system

Abstract

We provide the system. [Solution] A means of linking with information and communication terminals to collect health data, A means for storing the aforementioned health data in cloud storage, A generation engine means that analyzes the aforementioned health data and generates an individualized health management plan based on health goals, A means of providing a nutrition plan in conjunction with cooking appliances and equipment based on the aforementioned health management plan, A means of synchronizing with location information services to propose a body movement path, A means for obtaining feedback on the results of the execution of the aforementioned plan and updating the generation engine means, A health management system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, it has been difficult for busy modern people to appropriately manage their own health, and in particular, maintaining a lifestyle such as diet management and exercise plans has become an issue. In such a situation, there is a demand for a solution that can easily create and execute a health plan suitable for each individual. However, conventional systems have a problem that data is not sufficiently automatically collected and analyzed, resulting in a poor user experience.

Means for Solving the Problems

[0005] The present invention provides a health management system that works in conjunction with an information and communication terminal, a cooking appliance, and a location information service. Specifically, it includes means for collecting health data using the information and communication terminal and storing the data in cloud storage. Using this data, a generation engine generates an individualized health plan based on health goals. Furthermore, by providing a nutrition plan in conjunction with the cooking appliance, the system improves the user's dietary management, and by suggesting an exercise plan using a location information service, it achieves comprehensive health management. In addition, by obtaining the results of the plan's execution as feedback and updating the generation engine, it becomes possible to provide a more accurate individualized plan.

[0006] "Information and communication terminals" is a general term for electronic devices that have the function of collecting, storing, and transmitting health data.

[0007] "Health data" refers to a collection of information that indicates an individual's health status, such as physical activity, heart rate, sleep duration, and dietary habits.

[0008] "Cloud storage" is an online data storage service that allows you to save data via the internet and access it from any device.

[0009] A "generation engine" is a software module that automatically generates individualized plans tailored to health goals based on collected data.

[0010] "Cooking appliances" refer to household devices that can share data, including household electrical appliances such as refrigerators and ovens.

[0011] "Location-based services" are technologies that provide information and services about specific locations using geographical location data.

[0012] "Physical movement pathways" refer to route information for running and walking tailored to the user's exercise goals. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a comprehensive system for supporting user health management, and functions in conjunction with information and communication terminals, cloud storage, a generation engine, and external devices.

[0035] Users first collect their own health data using information and communication devices such as smartphones and wearable devices. This health data includes daily steps, heart rate, sleep duration, and dietary information. This data is automatically sent to cloud storage and stored securely.

[0036] The server receives data stored in cloud storage, and the generation engine uses this data to create a personalized health plan for each user. This health plan is designed based on the user's set goals, past health data, and real-time behavioral data. For example, if a user aims to "lose 5 kg in 3 months," the generation engine will provide specific instructions to optimize their diet and exercise.

[0037] Furthermore, the terminal communicates with cooking appliances and suggests menus that can be made using the ingredients in the refrigerator. This allows users to easily plan nutritionally balanced meals using the ingredients they have on hand.

[0038] Furthermore, devices synchronized with location services can also suggest exercise routes based on the user's current location and time of day. For example, they might suggest running routes using nearby parks, helping users maintain their exercise habits.

[0039] As users progress through their daily activities according to their health plan, data is collected again to evaluate whether they achieved their goals as planned. The server updates the generation engine based on this feedback data, further improving the accuracy and personalization of the next plan provided. For example, if a user achieves their weight management goal within a month, the plan content is analyzed and used to improve the plan for the following month.

[0040] This system allows users to efficiently manage their health while creating an optimal lifestyle tailored to their individual needs and goals.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users collect daily health data using information and communication devices. This data includes steps taken, heart rate, sleep duration, and dietary information.

[0044] Step 2:

[0045] The device sends collected health data to cloud storage. Communication is secure using encryption protocols.

[0046] Step 3:

[0047] The server retrieves health data from cloud storage. The retrieved data is stored in a database and used for analysis.

[0048] Step 4:

[0049] The server's generation engine analyzes acquired health data and generates a personalized health management plan based on the user's health goals. This may include adjusting the nutritional balance of meals and the amount of exercise.

[0050] Step 5:

[0051] The terminal connects with the cooking appliance and retrieves information about the ingredients inside the refrigerator. Based on this ingredient information, it suggests a menu.

[0052] Step 6:

[0053] The device works in conjunction with location services to calculate and suggest an appropriate exercise route based on the user's current location and schedule information.

[0054] Step 7:

[0055] Users perform daily activities based on the provided health plan and record their progress on their device.

[0056] Step 8:

[0057] After the user completes the activities according to the plan, they enter the results into their device and send the data to cloud storage.

[0058] Step 9:

[0059] The server analyzes the feedback data and updates the generation engine, thereby improving the accuracy of the next health plan provided.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In today's living environment, there is a need to efficiently and individually manage each person's health status. However, it is not easy for users to properly collect and analyze their own health data and implement an appropriate health management plan based on that data. Furthermore, receiving specific guidance on nutrition and physical exercise often requires specialized knowledge or complex equipment, posing a challenge to its practical application.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for cooperating with a computer terminal to collect user health data, means for storing the health data in a remote storage device, and a generation device for analyzing the health data and generating an individualized health management plan based on health goals. This enables users to efficiently and individually manage and improve their health status using digital technology and obtain concrete and actionable guidelines for their daily lives.

[0065] "Health data" refers to information that indicates a user's physical activity and physiological state, including information such as steps taken, heart rate, sleep duration, and dietary content.

[0066] A "computer terminal" is an electronic device used by users to collect and review health data, and includes terminals such as smartphones and wearable devices.

[0067] A "remote storage device" is a device that stores and manages data via the internet, and refers to devices such as cloud storage and data centers.

[0068] A "generating device" is a device that includes software or hardware for creating individualized health management plans based on collected health data and the user's health goals.

[0069] A "nutrition plan" is a guideline for dietary habits that takes into account the content of meals and the nutrients consumed, in order to support users in achieving their health goals.

[0070] A "physical exercise pathway" refers to the geographical route or exercise plan for physical activity that a user should engage in, and is a pathway aimed at maintaining or improving health.

[0071] "Feedback" refers to data and information based on the user's activity results, which are used to adjust subsequent health management plans.

[0072] This system provides comprehensive support for users' health management and is implemented through the collaboration of hardware and software. A specific implementation is described below.

[0073] The device functions as an information and communication device, such as a smartphone or wearable device, and collects health data from the user's daily life. This includes data obtained from a wide variety of sensors, such as steps taken, heart rate, sleep duration, and dietary information. This collected data is controlled by application software on the device and transmitted via the internet to cloud storage, which is a remote storage device.

[0074] The server receives health data stored in cloud storage and organizes and manages the data using high-performance database software. A generative device equipped with a generative AI model is used for analysis, and a personalized health management plan is created based on the user's goals, past health data, and real-time behavioral data. This generative AI model uses deep learning algorithms to learn data patterns and improve the accuracy of predictions.

[0075] Based on this generated health management plan, the terminal communicates with cooking appliances and other devices. Specifically, it uses IoT technology to acquire information about the ingredients in the refrigerator and proposes a nutrition plan based on that information. This allows users to plan nutritionally balanced meals while making use of the ingredients they have on hand.

[0076] Furthermore, the device synchronizes with location services to suggest exercise routes based on the user's current location and time of day. This utilizes APIs from geographic information systems to present the user with the most suitable running or walking routes.

[0077] For example, if a user has the goal of "building up physical strength for next month's marathon," an example of a prompt to the generative AI model would be, "Create an optimal training plan for next month's marathon." This prompt allows the AI ​​to provide a personalized training plan based on the user's current health data and goals.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users collect health data using smartphones and wearable devices. For example, they use pedometer apps or activity monitors to record their daily steps, heart rate, and sleep duration. This information is input data acquired from sensors on the device. The device converts this data into a digital format, organizes and optimizes the data in the background, and generates output ready for transmission to cloud storage.

[0081] Step 2:

[0082] The device automatically sends the ready health data to cloud storage. Here, the data is securely transmitted over the internet. This data transfer utilizes encryption and compression to improve transmission speed and security. Furthermore, because the data stored in the cloud is centrally managed, it becomes easily accessible and usable.

[0083] Step 3:

[0084] The server receives health data from cloud storage. The server uses dedicated database software to organize the data and convert it into an analyzable format. This process involves cross-referencing and filtering the data to produce an output suitable for input to a generative AI model.

[0085] Step 4:

[0086] The server's generation engine creates prompts that generate personalized health management plans based on health data organized using a generation AI model and the user's goals. Here, historical data patterns and AI predictive algorithms are leveraged to formulate the optimal health plan for the user. The output is in report format, including specific activity plans and dietary guidelines.

[0087] Step 5:

[0088] The device notifies the user of the generated health management plan and assists in carrying out daily tasks. The user reviews this plan through the application interface and puts it into practice. In this phase, user feedback and additional data are entered into the app to support behavioral improvement and goal achievement. Furthermore, when integrating with cooking appliances to provide nutritional plans, the device will suggest menus based on information about the ingredients in the refrigerator.

[0089] Step 6:

[0090] The server updates the system based on feedback data provided by users. At this time, the generation engine further optimizes individual health management plans, incorporating the information to enable more accurate suggestions for the next planning cycle. This results in a health management plan for the next cycle that is reshaped into a more personalized, specific, and actionable plan for each user.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In modern society, efficiently managing individual health conditions and individually optimizing different types of health-related products and services is extremely difficult. In particular, there is a lack of systems that allow users to select the most suitable products based on their own health data and intuitively understand their effects. As a result, users often suffer from information overload and a lack of choices, making it difficult to achieve optimal health management.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for coordinating with a communication device to collect health information, means for storing the health information in a memory area, and means for a generation mechanism that analyzes the health information and generates an individualized health management plan based on health goals. This allows users to receive an individually optimized health management plan based on their own health data, and further enables them to intuitively and effectively select and experience health-related products and services using a virtual reality device.

[0096] "Health information" refers to data that indicates an individual's health status, including information such as heart rate, steps taken, sleep duration, and dietary content.

[0097] A "communication device" is a device that has the ability to transmit and receive data, and includes smartphones and wearable devices.

[0098] "Storage space" refers to a place for storing information, and includes cloud storage and local storage.

[0099] A "generation mechanism" refers to an engine or algorithm for generating individual plans or designs based on input data.

[0100] A "virtual reality device" is a hardware device that allows users to experience a virtual environment, and head-mounted displays are an example of this.

[0101] "Health-related products" are items intended for users' health management and improvement, and include supplements, fitness equipment, and other similar products.

[0102] A "virtual space" is an interactive environment created by digital technology, a digital world represented in three dimensions.

[0103] This system constitutes a comprehensive information processing device for effectively managing the health status of individual users. Communication devices owned by the user, such as smartphones and wearable devices, play a role in collecting health information and instantly transmitting it to cloud storage. The data stored in the cloud storage is then analyzed by a generation mechanism. This generation mechanism incorporates a generation AI model to generate personalized health management plans based on the user's health goals.

[0104] Based on this generated plan, the server interacts with the user's cooking equipment to propose a nutritional plan. If the user is using a virtual reality device, the system visually and experientially presents health-related products within the virtual space. This allows the user to intuitively understand the effects of different health-related products.

[0105] The hardware used includes, for example, a "head-mounted display" for the virtual reality device, the "Unity engine" for VR system development, and "GOOGLE FI® rebase" for data management. As a result, the user experience is improved in real time, and the generation mechanism can propose even more accurate plans based on the feedback.

[0106] For example, if a user sets a health goal of "improving physical fitness in one month," they can try out protein supplements and specific fitness equipment within the virtual reality device, and even simulate their effects.

[0107] An example of a prompt message would be: "This user's health goal is to improve their physical fitness. Based on past health data and current goals, select the most suitable product and propose a VR simulation."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The device collects health information. It collects health information such as heart rate, steps taken, sleep duration, and diet from smartphones and wearable devices, and sends it to cloud storage. The input is this raw data, and the output is data stored in the cloud. Data processing involves formatting and converting the data into a format suitable for storage.

[0111] Step 2:

[0112] The server retrieves health information from cloud storage and performs analysis. Here, a generative AI model is used to analyze the collected health information and generate a personalized health management plan based on the user's health goals. The input is health information stored in cloud storage, and the output is a personalized health management plan.

[0113] Step 3:

[0114] The server generates a health management plan and then works in conjunction with the cooking device to propose a nutrition plan. It develops a nutrition plan tailored to the user's health status and goals, and obtains information on available ingredients from the cooking device to provide specific meal suggestions. Inputs are the individual health management plan and data from the cooking device; output is a specific nutrition plan.

[0115] Step 4:

[0116] Users use a virtual reality device to visually and experientially experience health-related products. The server presents recommended products within the VR space and provides data to simulate their effects. Inputs are the user's health goals and the generated plan, and output is the VR simulation result.

[0117] Step 5:

[0118] The server retrieves user feedback and uses it when generating the next plan. Here, user behavior data and task completion scores are collected and used as material to update the generation AI model. Input is user feedback and activity data, and output is the updated generation engine.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention is a comprehensive system that supports user health management and functions in conjunction with information and communication terminals, cloud storage, a generation engine, an emotion engine, and external devices.

[0121] First, users collect daily health data using an information and communication device. This data includes steps taken, heart rate, sleep duration, diet, and emotional data obtained from facial expressions and voice. The device uses sensors from smartphones and wearable devices to analyze the user's facial expressions and voice tone to recognize their emotions.

[0122] The health and emotional data collected by the device are sent to cloud storage and stored securely. The server retrieves this data from the cloud storage, and a generation engine creates a personalized health plan for the user. The generation engine makes adjustments based on the user's set health goals, past health history, real-time behavioral data, and even emotional state.

[0123] For example, if a user has set a weight loss goal and the emotion engine analyzes that the user's stress level is high, it will suggest a health plan that includes relaxation activities. Furthermore, if the user loses motivation for their exercise or diet plan, the emotion engine may add encouraging messages or set smaller goals.

[0124] The device communicates with cooking appliances and other devices to obtain information about ingredients in the refrigerator and suggest menus. It also synchronizes with location services to calculate and suggest appropriate exercise routes based on the user's current location and schedule information.

[0125] Users record the results of their daily activities on their devices and send this progress to the server. The server analyzes this feedback data and updates the generation engine and sentiment engine to further improve the accuracy and personalization of the next plan provided.

[0126] This system allows users to manage their health while receiving personalized support tailored to their emotions, enabling them to more effectively achieve their health goals.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users collect daily health and emotional data using information and communication devices. Health data includes steps taken, heart rate, and sleep duration, while emotional data is obtained from facial expressions and voice through the device's camera and microphone.

[0130] Step 2:

[0131] The device sends the collected data to cloud storage. This communication is secured by an encryption protocol.

[0132] Step 3:

[0133] The server retrieves data from cloud storage and saves it to the database.

[0134] Step 4:

[0135] The server's generation engine analyzes health data and evaluates the user's current emotional state based on emotional data. At this stage, it identifies stress levels and motivation levels.

[0136] Step 5:

[0137] The server's generation engine creates a personalized health plan that takes into account the user's health goals and emotional state. For example, a plan that includes relaxation activities will be suggested for a user experiencing high stress levels.

[0138] Step 6:

[0139] The device interacts with the cooking appliances, scanning the current ingredients in the refrigerator to suggest tonight's menu. Depending on your emotional state, it may even recommend foods that have a mood-boosting effect.

[0140] Step 7:

[0141] The device uses location services to suggest an exercise route that takes into account the user's current location and schedule for the day. For example, if the user is looking to relax, it will select a route that goes through a park.

[0142] Step 8:

[0143] Users follow their health plan and record their progress on their devices.

[0144] Step 9:

[0145] The device sends plan progress and user feedback to cloud storage.

[0146] Step 10:

[0147] The server analyzes the feedback and updates the generation and sentiment engines to make the next plan it delivers more personalized and accurate.

[0148] This process allows users to effectively work towards achieving their goals while receiving personalized health support that takes their emotional state into consideration.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In modern lifestyles, while a vast amount of information for managing individual health is available, there is a challenge in that systems for effectively utilizing this information are not yet fully developed. Furthermore, there is a growing need for personalized health plans that take into account the emotional state of the individual, but integrated technologies to achieve this are lacking. Therefore, there is a need to develop a system that comprehensively utilizes health and emotional information to provide individualized health plans.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for coordinating with wireless communication equipment to acquire health information, means for storing the health information and emotional information in a storage device, and means for generating a personalized management plan based on health goals by analyzing the health information and emotional information. This makes it possible to provide an individually optimized health plan that takes into account both the individual's health and emotional state.

[0154] "Health information" refers to information related to an individual's physical activity and biometric data, including data such as steps taken, heart rate, and sleep duration.

[0155] "Emotional information" refers to data that indicates an individual's psychological state, and is information analyzed from facial expressions and tone of voice.

[0156] "Wireless communication devices" are devices that transmit and receive data wirelessly, and examples include smartphones and wearable devices.

[0157] A "storage device" is an information storage system for organizing and securely storing acquired data.

[0158] The "generation device means" refers to a program that includes a generative AI model for designing and providing individual health plans, and which operates while taking into account the user's health goals and emotional state.

[0159] "Cooking appliances" are household appliances used for preparing food, and include devices such as refrigerators and microwave ovens.

[0160] A "mobility information service" is a service technology that supports routes and actions based on the user's current location and schedule.

[0161] "Feedback" refers to records of improvements and progress based on the user's activity results and implementation data, and is information used to help generate future plans.

[0162] The embodiments for carrying out this invention are as follows:

[0163] Users first collect their health and emotional information using wireless communication devices such as smartphones and wearable devices. Sensors in these devices record steps, heart rate, sleep duration, as well as facial expressions and voice tone. The devices transmit the acquired information to a storage device in real time, where the data is securely stored.

[0164] The server retrieves health and emotional information stored in the storage device. Using a generative AI model as a generating device, it analyzes each user's health goals and emotional state and designs a personalized health management plan. In this process, the generative AI model proposes the optimal plan based on the user's past data, real-time status, and emotional information.

[0165] The system can obtain information about ingredients from cooking appliances, such as the refrigerator, and propose a nutritional plan based on the user's health plan. Furthermore, it can obtain location information using a mobility information service and calculate the optimal activity route tailored to the user's schedule and current location.

[0166] The feedback function allows users to record the results of their daily activities on their devices, and this feedback data is sent to the server. The server analyzes this data, updates the generation mechanism, and uses it to create the next plan. This ensures that users are provided with plans that are responsive to their constantly changing circumstances.

[0167] For example, if a user enters the prompt "Please suggest an exercise plan to relieve stress," the server can analyze the user's emotional information and suggest plans such as yoga or walking that can help with relaxation.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The device collects health and emotional information from the user. It uses sensor data from smartphones and wearable devices as input. The device's sensors measure steps, heart rate, and sleep duration, and emotional information is obtained by analyzing facial expressions and voice tone. The output is a collection of the collected data.

[0171] Step 2:

[0172] The device transmits the data collected in Step 1 to the storage device. The input consists of health and emotional information stored within the device. This data is stored in the storage device in real time using a secure protocol via wireless communication. The output is data securely stored in the cloud.

[0173] Step 3:

[0174] The server retrieves data stored in the storage device and generates personalized health management plans using a generative AI model. The input consists of health and emotional information retrieved from the cloud. The generative AI model processes the data by analyzing the user's health goals, past history, and current emotional state, and creates a personalized health management plan as output.

[0175] Step 4:

[0176] The terminal receives a health management plan generated from the server and presents it to the user. The input is the management plan sent from the server. The terminal visually displays the plan, allowing the user to review its contents. The output is the on-screen interface presenting the management plan.

[0177] Step 5:

[0178] The user uses the device to record their daily activity results and provides feedback on the execution of the plan presented in Step 4. Input is the activity data entered by the user into the device. This data is then sent back from the device to the server. Output is the collected data as feedback.

[0179] Step 6:

[0180] The server analyzes feedback data and updates the generated AI model to incorporate the feedback into the next health management plan. The input is user feedback data. Data analysis techniques are used to fine-tune the model, preparing to provide an improved plan next time. The output is the updated AI model and the new health management plan.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] In modern society, as the importance of individual health management increases, there is a growing demand for personalized health management services. However, conventional systems do not adequately provide motivation based on health data and emotional changes, making it difficult to offer timely advice tailored to user needs. Furthermore, there is a problem with insufficient integration between physical exercise activities and digital data, making real-time planning adjustments difficult.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for cooperating with an information and communication terminal to collect health data, means for storing the health data in cloud storage, and generation engine means for analyzing the health data and generating individual health management plans based on health goals. This enables the comprehensive use of the user's health data and emotional data to provide personalized health support and real-time adjustment of exercise plans to meet individual needs.

[0186] "Health data" refers to physical information such as the user's physical activity, heart rate, sleep duration, and diet.

[0187] An "information and communication terminal" is a device used to collect health data from users, and includes smartphones and wearable devices.

[0188] "Cloud storage" is an online data storage service for securely storing collected data.

[0189] A "generation engine" is a data processing system that creates individualized health management plans based on the user's health status and goals.

[0190] "Emotional data" refers to information that indicates the user's emotional state at a given time, recognized by analyzing their facial expressions and tone of voice.

[0191] A "cooking appliance" is a kitchen device that integrates with food information to provide nutritional planning in order to support the user's health management.

[0192] "Exercise equipment" refers to fitness devices used in physical stores, which are devices that acquire and share users' exercise data in real time.

[0193] "Location-based services" refer to a function that uses the user's current location information to perform location verification in order to suggest a physical movement path.

[0194] In the system implementing this invention, health data and emotional data are first collected from the user using information and communication terminals such as smartphones and wearable devices. The collected health data includes steps taken, heart rate, and sleep duration. Emotional data is obtained by analyzing the user's facial expressions and voice tone using the smartphone's camera and microphone. This data is transmitted to cloud storage and managed securely and efficiently.

[0195] The server uses a generation engine to create personalized health management plans for users based on data retrieved from cloud storage. This generation engine considers past health history, current behavioral data, user-set goals, and emotional state. For example, if a user is aiming to lose weight, an exercise plan that includes relaxation may be suggested.

[0196] Furthermore, the server sends messages to maintain motivation based on the user's emotional data. In physical locations such as fitness gyms, it integrates with exercise equipment and adjusts exercise plans using real-time exercise information.

[0197] For example, if your step count for the week hasn't reached your goal, a plan to increase your cardio workouts at the gym will be suggested. Also, if your stress levels are analyzed as high, a message will be provided encouraging you to try a yoga class as a relaxation activity.

[0198] Examples of prompts include, "Based on the user's data, recommend a workout plan to reduce stress," and "Considering recent steps and exercise data, suggest an optimal exercise plan for your next gym visit." In this way, it becomes possible to efficiently manage the user's health status and provide personalized support.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The device uses sensors from wearable devices and smartphones to collect the user's health data (steps, heart rate, sleep duration, etc.) and emotional data (facial expressions and voice tone). During this process, it receives biometric information detected by the sensors as input and records it as digital health data as output. Data processing involves facial expression analysis and voice tone analysis to quantify the emotional state.

[0202] Step 2:

[0203] The device transmits collected health and emotional data to cloud storage. It receives collected digital data as input and stores it in cloud storage via the internet as output. There is no specific data processing; communication technology is used for data transfer and secure storage.

[0204] Step 3:

[0205] The server retrieves health and emotional data from cloud storage and uses a generation engine to create a personalized health management plan for each user. Inputs include the user's past health history, behavioral data, and set goals, which are then used for data analysis and calculations. The output is a tailored health management plan. Specifically, an AI model analyzes data patterns and presents the optimal plan.

[0206] Step 4:

[0207] The server notifies the user's device based on the generated health management plan. Input includes the generated plan and the user's current emotional state, and output is the plan's notification content sent to the device. This allows the user to receive specific action guidelines. The operation involves displaying notifications on the user interface and pop-up messages about the next action.

[0208] Step 5:

[0209] When a user arrives at a physical location such as a fitness gym, the exercise equipment and the terminal work together to acquire exercise data in real time and send it to the server. The input is exercise information acquired by the sensors on the exercise equipment, and the output is sent to the server. This allows the exercise plan to be adjusted in real time. Specifically, this involves providing immediate feedback according to the progress of the exercise.

[0210] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0226] This invention is a comprehensive system for supporting user health management, and functions in conjunction with information and communication terminals, cloud storage, a generation engine, and external devices.

[0227] Users first collect their own health data using information and communication devices such as smartphones and wearable devices. This health data includes daily steps, heart rate, sleep duration, and dietary information. This data is automatically sent to cloud storage and stored securely.

[0228] The server receives data stored in cloud storage, and the generation engine uses this data to create a personalized health plan for each user. This health plan is designed based on the user's set goals, past health data, and real-time behavioral data. For example, if a user aims to "lose 5 kg in 3 months," the generation engine will provide specific instructions to optimize their diet and exercise.

[0229] Furthermore, the terminal communicates with cooking appliances and suggests menus that can be made using the ingredients in the refrigerator. This allows users to easily plan nutritionally balanced meals using the ingredients they have on hand.

[0230] Furthermore, devices synchronized with location services can also suggest exercise routes based on the user's current location and time of day. For example, they might suggest running routes using nearby parks, helping users maintain their exercise habits.

[0231] As users progress through their daily activities according to their health plan, data is collected again to evaluate whether they achieved their goals as planned. The server updates the generation engine based on this feedback data, further improving the accuracy and personalization of the next plan provided. For example, if a user achieves their weight management goal within a month, the plan content is analyzed and used to improve the plan for the following month.

[0232] This system allows users to efficiently manage their health while creating an optimal lifestyle tailored to their individual needs and goals.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] Users collect daily health data using information and communication devices. This data includes steps taken, heart rate, sleep duration, and dietary information.

[0236] Step 2:

[0237] The device sends collected health data to cloud storage. Communication is secure using encryption protocols.

[0238] Step 3:

[0239] The server retrieves health data from cloud storage. The retrieved data is stored in a database and used for analysis.

[0240] Step 4:

[0241] The server's generation engine analyzes acquired health data and generates a personalized health management plan based on the user's health goals. This may include adjusting the nutritional balance of meals and the amount of exercise.

[0242] Step 5:

[0243] The terminal connects with the cooking appliance and retrieves information about the ingredients inside the refrigerator. Based on this ingredient information, it suggests a menu.

[0244] Step 6:

[0245] The device works in conjunction with location services to calculate and suggest an appropriate exercise route based on the user's current location and schedule information.

[0246] Step 7:

[0247] Users perform daily activities based on the provided health plan and record their progress on their device.

[0248] Step 8:

[0249] After the user completes the activities according to the plan, they enter the results into their device and send the data to cloud storage.

[0250] Step 9:

[0251] The server analyzes the feedback data and updates the generation engine, thereby improving the accuracy of the next health plan provided.

[0252] (Example 1)

[0253] Next, we will describe Example 1. 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."

[0254] In today's living environment, there is a need to efficiently and individually manage each person's health status. However, it is not easy for users to properly collect and analyze their own health data and implement an appropriate health management plan based on that data. Furthermore, receiving specific guidance on nutrition and physical exercise often requires specialized knowledge or complex equipment, posing a challenge to its practical application.

[0255] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0256] In this invention, the server includes means for cooperating with a computer terminal to collect user health data, means for storing the health data in a remote storage device, and a generation device for analyzing the health data and generating an individualized health management plan based on health goals. This enables users to efficiently and individually manage and improve their health status using digital technology and obtain concrete and actionable guidelines for their daily lives.

[0257] "Health data" refers to information that indicates a user's physical activity and physiological state, including information such as steps taken, heart rate, sleep duration, and dietary content.

[0258] A "computer terminal" is an electronic device used by users to collect and review health data, and includes terminals such as smartphones and wearable devices.

[0259] A "remote storage device" is a device that stores and manages data via the internet, and refers to devices such as cloud storage and data centers.

[0260] A "generating device" is a device that includes software or hardware for creating individualized health management plans based on collected health data and the user's health goals.

[0261] A "nutrition plan" is a guideline for dietary habits that takes into account the content of meals and the nutrients consumed, in order to support users in achieving their health goals.

[0262] A "physical exercise pathway" refers to the geographical route or exercise plan for physical activity that a user should engage in, and is a pathway aimed at maintaining or improving health.

[0263] "Feedback" refers to data and information based on the user's activity results, which are used to adjust subsequent health management plans.

[0264] This system provides comprehensive support for users' health management and is implemented through the collaboration of hardware and software. A specific implementation is described below.

[0265] The device functions as an information and communication device, such as a smartphone or wearable device, and collects health data from the user's daily life. This includes data obtained from a wide variety of sensors, such as steps taken, heart rate, sleep duration, and dietary information. This collected data is controlled by application software on the device and transmitted via the internet to cloud storage, which is a remote storage device.

[0266] The server receives health data stored in cloud storage and organizes and manages the data using high-performance database software. A generative device equipped with a generative AI model is used for analysis, and a personalized health management plan is created based on the user's goals, past health data, and real-time behavioral data. This generative AI model uses deep learning algorithms to learn data patterns and improve the accuracy of predictions.

[0267] Based on this generated health management plan, the terminal communicates with cooking appliances and other devices. Specifically, it uses IoT technology to acquire information about the ingredients in the refrigerator and proposes a nutrition plan based on that information. This allows users to plan nutritionally balanced meals while making use of the ingredients they have on hand.

[0268] Furthermore, the device synchronizes with location services to suggest exercise routes based on the user's current location and time of day. This utilizes APIs from geographic information systems to present the user with the most suitable running or walking routes.

[0269] For example, if a user has the goal of "building up physical strength for next month's marathon," an example of a prompt to the generative AI model would be, "Create an optimal training plan for next month's marathon." This prompt allows the AI ​​to provide a personalized training plan based on the user's current health data and goals.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] Users collect health data using smartphones and wearable devices. For example, they use pedometer apps or activity monitors to record their daily steps, heart rate, and sleep duration. This information is input data acquired from sensors on the device. The device converts this data into a digital format, organizes and optimizes the data in the background, and generates output ready for transmission to cloud storage.

[0273] Step 2:

[0274] The device automatically sends the ready health data to cloud storage. Here, the data is securely transmitted over the internet. This data transfer utilizes encryption and compression to improve transmission speed and security. Furthermore, because the data stored in the cloud is centrally managed, it becomes easily accessible and usable.

[0275] Step 3:

[0276] The server receives health data from cloud storage. The server uses dedicated database software to organize the data and convert it into an analyzable format. This process involves cross-referencing and filtering the data to produce an output suitable for input to a generative AI model.

[0277] Step 4:

[0278] The server's generation engine creates prompts that generate personalized health management plans based on health data organized using a generation AI model and the user's goals. Here, historical data patterns and AI predictive algorithms are leveraged to formulate the optimal health plan for the user. The output is in report format, including specific activity plans and dietary guidelines.

[0279] Step 5:

[0280] The terminal notifies the user of the generated health management plan and supports the execution of daily tasks. The user can check this plan from the application interface and put it into practice. In this phase, the user's feedback and additional data are input into the application, which supports the improvement of behavior and the achievement of goals. Furthermore, when providing a nutrition plan in cooperation with a cooking appliance device, the terminal operates to propose a menu based on the food ingredients information in the refrigerator.

[0281] Step 6:

[0282] The server updates the system based on the feedback data provided by the user. At this time, the generation engine further optimizes the individual health management plan and reflects the information so that more accurate proposals can be made in the next planning. As a result, the health management plan for the next cycle is re-shaped as an output that is more personalized, specific, and feasible for each user.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0285] In modern society, it is very difficult to efficiently manage individual health conditions and individually optimize different forms of health-related products and services. In particular, there is a lack of a mechanism that allows users to select the optimal products based on their own health data and intuitively understand the effects. As a result, users are often troubled by an overabundance of information and options, and there is a problem that optimal health management is difficult to achieve.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0287] In this invention, the server includes means for coordinating with a communication device to collect health information, means for storing the health information in a memory area, and means for a generation mechanism that analyzes the health information and generates an individualized health management plan based on health goals. This allows users to receive an individually optimized health management plan based on their own health data, and further enables them to intuitively and effectively select and experience health-related products and services using a virtual reality device.

[0288] "Health information" refers to data that indicates an individual's health status, including information such as heart rate, steps taken, sleep duration, and dietary content.

[0289] A "communication device" is a device that has the ability to transmit and receive data, and includes smartphones and wearable devices.

[0290] "Storage space" refers to a place for storing information, and includes cloud storage and local storage.

[0291] A "generation mechanism" refers to an engine or algorithm for generating individual plans or designs based on input data.

[0292] A "virtual reality device" is a hardware device that allows users to experience a virtual environment, and head-mounted displays are an example of this.

[0293] "Health-related products" are items intended for users' health management and improvement, and include supplements, fitness equipment, and other similar products.

[0294] A "virtual space" is an interactive environment created by digital technology, a digital world represented in three dimensions.

[0295] This system constitutes a comprehensive information processing device for effectively managing the health status of individual users. Communication devices owned by the user, such as smartphones and wearable devices, play a role in collecting health information and instantly transmitting it to cloud storage. The data stored in the cloud storage is then analyzed by a generation mechanism. This generation mechanism incorporates a generation AI model to generate personalized health management plans based on the user's health goals.

[0296] Based on this generated plan, the server interacts with the user's cooking equipment to propose a nutritional plan. If the user is using a virtual reality device, the system visually and experientially presents health-related products within the virtual space. This allows the user to intuitively understand the effects of different health-related products.

[0297] The hardware used includes, for example, a "head-mounted display" for the virtual reality device, and for the software, the "Unity engine" for VR system development and "Google® Firebase" for data management. As a result, the user experience is improved in real time, and the generation mechanism can propose even more accurate plans based on the feedback.

[0298] For example, if a user sets a health goal of "improving physical fitness in one month," they can try out protein supplements and specific fitness equipment within the virtual reality device, and even simulate their effects.

[0299] An example of a prompt message would be: "This user's health goal is to improve their physical fitness. Based on past health data and current goals, select the most suitable product and propose a VR simulation."

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] The terminal collects health information. Health information such as heart rate, number of steps, sleep time, and meal content is collected from smartphones and wearable devices and transmitted to cloud storage. The input is these raw data, and the output is the data stored in the cloud. In data processing, the format is adjusted and converted into a format suitable for storage.

[0303] Step 2:

[0304] The server obtains health information from cloud storage and performs analysis. Here, a process is carried out using a generative AI model to analyze the collected health information and generate an individualized health management plan based on the user's health goals. The input is the health information stored in cloud storage, and the output is an individualized health management plan.

[0305] Step 3:

[0306] Based on the generated health management plan, the server cooperates with the cooking device to propose a nutrition plan. A nutrition plan is formulated according to the user's health status and goals, and specific meal suggestions are made by obtaining available food ingredient information from the cooking device. The input is the individual health management plan and data from the cooking device, and the output is a specific nutrition plan.

[0307] Step 4:

[0308] The user uses a virtual reality device to visually and physically experience health-related products. The server presents recommended products within the VR space and provides data for simulating their effects. The input is the user's health goals and the generated plan, and the output is the VR simulation result.

[0309] Step 5:

[0310] The server retrieves user feedback and uses it when generating the next plan. Here, user behavior data and task completion scores are collected and used as material to update the generation AI model. Input is user feedback and activity data, and output is the updated generation engine.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention is a comprehensive system that supports user health management and functions in conjunction with information and communication terminals, cloud storage, a generation engine, an emotion engine, and external devices.

[0313] First, users collect daily health data using an information and communication device. This data includes steps taken, heart rate, sleep duration, diet, and emotional data obtained from facial expressions and voice. The device uses sensors from smartphones and wearable devices to analyze the user's facial expressions and voice tone to recognize their emotions.

[0314] The health and emotional data collected by the device are sent to cloud storage and stored securely. The server retrieves this data from the cloud storage, and a generation engine creates a personalized health plan for the user. The generation engine makes adjustments based on the user's set health goals, past health history, real-time behavioral data, and even emotional state.

[0315] For example, if a user has set a weight loss goal and the emotion engine analyzes that the user's stress level is high, it will suggest a health plan that includes relaxation activities. Furthermore, if the user loses motivation for their exercise or diet plan, the emotion engine may add encouraging messages or set smaller goals.

[0316] The device communicates with cooking appliances and other devices to obtain information about ingredients in the refrigerator and suggest menus. It also synchronizes with location services to calculate and suggest appropriate exercise routes based on the user's current location and schedule information.

[0317] Users record the results of their daily activities on their devices and send this progress to the server. The server analyzes this feedback data and updates the generation engine and sentiment engine to further improve the accuracy and personalization of the next plan provided.

[0318] This system allows users to manage their health while receiving personalized support tailored to their emotions, enabling them to more effectively achieve their health goals.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] Users collect daily health and emotional data using information and communication devices. Health data includes steps taken, heart rate, and sleep duration, while emotional data is obtained from facial expressions and voice through the device's camera and microphone.

[0322] Step 2:

[0323] The device sends the collected data to cloud storage. This communication is secured by an encryption protocol.

[0324] Step 3:

[0325] The server retrieves data from cloud storage and saves it to the database.

[0326] Step 4:

[0327] The server's generation engine analyzes health data and evaluates the user's current emotional state based on emotional data. At this stage, it identifies stress levels and motivation levels.

[0328] Step 5:

[0329] The server's generation engine creates a personalized health plan that takes into account the user's health goals and emotional state. For example, a plan that includes relaxation activities will be suggested for a user experiencing high stress levels.

[0330] Step 6:

[0331] The device interacts with the cooking appliances, scanning the current ingredients in the refrigerator to suggest tonight's menu. Depending on your emotional state, it may even recommend foods that have a mood-boosting effect.

[0332] Step 7:

[0333] The device uses location services to suggest an exercise route that takes into account the user's current location and schedule for the day. For example, if the user is looking to relax, it will select a route that goes through a park.

[0334] Step 8:

[0335] Users follow their health plan and record their progress on their devices.

[0336] Step 9:

[0337] The device sends plan progress and user feedback to cloud storage.

[0338] Step 10:

[0339] The server analyzes the feedback and updates the generation and sentiment engines to make the next plan it delivers more personalized and accurate.

[0340] This process allows users to effectively work towards achieving their goals while receiving personalized health support that takes their emotional state into consideration.

[0341] (Example 2)

[0342] Next, we will describe Example 2. 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".

[0343] In modern lifestyles, while a vast amount of information for managing individual health is available, there is a challenge in that systems for effectively utilizing this information are not yet fully developed. Furthermore, there is a growing need for personalized health plans that take into account the emotional state of the individual, but integrated technologies to achieve this are lacking. Therefore, there is a need to develop a system that comprehensively utilizes health and emotional information to provide individualized health plans.

[0344] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0345] In this invention, the server includes means for coordinating with wireless communication equipment to acquire health information, means for storing the health information and emotional information in a storage device, and means for generating a personalized management plan based on health goals by analyzing the health information and emotional information. This makes it possible to provide an individually optimized health plan that takes into account both the individual's health and emotional state.

[0346] "Health information" refers to information related to an individual's physical activity and biometric data, including data such as steps taken, heart rate, and sleep duration.

[0347] "Emotional information" refers to data that indicates an individual's psychological state, and is information analyzed from facial expressions and tone of voice.

[0348] "Wireless communication devices" are devices that transmit and receive data wirelessly, and examples include smartphones and wearable devices.

[0349] A "storage device" is an information storage system for organizing and securely storing acquired data.

[0350] The "generation device means" refers to a program that includes a generative AI model for designing and providing individual health plans, and which operates while taking into account the user's health goals and emotional state.

[0351] "Cooking appliances" are household appliances used for preparing food, and include devices such as refrigerators and microwave ovens.

[0352] A "mobility information service" is a service technology that supports routes and actions based on the user's current location and schedule.

[0353] "Feedback" refers to records of improvements and progress based on the user's activity results and implementation data, and is information used to help generate future plans.

[0354] The embodiments for carrying out this invention are as follows:

[0355] Users first collect their health and emotional information using wireless communication devices such as smartphones and wearable devices. Sensors in these devices record steps, heart rate, sleep duration, as well as facial expressions and voice tone. The devices transmit the acquired information to a storage device in real time, where the data is securely stored.

[0356] The server retrieves health and emotional information stored in the storage device. Using a generative AI model as a generating device, it analyzes each user's health goals and emotional state and designs a personalized health management plan. In this process, the generative AI model proposes the optimal plan based on the user's past data, real-time status, and emotional information.

[0357] The system can obtain information about ingredients from cooking appliances, such as the refrigerator, and propose a nutritional plan based on the user's health plan. Furthermore, it can obtain location information using a mobility information service and calculate the optimal activity route tailored to the user's schedule and current location.

[0358] The feedback function allows users to record the results of their daily activities on their devices, and this feedback data is sent to the server. The server analyzes this data, updates the generation mechanism, and uses it to create the next plan. This ensures that users are provided with plans that are responsive to their constantly changing circumstances.

[0359] For example, if a user enters the prompt "Please suggest an exercise plan to relieve stress," the server can analyze the user's emotional information and suggest plans such as yoga or walking that can help with relaxation.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The device collects health and emotional information from the user. It uses sensor data from smartphones and wearable devices as input. The device's sensors measure steps, heart rate, and sleep duration, and emotional information is obtained by analyzing facial expressions and voice tone. The output is a collection of the collected data.

[0363] Step 2:

[0364] The device transmits the data collected in Step 1 to the storage device. The input consists of health and emotional information stored within the device. This data is stored in the storage device in real time using a secure protocol via wireless communication. The output is data securely stored in the cloud.

[0365] Step 3:

[0366] The server retrieves data stored in the storage device and generates personalized health management plans using a generative AI model. The input consists of health and emotional information retrieved from the cloud. The generative AI model processes the data by analyzing the user's health goals, past history, and current emotional state, and creates a personalized health management plan as output.

[0367] Step 4:

[0368] The terminal receives a health management plan generated from the server and presents it to the user. The input is the management plan sent from the server. The terminal visually displays the plan, allowing the user to review its contents. The output is the on-screen interface presenting the management plan.

[0369] Step 5:

[0370] The user uses the device to record their daily activity results and provides feedback on the execution of the plan presented in Step 4. Input is the activity data entered by the user into the device. This data is then sent back from the device to the server. Output is the collected data as feedback.

[0371] Step 6:

[0372] The server analyzes feedback data and updates the generated AI model to incorporate the feedback into the next health management plan. The input is user feedback data. Data analysis techniques are used to fine-tune the model, preparing to provide an improved plan next time. The output is the updated AI model and the new health management plan.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] In modern society, as the importance of individual health management increases, there is a growing demand for personalized health management services. However, conventional systems do not adequately provide motivation based on health data and emotional changes, making it difficult to offer timely advice tailored to user needs. Furthermore, there is a problem with insufficient integration between physical exercise activities and digital data, making real-time planning adjustments difficult.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0377] In this invention, the server includes means for cooperating with an information and communication terminal to collect health data, means for storing the health data in cloud storage, and generation engine means for analyzing the health data and generating individual health management plans based on health goals. This enables the comprehensive use of the user's health data and emotional data to provide personalized health support and real-time adjustment of exercise plans to meet individual needs.

[0378] "Health data" refers to physical information such as the user's physical activity, heart rate, sleep duration, and diet.

[0379] An "information and communication terminal" is a device used to collect health data from users, and includes smartphones and wearable devices.

[0380] "Cloud storage" is an online data storage service for securely storing collected data.

[0381] A "generation engine" is a data processing system that creates individualized health management plans based on the user's health status and goals.

[0382] "Emotional data" refers to information that indicates the user's emotional state at a given time, recognized by analyzing their facial expressions and tone of voice.

[0383] A "cooking appliance" is a kitchen device that integrates with food information to provide nutritional planning in order to support the user's health management.

[0384] "Exercise equipment" refers to fitness devices used in physical stores, which are devices that acquire and share users' exercise data in real time.

[0385] "Location-based services" refer to a function that uses the user's current location information to perform location verification in order to suggest a physical movement path.

[0386] In the system implementing this invention, health data and emotional data are first collected from the user using information and communication terminals such as smartphones and wearable devices. The collected health data includes steps taken, heart rate, and sleep duration. Emotional data is obtained by analyzing the user's facial expressions and voice tone using the smartphone's camera and microphone. This data is transmitted to cloud storage and managed securely and efficiently.

[0387] The server uses a generation engine to create personalized health management plans for users based on data retrieved from cloud storage. This generation engine considers past health history, current behavioral data, user-set goals, and emotional state. For example, if a user is aiming to lose weight, an exercise plan that includes relaxation may be suggested.

[0388] Furthermore, the server sends messages to maintain motivation based on the user's emotional data. In physical locations such as fitness gyms, it integrates with exercise equipment and adjusts exercise plans using real-time exercise information.

[0389] For example, if your step count for the week hasn't reached your goal, a plan to increase your cardio workouts at the gym will be suggested. Also, if your stress levels are analyzed as high, a message will be provided encouraging you to try a yoga class as a relaxation activity.

[0390] Examples of prompts include, "Based on the user's data, recommend a workout plan to reduce stress," and "Considering recent steps and exercise data, suggest an optimal exercise plan for your next gym visit." In this way, it becomes possible to efficiently manage the user's health status and provide personalized support.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The device uses sensors from wearable devices and smartphones to collect the user's health data (steps, heart rate, sleep duration, etc.) and emotional data (facial expressions and voice tone). During this process, it receives biometric information detected by the sensors as input and records it as digital health data as output. Data processing involves facial expression analysis and voice tone analysis to quantify the emotional state.

[0394] Step 2:

[0395] The device transmits collected health and emotional data to cloud storage. It receives collected digital data as input and stores it in cloud storage via the internet as output. There is no specific data processing; communication technology is used for data transfer and secure storage.

[0396] Step 3:

[0397] The server retrieves health and emotional data from cloud storage and uses a generation engine to create a personalized health management plan for each user. Inputs include the user's past health history, behavioral data, and set goals, which are then used for data analysis and calculations. The output is a tailored health management plan. Specifically, an AI model analyzes data patterns and presents the optimal plan.

[0398] Step 4:

[0399] The server notifies the user's device based on the generated health management plan. Input includes the generated plan and the user's current emotional state, and output is the plan's notification content sent to the device. This allows the user to receive specific action guidelines. The operation involves displaying notifications on the user interface and pop-up messages about the next action.

[0400] Step 5:

[0401] When a user arrives at a physical location such as a fitness gym, the exercise equipment and the terminal work together to acquire exercise data in real time and send it to the server. The input is exercise information acquired by the sensors on the exercise equipment, and the output is sent to the server. This allows the exercise plan to be adjusted in real time. Specifically, this involves providing immediate feedback according to the progress of the exercise.

[0402] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0413] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0414] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0415] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] This invention is a comprehensive system for supporting user health management, and functions in conjunction with information and communication terminals, cloud storage, a generation engine, and external devices.

[0419] Users first collect their own health data using information and communication devices such as smartphones and wearable devices. This health data includes daily steps, heart rate, sleep duration, and dietary information. This data is automatically sent to cloud storage and stored securely.

[0420] The server receives data stored in cloud storage, and the generation engine uses this data to create a personalized health plan for each user. This health plan is designed based on the user's set goals, past health data, and real-time behavioral data. For example, if a user aims to "lose 5 kg in 3 months," the generation engine will provide specific instructions to optimize their diet and exercise.

[0421] Furthermore, the terminal communicates with cooking appliances and suggests menus that can be made using the ingredients in the refrigerator. This allows users to easily plan nutritionally balanced meals using the ingredients they have on hand.

[0422] Furthermore, devices synchronized with location services can also suggest exercise routes based on the user's current location and time of day. For example, they might suggest running routes using nearby parks, helping users maintain their exercise habits.

[0423] As users progress through their daily activities according to their health plan, data is collected again to evaluate whether they achieved their goals as planned. The server updates the generation engine based on this feedback data, further improving the accuracy and personalization of the next plan provided. For example, if a user achieves their weight management goal within a month, the plan content is analyzed and used to improve the plan for the following month.

[0424] This system allows users to efficiently manage their health while creating an optimal lifestyle tailored to their individual needs and goals.

[0425] The following describes the processing flow.

[0426] Step 1:

[0427] Users collect daily health data using information and communication devices. This data includes steps taken, heart rate, sleep duration, and dietary information.

[0428] Step 2:

[0429] The device sends collected health data to cloud storage. Communication is secure using encryption protocols.

[0430] Step 3:

[0431] The server retrieves health data from cloud storage. The retrieved data is stored in a database and used for analysis.

[0432] Step 4:

[0433] The server's generation engine analyzes acquired health data and generates a personalized health management plan based on the user's health goals. This may include adjusting the nutritional balance of meals and the amount of exercise.

[0434] Step 5:

[0435] The terminal connects with the cooking appliance and retrieves information about the ingredients inside the refrigerator. Based on this ingredient information, it suggests a menu.

[0436] Step 6:

[0437] The device works in conjunction with location services to calculate and suggest an appropriate exercise route based on the user's current location and schedule information.

[0438] Step 7:

[0439] Users perform daily activities based on the provided health plan and record their progress on their device.

[0440] Step 8:

[0441] After the user completes the activities according to the plan, they enter the results into their device and send the data to cloud storage.

[0442] Step 9:

[0443] The server analyzes the feedback data and updates the generation engine, thereby improving the accuracy of the next health plan provided.

[0444] (Example 1)

[0445] Next, we will describe Example 1. 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."

[0446] In today's living environment, there is a need to efficiently and individually manage each person's health status. However, it is not easy for users to properly collect and analyze their own health data and implement an appropriate health management plan based on that data. Furthermore, receiving specific guidance on nutrition and physical exercise often requires specialized knowledge or complex equipment, posing a challenge to its practical application.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for cooperating with a computer terminal to collect user health data, means for storing the health data in a remote storage device, and a generation device for analyzing the health data and generating an individualized health management plan based on health goals. This enables users to efficiently and individually manage and improve their health status using digital technology and obtain concrete and actionable guidelines for their daily lives.

[0449] "Health data" refers to information that indicates a user's physical activity and physiological state, including information such as steps taken, heart rate, sleep duration, and dietary content.

[0450] A "computer terminal" is an electronic device used by users to collect and review health data, and includes terminals such as smartphones and wearable devices.

[0451] A "remote storage device" is a device that stores and manages data via the internet, and refers to devices such as cloud storage and data centers.

[0452] A "generating device" is a device that includes software or hardware for creating individualized health management plans based on collected health data and the user's health goals.

[0453] A "nutrition plan" is a guideline for dietary habits that takes into account the content of meals and the nutrients consumed, in order to support users in achieving their health goals.

[0454] A "physical exercise pathway" refers to the geographical route or exercise plan for physical activity that a user should engage in, and is a pathway aimed at maintaining or improving health.

[0455] "Feedback" refers to data and information based on the user's activity results, which are used to adjust subsequent health management plans.

[0456] This system provides comprehensive support for users' health management and is implemented through the collaboration of hardware and software. A specific implementation is described below.

[0457] The device functions as an information and communication device, such as a smartphone or wearable device, and collects health data from the user's daily life. This includes data obtained from a wide variety of sensors, such as steps taken, heart rate, sleep duration, and dietary information. This collected data is controlled by application software on the device and transmitted via the internet to cloud storage, which is a remote storage device.

[0458] The server receives health data stored in cloud storage and organizes and manages the data using high-performance database software. A generative device equipped with a generative AI model is used for analysis, and a personalized health management plan is created based on the user's goals, past health data, and real-time behavioral data. This generative AI model uses deep learning algorithms to learn data patterns and improve the accuracy of predictions.

[0459] Based on this generated health management plan, the terminal communicates with cooking appliances and other devices. Specifically, it uses IoT technology to acquire information about the ingredients in the refrigerator and proposes a nutrition plan based on that information. This allows users to plan nutritionally balanced meals while making use of the ingredients they have on hand.

[0460] Furthermore, the device synchronizes with location services to suggest exercise routes based on the user's current location and time of day. This utilizes APIs from geographic information systems to present the user with the most suitable running or walking routes.

[0461] For example, if a user has the goal of "building up physical strength for next month's marathon," an example of a prompt to the generative AI model would be, "Create an optimal training plan for next month's marathon." This prompt allows the AI ​​to provide a personalized training plan based on the user's current health data and goals.

[0462] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0463] Step 1:

[0464] Users collect health data using smartphones and wearable devices. For example, they use pedometer apps or activity monitors to record their daily steps, heart rate, and sleep duration. This information is input data acquired from sensors on the device. The device converts this data into a digital format, organizes and optimizes the data in the background, and generates output ready for transmission to cloud storage.

[0465] Step 2:

[0466] The device automatically sends the ready health data to cloud storage. Here, the data is securely transmitted over the internet. This data transfer utilizes encryption and compression to improve transmission speed and security. Furthermore, because the data stored in the cloud is centrally managed, it becomes easily accessible and usable.

[0467] Step 3:

[0468] The server receives health data from cloud storage. The server uses dedicated database software to organize the data and convert it into an analyzable format. This process involves cross-referencing and filtering the data to produce an output suitable for input to a generative AI model.

[0469] Step 4:

[0470] The server's generation engine creates prompts that generate personalized health management plans based on health data organized using a generation AI model and the user's goals. Here, historical data patterns and AI predictive algorithms are leveraged to formulate the optimal health plan for the user. The output is in report format, including specific activity plans and dietary guidelines.

[0471] Step 5:

[0472] The device notifies the user of the generated health management plan and assists in carrying out daily tasks. The user reviews this plan through the application interface and puts it into practice. In this phase, user feedback and additional data are entered into the app to support behavioral improvement and goal achievement. Furthermore, when integrating with cooking appliances to provide nutritional plans, the device will suggest menus based on information about the ingredients in the refrigerator.

[0473] Step 6:

[0474] The server updates the system based on feedback data provided by users. At this time, the generation engine further optimizes individual health management plans, incorporating the information to enable more accurate suggestions for the next planning cycle. This results in a health management plan for the next cycle that is reshaped into a more personalized, specific, and actionable plan for each user.

[0475] (Application Example 1)

[0476] Next, we will explain Application Example 1. In the following explanation, 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."

[0477] In modern society, efficiently managing individual health conditions and individually optimizing different types of health-related products and services is extremely difficult. In particular, there is a lack of systems that allow users to select the most suitable products based on their own health data and intuitively understand their effects. As a result, users often suffer from information overload and a lack of choices, making it difficult to achieve optimal health management.

[0478] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0479] In this invention, the server includes means for coordinating with a communication device to collect health information, means for storing the health information in a memory area, and means for a generation mechanism that analyzes the health information and generates an individualized health management plan based on health goals. This allows users to receive an individually optimized health management plan based on their own health data, and further enables them to intuitively and effectively select and experience health-related products and services using a virtual reality device.

[0480] "Health information" refers to data that indicates an individual's health status, including information such as heart rate, steps taken, sleep duration, and dietary content.

[0481] A "communication device" is a device that has the ability to transmit and receive data, and includes smartphones and wearable devices.

[0482] "Storage space" refers to a place for storing information, and includes cloud storage and local storage.

[0483] A "generation mechanism" refers to an engine or algorithm for generating individual plans or designs based on input data.

[0484] A "virtual reality device" is a hardware device that allows users to experience a virtual environment, and head-mounted displays are an example of this.

[0485] "Health-related products" are items intended for users' health management and improvement, and include supplements, fitness equipment, and other similar products.

[0486] A "virtual space" is an interactive environment created by digital technology, a digital world represented in three dimensions.

[0487] This system constitutes a comprehensive information processing device for effectively managing the health status of individual users. Communication devices owned by the user, such as smartphones and wearable devices, play a role in collecting health information and instantly transmitting it to cloud storage. The data stored in the cloud storage is then analyzed by a generation mechanism. This generation mechanism incorporates a generation AI model to generate personalized health management plans based on the user's health goals.

[0488] Based on this generated plan, the server interacts with the user's cooking equipment to propose a nutritional plan. If the user is using a virtual reality device, the system visually and experientially presents health-related products within the virtual space. This allows the user to intuitively understand the effects of different health-related products.

[0489] The hardware used includes, for example, a head-mounted display for virtual reality, the Unity engine for VR system development, and Google Firebase for data management. This allows the user experience to be improved in real time, and the generation mechanism can propose even more accurate plans based on the feedback.

[0490] For example, if a user sets a health goal of "improving physical fitness in one month," they can try out protein supplements and specific fitness equipment within the virtual reality device, and even simulate their effects.

[0491] An example of a prompt message would be: "This user's health goal is to improve their physical fitness. Based on past health data and current goals, select the most suitable product and propose a VR simulation."

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The device collects health information. It collects health information such as heart rate, steps taken, sleep duration, and diet from smartphones and wearable devices, and sends it to cloud storage. The input is this raw data, and the output is data stored in the cloud. Data processing involves formatting and converting the data into a format suitable for storage.

[0495] Step 2:

[0496] The server retrieves health information from cloud storage and performs analysis. Here, a generative AI model is used to analyze the collected health information and generate a personalized health management plan based on the user's health goals. The input is health information stored in cloud storage, and the output is a personalized health management plan.

[0497] Step 3:

[0498] The server generates a health management plan and then works in conjunction with the cooking device to propose a nutrition plan. It develops a nutrition plan tailored to the user's health status and goals, and obtains information on available ingredients from the cooking device to provide specific meal suggestions. Inputs are the individual health management plan and data from the cooking device; output is a specific nutrition plan.

[0499] Step 4:

[0500] Users use a virtual reality device to visually and experientially experience health-related products. The server presents recommended products within the VR space and provides data to simulate their effects. Inputs are the user's health goals and the generated plan, and output is the VR simulation result.

[0501] Step 5:

[0502] The server retrieves user feedback and uses it when generating the next plan. Here, user behavior data and task completion scores are collected and used as material to update the generation AI model. Input is user feedback and activity data, and output is the updated generation engine.

[0503] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0504] This invention is a comprehensive system that supports user health management and functions in conjunction with information and communication terminals, cloud storage, a generation engine, an emotion engine, and external devices.

[0505] First, users collect daily health data using an information and communication device. This data includes steps taken, heart rate, sleep duration, diet, and emotional data obtained from facial expressions and voice. The device uses sensors from smartphones and wearable devices to analyze the user's facial expressions and voice tone to recognize their emotions.

[0506] The health and emotional data collected by the device are sent to cloud storage and stored securely. The server retrieves this data from the cloud storage, and a generation engine creates a personalized health plan for the user. The generation engine makes adjustments based on the user's set health goals, past health history, real-time behavioral data, and even emotional state.

[0507] For example, if a user has set a weight loss goal and the emotion engine analyzes that the user's stress level is high, it will suggest a health plan that includes relaxation activities. Furthermore, if the user loses motivation for their exercise or diet plan, the emotion engine may add encouraging messages or set smaller goals.

[0508] The device communicates with cooking appliances and other devices to obtain information about ingredients in the refrigerator and suggest menus. It also synchronizes with location services to calculate and suggest appropriate exercise routes based on the user's current location and schedule information.

[0509] Users record the results of their daily activities on their devices and send this progress to the server. The server analyzes this feedback data and updates the generation engine and sentiment engine to further improve the accuracy and personalization of the next plan provided.

[0510] This system allows users to manage their health while receiving personalized support tailored to their emotions, enabling them to more effectively achieve their health goals.

[0511] The following describes the processing flow.

[0512] Step 1:

[0513] Users collect daily health and emotional data using information and communication devices. Health data includes steps taken, heart rate, and sleep duration, while emotional data is obtained from facial expressions and voice through the device's camera and microphone.

[0514] Step 2:

[0515] The device sends the collected data to cloud storage. This communication is secured by an encryption protocol.

[0516] Step 3:

[0517] The server retrieves data from cloud storage and saves it to the database.

[0518] Step 4:

[0519] The server's generation engine analyzes health data and evaluates the user's current emotional state based on emotional data. At this stage, it identifies stress levels and motivation levels.

[0520] Step 5:

[0521] The server's generation engine creates a personalized health plan that takes into account the user's health goals and emotional state. For example, a plan that includes relaxation activities will be suggested for a user experiencing high stress levels.

[0522] Step 6:

[0523] The device interacts with the cooking appliances, scanning the current ingredients in the refrigerator to suggest tonight's menu. Depending on your emotional state, it may even recommend foods that have a mood-boosting effect.

[0524] Step 7:

[0525] The device uses location services to suggest an exercise route that takes into account the user's current location and schedule for the day. For example, if the user is looking to relax, it will select a route that goes through a park.

[0526] Step 8:

[0527] Users follow their health plan and record their progress on their devices.

[0528] Step 9:

[0529] The device sends plan progress and user feedback to cloud storage.

[0530] Step 10:

[0531] The server analyzes the feedback and updates the generation and sentiment engines to make the next plan it delivers more personalized and accurate.

[0532] This process allows users to effectively work towards achieving their goals while receiving personalized health support that takes their emotional state into consideration.

[0533] (Example 2)

[0534] Next, we will describe Example 2. 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."

[0535] In modern lifestyles, while a vast amount of information for managing individual health is available, there is a challenge in that systems for effectively utilizing this information are not yet fully developed. Furthermore, there is a growing need for personalized health plans that take into account the emotional state of the individual, but integrated technologies to achieve this are lacking. Therefore, there is a need to develop a system that comprehensively utilizes health and emotional information to provide individualized health plans.

[0536] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0537] In this invention, the server includes means for coordinating with wireless communication equipment to acquire health information, means for storing the health information and emotional information in a storage device, and means for generating a personalized management plan based on health goals by analyzing the health information and emotional information. This makes it possible to provide an individually optimized health plan that takes into account both the individual's health and emotional state.

[0538] "Health information" refers to information related to an individual's physical activity and biometric data, including data such as steps taken, heart rate, and sleep duration.

[0539] "Emotional information" refers to data that indicates an individual's psychological state, and is information analyzed from facial expressions and tone of voice.

[0540] "Wireless communication devices" are devices that transmit and receive data wirelessly, and examples include smartphones and wearable devices.

[0541] A "storage device" is an information storage system for organizing and securely storing acquired data.

[0542] The "generation device means" refers to a program that includes a generative AI model for designing and providing individual health plans, and which operates while taking into account the user's health goals and emotional state.

[0543] "Cooking appliances" are household appliances used for preparing food, and include devices such as refrigerators and microwave ovens.

[0544] A "mobility information service" is a service technology that supports routes and actions based on the user's current location and schedule.

[0545] "Feedback" refers to records of improvements and progress based on the user's activity results and implementation data, and is information used to help generate future plans.

[0546] The embodiments for carrying out this invention are as follows:

[0547] Users first collect their health and emotional information using wireless communication devices such as smartphones and wearable devices. Sensors in these devices record steps, heart rate, sleep duration, as well as facial expressions and voice tone. The devices transmit the acquired information to a storage device in real time, where the data is securely stored.

[0548] The server retrieves health and emotional information stored in the storage device. Using a generative AI model as a generating device, it analyzes each user's health goals and emotional state and designs a personalized health management plan. In this process, the generative AI model proposes the optimal plan based on the user's past data, real-time status, and emotional information.

[0549] The system can obtain information about ingredients from cooking appliances, such as the refrigerator, and propose a nutritional plan based on the user's health plan. Furthermore, it can obtain location information using a mobility information service and calculate the optimal activity route tailored to the user's schedule and current location.

[0550] The feedback function allows users to record the results of their daily activities on their devices, and this feedback data is sent to the server. The server analyzes this data, updates the generation mechanism, and uses it to create the next plan. This ensures that users are provided with plans that are responsive to their constantly changing circumstances.

[0551] For example, if a user enters the prompt "Please suggest an exercise plan to relieve stress," the server can analyze the user's emotional information and suggest plans such as yoga or walking that can help with relaxation.

[0552] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0553] Step 1:

[0554] The device collects health and emotional information from the user. It uses sensor data from smartphones and wearable devices as input. The device's sensors measure steps, heart rate, and sleep duration, and emotional information is obtained by analyzing facial expressions and voice tone. The output is a collection of the collected data.

[0555] Step 2:

[0556] The device transmits the data collected in Step 1 to the storage device. The input consists of health and emotional information stored within the device. This data is stored in the storage device in real time using a secure protocol via wireless communication. The output is data securely stored in the cloud.

[0557] Step 3:

[0558] The server retrieves data stored in the storage device and generates personalized health management plans using a generative AI model. The input consists of health and emotional information retrieved from the cloud. The generative AI model processes the data by analyzing the user's health goals, past history, and current emotional state, and creates a personalized health management plan as output.

[0559] Step 4:

[0560] The terminal receives a health management plan generated from the server and presents it to the user. The input is the management plan sent from the server. The terminal visually displays the plan, allowing the user to review its contents. The output is the on-screen interface presenting the management plan.

[0561] Step 5:

[0562] The user uses the device to record their daily activity results and provides feedback on the execution of the plan presented in Step 4. Input is the activity data entered by the user into the device. This data is then sent back from the device to the server. Output is the collected data as feedback.

[0563] Step 6:

[0564] The server analyzes feedback data and updates the generated AI model to incorporate the feedback into the next health management plan. The input is user feedback data. Data analysis techniques are used to fine-tune the model, preparing to provide an improved plan next time. The output is the updated AI model and the new health management plan.

[0565] (Application Example 2)

[0566] Next, we will explain Application Example 2. In the following explanation, 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."

[0567] In modern society, as the importance of individual health management increases, there is a growing demand for personalized health management services. However, conventional systems do not adequately provide motivation based on health data and emotional changes, making it difficult to offer timely advice tailored to user needs. Furthermore, there is a problem with insufficient integration between physical exercise activities and digital data, making real-time planning adjustments difficult.

[0568] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0569] In this invention, the server includes means for cooperating with an information and communication terminal to collect health data, means for storing the health data in cloud storage, and generation engine means for analyzing the health data and generating individual health management plans based on health goals. This enables the comprehensive use of the user's health data and emotional data to provide personalized health support and real-time adjustment of exercise plans to meet individual needs.

[0570] "Health data" refers to physical information such as the user's physical activity, heart rate, sleep duration, and diet.

[0571] An "information and communication terminal" is a device used to collect health data from users, and includes smartphones and wearable devices.

[0572] "Cloud storage" is an online data storage service for securely storing collected data.

[0573] A "generation engine" is a data processing system that creates individualized health management plans based on the user's health status and goals.

[0574] "Emotional data" refers to information that indicates the user's emotional state at a given time, recognized by analyzing their facial expressions and tone of voice.

[0575] A "cooking appliance" is a kitchen device that integrates with food information to provide nutritional planning in order to support the user's health management.

[0576] "Exercise equipment" refers to fitness devices used in physical stores, which are devices that acquire and share users' exercise data in real time.

[0577] "Location-based services" refer to a function that uses the user's current location information to perform location verification in order to suggest a physical movement path.

[0578] In the system implementing this invention, health data and emotional data are first collected from the user using information and communication terminals such as smartphones and wearable devices. The collected health data includes steps taken, heart rate, and sleep duration. Emotional data is obtained by analyzing the user's facial expressions and voice tone using the smartphone's camera and microphone. This data is transmitted to cloud storage and managed securely and efficiently.

[0579] The server uses a generation engine to create personalized health management plans for users based on data retrieved from cloud storage. This generation engine considers past health history, current behavioral data, user-set goals, and emotional state. For example, if a user is aiming to lose weight, an exercise plan that includes relaxation may be suggested.

[0580] Furthermore, the server sends messages to maintain motivation based on the user's emotional data. In physical locations such as fitness gyms, it integrates with exercise equipment and adjusts exercise plans using real-time exercise information.

[0581] For example, if your step count for the week hasn't reached your goal, a plan to increase your cardio workouts at the gym will be suggested. Also, if your stress levels are analyzed as high, a message will be provided encouraging you to try a yoga class as a relaxation activity.

[0582] Examples of prompts include, "Based on the user's data, recommend a workout plan to reduce stress," and "Considering recent steps and exercise data, suggest an optimal exercise plan for your next gym visit." In this way, it becomes possible to efficiently manage the user's health status and provide personalized support.

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The device uses sensors from wearable devices and smartphones to collect the user's health data (steps, heart rate, sleep duration, etc.) and emotional data (facial expressions and voice tone). During this process, it receives biometric information detected by the sensors as input and records it as digital health data as output. Data processing involves facial expression analysis and voice tone analysis to quantify the emotional state.

[0586] Step 2:

[0587] The device transmits collected health and emotional data to cloud storage. It receives collected digital data as input and stores it in cloud storage via the internet as output. There is no specific data processing; communication technology is used for data transfer and secure storage.

[0588] Step 3:

[0589] The server retrieves health and emotional data from cloud storage and uses a generation engine to create a personalized health management plan for each user. Inputs include the user's past health history, behavioral data, and set goals, which are then used for data analysis and calculations. The output is a tailored health management plan. Specifically, an AI model analyzes data patterns and presents the optimal plan.

[0590] Step 4:

[0591] The server notifies the user's device based on the generated health management plan. Input includes the generated plan and the user's current emotional state, and output is the plan's notification content sent to the device. This allows the user to receive specific action guidelines. The operation involves displaying notifications on the user interface and pop-up messages about the next action.

[0592] Step 5:

[0593] When a user arrives at a physical location such as a fitness gym, the exercise equipment and the terminal work together to acquire exercise data in real time and send it to the server. The input is exercise information acquired by the sensors on the exercise equipment, and the output is sent to the server. This allows the exercise plan to be adjusted in real time. Specifically, this involves providing immediate feedback according to the progress of the exercise.

[0594] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0601] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0603] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0605] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0607] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0608] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] This invention is a comprehensive system for supporting user health management, and functions in conjunction with information and communication terminals, cloud storage, a generation engine, and external devices.

[0612] Users first collect their own health data using information and communication devices such as smartphones and wearable devices. This health data includes daily steps, heart rate, sleep duration, and dietary information. This data is automatically sent to cloud storage and stored securely.

[0613] The server receives data stored in cloud storage, and the generation engine uses this data to create a personalized health plan for each user. This health plan is designed based on the user's set goals, past health data, and real-time behavioral data. For example, if a user aims to "lose 5 kg in 3 months," the generation engine will provide specific instructions to optimize their diet and exercise.

[0614] Furthermore, the terminal communicates with cooking appliances and suggests menus that can be made using the ingredients in the refrigerator. This allows users to easily plan nutritionally balanced meals using the ingredients they have on hand.

[0615] Furthermore, devices synchronized with location services can also suggest exercise routes based on the user's current location and time of day. For example, they might suggest running routes using nearby parks, helping users maintain their exercise habits.

[0616] As users progress through their daily activities according to their health plan, data is collected again to evaluate whether they achieved their goals as planned. The server updates the generation engine based on this feedback data, further improving the accuracy and personalization of the next plan provided. For example, if a user achieves their weight management goal within a month, the plan content is analyzed and used to improve the plan for the following month.

[0617] This system allows users to efficiently manage their health while creating an optimal lifestyle tailored to their individual needs and goals.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] Users collect daily health data using information and communication devices. This data includes steps taken, heart rate, sleep duration, and dietary information.

[0621] Step 2:

[0622] The device sends collected health data to cloud storage. Communication is secure using encryption protocols.

[0623] Step 3:

[0624] The server retrieves health data from cloud storage. The retrieved data is stored in a database and used for analysis.

[0625] Step 4:

[0626] The server's generation engine analyzes acquired health data and generates a personalized health management plan based on the user's health goals. This may include adjusting the nutritional balance of meals and the amount of exercise.

[0627] Step 5:

[0628] The terminal connects with the cooking appliance and retrieves information about the ingredients inside the refrigerator. Based on this ingredient information, it suggests a menu.

[0629] Step 6:

[0630] The device works in conjunction with location services to calculate and suggest an appropriate exercise route based on the user's current location and schedule information.

[0631] Step 7:

[0632] Users perform daily activities based on the provided health plan and record their progress on their device.

[0633] Step 8:

[0634] After the user completes the activities according to the plan, they enter the results into their device and send the data to cloud storage.

[0635] Step 9:

[0636] The server analyzes the feedback data and updates the generation engine, thereby improving the accuracy of the next health plan provided.

[0637] (Example 1)

[0638] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0639] In today's living environment, there is a need to efficiently and individually manage each person's health status. However, it is not easy for users to properly collect and analyze their own health data and implement an appropriate health management plan based on that data. Furthermore, receiving specific guidance on nutrition and physical exercise often requires specialized knowledge or complex equipment, posing a challenge to its practical application.

[0640] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0641] In this invention, the server includes means for cooperating with a computer terminal to collect user health data, means for storing the health data in a remote storage device, and a generation device for analyzing the health data and generating an individualized health management plan based on health goals. This enables users to efficiently and individually manage and improve their health status using digital technology and obtain concrete and actionable guidelines for their daily lives.

[0642] "Health data" refers to information that indicates a user's physical activity and physiological state, including information such as steps taken, heart rate, sleep duration, and dietary content.

[0643] A "computer terminal" is an electronic device used by users to collect and review health data, and includes terminals such as smartphones and wearable devices.

[0644] A "remote storage device" is a device that stores and manages data via the internet, and refers to devices such as cloud storage and data centers.

[0645] A "generating device" is a device that includes software or hardware for creating individualized health management plans based on collected health data and the user's health goals.

[0646] A "nutrition plan" is a guideline for dietary habits that takes into account the content of meals and the nutrients consumed, in order to support users in achieving their health goals.

[0647] A "physical exercise pathway" refers to the geographical route or exercise plan for physical activity that a user should engage in, and is a pathway aimed at maintaining or improving health.

[0648] "Feedback" refers to data and information based on the user's activity results, which are used to adjust subsequent health management plans.

[0649] This system provides comprehensive support for users' health management and is implemented through the collaboration of hardware and software. A specific implementation is described below.

[0650] The device functions as an information and communication device, such as a smartphone or wearable device, and collects health data from the user's daily life. This includes data obtained from a wide variety of sensors, such as steps taken, heart rate, sleep duration, and dietary information. This collected data is controlled by application software on the device and transmitted via the internet to cloud storage, which is a remote storage device.

[0651] The server receives health data stored in cloud storage and organizes and manages the data using high-performance database software. A generative device equipped with a generative AI model is used for analysis, and a personalized health management plan is created based on the user's goals, past health data, and real-time behavioral data. This generative AI model uses deep learning algorithms to learn data patterns and improve the accuracy of predictions.

[0652] Based on this generated health management plan, the terminal communicates with cooking appliances and other devices. Specifically, it uses IoT technology to acquire information about the ingredients in the refrigerator and proposes a nutrition plan based on that information. This allows users to plan nutritionally balanced meals while making use of the ingredients they have on hand.

[0653] Furthermore, the device synchronizes with location services to suggest exercise routes based on the user's current location and time of day. This utilizes APIs from geographic information systems to present the user with the most suitable running or walking routes.

[0654] For example, if a user has the goal of "building up physical strength for next month's marathon," an example of a prompt to the generative AI model would be, "Create an optimal training plan for next month's marathon." This prompt allows the AI ​​to provide a personalized training plan based on the user's current health data and goals.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] Users collect health data using smartphones and wearable devices. For example, they use pedometer apps or activity monitors to record their daily steps, heart rate, and sleep duration. This information is input data acquired from sensors on the device. The device converts this data into a digital format, organizes and optimizes the data in the background, and generates output ready for transmission to cloud storage.

[0658] Step 2:

[0659] The device automatically sends the ready health data to cloud storage. Here, the data is securely transmitted over the internet. This data transfer utilizes encryption and compression to improve transmission speed and security. Furthermore, because the data stored in the cloud is centrally managed, it becomes easily accessible and usable.

[0660] Step 3:

[0661] The server receives health data from cloud storage. The server uses dedicated database software to organize the data and convert it into an analyzable format. This process involves cross-referencing and filtering the data to produce an output suitable for input to a generative AI model.

[0662] Step 4:

[0663] The server's generation engine creates prompts that generate personalized health management plans based on health data organized using a generation AI model and the user's goals. Here, historical data patterns and AI predictive algorithms are leveraged to formulate the optimal health plan for the user. The output is in report format, including specific activity plans and dietary guidelines.

[0664] Step 5:

[0665] The device notifies the user of the generated health management plan and assists in carrying out daily tasks. The user reviews this plan through the application interface and puts it into practice. In this phase, user feedback and additional data are entered into the app to support behavioral improvement and goal achievement. Furthermore, when integrating with cooking appliances to provide nutritional plans, the device will suggest menus based on information about the ingredients in the refrigerator.

[0666] Step 6:

[0667] The server updates the system based on feedback data provided by users. At this time, the generation engine further optimizes individual health management plans, incorporating the information to enable more accurate suggestions for the next planning cycle. This results in a health management plan for the next cycle that is reshaped into a more personalized, specific, and actionable plan for each user.

[0668] (Application Example 1)

[0669] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0670] In modern society, efficiently managing individual health conditions and individually optimizing different types of health-related products and services is extremely difficult. In particular, there is a lack of systems that allow users to select the most suitable products based on their own health data and intuitively understand their effects. As a result, users often suffer from information overload and a lack of choices, making it difficult to achieve optimal health management.

[0671] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0672] In this invention, the server includes means for coordinating with a communication device to collect health information, means for storing the health information in a memory area, and means for a generation mechanism that analyzes the health information and generates an individualized health management plan based on health goals. This allows users to receive an individually optimized health management plan based on their own health data, and further enables them to intuitively and effectively select and experience health-related products and services using a virtual reality device.

[0673] "Health information" refers to data that indicates an individual's health status, including information such as heart rate, steps taken, sleep duration, and dietary content.

[0674] A "communication device" is a device that has the ability to transmit and receive data, and includes smartphones and wearable devices.

[0675] "Storage space" refers to a place for storing information, and includes cloud storage and local storage.

[0676] A "generation mechanism" refers to an engine or algorithm for generating individual plans or designs based on input data.

[0677] A "virtual reality device" is a hardware device that allows users to experience a virtual environment, and head-mounted displays are an example of this.

[0678] "Health-related products" are items intended for users' health management and improvement, and include supplements, fitness equipment, and other similar products.

[0679] A "virtual space" is an interactive environment created by digital technology, a digital world represented in three dimensions.

[0680] This system constitutes a comprehensive information processing device for effectively managing the health status of individual users. Communication devices owned by the user, such as smartphones and wearable devices, play a role in collecting health information and instantly transmitting it to cloud storage. The data stored in the cloud storage is then analyzed by a generation mechanism. This generation mechanism incorporates a generation AI model to generate personalized health management plans based on the user's health goals.

[0681] Based on this generated plan, the server interacts with the user's cooking equipment to propose a nutritional plan. If the user is using a virtual reality device, the system visually and experientially presents health-related products within the virtual space. This allows the user to intuitively understand the effects of different health-related products.

[0682] The hardware used includes, for example, a head-mounted display for virtual reality, the Unity engine for VR system development, and Google Firebase for data management. This allows the user experience to be improved in real time, and the generation mechanism can propose even more accurate plans based on the feedback.

[0683] For example, if a user sets a health goal of "improving physical fitness in one month," they can try out protein supplements and specific fitness equipment within the virtual reality device, and even simulate their effects.

[0684] An example of a prompt message would be: "This user's health goal is to improve their physical fitness. Based on past health data and current goals, select the most suitable product and propose a VR simulation."

[0685] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0686] Step 1:

[0687] The device collects health information. It collects health information such as heart rate, steps taken, sleep duration, and diet from smartphones and wearable devices, and sends it to cloud storage. The input is this raw data, and the output is data stored in the cloud. Data processing involves formatting and converting the data into a format suitable for storage.

[0688] Step 2:

[0689] The server retrieves health information from cloud storage and performs analysis. Here, a generative AI model is used to analyze the collected health information and generate a personalized health management plan based on the user's health goals. The input is health information stored in cloud storage, and the output is a personalized health management plan.

[0690] Step 3:

[0691] The server generates a health management plan and then works in conjunction with the cooking device to propose a nutrition plan. It develops a nutrition plan tailored to the user's health status and goals, and obtains information on available ingredients from the cooking device to provide specific meal suggestions. Inputs are the individual health management plan and data from the cooking device; output is a specific nutrition plan.

[0692] Step 4:

[0693] Users use a virtual reality device to visually and experientially experience health-related products. The server presents recommended products within the VR space and provides data to simulate their effects. Inputs are the user's health goals and the generated plan, and output is the VR simulation result.

[0694] Step 5:

[0695] The server retrieves user feedback and uses it when generating the next plan. Here, user behavior data and task completion scores are collected and used as material to update the generation AI model. Input is user feedback and activity data, and output is the updated generation engine.

[0696] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0697] This invention is a comprehensive system that supports user health management and functions in conjunction with information and communication terminals, cloud storage, a generation engine, an emotion engine, and external devices.

[0698] First, users collect daily health data using an information and communication device. This data includes steps taken, heart rate, sleep duration, diet, and emotional data obtained from facial expressions and voice. The device uses sensors from smartphones and wearable devices to analyze the user's facial expressions and voice tone to recognize their emotions.

[0699] The health and emotional data collected by the device are sent to cloud storage and stored securely. The server retrieves this data from the cloud storage, and a generation engine creates a personalized health plan for the user. The generation engine makes adjustments based on the user's set health goals, past health history, real-time behavioral data, and even emotional state.

[0700] For example, if a user has set a weight loss goal and the emotion engine analyzes that the user's stress level is high, it will suggest a health plan that includes relaxation activities. Furthermore, if the user loses motivation for their exercise or diet plan, the emotion engine may add encouraging messages or set smaller goals.

[0701] The device communicates with cooking appliances and other devices to obtain information about ingredients in the refrigerator and suggest menus. It also synchronizes with location services to calculate and suggest appropriate exercise routes based on the user's current location and schedule information.

[0702] Users record the results of their daily activities on their devices and send this progress to the server. The server analyzes this feedback data and updates the generation engine and sentiment engine to further improve the accuracy and personalization of the next plan provided.

[0703] This system allows users to manage their health while receiving personalized support tailored to their emotions, enabling them to more effectively achieve their health goals.

[0704] The following describes the processing flow.

[0705] Step 1:

[0706] Users collect daily health and emotional data using information and communication devices. Health data includes steps taken, heart rate, and sleep duration, while emotional data is obtained from facial expressions and voice through the device's camera and microphone.

[0707] Step 2:

[0708] The device sends the collected data to cloud storage. This communication is secured by an encryption protocol.

[0709] Step 3:

[0710] The server retrieves data from cloud storage and saves it to the database.

[0711] Step 4:

[0712] The server's generation engine analyzes health data and evaluates the user's current emotional state based on emotional data. At this stage, it identifies stress levels and motivation levels.

[0713] Step 5:

[0714] The server's generation engine creates a personalized health plan that takes into account the user's health goals and emotional state. For example, a plan that includes relaxation activities will be suggested for a user experiencing high stress levels.

[0715] Step 6:

[0716] The device interacts with the cooking appliances, scanning the current ingredients in the refrigerator to suggest tonight's menu. Depending on your emotional state, it may even recommend foods that have a mood-boosting effect.

[0717] Step 7:

[0718] The device uses location services to suggest an exercise route that takes into account the user's current location and schedule for the day. For example, if the user is looking to relax, it will select a route that goes through a park.

[0719] Step 8:

[0720] Users follow their health plan and record their progress on their devices.

[0721] Step 9:

[0722] The device sends plan progress and user feedback to cloud storage.

[0723] Step 10:

[0724] The server analyzes the feedback and updates the generation and sentiment engines to make the next plan it delivers more personalized and accurate.

[0725] This process allows users to effectively work towards achieving their goals while receiving personalized health support that takes their emotional state into consideration.

[0726] (Example 2)

[0727] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] In modern lifestyles, while a vast amount of information for managing individual health is available, there is a challenge in that systems for effectively utilizing this information are not yet fully developed. Furthermore, there is a growing need for personalized health plans that take into account the emotional state of the individual, but integrated technologies to achieve this are lacking. Therefore, there is a need to develop a system that comprehensively utilizes health and emotional information to provide individualized health plans.

[0729] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0730] In this invention, the server includes means for coordinating with wireless communication equipment to acquire health information, means for storing the health information and emotional information in a storage device, and means for generating a personalized management plan based on health goals by analyzing the health information and emotional information. This makes it possible to provide an individually optimized health plan that takes into account both the individual's health and emotional state.

[0731] "Health information" refers to information related to an individual's physical activity and biometric data, including data such as steps taken, heart rate, and sleep duration.

[0732] "Emotional information" refers to data that indicates an individual's psychological state, and is information analyzed from facial expressions and tone of voice.

[0733] "Wireless communication devices" are devices that transmit and receive data wirelessly, and examples include smartphones and wearable devices.

[0734] A "storage device" is an information storage system for organizing and securely storing acquired data.

[0735] The "generation device means" refers to a program that includes a generative AI model for designing and providing individual health plans, and which operates while taking into account the user's health goals and emotional state.

[0736] "Cooking appliances" are household appliances used for preparing food, and include devices such as refrigerators and microwave ovens.

[0737] A "mobility information service" is a service technology that supports routes and actions based on the user's current location and schedule.

[0738] "Feedback" refers to records of improvements and progress based on the user's activity results and implementation data, and is information used to help generate future plans.

[0739] The embodiments for carrying out this invention are as follows:

[0740] Users first collect their health and emotional information using wireless communication devices such as smartphones and wearable devices. Sensors in these devices record steps, heart rate, sleep duration, as well as facial expressions and voice tone. The devices transmit the acquired information to a storage device in real time, where the data is securely stored.

[0741] The server retrieves health and emotional information stored in the storage device. Using a generative AI model as a generating device, it analyzes each user's health goals and emotional state and designs a personalized health management plan. In this process, the generative AI model proposes the optimal plan based on the user's past data, real-time status, and emotional information.

[0742] The system can obtain information about ingredients from cooking appliances, such as the refrigerator, and propose a nutritional plan based on the user's health plan. Furthermore, it can obtain location information using a mobility information service and calculate the optimal activity route tailored to the user's schedule and current location.

[0743] The feedback function allows users to record the results of their daily activities on their devices, and this feedback data is sent to the server. The server analyzes this data, updates the generation mechanism, and uses it to create the next plan. This ensures that users are provided with plans that are responsive to their constantly changing circumstances.

[0744] For example, if a user enters the prompt "Please suggest an exercise plan to relieve stress," the server can analyze the user's emotional information and suggest plans such as yoga or walking that can help with relaxation.

[0745] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0746] Step 1:

[0747] The device collects health and emotional information from the user. It uses sensor data from smartphones and wearable devices as input. The device's sensors measure steps, heart rate, and sleep duration, and emotional information is obtained by analyzing facial expressions and voice tone. The output is a collection of the collected data.

[0748] Step 2:

[0749] The device transmits the data collected in Step 1 to the storage device. The input consists of health and emotional information stored within the device. This data is stored in the storage device in real time using a secure protocol via wireless communication. The output is data securely stored in the cloud.

[0750] Step 3:

[0751] The server retrieves data stored in the storage device and generates personalized health management plans using a generative AI model. The input consists of health and emotional information retrieved from the cloud. The generative AI model processes the data by analyzing the user's health goals, past history, and current emotional state, and creates a personalized health management plan as output.

[0752] Step 4:

[0753] The terminal receives a health management plan generated from the server and presents it to the user. The input is the management plan sent from the server. The terminal visually displays the plan, allowing the user to review its contents. The output is the on-screen interface presenting the management plan.

[0754] Step 5:

[0755] The user uses the device to record their daily activity results and provides feedback on the execution of the plan presented in Step 4. Input is the activity data entered by the user into the device. This data is then sent back from the device to the server. Output is the collected data as feedback.

[0756] Step 6:

[0757] The server analyzes feedback data and updates the generated AI model to incorporate the feedback into the next health management plan. The input is user feedback data. Data analysis techniques are used to fine-tune the model, preparing to provide an improved plan next time. The output is the updated AI model and the new health management plan.

[0758] (Application Example 2)

[0759] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] In modern society, as the importance of individual health management increases, there is a growing demand for personalized health management services. However, conventional systems do not adequately provide motivation based on health data and emotional changes, making it difficult to offer timely advice tailored to user needs. Furthermore, there is a problem with insufficient integration between physical exercise activities and digital data, making real-time planning adjustments difficult.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0762] In this invention, the server includes means for cooperating with an information and communication terminal to collect health data, means for storing the health data in cloud storage, and generation engine means for analyzing the health data and generating individual health management plans based on health goals. This enables the comprehensive use of the user's health data and emotional data to provide personalized health support and real-time adjustment of exercise plans to meet individual needs.

[0763] "Health data" refers to physical information such as the user's physical activity, heart rate, sleep duration, and diet.

[0764] An "information and communication terminal" is a device used to collect health data from users, and includes smartphones and wearable devices.

[0765] "Cloud storage" is an online data storage service for securely storing collected data.

[0766] A "generation engine" is a data processing system that creates individualized health management plans based on the user's health status and goals.

[0767] "Emotional data" refers to information that indicates the user's emotional state at a given time, recognized by analyzing their facial expressions and tone of voice.

[0768] A "cooking appliance" is a kitchen device that integrates with food information to provide nutritional planning in order to support the user's health management.

[0769] "Exercise equipment" refers to fitness devices used in physical stores, which are devices that acquire and share users' exercise data in real time.

[0770] "Location-based services" refer to a function that uses the user's current location information to perform location verification in order to suggest a physical movement path.

[0771] In the system implementing this invention, health data and emotional data are first collected from the user using information and communication terminals such as smartphones and wearable devices. The collected health data includes steps taken, heart rate, and sleep duration. Emotional data is obtained by analyzing the user's facial expressions and voice tone using the smartphone's camera and microphone. This data is transmitted to cloud storage and managed securely and efficiently.

[0772] The server uses a generation engine to create personalized health management plans for users based on data retrieved from cloud storage. This generation engine considers past health history, current behavioral data, user-set goals, and emotional state. For example, if a user is aiming to lose weight, an exercise plan that includes relaxation may be suggested.

[0773] Furthermore, the server sends messages to maintain motivation based on the user's emotional data. In physical locations such as fitness gyms, it integrates with exercise equipment and adjusts exercise plans using real-time exercise information.

[0774] For example, if your step count for the week hasn't reached your goal, a plan to increase your cardio workouts at the gym will be suggested. Also, if your stress levels are analyzed as high, a message will be provided encouraging you to try a yoga class as a relaxation activity.

[0775] Examples of prompts include, "Based on the user's data, recommend a workout plan to reduce stress," and "Considering recent steps and exercise data, suggest an optimal exercise plan for your next gym visit." In this way, it becomes possible to efficiently manage the user's health status and provide personalized support.

[0776] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0777] Step 1:

[0778] The device uses sensors from wearable devices and smartphones to collect the user's health data (steps, heart rate, sleep duration, etc.) and emotional data (facial expressions and voice tone). During this process, it receives biometric information detected by the sensors as input and records it as digital health data as output. Data processing involves facial expression analysis and voice tone analysis to quantify the emotional state.

[0779] Step 2:

[0780] The device transmits collected health and emotional data to cloud storage. It receives collected digital data as input and stores it in cloud storage via the internet as output. There is no specific data processing; communication technology is used for data transfer and secure storage.

[0781] Step 3:

[0782] The server retrieves health and emotional data from cloud storage and uses a generation engine to create a personalized health management plan for each user. Inputs include the user's past health history, behavioral data, and set goals, which are then used for data analysis and calculations. The output is a tailored health management plan. Specifically, an AI model analyzes data patterns and presents the optimal plan.

[0783] Step 4:

[0784] The server notifies the user's device based on the generated health management plan. Input includes the generated plan and the user's current emotional state, and output is the plan's notification content sent to the device. This allows the user to receive specific action guidelines. The operation involves displaying notifications on the user interface and pop-up messages about the next action.

[0785] Step 5:

[0786] When a user arrives at a physical location such as a fitness gym, the exercise equipment and the terminal work together to acquire exercise data in real time and send it to the server. The input is exercise information acquired by the sensors on the exercise equipment, and the output is sent to the server. This allows the exercise plan to be adjusted in real time. Specifically, this involves providing immediate feedback according to the progress of the exercise.

[0787] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0788] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0791] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0792] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0793] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0794] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0795] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0796] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0797] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0798] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0799] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0800] 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.

[0801] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0802] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0803] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0804] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0805] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0806] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0807] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A means of linking with information and communication terminals to collect health data,

[0811] A means for storing the aforementioned health data in cloud storage,

[0812] A generation engine means that analyzes the aforementioned health data and generates an individualized health management plan based on health goals,

[0813] A means of providing a nutrition plan in conjunction with cooking appliances and equipment based on the aforementioned health management plan,

[0814] A means of synchronizing with location information services to propose a body movement path,

[0815] A means for obtaining feedback on the results of the execution of the aforementioned plan and updating the generation engine means,

[0816] A health management system that includes this.

[0817] (Claim 2)

[0818] The health management system according to claim 1, wherein the information and communication terminal acquires meal information using image recognition technology.

[0819] (Claim 3)

[0820] The health management system according to claim 1, wherein the generation engine means proposes an exercise plan taking into account the user's schedule information.

[0821] "Example 1"

[0822] (Claim 1)

[0823] A means of linking with a computer terminal to collect user health data,

[0824] Means for storing the aforementioned health data in a remote storage device,

[0825] A generating device that analyzes the aforementioned health data and generates an individualized health management plan based on health goals,

[0826] A means for providing a nutrition plan in conjunction with cooking equipment and devices, based on the aforementioned health management plan,

[0827] A means of synchronizing with location information services to propose a body movement path,

[0828] Means for obtaining feedback on the results of the aforementioned plan execution and updating the generation device,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the computer terminal acquires food and beverage information using image recognition technology.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the generating device proposes an exercise plan taking into account the user's event information.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] A means of linking with communication devices to collect health information,

[0837] means for storing the aforementioned health information in a memory area,

[0838] A generation mechanism means that analyzes the aforementioned health information and generates an individual health management plan based on health goals,

[0839] A means for providing a nutrition plan in conjunction with a cooking device, based on the aforementioned health management plan,

[0840] A means of suggesting a movement route in synchronization with a location information service,

[0841] A means for obtaining feedback on the results of the aforementioned plan execution and updating the generation mechanism means,

[0842] A means of exploring health-related products using virtual reality equipment and experiencing the effects of those products in a virtual space,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, wherein the communication device acquires food information using image analysis technology.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the generation mechanism means proposes an exercise plan taking into account the user's planned information.

[0848] "Example 2 of combining an emotion engine"

[0849] (Claim 1)

[0850] A means of cooperating with wireless communication devices to obtain health information,

[0851] Means for storing the aforementioned health information and emotional information in a storage device,

[0852] A generation device means that analyzes the aforementioned health information and emotional information and generates an individualized management plan based on health goals,

[0853] An emotion analysis device that modifies the management plan according to the emotional state,

[0854] Based on the aforementioned management plan, a means of providing a nutrition plan in conjunction with cooking equipment,

[0855] A means of synchronizing with mobility information services and suggesting activity routes,

[0856] A means for obtaining feedback on the results of the execution of the aforementioned plan and updating the generation device means,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the wireless communication device acquires nutritional information using image processing technology.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the generating device means proposes an exercise plan taking into account the user's timetable information.

[0862] "Application example 2 when combining with an emotional engine"

[0863] (Claim 1)

[0864] A means of linking with information and communication terminals to collect health data,

[0865] A means for storing the aforementioned health data in cloud storage,

[0866] A generation engine means that analyzes the aforementioned health data and generates an individual health management plan based on health goals,

[0867] A means of providing a nutrition plan in conjunction with cooking equipment, based on the aforementioned health management plan,

[0868] A means of synchronizing with location information services to propose a body movement path,

[0869] A means for obtaining feedback on the plan execution results and updating the generation engine means,

[0870] A means of analyzing user emotional data and providing messages to maintain motivation,

[0871] A method to connect with exercise equipment in physical stores and use on-the-spot exercise information to adjust exercise plans in real time.

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein an information and communication terminal acquires food and beverage information using image recognition technology.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein the generation engine means proposes an exercise plan taking into account the user's scheduled information. [Explanation of Symbols]

[0877] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of linking with information and communication terminals to collect health data, A means for storing the aforementioned health data in cloud storage, A generation engine means that analyzes the aforementioned health data and generates an individualized health management plan based on health goals, A means of providing a nutrition plan in conjunction with cooking appliances and equipment based on the aforementioned health management plan, A means of synchronizing with location information services to propose a body movement path, A means for obtaining feedback on the results of the execution of the aforementioned plan and updating the generation engine means, A health management system that includes this.

2. The health management system according to claim 1, wherein the information and communication terminal acquires meal information using image recognition technology.

3. The health management system according to claim 1, wherein the generation engine means proposes an exercise plan taking into account the user's schedule information.

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