system

Abstract

We provide the system. [Solution] An input method for users to input health-related data, A server for collecting entered health-related data and storing it in a database, An AI model for analyzing stored data and generating personalized health plans for each user, A terminal means for notifying the user of the generated health plan through a user interface, A feedback collection means for collecting user feedback data and sending it to a server, A feedback analysis tool for adjusting health plans based on collected feedback data, A 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, the method 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 order to provide a health management and fitness plan suitable for individual users, it is required to efficiently collect and analyze a large amount of health-related data and quickly create a personalized plan based on the results. However, existing systems have difficulties in sufficient individual optimization and cannot provide advice according to user characteristics and lifestyles. In addition, there is a problem that it is difficult to continuously support the improvement of the user's health condition because the function of reflecting the feedback of the health plan and improving the next plan is lacking.

Means for Solving the Problems

[0005] This invention provides an input means for users to input health-related data, thereby collecting information such as the user's weight, height, diet, and exercise history. The collected data is stored in a database by a server and analyzed by an AI model to generate a personalized health plan for each user. The generated health plan is notified to the user interface via a terminal, providing the user with specific action guidelines. Furthermore, feedback data from the user is collected and analyzed by the server to dynamically adjust the next health plan. This series of processes enables individually optimized health management for each user, supporting the continuous improvement of their health status.

[0006] A "user" refers to an entity that inputs health-related data and receives a personalized health plan.

[0007] "Health-related data" refers to information necessary for analyzing a user's health status, such as their weight, height, diet, and exercise history.

[0008] "Input method" refers to the interface that allows users to input health-related data into the system.

[0009] A "server system" refers to a computer system that has the function of receiving and storing input data and providing that data to the AI ​​model system.

[0010] A "database" refers to digital information storage used to store collected health-related data.

[0011] "AI modeling tools" refer to algorithms and software that analyze collected health-related data and generate personalized health plans for each user.

[0012] A "health plan" refers to personalized suggestions for diet, exercise, stress management, and sleep improvement, generated to improve the user's health.

[0013] "Terminal means" refers to an electronic device used to provide the generated health plan to the user through a user interface.

[0014] "Feedback collection method" refers to an interface for collecting user feedback and opinions on the results of their health plans.

[0015] "Feedback analysis tools" refer to functions that analyze feedback data collected from users and adjust the next health plan accordingly. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference number (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.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that provides users with personalized health management and fitness plans. Users input daily health-related data such as weight, height, diet, and exercise through the app.

[0038] The data provided by the user is transmitted to the server via a secure communication method from the device. The server stores the received data in a database and calls an AI model to analyze the data. The AI ​​model uses the collected data to evaluate the user's diet and exercise habits.

[0039] Based on the analysis results, the server generates a health plan optimized for each user. This health plan includes dietary suggestions tailored to the individual's health condition, suggestions for type and duration of exercise, advice for stress management, and guidelines for improving sleep quality. For example, if calorie expenditure is not meeting the target, it may include specific action plans such as recommending a 30-minute run.

[0040] The generated health plan is sent from the server to the terminal. The terminal then notifies the user of this health plan through the user interface. The notification is provided in a format that is easy for the user to understand and implement in their daily life.

[0041] Users adjust their actions based on the provided plan and record the results on their device. The recorded data is sent back to the server as feedback and incorporated into the next plan. This allows for flexible adjustments in response to changes in the user's health status.

[0042] For example, if a user aims to lose weight and their daily food diary reveals excessive carbohydrate intake, the system will suggest a diet high in protein and recommend three sessions of aerobic exercise per week. By following this plan and providing daily feedback, the user can receive a more effective health plan in the next step.

[0043] Through this process, the system enables personalized health management tailored to the user's lifestyle and supports the continuous improvement of the user's health.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] Users input daily health-related data (such as weight, height, diet, type and duration of exercise) through the app's interface, and then submit that data.

[0047] Step 2:

[0048] The device verifies the entered health-related data, formats it, and encrypts it to ensure security. It then sends the data to the server.

[0049] Step 3:

[0050] The server receives data sent from the terminal and stores it in the database. It also verifies the integrity of the data and checks for inconsistencies or errors.

[0051] Step 4:

[0052] The server passes health-related data stored in the database to an AI model for analysis. The AI ​​model evaluates the user's diet and exercise habits and assesses their health status.

[0053] Step 5:

[0054] The server generates a personalized health plan for each user based on the analysis results of the AI ​​model. The plan includes dietary suggestions, exercise recommendations, stress management techniques, and sleep improvement methods.

[0055] Step 6:

[0056] The server sends the generated health plan to the device. The device receives the plan and displays it clearly in the user interface. It then notifies the user with specific action guidelines.

[0057] Step 7:

[0058] Users take daily actions based on the notified health plan and record the results and their opinions within the app.

[0059] Step 8:

[0060] The device collects user feedback data, formats it, and then sends it to the server.

[0061] Step 9:

[0062] The server analyzes the feedback data and makes necessary adjustments to the next health plan. Based on this information, the plan is updated again using a process similar to step 5.

[0063] By repeatedly operating the system through these steps, we support the continuous improvement of users' health.

[0064] (Example 1)

[0065] 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."

[0066] There is a need to provide a system that offers effective health management and fitness plans tailored to individual users, supporting the continuous improvement of their health status. Conventional technologies have struggled to generate personalized health plans that take into account individual user information, and have been insufficient in dynamically adjusting plans based on feedback.

[0067] 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.

[0068] In this invention, the server includes an information input means for inputting health-related information from a user, an information recording medium means for collecting and storing the input information, and a machine learning model means for analyzing the stored information and generating an optimal health plan. This enables the provision of a personalized health plan to the user and the optimization of continuous health management.

[0069] An "information input method" is a mechanism for users to input health-related information, thereby enabling the system to provide daily information such as the user's weight and diet.

[0070] A "central control device" is a device that has the function of collecting information entered by the user and recording it on a storage medium, and it receives and records data through secure communication.

[0071] A "machine learning model" is a means of performing computational processing to analyze collected health-related information and generate a health plan optimized for each individual user, and it uses AI technology to evaluate the information.

[0072] An "information provision device" is a mechanism for notifying the user of the generated health plan, providing information in an easily understandable format through a user interface.

[0073] The "response collection means" is a component that collects feedback information from the user and transmits it back to the central control unit, providing information about changes in the user's lifestyle.

[0074] A "response analysis means" is a means for adjusting the health plan based on collected user feedback information and dynamically optimizing the plan according to the user's individual health condition.

[0075] This system is designed to provide users with personalized health management and fitness plans. Users can use a health management application to input information such as their daily weight, height, diet, and exercise. The device then transmits this input information to the server using the HTTPS protocol as a secure communication method.

[0076] The server receives information and records it in a database. This database has a table structure organized by date and type of information. The server also calls a machine learning model to analyze the information. This model uses programming languages ​​such as Python and R, and machine learning libraries such as TENSORFLOW®, and includes algorithms to evaluate the user's diet and exercise habits.

[0077] Based on the analyzed results, the server generates a personalized health plan for each user. Specific suggestions can be incorporated into the plan using JavaScript® or Python scripts. This generated plan includes specific guidance on diet, exercise, stress management, and sleep improvement, as needed.

[0078] The app sends a health plan generated from the server to the device, which then notifies the user via the user interface. The app's UI / UX design is carefully crafted to present the information in a format that is easy for the user to understand.

[0079] Users adjust their daily activities based on the plan and record the results on their device. The device then sends the recorded feedback back to the server, which is used to adjust the next plan. This feedback loop allows for flexible plan adjustments based on the user's health condition.

[0080] For example, the following input is sent to the generating AI model as a prompt:

[0081] "The user's weight has not decreased in the past month, but their activity level has remained the same. Their food diary shows that their carbohydrate intake exceeds the recommended limit. Based on this data, please adjust their health plan to reduce carbohydrates and increase protein intake."

[0082] This allows the system to provide individualized plans tailored to each user's lifestyle and health condition, supporting continuous improvement in their health.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users input daily health-related data such as weight, height, diet, and exercise through a health management app. This data is temporarily stored on the device. The data can be entered in numerical or text format and is designed for easy input through a graphical user interface.

[0086] Step 2:

[0087] The terminal securely transmits user-entered data to the server using the HTTPS protocol. The transmitted data is structured in JSON or XML format and delivered to the server while protecting user privacy through encryption.

[0088] Step 3:

[0089] The server stores the received user data in a database. The database organizes and stores the data based on date, data type, and user ID. Data formatting and cleaning are performed here, ensuring data consistency and making it available for subsequent analysis.

[0090] Step 4:

[0091] The server inputs the stored data into an AI model for analysis. The AI ​​model utilizes machine learning algorithms and is built using Python and TensorFlow libraries. Based on the input data, it evaluates the user's nutritional status and exercise habits, extracting important insights. The output information serves as a useful indicator for improving the user's lifestyle.

[0092] Step 5:

[0093] The server generates an optimal health plan for the user based on the analysis results from the AI ​​model. The plan generation includes specific instructions tailored to the user's goals and health condition (e.g., "Reduce calorie intake by 200kcal," "Recommend yoga 5 days a week"). The generated plan is structured as an actionable plan.

[0094] Step 6:

[0095] The server sends the generated health plan to the device in JSON or XML format. The device then notifies the user of this plan through its user interface, displaying it in a visually easy-to-understand format. Notifications include push notifications and in-app alerts.

[0096] Step 7:

[0097] Users adjust their daily lifestyles based on the health plan they receive. They re-enter the results and impressions obtained during the adjustment process into their device and provide them as feedback. Specifically, users record the frequency of their exercise and the content of their meals, and save the data on their device.

[0098] Step 8:

[0099] The device sends the feedback received from the user back to the server. The server analyzes this feedback and incorporates it into generating the next health plan. This response loop enables continuous optimization of the plan based on the user's health status.

[0100] (Application Example 1)

[0101] 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."

[0102] Conventional health management systems have difficulty supporting users in making specific behavioral choices in their daily lives, and in particular, they lack practical suggestions for selecting products that align with their health plans when purchasing products in stores.

[0103] 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.

[0104] In this invention, the server includes a suggestion generation means for suggesting products to the user within the store, a product adjustment means for adjusting the suggested products and health plan based on the user's health-related data, and a feedback utilization means for optimizing in-store suggestions through user feedback. This enables the user to receive support in selecting products that are suitable for their health plan when shopping for everyday items.

[0105] A "proposal generation method" is a method for suggesting products suitable for an individual's health plan within a store, based on the user's health-related data.

[0106] "Product adjustment means" refers to a method of adjusting the content of a proposed product based on the user's health-related data and health plan, in order to present a more appropriate option.

[0107] "Feedback utilization methods" refer to methods for optimizing in-store product recommendations based on user feedback and improving the accuracy of future recommendations.

[0108] The system that implements this application optimizes product recommendations in a retail environment using users' health-related data. The system works by users inputting daily health-related data via a smartphone app, which is then transmitted to a server via secure communication. The server uses an AI model to analyze and generate personalized health plans based on past and present data stored in a database. Specifically, it uses machine learning libraries such as TensorFlow to analyze the user's nutritional status and exercise habits.

[0109] Based on the analysis results, the server uses a suggestion generation mechanism to propose products suitable for the individual's health plan. Furthermore, a product adjustment mechanism adjusts the product content based on the user's current condition. These suggestions are then communicated to the user via a smartphone or other device.

[0110] Users can select products in stores based on suggestions displayed on their devices. Feedback on selected products is collected through feedback mechanisms and used to improve the accuracy of future suggestions. This enables a personalized shopping experience for each user.

[0111] For example, if a user has a weight loss plan and visits a store, the app will list low-calorie foods for that day and suggest specific foods or fitness products as recommended items. Examples of prompts might include, "Analyze user A's latest health data and suggest a meal plan and recommended products for today," or "Generate a food list suitable for user B, who is on a weight loss plan." This system provides practical support for users to achieve a healthy lifestyle.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server stores health-related data received from users in a database. Input includes weight, height, diet, and exercise details entered by the user via a smartphone app. The server receives this data via secure communication and stores it in the database. This stored data is used for subsequent analysis.

[0115] Step 2:

[0116] The server analyzes health-related data stored in a database using an AI model. The input is the health-related data of each user stored in the database. The server uses generative AI models such as TensorFlow to perform data calculations to evaluate the user's diet and exercise habits. As a result of the analysis, it generates a health plan optimized for each individual user.

[0117] Step 3:

[0118] The server sends the generated health plan to the terminal and notifies the user. The input is a health plan generated by an AI model. The server sends this plan to the user's smartphone or terminal via electronic communication and notifies the user of the plan's details through the user interface. The output is a health plan displayed in a format that is easy for the user to understand and implement.

[0119] Step 4:

[0120] The user selects products based on product suggestions provided in the store. The input is the product suggestion notified to the terminal. The user then selects and purchases appropriate products in the physical store, referring to the application's suggestions. The output is a product selection suitable for the health plan.

[0121] Step 5:

[0122] Users input feedback into the app, including purchased items and actions taken based on suggestions. This input consists of the user's actual purchase history and activity records. By inputting this as feedback into the app, users can have it reflected in their next health plan. The output is feedback data used to improve future suggestions.

[0123] Step 6:

[0124] The server analyzes feedback data and dynamically adjusts the next product recommendation and health plan. The input is user-provided feedback data. The server then re-introduces this data into an AI model and performs data calculations to optimize the next recommendation based on the user's state and behavior. The output is the next product recommendation and health plan.

[0125] 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.

[0126] This invention is a system that provides individually optimized health plans to users, and in particular, by combining it with an emotion engine that recognizes the user's emotions, it achieves more personalized advice. Users can input daily health-related data and feedback using the application, and can also provide emotional information through voice input.

[0127] The data entered by the user is sent from the terminal to the server. The server stores the received health-related data in a database and passes it to an AI model and an emotion engine for analysis. The AI ​​model evaluates and analyzes the user's diet and exercise habits in detail, while the emotion engine estimates the user's emotional state from voice input and other feedback.

[0128] Based on information generated by an AI model and an emotion engine, the server creates a health plan optimized for each user. The generated health plan includes suggestions tailored to the user's nutritional status, exercise habits, and even their emotional state on that day. For example, if the user is feeling stressed, suggestions for relaxation-oriented exercises and relaxing meals will be provided.

[0129] The created health plan is sent from the server to the device, which then notifies the user. The user can then implement the actions outlined in the plan in their daily life and record their progress in the application.

[0130] Feedback data is sent back to the server via the device, where it undergoes further analysis, including emotional analysis by the emotion engine. Based on the user's latest state, necessary adjustments are made to the next health plan, supporting continuous health management. This system allows users to manage their health in a way that takes their emotions into account, enabling them to enjoy a higher quality, personalized service.

[0131] As a concrete example, consider a user who is managing their weight through diet and exercise. If this user is experiencing stress due to their extremely busy daily life, the emotional engine will pick up on this information and provide specific guidance to balance stress and weight management by suggesting light meals such as smoothies and exercise including stretching in their health plan. This will improve the user's overall satisfaction with their health management.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] Users input health-related data (weight, height, diet, exercise, etc.) and feedback through the application interface. In addition, they can input information about their emotions using the voice input function.

[0135] Step 2:

[0136] The device encrypts data to securely process health-related data and emotional information, and then transmits that information to the server. Ensuring security plays a crucial role.

[0137] Step 3:

[0138] The server stores health-related data and emotional information received from the terminal in a database. The stored data is validated to ensure its integrity and consistency.

[0139] Step 4:

[0140] The server sends the stored health-related data to an AI model and begins analyzing the data. The AI ​​model evaluates not only the user's diet and exercise habits, but also their overall health status.

[0141] Step 5:

[0142] The server utilizes an emotion engine to analyze the user's voice input and recognize their emotional state. This is used to identify stress levels, mood tendencies, and other related information.

[0143] Step 6:

[0144] The server integrates the analysis results of the AI ​​model with the recognition results of the emotion engine to generate a personalized health plan for each user. The generated plan includes nutritional suggestions, exercise recommendations, and stress management advice based on emotional state. For example, on days with high stress levels, a plan emphasizing relaxation will be provided.

[0145] Step 7:

[0146] The server sends the generated health plan to the device. The device then notifies the user of this information through its user interface, supporting the user's conscious health management.

[0147] Step 8:

[0148] Users adjust their daily lives according to the notified health plan and record the results of their efforts and any new feedback in the application. The application tracks the progress of plan implementation.

[0149] Step 9:

[0150] The device sends user-recorded feedback data to the server. This feedback includes changes in the user's emotions.

[0151] Step 10:

[0152] The server analyzes the feedback data and adjusts the new health plan. It prepares to generate and deliver an optimal health plan again, tailored to the user's changes in health and emotional state.

[0153] In this way, by utilizing the emotion engine, it becomes possible to manage health while taking into account the user's mental state, and to continuously provide personalized advice that is tailored to the user's lifestyle.

[0154] (Example 2)

[0155] 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".

[0156] In modern times, health management requires a personalized approach that takes into account individual lifestyles and emotional states. However, conventional health management systems often offer generalized suggestions and fail to provide advice that considers the user's emotional state, resulting in low user satisfaction.

[0157] 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.

[0158] In this invention, the server includes an input device, a processing device, an artificial intelligence device, a communication device, a data collection device, an analysis device, an emotion engine device, and an adjustment device. This enables personalized health planning and highly satisfying health management that takes into account the user's nutritional status, exercise habits, and emotional state.

[0159] An "input device" refers to a means by which a user inputs health-related information as data.

[0160] A "processing device" refers to a device that has the function of collecting input information and storing it in a memory device.

[0161] "Artificial intelligence device" refers to a technological means for analyzing stored information and generating personalized health plans.

[0162] "Communication device" refers to a means of notifying the user of the generated health design through the user interface.

[0163] A "collection device" refers to a device that has the function of collecting user feedback information and transmitting it to a processing device.

[0164] An "analysis device" refers to a device that has analytical functions to improve health design based on collected feedback information.

[0165] An "emotional engine device" refers to a method that utilizes technology to analyze emotional information obtained through voice input.

[0166] A "regulating device" refers to a device that dynamically adjusts and improves the health design generated based on emotional information.

[0167] This invention provides a system that allows users to manage their own health status on a daily basis. The user inputs health-related information, such as diet, exercise, and emotional state, using a dedicated application. This terminal transmits the input data to a server. The server processes this data and stores it in its storage device.

[0168] The server uses artificial intelligence technology to analyze the user's current health status based on stored information. This AI device includes a machine learning model implemented in Python, which analyzes the user's nutritional intake, exercise patterns, and emotional state in detail. Meanwhile, the emotion engine device analyzes the user's emotions from voice data and uses natural language processing technology to identify their emotional state.

[0169] Based on the analysis results, the server generates a personalized health plan. This plan includes nutritional balance, exercise suggestions, and relaxation methods tailored to the user's emotional state. For example, if the analysis reveals that a particular user is experiencing stress, the server will incorporate relaxation-enhancing exercises and meals into the plan.

[0170] The generated health plan is sent from the server to the device. The device notifies the user of this. The user executes the provided plan according to their daily activities and records feedback in the application. This feedback is sent back to the server via the device and is reflected in the next health plan based on the latest information and emotional state.

[0171] As a concrete example, when a user uses the prompt "Suggest exercise and diet recommendations for when the user is feeling stressed," the generative AI model provides appropriate suggestions. This mechanism allows users to achieve highly satisfying health management.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] Users use their mobile devices to input information about their meals, exercise, and emotional state via voice. This input data includes both text and audio. Users review this data within the app to ensure it is entered accurately.

[0175] Step 2:

[0176] The terminal sends the entered data to the server. This transmission takes place via an internet connection, and the data is stored in the server's storage device. The transmitted data is managed in conjunction with the user ID.

[0177] Step 3:

[0178] The server processes the received data and stores it in the database. During processing, the data format is standardized and converted into a format suitable for analysis by AI models. This enables high-quality analysis.

[0179] Step 4:

[0180] The server performs analysis on the stored data using a generative AI model. Nutritional information and exercise data are provided to the model as input, and the user's health trends are analyzed as output. The AI ​​model identifies user patterns and generates personalized health recommendations.

[0181] Step 5:

[0182] The emotion engine device analyzes voice data to identify the user's emotional state. Voice data is provided as input, and analysis is performed using natural language processing technology. The output is an emotional state such as stress, joy, or sadness.

[0183] Step 6:

[0184] The server generates a customized health plan that takes into account both health trends and emotional state. The plan includes suggestions that consider the user's nutrition, exercise, and emotions. Specifically, the suggestions are generated by a generative AI model.

[0185] Step 7:

[0186] The server sends the generated health design to the device. The device notifies the user, who can then review the design. The device uses push notifications to provide real-time notifications.

[0187] Step 8:

[0188] Users perform daily activities according to their health plan and input the results as feedback into the application. This feedback includes successes and challenges, and is recorded within the app.

[0189] Step 9:

[0190] The device sends the feedback back to the server. The server analyzes the received feedback data and adjusts the next health design. This reanalysis uses the feedback information as input and generates an adjusted health design as output.

[0191] (Application Example 2)

[0192] 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".

[0193] Traditional health management systems have struggled to provide health strategies that take into account the user's emotional state, resulting in users not receiving appropriate health guidance tailored to their individual circumstances and emotional state. Furthermore, in face-to-face services such as fitness gyms, personalized guidance that immediately reflects each individual's emotional and health state is required, but achieving this has been difficult.

[0194] 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.

[0195] In this invention, the server includes information gathering means, information management means, and artificial intelligence model means. This makes it possible to collect user health-related information and emotional information and immediately propose customized health strategies that take into account the user's emotional state.

[0196] "Information gathering means" refers to devices and methods for inputting health-related information and emotional information from users.

[0197] "Information management means" refers to devices and methods for storing accumulated data in a memory device, enabling later analysis and use.

[0198] An "artificial intelligence model means" is a program or device that analyzes stored data and creates customized health strategies for each user.

[0199] "Notification means" refers to output devices or methods for providing users with the created health measures.

[0200] "Feedback information collection means" refers to a method or device for collecting user feedback information and transmitting it to an information management means.

[0201] A "feedback analysis tool" is a method or device for modifying health policies based on accumulated feedback information.

[0202] "Device means" refers to equipment or software that proposes exercise programs that take emotional states into consideration.

[0203] To implement this invention, the following system is constructed. The server acquires health-related information and emotional information input from the user using information collection means. Sensors and applications installed on smartphones or smart devices can be used as information collection means. This collects the user's voice data and input information.

[0204] The collected data is transmitted to a server via information management means and stored in a memory device. This process often utilizes remote servers such as cloud servers. Next, the stored data is analyzed using artificial intelligence model means. The software used here includes TensorFlow and OpenAI® API, which are used to determine the user's health and emotional state and generate customized health strategies.

[0205] The generated health measures are communicated to the user's device via a notification system. This notification is provided through a smartphone, tablet, or glasses-type device with a transparent display. The user can then implement the suggested health measures and send the results back to the server using a feedback information collection system.

[0206] To achieve further personalization, feedback analysis tools analyze feedback data, and health strategies are modified as needed. This cycle enables the provision of an optimal health plan tailored to the user's health and emotional state.

[0207] A concrete example would be a user stopping by a fitness gym on their way home from work, where a device that detects fatigue and stress suggests relaxation yoga. An example of a prompt for the generative AI model used here would be a sentence describing the specific situation, such as, "The user is tired after stopping by the gym on their way home from work." Based on this information, the AI ​​generates appropriate advice.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server receives health-related and emotional information from the terminal. Users use a smartphone application to input data and voice information about their daily lives, and the information gathering system collects this information and sends it to the server. The input information includes dietary details, exercise history, and emotional data extracted from voice.

[0211] Step 2:

[0212] The server stores the received data in a database using information management tools. This storage step enables centralized data management and structures the data for later analysis. Database software is used in this process to ensure that the stored data is efficiently accessible and updatable.

[0213] Step 3:

[0214] The server analyzes the stored data using an artificial intelligence model. Receiving input health-related and emotional information, an AI model utilizing TensorFlow generates health strategies optimized for each individual user. In this process, it learns the correlations between data and outputs an appropriate health plan.

[0215] Step 4:

[0216] The server notifies the terminal of the generated health plan. Notification methods include smartphone push notifications and head-mounted display displays, immediately informing the user of the suggested content. The generated health plan is visualized and designed to be easily understood by the user.

[0217] Step 5:

[0218] The user implements the health measures they receive notifications about and sends feedback information from their device to the server. The feedback information collection mechanism is activated when the user re-enters the type of exercise they performed, their diet, and their emotional state at the time.

[0219] Step 6:

[0220] The server re-evaluates the received feedback information using feedback analysis tools. In this step, the server dynamically modifies the health measures to be provided next based on the newly received user input data. The re-evaluated data is then used for the next suggestion.

[0221] Step 7:

[0222] The server provides prompt statements to the generating AI model as needed, helping to generate the next suggested health measures. These prompt statements are based on the specific user's condition and the information required, and may include details such as "the user stopped by the gym after work and is tired."

[0223] 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.

[0224] 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 the following. 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 indicated 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is a system that provides users with personalized health management and fitness plans. Users input daily health-related data such as weight, height, diet, and exercise through the app.

[0240] The data provided by the user is transmitted to the server via a secure communication method from the device. The server stores the received data in a database and calls an AI model to analyze the data. The AI ​​model uses the collected data to evaluate the user's diet and exercise habits.

[0241] Based on the analysis results, the server generates a health plan optimized for each user. This health plan includes dietary suggestions tailored to the individual's health condition, suggestions for type and duration of exercise, advice for stress management, and guidelines for improving sleep quality. For example, if calorie expenditure is not meeting the target, it may include specific action plans such as recommending a 30-minute run.

[0242] The generated health plan is sent from the server to the terminal. The terminal then notifies the user of this health plan through the user interface. The notification is provided in a format that is easy for the user to understand and implement in their daily life.

[0243] Users adjust their actions based on the provided plan and record the results on their device. The recorded data is sent back to the server as feedback and incorporated into the next plan. This allows for flexible adjustments in response to changes in the user's health status.

[0244] For example, if a user aims to lose weight and their daily food diary reveals excessive carbohydrate intake, the system will suggest a diet high in protein and recommend three sessions of aerobic exercise per week. By following this plan and providing daily feedback, the user can receive a more effective health plan in the next step.

[0245] Through this process, the system enables personalized health management tailored to the user's lifestyle and supports the continuous improvement of the user's health.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] Users input daily health-related data (such as weight, height, diet, type and duration of exercise) through the app's interface, and then submit that data.

[0249] Step 2:

[0250] The device verifies the entered health-related data, formats it, and encrypts it to ensure security. It then sends the data to the server.

[0251] Step 3:

[0252] The server receives data sent from the terminal and stores it in the database. It also verifies the integrity of the data and checks for inconsistencies or errors.

[0253] Step 4:

[0254] The server passes health-related data stored in the database to an AI model for analysis. The AI ​​model evaluates the user's diet and exercise habits and assesses their health status.

[0255] Step 5:

[0256] The server generates a personalized health plan for each user based on the analysis results of the AI ​​model. The plan includes dietary suggestions, exercise recommendations, stress management techniques, and sleep improvement methods.

[0257] Step 6:

[0258] The server sends the generated health plan to the device. The device receives the plan and displays it clearly in the user interface. It then notifies the user with specific action guidelines.

[0259] Step 7:

[0260] Users take daily actions based on the notified health plan and record the results and their opinions within the app.

[0261] Step 8:

[0262] The device collects user feedback data, formats it, and then sends it to the server.

[0263] Step 9:

[0264] The server analyzes the feedback data and makes necessary adjustments to the next health plan. Based on this information, the plan is updated again using a process similar to step 5.

[0265] By repeatedly operating the system through these steps, we support the continuous improvement of users' health.

[0266] (Example 1)

[0267] 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."

[0268] There is a need to provide a system that offers effective health management and fitness plans tailored to individual users, supporting the continuous improvement of their health status. Conventional technologies have struggled to generate personalized health plans that take into account individual user information, and have been insufficient in dynamically adjusting plans based on feedback.

[0269] 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.

[0270] In this invention, the server includes an information input means for inputting health-related information from a user, an information recording medium means for collecting and storing the input information, and a machine learning model means for analyzing the stored information and generating an optimal health plan. This enables the provision of a personalized health plan to the user and the optimization of continuous health management.

[0271] An "information input method" is a mechanism for users to input health-related information, thereby enabling the system to provide daily information such as the user's weight and diet.

[0272] A "central control device" is a device that has the function of collecting information entered by the user and recording it on a storage medium, and it receives and records data through secure communication.

[0273] A "machine learning model" is a means of performing computational processing to analyze collected health-related information and generate a health plan optimized for each individual user, and it uses AI technology to evaluate the information.

[0274] An "information provision device" is a mechanism for notifying the user of the generated health plan, providing information in an easily understandable format through a user interface.

[0275] The "response collection means" is a component that collects feedback information from the user and transmits it back to the central control unit, providing information about changes in the user's lifestyle.

[0276] A "response analysis means" is a means for adjusting the health plan based on collected user feedback information and dynamically optimizing the plan according to the user's individual health condition.

[0277] This system is designed to provide users with personalized health management and fitness plans. Users can use a health management application to input information such as their daily weight, height, diet, and exercise. The device then transmits this input information to the server using the HTTPS protocol as a secure communication method.

[0278] The server receives information and records it in a database. This database has a table structure organized by date and type of information. The server also calls a machine learning model to analyze the information. This model uses programming languages ​​such as Python and R, and machine learning libraries such as TensorFlow, and includes algorithms to evaluate the user's diet and exercise habits.

[0279] Based on the analyzed results, the server generates a health plan optimized for each individual user. Specific suggestions can be incorporated into the plan using JavaScript or Python scripts. This generated plan includes specific guidance on diet, exercise, stress management, and sleep improvement, as needed.

[0280] The app sends a health plan generated from the server to the device, which then notifies the user via the user interface. The app's UI / UX design is carefully crafted to present the information in a format that is easy for the user to understand.

[0281] Users adjust their daily activities based on the plan and record the results on their device. The device then sends the recorded feedback back to the server, which is used to adjust the next plan. This feedback loop allows for flexible plan adjustments based on the user's health condition.

[0282] For example, the following input is sent to the generating AI model as a prompt:

[0283] "The user's weight has not decreased in the past month, but their activity level has remained the same. Their food diary shows that their carbohydrate intake exceeds the recommended limit. Based on this data, please adjust their health plan to reduce carbohydrates and increase protein intake."

[0284] This allows the system to provide individualized plans tailored to each user's lifestyle and health condition, supporting continuous improvement in their health.

[0285] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0286] Step 1:

[0287] The user inputs daily health-related data such as weight, height, diet content, and exercise content through a health management app. At this time, the input data is temporarily stored in the terminal. The format of the input data is numerical or text, and it is designed to be easily input through a graphical user interface.

[0288] Step 2:

[0289] The terminal securely transmits the data input by the user to the server using the HTTPS protocol. The transmitted data is structured in JSON or XML format and is delivered to the server while protecting the user's privacy through encryption.

[0290] Step 3:

[0291] The server stores the received user data in a database. The database organizes and stores the data based on the date, data type, and user ID. Here, data formatting and cleaning are performed to ensure data consistency and make it available for subsequent analysis processing.

[0292] Step 4:

[0293] The server inputs the stored data into an AI model for analysis. The AI model uses machine learning algorithms and is built with Python and TensorFlow libraries. Based on the input data, it evaluates the user's nutritional status and exercise habits and extracts important insights. The output information becomes an indicator useful for improving the user's life.

[0294] Step 5:

[0295] The server generates an optimal health plan for the user based on the analysis results from the AI ​​model. The plan generation includes specific instructions tailored to the user's goals and health condition (e.g., "Reduce calorie intake by 200kcal," "Recommend yoga 5 days a week"). The generated plan is structured as an actionable plan.

[0296] Step 6:

[0297] The server sends the generated health plan to the device in JSON or XML format. The device then notifies the user of this plan through its user interface, displaying it in a visually easy-to-understand format. Notifications include push notifications and in-app alerts.

[0298] Step 7:

[0299] Users adjust their daily lifestyles based on the health plan they receive. They re-enter the results and impressions obtained during the adjustment process into their device and provide them as feedback. Specifically, users record the frequency of their exercise and the content of their meals, and save the data on their device.

[0300] Step 8:

[0301] The device sends the feedback received from the user back to the server. The server analyzes this feedback and incorporates it into generating the next health plan. This response loop enables continuous optimization of the plan based on the user's health status.

[0302] (Application Example 1)

[0303] 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 glasses 214 will be referred to as the "terminal."

[0304] In conventional health management systems, it is difficult to support specific action choices in the daily lives of users. In particular, when purchasing products in a store, there is a problem that practical suggestions for selecting products that match a health plan are lacking.

[0305] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0306] In this invention, the server includes a proposal generation means for proposing products in a store to a user, a product adjustment means for adjusting the proposed products and the health plan based on the user's health-related data, and a feedback utilization means for optimizing the in-store proposal through feedback from the user. Thereby, the user can receive support for selecting products suitable for the health plan in daily shopping.

[0307] The "proposal generation means" is a method for proposing products suitable for an individual health plan in a store based on the user's health-related data.

[0308] The "product adjustment means" is a method for adjusting the content of the proposed products and presenting more appropriate options based on the user's health-related data and health plan.

[0309] The "feedback utilization means" is a method for optimizing the product proposal in a store and improving the accuracy of the next proposal based on feedback from the user.

[0310] The system that implements this application optimizes product recommendations in a retail environment using users' health-related data. The system works by users inputting daily health-related data via a smartphone app, which is then transmitted to a server via secure communication. The server uses an AI model to analyze and generate personalized health plans based on past and present data stored in a database. Specifically, it uses machine learning libraries such as TensorFlow to analyze the user's nutritional status and exercise habits.

[0311] Based on the analysis results, the server uses a suggestion generation mechanism to propose products suitable for the individual's health plan. Furthermore, a product adjustment mechanism adjusts the product content based on the user's current condition. These suggestions are then communicated to the user via a smartphone or other device.

[0312] Users can select products in stores based on suggestions displayed on their devices. Feedback on selected products is collected through feedback mechanisms and used to improve the accuracy of future suggestions. This enables a personalized shopping experience for each user.

[0313] For example, if a user has a weight loss plan and visits a store, the app will list low-calorie foods for that day and suggest specific foods or fitness products as recommended items. Examples of prompts might include, "Analyze user A's latest health data and suggest a meal plan and recommended products for today," or "Generate a food list suitable for user B, who is on a weight loss plan." This system provides practical support for users to achieve a healthy lifestyle.

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The server stores health-related data received from users in a database. Input includes weight, height, diet, and exercise details entered by the user via a smartphone app. The server receives this data via secure communication and stores it in the database. This stored data is used for subsequent analysis.

[0317] Step 2:

[0318] The server analyzes health-related data stored in a database using an AI model. The input is the health-related data of each user stored in the database. The server uses generative AI models such as TensorFlow to perform data calculations to evaluate the user's diet and exercise habits. As a result of the analysis, it generates a health plan optimized for each individual user.

[0319] Step 3:

[0320] The server sends the generated health plan to the terminal and notifies the user. The input is a health plan generated by an AI model. The server sends this plan to the user's smartphone or terminal via electronic communication and notifies the user of the plan's details through the user interface. The output is a health plan displayed in a format that is easy for the user to understand and implement.

[0321] Step 4:

[0322] The user selects products based on product suggestions provided in the store. The input is the product suggestion notified to the terminal. The user then selects and purchases appropriate products in the physical store, referring to the application's suggestions. The output is a product selection suitable for the health plan.

[0323] Step 5:

[0324] Users input feedback into the app, including purchased items and actions taken based on suggestions. This input consists of the user's actual purchase history and activity records. By inputting this as feedback into the app, users can have it reflected in their next health plan. The output is feedback data used to improve future suggestions.

[0325] Step 6:

[0326] The server analyzes feedback data and dynamically adjusts the next product recommendation and health plan. The input is user-provided feedback data. The server then re-introduces this data into an AI model and performs data calculations to optimize the next recommendation based on the user's state and behavior. The output is the next product recommendation and health plan.

[0327] 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.

[0328] This invention is a system that provides individually optimized health plans to users, and in particular, by combining it with an emotion engine that recognizes the user's emotions, it achieves more personalized advice. Users can input daily health-related data and feedback using the application, and can also provide emotional information through voice input.

[0329] The data entered by the user is sent from the terminal to the server. The server stores the received health-related data in a database and passes it to an AI model and an emotion engine for analysis. The AI ​​model evaluates and analyzes the user's diet and exercise habits in detail, while the emotion engine estimates the user's emotional state from voice input and other feedback.

[0330] Based on information generated by an AI model and an emotion engine, the server creates a health plan optimized for each user. The generated health plan includes suggestions tailored to the user's nutritional status, exercise habits, and even their emotional state on that day. For example, if the user is feeling stressed, suggestions for relaxation-oriented exercises and relaxing meals will be provided.

[0331] The created health plan is sent from the server to the device, which then notifies the user. The user can then implement the actions outlined in the plan in their daily life and record their progress in the application.

[0332] Feedback data is sent back to the server via the device, where it undergoes further analysis, including emotional analysis by the emotion engine. Based on the user's latest state, necessary adjustments are made to the next health plan, supporting continuous health management. This system allows users to manage their health in a way that takes their emotions into account, enabling them to enjoy a higher quality, personalized service.

[0333] As a concrete example, consider a user who is managing their weight through diet and exercise. If this user is experiencing stress due to their extremely busy daily life, the emotional engine will pick up on this information and provide specific guidance to balance stress and weight management by suggesting light meals such as smoothies and exercise including stretching in their health plan. This will improve the user's overall satisfaction with their health management.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] Users input health-related data (weight, height, diet, exercise, etc.) and feedback through the application interface. In addition, they can input information about their emotions using the voice input function.

[0337] Step 2:

[0338] The device encrypts data to securely process health-related data and emotional information, and then transmits that information to the server. Ensuring security plays a crucial role.

[0339] Step 3:

[0340] The server stores health-related data and emotional information received from the terminal in a database. The stored data is validated to ensure its integrity and consistency.

[0341] Step 4:

[0342] The server sends the stored health-related data to an AI model and begins analyzing the data. The AI ​​model evaluates not only the user's diet and exercise habits, but also their overall health status.

[0343] Step 5:

[0344] The server utilizes an emotion engine to analyze the user's voice input and recognize their emotional state. This is used to identify stress levels, mood tendencies, and other related information.

[0345] Step 6:

[0346] The server integrates the analysis results of the AI ​​model with the recognition results of the emotion engine to generate a personalized health plan for each user. The generated plan includes nutritional suggestions, exercise recommendations, and stress management advice based on emotional state. For example, on days with high stress levels, a plan emphasizing relaxation will be provided.

[0347] Step 7:

[0348] The server sends the generated health plan to the device. The device then notifies the user of this information through its user interface, supporting the user's conscious health management.

[0349] Step 8:

[0350] Users adjust their daily lives according to the notified health plan and record the results of their efforts and any new feedback in the application. The application tracks the progress of plan implementation.

[0351] Step 9:

[0352] The device sends user-recorded feedback data to the server. This feedback includes changes in the user's emotions.

[0353] Step 10:

[0354] The server analyzes the feedback data and adjusts the new health plan. It prepares to generate and deliver an optimal health plan again, tailored to the user's changes in health and emotional state.

[0355] In this way, by utilizing the emotion engine, it becomes possible to manage health while taking into account the user's mental state, and to continuously provide personalized advice that is tailored to the user's lifestyle.

[0356] (Example 2)

[0357] 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".

[0358] In modern times, health management requires a personalized approach that takes into account individual lifestyles and emotional states. However, conventional health management systems often offer generalized suggestions and fail to provide advice that considers the user's emotional state, resulting in low user satisfaction.

[0359] 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.

[0360] In this invention, the server includes an input device, a processing device, an artificial intelligence device, a communication device, a data collection device, an analysis device, an emotion engine device, and an adjustment device. This enables personalized health planning and highly satisfying health management that takes into account the user's nutritional status, exercise habits, and emotional state.

[0361] An "input device" refers to a means by which a user inputs health-related information as data.

[0362] A "processing device" refers to a device that has the function of collecting input information and storing it in a memory device.

[0363] "Artificial intelligence device" refers to a technological means for analyzing stored information and generating personalized health plans.

[0364] "Communication device" refers to a means of notifying the user of the generated health design through the user interface.

[0365] A "collection device" refers to a device that has the function of collecting user feedback information and transmitting it to a processing device.

[0366] An "analysis device" refers to a device that has analytical functions to improve health design based on collected feedback information.

[0367] An "emotional engine device" refers to a method that utilizes technology to analyze emotional information obtained through voice input.

[0368] A "regulating device" refers to a device that dynamically adjusts and improves the health design generated based on emotional information.

[0369] This invention provides a system that allows users to manage their own health status on a daily basis. The user inputs health-related information, such as diet, exercise, and emotional state, using a dedicated application. This terminal transmits the input data to a server. The server processes this data and stores it in its storage device.

[0370] The server uses artificial intelligence technology to analyze the user's current health status based on stored information. This AI device includes a machine learning model implemented in Python, which analyzes the user's nutritional intake, exercise patterns, and emotional state in detail. Meanwhile, the emotion engine device analyzes the user's emotions from voice data and uses natural language processing technology to identify their emotional state.

[0371] Based on the analysis results, the server generates a personalized health plan. This plan includes nutritional balance, exercise suggestions, and relaxation methods tailored to the user's emotional state. For example, if the analysis reveals that a particular user is experiencing stress, the server will incorporate relaxation-enhancing exercises and meals into the plan.

[0372] The generated health plan is sent from the server to the device. The device notifies the user of this. The user executes the provided plan according to their daily activities and records feedback in the application. This feedback is sent back to the server via the device and is reflected in the next health plan based on the latest information and emotional state.

[0373] As a concrete example, when a user uses the prompt "Suggest exercise and diet recommendations for when the user is feeling stressed," the generative AI model provides appropriate suggestions. This mechanism allows users to achieve highly satisfying health management.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] Users use their mobile devices to input information about their meals, exercise, and emotional state via voice. This input data includes both text and audio. Users review this data within the app to ensure it is entered accurately.

[0377] Step 2:

[0378] The terminal sends the entered data to the server. This transmission takes place via an internet connection, and the data is stored in the server's storage device. The transmitted data is managed in conjunction with the user ID.

[0379] Step 3:

[0380] The server processes the received data and stores it in the database. During processing, the data format is standardized and converted into a format suitable for analysis by AI models. This enables high-quality analysis.

[0381] Step 4:

[0382] The server performs analysis on the stored data using a generative AI model. Nutritional information and exercise data are provided to the model as input, and the user's health trends are analyzed as output. The AI ​​model identifies user patterns and generates personalized health recommendations.

[0383] Step 5:

[0384] The emotion engine device analyzes voice data to identify the user's emotional state. Voice data is provided as input, and analysis is performed using natural language processing technology. The output is an emotional state such as stress, joy, or sadness.

[0385] Step 6:

[0386] The server generates a customized health plan that takes into account both health trends and emotional state. The plan includes suggestions that consider the user's nutrition, exercise, and emotions. Specifically, the suggestions are generated by a generative AI model.

[0387] Step 7:

[0388] The server sends the generated health design to the device. The device notifies the user, who can then review the design. The device uses push notifications to provide real-time notifications.

[0389] Step 8:

[0390] Users perform daily activities according to their health plan and input the results as feedback into the application. This feedback includes successes and challenges, and is recorded within the app.

[0391] Step 9:

[0392] The device sends the feedback back to the server. The server analyzes the received feedback data and adjusts the next health design. This reanalysis uses the feedback information as input and generates an adjusted health design as output.

[0393] (Application Example 2)

[0394] 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."

[0395] Traditional health management systems have struggled to provide health strategies that take into account the user's emotional state, resulting in users not receiving appropriate health guidance tailored to their individual circumstances and emotional state. Furthermore, in face-to-face services such as fitness gyms, personalized guidance that immediately reflects each individual's emotional and health state is required, but achieving this has been difficult.

[0396] 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.

[0397] In this invention, the server includes information gathering means, information management means, and artificial intelligence model means. This makes it possible to collect user health-related information and emotional information and immediately propose customized health strategies that take into account the user's emotional state.

[0398] "Information gathering means" refers to devices and methods for inputting health-related information and emotional information from users.

[0399] "Information management means" refers to devices and methods for storing accumulated data in a memory device, enabling later analysis and use.

[0400] An "artificial intelligence model means" is a program or device that analyzes stored data and creates customized health strategies for each user.

[0401] "Notification means" refers to output devices or methods for providing users with the created health measures.

[0402] "Feedback information collection means" refers to a method or device for collecting user feedback information and transmitting it to an information management means.

[0403] A "feedback analysis tool" is a method or device for modifying health policies based on accumulated feedback information.

[0404] "Device means" refers to equipment or software that proposes exercise programs that take emotional states into consideration.

[0405] To implement this invention, the following system is constructed. The server acquires health-related information and emotional information input from the user using information collection means. Sensors and applications installed on smartphones or smart devices can be used as information collection means. This collects the user's voice data and input information.

[0406] The collected data is transmitted to a server via information management means and stored in a memory device. This process often utilizes remote servers such as cloud servers. Next, the stored data is analyzed using artificial intelligence model means. The software used here includes TensorFlow and OpenAI API, which are used to determine the user's health and emotional state and generate customized health strategies.

[0407] The generated health measures are communicated to the user's device via a notification system. This notification is provided through a smartphone, tablet, or glasses-type device with a transparent display. The user can then implement the suggested health measures and send the results back to the server using a feedback information collection system.

[0408] To achieve further personalization, feedback analysis tools analyze feedback data, and health strategies are modified as needed. This cycle enables the provision of an optimal health plan tailored to the user's health and emotional state.

[0409] A concrete example would be a user stopping by a fitness gym on their way home from work, where a device that detects fatigue and stress suggests relaxation yoga. An example of a prompt for the generative AI model used here would be a sentence describing the specific situation, such as, "The user is tired after stopping by the gym on their way home from work." Based on this information, the AI ​​generates appropriate advice.

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The server receives health-related and emotional information from the terminal. Users use a smartphone application to input data and voice information about their daily lives, and the information gathering system collects this information and sends it to the server. The input information includes dietary details, exercise history, and emotional data extracted from voice.

[0413] Step 2:

[0414] The server stores the received data in a database using information management tools. This storage step enables centralized data management and structures the data for later analysis. Database software is used in this process to ensure that the stored data is efficiently accessible and updatable.

[0415] Step 3:

[0416] The server analyzes the stored data using an artificial intelligence model. Receiving input health-related and emotional information, an AI model utilizing TensorFlow generates health strategies optimized for each individual user. In this process, it learns the correlations between data and outputs an appropriate health plan.

[0417] Step 4:

[0418] The server notifies the terminal of the generated health plan. Notification methods include smartphone push notifications and head-mounted display displays, immediately informing the user of the suggested content. The generated health plan is visualized and designed to be easily understood by the user.

[0419] Step 5:

[0420] The user implements the health measures they receive notifications about and sends feedback information from their device to the server. The feedback information collection mechanism is activated when the user re-enters the type of exercise they performed, their diet, and their emotional state at the time.

[0421] Step 6:

[0422] The server re-evaluates the received feedback information using feedback analysis tools. In this step, the server dynamically modifies the health measures to be provided next based on the newly received user input data. The re-evaluated data is then used for the next suggestion.

[0423] Step 7:

[0424] The server provides prompt statements to the generating AI model as needed, helping to generate the next suggested health measures. These prompt statements are based on the specific user's condition and the information required, and may include details such as "the user stopped by the gym after work and is tired."

[0425] 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.

[0426] 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 the following. 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 indicated 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] This invention is a system that provides users with personalized health management and fitness plans. Users input daily health-related data such as weight, height, diet, and exercise through the app.

[0442] The data provided by the user is transmitted to the server via a secure communication method from the device. The server stores the received data in a database and calls an AI model to analyze the data. The AI ​​model uses the collected data to evaluate the user's diet and exercise habits.

[0443] Based on the analysis results, the server generates a health plan optimized for each user. This health plan includes dietary suggestions tailored to the individual's health condition, suggestions for type and duration of exercise, advice for stress management, and guidelines for improving sleep quality. For example, if calorie expenditure is not meeting the target, it may include specific action plans such as recommending a 30-minute run.

[0444] The generated health plan is sent from the server to the terminal. The terminal then notifies the user of this health plan through the user interface. The notification is provided in a format that is easy for the user to understand and implement in their daily life.

[0445] Users adjust their actions based on the provided plan and record the results on their device. The recorded data is sent back to the server as feedback and incorporated into the next plan. This allows for flexible adjustments in response to changes in the user's health status.

[0446] For example, if a user aims to lose weight and their daily food diary reveals excessive carbohydrate intake, the system will suggest a diet high in protein and recommend three sessions of aerobic exercise per week. By following this plan and providing daily feedback, the user can receive a more effective health plan in the next step.

[0447] Through this process, the system enables personalized health management tailored to the user's lifestyle and supports the continuous improvement of the user's health.

[0448] The following describes the processing flow.

[0449] Step 1:

[0450] Users input daily health-related data (such as weight, height, diet, type and duration of exercise) through the app's interface, and then submit that data.

[0451] Step 2:

[0452] The device verifies the entered health-related data, formats it, and encrypts it to ensure security. It then sends the data to the server.

[0453] Step 3:

[0454] The server receives data sent from the terminal and stores it in the database. It also verifies the integrity of the data and checks for inconsistencies or errors.

[0455] Step 4:

[0456] The server passes health-related data stored in the database to an AI model for analysis. The AI ​​model evaluates the user's diet and exercise habits and assesses their health status.

[0457] Step 5:

[0458] The server generates a personalized health plan for each user based on the analysis results of the AI ​​model. The plan includes dietary suggestions, exercise recommendations, stress management techniques, and sleep improvement methods.

[0459] Step 6:

[0460] The server sends the generated health plan to the device. The device receives the plan and displays it clearly in the user interface. It then notifies the user with specific action guidelines.

[0461] Step 7:

[0462] Users take daily actions based on the notified health plan and record the results and their opinions within the app.

[0463] Step 8:

[0464] The device collects user feedback data, formats it, and then sends it to the server.

[0465] Step 9:

[0466] The server analyzes the feedback data and makes necessary adjustments to the next health plan. Based on this information, the plan is updated again using a process similar to step 5.

[0467] By repeatedly operating the system through these steps, we support the continuous improvement of users' health.

[0468] (Example 1)

[0469] 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."

[0470] There is a need to provide a system that offers effective health management and fitness plans tailored to individual users, supporting the continuous improvement of their health status. Conventional technologies have struggled to generate personalized health plans that take into account individual user information, and have been insufficient in dynamically adjusting plans based on feedback.

[0471] 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.

[0472] In this invention, the server includes an information input means for inputting health-related information from a user, an information recording medium means for collecting and storing the input information, and a machine learning model means for analyzing the stored information and generating an optimal health plan. This enables the provision of a personalized health plan to the user and the optimization of continuous health management.

[0473] An "information input method" is a mechanism for users to input health-related information, thereby enabling the system to provide daily information such as the user's weight and diet.

[0474] A "central control device" is a device that has the function of collecting information entered by the user and recording it on a storage medium, and it receives and records data through secure communication.

[0475] A "machine learning model" is a means of performing computational processing to analyze collected health-related information and generate a health plan optimized for each individual user, and it uses AI technology to evaluate the information.

[0476] An "information provision device" is a mechanism for notifying the user of the generated health plan, providing information in an easily understandable format through a user interface.

[0477] The "response collection means" is a component that collects feedback information from the user and transmits it back to the central control unit, providing information about changes in the user's lifestyle.

[0478] A "response analysis means" is a means for adjusting the health plan based on collected user feedback information and dynamically optimizing the plan according to the user's individual health condition.

[0479] This system is designed to provide users with personalized health management and fitness plans. Users can use a health management application to input information such as their daily weight, height, diet, and exercise. The device then transmits this input information to the server using the HTTPS protocol as a secure communication method.

[0480] The server receives information and records it in a database. This database has a table structure organized by date and type of information. The server also calls a machine learning model to analyze the information. This model uses programming languages ​​such as Python and R, and machine learning libraries such as TensorFlow, and includes algorithms to evaluate the user's diet and exercise habits.

[0481] Based on the analyzed results, the server generates a health plan optimized for each individual user. Specific suggestions can be incorporated into the plan using JavaScript or Python scripts. This generated plan includes specific guidance on diet, exercise, stress management, and sleep improvement, as needed.

[0482] The app sends a health plan generated from the server to the device, which then notifies the user via the user interface. The app's UI / UX design is carefully crafted to present the information in a format that is easy for the user to understand.

[0483] Users adjust their daily activities based on the plan and record the results on their device. The device then sends the recorded feedback back to the server, which is used to adjust the next plan. This feedback loop allows for flexible plan adjustments based on the user's health condition.

[0484] For example, the following input is sent to the generating AI model as a prompt:

[0485] "The user's weight has not decreased in the past month, but their activity level has remained the same. Their food diary shows that their carbohydrate intake exceeds the recommended limit. Based on this data, please adjust their health plan to reduce carbohydrates and increase protein intake."

[0486] This allows the system to provide individualized plans tailored to each user's lifestyle and health condition, supporting continuous improvement in their health.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] Users input daily health-related data such as weight, height, diet, and exercise through a health management app. This data is temporarily stored on the device. The data can be entered in numerical or text format and is designed for easy input through a graphical user interface.

[0490] Step 2:

[0491] The terminal securely transmits user-entered data to the server using the HTTPS protocol. The transmitted data is structured in JSON or XML format and delivered to the server while protecting user privacy through encryption.

[0492] Step 3:

[0493] The server stores the received user data in a database. The database organizes and stores the data based on date, data type, and user ID. Data formatting and cleaning are performed here, ensuring data consistency and making it available for subsequent analysis.

[0494] Step 4:

[0495] The server inputs the stored data into an AI model for analysis. The AI ​​model utilizes machine learning algorithms and is built using Python and TensorFlow libraries. Based on the input data, it evaluates the user's nutritional status and exercise habits, extracting important insights. The output information serves as a useful indicator for improving the user's lifestyle.

[0496] Step 5:

[0497] The server generates an optimal health plan for the user based on the analysis results from the AI ​​model. The plan generation includes specific instructions tailored to the user's goals and health condition (e.g., "Reduce calorie intake by 200kcal," "Recommend yoga 5 days a week"). The generated plan is structured as an actionable plan.

[0498] Step 6:

[0499] The server sends the generated health plan to the device in JSON or XML format. The device then notifies the user of this plan through its user interface, displaying it in a visually easy-to-understand format. Notifications include push notifications and in-app alerts.

[0500] Step 7:

[0501] Users adjust their daily lifestyles based on the health plan they receive. They re-enter the results and impressions obtained during the adjustment process into their device and provide them as feedback. Specifically, users record the frequency of their exercise and the content of their meals, and save the data on their device.

[0502] Step 8:

[0503] The device sends the feedback received from the user back to the server. The server analyzes this feedback and incorporates it into generating the next health plan. This response loop enables continuous optimization of the plan based on the user's health status.

[0504] (Application Example 1)

[0505] 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."

[0506] Conventional health management systems have difficulty supporting users in making specific behavioral choices in their daily lives, and in particular, they lack practical suggestions for selecting products that align with their health plans when purchasing products in stores.

[0507] 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.

[0508] In this invention, the server includes a suggestion generation means for suggesting products to the user within the store, a product adjustment means for adjusting the suggested products and health plan based on the user's health-related data, and a feedback utilization means for optimizing in-store suggestions through user feedback. This enables the user to receive support in selecting products that are suitable for their health plan when shopping for everyday items.

[0509] A "proposal generation method" is a method for suggesting products suitable for an individual's health plan within a store, based on the user's health-related data.

[0510] "Product adjustment means" refers to a method of adjusting the content of a proposed product based on the user's health-related data and health plan, in order to present a more appropriate option.

[0511] "Feedback utilization methods" refer to methods for optimizing in-store product recommendations based on user feedback and improving the accuracy of future recommendations.

[0512] The system that implements this application optimizes product recommendations in a retail environment using users' health-related data. The system works by users inputting daily health-related data via a smartphone app, which is then transmitted to a server via secure communication. The server uses an AI model to analyze and generate personalized health plans based on past and present data stored in a database. Specifically, it uses machine learning libraries such as TensorFlow to analyze the user's nutritional status and exercise habits.

[0513] Based on the analysis results, the server uses a suggestion generation mechanism to propose products suitable for the individual's health plan. Furthermore, a product adjustment mechanism adjusts the product content based on the user's current condition. These suggestions are then communicated to the user via a smartphone or other device.

[0514] Users can select products in stores based on suggestions displayed on their devices. Feedback on selected products is collected through feedback mechanisms and used to improve the accuracy of future suggestions. This enables a personalized shopping experience for each user.

[0515] For example, if a user has a weight loss plan and visits a store, the app will list low-calorie foods for that day and suggest specific foods or fitness products as recommended items. Examples of prompts might include, "Analyze user A's latest health data and suggest a meal plan and recommended products for today," or "Generate a food list suitable for user B, who is on a weight loss plan." This system provides practical support for users to achieve a healthy lifestyle.

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] The server stores health-related data received from users in a database. Input includes weight, height, diet, and exercise details entered by the user via a smartphone app. The server receives this data via secure communication and stores it in the database. This stored data is used for subsequent analysis.

[0519] Step 2:

[0520] The server analyzes health-related data stored in a database using an AI model. The input is the health-related data of each user stored in the database. The server uses generative AI models such as TensorFlow to perform data calculations to evaluate the user's diet and exercise habits. As a result of the analysis, it generates a health plan optimized for each individual user.

[0521] Step 3:

[0522] The server sends the generated health plan to the terminal and notifies the user. The input is a health plan generated by an AI model. The server sends this plan to the user's smartphone or terminal via electronic communication and notifies the user of the plan's details through the user interface. The output is a health plan displayed in a format that is easy for the user to understand and implement.

[0523] Step 4:

[0524] The user selects products based on product suggestions provided in the store. The input is the product suggestion notified to the terminal. The user then selects and purchases appropriate products in the physical store, referring to the application's suggestions. The output is a product selection suitable for the health plan.

[0525] Step 5:

[0526] Users input feedback into the app, including purchased items and actions taken based on suggestions. This input consists of the user's actual purchase history and activity records. By inputting this as feedback into the app, users can have it reflected in their next health plan. The output is feedback data used to improve future suggestions.

[0527] Step 6:

[0528] The server analyzes feedback data and dynamically adjusts the next product recommendation and health plan. The input is user-provided feedback data. The server then re-introduces this data into an AI model and performs data calculations to optimize the next recommendation based on the user's state and behavior. The output is the next product recommendation and health plan.

[0529] 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.

[0530] This invention is a system that provides individually optimized health plans to users, and in particular, by combining it with an emotion engine that recognizes the user's emotions, it achieves more personalized advice. Users can input daily health-related data and feedback using the application, and can also provide emotional information through voice input.

[0531] The data entered by the user is sent from the terminal to the server. The server stores the received health-related data in a database and passes it to an AI model and an emotion engine for analysis. The AI ​​model evaluates and analyzes the user's diet and exercise habits in detail, while the emotion engine estimates the user's emotional state from voice input and other feedback.

[0532] Based on information generated by an AI model and an emotion engine, the server creates a health plan optimized for each user. The generated health plan includes suggestions tailored to the user's nutritional status, exercise habits, and even their emotional state on that day. For example, if the user is feeling stressed, suggestions for relaxation-oriented exercises and relaxing meals will be provided.

[0533] The created health plan is sent from the server to the device, which then notifies the user. The user can then implement the actions outlined in the plan in their daily life and record their progress in the application.

[0534] Feedback data is sent back to the server via the device, where it undergoes further analysis, including emotional analysis by the emotion engine. Based on the user's latest state, necessary adjustments are made to the next health plan, supporting continuous health management. This system allows users to manage their health in a way that takes their emotions into account, enabling them to enjoy a higher quality, personalized service.

[0535] As a concrete example, consider a user who is managing their weight through diet and exercise. If this user is experiencing stress due to their extremely busy daily life, the emotional engine will pick up on this information and provide specific guidance to balance stress and weight management by suggesting light meals such as smoothies and exercise including stretching in their health plan. This will improve the user's overall satisfaction with their health management.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] Users input health-related data (weight, height, diet, exercise, etc.) and feedback through the application interface. In addition, they can input information about their emotions using the voice input function.

[0539] Step 2:

[0540] The device encrypts data to securely process health-related data and emotional information, and then transmits that information to the server. Ensuring security plays a crucial role.

[0541] Step 3:

[0542] The server stores health-related data and emotional information received from the terminal in a database. The stored data is validated to ensure its integrity and consistency.

[0543] Step 4:

[0544] The server sends the stored health-related data to an AI model and begins analyzing the data. The AI ​​model evaluates not only the user's diet and exercise habits, but also their overall health status.

[0545] Step 5:

[0546] The server utilizes an emotion engine to analyze the user's voice input and recognize their emotional state. This is used to identify stress levels, mood tendencies, and other related information.

[0547] Step 6:

[0548] The server integrates the analysis results of the AI ​​model with the recognition results of the emotion engine to generate a personalized health plan for each user. The generated plan includes nutritional suggestions, exercise recommendations, and stress management advice based on emotional state. For example, on days with high stress levels, a plan emphasizing relaxation will be provided.

[0549] Step 7:

[0550] The server sends the generated health plan to the device. The device then notifies the user of this information through its user interface, supporting the user's conscious health management.

[0551] Step 8:

[0552] Users adjust their daily lives according to the notified health plan and record the results of their efforts and any new feedback in the application. The application tracks the progress of plan implementation.

[0553] Step 9:

[0554] The device sends user-recorded feedback data to the server. This feedback includes changes in the user's emotions.

[0555] Step 10:

[0556] The server analyzes the feedback data and adjusts the new health plan. It prepares to generate and deliver an optimal health plan again, tailored to the user's changes in health and emotional state.

[0557] In this way, by utilizing the emotion engine, it becomes possible to manage health while taking into account the user's mental state, and to continuously provide personalized advice that is tailored to the user's lifestyle.

[0558] (Example 2)

[0559] 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."

[0560] In modern times, health management requires a personalized approach that takes into account individual lifestyles and emotional states. However, conventional health management systems often offer generalized suggestions and fail to provide advice that considers the user's emotional state, resulting in low user satisfaction.

[0561] 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.

[0562] In this invention, the server includes an input device, a processing device, an artificial intelligence device, a communication device, a data collection device, an analysis device, an emotion engine device, and an adjustment device. This enables personalized health planning and highly satisfying health management that takes into account the user's nutritional status, exercise habits, and emotional state.

[0563] An "input device" refers to a means by which a user inputs health-related information as data.

[0564] A "processing device" refers to a device that has the function of collecting input information and storing it in a memory device.

[0565] "Artificial intelligence device" refers to a technological means for analyzing stored information and generating personalized health plans.

[0566] "Communication device" refers to a means of notifying the user of the generated health design through the user interface.

[0567] A "collection device" refers to a device that has the function of collecting user feedback information and transmitting it to a processing device.

[0568] An "analysis device" refers to a device that has analytical functions to improve health design based on collected feedback information.

[0569] An "emotional engine device" refers to a method that utilizes technology to analyze emotional information obtained through voice input.

[0570] A "regulating device" refers to a device that dynamically adjusts and improves the health design generated based on emotional information.

[0571] This invention provides a system that allows users to manage their own health status on a daily basis. The user inputs health-related information, such as diet, exercise, and emotional state, using a dedicated application. This terminal transmits the input data to a server. The server processes this data and stores it in its storage device.

[0572] The server uses artificial intelligence technology to analyze the user's current health status based on stored information. This AI device includes a machine learning model implemented in Python, which analyzes the user's nutritional intake, exercise patterns, and emotional state in detail. Meanwhile, the emotion engine device analyzes the user's emotions from voice data and uses natural language processing technology to identify their emotional state.

[0573] Based on the analysis results, the server generates a personalized health plan. This plan includes nutritional balance, exercise suggestions, and relaxation methods tailored to the user's emotional state. For example, if the analysis reveals that a particular user is experiencing stress, the server will incorporate relaxation-enhancing exercises and meals into the plan.

[0574] The generated health plan is sent from the server to the device. The device notifies the user of this. The user executes the provided plan according to their daily activities and records feedback in the application. This feedback is sent back to the server via the device and is reflected in the next health plan based on the latest information and emotional state.

[0575] As a concrete example, when a user uses the prompt "Suggest exercise and diet recommendations for when the user is feeling stressed," the generative AI model provides appropriate suggestions. This mechanism allows users to achieve highly satisfying health management.

[0576] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0577] Step 1:

[0578] Users use their mobile devices to input information about their meals, exercise, and emotional state via voice. This input data includes both text and audio. Users review this data within the app to ensure it is entered accurately.

[0579] Step 2:

[0580] The terminal sends the entered data to the server. This transmission takes place via an internet connection, and the data is stored in the server's storage device. The transmitted data is managed in conjunction with the user ID.

[0581] Step 3:

[0582] The server processes the received data and stores it in the database. During processing, the data format is standardized and converted into a format suitable for analysis by AI models. This enables high-quality analysis.

[0583] Step 4:

[0584] The server performs analysis on the stored data using a generative AI model. Nutritional information and exercise data are provided to the model as input, and the user's health trends are analyzed as output. The AI ​​model identifies user patterns and generates personalized health recommendations.

[0585] Step 5:

[0586] The emotion engine device analyzes voice data to identify the user's emotional state. Voice data is provided as input, and analysis is performed using natural language processing technology. The output is an emotional state such as stress, joy, or sadness.

[0587] Step 6:

[0588] The server generates a customized health plan that takes into account both health trends and emotional state. The plan includes suggestions that consider the user's nutrition, exercise, and emotions. Specifically, the suggestions are generated by a generative AI model.

[0589] Step 7:

[0590] The server sends the generated health design to the device. The device notifies the user, who can then review the design. The device uses push notifications to provide real-time notifications.

[0591] Step 8:

[0592] Users perform daily activities according to their health plan and input the results as feedback into the application. This feedback includes successes and challenges, and is recorded within the app.

[0593] Step 9:

[0594] The device sends the feedback back to the server. The server analyzes the received feedback data and adjusts the next health design. This reanalysis uses the feedback information as input and generates an adjusted health design as output.

[0595] (Application Example 2)

[0596] 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."

[0597] Traditional health management systems have struggled to provide health strategies that take into account the user's emotional state, resulting in users not receiving appropriate health guidance tailored to their individual circumstances and emotional state. Furthermore, in face-to-face services such as fitness gyms, personalized guidance that immediately reflects each individual's emotional and health state is required, but achieving this has been difficult.

[0598] 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.

[0599] In this invention, the server includes information gathering means, information management means, and artificial intelligence model means. This makes it possible to collect user health-related information and emotional information and immediately propose customized health strategies that take into account the user's emotional state.

[0600] "Information gathering means" refers to devices and methods for inputting health-related information and emotional information from users.

[0601] "Information management means" refers to devices and methods for storing accumulated data in a memory device, enabling later analysis and use.

[0602] An "artificial intelligence model means" is a program or device that analyzes stored data and creates customized health strategies for each user.

[0603] "Notification means" refers to output devices or methods for providing users with the created health measures.

[0604] "Feedback information collection means" refers to a method or device for collecting user feedback information and transmitting it to an information management means.

[0605] A "feedback analysis tool" is a method or device for modifying health policies based on accumulated feedback information.

[0606] "Device means" refers to equipment or software that proposes exercise programs that take emotional states into consideration.

[0607] To implement this invention, the following system is constructed. The server acquires health-related information and emotional information input from the user using information collection means. Sensors and applications installed on smartphones or smart devices can be used as information collection means. This collects the user's voice data and input information.

[0608] The collected data is transmitted to a server via information management means and stored in a memory device. This process often utilizes remote servers such as cloud servers. Next, the stored data is analyzed using artificial intelligence model means. The software used here includes TensorFlow and OpenAI API, which are used to determine the user's health and emotional state and generate customized health strategies.

[0609] The generated health measures are communicated to the user's device via a notification system. This notification is provided through a smartphone, tablet, or glasses-type device with a transparent display. The user can then implement the suggested health measures and send the results back to the server using a feedback information collection system.

[0610] To achieve further personalization, feedback analysis tools analyze feedback data, and health strategies are modified as needed. This cycle enables the provision of an optimal health plan tailored to the user's health and emotional state.

[0611] A concrete example would be a user stopping by a fitness gym on their way home from work, where a device that detects fatigue and stress suggests relaxation yoga. An example of a prompt for the generative AI model used here would be a sentence describing the specific situation, such as, "The user is tired after stopping by the gym on their way home from work." Based on this information, the AI ​​generates appropriate advice.

[0612] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0613] Step 1:

[0614] The server receives health-related and emotional information from the terminal. Users use a smartphone application to input data and voice information about their daily lives, and the information gathering system collects this information and sends it to the server. The input information includes dietary details, exercise history, and emotional data extracted from voice.

[0615] Step 2:

[0616] The server stores the received data in a database using information management tools. This storage step enables centralized data management and structures the data for later analysis. Database software is used in this process to ensure that the stored data is efficiently accessible and updatable.

[0617] Step 3:

[0618] The server analyzes the stored data using an artificial intelligence model. Receiving input health-related and emotional information, an AI model utilizing TensorFlow generates health strategies optimized for each individual user. In this process, it learns the correlations between data and outputs an appropriate health plan.

[0619] Step 4:

[0620] The server notifies the terminal of the generated health plan. Notification methods include smartphone push notifications and head-mounted display displays, immediately informing the user of the suggested content. The generated health plan is visualized and designed to be easily understood by the user.

[0621] Step 5:

[0622] The user implements the health measures they receive notifications about and sends feedback information from their device to the server. The feedback information collection mechanism is activated when the user re-enters the type of exercise they performed, their diet, and their emotional state at the time.

[0623] Step 6:

[0624] The server re-evaluates the received feedback information using feedback analysis tools. In this step, the server dynamically modifies the health measures to be provided next based on the newly received user input data. The re-evaluated data is then used for the next suggestion.

[0625] Step 7:

[0626] The server provides prompt statements to the generating AI model as needed, helping to generate the next suggested health measures. These prompt statements are based on the specific user's condition and the information required, and may include details such as "the user stopped by the gym after work and is tired."

[0627] 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.

[0628] 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 the following. 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 indicated 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.

[0629] 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.

[0630] [Fourth Embodiment]

[0631] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0632] 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.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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".

[0644] This invention is a system that provides users with personalized health management and fitness plans. Users input daily health-related data such as weight, height, diet, and exercise through the app.

[0645] The data provided by the user is transmitted to the server via a secure communication method from the device. The server stores the received data in a database and calls an AI model to analyze the data. The AI ​​model uses the collected data to evaluate the user's diet and exercise habits.

[0646] Based on the analysis results, the server generates a health plan optimized for each user. This health plan includes dietary suggestions tailored to the individual's health condition, suggestions for type and duration of exercise, advice for stress management, and guidelines for improving sleep quality. For example, if calorie expenditure is not meeting the target, it may include specific action plans such as recommending a 30-minute run.

[0647] The generated health plan is sent from the server to the terminal. The terminal then notifies the user of this health plan through the user interface. The notification is provided in a format that is easy for the user to understand and implement in their daily life.

[0648] Users adjust their actions based on the provided plan and record the results on their device. The recorded data is sent back to the server as feedback and incorporated into the next plan. This allows for flexible adjustments in response to changes in the user's health status.

[0649] For example, if a user aims to lose weight and their daily food diary reveals excessive carbohydrate intake, the system will suggest a diet high in protein and recommend three sessions of aerobic exercise per week. By following this plan and providing daily feedback, the user can receive a more effective health plan in the next step.

[0650] Through this process, the system enables personalized health management tailored to the user's lifestyle and supports the continuous improvement of the user's health.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] Users input daily health-related data (such as weight, height, diet, type and duration of exercise) through the app's interface, and then submit that data.

[0654] Step 2:

[0655] The device verifies the entered health-related data, formats it, and encrypts it to ensure security. It then sends the data to the server.

[0656] Step 3:

[0657] The server receives data sent from the terminal and stores it in the database. It also verifies the integrity of the data and checks for inconsistencies or errors.

[0658] Step 4:

[0659] The server passes health-related data stored in the database to an AI model for analysis. The AI ​​model evaluates the user's diet and exercise habits and assesses their health status.

[0660] Step 5:

[0661] The server generates a personalized health plan for each user based on the analysis results of the AI ​​model. The plan includes dietary suggestions, exercise recommendations, stress management techniques, and sleep improvement methods.

[0662] Step 6:

[0663] The server sends the generated health plan to the device. The device receives the plan and displays it clearly in the user interface. It then notifies the user with specific action guidelines.

[0664] Step 7:

[0665] Users take daily actions based on the notified health plan and record the results and their opinions within the app.

[0666] Step 8:

[0667] The device collects user feedback data, formats it, and then sends it to the server.

[0668] Step 9:

[0669] The server analyzes the feedback data and makes necessary adjustments to the next health plan. Based on this information, the plan is updated again using a process similar to step 5.

[0670] By repeatedly operating the system through these steps, we support the continuous improvement of users' health.

[0671] (Example 1)

[0672] 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".

[0673] There is a need to provide a system that offers effective health management and fitness plans tailored to individual users, supporting the continuous improvement of their health status. Conventional technologies have struggled to generate personalized health plans that take into account individual user information, and have been insufficient in dynamically adjusting plans based on feedback.

[0674] 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.

[0675] In this invention, the server includes an information input means for inputting health-related information from a user, an information recording medium means for collecting and storing the input information, and a machine learning model means for analyzing the stored information and generating an optimal health plan. This enables the provision of a personalized health plan to the user and the optimization of continuous health management.

[0676] An "information input method" is a mechanism for users to input health-related information, thereby enabling the system to provide daily information such as the user's weight and diet.

[0677] A "central control device" is a device that has the function of collecting information entered by the user and recording it on a storage medium, and it receives and records data through secure communication.

[0678] A "machine learning model" is a means of performing computational processing to analyze collected health-related information and generate a health plan optimized for each individual user, and it uses AI technology to evaluate the information.

[0679] An "information provision device" is a mechanism for notifying the user of the generated health plan, providing information in an easily understandable format through a user interface.

[0680] The "response collection means" is a component that collects feedback information from the user and transmits it back to the central control unit, providing information about changes in the user's lifestyle.

[0681] A "response analysis means" is a means for adjusting the health plan based on collected user feedback information and dynamically optimizing the plan according to the user's individual health condition.

[0682] This system is designed to provide users with personalized health management and fitness plans. Users can use a health management application to input information such as their daily weight, height, diet, and exercise. The device then transmits this input information to the server using the HTTPS protocol as a secure communication method.

[0683] The server receives information and records it in a database. This database has a table structure organized by date and type of information. The server also calls a machine learning model to analyze the information. This model uses programming languages ​​such as Python and R, and machine learning libraries such as TensorFlow, and includes algorithms to evaluate the user's diet and exercise habits.

[0684] Based on the analyzed results, the server generates a health plan optimized for each individual user. Specific suggestions can be incorporated into the plan using JavaScript or Python scripts. This generated plan includes specific guidance on diet, exercise, stress management, and sleep improvement, as needed.

[0685] The app sends a health plan generated from the server to the device, which then notifies the user via the user interface. The app's UI / UX design is carefully crafted to present the information in a format that is easy for the user to understand.

[0686] Users adjust their daily activities based on the plan and record the results on their device. The device then sends the recorded feedback back to the server, which is used to adjust the next plan. This feedback loop allows for flexible plan adjustments based on the user's health condition.

[0687] For example, the following input is sent to the generating AI model as a prompt:

[0688] "The user's weight has not decreased in the past month, but their activity level has remained the same. Their food diary shows that their carbohydrate intake exceeds the recommended limit. Based on this data, please adjust their health plan to reduce carbohydrates and increase protein intake."

[0689] This allows the system to provide individualized plans tailored to each user's lifestyle and health condition, supporting continuous improvement in their health.

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1:

[0692] Users input daily health-related data such as weight, height, diet, and exercise through a health management app. This data is temporarily stored on the device. The data can be entered in numerical or text format and is designed for easy input through a graphical user interface.

[0693] Step 2:

[0694] The terminal securely transmits user-entered data to the server using the HTTPS protocol. The transmitted data is structured in JSON or XML format and delivered to the server while protecting user privacy through encryption.

[0695] Step 3:

[0696] The server stores the received user data in a database. The database organizes and stores the data based on date, data type, and user ID. Data formatting and cleaning are performed here, ensuring data consistency and making it available for subsequent analysis.

[0697] Step 4:

[0698] The server inputs the stored data into an AI model for analysis. The AI ​​model utilizes machine learning algorithms and is built using Python and TensorFlow libraries. Based on the input data, it evaluates the user's nutritional status and exercise habits, extracting important insights. The output information serves as a useful indicator for improving the user's lifestyle.

[0699] Step 5:

[0700] The server generates an optimal health plan for the user based on the analysis results from the AI ​​model. The plan generation includes specific instructions tailored to the user's goals and health condition (e.g., "Reduce calorie intake by 200kcal," "Recommend yoga 5 days a week"). The generated plan is structured as an actionable plan.

[0701] Step 6:

[0702] The server sends the generated health plan to the device in JSON or XML format. The device then notifies the user of this plan through its user interface, displaying it in a visually easy-to-understand format. Notifications include push notifications and in-app alerts.

[0703] Step 7:

[0704] Users adjust their daily lifestyles based on the health plan they receive. They re-enter the results and impressions obtained during the adjustment process into their device and provide them as feedback. Specifically, users record the frequency of their exercise and the content of their meals, and save the data on their device.

[0705] Step 8:

[0706] The device sends the feedback received from the user back to the server. The server analyzes this feedback and incorporates it into generating the next health plan. This response loop enables continuous optimization of the plan based on the user's health status.

[0707] (Application Example 1)

[0708] 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".

[0709] Conventional health management systems have difficulty supporting users in making specific behavioral choices in their daily lives, and in particular, they lack practical suggestions for selecting products that align with their health plans when purchasing products in stores.

[0710] 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.

[0711] In this invention, the server includes a suggestion generation means for suggesting products to the user within the store, a product adjustment means for adjusting the suggested products and health plan based on the user's health-related data, and a feedback utilization means for optimizing in-store suggestions through user feedback. This enables the user to receive support in selecting products that are suitable for their health plan when shopping for everyday items.

[0712] A "proposal generation method" is a method for suggesting products suitable for an individual's health plan within a store, based on the user's health-related data.

[0713] "Product adjustment means" refers to a method of adjusting the content of a proposed product based on the user's health-related data and health plan, in order to present a more appropriate option.

[0714] "Feedback utilization methods" refer to methods for optimizing in-store product recommendations based on user feedback and improving the accuracy of future recommendations.

[0715] The system that implements this application optimizes product recommendations in a retail environment using users' health-related data. The system works by users inputting daily health-related data via a smartphone app, which is then transmitted to a server via secure communication. The server uses an AI model to analyze and generate personalized health plans based on past and present data stored in a database. Specifically, it uses machine learning libraries such as TensorFlow to analyze the user's nutritional status and exercise habits.

[0716] Based on the analysis results, the server uses a suggestion generation mechanism to propose products suitable for the individual's health plan. Furthermore, a product adjustment mechanism adjusts the product content based on the user's current condition. These suggestions are then communicated to the user via a smartphone or other device.

[0717] Users can select products in stores based on suggestions displayed on their devices. Feedback on selected products is collected through feedback mechanisms and used to improve the accuracy of future suggestions. This enables a personalized shopping experience for each user.

[0718] For example, if a user has a weight loss plan and visits a store, the app will list low-calorie foods for that day and suggest specific foods or fitness products as recommended items. Examples of prompts might include, "Analyze user A's latest health data and suggest a meal plan and recommended products for today," or "Generate a food list suitable for user B, who is on a weight loss plan." This system provides practical support for users to achieve a healthy lifestyle.

[0719] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0720] Step 1:

[0721] The server stores health-related data received from users in a database. Input includes weight, height, diet, and exercise details entered by the user via a smartphone app. The server receives this data via secure communication and stores it in the database. This stored data is used for subsequent analysis.

[0722] Step 2:

[0723] The server analyzes health-related data stored in a database using an AI model. The input is the health-related data of each user stored in the database. The server uses generative AI models such as TensorFlow to perform data calculations to evaluate the user's diet and exercise habits. As a result of the analysis, it generates a health plan optimized for each individual user.

[0724] Step 3:

[0725] The server sends the generated health plan to the terminal and notifies the user. The input is a health plan generated by an AI model. The server sends this plan to the user's smartphone or terminal via electronic communication and notifies the user of the plan's details through the user interface. The output is a health plan displayed in a format that is easy for the user to understand and implement.

[0726] Step 4:

[0727] The user selects products based on product suggestions provided in the store. The input is the product suggestion notified to the terminal. The user then selects and purchases appropriate products in the physical store, referring to the application's suggestions. The output is a product selection suitable for the health plan.

[0728] Step 5:

[0729] Users input feedback into the app, including purchased items and actions taken based on suggestions. This input consists of the user's actual purchase history and activity records. By inputting this as feedback into the app, users can have it reflected in their next health plan. The output is feedback data used to improve future suggestions.

[0730] Step 6:

[0731] The server analyzes feedback data and dynamically adjusts the next product recommendation and health plan. The input is user-provided feedback data. The server then re-introduces this data into an AI model and performs data calculations to optimize the next recommendation based on the user's state and behavior. The output is the next product recommendation and health plan.

[0732] 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.

[0733] This invention is a system that provides individually optimized health plans to users, and in particular, by combining it with an emotion engine that recognizes the user's emotions, it achieves more personalized advice. Users can input daily health-related data and feedback using the application, and can also provide emotional information through voice input.

[0734] The data entered by the user is sent from the terminal to the server. The server stores the received health-related data in a database and passes it to an AI model and an emotion engine for analysis. The AI ​​model evaluates and analyzes the user's diet and exercise habits in detail, while the emotion engine estimates the user's emotional state from voice input and other feedback.

[0735] Based on information generated by an AI model and an emotion engine, the server creates a health plan optimized for each user. The generated health plan includes suggestions tailored to the user's nutritional status, exercise habits, and even their emotional state on that day. For example, if the user is feeling stressed, suggestions for relaxation-oriented exercises and relaxing meals will be provided.

[0736] The created health plan is sent from the server to the device, which then notifies the user. The user can then implement the actions outlined in the plan in their daily life and record their progress in the application.

[0737] Feedback data is sent back to the server via the device, where it undergoes further analysis, including emotional analysis by the emotion engine. Based on the user's latest state, necessary adjustments are made to the next health plan, supporting continuous health management. This system allows users to manage their health in a way that takes their emotions into account, enabling them to enjoy a higher quality, personalized service.

[0738] As a concrete example, consider a user who is managing their weight through diet and exercise. If this user is experiencing stress due to their extremely busy daily life, the emotional engine will pick up on this information and provide specific guidance to balance stress and weight management by suggesting light meals such as smoothies and exercise including stretching in their health plan. This will improve the user's overall satisfaction with their health management.

[0739] The following describes the processing flow.

[0740] Step 1:

[0741] Users input health-related data (weight, height, diet, exercise, etc.) and feedback through the application interface. In addition, they can input information about their emotions using the voice input function.

[0742] Step 2:

[0743] The device encrypts data to securely process health-related data and emotional information, and then transmits that information to the server. Ensuring security plays a crucial role.

[0744] Step 3:

[0745] The server stores health-related data and emotional information received from the terminal in a database. The stored data is validated to ensure its integrity and consistency.

[0746] Step 4:

[0747] The server sends the stored health-related data to an AI model and begins analyzing the data. The AI ​​model evaluates not only the user's diet and exercise habits, but also their overall health status.

[0748] Step 5:

[0749] The server utilizes an emotion engine to analyze the user's voice input and recognize their emotional state. This is used to identify stress levels, mood tendencies, and other related information.

[0750] Step 6:

[0751] The server integrates the analysis results of the AI ​​model with the recognition results of the emotion engine to generate a personalized health plan for each user. The generated plan includes nutritional suggestions, exercise recommendations, and stress management advice based on emotional state. For example, on days with high stress levels, a plan emphasizing relaxation will be provided.

[0752] Step 7:

[0753] The server sends the generated health plan to the device. The device then notifies the user of this information through its user interface, supporting the user's conscious health management.

[0754] Step 8:

[0755] Users adjust their daily lives according to the notified health plan and record the results of their efforts and any new feedback in the application. The application tracks the progress of plan implementation.

[0756] Step 9:

[0757] The device sends user-recorded feedback data to the server. This feedback includes changes in the user's emotions.

[0758] Step 10:

[0759] The server analyzes the feedback data and adjusts the new health plan. It prepares to generate and deliver an optimal health plan again, tailored to the user's changes in health and emotional state.

[0760] In this way, by utilizing the emotion engine, it becomes possible to manage health while taking into account the user's mental state, and to continuously provide personalized advice that is tailored to the user's lifestyle.

[0761] (Example 2)

[0762] 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".

[0763] In modern times, health management requires a personalized approach that takes into account individual lifestyles and emotional states. However, conventional health management systems often offer generalized suggestions and fail to provide advice that considers the user's emotional state, resulting in low user satisfaction.

[0764] 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.

[0765] In this invention, the server includes an input device, a processing device, an artificial intelligence device, a communication device, a data collection device, an analysis device, an emotion engine device, and an adjustment device. This enables personalized health planning and highly satisfying health management that takes into account the user's nutritional status, exercise habits, and emotional state.

[0766] An "input device" refers to a means by which a user inputs health-related information as data.

[0767] A "processing device" refers to a device that has the function of collecting input information and storing it in a memory device.

[0768] "Artificial intelligence device" refers to a technological means for analyzing stored information and generating personalized health plans.

[0769] "Communication device" refers to a means of notifying the user of the generated health design through the user interface.

[0770] A "collection device" refers to a device that has the function of collecting user feedback information and transmitting it to a processing device.

[0771] An "analysis device" refers to a device that has analytical functions to improve health design based on collected feedback information.

[0772] An "emotional engine device" refers to a method that utilizes technology to analyze emotional information obtained through voice input.

[0773] A "regulating device" refers to a device that dynamically adjusts and improves the health design generated based on emotional information.

[0774] This invention provides a system that allows users to manage their own health status on a daily basis. The user inputs health-related information, such as diet, exercise, and emotional state, using a dedicated application. This terminal transmits the input data to a server. The server processes this data and stores it in its storage device.

[0775] The server uses artificial intelligence technology to analyze the user's current health status based on stored information. This AI device includes a machine learning model implemented in Python, which analyzes the user's nutritional intake, exercise patterns, and emotional state in detail. Meanwhile, the emotion engine device analyzes the user's emotions from voice data and uses natural language processing technology to identify their emotional state.

[0776] Based on the analysis results, the server generates a personalized health plan. This plan includes nutritional balance, exercise suggestions, and relaxation methods tailored to the user's emotional state. For example, if the analysis reveals that a particular user is experiencing stress, the server will incorporate relaxation-enhancing exercises and meals into the plan.

[0777] The generated health plan is sent from the server to the device. The device notifies the user of this. The user executes the provided plan according to their daily activities and records feedback in the application. This feedback is sent back to the server via the device and is reflected in the next health plan based on the latest information and emotional state.

[0778] As a concrete example, when a user uses the prompt "Suggest exercise and diet recommendations for when the user is feeling stressed," the generative AI model provides appropriate suggestions. This mechanism allows users to achieve highly satisfying health management.

[0779] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0780] Step 1:

[0781] Users use their mobile devices to input information about their meals, exercise, and emotional state via voice. This input data includes both text and audio. Users review this data within the app to ensure it is entered accurately.

[0782] Step 2:

[0783] The terminal sends the entered data to the server. This transmission takes place via an internet connection, and the data is stored in the server's storage device. The transmitted data is managed in conjunction with the user ID.

[0784] Step 3:

[0785] The server processes the received data and stores it in the database. During processing, the data format is standardized and converted into a format suitable for analysis by AI models. This enables high-quality analysis.

[0786] Step 4:

[0787] The server performs analysis on the stored data using a generative AI model. Nutritional information and exercise data are provided to the model as input, and the user's health trends are analyzed as output. The AI ​​model identifies user patterns and generates personalized health recommendations.

[0788] Step 5:

[0789] The emotion engine device analyzes voice data to identify the user's emotional state. Voice data is provided as input, and analysis is performed using natural language processing technology. The output is an emotional state such as stress, joy, or sadness.

[0790] Step 6:

[0791] The server generates a customized health plan that takes into account both health trends and emotional state. The plan includes suggestions that consider the user's nutrition, exercise, and emotions. Specifically, the suggestions are generated by a generative AI model.

[0792] Step 7:

[0793] The server sends the generated health design to the device. The device notifies the user, who can then review the design. The device uses push notifications to provide real-time notifications.

[0794] Step 8:

[0795] Users perform daily activities according to their health plan and input the results as feedback into the application. This feedback includes successes and challenges, and is recorded within the app.

[0796] Step 9:

[0797] The device sends the feedback back to the server. The server analyzes the received feedback data and adjusts the next health design. This reanalysis uses the feedback information as input and generates an adjusted health design as output.

[0798] (Application Example 2)

[0799] 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".

[0800] Traditional health management systems have struggled to provide health strategies that take into account the user's emotional state, resulting in users not receiving appropriate health guidance tailored to their individual circumstances and emotional state. Furthermore, in face-to-face services such as fitness gyms, personalized guidance that immediately reflects each individual's emotional and health state is required, but achieving this has been difficult.

[0801] 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.

[0802] In this invention, the server includes information gathering means, information management means, and artificial intelligence model means. This makes it possible to collect user health-related information and emotional information and immediately propose customized health strategies that take into account the user's emotional state.

[0803] "Information gathering means" refers to devices and methods for inputting health-related information and emotional information from users.

[0804] "Information management means" refers to devices and methods for storing accumulated data in a memory device, enabling later analysis and use.

[0805] An "artificial intelligence model means" is a program or device that analyzes stored data and creates customized health strategies for each user.

[0806] "Notification means" refers to output devices or methods for providing users with the created health measures.

[0807] "Feedback information collection means" refers to a method or device for collecting user feedback information and transmitting it to an information management means.

[0808] A "feedback analysis tool" is a method or device for modifying health policies based on accumulated feedback information.

[0809] "Device means" refers to equipment or software that proposes exercise programs that take emotional states into consideration.

[0810] To implement this invention, the following system is constructed. The server acquires health-related information and emotional information input from the user using information collection means. Sensors and applications installed on smartphones or smart devices can be used as information collection means. This collects the user's voice data and input information.

[0811] The collected data is transmitted to a server via information management means and stored in a memory device. This process often utilizes remote servers such as cloud servers. Next, the stored data is analyzed using artificial intelligence model means. The software used here includes TensorFlow and OpenAI API, which are used to determine the user's health and emotional state and generate customized health strategies.

[0812] The generated health measures are communicated to the user's device via a notification system. This notification is provided through a smartphone, tablet, or glasses-type device with a transparent display. The user can then implement the suggested health measures and send the results back to the server using a feedback information collection system.

[0813] To achieve further personalization, feedback analysis tools analyze feedback data, and health strategies are modified as needed. This cycle enables the provision of an optimal health plan tailored to the user's health and emotional state.

[0814] A concrete example would be a user stopping by a fitness gym on their way home from work, where a device that detects fatigue and stress suggests relaxation yoga. An example of a prompt for the generative AI model used here would be a sentence describing the specific situation, such as, "The user is tired after stopping by the gym on their way home from work." Based on this information, the AI ​​generates appropriate advice.

[0815] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0816] Step 1:

[0817] The server receives health-related and emotional information from the terminal. Users use a smartphone application to input data and voice information about their daily lives, and the information gathering system collects this information and sends it to the server. The input information includes dietary details, exercise history, and emotional data extracted from voice.

[0818] Step 2:

[0819] The server stores the received data in a database using information management tools. This storage step enables centralized data management and structures the data for later analysis. Database software is used in this process to ensure that the stored data is efficiently accessible and updatable.

[0820] Step 3:

[0821] The server analyzes the stored data using an artificial intelligence model. Receiving input health-related and emotional information, an AI model utilizing TensorFlow generates health strategies optimized for each individual user. In this process, it learns the correlations between data and outputs an appropriate health plan.

[0822] Step 4:

[0823] The server notifies the terminal of the generated health plan. Notification methods include smartphone push notifications and head-mounted display displays, immediately informing the user of the suggested content. The generated health plan is visualized and designed to be easily understood by the user.

[0824] Step 5:

[0825] The user implements the health measures they receive notifications about and sends feedback information from their device to the server. The feedback information collection mechanism is activated when the user re-enters the type of exercise they performed, their diet, and their emotional state at the time.

[0826] Step 6:

[0827] The server re-evaluates the received feedback information using feedback analysis tools. In this step, the server dynamically modifies the health measures to be provided next based on the newly received user input data. The re-evaluated data is then used for the next suggestion.

[0828] Step 7:

[0829] The server provides prompt statements to the generating AI model as needed, helping to generate the next suggested health measures. These prompt statements are based on the specific user's condition and the information required, and may include details such as "the user stopped by the gym after work and is tired."

[0830] 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.

[0831] 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 the following. 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 indicated 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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 as being incorporated by reference.

[0851] The following is further disclosed regarding the embodiments described above.

[0852] (Claim 1)

[0853] An input method for users to input health-related data,

[0854] A server for collecting entered health-related data and storing it in a database,

[0855] An AI model for analyzing stored data and generating personalized health plans for each user,

[0856] A terminal means for notifying the user of the generated health plan through a user interface,

[0857] A feedback collection means for collecting user feedback data and sending it to a server,

[0858] A feedback analysis tool for adjusting health plans based on collected feedback data,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, characterized in that the analysis of health-related data by an AI model includes a step of analyzing the user's nutritional status and exercise tendencies.

[0862] (Claim 3)

[0863] The system according to claim 1, characterized in that the server means dynamically adjusts the next health plan using user feedback data.

[0864] "Example 1"

[0865] (Claim 1)

[0866] An information input method for users to input health-related information,

[0867] A central control device means for collecting input health-related information and storing it in an information recording medium,

[0868] A machine learning model for analyzing stored information and generating a health plan optimized for each individual user,

[0869] Information provision device means for notifying the generated health plan via a user interface,

[0870] A response collection means for collecting response information from the user and transmitting it to a central control unit,

[0871] A response analysis means for adjusting the health plan based on the collected response information,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, characterized in that the analysis of health-related information using a machine learning model includes a step of analyzing the user's nutritional status and exercise tendencies.

[0875] (Claim 3)

[0876] The system according to claim 1, characterized in that a central control device dynamically adjusts the next health plan using user response information.

[0877] "Application Example 1"

[0878] (Claim 1)

[0879] A means for generating suggestions to suggest products to users within a store,

[0880] Product adjustment means for adjusting suggested products and health plans based on the user's health-related data,

[0881] A method for utilizing user feedback to optimize in-store suggestions,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, characterized in that the analysis of health-related data by an AI model includes a step of analyzing the user's nutritional status and exercise tendencies.

[0885] (Claim 3)

[0886] The system according to claim 1, characterized in that the server means dynamically adjusts the next health plan and product suggestions using user feedback data.

[0887] "Example 2 of combining an emotion engine"

[0888] (Claim 1)

[0889] An input device for users to input health-related information,

[0890] A processing device for collecting input health-related information and storing it in a storage device,

[0891] An artificial intelligence device for analyzing stored information and generating personalized health plans for each user,

[0892] A communication device for notifying the generated health design through a user interface,

[0893] A collection device for collecting user feedback information and transmitting it to a processing device,

[0894] An analytical device for improving health design based on collected feedback information,

[0895] An emotion engine device for analyzing emotional information obtained through voice input,

[0896] A device for adjusting health designs generated based on emotional information,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, characterized in that the analysis of health-related information by an artificial intelligence device includes a step of analyzing the user's nutritional status, exercise habits, and emotional state.

[0900] (Claim 3)

[0901] The system according to claim 1, characterized in that the processing device dynamically improves the next health design using user feedback information and emotional information.

[0902] "Application example 2 when combining with an emotional engine"

[0903] (Claim 1)

[0904] A means of collecting information for users to input health-related information and emotional information,

[0905] Information management means for collecting input information and storing it in a memory device,

[0906] An artificial intelligence model for analyzing stored data and creating customized health strategies for each user,

[0907] A notification means for providing the created health measures through an output device,

[0908] A feedback information collection means for collecting user feedback information and transmitting it to an information management means,

[0909] A feedback analysis means for modifying health policies based on accumulated feedback information,

[0910] A device for proposing an exercise program that takes emotional state into consideration,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, characterized in that the analysis of health-related information by an artificial intelligence model includes the step of analyzing the user's nutritional status, exercise tendencies, and emotional state.

[0914] (Claim 3)

[0915] The system according to claim 1, characterized in that the information management means dynamically modifies the next health measure using user feedback information. [Explanation of symbols]

[0916] 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. An input method for users to input health-related data, A server for collecting entered health-related data and storing it in a database, An AI model for analyzing stored data and generating personalized health plans for each user, A terminal means for notifying the user of the generated health plan through a user interface, A feedback collection means for collecting user feedback data and sending it to a server, A feedback analysis tool for adjusting health plans based on collected feedback data, A system that includes this.

2. The system according to claim 1, characterized in that the analysis of health-related data by an AI model includes a step of analyzing the user's nutritional status and exercise tendencies.

3. The system according to claim 1, characterized in that the server means dynamically adjusts the next health plan using user feedback data.

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