server monitors whether the user has implemented the suggested health improvements, checking against new data recorded in the database. If the recommendations have been achieved, the server recalculates the next insurance premium and notifies the user of the results.

[1321] Utilizing medical big data

[1322] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[1323] Specific examples

[1324] A specific example is shown below.

[1325] The device records that User A's heart rate is 85 bpm at "2023-10-05 07:30:00" and collects data on a 1 km walk and breakfast (bread and milk). Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and evaluates that he is in a "stressed state."

[1326] The server receives this data and calculates User A's health score as "70 points." It also generates a stress reduction suggestion of "meditating three times a week" from the emotional data and notifies User A.

[1327] If User A follows the suggested meditation and improves his / her lifestyle, the insurance premium for the next year will be reduced by 500 yen based on that data.

[1328] The server will integrate the new data into medical big data and share the analysis results with medical research institutions, thereby advancing research into new treatments for stress management.

[1329] In this way, by linking wearable devices, servers, and emotion engines and utilizing health and emotion data, a system can be built that provides optimal health management and dynamic adjustment of insurance premiums for each individual user.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] The wearable device allows users to monitor their daily heart rate, electrocardiogram, exercise data, and emotional status in real time, and also allows users to input their dietary information using a smartphone application.

[1333] Step 2:

[1334] The device periodically transmits the acquired heart rate, electrocardiogram, exercise data, dietary data, and emotion data to the server. The transmitted data includes each data item and a corresponding timestamp.

[1335] Step 3:

[1336] The server receives the data sent from the device, associates it with each user's ID, and stores it in a database, recording their heart rate, exercise volume, dietary habits, and emotional state.

[1337] Step 4:

[1338] The server analyzes the stored data and calculates the user's health score, taking into account heart rate fluctuation patterns, exercise volume, nutritional balance of meals, and emotional state.

[1339] Step 5:

[1340] The server references medical big data and uses generative AI models to complement and refine the health score analysis results, enabling more accurate predictions and assessments.

[1341] Step 6:

[1342] The server dynamically calculates insurance premiums based on the calculated health score, with the calculation algorithm taking into account the user's health risks, emotional state, and lifestyle improvement efforts.

[1343] Step 7:

[1344] The server generates specific suggestions for maintaining or improving health based on the user's health score and emotional state, and the suggestions are customized taking into account the user's emotional data.

[1345] Step 8:

[1346] The server notifies the user of the generated proposal through a notification means, which is displayed on the smartphone application or the display of the wearable device.

[1347] Step 9:

[1348] Users receive suggestions from the server and use them to improve their lifestyle, such as doing aerobic exercise three times a week or performing meditation sessions to reduce stress.

[1349] Step 10:

[1350] The server monitors whether the proposed health improvement measures have been implemented, checking against new data recorded in the database, and if implementation is confirmed, records the result in the database.

[1351] Step 11:

[1352] If the proposal is achieved, the server recalculates the next premium, applying an additional discount according to the achievement level.

[1353] Step 12:

[1354] The server integrates the collected health and emotion data with medical big data and analyzes it using a generative AI model. Based on the analysis results, the data will be shared with medical research institutions, contributing to the development of new treatments and preventative measures.

[1355] In this way, each step works in tandem to create a system that comprehensively manages users' health and emotions, providing individually optimized recommendations and dynamically adjusting insurance premiums. The collected data will also be used in medical research, making a wide-ranging contribution to society.

[1356] Example 2

[1357] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1358] In modern society, there is a demand for accurate understanding of individual health conditions and stress levels, and for dynamic calculation of insurance premiums. However, achieving this requires a system that can collect and analyze a variety of health and emotional data in real time and automatically generate optimal health management proposals for each individual user. Conventional systems have had difficulty dynamically calculating insurance premiums that take emotional states into account, or recalculating premiums based on the implementation of proposals. It has also been difficult to integrate collected data with medical big data and analyze it using generative AI models to contribute to new medical research.

[1359] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1360] In this invention, the server includes a means for receiving health data and emotion data and storing them in a database, a means for analyzing the health data and emotion data to calculate a health score, a means for dynamically calculating insurance premiums based on the health score, and a means for generating health maintenance and improvement proposals. This allows for comprehensive management of the user's health status, dynamic calculation of insurance premiums, and real-time healthcare proposals. Furthermore, insurance premiums can be recalculated by monitoring the implementation status of health improvement measures, and the collected data can be integrated with medical big data to contribute to new medical research.

[1361] A "wearable device" is a device that is worn on the user's body and collects health and emotional data in real time.

[1362] "Health data" refers to physiological data that indicates the user's health condition, such as heart rate, electrocardiogram, and exercise data.

[1363] "Emotional data" refers to data that indicates the emotional state of the user, such as skin galvanic response, heart rate variability, and facial expression data.

[1364] The "server means" refers to a device and software for receiving health data and emotion data transmitted from a wearable device and storing the data in a database.

[1365] The "analysis means" refers to a device and software for analyzing the health data and emotion data stored on the server and calculating the user's health score.

[1366] The "health score" is a numerical value calculated by the analysis means to comprehensively evaluate the health condition of the user.

[1367] A "price calculator" is a device and software for dynamically calculating insurance premiums based on health scores.

[1368] The "generation means" refers to a device and software for generating health maintenance and improvement suggestions to improve the user's health score.

[1369] The "notification means" refers to devices and software for notifying users of generated suggestions.

[1370] "Monitoring means" means the equipment and software used to monitor whether the proposed health improvement measures have been implemented and to recalculate the next premium.

[1371] "Medical big data" refers to a large-scale dataset that compiles a wide range of health and medical data.

[1372] A "generative AI model" is a machine learning model that uses AI technology to generate specific suggestions for maintaining and improving health.

[1373] This invention is a system that collects health and emotional data from users, dynamically calculates insurance premiums based on that data, and makes specific suggestions for maintaining and improving their health. This system is composed of a wearable device (terminal), a server, and a generative AI model.

[1374] Wearable devices (terminals)

[1375] The wearable device is equipped with an emotion engine that recognizes the user's heart rate, electrocardiogram, exercise data, and emotional state. The device collects and records this data in real time. The user can also input their dietary information through a smartphone application. For example, when the user goes for a daily walk or jog, the device uses GPS to record the distance and elapsed time. It is also equipped with a heart rate sensor to monitor the heart rate during exercise.

[1376] Emotion Engine

[1377] The device's built-in emotion engine can analyze the user's emotional state using data on skin electrodermal response, heart rate variability, and facial expressions. For example, if skin electrodermal response increases and heart rate variability increases, it will recognize this as a "stress state" and provide a real-time evaluation.

[1378] server

[1379] The server receives the health and emotion data sent from the device and stores it in a database. This data is linked to each user's ID and managed as individual health and emotion information. Each time the server receives data, it stores it in the database and performs a comparative analysis with the user's past data.

[1380] Data analysis

[1381] The server analyzes the received health and emotional data and calculates the user's health score. The analysis takes into account heart rate fluctuations, exercise volume, nutritional balance of meals, emotional state, and other factors. For example, if the user exercises less and is under stress for a long period of time, the health score will be calculated as low.

[1382] Dynamic calculation of insurance premiums

[1383] The server dynamically calculates insurance premiums based on the analyzed health score. This calculation is designed to reflect individual health risks, emotional state, and efforts to improve lifestyle habits. For example, premiums can be set lower for users with a high health score, and higher for users with a low health score.

[1384] Proposal generation and notification

[1385] The server uses a generative AI model to generate specific suggestions for maintaining or improving health based on the user's health score and emotional state. For example, a specific suggestion such as "Meditate three times a week to reduce stress" may be generated. These suggestions are customized taking into account the user's emotional data and are communicated via a wearable device or smartphone application.

[1386] Example (prompt):

[1387] "If the user is stressed, suggest specific actions to reduce stress."

[1388] Proposal implementation and monitoring

[1389] The server monitors whether the user has implemented the suggested health improvements. This data is compared with the recommendations, and if implementation is confirmed, the next insurance premium is recalculated. For example, if a user meditates three times a week, the insurance premium will be reduced based on that data.

[1390] Utilizing medical big data

[1391] The server integrates the collected health and emotional data with medical big data and analyzes it using a generative AI model. The results of this analysis will be shared with medical research institutions, contributing to the development of new treatments and preventative measures. Specifically, it is expected that research into new treatments for stress management will progress.

[1392] In this way, by linking wearable devices, a server, and an emotion engine, this system effectively utilizes users' health and emotion data to dynamically adjust insurance premiums and provide suggestions for maintaining and improving health.

[1393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1394] Step 1:

[1395] The device collects heart rate, electrocardiogram, exercise data, and emotional data in real time. Specifically, sensors measure heart rate every second and record exercise data (e.g., walking distance) using GPS. The emotion engine analyzes electrodermal response and heart rate variability to determine emotional state. The input is real-time data from the sensors, and the output is collected health data and emotional data.

[1396] Step 2:

[1397] The terminal sends the collected data to the server at regular intervals. Specifically, the device packets the data every 30 seconds and sends it to the server via the smartphone. The input is the data collected by the terminal, and the output is the data sent to the server.

[1398] Step 3:

[1399] The server receives the health and emotion data sent from the device and stores it in a database. Specifically, the server associates the received data with the user's ID and stores it in the corresponding table in the database. The input is the data sent from the device, and the output is the data stored in the database.

[1400] Step 4:

[1401] The server calculates the user's health score based on the data received. Specifically, the server's analysis engine calculates a comprehensive health score by taking into account heart rate fluctuations, exercise volume, nutritional balance of meals, and emotional state. The input is the health and emotional data stored in the database, and the output is the calculated health score.

[1402] Step 5:

[1403] The server dynamically calculates the insurance premium based on the analyzed health score. Specifically, it inputs the health score and calculates a new insurance premium according to the insurance premium setting rules. The input is the health score, and the output is the calculated new insurance premium.

[1404] Step 6:

[1405] The server uses the generative AI model to generate suggestions for maintaining or improving health, such as suggesting "meditation methods to reduce stress." The input is the health score and emotion data, and the output is the generated health suggestions.

[1406] Step 7:

[1407] The server notifies the user of the generated suggestions. Specifically, the suggestions are notified via a wearable device or a smartphone app. The input is the generated health suggestions, and the output is the notified information.

[1408] Step 8:

[1409] The server monitors whether the user has implemented the suggested health improvement measures and recalculates the next insurance premium. Specifically, it compares the new data sent from the device with the proposed measures. The input is the new health data and the proposed measures, and the output is the recalculated insurance premium.

[1410] Step 9:

[1411] The server integrates the collected data with medical big data, analyzes it using a generative AI model, and shares the analysis results with medical research institutions. Specifically, the data is uploaded to a large dataset and the analysis results are provided through a shared API. The input is the collected health data and emotion data, and the output is the analysis results.

[1412] (Application example 2)

[1413] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1414] While modern insurance systems calculate insurance premiums based on users' health data, they do not dynamically calculate premiums that take into account the user's emotional state or provide customized suggestions to improve the health score itself. Furthermore, there is a lack of mechanisms for providing services and promotions based on the user's health status in physical stores. This makes it difficult to implement specific measures for users' health management and improvement of their emotional state, making it difficult to promote the overall health of users.

[1415] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1416] In this invention, the server includes means for receiving data from a wearable device to acquire a user's health data and emotional data and storing the data in a database, means for analyzing the health data and emotional data stored in the server means and calculating the user's health score, means for generating suggestions for diet, exercise, and relaxation to improve the health score, and means for notifying the user of the generated suggestions as services and promotions in a physical store. This makes it possible not only to precisely analyze a user's health data and emotional data and provide individually optimized suggestions for maintaining and improving health, but also to effectively utilize and notify the suggestions in a physical store.

[1417] A "wearable device" is a device for collecting health and emotional data of a user. It has the function of collecting heart rate, electrocardiogram, exercise data, and emotional state in real time.

[1418] The "server means" is a device that receives the health data and emotion data sent from the wearable device and stores them in a database.

[1419] The "analysis means" is a device that analyzes the health data and emotion data stored on the server and calculates the user's health score.

[1420] The "price calculation means" is a device that dynamically calculates insurance premiums based on the health score calculated by the analysis means.

[1421] The "generation means" is a device that generates diet and exercise suggestions, as well as relaxation suggestions, to improve the health score.

[1422] The "notification means" is a device for notifying the user of the generated proposal, which is provided to the user as a service or promotion in a physical store.

[1423] "Health Data" refers to physiological data such as a user's heart rate, electrocardiogram, and exercise data.

[1424] "Emotion data" refers to data on the user's emotional state that is analyzed based on the user's skin galvanic response, heart rate variability, facial expression data, and the like.

[1425] The "health score" is an index showing the user's health condition calculated by the analysis means by integrating health data and emotional data.

[1426] "Dietary Suggestions" are specific dietary recommendations to improve a user's health score.

[1427] "Exercise Suggestions" are recommendations for specific exercises to improve a user's health score.

[1428] "Relaxation suggestions" are recommendations for specific activities to improve the user's emotional state and reduce stress.

[1429] "Services and Promotions" refers to promotional activities, including discounts and special offers on specific services and products offered in physical stores.

[1430] As an embodiment of the present invention, we will specifically explain a system that acquires health data and emotional data of users, dynamically calculates insurance premiums based on this data, and makes appropriate health maintenance and improvement proposals. This system is composed of the following main components.

[1431] 1. Wearable devices

[1432] The wearable device has the function of acquiring the following data in real time:

[1433] Heart rate

[1434] electro-cardiogram

[1435] Exercise data

[1436] Emotional state (analysis based on skin galvanic response, heart rate variability, and facial expression data)

[1437] When worn by the user, these physiological and emotional data are collected and sent to a server via a smartphone application.

[1438] 2. Server Means

[1439] The server receives the health data and emotion data sent from the wearable device and stores them in a database. The server has the following functions:

[1440] Receiving and storing data

[1441] Health and emotional data analysis

[1442] Calculating your health score

[1443] Dynamic calculation of insurance premiums

[1444] Proposal Generation

[1445] 3. Analysis method

[1446] The analysis method comprehensively analyzes the health data and emotional data stored on the server and calculates the user's health score. The analysis uses medical big data and generative AI models. For example, if a person is experiencing persistent stress, the health score can be set low.

[1447] 4. Generation means

[1448] The generation means generates dietary and exercise suggestions, as well as relaxation suggestions, based on the health score calculated by the analysis means. Using the generative AI model, the following specific suggestions are generated:

[1449] "Meditate three times a week"

[1450] "I walk in nature to relax."

[1451] 5. Means of notification

[1452] The notification means notifies the user of the proposal created by the generation means. In particular, the proposal can be provided as a service or promotion in a physical store. The notification is performed via a smartphone application or smart glasses.

[1453] Specific examples

[1454] A specific example is given below.

[1455] At 2023-10-05 07:30:00, User A's heart rate is recorded as 85 bpm, and data on 1 km of walking and breakfast (bread and milk) is collected. Furthermore, the emotion engine analyzes User A's electrodermal response and heart rate variability and assesses that he is in a stressed state.

[1456] The server receives this data and calculates User A's health score as "70 points." From the emotional data, it generates a stress reduction suggestion of "meditating three times a week" and notifies User A. An example of a specific notification would be "Please relax in the relaxation area."

[1457] An example of a prompt sentence is, "Generate recommended service suggestions if the user's health score is 80 or less. Input example: 'My heart rate is 85 bpm and I'm in a stressed state. What services would you recommend?'" By inputting this prompt into the generation AI model, it is possible to automatically generate appropriate suggestions.

[1458] The embodiment of the present invention is as described above. It is a system that comprehensively manages a user's health and emotional state and provides individualized suggestions to promote health improvement and dynamically adjust insurance premiums.

[1459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1460] Step 1:

[1461] The wearable device collects the user's health data (heart rate, electrocardiogram, exercise data) and emotional data (galvanic skin response, heart rate variability, facial expression data) in real time and transmits it to a smartphone application.

[1462] Input: Health and emotion data

[1463] Output: Send data to a smartphone application

[1464] How it works: The wearable device stores data collected by sensors in its internal memory and periodically transmits it to a smartphone application via Bluetooth or Wi-Fi.

[1465] Step 2:

[1466] The smartphone application transmits the data received from the wearable device to a server.

[1467] Input: Health and emotion data from wearable devices

[1468] Output: Send data to the server

[1469] Operation: The smartphone application sends the received data to the server as an HTTP request, along with the user ID.

[1470] Step 3:

[1471] The server stores the data received from the smartphone application in a database.

[1472] Input: Data from a smartphone application

[1473] Output: Data storage in database

[1474] Operation: The server stores the received data in the appropriate database table based on the user ID.

[1475] Step 4:

[1476] The server analyzes the health data and emotion data stored in the database and calculates the user's health score.

[1477] Input: Health and emotion data stored in a database

[1478] Output: Health score

[1479] How it works: The server uses a generative AI model to analyze the data and calculate a health score based on information such as heart rate and emotional state, with medical big data also being taken into account.

[1480] Step 5:

[1481] The server dynamically calculates the insurance premium based on the calculated health score.

[1482] Input: Health score

[1483] Output: Insurance premium

[1484] Operation: The server recalculates the insurance premium based on the health risk assessment. This calculation is performed by the insurance risk algorithm.

[1485] Step 6:

[1486] The server generates diet, exercise, and relaxation suggestions to improve the health score.

[1487] Input: Health score

[1488] Output: Specific proposals

[1489] How it works: Based on your health score, the server uses a generative AI model to generate personalized health maintenance and improvement suggestions, such as meditation and relaxation exercises to reduce stress.

[1490] Step 7:

[1491] The server notifies the user of the generated proposals as services or promotions in the physical store.

[1492] Input: Specific proposal details

[1493] Output: Notification to user

[1494] Operation: The server notifies the user of the proposed offer via a smartphone application or smart glasses. It also provides promotional information in conjunction with in-store displays and digital signage.

[1495] Examples and prompts

[1496] As a specific example, consider the case where user A is suggested to meditate three times a week and is notified through the application to "relax in the relaxation area."

[1497] Example prompt: "Generate recommended services if the user's health score is below 80. Example input: 'My heart rate is 85 bpm and I'm stressed. What services would you recommend?'"

[1498] Through the above steps, the system of the present invention comprehensively manages the user's health and emotional state, provides personalized recommendations, promotes health improvement, and dynamically adjusts insurance premiums.

[1499] 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.

[1500] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1501] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1502] 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.

[1503] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1504] 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.

[1505] 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).

[1506] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1507] 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."

[1508] 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.

[1509] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1510] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1511] 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.

[1512] 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.

[1513] 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.

[1514] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[1515] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] The following is further disclosed regarding the above embodiment.

[1521] (Claim 1)

[1522] Wearable devices for collecting user health data;

[1523] a server means for receiving health data from the wearable device and storing the data in a database;

[1524] an analysis means for analyzing the health data stored in the server means and calculating the user's health score;

[1525] a price calculation means for dynamically calculating a premium based on the health score;

[1526] generating means for generating diet and exercise suggestions to improve said health score;

[1527] a notification means for notifying a user of the generated proposal;

[1528] A system including:

[1529] (Claim 2)

[1530] 10. The system of claim 1, wherein the wearable device acquires heart rate, electrocardiogram, and movement data.

[1531] (Claim 3)

[1532] The system according to claim 1, wherein the analysis means refers to medical big data and calculates a health score using a generative AI model.

[1533] "Example 1"

[1534] Claims

[1535] (Claim 1)

[1536] A wearable device for collecting user health data;

[1537] a computing device that receives health data from the wearable device and stores the health data in a data storage device;

[1538] a data analysis device that analyzes the health data stored in the computer device and calculates a health evaluation of the user;

[1539] a price calculation device that dynamically calculates insurance premiums based on said health assessment;

[1540] a suggestion generator that generates suggestions for diet and exercise to improve the health assessment;

[1541] a notification device for notifying a user of the generated proposal;

[1542] A system including:

[1543] (Claim 2)

[1544] 10. The system of claim 1, wherein the wearable device acquires heart rate, electrocardiogram, and movement data.

[1545] (Claim 3)

[1546] The system of claim 1, wherein the data analysis device refers to large amounts of medical data and calculates a health assessment using a generative AI model.

[1547] "Application Example 1"

[1548] (Claim 1)

[1549] a wearable device means for acquiring health data of a user;

[1550] a server means for receiving health data from the wearable device and storing the data in a database;

[1551] an analysis means for analyzing the health data stored in the server means and calculating the user's health score;

[1552] a price calculation means for dynamically calculating a premium based on the health score;

[1553] a generating means for generating diet and exercise suggestions for improving said health score;

[1554] a notification means for notifying a user of the generated proposal;

[1555] a payment data aggregation means for receiving payment data and reflecting the data in the calculation of a health score;

[1556] A system including:

[1557] (Claim 2)

[1558] 10. The system of claim 1, wherein the wearable device acquires heart rate, electrocardiogram, and movement data.

[1559] (Claim 3)

[1560] The system according to claim 1, wherein the analysis means refers to medical big data and calculates a health score using a generative AI model.

[1561] "Example 2: Combining Emotion Engines"

[1562] (Claim 1)

[1563] Wearable devices for collecting user health data;

[1564] a server means for receiving health data and emotion data from the wearable device and storing the data in a database;

[1565] an analysis means for analyzing the health data and emotion data stored in the server means and calculating a health score of the user;

[1566] a price calculation means for dynamically calculating a premium based on the health score;

[1567] A generating means for generating health maintenance and improvement suggestions for improving the health score;

[1568] a notification means for notifying a user of the generated proposal;

[1569] A means of monitoring whether the proposed health improvements have been implemented and recalculating the next premium; and

[1570] A system that integrates collected data with medical big data, analyzes it using generative AI models, and includes a means to share the analysis results with medical research institutions.

[1571] (Claim 2)

[1572] 10. The system of claim 1, wherein the wearable device acquires heart rate, electrocardiogram, movement data, and emotion data.

[1573] (Claim 3)

[1574] The system according to claim 1, wherein the analysis means refers to medical big data and calculates a health score using a generative AI model.

[1575] "Application example 2 when combining emotion engines"

[1576] (Claim 1)

[1577] a wearable device for acquiring health and emotional data of a user;

[1578] a server means for receiving health data and emotion data from the wearable device and storing the data in a database;

[1579] an analysis means for analyzing the health data and emotion data stored in the server means and calculating the user's health score;

[1580] a price calculation means for dynamically calculating a premium based on the health score;

[1581] a generating means for generating dietary and exercise suggestions, as well as relaxation suggestions, to improve said health score;

[1582] a notification means for notifying the user of the generated proposal as a service or promotion in a physical store;

[1583] A system including:

[1584] (Claim 2)

[1585] 10. The system of claim 1, wherein the wearable device acquires heart rate, electrocardiogram, and movement data.

[1586] (Claim 3)

[1587] The system according to claim 1, wherein the analysis means refers to medical big data and calculates a health score using a generative AI model. [Explanation of symbols]

[1588] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. Wearable devices for collecting user health data; a server means for receiving health data from the wearable device and storing the data in a database; an analysis means for analyzing the health data stored in the server means and calculating the user's health score; a price calculation means for dynamically calculating a premium based on the health score; generating means for generating diet and exercise suggestions to improve said health score; a notification means for notifying a user of the generated proposal; A system including:

2. The system of claim 1 , wherein the wearable device acquires heart rate, electrocardiogram, and movement data.

3. The system according to claim 1 , wherein the analysis means refers to medical big data and calculates a health score using a generative AI model.

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