System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting and analyzing genetic information of a user; means for collecting and analyzing lifestyle data of the user; means for integrating the analysis results of the genetic information and the lifestyle data to generate a personalized health care plan; and means for providing the generated health care plan to the user.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] In modern society, the increase in lifestyle-related diseases has become a serious problem, necessitating the effective implementation of personalized prevention methods and health management. Conventional health management systems adopt a uniform approach and do not adequately consider the genetic factors and lifestyle habits of individual users. This makes it difficult to provide effective risk assessments and appropriate health management plans. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, means for integrating the results of these analyses to generate a personalized health management plan, and means for providing the generated health management plan to the user. The system further includes means for predicting health risks using a generation AI based on the user's genetic information and lifestyle data, and means for continuously collecting and updating the user's lifestyle data, which the generation AI learns from and appropriately updates the personalized health management plan. This allows the user to receive an effective health management plan based on their genetic risk and lifestyle, enabling efficient prevention and management of lifestyle-related diseases.

[0006] "Genetic information" refers to information based on the DNA and RNA that an individual organism possesses, and includes hereditary characteristics and risk factors.

[0007] "Lifestyle data" includes information on diet, exercise, sleep, stress, and the like in the user's daily life, and is used to evaluate an individual's health condition.

[0008] "Analysis" refers to the process of using collected data (genetic information and lifestyle data) to evaluate them using certain algorithms or models and obtain specific results.

[0009] "Synthesis" refers to the process of combining multiple data and analytical results to produce a single, coherent output.

[0010] "Generative AI" refers to a system or algorithm that uses artificial intelligence technology to learn from data and output newly generated information or plans.

[0011] A "health management plan" includes specific recommended action plans and advice for maintaining or improving the health of an individual user.

[0012] "Forecasting" refers to the process of estimating future events and risks based on currently available data.

[0013] "Update" is the process of adding new information to existing data or plans to bring them up to date. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be specifically described.

[0036] System Overview

[0037] This system is mainly composed of a user, a device, and a server. First, the user provides genetic information and inputs lifestyle data daily. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to generate a health management plan and provides it to the user.

[0038] Program processing overview

[0039] Collection and analysis of genetic information

[0040] User:

[0041] Users use a genetic analysis kit to collect genetic samples and upload the results to the system's web portal or dedicated app. The genetic information, including DNA and RNA analysis results, reveals individual genetic characteristics and risk factors.

[0042] Device:

[0043] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure communication protocol.

[0044] server:

[0045] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[0046] Collection and analysis of lifestyle data

[0047] User:

[0048] Users input or automatically record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[0049] Device:

[0050] The device sends the lifestyle data entered by the user to a cloud server in real time, also using a secure communication protocol.

[0051] server:

[0052] The server analyzes the lifestyle data it receives. For example, it evaluates nutritional balance for diet, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database.

[0053] Generate and provide health management plans

[0054] server:

[0055] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[0056] Device:

[0057] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[0058] Ongoing support

[0059] User:

[0060] Users continually input their lifestyle data, so the system always has the most up-to-date information.

[0061] Device:

[0062] The device continues to send these new data to the cloud server.

[0063] server:

[0064] The server updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly, for example, by suggesting a new exercise plan for a user who has increased their physical activity.

[0065] For example, Alice provides her genetic information to the system and continues to input her daily lifestyle habits using her smartphone. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan in real time. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis result file to a web portal or dedicated app.

[0069] Step 2:

[0070] Device: The device (smartphone or PC) sends the genetic information data uploaded by the user to the cloud server. This transmission is carried out using a secure communication protocol such as SSL / TLS.

[0071] Step 3:

[0072] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and organized for each user.

[0073] Step 4:

[0074] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[0075] Step 5:

[0076] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[0077] Step 6:

[0078] Server: The server analyzes the received lifestyle data. The dietary data is used to evaluate nutritional balance, the exercise data is used to evaluate the amount of exercise and heart rate, and the sleep data is used to evaluate the quality and quantity of sleep. The evaluation results are stored in a database.

[0079] Step 7:

[0080] Server: The server combines the genetic information and lifestyle data analysis results, inputs them into a generative AI model, and generates a personalized health plan for the user, including recommended diet, exercise programs, and lifestyle modifications.

[0081] Step 8:

[0082] Device: The device presents the health management plan provided by the server to the user via push notifications, emails, in-app messages, etc.

[0083] Step 9:

[0084] User: The user continuously inputs lifestyle data, such as updating daily diet, exercise, and sleep records.

[0085] Step 10:

[0086] Device: The device continues to send newly entered lifestyle habit data to the cloud server.

[0087] Step 11:

[0088] Server: The server analyzes newly collected data and updates the generative AI model, resulting in appropriate improvements to the personalized health plan. For example, if the user increases their physical activity, new exercise and meal plans will be presented.

[0089] For example, Alice uploads her genetic information and continues to input her daily lifestyle habits. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[0090] Example 1

[0091] 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."

[0092] In modern society, individuals face the challenge of effectively managing their own health. In particular, accurately understanding health risks due to genetic risk factors and lifestyle habits and formulating an appropriate health management plan based on that information requires specialized knowledge, making it difficult for many people. Therefore, there is a need for a system that can solve these challenges and provide an optimized health management plan for each individual user.

[0093] 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.

[0094] In this invention, the server includes means for collecting a user's genetic information and transmitting it to a cloud server, means for collecting a user's lifestyle data and transmitting it to the cloud server, means for analyzing the genetic information and lifestyle data in the cloud server and storing the analysis results, means for integrating the analysis results of the genetic information and lifestyle data and generating an individualized health management plan using a generative AI model, and means for notifying the user of the generated health management plan, thereby enabling the user to implement an optimal health management plan based on their genetic risks and lifestyle habits and effectively maintain and improve their health.

[0095] "User" refers to an individual who utilizes the System to provide genetic and lifestyle data and receive a health management plan.

[0096] "Genetic information" refers to data obtained as a result of analyzing a user's DNA or RNA, including individual genetic characteristics and risk factors.

[0097] "Lifestyle data" refers to information including data on the user's daily diet, exercise, sleep, etc.

[0098] A "cloud server" refers to a server on a network that remotely receives, analyzes, and stores genetic information and lifestyle data sent by users.

[0099] "Analysis algorithm" refers to a computational method or mathematical model that processes genetic information and lifestyle data to assess a user's health status and risks.

[0100] "Generative AI model" refers to an artificial intelligence model that generates personalized health management plans based on genetic information and lifestyle data.

[0101] A "health management plan" refers to a plan that includes specific guidelines and recommendations for diet, exercise, sleep, etc., generated based on a user's genetic information and lifestyle data.

[0102] "Notification means" refers to the methods and technologies used to communicate the generated health management plan to the user, and examples include push notifications, emails, and in-app messages.

[0103] The present invention relates to a system that collects a user's genetic information and lifestyle data, analyzes the data, and generates and provides an individualized health management plan. This system is mainly composed of a user, a terminal, and a server.

[0104] Collection and analysis of genetic information

[0105] User:

[0106] First, the user collects a saliva or blood sample using a genetic analysis kit. The sample is then sent to a specialized analysis facility, and the analysis results are provided to the user. The user then uploads the analysis results to the system's web portal or a dedicated app. This genetic information includes DNA and RNA analysis data.

[0107] Device:

[0108] The terminal (e.g., a smartphone or personal computer) receives the genetic information data entered by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[0109] server:

[0110] The server then applies an analysis algorithm to the received genetic information data (using a machine learning library such as Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored for each user in a database such as MySQL.

[0111] Collection and analysis of lifestyle data

[0112] User:

[0113] Users use smartphone apps or wearable devices (e.g., fitness trackers or smartwatches) to input or automatically record daily food, exercise, and sleep data. For example, food logs include the types and amounts of food consumed, exercise logs include the types and durations of exercise, and sleep logs include total sleep time and quality.

[0114] Device:

[0115] The device transmits the lifestyle data collected by the user to a cloud server in real time, again using a secure communication protocol.

[0116] server:

[0117] The server analyzes the lifestyle data it receives. For example, dietary data is evaluated for nutritional balance, exercise data is evaluated for exercise volume and heart rate, and sleep data is evaluated for quality and quantity. The results of these analyses are stored in a database along with genetic information.

[0118] Generate and provide health management plans

[0119] server:

[0120] The server combines the results of the analysis of the genetic information and lifestyle data and inputs them into a generative AI model (e.g., OpenAI's GPT-4). The generative AI model uses this data to generate an optimized health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle improvements. For example, the following prompt could be input into the generative AI model: "Based on the user's genetic information and the collected lifestyle data, please generate a personalized health management plan. Specifically, please include recommendations for diet, exercise, and sleep, as well as measures to address the user's genetic risks."

[0121] Device:

[0122] The device notifies the user of the health management plan provided by the server. Notification methods include push notifications, emails, and in-app messages. For example, a message such as "Today's recommended exercise plan" may be displayed as a push notification on a smartphone.

[0123] Continuous data collection and analysis

[0124] User:

[0125] Users continuously input lifestyle data, for example, by recording their daily diet and exercise.

[0126] Device:

[0127] The device continues to send these new data to the cloud server.

[0128] server:

[0129] The server updates the generative AI model based on newly collected lifestyle data and improves the personalized health management plan accordingly. For example, it can present a new exercise plan with a higher intensity to a user who has increased their exercise volume.

[0130] Specific examples

[0131] When a user uses this system, they first provide their genetic information and then collect daily lifestyle data using a smartphone or wearable device. The server analyzes this data and uses generative AI to provide the user with an optimal health management plan in real time. By following this plan, users can reduce their risk of lifestyle-related diseases and maintain and improve their health.

[0132] This system allows users to practice optimal health management based on their genetic factors and lifestyle habits, enabling them to effectively maintain and improve their health.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] User:

[0136] Users collect a saliva sample using a genetic analysis kit and send it to an analysis laboratory, after which the genetic information provided by the analysis laboratory is uploaded to the system's web portal or dedicated app.

[0137] Input: Genetic information (DNA / RNA analysis results)

[0138] Output: Genetic information is uploaded to the system

[0139] Step 2:

[0140] Device:

[0141] The device (e.g., smartphone, personal computer) receives the genetic information uploaded by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[0142] Input: Genetic information

[0143] Output: Genetic information is sent to the cloud server

[0144] Step 3:

[0145] server:

[0146] The server then applies the received genetic information to an analysis algorithm (e.g., Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored in a database (e.g., MySQL).

[0147] Input: Genetic information

[0148] Output: The analysis results of genetic risk factors and physical predispositions are saved in a database.

[0149] Step 4:

[0150] User:

[0151] Users input or automatically record their daily diet, exercise, and sleep data using smartphone apps or wearable devices (e.g., fitness trackers, smartwatches).

[0152] Input: diet, exercise, sleep data

[0153] Output: Lifestyle data is recorded on the device

[0154] Step 5:

[0155] Device:

[0156] The device receives lifestyle data entered or recorded by the user and transmits it in real time to a cloud server using a secure communication protocol.

[0157] Input: Lifestyle data

[0158] Output: Lifestyle data is sent to the cloud server

[0159] Step 6:

[0160] server:

[0161] The server analyzes the lifestyle data it receives. Dietary data evaluates nutritional balance, exercise data evaluates exercise volume and heart rate, and sleep data evaluates quality and quantity. The analysis results are stored in a database.

[0162] Input: Lifestyle data

[0163] Output: Analysis results are saved in a database

[0164] Step 7:

[0165] server:

[0166] The server combines the genetic and lifestyle analysis results and inputs them into a generative AI model (e.g., OpenAI's GPT-4), which then uses this data to generate an optimal health management plan for the user.

[0167] Input: Analysis results of genetic information and lifestyle data

[0168] Output: Generate a personalized healthcare plan

[0169] Step 8:

[0170] server:

[0171] The generated health management plan is notified to the user via push notification, email, in-app message, etc. The server uses these methods to send the health management plan to the device.

[0172] Input: Health Care Plan

[0173] Output: The health management plan is sent to the device.

[0174] Step 9:

[0175] Device:

[0176] The device notifies the user of the health management plan sent from the server via push notification, email, in-app message, etc.

[0177] Input: Health Care Plan

[0178] Output: The health management plan is notified to the user.

[0179] Step 10:

[0180] User:

[0181] Users continue to input lifestyle habits data, which is then sent back to the server, where the generative AI model learns from it and updates the health management plan accordingly.

[0182] Input: New lifestyle data

[0183] Output: An updated health management plan from a generative AI model

[0184] (Application example 1)

[0185] 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."

[0186] Conventionally, personalized health management plans are designed for users to complete at home, and use in physical stores has not been considered, making it difficult to provide advice on which specific products users should purchase in physical stores. Also, there has been insufficient support for users to select appropriate products according to their individual health management plans, resulting in problems such as users having difficulty selecting the health products that are best suited to them.

[0187] 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.

[0188] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, means for integrating the analysis results of the genetic information and lifestyle data to generate an individualized health management plan, means for associating the generated health management plan with store product data to suggest optimal products for the user to purchase in a physical store, and means for providing the generated health management plan to the user. This enables the user to select products based on an appropriate health management plan while shopping in a physical store, and more effectively maintain and improve their health.

[0189] "Genetic information" is based on the results of DNA and RNA analysis provided by users and is data that indicates individual genetic characteristics and risk factors.

[0190] "Lifestyle data" is data that includes information about the user's daily habits, such as what they eat, how much they exercise, and how much they sleep.

[0191] A "health management plan" is a plan that includes personalized diet, exercise, and lifestyle recommendations based on the analysis of genetic and lifestyle data.

[0192] A "server" is a device that has the computing resources to analyze a user's genetic information and lifestyle data, and to generate and provide a health management plan.

[0193] "Generative AI" is an artificial intelligence technology that predicts health risks based on a user's genetic information and lifestyle data, and generates a personalized health management plan.

[0194] "Brick and mortar store" refers to a physical sales location where users can actually visit and select products.

[0195] "Products" are items that are useful for the user's health management, such as food and health products sold in physical stores.

[0196] "Scanning" is the act of reading the product's identification information using a barcode reader or camera.

[0197] "Analysis" is the process of finding certain regularities and patterns based on collected data and turning it into meaningful information.

[0198] "Notification" refers to the act of communicating the generated health management plan and other important information to the user, and can take various forms such as email or push notification.

[0199] "Association" refers to the act of matching a user's health management plan with product data from a physical store.

[0200] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be described in detail.

[0201] System Overview

[0202] This system is primarily composed of a user, a device (e.g., a smartphone), and a server. First, the user provides genetic information and inputs daily lifestyle habits. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to create a health management plan and provides it to the user. The system also has a function that suggests optimal products for the user to purchase in physical stores.

[0203] Collection and analysis of genetic and lifestyle data

[0204] Users use a genetic analysis kit to collect a genetic sample and upload the results to the system's dedicated app. Lifestyle data is recorded automatically or entered via a smartphone app or wearable device, including daily diet, exercise, and sleep data.

[0205] The device transmits this genetic information and lifestyle data to a cloud server using a secure communication protocol (e.g., HTTPS).

[0206] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data. The analysis includes evaluation of genetic risk factors, nutritional balance, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database and managed for each user.

[0207] Generate and provide health management plans

[0208] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[0209] The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[0210] Health support in physical stores

[0211] When users visit a physical store, they can use the app to scan products to see if they fit into their health management plan.

[0212] When the terminal (smartphone) scans a product in the store, it uses its barcode reader function to send the product information to the server.

[0213] The server associates this product information with the user's personalized health care plan and evaluates whether the product conforms to the health care plan. The evaluation results are returned to the terminal in real time and notified to the user.

[0214] Ongoing support

[0215] Users continually input their lifestyle data, which causes the device to continuously send new data to the cloud server, which then updates the generative AI model with the newly collected data and improves the personalized health management plan accordingly.

[0216] Specific examples

[0217] A specific example scenario could be when a user is searching for low-calorie foods in a store and uses an app to scan the product's barcode to check calorie information and suitability based on a health management plan.

[0218] Prompt Sentence Examples

[0219] We would like to develop an app that allows users to use an advanced smartphone app to suggest optimal low-calorie foods to purchase in stores based on their health management plan. The generative AI should analyze genetic information and lifestyle data to produce a list of specific foods. Furthermore, we would like the scanning function to display detailed product information in the store.

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] Users provide genetic information and lifestyle data

[0223] Users use a genetic analysis kit to collect a genetic sample and upload the results to a dedicated app. They also input or automatically record lifestyle data such as daily diet, exercise, and sleep data using a smartphone app or wearable device.

[0224] Input: Genetic information, lifestyle data

[0225] Output: Provided data (genetic information, lifestyle data)

[0226] Step 2:

[0227] The device sends data to the cloud server

[0228] The device (smartphone) transmits the genetic information and lifestyle data provided by the user to a cloud server using a secure communication protocol (e.g., HTTPS).

[0229] Input: Genetic information, lifestyle data

[0230] Output: Data sent to the cloud server

[0231] Step 3:

[0232] The server analyzes genetic information and lifestyle data

[0233] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data, which evaluates genetic risk factors and nutritional balance, exercise volume, heart rate, and sleep quality and quantity.

[0234] Input: Transmitted genetic information, lifestyle data

[0235] Output: Analyzed genetic risk factors and lifestyle assessment results

[0236] Step 4:

[0237] The server generates a health management plan

[0238] The server inputs the results of the genetic and lifestyle analysis into a generative AI model to generate a personalized health management plan, including recommended dietary choices, specific exercise programs, and lifestyle changes.

[0239] Input: Analysis results (genetic risk factors, nutritional balance, amount of exercise, quality of sleep)

[0240] Output: personalized health care plan

[0241] Step 5:

[0242] The device notifies the user of the health management plan

[0243] The device (smartphone) notifies the user of the health management plan provided by the server via push notifications or in-app messages.

[0244] Input: Generated health care plan

[0245] Output: Notification to the user (push notification, in-app message)

[0246] Step 6:

[0247] User scans item in store

[0248] When a user visits a physical store, they use the app to scan the barcode of a product, which provides the product's identification information.

[0249] Input: Product barcode

[0250] Output: Product identification information

[0251] Step 7:

[0252] The device sends product information to the server

[0253] The terminal sends the scanned product's identification information to the server, which then obtains detailed product information.

[0254] Input: Product identification information

[0255] Output: Product information sent to the server

[0256] Step 8:

[0257] The server matches the product with the health management plan

[0258] The server associates the product information with the user's personalized health care plan and evaluates whether the product is suitable for the plan. A suitability evaluation result is generated.

[0259] Input: Product information, health management plan

[0260] Output: Product conformity assessment results

[0261] Step 9:

[0262] The device notifies the user of the evaluation results

[0263] The terminal (smartphone) notifies the user of the product compatibility evaluation results received from the server, allowing the user to select the appropriate product in the store.

[0264] Input: Product conformity assessment results

[0265] Output: Notification to user (evaluation result)

[0266] 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.

[0267] The present invention relates to a health support system that utilizes generative AI and combines it with an emotion engine that recognizes a user's emotions. This system analyzes the user's genetic information and lifestyle data, and also adds emotion data to provide the user with a personalized health management plan. A specific embodiment of the present invention is described below.

[0268] System Overview

[0269] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data. The device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and updating of the health management plan.

[0270] Program processing overview

[0271] Collection and analysis of genetic information

[0272] User:

[0273] Users use a genetic analysis kit to collect genetic samples and upload the analysis results to the system's web portal or dedicated app.

[0274] Device:

[0275] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure protocol.

[0276] server:

[0277] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[0278] Collection and analysis of lifestyle data

[0279] User:

[0280] Users input or record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[0281] Device:

[0282] The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[0283] server:

[0284] The server analyzes the lifestyle data it receives. It evaluates nutritional balance for dietary data, exercise volume and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results of these analyses are stored in a database.

[0285] Emotion data collection and analysis

[0286] User:

[0287] Users collect their own emotional data using devices with emotion detection capabilities (e.g., smartwatches or smartphone apps).

[0288] Device:

[0289] The device transmits the user's emotional data to a cloud server, which is also transmitted using a secure protocol.

[0290] Server and Emotion Engine:

[0291] The server uses an emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the analysis results in a database along with other data.

[0292] Generate and provide health management plans

[0293] server:

[0294] The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[0295] Device:

[0296] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[0297] Ongoing support

[0298] User:

[0299] Users continuously input lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[0300] Device:

[0301] The device continues to send newly entered data to the cloud server.

[0302] server:

[0303] The server updates the generative AI model with newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it can suggest new emotional management or relaxation techniques.

[0304] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[0305] The processing flow will be explained below.

[0306] Step 1:

[0307] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis results file to a web portal or dedicated app.

[0308] Step 2:

[0309] Terminal: The terminal (smartphone or PC) sends the genetic information data provided by the user to the cloud server. This transmission uses a secure communication protocol such as SSL / TLS.

[0310] Step 3:

[0311] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and managed for each user.

[0312] Step 4:

[0313] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[0314] Step 5:

[0315] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[0316] Step 6:

[0317] Server: The server analyzes the received lifestyle data, evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The evaluation results are stored in a database.

[0318] Step 7:

[0319] User: The user uses an emotion-sensing device (e.g., a smartwatch or smartphone app) to collect their own emotional data, including stress levels, excitement levels, and relaxation levels.

[0320] Step 8:

[0321] Device: The device transmits the user's emotional data to the cloud server in real time. The emotional data is also transmitted using a secure protocol.

[0322] Step 9:

[0323] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, to evaluate whether the user is stressed or relaxed, and stores the results in a database.

[0324] Step 10:

[0325] Server: The server integrates the results of the analysis of genetic information, lifestyle data, and emotional data. It then inputs this data into a generative AI model to generate an optimal health management plan for the user. This plan includes recommended dietary content, exercise programs, and emotional management methods.

[0326] Step 11:

[0327] Device: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[0328] Step 12:

[0329] User: The user continuously inputs lifestyle and emotional data, such as daily updates on diet, exercise, sleep, and emotional state.

[0330] Step 13:

[0331] Device: The device continues to send newly input lifestyle data and emotion data to the cloud server.

[0332] Step 14:

[0333] Server: The server updates the generative AI model based on newly collected data, resulting in appropriate improvements to the personalized health management plan. For example, if the user's emotional state changes, new emotional management or relaxation techniques will be suggested.

[0334] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[0335] Example 2

[0336] 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."

[0337] In modern society, personalized health management is becoming increasingly important. However, conventional health management systems are unable to comprehensively analyze a user's genetic information, lifestyle data, and emotional data to provide an individually optimized health management plan. As a result, many users struggle with self-management and are unable to effectively reduce their risk of lifestyle-related diseases. Another issue is the inability to respond appropriately to changes in emotional state.

[0338] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing the user's genetic information, means for collecting and analyzing the user's lifestyle habit data, and means for collecting and analyzing the user's emotional data. This makes it possible to generate and provide an individualized health management plan based on a comprehensive analysis of the genetic information, lifestyle habit data, and emotional data.

[0339] "Genetic information" refers to all genetic information obtained from a user's DNA, including data regarding specific disease risks and physical predispositions.

[0340] "Lifestyle data" refers to information about a user's daily activities and lifestyle patterns, including dietary content, amount of exercise, and sleep duration.

[0341] "Emotional data" refers to information about the user's psychological and emotional state, including stress level and relaxation state.

[0342] A "generative AI model" refers to an algorithm that uses artificial intelligence techniques such as machine learning to generate optimal outputs (in this case, health management plans) based on multiple input data.

[0343] "Health Management Plan" means specific guidance and recommendations for improving and maintaining health that are individually tailored to a User's specific needs and circumstances.

[0344] A "cloud server" refers to a system that processes and stores data using a server installed in a remote location and is accessible via the Internet.

[0345] "Analysis" refers to the general process of extracting information based on collected data and making evaluations and judgments.

[0346] System Overview

[0347] This invention is a health support system utilizing generative AI, which is mainly composed of a user, a terminal, a server, and an emotion engine. This system analyzes the user's genetic information, lifestyle data, and emotion data, and provides an individualized health management plan.

[0348] Hardware and software used

[0349] Devices: Smartphones, PCs, wearable devices (e.g., smartwatches) that are used to collect and transmit data.

[0350] Server: Cloud server, used to analyze and store data and run generative AI models.

[0351] Emotion Engine: Software with emotion recognition algorithms used to analyze emotion data.

[0352] Generative AI model: Software containing machine learning algorithms used to generate optimal health management plans for users.

[0353] Program processing overview

[0354] The system operates as follows.

[0355] Collection and analysis of genetic information

[0356] User: Collects genetic samples using a genetic analysis kit and uploads the analysis results to the system's web portal or dedicated app.

[0357] Terminal: Sends the genetic information data uploaded by the user to the cloud server. Safety is ensured by using a secure protocol (e.g., HTTPS).

[0358] Server: The received genetic information data is run through a dedicated analysis algorithm to evaluate specific genetic risk factors and predispositions, and the results are stored in a database.

[0359] Example prompt: "Please upload the genetic information obtained using the genetic analysis kit and send the data to the cloud server."

[0360] Collection and analysis of lifestyle data

[0361] User: Uses a smartphone app or wearable device to enter or automatically record daily diet, exercise, and sleep data.

[0362] Device: The lifestyle habit data entered by the user is sent to the cloud server in real time using a secure protocol.

[0363] Server: Analyzes the received lifestyle data, evaluating nutritional balance from dietary data, exercise amount and heart rate from exercise data, and quality and quantity of sleep from sleep data. The results are stored in a database.

[0364] Example prompt: "Enter your daily diet, exercise, and sleep data into a smartphone app."

[0365] Emotion data collection and analysis

[0366] User: Collects their own emotional data using a device with emotion detection capabilities (such as a smartwatch or smartphone app).

[0367] Device: Emotion data is sent to the cloud server using a secure protocol, ensuring safety.

[0368] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the results in a database.

[0369] Example prompt: "Please wear a device with emotion detection capabilities and send the collected emotion data to the cloud server."

[0370] Generate and provide health management plans

[0371] Server: The results of the analysis of genetic information, lifestyle data, and emotional data are combined and input into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[0372] Device: The health management plan provided by the server is notified to the user via push notification, email, in-app message, etc.

[0373] Example prompt: "Check out the health management plan generated based on your lifestyle and emotional data."

[0374] Ongoing support

[0375] User: By continuously inputting daily lifestyle and emotional data, the system can always keep up to date with the latest information.

[0376] Device: Continues to send newly entered data to the cloud server. Safety is ensured by using a secure protocol.

[0377] Server: Updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it suggests new emotional management or relaxation techniques.

[0378] Example prompt: "Please continue to enter your daily habits and emotions."

[0379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0380] Processing flow and specific explanation

[0381] Step 1: User prepares genetic information

[0382] Input: Genetic analysis kit

[0383] How it works: Users use a genetic analysis kit to collect a genetic sample and send it to an analysis service. After a few days, they upload the analysis results to the system's web portal or a dedicated app.

[0384] Output: Genetic information data

[0385] Step 2: Sending genetic information via the device

[0386] Input: Genetic information data

[0387] How it works: A device (such as a smartphone or PC) sends the genetic information uploaded by the user to a cloud server using a secure protocol (HTTPS).

[0388] Output: Genetic information data stored on a cloud server

[0389] Step 3: Analysis of genetic information by the server

[0390] Input: Genetic information data stored on a cloud server

[0391] How it works: The server runs the received genetic information data through a specialized analysis algorithm. Specifically, it analyzes the DNA sequence data and evaluates specific genetic risk factors and predispositions. The analysis results are stored in a database.

[0392] Output: Analyzed genetic information

[0393] Step 4: User enters lifestyle data

[0394] Input: Smartphones and wearable devices

[0395] How it works: Users input or automatically record daily diet, exercise, and sleep data using a smartphone app or wearable device. Specifically, the diet app records the food consumed, and the wearable device measures the amount of exercise and sleep time.

[0396] Output: Lifestyle data

[0397] Step 5: Send lifestyle data via your device

[0398] Input: Lifestyle data

[0399] How it works: The device sends collected lifestyle data to a cloud server in real time using a secure protocol (HTTPS), ensuring data security.

[0400] Output: Lifestyle data stored on a cloud server

[0401] Step 6: Analysis of lifestyle data by the server

[0402] Input: Lifestyle data stored on a cloud server

[0403] Operation: The server analyzes the received lifestyle data. Specifically, it evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results are stored in a database.

[0404] Output: Analyzed lifestyle data

[0405] Step 7: Obtaining user emotion data

[0406] Input: Device with emotion detection function (smartwatch or smartphone app)

[0407] How it works: Users use emotion-sensing devices to collect their own emotional data, specifically measuring their emotions based on heart rate and facial expression recognition.

[0408] Output: Emotion data

[0409] Step 8: Device sends emotion data

[0410] Input: Emotion data

[0411] How it works: The device sends the user's emotional data to a cloud server. This data is also sent using a secure protocol (HTTPS), so it is safely protected.

[0412] Output: Emotion data stored on a cloud server

[0413] Step 9: Emotion data analysis by the server and emotion engine

[0414] Input: Emotion data stored on a cloud server

[0415] How it works: The server uses an emotion engine to analyze the received emotion data, for example to assess whether the user is stressed or relaxed. The results are stored in a database.

[0416] Output: Parsed emotion data

[0417] Step 10: Server generates health management plan

[0418] Input: Analyzed genetic information, lifestyle data, and emotional data

[0419] How it works: The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[0420] Output: Health management plan

[0421] Step 11: Notification of health management plan via device

[0422] Input: Health Care Plan

[0423] Operation: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[0424] Output: Health plan provided to the user

[0425] Step 12: User continues to enter data

[0426] Input: New lifestyle data, emotional data

[0427] How it works: Users continuously input their daily lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[0428] Output: New lifestyle data, emotional data

[0429] Step 13: Device continues to send data

[0430] Input: New lifestyle data, emotional data

[0431] How it works: The device continues to send newly entered data to the cloud server. Data transmission is securely protected by a secure protocol.

[0432] Output: New lifestyle and emotion data stored on a cloud server

[0433] Step 14: Server updates the generative AI model

[0434] Input: New lifestyle data and emotion data stored on the cloud server

[0435] How it works: The server updates the generative AI model based on newly collected data and refines the personalized health plan accordingly, for example by suggesting new emotional management or relaxation techniques when the user's emotional state changes.

[0436] Output: Updated health plan

[0437] Specific examples of each step

[0438] As a concrete example of step 1, a user uses a genetic analysis kit at home to collect a sample and uploads the analysis results to a dedicated app.

[0439] As a concrete example of step 4, a user enters food records into a smartphone app and collects exercise and sleep data using a wearable device.

[0440] Example prompt: "Please continue to enter your daily lifestyle and emotional data. The server will optimize your health management plan based on the latest information."

[0441] (Application example 2)

[0442] 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."

[0443] Today's consumers want personalized services and product recommendations based on their health and emotional state. However, current health management systems and services offered in brick-and-mortar stores are not fully capable of integrating and analyzing individual users' genetic information, lifestyle data, and emotional data to provide appropriate health management plans and recommendations. As a result, users are unable to optimally manage their health and their brick-and-mortar store experiences are unsatisfactory.

[0444] 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.

[0445] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, and means for collecting and analyzing the user's emotional data in real time. This makes it possible to integrate the analysis results of the user's genetic information, lifestyle data, and emotional data, generate a personalized health management plan, and provide the generated health management plan to the user. Furthermore, optimal products and services can be suggested in physical stores based on the user's emotional state, making it possible to provide more effective and satisfying services to the user.

[0446] "User's genetic information" refers to data regarding the user's genetic characteristics and constitution obtained from the user's DNA.

[0447] "Lifestyle data" is information about the user's daily lifestyle habits such as diet, exercise, and sleep.

[0448] "Emotional data" is data that indicates the user's state of mind and emotional expression, and is often collected in real time.

[0449] "Analysis tools" refers to the processes or devices that use algorithms to analyze collected data and convert it into meaningful information.

[0450] A "personalized health management plan" is a plan for optimal health management created based on a user's individual genetic information, lifestyle data, and emotional data.

[0451] "Generative AI" is a system that uses artificial intelligence technology to generate new information and plans based on data.

[0452] A "brick and mortar store" is a store located in a physical location where customers can visit in person to purchase goods or services.

[0453] "Product and service suggestions" refers to the act of recommending appropriate products and services based on user data.

[0454] The present invention provides a system for integrating and analyzing a user's genetic information, lifestyle data, and emotional data to generate an individualized health management plan. Specific embodiments for carrying out the present invention will be described below.

[0455] System Overview

[0456] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data, and the device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and update of the health management plan.

[0457] Hardware and software used

[0458] Smartphones and smartwatches: These devices collect genetic information, lifestyle data, and emotional data from users and transmit it to a cloud server.

[0459] Cloud server (e.g., AWS): Stores collected data and processes it for analysis and generative AI models.

[0460] Emotion engine (e.g. Microsoft Azure Emotion API): Analyzes the user's emotional state.

[0461] Generative AI models (e.g., OpenAI GPT-4): Generate health management plans based on genetic information, lifestyle data, and emotional data.

[0462] Data collection and transmission

[0463] Users use a smartphone or smartwatch to collect genetic information, lifestyle data, and emotional data. Genetic information is collected using a genetic analysis kit, and the results are uploaded to the app. Lifestyle data, such as daily diet, exercise records, and sleep duration, is entered into the smartphone app. Emotional data is recorded in real time using a smartwatch with emotion detection capabilities.

[0464] Analyzing data and using generative AI models

[0465] The server receives the genetic information, lifestyle data, and emotional data sent from the device. The genetic information data is passed through an analysis algorithm to evaluate the user's genetic risk factors and constitution. The lifestyle data evaluates the nutritional balance of the dietary data, the amount of exercise and heart rate of the exercise data, and the quality and quantity of sleep data. The emotional engine analyzes the emotional data to evaluate the user's stress and relaxation state.

[0466] The results of these analyses are then integrated and fed into a generative AI model, which takes into account genetic information, lifestyle habits, and emotional data to generate a personalized health plan for each user, including recommended dietary choices, specific exercise programs, and emotional management techniques.

[0467] Prompt Sentence Examples

[0468] The generated health management plan will be sent to the user's smartphone. Below is an example of a prompt to be input to the generation AI.

[0469] Example prompt sentence:

[0470] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[0471] Providing services in physical stores

[0472] When a user visits a physical store, the emotion engine analyzes the user's emotional data in real time and suggests the most appropriate products and services based on their emotional state. Store staff can also use the app to provide personalized support to users, such as counseling based on specific health concerns or suggesting relaxation items suited to their emotional state.

[0473] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0474] Step 1:

[0475] User provides genetic information

[0476] Input: User's DNA sample

[0477] How it works: A user uses a genetic analysis kit to collect a DNA sample and enters the analysis results into a smartphone app.

[0478] Output: Genetic information is stored in the app and sent to a cloud server.

[0479] Step 2:

[0480] Collection of lifestyle data

[0481] Input: User's daily diet, exercise, and sleep data

[0482] How it works: Users enter their daily lifestyle data into a smartphone app, recording their diet, exercise, and sleep.

[0483] Output: Lifestyle data is sent to the cloud server.

[0484] Step 3:

[0485] Real-time collection of emotional data

[0486] Input: User emotion recognition data

[0487] How it works: Users wear a smartwatch with emotion detection capabilities to record real-time emotional data, including heart rate and facial expression data.

[0488] Output: Emotion data is sent to a cloud server via a smartphone app.

[0489] Step 4:

[0490] Sending data to the cloud server

[0491] Input: Genetic information, lifestyle data, emotional data

[0492] How it works: The smartphone app sends all collected data to a cloud server using a secure protocol.

[0493] Output: Various user data is stored on the cloud server.

[0494] Step 5:

[0495] Data analysis

[0496] Input: User data stored on the cloud server

[0497] How it works: The server uses an algorithm to analyze genetic information to assess genetic risk factors, analyzes lifestyle data to assess nutritional balance, exercise volume, and sleep quality, and analyzes emotional data using an emotion engine.

[0498] Output: Analysis results are generated and stored in a database.

[0499] Step 6:

[0500] Generating health management plans using generative AI models

[0501] Input: Analysis results (genetic information, lifestyle data, emotional data)

[0502] How it works: The server inputs these analysis results into a generative AI model to generate an optimal health management plan for the user. To generate the plan, the generative AI model (e.g., OpenAI GPT-4) uses prompt statements.

[0503] Output: A health care plan is generated.

[0504] Step 7:

[0505] Providing health management plans to users

[0506] Input: Generated health care plan

[0507] Operation: The server sends the generated health management plan to the smartphone app and notifies the user.

[0508] Output: The user receives the healthcare plan.

[0509] Step 8:

[0510] Emotional data analysis and proposals in physical stores

[0511] Input: Real-time sentiment data from physical store visits

[0512] How it works: When a user visits a physical store, the smartwatch records emotional data in real time and sends it to the server. The server analyzes the emotional data and generates and recommends optimal products and services.

[0513] Output: Optimal product and service recommendations are generated for the physical store.

[0514] Detailed operation example

[0515] Example prompt sentence:

[0516] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[0517] This process integrates the data collected and analyzed at each step to provide optimal health management plans and personalized services in physical stores.

[0518] 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.

[0519] 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.

[0520] 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.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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).

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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."

[0534] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be specifically described.

[0535] System Overview

[0536] This system is mainly composed of a user, a device, and a server. First, the user provides genetic information and inputs lifestyle data daily. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to generate a health management plan and provides it to the user.

[0537] Program processing overview

[0538] Collection and analysis of genetic information

[0539] User:

[0540] Users use a genetic analysis kit to collect genetic samples and upload the results to the system's web portal or dedicated app. The genetic information, including DNA and RNA analysis results, reveals individual genetic characteristics and risk factors.

[0541] Device:

[0542] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure communication protocol.

[0543] server:

[0544] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[0545] Collection and analysis of lifestyle data

[0546] User:

[0547] Users input or automatically record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[0548] Device:

[0549] The device sends the lifestyle data entered by the user to a cloud server in real time, also using a secure communication protocol.

[0550] server:

[0551] The server analyzes the lifestyle data it receives. For example, it evaluates nutritional balance for diet, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database.

[0552] Generate and provide health management plans

[0553] server:

[0554] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[0555] Device:

[0556] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[0557] Ongoing support

[0558] User:

[0559] Users continually input their lifestyle data, so the system always has the most up-to-date information.

[0560] Device:

[0561] The device continues to send these new data to the cloud server.

[0562] server:

[0563] The server updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly, for example, by suggesting a new exercise plan for a user who has increased their physical activity.

[0564] For example, Alice provides her genetic information to the system and continues to input her daily lifestyle habits using her smartphone. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan in real time. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[0565] The processing flow will be explained below.

[0566] Step 1:

[0567] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis result file to a web portal or dedicated app.

[0568] Step 2:

[0569] Device: The device (smartphone or PC) sends the genetic information data uploaded by the user to the cloud server. This transmission is carried out using a secure communication protocol such as SSL / TLS.

[0570] Step 3:

[0571] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and organized for each user.

[0572] Step 4:

[0573] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[0574] Step 5:

[0575] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[0576] Step 6:

[0577] Server: The server analyzes the received lifestyle data. The dietary data is used to evaluate nutritional balance, the exercise data is used to evaluate the amount of exercise and heart rate, and the sleep data is used to evaluate the quality and quantity of sleep. The evaluation results are stored in a database.

[0578] Step 7:

[0579] Server: The server combines the genetic information and lifestyle data analysis results, inputs them into a generative AI model, and generates a personalized health plan for the user, including recommended diet, exercise programs, and lifestyle modifications.

[0580] Step 8:

[0581] Device: The device presents the health management plan provided by the server to the user via push notifications, emails, in-app messages, etc.

[0582] Step 9:

[0583] User: The user continuously inputs lifestyle data, such as updating daily diet, exercise, and sleep records.

[0584] Step 10:

[0585] Device: The device continues to send newly entered lifestyle habit data to the cloud server.

[0586] Step 11:

[0587] Server: The server analyzes newly collected data and updates the generative AI model, resulting in appropriate improvements to the personalized health plan. For example, if the user increases their physical activity, new exercise and meal plans will be presented.

[0588] For example, Alice uploads her genetic information and continues to input her daily lifestyle habits. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[0589] Example 1

[0590] 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."

[0591] In modern society, individuals face the challenge of effectively managing their own health. In particular, accurately understanding health risks due to genetic risk factors and lifestyle habits and formulating an appropriate health management plan based on that information requires specialized knowledge, making it difficult for many people. Therefore, there is a need for a system that can solve these challenges and provide an optimized health management plan for each individual user.

[0592] 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.

[0593] In this invention, the server includes means for collecting a user's genetic information and transmitting it to a cloud server, means for collecting a user's lifestyle data and transmitting it to the cloud server, means for analyzing the genetic information and lifestyle data in the cloud server and storing the analysis results, means for integrating the analysis results of the genetic information and lifestyle data and generating an individualized health management plan using a generative AI model, and means for notifying the user of the generated health management plan, thereby enabling the user to implement an optimal health management plan based on their genetic risks and lifestyle habits and effectively maintain and improve their health.

[0594] "User" refers to an individual who utilizes the System to provide genetic and lifestyle data and receive a health management plan.

[0595] "Genetic information" refers to data obtained as a result of analyzing a user's DNA or RNA, including individual genetic characteristics and risk factors.

[0596] "Lifestyle data" refers to information including data on the user's daily diet, exercise, sleep, etc.

[0597] A "cloud server" refers to a server on a network that remotely receives, analyzes, and stores genetic information and lifestyle data sent by users.

[0598] "Analysis algorithm" refers to a computational method or mathematical model that processes genetic information and lifestyle data to assess a user's health status and risks.

[0599] "Generative AI model" refers to an artificial intelligence model that generates personalized health management plans based on genetic information and lifestyle data.

[0600] A "health management plan" refers to a plan that includes specific guidelines and recommendations for diet, exercise, sleep, etc., generated based on a user's genetic information and lifestyle data.

[0601] "Notification means" refers to the methods and technologies used to communicate the generated health management plan to the user, and examples include push notifications, emails, and in-app messages.

[0602] The present invention relates to a system that collects a user's genetic information and lifestyle data, analyzes the data, and generates and provides an individualized health management plan. This system is mainly composed of a user, a terminal, and a server.

[0603] Collection and analysis of genetic information

[0604] User:

[0605] First, the user collects a saliva or blood sample using a genetic analysis kit. The sample is then sent to a specialized analysis facility, and the analysis results are provided to the user. The user then uploads the analysis results to the system's web portal or a dedicated app. This genetic information includes DNA and RNA analysis data.

[0606] Device:

[0607] The terminal (e.g., a smartphone or personal computer) receives the genetic information data entered by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[0608] server:

[0609] The server then applies an analysis algorithm to the received genetic information data (using a machine learning library such as Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored for each user in a database such as MySQL.

[0610] Collection and analysis of lifestyle data

[0611] User:

[0612] Users use smartphone apps or wearable devices (e.g., fitness trackers or smartwatches) to input or automatically record daily food, exercise, and sleep data. For example, food logs include the types and amounts of food consumed, exercise logs include the types and durations of exercise, and sleep logs include total sleep time and quality.

[0613] Device:

[0614] The device transmits the lifestyle data collected by the user to a cloud server in real time, again using a secure communication protocol.

[0615] server:

[0616] The server analyzes the lifestyle data it receives. For example, dietary data is evaluated for nutritional balance, exercise data is evaluated for exercise volume and heart rate, and sleep data is evaluated for quality and quantity. The results of these analyses are stored in a database along with genetic information.

[0617] Generate and provide health management plans

[0618] server:

[0619] The server combines the results of the analysis of the genetic information and lifestyle data and inputs them into a generative AI model (e.g., OpenAI's GPT-4). The generative AI model uses this data to generate an optimized health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle improvements. For example, the following prompt could be input into the generative AI model: "Based on the user's genetic information and the collected lifestyle data, please generate a personalized health management plan. Specifically, please include recommendations for diet, exercise, and sleep, as well as measures to address the user's genetic risks."

[0620] Device:

[0621] The device notifies the user of the health management plan provided by the server. Notification methods include push notifications, emails, and in-app messages. For example, a message such as "Today's recommended exercise plan" may be displayed as a push notification on a smartphone.

[0622] Continuous data collection and analysis

[0623] User:

[0624] Users continuously input lifestyle data, for example, by recording their daily diet and exercise.

[0625] Device:

[0626] The device continues to send these new data to the cloud server.

[0627] server:

[0628] The server updates the generative AI model based on newly collected lifestyle data and improves the personalized health management plan accordingly. For example, it can present a new exercise plan with a higher intensity to a user who has increased their exercise volume.

[0629] Specific examples

[0630] When a user uses this system, they first provide their genetic information and then collect daily lifestyle data using a smartphone or wearable device. The server analyzes this data and uses generative AI to provide the user with an optimal health management plan in real time. By following this plan, users can reduce their risk of lifestyle-related diseases and maintain and improve their health.

[0631] This system allows users to practice optimal health management based on their genetic factors and lifestyle habits, enabling them to effectively maintain and improve their health.

[0632] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0633] Step 1:

[0634] User:

[0635] Users collect a saliva sample using a genetic analysis kit and send it to an analysis laboratory, after which the genetic information provided by the analysis laboratory is uploaded to the system's web portal or dedicated app.

[0636] Input: Genetic information (DNA / RNA analysis results)

[0637] Output: Genetic information is uploaded to the system

[0638] Step 2:

[0639] Device:

[0640] The device (e.g., smartphone, personal computer) receives the genetic information uploaded by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[0641] Input: Genetic information

[0642] Output: Genetic information is sent to the cloud server

[0643] Step 3:

[0644] server:

[0645] The server then applies the received genetic information to an analysis algorithm (e.g., Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored in a database (e.g., MySQL).

[0646] Input: Genetic information

[0647] Output: The analysis results of genetic risk factors and physical predispositions are saved in a database.

[0648] Step 4:

[0649] User:

[0650] Users input or automatically record their daily diet, exercise, and sleep data using smartphone apps or wearable devices (e.g., fitness trackers, smartwatches).

[0651] Input: diet, exercise, sleep data

[0652] Output: Lifestyle data is recorded on the device

[0653] Step 5:

[0654] Device:

[0655] The device receives lifestyle data entered or recorded by the user and transmits it in real time to a cloud server using a secure communication protocol.

[0656] Input: Lifestyle data

[0657] Output: Lifestyle data is sent to the cloud server

[0658] Step 6:

[0659] server:

[0660] The server analyzes the lifestyle data it receives. Dietary data evaluates nutritional balance, exercise data evaluates exercise volume and heart rate, and sleep data evaluates quality and quantity. The analysis results are stored in a database.

[0661] Input: Lifestyle data

[0662] Output: Analysis results are saved in a database

[0663] Step 7:

[0664] server:

[0665] The server combines the genetic and lifestyle analysis results and inputs them into a generative AI model (e.g., OpenAI's GPT-4), which then uses this data to generate an optimal health management plan for the user.

[0666] Input: Analysis results of genetic information and lifestyle data

[0667] Output: Generate a personalized healthcare plan

[0668] Step 8:

[0669] server:

[0670] The generated health management plan is notified to the user via push notification, email, in-app message, etc. The server uses these methods to send the health management plan to the device.

[0671] Input: Health Care Plan

[0672] Output: The health management plan is sent to the device.

[0673] Step 9:

[0674] Device:

[0675] The device notifies the user of the health management plan sent from the server via push notification, email, in-app message, etc.

[0676] Input: Health Care Plan

[0677] Output: The health management plan is notified to the user.

[0678] Step 10:

[0679] User:

[0680] Users continue to input lifestyle habits data, which is then sent back to the server, where the generative AI model learns from it and updates the health management plan accordingly.

[0681] Input: New lifestyle data

[0682] Output: An updated health management plan from a generative AI model

[0683] (Application example 1)

[0684] 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."

[0685] Conventionally, personalized health management plans are designed for users to complete at home, and use in physical stores has not been considered, making it difficult to provide advice on which specific products users should purchase in physical stores. Also, there has been insufficient support for users to select appropriate products according to their individual health management plans, resulting in problems such as users having difficulty selecting the health products that are best suited to them.

[0686] 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.

[0687] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, means for integrating the analysis results of the genetic information and lifestyle data to generate an individualized health management plan, means for associating the generated health management plan with store product data to suggest optimal products for the user to purchase in a physical store, and means for providing the generated health management plan to the user. This enables the user to select products based on an appropriate health management plan while shopping in a physical store, and more effectively maintain and improve their health.

[0688] "Genetic information" is based on the results of DNA and RNA analysis provided by users and is data that indicates individual genetic characteristics and risk factors.

[0689] "Lifestyle data" is data that includes information about the user's daily habits, such as what they eat, how much they exercise, and how much they sleep.

[0690] A "health management plan" is a plan that includes personalized diet, exercise, and lifestyle recommendations based on the analysis of genetic and lifestyle data.

[0691] A "server" is a device that has the computing resources to analyze a user's genetic information and lifestyle data, and to generate and provide a health management plan.

[0692] "Generative AI" is an artificial intelligence technology that predicts health risks based on a user's genetic information and lifestyle data, and generates a personalized health management plan.

[0693] "Brick and mortar store" refers to a physical sales location where users can actually visit and select products.

[0694] "Products" are items that are useful for the user's health management, such as food and health products sold in physical stores.

[0695] "Scanning" is the act of reading the product's identification information using a barcode reader or camera.

[0696] "Analysis" is the process of finding certain regularities and patterns based on collected data and turning it into meaningful information.

[0697] "Notification" refers to the act of communicating the generated health management plan and other important information to the user, and can take various forms such as email or push notification.

[0698] "Association" refers to the act of matching a user's health management plan with product data from a physical store.

[0699] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be described in detail.

[0700] System Overview

[0701] This system is primarily composed of a user, a device (e.g., a smartphone), and a server. First, the user provides genetic information and inputs daily lifestyle habits. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to create a health management plan and provides it to the user. The system also has a function that suggests optimal products for the user to purchase in physical stores.

[0702] Collection and analysis of genetic and lifestyle data

[0703] Users use a genetic analysis kit to collect a genetic sample and upload the results to the system's dedicated app. Lifestyle data is recorded automatically or entered via a smartphone app or wearable device, including daily diet, exercise, and sleep data.

[0704] The device transmits this genetic information and lifestyle data to a cloud server using a secure communication protocol (e.g., HTTPS).

[0705] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data. The analysis includes evaluation of genetic risk factors, nutritional balance, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database and managed for each user.

[0706] Generate and provide health management plans

[0707] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[0708] The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[0709] Health support in physical stores

[0710] When users visit a physical store, they can use the app to scan products to see if they fit into their health management plan.

[0711] When the terminal (smartphone) scans a product in the store, it uses its barcode reader function to send the product information to the server.

[0712] The server associates this product information with the user's personalized health care plan and evaluates whether the product conforms to the health care plan. The evaluation results are returned to the terminal in real time and notified to the user.

[0713] Ongoing support

[0714] Users continually input their lifestyle data, which causes the device to continuously send new data to the cloud server, which then updates the generative AI model with the newly collected data and improves the personalized health management plan accordingly.

[0715] Specific examples

[0716] A specific example scenario could be when a user is searching for low-calorie foods in a store and uses an app to scan the product's barcode to check calorie information and suitability based on a health management plan.

[0717] Prompt Sentence Examples

[0718] We would like to develop an app that allows users to use an advanced smartphone app to suggest optimal low-calorie foods to purchase in stores based on their health management plan. The generative AI should analyze genetic information and lifestyle data to produce a list of specific foods. Furthermore, we would like the scanning function to display detailed product information in the store.

[0719] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0720] Step 1:

[0721] Users provide genetic information and lifestyle data

[0722] Users use a genetic analysis kit to collect a genetic sample and upload the results to a dedicated app. They also input or automatically record lifestyle data such as daily diet, exercise, and sleep data using a smartphone app or wearable device.

[0723] Input: Genetic information, lifestyle data

[0724] Output: Provided data (genetic information, lifestyle data)

[0725] Step 2:

[0726] The device sends data to the cloud server

[0727] The device (smartphone) transmits the genetic information and lifestyle data provided by the user to a cloud server using a secure communication protocol (e.g., HTTPS).

[0728] Input: Genetic information, lifestyle data

[0729] Output: Data sent to the cloud server

[0730] Step 3:

[0731] The server analyzes genetic information and lifestyle data

[0732] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data, which evaluates genetic risk factors and nutritional balance, exercise volume, heart rate, and sleep quality and quantity.

[0733] Input: Transmitted genetic information, lifestyle data

[0734] Output: Analyzed genetic risk factors and lifestyle assessment results

[0735] Step 4:

[0736] The server generates a health management plan

[0737] The server inputs the results of the genetic and lifestyle analysis into a generative AI model to generate a personalized health management plan, including recommended dietary choices, specific exercise programs, and lifestyle changes.

[0738] Input: Analysis results (genetic risk factors, nutritional balance, amount of exercise, quality of sleep)

[0739] Output: personalized health care plan

[0740] Step 5:

[0741] The device notifies the user of the health management plan

[0742] The device (smartphone) notifies the user of the health management plan provided by the server via push notifications or in-app messages.

[0743] Input: Generated health care plan

[0744] Output: Notification to the user (push notification, in-app message)

[0745] Step 6:

[0746] User scans item in store

[0747] When a user visits a physical store, they use the app to scan the barcode of a product, which provides the product's identification information.

[0748] Input: Product barcode

[0749] Output: Product identification information

[0750] Step 7:

[0751] The device sends product information to the server

[0752] The terminal sends the scanned product's identification information to the server, which then obtains detailed product information.

[0753] Input: Product identification information

[0754] Output: Product information sent to the server

[0755] Step 8:

[0756] The server matches the product with the health management plan

[0757] The server associates the product information with the user's personalized health care plan and evaluates whether the product is suitable for the plan. A suitability evaluation result is generated.

[0758] Input: Product information, health management plan

[0759] Output: Product conformity assessment results

[0760] Step 9:

[0761] The device notifies the user of the evaluation results

[0762] The terminal (smartphone) notifies the user of the product compatibility evaluation results received from the server, allowing the user to select the appropriate product in the store.

[0763] Input: Product conformity assessment results

[0764] Output: Notification to user (evaluation result)

[0765] 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.

[0766] The present invention relates to a health support system that utilizes generative AI and combines it with an emotion engine that recognizes a user's emotions. This system analyzes the user's genetic information and lifestyle data, and also adds emotion data to provide the user with a personalized health management plan. A specific embodiment of the present invention is described below.

[0767] System Overview

[0768] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data. The device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and updating of the health management plan.

[0769] Program processing overview

[0770] Collection and analysis of genetic information

[0771] User:

[0772] Users use a genetic analysis kit to collect genetic samples and upload the analysis results to the system's web portal or dedicated app.

[0773] Device:

[0774] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure protocol.

[0775] server:

[0776] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[0777] Collection and analysis of lifestyle data

[0778] User:

[0779] Users input or record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[0780] Device:

[0781] The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[0782] server:

[0783] The server analyzes the lifestyle data it receives. It evaluates nutritional balance for dietary data, exercise volume and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results of these analyses are stored in a database.

[0784] Emotion data collection and analysis

[0785] User:

[0786] Users collect their own emotional data using devices with emotion detection capabilities (e.g., smartwatches or smartphone apps).

[0787] Device:

[0788] The device transmits the user's emotional data to a cloud server, which is also transmitted using a secure protocol.

[0789] Server and Emotion Engine:

[0790] The server uses an emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the analysis results in a database along with other data.

[0791] Generate and provide health management plans

[0792] server:

[0793] The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[0794] Device:

[0795] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[0796] Ongoing support

[0797] User:

[0798] Users continuously input lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[0799] Device:

[0800] The device continues to send newly entered data to the cloud server.

[0801] server:

[0802] The server updates the generative AI model with newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it can suggest new emotional management or relaxation techniques.

[0803] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[0804] The processing flow will be explained below.

[0805] Step 1:

[0806] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis results file to a web portal or dedicated app.

[0807] Step 2:

[0808] Terminal: The terminal (smartphone or PC) sends the genetic information data provided by the user to the cloud server. This transmission uses a secure communication protocol such as SSL / TLS.

[0809] Step 3:

[0810] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and managed for each user.

[0811] Step 4:

[0812] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[0813] Step 5:

[0814] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[0815] Step 6:

[0816] Server: The server analyzes the received lifestyle data, evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The evaluation results are stored in a database.

[0817] Step 7:

[0818] User: The user uses an emotion-sensing device (e.g., a smartwatch or smartphone app) to collect their own emotional data, including stress levels, excitement levels, and relaxation levels.

[0819] Step 8:

[0820] Device: The device transmits the user's emotional data to the cloud server in real time. The emotional data is also transmitted using a secure protocol.

[0821] Step 9:

[0822] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, to evaluate whether the user is stressed or relaxed, and stores the results in a database.

[0823] Step 10:

[0824] Server: The server integrates the results of the analysis of genetic information, lifestyle data, and emotional data. It then inputs this data into a generative AI model to generate an optimal health management plan for the user. This plan includes recommended dietary content, exercise programs, and emotional management methods.

[0825] Step 11:

[0826] Device: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[0827] Step 12:

[0828] User: The user continuously inputs lifestyle and emotional data, such as daily updates on diet, exercise, sleep, and emotional state.

[0829] Step 13:

[0830] Device: The device continues to send newly input lifestyle data and emotion data to the cloud server.

[0831] Step 14:

[0832] Server: The server updates the generative AI model based on newly collected data, resulting in appropriate improvements to the personalized health management plan. For example, if the user's emotional state changes, new emotional management or relaxation techniques will be suggested.

[0833] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[0834] Example 2

[0835] 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."

[0836] In modern society, personalized health management is becoming increasingly important. However, conventional health management systems are unable to comprehensively analyze a user's genetic information, lifestyle data, and emotional data to provide an individually optimized health management plan. As a result, many users struggle with self-management and are unable to effectively reduce their risk of lifestyle-related diseases. Another issue is the inability to respond appropriately to changes in emotional state.

[0837] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing the user's genetic information, means for collecting and analyzing the user's lifestyle habit data, and means for collecting and analyzing the user's emotional data. This makes it possible to generate and provide an individualized health management plan based on a comprehensive analysis of the genetic information, lifestyle habit data, and emotional data.

[0838] "Genetic information" refers to all genetic information obtained from a user's DNA, including data regarding specific disease risks and physical predispositions.

[0839] "Lifestyle data" refers to information about a user's daily activities and lifestyle patterns, including dietary content, amount of exercise, and sleep duration.

[0840] "Emotional data" refers to information about the user's psychological and emotional state, including stress level and relaxation state.

[0841] A "generative AI model" refers to an algorithm that uses artificial intelligence techniques such as machine learning to generate optimal outputs (in this case, health management plans) based on multiple input data.

[0842] "Health Management Plan" means specific guidance and recommendations for improving and maintaining health that are individually tailored to a User's specific needs and circumstances.

[0843] A "cloud server" refers to a system that processes and stores data using a server installed in a remote location and is accessible via the Internet.

[0844] "Analysis" refers to the general process of extracting information based on collected data and making evaluations and judgments.

[0845] System Overview

[0846] This invention is a health support system utilizing generative AI, which is mainly composed of a user, a terminal, a server, and an emotion engine. This system analyzes the user's genetic information, lifestyle data, and emotion data, and provides an individualized health management plan.

[0847] Hardware and software used

[0848] Devices: Smartphones, PCs, wearable devices (e.g., smartwatches) that are used to collect and transmit data.

[0849] Server: Cloud server, used to analyze and store data and run generative AI models.

[0850] Emotion Engine: Software with emotion recognition algorithms used to analyze emotion data.

[0851] Generative AI model: Software containing machine learning algorithms used to generate optimal health management plans for users.

[0852] Program processing overview

[0853] The system operates as follows.

[0854] Collection and analysis of genetic information

[0855] User: Collects genetic samples using a genetic analysis kit and uploads the analysis results to the system's web portal or dedicated app.

[0856] Terminal: Sends the genetic information data uploaded by the user to the cloud server. Safety is ensured by using a secure protocol (e.g., HTTPS).

[0857] Server: The received genetic information data is run through a dedicated analysis algorithm to evaluate specific genetic risk factors and predispositions, and the results are stored in a database.

[0858] Example prompt: "Please upload the genetic information obtained using the genetic analysis kit and send the data to the cloud server."

[0859] Collection and analysis of lifestyle data

[0860] User: Uses a smartphone app or wearable device to enter or automatically record daily diet, exercise, and sleep data.

[0861] Device: The lifestyle habit data entered by the user is sent to the cloud server in real time using a secure protocol.

[0862] Server: Analyzes the received lifestyle data, evaluating nutritional balance from dietary data, exercise amount and heart rate from exercise data, and quality and quantity of sleep from sleep data. The results are stored in a database.

[0863] Example prompt: "Enter your daily diet, exercise, and sleep data into a smartphone app."

[0864] Emotion data collection and analysis

[0865] User: Collects their own emotional data using a device with emotion detection capabilities (such as a smartwatch or smartphone app).

[0866] Device: Emotion data is sent to the cloud server using a secure protocol, ensuring safety.

[0867] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the results in a database.

[0868] Example prompt: "Please wear a device with emotion detection capabilities and send the collected emotion data to the cloud server."

[0869] Generate and provide health management plans

[0870] Server: The results of the analysis of genetic information, lifestyle data, and emotional data are combined and input into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[0871] Device: The health management plan provided by the server is notified to the user via push notification, email, in-app message, etc.

[0872] Example prompt: "Check out the health management plan generated based on your lifestyle and emotional data."

[0873] Ongoing support

[0874] User: By continuously inputting daily lifestyle and emotional data, the system can always keep up to date with the latest information.

[0875] Device: Continues to send newly entered data to the cloud server. Safety is ensured by using a secure protocol.

[0876] Server: Updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it suggests new emotional management or relaxation techniques.

[0877] Example prompt: "Please continue to enter your daily habits and emotions."

[0878] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0879] Processing flow and specific explanation

[0880] Step 1: User prepares genetic information

[0881] Input: Genetic analysis kit

[0882] How it works: Users use a genetic analysis kit to collect a genetic sample and send it to an analysis service. After a few days, they upload the analysis results to the system's web portal or a dedicated app.

[0883] Output: Genetic information data

[0884] Step 2: Sending genetic information via the device

[0885] Input: Genetic information data

[0886] How it works: A device (such as a smartphone or PC) sends the genetic information uploaded by the user to a cloud server using a secure protocol (HTTPS).

[0887] Output: Genetic information data stored on a cloud server

[0888] Step 3: Analysis of genetic information by the server

[0889] Input: Genetic information data stored on a cloud server

[0890] How it works: The server runs the received genetic information data through a specialized analysis algorithm. Specifically, it analyzes the DNA sequence data and evaluates specific genetic risk factors and predispositions. The analysis results are stored in a database.

[0891] Output: Analyzed genetic information

[0892] Step 4: User enters lifestyle data

[0893] Input: Smartphones and wearable devices

[0894] How it works: Users input or automatically record daily diet, exercise, and sleep data using a smartphone app or wearable device. Specifically, the diet app records the food consumed, and the wearable device measures the amount of exercise and sleep time.

[0895] Output: Lifestyle data

[0896] Step 5: Send lifestyle data via your device

[0897] Input: Lifestyle data

[0898] How it works: The device sends collected lifestyle data to a cloud server in real time using a secure protocol (HTTPS), ensuring data security.

[0899] Output: Lifestyle data stored on a cloud server

[0900] Step 6: Analysis of lifestyle data by the server

[0901] Input: Lifestyle data stored on a cloud server

[0902] Operation: The server analyzes the received lifestyle data. Specifically, it evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results are stored in a database.

[0903] Output: Analyzed lifestyle data

[0904] Step 7: Obtaining user emotion data

[0905] Input: Device with emotion detection function (smartwatch or smartphone app)

[0906] How it works: Users use emotion-sensing devices to collect their own emotional data, specifically measuring their emotions based on heart rate and facial expression recognition.

[0907] Output: Emotion data

[0908] Step 8: Device sends emotion data

[0909] Input: Emotion data

[0910] How it works: The device sends the user's emotional data to a cloud server. This data is also sent using a secure protocol (HTTPS), so it is safely protected.

[0911] Output: Emotion data stored on a cloud server

[0912] Step 9: Emotion data analysis by the server and emotion engine

[0913] Input: Emotion data stored on a cloud server

[0914] How it works: The server uses an emotion engine to analyze the received emotion data, for example to assess whether the user is stressed or relaxed. The results are stored in a database.

[0915] Output: Parsed emotion data

[0916] Step 10: Server generates health management plan

[0917] Input: Analyzed genetic information, lifestyle data, and emotional data

[0918] How it works: The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[0919] Output: Health management plan

[0920] Step 11: Notification of health management plan via device

[0921] Input: Health Care Plan

[0922] Operation: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[0923] Output: Health plan provided to the user

[0924] Step 12: User continues to enter data

[0925] Input: New lifestyle data, emotional data

[0926] How it works: Users continuously input their daily lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[0927] Output: New lifestyle data, emotional data

[0928] Step 13: Device continues to send data

[0929] Input: New lifestyle data, emotional data

[0930] How it works: The device continues to send newly entered data to the cloud server. Data transmission is securely protected by a secure protocol.

[0931] Output: New lifestyle and emotion data stored on a cloud server

[0932] Step 14: Server updates the generative AI model

[0933] Input: New lifestyle data and emotion data stored on the cloud server

[0934] How it works: The server updates the generative AI model based on newly collected data and refines the personalized health plan accordingly, for example by suggesting new emotional management or relaxation techniques when the user's emotional state changes.

[0935] Output: Updated health plan

[0936] Specific examples of each step

[0937] As a concrete example of step 1, a user uses a genetic analysis kit at home to collect a sample and uploads the analysis results to a dedicated app.

[0938] As a concrete example of step 4, a user enters food records into a smartphone app and collects exercise and sleep data using a wearable device.

[0939] Example prompt: "Please continue to enter your daily lifestyle and emotional data. The server will optimize your health management plan based on the latest information."

[0940] (Application example 2)

[0941] 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."

[0942] Today's consumers want personalized services and product recommendations based on their health and emotional state. However, current health management systems and services offered in brick-and-mortar stores are not fully capable of integrating and analyzing individual users' genetic information, lifestyle data, and emotional data to provide appropriate health management plans and recommendations. As a result, users are unable to optimally manage their health and their brick-and-mortar store experiences are unsatisfactory.

[0943] 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.

[0944] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, and means for collecting and analyzing the user's emotional data in real time. This makes it possible to integrate the analysis results of the user's genetic information, lifestyle data, and emotional data, generate a personalized health management plan, and provide the generated health management plan to the user. Furthermore, optimal products and services can be suggested in physical stores based on the user's emotional state, making it possible to provide more effective and satisfying services to the user.

[0945] "User's genetic information" refers to data regarding the user's genetic characteristics and constitution obtained from the user's DNA.

[0946] "Lifestyle data" is information about the user's daily lifestyle habits such as diet, exercise, and sleep.

[0947] "Emotional data" is data that indicates the user's state of mind and emotional expression, and is often collected in real time.

[0948] "Analysis tools" refers to the processes or devices that use algorithms to analyze collected data and convert it into meaningful information.

[0949] A "personalized health management plan" is a plan for optimal health management created based on a user's individual genetic information, lifestyle data, and emotional data.

[0950] "Generative AI" is a system that uses artificial intelligence technology to generate new information and plans based on data.

[0951] A "brick and mortar store" is a store located in a physical location where customers can visit in person to purchase goods or services.

[0952] "Product and service suggestions" refers to the act of recommending appropriate products and services based on user data.

[0953] The present invention provides a system for integrating and analyzing a user's genetic information, lifestyle data, and emotional data to generate an individualized health management plan. Specific embodiments for carrying out the present invention will be described below.

[0954] System Overview

[0955] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data, and the device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and update of the health management plan.

[0956] Hardware and software used

[0957] Smartphones and smartwatches: These devices collect genetic information, lifestyle data, and emotional data from users and transmit it to a cloud server.

[0958] Cloud server (e.g., AWS): Stores collected data and processes it for analysis and generative AI models.

[0959] Emotion engine (e.g. Microsoft Azure Emotion API): Analyzes the user's emotional state.

[0960] Generative AI models (e.g., OpenAI GPT-4): Generate health management plans based on genetic information, lifestyle data, and emotional data.

[0961] Data collection and transmission

[0962] Users use a smartphone or smartwatch to collect genetic information, lifestyle data, and emotional data. Genetic information is collected using a genetic analysis kit, and the results are uploaded to the app. Lifestyle data, such as daily diet, exercise records, and sleep duration, is entered into the smartphone app. Emotional data is recorded in real time using a smartwatch with emotion detection capabilities.

[0963] Analyzing data and using generative AI models

[0964] The server receives the genetic information, lifestyle data, and emotional data sent from the device. The genetic information data is passed through an analysis algorithm to evaluate the user's genetic risk factors and constitution. The lifestyle data evaluates the nutritional balance of the dietary data, the amount of exercise and heart rate of the exercise data, and the quality and quantity of sleep data. The emotional engine analyzes the emotional data to evaluate the user's stress and relaxation state.

[0965] The results of these analyses are then integrated and fed into a generative AI model, which takes into account genetic information, lifestyle habits, and emotional data to generate a personalized health plan for each user, including recommended dietary choices, specific exercise programs, and emotional management techniques.

[0966] Prompt Sentence Examples

[0967] The generated health management plan will be sent to the user's smartphone. Below is an example of a prompt to be input to the generation AI.

[0968] Example prompt sentence:

[0969] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[0970] Providing services in physical stores

[0971] When a user visits a physical store, the emotion engine analyzes the user's emotional data in real time and suggests the most appropriate products and services based on their emotional state. Store staff can also use the app to provide personalized support to users, such as counseling based on specific health concerns or suggesting relaxation items suited to their emotional state.

[0972] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0973] Step 1:

[0974] User provides genetic information

[0975] Input: User's DNA sample

[0976] How it works: A user uses a genetic analysis kit to collect a DNA sample and enters the analysis results into a smartphone app.

[0977] Output: Genetic information is stored in the app and sent to a cloud server.

[0978] Step 2:

[0979] Collection of lifestyle data

[0980] Input: User's daily diet, exercise, and sleep data

[0981] How it works: Users enter their daily lifestyle data into a smartphone app, recording their diet, exercise, and sleep.

[0982] Output: Lifestyle data is sent to the cloud server.

[0983] Step 3:

[0984] Real-time collection of emotional data

[0985] Input: User emotion recognition data

[0986] How it works: Users wear a smartwatch with emotion detection capabilities to record real-time emotional data, including heart rate and facial expression data.

[0987] Output: Emotion data is sent to a cloud server via a smartphone app.

[0988] Step 4:

[0989] Sending data to the cloud server

[0990] Input: Genetic information, lifestyle data, emotional data

[0991] How it works: The smartphone app sends all collected data to a cloud server using a secure protocol.

[0992] Output: Various user data is stored on the cloud server.

[0993] Step 5:

[0994] Data analysis

[0995] Input: User data stored on the cloud server

[0996] How it works: The server uses an algorithm to analyze genetic information to assess genetic risk factors, analyzes lifestyle data to assess nutritional balance, exercise volume, and sleep quality, and analyzes emotional data using an emotion engine.

[0997] Output: Analysis results are generated and stored in a database.

[0998] Step 6:

[0999] Generating health management plans using generative AI models

[1000] Input: Analysis results (genetic information, lifestyle data, emotional data)

[1001] How it works: The server inputs these analysis results into a generative AI model to generate an optimal health management plan for the user. To generate the plan, the generative AI model (e.g., OpenAI GPT-4) uses prompt statements.

[1002] Output: A health care plan is generated.

[1003] Step 7:

[1004] Providing health management plans to users

[1005] Input: Generated health care plan

[1006] Operation: The server sends the generated health management plan to the smartphone app and notifies the user.

[1007] Output: The user receives the healthcare plan.

[1008] Step 8:

[1009] Emotional data analysis and proposals in physical stores

[1010] Input: Real-time sentiment data from physical store visits

[1011] How it works: When a user visits a physical store, the smartwatch records emotional data in real time and sends it to the server. The server analyzes the emotional data and generates and recommends optimal products and services.

[1012] Output: Optimal product and service recommendations are generated for the physical store.

[1013] Detailed operation example

[1014] Example prompt sentence:

[1015] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[1016] This process integrates the data collected and analyzed at each step to provide optimal health management plans and personalized services in physical stores.

[1017] 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.

[1018] 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.

[1019] 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.

[1020] [Third embodiment]

[1021] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1022] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1023] 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).

[1024] 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.

[1025] 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.

[1026] 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).

[1027] 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.

[1028] 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.

[1029] 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.

[1030] 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.

[1031] 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.

[1032] 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."

[1033] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be specifically described.

[1034] System Overview

[1035] This system is mainly composed of a user, a device, and a server. First, the user provides genetic information and inputs lifestyle data daily. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to generate a health management plan and provides it to the user.

[1036] Program processing overview

[1037] Collection and analysis of genetic information

[1038] User:

[1039] Users use a genetic analysis kit to collect genetic samples and upload the results to the system's web portal or dedicated app. The genetic information, including DNA and RNA analysis results, reveals individual genetic characteristics and risk factors.

[1040] Device:

[1041] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure communication protocol.

[1042] server:

[1043] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[1044] Collection and analysis of lifestyle data

[1045] User:

[1046] Users input or automatically record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[1047] Device:

[1048] The device sends the lifestyle data entered by the user to a cloud server in real time, also using a secure communication protocol.

[1049] server:

[1050] The server analyzes the lifestyle data it receives. For example, it evaluates nutritional balance for diet, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database.

[1051] Generate and provide health management plans

[1052] server:

[1053] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[1054] Device:

[1055] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[1056] Ongoing support

[1057] User:

[1058] Users continually input their lifestyle data, so the system always has the most up-to-date information.

[1059] Device:

[1060] The device continues to send these new data to the cloud server.

[1061] server:

[1062] The server updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly, for example, by suggesting a new exercise plan for a user who has increased their physical activity.

[1063] For example, Alice provides her genetic information to the system and continues to input her daily lifestyle habits using her smartphone. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan in real time. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[1064] The processing flow will be explained below.

[1065] Step 1:

[1066] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis result file to a web portal or dedicated app.

[1067] Step 2:

[1068] Device: The device (smartphone or PC) sends the genetic information data uploaded by the user to the cloud server. This transmission is carried out using a secure communication protocol such as SSL / TLS.

[1069] Step 3:

[1070] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and organized for each user.

[1071] Step 4:

[1072] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[1073] Step 5:

[1074] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[1075] Step 6:

[1076] Server: The server analyzes the received lifestyle data. The dietary data is used to evaluate nutritional balance, the exercise data is used to evaluate the amount of exercise and heart rate, and the sleep data is used to evaluate the quality and quantity of sleep. The evaluation results are stored in a database.

[1077] Step 7:

[1078] Server: The server combines the genetic information and lifestyle data analysis results, inputs them into a generative AI model, and generates a personalized health plan for the user, including recommended diet, exercise programs, and lifestyle modifications.

[1079] Step 8:

[1080] Device: The device presents the health management plan provided by the server to the user via push notifications, emails, in-app messages, etc.

[1081] Step 9:

[1082] User: The user continuously inputs lifestyle data, such as updating daily diet, exercise, and sleep records.

[1083] Step 10:

[1084] Device: The device continues to send newly entered lifestyle habit data to the cloud server.

[1085] Step 11:

[1086] Server: The server analyzes newly collected data and updates the generative AI model, resulting in appropriate improvements to the personalized health plan. For example, if the user increases their physical activity, new exercise and meal plans will be presented.

[1087] For example, Alice uploads her genetic information and continues to input her daily lifestyle habits. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[1088] Example 1

[1089] 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."

[1090] In modern society, individuals face the challenge of effectively managing their own health. In particular, accurately understanding health risks due to genetic risk factors and lifestyle habits and formulating an appropriate health management plan based on that information requires specialized knowledge, making it difficult for many people. Therefore, there is a need for a system that can solve these challenges and provide an optimized health management plan for each individual user.

[1091] 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.

[1092] In this invention, the server includes means for collecting a user's genetic information and transmitting it to a cloud server, means for collecting a user's lifestyle data and transmitting it to the cloud server, means for analyzing the genetic information and lifestyle data in the cloud server and storing the analysis results, means for integrating the analysis results of the genetic information and lifestyle data and generating an individualized health management plan using a generative AI model, and means for notifying the user of the generated health management plan, thereby enabling the user to implement an optimal health management plan based on their genetic risks and lifestyle habits and effectively maintain and improve their health.

[1093] "User" refers to an individual who utilizes the System to provide genetic and lifestyle data and receive a health management plan.

[1094] "Genetic information" refers to data obtained as a result of analyzing a user's DNA or RNA, including individual genetic characteristics and risk factors.

[1095] "Lifestyle data" refers to information including data on the user's daily diet, exercise, sleep, etc.

[1096] A "cloud server" refers to a server on a network that remotely receives, analyzes, and stores genetic information and lifestyle data sent by users.

[1097] "Analysis algorithm" refers to a computational method or mathematical model that processes genetic information and lifestyle data to assess a user's health status and risks.

[1098] "Generative AI model" refers to an artificial intelligence model that generates personalized health management plans based on genetic information and lifestyle data.

[1099] A "health management plan" refers to a plan that includes specific guidelines and recommendations for diet, exercise, sleep, etc., generated based on a user's genetic information and lifestyle data.

[1100] "Notification means" refers to the methods and technologies used to communicate the generated health management plan to the user, and examples include push notifications, emails, and in-app messages.

[1101] The present invention relates to a system that collects a user's genetic information and lifestyle data, analyzes the data, and generates and provides an individualized health management plan. This system is mainly composed of a user, a terminal, and a server.

[1102] Collection and analysis of genetic information

[1103] User:

[1104] First, the user collects a saliva or blood sample using a genetic analysis kit. The sample is then sent to a specialized analysis facility, and the analysis results are provided to the user. The user then uploads the analysis results to the system's web portal or a dedicated app. This genetic information includes DNA and RNA analysis data.

[1105] Device:

[1106] The terminal (e.g., a smartphone or personal computer) receives the genetic information data entered by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[1107] server:

[1108] The server then applies an analysis algorithm to the received genetic information data (using a machine learning library such as Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored for each user in a database such as MySQL.

[1109] Collection and analysis of lifestyle data

[1110] User:

[1111] Users use smartphone apps or wearable devices (e.g., fitness trackers or smartwatches) to input or automatically record daily food, exercise, and sleep data. For example, food logs include the types and amounts of food consumed, exercise logs include the types and durations of exercise, and sleep logs include total sleep time and quality.

[1112] Device:

[1113] The device transmits the lifestyle data collected by the user to a cloud server in real time, again using a secure communication protocol.

[1114] server:

[1115] The server analyzes the lifestyle data it receives. For example, dietary data is evaluated for nutritional balance, exercise data is evaluated for exercise volume and heart rate, and sleep data is evaluated for quality and quantity. The results of these analyses are stored in a database along with genetic information.

[1116] Generate and provide health management plans

[1117] server:

[1118] The server combines the results of the analysis of the genetic information and lifestyle data and inputs them into a generative AI model (e.g., OpenAI's GPT-4). The generative AI model uses this data to generate an optimized health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle improvements. For example, the following prompt could be input into the generative AI model: "Based on the user's genetic information and the collected lifestyle data, please generate a personalized health management plan. Specifically, please include recommendations for diet, exercise, and sleep, as well as measures to address the user's genetic risks."

[1119] Device:

[1120] The device notifies the user of the health management plan provided by the server. Notification methods include push notifications, emails, and in-app messages. For example, a message such as "Today's recommended exercise plan" may be displayed as a push notification on a smartphone.

[1121] Continuous data collection and analysis

[1122] User:

[1123] Users continuously input lifestyle data, for example, by recording their daily diet and exercise.

[1124] Device:

[1125] The device continues to send these new data to the cloud server.

[1126] server:

[1127] The server updates the generative AI model based on newly collected lifestyle data and improves the personalized health management plan accordingly. For example, it can present a new exercise plan with a higher intensity to a user who has increased their exercise volume.

[1128] Specific examples

[1129] When a user uses this system, they first provide their genetic information and then collect daily lifestyle data using a smartphone or wearable device. The server analyzes this data and uses generative AI to provide the user with an optimal health management plan in real time. By following this plan, users can reduce their risk of lifestyle-related diseases and maintain and improve their health.

[1130] This system allows users to practice optimal health management based on their genetic factors and lifestyle habits, enabling them to effectively maintain and improve their health.

[1131] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1132] Step 1:

[1133] User:

[1134] Users collect a saliva sample using a genetic analysis kit and send it to an analysis laboratory, after which the genetic information provided by the analysis laboratory is uploaded to the system's web portal or dedicated app.

[1135] Input: Genetic information (DNA / RNA analysis results)

[1136] Output: Genetic information is uploaded to the system

[1137] Step 2:

[1138] Device:

[1139] The device (e.g., smartphone, personal computer) receives the genetic information uploaded by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[1140] Input: Genetic information

[1141] Output: Genetic information is sent to the cloud server

[1142] Step 3:

[1143] server:

[1144] The server then applies the received genetic information to an analysis algorithm (e.g., Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored in a database (e.g., MySQL).

[1145] Input: Genetic information

[1146] Output: The analysis results of genetic risk factors and physical predispositions are saved in a database.

[1147] Step 4:

[1148] User:

[1149] Users input or automatically record their daily diet, exercise, and sleep data using smartphone apps or wearable devices (e.g., fitness trackers, smartwatches).

[1150] Input: diet, exercise, sleep data

[1151] Output: Lifestyle data is recorded on the device

[1152] Step 5:

[1153] Device:

[1154] The device receives lifestyle data entered or recorded by the user and transmits it in real time to a cloud server using a secure communication protocol.

[1155] Input: Lifestyle data

[1156] Output: Lifestyle data is sent to the cloud server

[1157] Step 6:

[1158] server:

[1159] The server analyzes the lifestyle data it receives. Dietary data evaluates nutritional balance, exercise data evaluates exercise volume and heart rate, and sleep data evaluates quality and quantity. The analysis results are stored in a database.

[1160] Input: Lifestyle data

[1161] Output: Analysis results are saved in a database

[1162] Step 7:

[1163] server:

[1164] The server combines the genetic and lifestyle analysis results and inputs them into a generative AI model (e.g., OpenAI's GPT-4), which then uses this data to generate an optimal health management plan for the user.

[1165] Input: Analysis results of genetic information and lifestyle data

[1166] Output: Generate a personalized healthcare plan

[1167] Step 8:

[1168] server:

[1169] The generated health management plan is notified to the user via push notification, email, in-app message, etc. The server uses these methods to send the health management plan to the device.

[1170] Input: Health Care Plan

[1171] Output: The health management plan is sent to the device.

[1172] Step 9:

[1173] Device:

[1174] The device notifies the user of the health management plan sent from the server via push notification, email, in-app message, etc.

[1175] Input: Health Care Plan

[1176] Output: The health management plan is notified to the user.

[1177] Step 10:

[1178] User:

[1179] Users continue to input lifestyle habits data, which is then sent back to the server, where the generative AI model learns from it and updates the health management plan accordingly.

[1180] Input: New lifestyle data

[1181] Output: An updated health management plan from a generative AI model

[1182] (Application example 1)

[1183] 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."

[1184] Conventionally, personalized health management plans are designed for users to complete at home, and use in physical stores has not been considered, making it difficult to provide advice on which specific products users should purchase in physical stores. Also, there has been insufficient support for users to select appropriate products according to their individual health management plans, resulting in problems such as users having difficulty selecting the health products that are best suited to them.

[1185] 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.

[1186] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, means for integrating the analysis results of the genetic information and lifestyle data to generate an individualized health management plan, means for associating the generated health management plan with store product data to suggest optimal products for the user to purchase in a physical store, and means for providing the generated health management plan to the user. This enables the user to select products based on an appropriate health management plan while shopping in a physical store, and more effectively maintain and improve their health.

[1187] "Genetic information" is based on the results of DNA and RNA analysis provided by users and is data that indicates individual genetic characteristics and risk factors.

[1188] "Lifestyle data" is data that includes information about the user's daily habits, such as what they eat, how much they exercise, and how much they sleep.

[1189] A "health management plan" is a plan that includes personalized diet, exercise, and lifestyle recommendations based on the analysis of genetic and lifestyle data.

[1190] A "server" is a device that has the computing resources to analyze a user's genetic information and lifestyle data, and to generate and provide a health management plan.

[1191] "Generative AI" is an artificial intelligence technology that predicts health risks based on a user's genetic information and lifestyle data, and generates a personalized health management plan.

[1192] "Brick and mortar store" refers to a physical sales location where users can actually visit and select products.

[1193] "Products" are items that are useful for the user's health management, such as food and health products sold in physical stores.

[1194] "Scanning" is the act of reading the product's identification information using a barcode reader or camera.

[1195] "Analysis" is the process of finding certain regularities and patterns based on collected data and turning it into meaningful information.

[1196] "Notification" refers to the act of communicating the generated health management plan and other important information to the user, and can take various forms such as email or push notification.

[1197] "Association" refers to the act of matching a user's health management plan with product data from a physical store.

[1198] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be described in detail.

[1199] System Overview

[1200] This system is primarily composed of a user, a device (e.g., a smartphone), and a server. First, the user provides genetic information and inputs daily lifestyle habits. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to create a health management plan and provides it to the user. The system also has a function that suggests optimal products for the user to purchase in physical stores.

[1201] Collection and analysis of genetic and lifestyle data

[1202] Users use a genetic analysis kit to collect a genetic sample and upload the results to the system's dedicated app. Lifestyle data is recorded automatically or entered via a smartphone app or wearable device, including daily diet, exercise, and sleep data.

[1203] The device transmits this genetic information and lifestyle data to a cloud server using a secure communication protocol (e.g., HTTPS).

[1204] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data. The analysis includes evaluation of genetic risk factors, nutritional balance, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database and managed for each user.

[1205] Generate and provide health management plans

[1206] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[1207] The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[1208] Health support in physical stores

[1209] When users visit a physical store, they can use the app to scan products to see if they fit into their health management plan.

[1210] When the terminal (smartphone) scans a product in the store, it uses its barcode reader function to send the product information to the server.

[1211] The server associates this product information with the user's personalized health care plan and evaluates whether the product conforms to the health care plan. The evaluation results are returned to the terminal in real time and notified to the user.

[1212] Ongoing support

[1213] Users continually input their lifestyle data, which causes the device to continuously send new data to the cloud server, which then updates the generative AI model with the newly collected data and improves the personalized health management plan accordingly.

[1214] Specific examples

[1215] A specific example scenario could be when a user is searching for low-calorie foods in a store and uses an app to scan the product's barcode to check calorie information and suitability based on a health management plan.

[1216] Prompt Sentence Examples

[1217] We would like to develop an app that allows users to use an advanced smartphone app to suggest optimal low-calorie foods to purchase in stores based on their health management plan. The generative AI should analyze genetic information and lifestyle data to produce a list of specific foods. Furthermore, we would like the scanning function to display detailed product information in the store.

[1218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1219] Step 1:

[1220] Users provide genetic information and lifestyle data

[1221] Users use a genetic analysis kit to collect a genetic sample and upload the results to a dedicated app. They also input or automatically record lifestyle data such as daily diet, exercise, and sleep data using a smartphone app or wearable device.

[1222] Input: Genetic information, lifestyle data

[1223] Output: Provided data (genetic information, lifestyle data)

[1224] Step 2:

[1225] The device sends data to the cloud server

[1226] The device (smartphone) transmits the genetic information and lifestyle data provided by the user to a cloud server using a secure communication protocol (e.g., HTTPS).

[1227] Input: Genetic information, lifestyle data

[1228] Output: Data sent to the cloud server

[1229] Step 3:

[1230] The server analyzes genetic information and lifestyle data

[1231] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data, which evaluates genetic risk factors and nutritional balance, exercise volume, heart rate, and sleep quality and quantity.

[1232] Input: Transmitted genetic information, lifestyle data

[1233] Output: Analyzed genetic risk factors and lifestyle assessment results

[1234] Step 4:

[1235] The server generates a health management plan

[1236] The server inputs the results of the genetic and lifestyle analysis into a generative AI model to generate a personalized health management plan, including recommended dietary choices, specific exercise programs, and lifestyle changes.

[1237] Input: Analysis results (genetic risk factors, nutritional balance, amount of exercise, quality of sleep)

[1238] Output: personalized health care plan

[1239] Step 5:

[1240] The device notifies the user of the health management plan

[1241] The device (smartphone) notifies the user of the health management plan provided by the server via push notifications or in-app messages.

[1242] Input: Generated health care plan

[1243] Output: Notification to the user (push notification, in-app message)

[1244] Step 6:

[1245] User scans item in store

[1246] When a user visits a physical store, they use the app to scan the barcode of a product, which provides the product's identification information.

[1247] Input: Product barcode

[1248] Output: Product identification information

[1249] Step 7:

[1250] The device sends product information to the server

[1251] The terminal sends the scanned product's identification information to the server, which then obtains detailed product information.

[1252] Input: Product identification information

[1253] Output: Product information sent to the server

[1254] Step 8:

[1255] The server matches the product with the health management plan

[1256] The server associates the product information with the user's personalized health care plan and evaluates whether the product is suitable for the plan. A suitability evaluation result is generated.

[1257] Input: Product information, health management plan

[1258] Output: Product conformity assessment results

[1259] Step 9:

[1260] The device notifies the user of the evaluation results

[1261] The terminal (smartphone) notifies the user of the product compatibility evaluation results received from the server, allowing the user to select the appropriate product in the store.

[1262] Input: Product conformity assessment results

[1263] Output: Notification to user (evaluation result)

[1264] 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.

[1265] The present invention relates to a health support system that utilizes generative AI and combines it with an emotion engine that recognizes a user's emotions. This system analyzes the user's genetic information and lifestyle data, and also adds emotion data to provide the user with a personalized health management plan. A specific embodiment of the present invention is described below.

[1266] System Overview

[1267] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data. The device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and updating of the health management plan.

[1268] Program processing overview

[1269] Collection and analysis of genetic information

[1270] User:

[1271] Users use a genetic analysis kit to collect genetic samples and upload the analysis results to the system's web portal or dedicated app.

[1272] Device:

[1273] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure protocol.

[1274] server:

[1275] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[1276] Collection and analysis of lifestyle data

[1277] User:

[1278] Users input or record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[1279] Device:

[1280] The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[1281] server:

[1282] The server analyzes the lifestyle data it receives. It evaluates nutritional balance for dietary data, exercise volume and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results of these analyses are stored in a database.

[1283] Emotion data collection and analysis

[1284] User:

[1285] Users collect their own emotional data using devices with emotion detection capabilities (e.g., smartwatches or smartphone apps).

[1286] Device:

[1287] The device transmits the user's emotional data to a cloud server, which is also transmitted using a secure protocol.

[1288] Server and Emotion Engine:

[1289] The server uses an emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the analysis results in a database along with other data.

[1290] Generate and provide health management plans

[1291] server:

[1292] The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[1293] Device:

[1294] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[1295] Ongoing support

[1296] User:

[1297] Users continuously input lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[1298] Device:

[1299] The device continues to send newly entered data to the cloud server.

[1300] server:

[1301] The server updates the generative AI model with newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it can suggest new emotional management or relaxation techniques.

[1302] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[1303] The processing flow will be explained below.

[1304] Step 1:

[1305] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis results file to a web portal or dedicated app.

[1306] Step 2:

[1307] Terminal: The terminal (smartphone or PC) sends the genetic information data provided by the user to the cloud server. This transmission uses a secure communication protocol such as SSL / TLS.

[1308] Step 3:

[1309] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and managed for each user.

[1310] Step 4:

[1311] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[1312] Step 5:

[1313] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[1314] Step 6:

[1315] Server: The server analyzes the received lifestyle data, evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The evaluation results are stored in a database.

[1316] Step 7:

[1317] User: The user uses an emotion-sensing device (e.g., a smartwatch or smartphone app) to collect their own emotional data, including stress levels, excitement levels, and relaxation levels.

[1318] Step 8:

[1319] Device: The device transmits the user's emotional data to the cloud server in real time. The emotional data is also transmitted using a secure protocol.

[1320] Step 9:

[1321] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, to evaluate whether the user is stressed or relaxed, and stores the results in a database.

[1322] Step 10:

[1323] Server: The server integrates the results of the analysis of genetic information, lifestyle data, and emotional data. It then inputs this data into a generative AI model to generate an optimal health management plan for the user. This plan includes recommended dietary content, exercise programs, and emotional management methods.

[1324] Step 11:

[1325] Device: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[1326] Step 12:

[1327] User: The user continuously inputs lifestyle and emotional data, such as daily updates on diet, exercise, sleep, and emotional state.

[1328] Step 13:

[1329] Device: The device continues to send newly input lifestyle data and emotion data to the cloud server.

[1330] Step 14:

[1331] Server: The server updates the generative AI model based on newly collected data, resulting in appropriate improvements to the personalized health management plan. For example, if the user's emotional state changes, new emotional management or relaxation techniques will be suggested.

[1332] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[1333] Example 2

[1334] 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."

[1335] In modern society, personalized health management is becoming increasingly important. However, conventional health management systems are unable to comprehensively analyze a user's genetic information, lifestyle data, and emotional data to provide an individually optimized health management plan. As a result, many users struggle with self-management and are unable to effectively reduce their risk of lifestyle-related diseases. Another issue is the inability to respond appropriately to changes in emotional state.

[1336] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing the user's genetic information, means for collecting and analyzing the user's lifestyle habit data, and means for collecting and analyzing the user's emotional data. This makes it possible to generate and provide an individualized health management plan based on a comprehensive analysis of the genetic information, lifestyle habit data, and emotional data.

[1337] "Genetic information" refers to all genetic information obtained from a user's DNA, including data regarding specific disease risks and physical predispositions.

[1338] "Lifestyle data" refers to information about a user's daily activities and lifestyle patterns, including dietary content, amount of exercise, and sleep duration.

[1339] "Emotional data" refers to information about the user's psychological and emotional state, including stress level and relaxation state.

[1340] A "generative AI model" refers to an algorithm that uses artificial intelligence techniques such as machine learning to generate optimal outputs (in this case, health management plans) based on multiple input data.

[1341] "Health Management Plan" means specific guidance and recommendations for improving and maintaining health that are individually tailored to a User's specific needs and circumstances.

[1342] A "cloud server" refers to a system that processes and stores data using a server installed in a remote location and is accessible via the Internet.

[1343] "Analysis" refers to the general process of extracting information based on collected data and making evaluations and judgments.

[1344] System Overview

[1345] This invention is a health support system utilizing generative AI, which is mainly composed of a user, a terminal, a server, and an emotion engine. This system analyzes the user's genetic information, lifestyle data, and emotion data, and provides an individualized health management plan.

[1346] Hardware and software used

[1347] Devices: Smartphones, PCs, wearable devices (e.g., smartwatches) that are used to collect and transmit data.

[1348] Server: Cloud server, used to analyze and store data and run generative AI models.

[1349] Emotion Engine: Software with emotion recognition algorithms used to analyze emotion data.

[1350] Generative AI model: Software containing machine learning algorithms used to generate optimal health management plans for users.

[1351] Program processing overview

[1352] The system operates as follows.

[1353] Collection and analysis of genetic information

[1354] User: Collects genetic samples using a genetic analysis kit and uploads the analysis results to the system's web portal or dedicated app.

[1355] Terminal: Sends the genetic information data uploaded by the user to the cloud server. Safety is ensured by using a secure protocol (e.g., HTTPS).

[1356] Server: The received genetic information data is run through a dedicated analysis algorithm to evaluate specific genetic risk factors and predispositions, and the results are stored in a database.

[1357] Example prompt: "Please upload the genetic information obtained using the genetic analysis kit and send the data to the cloud server."

[1358] Collection and analysis of lifestyle data

[1359] User: Uses a smartphone app or wearable device to enter or automatically record daily diet, exercise, and sleep data.

[1360] Device: The lifestyle habit data entered by the user is sent to the cloud server in real time using a secure protocol.

[1361] Server: Analyzes the received lifestyle data, evaluating nutritional balance from dietary data, exercise amount and heart rate from exercise data, and quality and quantity of sleep from sleep data. The results are stored in a database.

[1362] Example prompt: "Enter your daily diet, exercise, and sleep data into a smartphone app."

[1363] Emotion data collection and analysis

[1364] User: Collects their own emotional data using a device with emotion detection capabilities (such as a smartwatch or smartphone app).

[1365] Device: Emotion data is sent to the cloud server using a secure protocol, ensuring safety.

[1366] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the results in a database.

[1367] Example prompt: "Please wear a device with emotion detection capabilities and send the collected emotion data to the cloud server."

[1368] Generate and provide health management plans

[1369] Server: The results of the analysis of genetic information, lifestyle data, and emotional data are combined and input into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[1370] Device: The health management plan provided by the server is notified to the user via push notification, email, in-app message, etc.

[1371] Example prompt: "Check out the health management plan generated based on your lifestyle and emotional data."

[1372] Ongoing support

[1373] User: By continuously inputting daily lifestyle and emotional data, the system can always keep up to date with the latest information.

[1374] Device: Continues to send newly entered data to the cloud server. Safety is ensured by using a secure protocol.

[1375] Server: Updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it suggests new emotional management or relaxation techniques.

[1376] Example prompt: "Please continue to enter your daily habits and emotions."

[1377] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1378] Processing flow and specific explanation

[1379] Step 1: User prepares genetic information

[1380] Input: Genetic analysis kit

[1381] How it works: Users use a genetic analysis kit to collect a genetic sample and send it to an analysis service. After a few days, they upload the analysis results to the system's web portal or a dedicated app.

[1382] Output: Genetic information data

[1383] Step 2: Sending genetic information via the device

[1384] Input: Genetic information data

[1385] How it works: A device (such as a smartphone or PC) sends the genetic information uploaded by the user to a cloud server using a secure protocol (HTTPS).

[1386] Output: Genetic information data stored on a cloud server

[1387] Step 3: Analysis of genetic information by the server

[1388] Input: Genetic information data stored on a cloud server

[1389] How it works: The server runs the received genetic information data through a specialized analysis algorithm. Specifically, it analyzes the DNA sequence data and evaluates specific genetic risk factors and predispositions. The analysis results are stored in a database.

[1390] Output: Analyzed genetic information

[1391] Step 4: User enters lifestyle data

[1392] Input: Smartphones and wearable devices

[1393] How it works: Users input or automatically record daily diet, exercise, and sleep data using a smartphone app or wearable device. Specifically, the diet app records the food consumed, and the wearable device measures the amount of exercise and sleep time.

[1394] Output: Lifestyle data

[1395] Step 5: Send lifestyle data via your device

[1396] Input: Lifestyle data

[1397] How it works: The device sends collected lifestyle data to a cloud server in real time using a secure protocol (HTTPS), ensuring data security.

[1398] Output: Lifestyle data stored on a cloud server

[1399] Step 6: Analysis of lifestyle data by the server

[1400] Input: Lifestyle data stored on a cloud server

[1401] Operation: The server analyzes the received lifestyle data. Specifically, it evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results are stored in a database.

[1402] Output: Analyzed lifestyle data

[1403] Step 7: Obtaining user emotion data

[1404] Input: Device with emotion detection function (smartwatch or smartphone app)

[1405] How it works: Users use emotion-sensing devices to collect their own emotional data, specifically measuring their emotions based on heart rate and facial expression recognition.

[1406] Output: Emotion data

[1407] Step 8: Device sends emotion data

[1408] Input: Emotion data

[1409] How it works: The device sends the user's emotional data to a cloud server. This data is also sent using a secure protocol (HTTPS), so it is safely protected.

[1410] Output: Emotion data stored on a cloud server

[1411] Step 9: Emotion data analysis by the server and emotion engine

[1412] Input: Emotion data stored on a cloud server

[1413] How it works: The server uses an emotion engine to analyze the received emotion data, for example to assess whether the user is stressed or relaxed. The results are stored in a database.

[1414] Output: Parsed emotion data

[1415] Step 10: Server generates health management plan

[1416] Input: Analyzed genetic information, lifestyle data, and emotional data

[1417] How it works: The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[1418] Output: Health management plan

[1419] Step 11: Notification of health management plan via device

[1420] Input: Health Care Plan

[1421] Operation: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[1422] Output: Health plan provided to the user

[1423] Step 12: User continues to enter data

[1424] Input: New lifestyle data, emotional data

[1425] How it works: Users continuously input their daily lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[1426] Output: New lifestyle data, emotional data

[1427] Step 13: Device continues to send data

[1428] Input: New lifestyle data, emotional data

[1429] How it works: The device continues to send newly entered data to the cloud server. Data transmission is securely protected by a secure protocol.

[1430] Output: New lifestyle and emotion data stored on a cloud server

[1431] Step 14: Server updates the generative AI model

[1432] Input: New lifestyle data and emotion data stored on the cloud server

[1433] How it works: The server updates the generative AI model based on newly collected data and refines the personalized health plan accordingly, for example by suggesting new emotional management or relaxation techniques when the user's emotional state changes.

[1434] Output: Updated health plan

[1435] Specific examples of each step

[1436] As a concrete example of step 1, a user uses a genetic analysis kit at home to collect a sample and uploads the analysis results to a dedicated app.

[1437] As a concrete example of step 4, a user enters food records into a smartphone app and collects exercise and sleep data using a wearable device.

[1438] Example prompt: "Please continue to enter your daily lifestyle and emotional data. The server will optimize your health management plan based on the latest information."

[1439] (Application example 2)

[1440] 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."

[1441] Today's consumers want personalized services and product recommendations based on their health and emotional state. However, current health management systems and services offered in brick-and-mortar stores are not fully capable of integrating and analyzing individual users' genetic information, lifestyle data, and emotional data to provide appropriate health management plans and recommendations. As a result, users are unable to optimally manage their health and their brick-and-mortar store experiences are unsatisfactory.

[1442] 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.

[1443] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, and means for collecting and analyzing the user's emotional data in real time. This makes it possible to integrate the analysis results of the user's genetic information, lifestyle data, and emotional data, generate a personalized health management plan, and provide the generated health management plan to the user. Furthermore, optimal products and services can be suggested in physical stores based on the user's emotional state, making it possible to provide more effective and satisfying services to the user.

[1444] "User's genetic information" refers to data regarding the user's genetic characteristics and constitution obtained from the user's DNA.

[1445] "Lifestyle data" is information about the user's daily lifestyle habits such as diet, exercise, and sleep.

[1446] "Emotional data" is data that indicates the user's state of mind and emotional expression, and is often collected in real time.

[1447] "Analysis tools" refers to the processes or devices that use algorithms to analyze collected data and convert it into meaningful information.

[1448] A "personalized health management plan" is a plan for optimal health management created based on a user's individual genetic information, lifestyle data, and emotional data.

[1449] "Generative AI" is a system that uses artificial intelligence technology to generate new information and plans based on data.

[1450] A "brick and mortar store" is a store located in a physical location where customers can visit in person to purchase goods or services.

[1451] "Product and service suggestions" refers to the act of recommending appropriate products and services based on user data.

[1452] The present invention provides a system for integrating and analyzing a user's genetic information, lifestyle data, and emotional data to generate an individualized health management plan. Specific embodiments for carrying out the present invention will be described below.

[1453] System Overview

[1454] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data, and the device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and update of the health management plan.

[1455] Hardware and software used

[1456] Smartphones and smartwatches: These devices collect genetic information, lifestyle data, and emotional data from users and transmit it to a cloud server.

[1457] Cloud server (e.g., AWS): Stores collected data and processes it for analysis and generative AI models.

[1458] Emotion engine (e.g. Microsoft Azure Emotion API): Analyzes the user's emotional state.

[1459] Generative AI models (e.g., OpenAI GPT-4): Generate health management plans based on genetic information, lifestyle data, and emotional data.

[1460] Data collection and transmission

[1461] Users use a smartphone or smartwatch to collect genetic information, lifestyle data, and emotional data. Genetic information is collected using a genetic analysis kit, and the results are uploaded to the app. Lifestyle data, such as daily diet, exercise records, and sleep duration, is entered into the smartphone app. Emotional data is recorded in real time using a smartwatch with emotion detection capabilities.

[1462] Analyzing data and using generative AI models

[1463] The server receives the genetic information, lifestyle data, and emotional data sent from the device. The genetic information data is passed through an analysis algorithm to evaluate the user's genetic risk factors and constitution. The lifestyle data evaluates the nutritional balance of the dietary data, the amount of exercise and heart rate of the exercise data, and the quality and quantity of sleep data. The emotional engine analyzes the emotional data to evaluate the user's stress and relaxation state.

[1464] The results of these analyses are then integrated and fed into a generative AI model, which takes into account genetic information, lifestyle habits, and emotional data to generate a personalized health plan for each user, including recommended dietary choices, specific exercise programs, and emotional management techniques.

[1465] Prompt Sentence Examples

[1466] The generated health management plan will be sent to the user's smartphone. Below is an example of a prompt to be input to the generation AI.

[1467] Example prompt sentence:

[1468] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[1469] Providing services in physical stores

[1470] When a user visits a physical store, the emotion engine analyzes the user's emotional data in real time and suggests the most appropriate products and services based on their emotional state. Store staff can also use the app to provide personalized support to users, such as counseling based on specific health concerns or suggesting relaxation items suited to their emotional state.

[1471] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1472] Step 1:

[1473] User provides genetic information

[1474] Input: User's DNA sample

[1475] How it works: A user uses a genetic analysis kit to collect a DNA sample and enters the analysis results into a smartphone app.

[1476] Output: Genetic information is stored in the app and sent to a cloud server.

[1477] Step 2:

[1478] Collection of lifestyle data

[1479] Input: User's daily diet, exercise, and sleep data

[1480] How it works: Users enter their daily lifestyle data into a smartphone app, recording their diet, exercise, and sleep.

[1481] Output: Lifestyle data is sent to the cloud server.

[1482] Step 3:

[1483] Real-time collection of emotional data

[1484] Input: User emotion recognition data

[1485] How it works: Users wear a smartwatch with emotion detection capabilities to record real-time emotional data, including heart rate and facial expression data.

[1486] Output: Emotion data is sent to a cloud server via a smartphone app.

[1487] Step 4:

[1488] Sending data to the cloud server

[1489] Input: Genetic information, lifestyle data, emotional data

[1490] How it works: The smartphone app sends all collected data to a cloud server using a secure protocol.

[1491] Output: Various user data is stored on the cloud server.

[1492] Step 5:

[1493] Data analysis

[1494] Input: User data stored on the cloud server

[1495] How it works: The server uses an algorithm to analyze genetic information to assess genetic risk factors, analyzes lifestyle data to assess nutritional balance, exercise volume, and sleep quality, and analyzes emotional data using an emotion engine.

[1496] Output: Analysis results are generated and stored in a database.

[1497] Step 6:

[1498] Generating health management plans using generative AI models

[1499] Input: Analysis results (genetic information, lifestyle data, emotional data)

[1500] How it works: The server inputs these analysis results into a generative AI model to generate an optimal health management plan for the user. To generate the plan, the generative AI model (e.g., OpenAI GPT-4) uses prompt statements.

[1501] Output: A health care plan is generated.

[1502] Step 7:

[1503] Providing health management plans to users

[1504] Input: Generated health care plan

[1505] Operation: The server sends the generated health management plan to the smartphone app and notifies the user.

[1506] Output: The user receives the healthcare plan.

[1507] Step 8:

[1508] Emotional data analysis and proposals in physical stores

[1509] Input: Real-time sentiment data from physical store visits

[1510] How it works: When a user visits a physical store, the smartwatch records emotional data in real time and sends it to the server. The server analyzes the emotional data and generates and recommends optimal products and services.

[1511] Output: Optimal product and service recommendations are generated for the physical store.

[1512] Detailed operation example

[1513] Example prompt sentence:

[1514] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[1515] This process integrates the data collected and analyzed at each step to provide optimal health management plans and personalized services in physical stores.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] [Fourth embodiment]

[1520] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1521] 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.

[1522] 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).

[1523] 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.

[1524] 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.

[1525] 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).

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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.

[1532] 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."

[1533] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be specifically described.

[1534] System Overview

[1535] This system is mainly composed of a user, a device, and a server. First, the user provides genetic information and inputs lifestyle data daily. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to generate a health management plan and provides it to the user.

[1536] Program processing overview

[1537] Collection and analysis of genetic information

[1538] User:

[1539] Users use a genetic analysis kit to collect genetic samples and upload the results to the system's web portal or dedicated app. The genetic information, including DNA and RNA analysis results, reveals individual genetic characteristics and risk factors.

[1540] Device:

[1541] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure communication protocol.

[1542] server:

[1543] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[1544] Collection and analysis of lifestyle data

[1545] User:

[1546] Users input or automatically record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[1547] Device:

[1548] The device sends the lifestyle data entered by the user to a cloud server in real time, also using a secure communication protocol.

[1549] server:

[1550] The server analyzes the lifestyle data it receives. For example, it evaluates nutritional balance for diet, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database.

[1551] Generate and provide health management plans

[1552] server:

[1553] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[1554] Device:

[1555] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[1556] Ongoing support

[1557] User:

[1558] Users continually input their lifestyle data, so the system always has the most up-to-date information.

[1559] Device:

[1560] The device continues to send these new data to the cloud server.

[1561] server:

[1562] The server updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly, for example, by suggesting a new exercise plan for a user who has increased their physical activity.

[1563] For example, Alice provides her genetic information to the system and continues to input her daily lifestyle habits using her smartphone. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan in real time. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[1564] The processing flow will be explained below.

[1565] Step 1:

[1566] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis result file to a web portal or dedicated app.

[1567] Step 2:

[1568] Device: The device (smartphone or PC) sends the genetic information data uploaded by the user to the cloud server. This transmission is carried out using a secure communication protocol such as SSL / TLS.

[1569] Step 3:

[1570] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and organized for each user.

[1571] Step 4:

[1572] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[1573] Step 5:

[1574] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[1575] Step 6:

[1576] Server: The server analyzes the received lifestyle data. The dietary data is used to evaluate nutritional balance, the exercise data is used to evaluate the amount of exercise and heart rate, and the sleep data is used to evaluate the quality and quantity of sleep. The evaluation results are stored in a database.

[1577] Step 7:

[1578] Server: The server combines the genetic information and lifestyle data analysis results, inputs them into a generative AI model, and generates a personalized health plan for the user, including recommended diet, exercise programs, and lifestyle modifications.

[1579] Step 8:

[1580] Device: The device presents the health management plan provided by the server to the user via push notifications, emails, in-app messages, etc.

[1581] Step 9:

[1582] User: The user continuously inputs lifestyle data, such as updating daily diet, exercise, and sleep records.

[1583] Step 10:

[1584] Device: The device continues to send newly entered lifestyle habit data to the cloud server.

[1585] Step 11:

[1586] Server: The server analyzes newly collected data and updates the generative AI model, resulting in appropriate improvements to the personalized health plan. For example, if the user increases their physical activity, new exercise and meal plans will be presented.

[1587] For example, Alice uploads her genetic information and continues to input her daily lifestyle habits. The server analyzes this data and uses generative AI to provide Alice with an optimal health management plan. By following the plan, Alice can reduce her risk of lifestyle-related diseases and maintain and improve her health.

[1588] Example 1

[1589] 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."

[1590] In modern society, individuals face the challenge of effectively managing their own health. In particular, accurately understanding health risks due to genetic risk factors and lifestyle habits and formulating an appropriate health management plan based on that information requires specialized knowledge, making it difficult for many people. Therefore, there is a need for a system that can solve these challenges and provide an optimized health management plan for each individual user.

[1591] 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.

[1592] In this invention, the server includes means for collecting a user's genetic information and transmitting it to a cloud server, means for collecting a user's lifestyle data and transmitting it to the cloud server, means for analyzing the genetic information and lifestyle data in the cloud server and storing the analysis results, means for integrating the analysis results of the genetic information and lifestyle data and generating an individualized health management plan using a generative AI model, and means for notifying the user of the generated health management plan, thereby enabling the user to implement an optimal health management plan based on their genetic risks and lifestyle habits and effectively maintain and improve their health.

[1593] "User" refers to an individual who utilizes the System to provide genetic and lifestyle data and receive a health management plan.

[1594] "Genetic information" refers to data obtained as a result of analyzing a user's DNA or RNA, including individual genetic characteristics and risk factors.

[1595] "Lifestyle data" refers to information including data on the user's daily diet, exercise, sleep, etc.

[1596] A "cloud server" refers to a server on a network that remotely receives, analyzes, and stores genetic information and lifestyle data sent by users.

[1597] "Analysis algorithm" refers to a computational method or mathematical model that processes genetic information and lifestyle data to assess a user's health status and risks.

[1598] "Generative AI model" refers to an artificial intelligence model that generates personalized health management plans based on genetic information and lifestyle data.

[1599] A "health management plan" refers to a plan that includes specific guidelines and recommendations for diet, exercise, sleep, etc., generated based on a user's genetic information and lifestyle data.

[1600] "Notification means" refers to the methods and technologies used to communicate the generated health management plan to the user, and examples include push notifications, emails, and in-app messages.

[1601] The present invention relates to a system that collects a user's genetic information and lifestyle data, analyzes the data, and generates and provides an individualized health management plan. This system is mainly composed of a user, a terminal, and a server.

[1602] Collection and analysis of genetic information

[1603] User:

[1604] First, the user collects a saliva or blood sample using a genetic analysis kit. The sample is then sent to a specialized analysis facility, and the analysis results are provided to the user. The user then uploads the analysis results to the system's web portal or a dedicated app. This genetic information includes DNA and RNA analysis data.

[1605] Device:

[1606] The terminal (e.g., a smartphone or personal computer) receives the genetic information data entered by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[1607] server:

[1608] The server then applies an analysis algorithm to the received genetic information data (using a machine learning library such as Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored for each user in a database such as MySQL.

[1609] Collection and analysis of lifestyle data

[1610] User:

[1611] Users use smartphone apps or wearable devices (e.g., fitness trackers or smartwatches) to input or automatically record daily food, exercise, and sleep data. For example, food logs include the types and amounts of food consumed, exercise logs include the types and durations of exercise, and sleep logs include total sleep time and quality.

[1612] Device:

[1613] The device transmits the lifestyle data collected by the user to a cloud server in real time, again using a secure communication protocol.

[1614] server:

[1615] The server analyzes the lifestyle data it receives. For example, dietary data is evaluated for nutritional balance, exercise data is evaluated for exercise volume and heart rate, and sleep data is evaluated for quality and quantity. The results of these analyses are stored in a database along with genetic information.

[1616] Generate and provide health management plans

[1617] server:

[1618] The server combines the results of the analysis of the genetic information and lifestyle data and inputs them into a generative AI model (e.g., OpenAI's GPT-4). The generative AI model uses this data to generate an optimized health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle improvements. For example, the following prompt could be input into the generative AI model: "Based on the user's genetic information and the collected lifestyle data, please generate a personalized health management plan. Specifically, please include recommendations for diet, exercise, and sleep, as well as measures to address the user's genetic risks."

[1619] Device:

[1620] The device notifies the user of the health management plan provided by the server. Notification methods include push notifications, emails, and in-app messages. For example, a message such as "Today's recommended exercise plan" may be displayed as a push notification on a smartphone.

[1621] Continuous data collection and analysis

[1622] User:

[1623] Users continuously input lifestyle data, for example, by recording their daily diet and exercise.

[1624] Device:

[1625] The device continues to send these new data to the cloud server.

[1626] server:

[1627] The server updates the generative AI model based on newly collected lifestyle data and improves the personalized health management plan accordingly. For example, it can present a new exercise plan with a higher intensity to a user who has increased their exercise volume.

[1628] Specific examples

[1629] When a user uses this system, they first provide their genetic information and then collect daily lifestyle data using a smartphone or wearable device. The server analyzes this data and uses generative AI to provide the user with an optimal health management plan in real time. By following this plan, users can reduce their risk of lifestyle-related diseases and maintain and improve their health.

[1630] This system allows users to practice optimal health management based on their genetic factors and lifestyle habits, enabling them to effectively maintain and improve their health.

[1631] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1632] Step 1:

[1633] User:

[1634] Users collect a saliva sample using a genetic analysis kit and send it to an analysis laboratory, after which the genetic information provided by the analysis laboratory is uploaded to the system's web portal or dedicated app.

[1635] Input: Genetic information (DNA / RNA analysis results)

[1636] Output: Genetic information is uploaded to the system

[1637] Step 2:

[1638] Device:

[1639] The device (e.g., smartphone, personal computer) receives the genetic information uploaded by the user and transmits this data to a cloud server using a secure communication protocol such as SSL / TLS.

[1640] Input: Genetic information

[1641] Output: Genetic information is sent to the cloud server

[1642] Step 3:

[1643] server:

[1644] The server then applies the received genetic information to an analysis algorithm (e.g., Python's Scikit-learn) to evaluate the user's genetic risk factors and predispositions. The analysis results are then stored in a database (e.g., MySQL).

[1645] Input: Genetic information

[1646] Output: The analysis results of genetic risk factors and physical predispositions are saved in a database.

[1647] Step 4:

[1648] User:

[1649] Users input or automatically record their daily diet, exercise, and sleep data using smartphone apps or wearable devices (e.g., fitness trackers, smartwatches).

[1650] Input: diet, exercise, sleep data

[1651] Output: Lifestyle data is recorded on the device

[1652] Step 5:

[1653] Device:

[1654] The device receives lifestyle data entered or recorded by the user and transmits it in real time to a cloud server using a secure communication protocol.

[1655] Input: Lifestyle data

[1656] Output: Lifestyle data is sent to the cloud server

[1657] Step 6:

[1658] server:

[1659] The server analyzes the lifestyle data it receives. Dietary data evaluates nutritional balance, exercise data evaluates exercise volume and heart rate, and sleep data evaluates quality and quantity. The analysis results are stored in a database.

[1660] Input: Lifestyle data

[1661] Output: Analysis results are saved in a database

[1662] Step 7:

[1663] server:

[1664] The server combines the genetic and lifestyle analysis results and inputs them into a generative AI model (e.g., OpenAI's GPT-4), which then uses this data to generate an optimal health management plan for the user.

[1665] Input: Analysis results of genetic information and lifestyle data

[1666] Output: Generate a personalized healthcare plan

[1667] Step 8:

[1668] server:

[1669] The generated health management plan is notified to the user via push notification, email, in-app message, etc. The server uses these methods to send the health management plan to the device.

[1670] Input: Health Care Plan

[1671] Output: The health management plan is sent to the device.

[1672] Step 9:

[1673] Device:

[1674] The device notifies the user of the health management plan sent from the server via push notification, email, in-app message, etc.

[1675] Input: Health Care Plan

[1676] Output: The health management plan is notified to the user.

[1677] Step 10:

[1678] User:

[1679] Users continue to input lifestyle habits data, which is then sent back to the server, where the generative AI model learns from it and updates the health management plan accordingly.

[1680] Input: New lifestyle data

[1681] Output: An updated health management plan from a generative AI model

[1682] (Application example 1)

[1683] 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."

[1684] Conventionally, personalized health management plans are designed for users to complete at home, and use in physical stores has not been considered, making it difficult to provide advice on which specific products users should purchase in physical stores. Also, there has been insufficient support for users to select appropriate products according to their individual health management plans, resulting in problems such as users having difficulty selecting the health products that are best suited to them.

[1685] 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.

[1686] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, means for integrating the analysis results of the genetic information and lifestyle data to generate an individualized health management plan, means for associating the generated health management plan with store product data to suggest optimal products for the user to purchase in a physical store, and means for providing the generated health management plan to the user. This enables the user to select products based on an appropriate health management plan while shopping in a physical store, and more effectively maintain and improve their health.

[1687] "Genetic information" is based on the results of DNA and RNA analysis provided by users and is data that indicates individual genetic characteristics and risk factors.

[1688] "Lifestyle data" is data that includes information about the user's daily habits, such as what they eat, how much they exercise, and how much they sleep.

[1689] A "health management plan" is a plan that includes personalized diet, exercise, and lifestyle recommendations based on the analysis of genetic and lifestyle data.

[1690] A "server" is a device that has the computing resources to analyze a user's genetic information and lifestyle data, and to generate and provide a health management plan.

[1691] "Generative AI" is an artificial intelligence technology that predicts health risks based on a user's genetic information and lifestyle data, and generates a personalized health management plan.

[1692] "Brick and mortar store" refers to a physical sales location where users can actually visit and select products.

[1693] "Products" are items that are useful for the user's health management, such as food and health products sold in physical stores.

[1694] "Scanning" is the act of reading the product's identification information using a barcode reader or camera.

[1695] "Analysis" is the process of finding certain regularities and patterns based on collected data and turning it into meaningful information.

[1696] "Notification" refers to the act of communicating the generated health management plan and other important information to the user, and can take various forms such as email or push notification.

[1697] "Association" refers to the act of matching a user's health management plan with product data from a physical store.

[1698] The present invention relates to a health support system that utilizes generative AI, and aims to analyze a user's genetic information and lifestyle data to provide an individualized health management plan. Hereinafter, an embodiment of the present invention will be described in detail.

[1699] System Overview

[1700] This system is primarily composed of a user, a device (e.g., a smartphone), and a server. First, the user provides genetic information and inputs daily lifestyle habits. The device collects this data and sends it to a cloud server. The server then analyzes the collected data to create a health management plan and provides it to the user. The system also has a function that suggests optimal products for the user to purchase in physical stores.

[1701] Collection and analysis of genetic and lifestyle data

[1702] Users use a genetic analysis kit to collect a genetic sample and upload the results to the system's dedicated app. Lifestyle data is recorded automatically or entered via a smartphone app or wearable device, including daily diet, exercise, and sleep data.

[1703] The device transmits this genetic information and lifestyle data to a cloud server using a secure communication protocol (e.g., HTTPS).

[1704] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data. The analysis includes evaluation of genetic risk factors, nutritional balance, exercise volume and heart rate, and sleep quality and quantity. The results of these analyses are stored in a database and managed for each user.

[1705] Generate and provide health management plans

[1706] The server combines the genetic information and lifestyle data analysis results and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. This plan includes recommended dietary content, specific exercise programs, and lifestyle changes to be made.

[1707] The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[1708] Health support in physical stores

[1709] When users visit a physical store, they can use the app to scan products to see if they fit into their health management plan.

[1710] When the terminal (smartphone) scans a product in the store, it uses its barcode reader function to send the product information to the server.

[1711] The server associates this product information with the user's personalized health care plan and evaluates whether the product conforms to the health care plan. The evaluation results are returned to the terminal in real time and notified to the user.

[1712] Ongoing support

[1713] Users continually input their lifestyle data, which causes the device to continuously send new data to the cloud server, which then updates the generative AI model with the newly collected data and improves the personalized health management plan accordingly.

[1714] Specific examples

[1715] A specific example scenario could be when a user is searching for low-calorie foods in a store and uses an app to scan the product's barcode to check calorie information and suitability based on a health management plan.

[1716] Prompt Sentence Examples

[1717] We would like to develop an app that allows users to use an advanced smartphone app to suggest optimal low-calorie foods to purchase in stores based on their health management plan. The generative AI should analyze genetic information and lifestyle data to produce a list of specific foods. Furthermore, we would like the scanning function to display detailed product information in the store.

[1718] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1719] Step 1:

[1720] Users provide genetic information and lifestyle data

[1721] Users use a genetic analysis kit to collect a genetic sample and upload the results to a dedicated app. They also input or automatically record lifestyle data such as daily diet, exercise, and sleep data using a smartphone app or wearable device.

[1722] Input: Genetic information, lifestyle data

[1723] Output: Provided data (genetic information, lifestyle data)

[1724] Step 2:

[1725] The device sends data to the cloud server

[1726] The device (smartphone) transmits the genetic information and lifestyle data provided by the user to a cloud server using a secure communication protocol (e.g., HTTPS).

[1727] Input: Genetic information, lifestyle data

[1728] Output: Data sent to the cloud server

[1729] Step 3:

[1730] The server analyzes genetic information and lifestyle data

[1731] The server then applies the received genetic information to an analysis algorithm to analyze lifestyle data, which evaluates genetic risk factors and nutritional balance, exercise volume, heart rate, and sleep quality and quantity.

[1732] Input: Transmitted genetic information, lifestyle data

[1733] Output: Analyzed genetic risk factors and lifestyle assessment results

[1734] Step 4:

[1735] The server generates a health management plan

[1736] The server inputs the results of the genetic and lifestyle analysis into a generative AI model to generate a personalized health management plan, including recommended dietary choices, specific exercise programs, and lifestyle changes.

[1737] Input: Analysis results (genetic risk factors, nutritional balance, amount of exercise, quality of sleep)

[1738] Output: personalized health care plan

[1739] Step 5:

[1740] The device notifies the user of the health management plan

[1741] The device (smartphone) notifies the user of the health management plan provided by the server via push notifications or in-app messages.

[1742] Input: Generated health care plan

[1743] Output: Notification to the user (push notification, in-app message)

[1744] Step 6:

[1745] User scans item in store

[1746] When a user visits a physical store, they use the app to scan the barcode of a product, which provides the product's identification information.

[1747] Input: Product barcode

[1748] Output: Product identification information

[1749] Step 7:

[1750] The device sends product information to the server

[1751] The terminal sends the scanned product's identification information to the server, which then obtains detailed product information.

[1752] Input: Product identification information

[1753] Output: Product information sent to the server

[1754] Step 8:

[1755] The server matches the product with the health management plan

[1756] The server associates the product information with the user's personalized health care plan and evaluates whether the product is suitable for the plan. A suitability evaluation result is generated.

[1757] Input: Product information, health management plan

[1758] Output: Product conformity assessment results

[1759] Step 9:

[1760] The device notifies the user of the evaluation results

[1761] The terminal (smartphone) notifies the user of the product compatibility evaluation results received from the server, allowing the user to select the appropriate product in the store.

[1762] Input: Product conformity assessment results

[1763] Output: Notification to user (evaluation result)

[1764] 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.

[1765] The present invention relates to a health support system that utilizes generative AI and combines it with an emotion engine that recognizes a user's emotions. This system analyzes the user's genetic information and lifestyle data, and also adds emotion data to provide the user with a personalized health management plan. A specific embodiment of the present invention is described below.

[1766] System Overview

[1767] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data. The device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and updating of the health management plan.

[1768] Program processing overview

[1769] Collection and analysis of genetic information

[1770] User:

[1771] Users use a genetic analysis kit to collect genetic samples and upload the analysis results to the system's web portal or dedicated app.

[1772] Device:

[1773] The device (e.g., a smartphone or PC) transmits the genetic information data provided by the user to a cloud server using a secure protocol.

[1774] server:

[1775] The server then applies the received genetic information to an analysis algorithm to evaluate genetic risk factors and predispositions. The analysis results are stored in a database and managed for each user.

[1776] Collection and analysis of lifestyle data

[1777] User:

[1778] Users input or record daily food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the types and amounts of food consumed, exercise records include the types and times of exercise, and sleep records include sleep duration and quality.

[1779] Device:

[1780] The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[1781] server:

[1782] The server analyzes the lifestyle data it receives. It evaluates nutritional balance for dietary data, exercise volume and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results of these analyses are stored in a database.

[1783] Emotion data collection and analysis

[1784] User:

[1785] Users collect their own emotional data using devices with emotion detection capabilities (e.g., smartwatches or smartphone apps).

[1786] Device:

[1787] The device transmits the user's emotional data to a cloud server, which is also transmitted using a secure protocol.

[1788] Server and Emotion Engine:

[1789] The server uses an emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the analysis results in a database along with other data.

[1790] Generate and provide health management plans

[1791] server:

[1792] The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[1793] Device:

[1794] The device notifies the user of the health management plan provided by the server, which can take various forms such as push notification, email, or in-app message.

[1795] Ongoing support

[1796] User:

[1797] Users continuously input lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[1798] Device:

[1799] The device continues to send newly entered data to the cloud server.

[1800] server:

[1801] The server updates the generative AI model with newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it can suggest new emotional management or relaxation techniques.

[1802] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[1803] The processing flow will be explained below.

[1804] Step 1:

[1805] User: The user collects a genetic sample using a genetic analysis kit and uploads the analysis results file to a web portal or dedicated app.

[1806] Step 2:

[1807] Terminal: The terminal (smartphone or PC) sends the genetic information data provided by the user to the cloud server. This transmission uses a secure communication protocol such as SSL / TLS.

[1808] Step 3:

[1809] Server: The server applies the received genetic information to an analysis algorithm to extract data on genetic risk factors and physical predispositions. The analysis results are stored in a database and managed for each user.

[1810] Step 4:

[1811] User: Users routinely input or record food, exercise, and sleep data using smartphone apps or wearable devices. For example, food records include the type and amount of food consumed, exercise records include the type and time of exercise, and sleep records include sleep duration and quality.

[1812] Step 5:

[1813] Device: The device transmits the collected lifestyle data to a cloud server in real time using a secure protocol.

[1814] Step 6:

[1815] Server: The server analyzes the received lifestyle data, evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The evaluation results are stored in a database.

[1816] Step 7:

[1817] User: The user uses an emotion-sensing device (e.g., a smartwatch or smartphone app) to collect their own emotional data, including stress levels, excitement levels, and relaxation levels.

[1818] Step 8:

[1819] Device: The device transmits the user's emotional data to the cloud server in real time. The emotional data is also transmitted using a secure protocol.

[1820] Step 9:

[1821] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, to evaluate whether the user is stressed or relaxed, and stores the results in a database.

[1822] Step 10:

[1823] Server: The server integrates the results of the analysis of genetic information, lifestyle data, and emotional data. It then inputs this data into a generative AI model to generate an optimal health management plan for the user. This plan includes recommended dietary content, exercise programs, and emotional management methods.

[1824] Step 11:

[1825] Device: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[1826] Step 12:

[1827] User: The user continuously inputs lifestyle and emotional data, such as daily updates on diet, exercise, sleep, and emotional state.

[1828] Step 13:

[1829] Device: The device continues to send newly input lifestyle data and emotion data to the cloud server.

[1830] Step 14:

[1831] Server: The server updates the generative AI model based on newly collected data, resulting in appropriate improvements to the personalized health management plan. For example, if the user's emotional state changes, new emotional management or relaxation techniques will be suggested.

[1832] For example, a user uploads genetic information and continuously provides daily lifestyle and emotional data. The server then integrates this information and uses generative AI to provide the user with an optimal health management plan. By following the plan, the user can reduce the risk of lifestyle-related diseases and maintain and improve their physical and mental health. Furthermore, if the user's emotional state worsens, relaxation methods and stress management techniques are suggested, improving the user's overall quality of life.

[1833] Example 2

[1834] 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."

[1835] In modern society, personalized health management is becoming increasingly important. However, conventional health management systems are unable to comprehensively analyze a user's genetic information, lifestyle data, and emotional data to provide an individually optimized health management plan. As a result, many users struggle with self-management and are unable to effectively reduce their risk of lifestyle-related diseases. Another issue is the inability to respond appropriately to changes in emotional state.

[1836] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting and analyzing the user's genetic information, means for collecting and analyzing the user's lifestyle habit data, and means for collecting and analyzing the user's emotional data. This makes it possible to generate and provide an individualized health management plan based on a comprehensive analysis of the genetic information, lifestyle habit data, and emotional data.

[1837] "Genetic information" refers to all genetic information obtained from a user's DNA, including data regarding specific disease risks and physical predispositions.

[1838] "Lifestyle data" refers to information about a user's daily activities and lifestyle patterns, including dietary content, amount of exercise, and sleep duration.

[1839] "Emotional data" refers to information about the user's psychological and emotional state, including stress level and relaxation state.

[1840] A "generative AI model" refers to an algorithm that uses artificial intelligence techniques such as machine learning to generate optimal outputs (in this case, health management plans) based on multiple input data.

[1841] "Health Management Plan" means specific guidance and recommendations for improving and maintaining health that are individually tailored to a User's specific needs and circumstances.

[1842] A "cloud server" refers to a system that processes and stores data using a server installed in a remote location and is accessible via the Internet.

[1843] "Analysis" refers to the general process of extracting information based on collected data and making evaluations and judgments.

[1844] System Overview

[1845] This invention is a health support system utilizing generative AI, which is mainly composed of a user, a terminal, a server, and an emotion engine. This system analyzes the user's genetic information, lifestyle data, and emotion data, and provides an individualized health management plan.

[1846] Hardware and software used

[1847] Devices: Smartphones, PCs, wearable devices (e.g., smartwatches) that are used to collect and transmit data.

[1848] Server: Cloud server, used to analyze and store data and run generative AI models.

[1849] Emotion Engine: Software with emotion recognition algorithms used to analyze emotion data.

[1850] Generative AI model: Software containing machine learning algorithms used to generate optimal health management plans for users.

[1851] Program processing overview

[1852] The system operates as follows.

[1853] Collection and analysis of genetic information

[1854] User: Collects genetic samples using a genetic analysis kit and uploads the analysis results to the system's web portal or dedicated app.

[1855] Terminal: Sends the genetic information data uploaded by the user to the cloud server. Safety is ensured by using a secure protocol (e.g., HTTPS).

[1856] Server: The received genetic information data is run through a dedicated analysis algorithm to evaluate specific genetic risk factors and predispositions, and the results are stored in a database.

[1857] Example prompt: "Please upload the genetic information obtained using the genetic analysis kit and send the data to the cloud server."

[1858] Collection and analysis of lifestyle data

[1859] User: Uses a smartphone app or wearable device to enter or automatically record daily diet, exercise, and sleep data.

[1860] Device: The lifestyle habit data entered by the user is sent to the cloud server in real time using a secure protocol.

[1861] Server: Analyzes the received lifestyle data, evaluating nutritional balance from dietary data, exercise amount and heart rate from exercise data, and quality and quantity of sleep from sleep data. The results are stored in a database.

[1862] Example prompt: "Enter your daily diet, exercise, and sleep data into a smartphone app."

[1863] Emotion data collection and analysis

[1864] User: Collects their own emotional data using a device with emotion detection capabilities (such as a smartwatch or smartphone app).

[1865] Device: Emotion data is sent to the cloud server using a secure protocol, ensuring safety.

[1866] Server and Emotion Engine: The server uses the emotion engine to analyze the received emotion data, for example, assessing whether the user is stressed or relaxed, and stores the results in a database.

[1867] Example prompt: "Please wear a device with emotion detection capabilities and send the collected emotion data to the cloud server."

[1868] Generate and provide health management plans

[1869] Server: The results of the analysis of genetic information, lifestyle data, and emotional data are combined and input into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[1870] Device: The health management plan provided by the server is notified to the user via push notification, email, in-app message, etc.

[1871] Example prompt: "Check out the health management plan generated based on your lifestyle and emotional data."

[1872] Ongoing support

[1873] User: By continuously inputting daily lifestyle and emotional data, the system can always keep up to date with the latest information.

[1874] Device: Continues to send newly entered data to the cloud server. Safety is ensured by using a secure protocol.

[1875] Server: Updates the generative AI model based on newly collected data and improves the personalized health management plan accordingly. For example, if the user's emotional state changes, it suggests new emotional management or relaxation techniques.

[1876] Example prompt: "Please continue to enter your daily habits and emotions."

[1877] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1878] Processing flow and specific explanation

[1879] Step 1: User prepares genetic information

[1880] Input: Genetic analysis kit

[1881] How it works: Users use a genetic analysis kit to collect a genetic sample and send it to an analysis service. After a few days, they upload the analysis results to the system's web portal or a dedicated app.

[1882] Output: Genetic information data

[1883] Step 2: Sending genetic information via the device

[1884] Input: Genetic information data

[1885] How it works: A device (such as a smartphone or PC) sends the genetic information uploaded by the user to a cloud server using a secure protocol (HTTPS).

[1886] Output: Genetic information data stored on a cloud server

[1887] Step 3: Analysis of genetic information by the server

[1888] Input: Genetic information data stored on a cloud server

[1889] How it works: The server runs the received genetic information data through a specialized analysis algorithm. Specifically, it analyzes the DNA sequence data and evaluates specific genetic risk factors and predispositions. The analysis results are stored in a database.

[1890] Output: Analyzed genetic information

[1891] Step 4: User enters lifestyle data

[1892] Input: Smartphones and wearable devices

[1893] How it works: Users input or automatically record daily diet, exercise, and sleep data using a smartphone app or wearable device. Specifically, the diet app records the food consumed, and the wearable device measures the amount of exercise and sleep time.

[1894] Output: Lifestyle data

[1895] Step 5: Send lifestyle data via your device

[1896] Input: Lifestyle data

[1897] How it works: The device sends collected lifestyle data to a cloud server in real time using a secure protocol (HTTPS), ensuring data security.

[1898] Output: Lifestyle data stored on a cloud server

[1899] Step 6: Analysis of lifestyle data by the server

[1900] Input: Lifestyle data stored on a cloud server

[1901] Operation: The server analyzes the received lifestyle data. Specifically, it evaluates nutritional balance for dietary data, exercise amount and heart rate for exercise data, and quality and quantity of sleep for sleep data. The results are stored in a database.

[1902] Output: Analyzed lifestyle data

[1903] Step 7: Obtaining user emotion data

[1904] Input: Device with emotion detection function (smartwatch or smartphone app)

[1905] How it works: Users use emotion-sensing devices to collect their own emotional data, specifically measuring their emotions based on heart rate and facial expression recognition.

[1906] Output: Emotion data

[1907] Step 8: Device sends emotion data

[1908] Input: Emotion data

[1909] How it works: The device sends the user's emotional data to a cloud server. This data is also sent using a secure protocol (HTTPS), so it is safely protected.

[1910] Output: Emotion data stored on a cloud server

[1911] Step 9: Emotion data analysis by the server and emotion engine

[1912] Input: Emotion data stored on a cloud server

[1913] How it works: The server uses an emotion engine to analyze the received emotion data, for example to assess whether the user is stressed or relaxed. The results are stored in a database.

[1914] Output: Parsed emotion data

[1915] Step 10: Server generates health management plan

[1916] Input: Analyzed genetic information, lifestyle data, and emotional data

[1917] How it works: The server combines the results of the analysis of genetic information, lifestyle data, and emotional data and inputs them into a generative AI model. Based on this data, the generative AI model generates an optimal health management plan for the user. The plan includes recommended dietary content, specific exercise programs, and methods for managing emotions.

[1918] Output: Health management plan

[1919] Step 11: Notification of health management plan via device

[1920] Input: Health Care Plan

[1921] Operation: The device notifies the user of the health management plan provided by the server via push notification, email, in-app message, etc.

[1922] Output: Health plan provided to the user

[1923] Step 12: User continues to enter data

[1924] Input: New lifestyle data, emotional data

[1925] How it works: Users continuously input their daily lifestyle and emotional data, allowing the system to keep up to date with the latest information.

[1926] Output: New lifestyle data, emotional data

[1927] Step 13: Device continues to send data

[1928] Input: New lifestyle data, emotional data

[1929] How it works: The device continues to send newly entered data to the cloud server. Data transmission is securely protected by a secure protocol.

[1930] Output: New lifestyle and emotion data stored on a cloud server

[1931] Step 14: Server updates the generative AI model

[1932] Input: New lifestyle data and emotion data stored on the cloud server

[1933] How it works: The server updates the generative AI model based on newly collected data and refines the personalized health plan accordingly, for example by suggesting new emotional management or relaxation techniques when the user's emotional state changes.

[1934] Output: Updated health plan

[1935] Specific examples of each step

[1936] As a concrete example of step 1, a user uses a genetic analysis kit at home to collect a sample and uploads the analysis results to a dedicated app.

[1937] As a concrete example of step 4, a user enters food records into a smartphone app and collects exercise and sleep data using a wearable device.

[1938] Example prompt: "Please continue to enter your daily lifestyle and emotional data. The server will optimize your health management plan based on the latest information."

[1939] (Application example 2)

[1940] 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."

[1941] Today's consumers want personalized services and product recommendations based on their health and emotional state. However, current health management systems and services offered in brick-and-mortar stores are not fully capable of integrating and analyzing individual users' genetic information, lifestyle data, and emotional data to provide appropriate health management plans and recommendations. As a result, users are unable to optimally manage their health and their brick-and-mortar store experiences are unsatisfactory.

[1942] 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.

[1943] In this invention, the server includes means for collecting and analyzing a user's genetic information, means for collecting and analyzing the user's lifestyle data, and means for collecting and analyzing the user's emotional data in real time. This makes it possible to integrate the analysis results of the user's genetic information, lifestyle data, and emotional data, generate a personalized health management plan, and provide the generated health management plan to the user. Furthermore, optimal products and services can be suggested in physical stores based on the user's emotional state, making it possible to provide more effective and satisfying services to the user.

[1944] "User's genetic information" refers to data regarding the user's genetic characteristics and constitution obtained from the user's DNA.

[1945] "Lifestyle data" is information about the user's daily lifestyle habits such as diet, exercise, and sleep.

[1946] "Emotional data" is data that indicates the user's state of mind and emotional expression, and is often collected in real time.

[1947] "Analysis tools" refers to the processes or devices that use algorithms to analyze collected data and convert it into meaningful information.

[1948] A "personalized health management plan" is a plan for optimal health management created based on a user's individual genetic information, lifestyle data, and emotional data.

[1949] "Generative AI" is a system that uses artificial intelligence technology to generate new information and plans based on data.

[1950] A "brick and mortar store" is a store located in a physical location where customers can visit in person to purchase goods or services.

[1951] "Product and service suggestions" refers to the act of recommending appropriate products and services based on user data.

[1952] The present invention provides a system for integrating and analyzing a user's genetic information, lifestyle data, and emotional data to generate an individualized health management plan. Specific embodiments for carrying out the present invention will be described below.

[1953] System Overview

[1954] This system is primarily composed of a user, a device, a server, and an emotion engine. The user provides genetic information, lifestyle data, and emotion data, and the device collects this data and sends it to a cloud server. The server analyzes the data and generates a health management plan using a generative AI model, which is then provided to the user. The emotion engine recognizes and analyzes the user's emotions in real time and reflects this in the generation and update of the health management plan.

[1955] Hardware and software used

[1956] Smartphones and smartwatches: These devices collect genetic information, lifestyle data, and emotional data from users and transmit it to a cloud server.

[1957] Cloud server (e.g., AWS): Stores collected data and processes it for analysis and generative AI models.

[1958] Emotion engine (e.g. Microsoft Azure Emotion API): Analyzes the user's emotional state.

[1959] Generative AI models (e.g., OpenAI GPT-4): Generate health management plans based on genetic information, lifestyle data, and emotional data.

[1960] Data collection and transmission

[1961] Users use a smartphone or smartwatch to collect genetic information, lifestyle data, and emotional data. Genetic information is collected using a genetic analysis kit, and the results are uploaded to the app. Lifestyle data, such as daily diet, exercise records, and sleep duration, is entered into the smartphone app. Emotional data is recorded in real time using a smartwatch with emotion detection capabilities.

[1962] Analyzing data and using generative AI models

[1963] The server receives the genetic information, lifestyle data, and emotional data sent from the device. The genetic information data is passed through an analysis algorithm to evaluate the user's genetic risk factors and constitution. The lifestyle data evaluates the nutritional balance of the dietary data, the amount of exercise and heart rate of the exercise data, and the quality and quantity of sleep data. The emotional engine analyzes the emotional data to evaluate the user's stress and relaxation state.

[1964] The results of these analyses are then integrated and fed into a generative AI model, which takes into account genetic information, lifestyle habits, and emotional data to generate a personalized health plan for each user, including recommended dietary choices, specific exercise programs, and emotional management techniques.

[1965] Prompt Sentence Examples

[1966] The generated health management plan will be sent to the user's smartphone. Below is an example of a prompt to be input to the generation AI.

[1967] Example prompt sentence:

[1968] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[1969] Providing services in physical stores

[1970] When a user visits a physical store, the emotion engine analyzes the user's emotional data in real time and suggests the most appropriate products and services based on their emotional state. Store staff can also use the app to provide personalized support to users, such as counseling based on specific health concerns or suggesting relaxation items suited to their emotional state.

[1971] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1972] Step 1:

[1973] User provides genetic information

[1974] Input: User's DNA sample

[1975] How it works: A user uses a genetic analysis kit to collect a DNA sample and enters the analysis results into a smartphone app.

[1976] Output: Genetic information is stored in the app and sent to a cloud server.

[1977] Step 2:

[1978] Collection of lifestyle data

[1979] Input: User's daily diet, exercise, and sleep data

[1980] How it works: Users enter their daily lifestyle data into a smartphone app, recording their diet, exercise, and sleep.

[1981] Output: Lifestyle data is sent to the cloud server.

[1982] Step 3:

[1983] Real-time collection of emotional data

[1984] Input: User emotion recognition data

[1985] How it works: Users wear a smartwatch with emotion detection capabilities to record real-time emotional data, including heart rate and facial expression data.

[1986] Output: Emotion data is sent to a cloud server via a smartphone app.

[1987] Step 4:

[1988] Sending data to the cloud server

[1989] Input: Genetic information, lifestyle data, emotional data

[1990] How it works: The smartphone app sends all collected data to a cloud server using a secure protocol.

[1991] Output: Various user data is stored on the cloud server.

[1992] Step 5:

[1993] Data analysis

[1994] Input: User data stored on the cloud server

[1995] How it works: The server uses an algorithm to analyze genetic information to assess genetic risk factors, analyzes lifestyle data to assess nutritional balance, exercise volume, and sleep quality, and analyzes emotional data using an emotion engine.

[1996] Output: Analysis results are generated and stored in a database.

[1997] Step 6:

[1998] Generating health management plans using generative AI models

[1999] Input: Analysis results (genetic information, lifestyle data, emotional data)

[2000] How it works: The server inputs these analysis results into a generative AI model to generate an optimal health management plan for the user. To generate the plan, the generative AI model (e.g., OpenAI GPT-4) uses prompt statements.

[2001] Output: A health care plan is generated.

[2002] Step 7:

[2003] Providing health management plans to users

[2004] Input: Generated health care plan

[2005] Operation: The server sends the generated health management plan to the smartphone app and notifies the user.

[2006] Output: The user receives the healthcare plan.

[2007] Step 8:

[2008] Emotional data analysis and proposals in physical stores

[2009] Input: Real-time sentiment data from physical store visits

[2010] How it works: When a user visits a physical store, the smartwatch records emotional data in real time and sends it to the server. The server analyzes the emotional data and generates and recommends optimal products and services.

[2011] Output: Optimal product and service recommendations are generated for the physical store.

[2012] Detailed operation example

[2013] Example prompt sentence:

[2014] "Generate a health plan based on the user's genetic information, lifestyle data, and emotional data: Genetic information: xxx, Lifestyle data: Diet data: yyy, Exercise record: zzz, Emotional data: aaa."

[2015] This process integrates the data collected and analyzed at each step to provide optimal health management plans and personalized services in physical stores.

[2016] 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.

[2017] 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.

[2018] 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.

[2019] 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.

[2020] 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.

[2021] 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.

[2022] 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).

[2023] 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.

[2024] 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."

[2025] 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.

[2026] 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).

[2027] 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.

[2028] 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.

[2029] 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.

[2030] 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.

[2031] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2032] 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.

[2033] 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.

[2034] 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.

[2035] 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.

[2036] 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.

[2037] The following is further disclosed regarding the above embodiment.

[2038] (Claim 1)

[2039] means for collecting and analyzing genetic information of a user;

[2040] means for collecting and analyzing lifestyle data of users;

[2041] A means for integrating the analysis results of the genetic information and lifestyle data to generate an individualized health management plan;

[2042] means for providing the generated health management plan to a user;

[2043] A system including:

[2044] (Claim 2)

[2045] 10. The system of claim 1, further comprising means for predicting a user's health risk using the generative AI based on the user's genetic information and lifestyle data.

[2046] (Claim 3)

[2047] The system of claim 1, further comprising means for continuously collecting and updating lifestyle data of the user, the generating AI learning from the data, and updating the personalized health management plan accordingly.

[2048] "Example 1"

[2049] (Claim 1)

[2050] A means for collecting genetic information of a user and transmitting the information to a cloud server;

[2051] means for collecting lifestyle habit data of a user and transmitting the data to a cloud server;

[2052] A means for analyzing genetic information and lifestyle habit data in a cloud server and storing the analysis results;

[2053] A means for integrating the results of the analysis of genetic information and lifestyle data and generating a personalized health management plan using a generative AI model; and

[2054] means for notifying a user of the generated health management plan;

[2055] A system including:

[2056] (Claim 2)

[2057] 10. The system of claim 1, further comprising means for predicting a user's health risk using the generative AI model based on the user's genetic information and lifestyle data.

[2058] (Claim 3)

[2059] 10. The system of claim 1, further comprising means for continuously collecting and updating lifestyle data of the user, the generative AI model learning from the data, and updating the personalized health management plan accordingly.

[2060] "Application Example 1"

[2061] (Claim 1)

[2062] means for collecting and analyzing genetic information of a user;

[2063] means for collecting and analyzing lifestyle data of users;

[2064] A means for integrating the analysis results of the genetic information and lifestyle data to generate an individualized health management plan;

[2065] a means for associating the generated health management plan with store product data in order to suggest optimal products for the user to purchase in a physical store;

[2066] means for providing the generated health management plan to a user;

[2067] A system including:

[2068] (Claim 2)

[2069] 10. The system of claim 1, further comprising means for predicting a user's health risk using the generative AI based on the user's genetic information and lifestyle data.

[2070] (Claim 3)

[2071] 10. The system of claim 1, further comprising means for scanning items in a store to assess whether the items are suitable for a user's health management plan.

[2072] "Example 2: Combining Emotion Engines"

[2073] (Claim 1)

[2074] means for collecting and analyzing genetic information of a user;

[2075] means for collecting and analyzing lifestyle data of users;

[2076] means for collecting and analyzing user emotion data;

[2077] A means for using a generative AI model that integrates the analysis results of the genetic information, lifestyle data, and emotional data to generate a personalized health management plan;

[2078] means for providing the generated health management plan to a user;

[2079] A system including:

[2080] (Claim 2)

[2081] 10. The system of claim 1, further comprising means for predicting a user's health risk using generative AI based on the user's genetic information, lifestyle data, and emotional data.

[2082] (Claim 3)

[2083] The system of claim 1, further comprising means for continuously collecting and updating lifestyle and emotional data of the user, and for the generating AI to learn from this data and update the personalized health management plan accordingly.

[2084] "Application example 2 when combining emotion engines"

[2085] (Claim 1)

[2086] means for collecting and analyzing genetic information of a user;

[2087] means for collecting and analyzing lifestyle data of users;

[2088] A means for collecting and analyzing user emotion data in real time;

[2089] A means for integrating the analysis results of the genetic information, lifestyle data, and emotional data to generate an individualized health management plan;

[2090] means for providing the generated health management plan to a user;

[2091] A means for suggesting optimal products and services in a physical store based on the emotional state of the user;

[2092] A system including:

[2093] (Claim 2)

[2094] 10. The system of claim 1, further comprising means for predicting a user's health risk using the generative AI based on the user's genetic information and lifestyle data.

[2095] (Claim 3)

[2096] The system of claim 1, further comprising means for continuously collecting and updating lifestyle data of the user, the generating AI learning from the data, and updating the personalized health management plan accordingly. [Explanation of symbols]

[2097] 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. means for collecting and analyzing genetic information of a user; means for collecting and analyzing lifestyle data of users; A means for integrating the analysis results of the genetic information and lifestyle data to generate an individualized health management plan; means for providing the generated health management plan to a user; A system including:

2. The system of claim 1 further comprising means for predicting a user's health risk using a generating AI based on the user's genetic information and lifestyle data.

3. The system of claim 1, further comprising means for continuously collecting and updating the user's lifestyle data, allowing the generating AI to learn from this data and update the personalized health management plan accordingly.

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