system

Abstract

We provide the system. [Solution] A data analysis means that receives personal health data, analyzes the data, and predicts the future physical condition, A means for generating a video to visualize the generated future physical state, A recommendation system that generates personalized advice for improving health, An output means for displaying the generated video and advice on an output device, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order for obese patients to achieve and maintain a healthy weight, continuous motivation is required. However, with conventional methods, it is difficult to see results in the short term, and patients often get frustrated halfway. Furthermore, it has been difficult to provide specific guidance according to individual health conditions, and there has been a lot of dependence on general guidelines. There is a need for a new support system that solves such problems and provides motivation and specific advice tailored to each individual.

Means for Solving the Problems

[0005] This invention provides a data analysis means for receiving and analyzing an individual's health data, and a generation means for generating a video of the future physical condition based on the analysis results. Furthermore, it includes a recommendation means for generating personalized advice for health improvement, and enhances user motivation by presenting these generated products to the user. It also includes means for strengthening motivation and providing satisfaction by adjusting the system's output content based on user feedback.

[0006] "Personal health data" refers to a series of pieces of information related to a user's health status, such as weight, height, diet, exercise habits, and target weight.

[0007] "Data analysis means" refers to a component that has the function of performing various analyses based on received individual health data and evaluating the user's current and future health status.

[0008] "Generation means" refers to a component that has the function of generating a video to visualize the future state of the body from the analysis results of data analysis means.

[0009] A "recommendation tool" refers to a component that has the function of creating health improvement advice optimized for the user based on the analysis results.

[0010] "Output means" refers to a component that has the function of visually providing the generated video and advice to the user.

[0011] A "feedback processing mechanism" refers to a component that has the function of receiving information about opinions and experiences from users and reflecting that information in the output of the entire system.

[0012] A "means of generating satisfaction" refers to a component that has the function of generating information to temporarily suppress the desires experienced by the user or to provide alternative satisfaction. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention relates to a system that provides personalized support to promote healthy weight management. The system consists of three main components: a server, a terminal, and a user, each performing a specific function to support obese individuals as a whole.

[0035] The server has the function of receiving and analyzing individual health data. This analysis includes weight, height, diet, exercise habits, etc., and uses this data to evaluate the user's current health status. Based on the analysis results, it generates a realistic video of the user's future physical condition. This generation method visualizes the user's future body shape and predicted healthy changes, leading to increased motivation. Furthermore, it generates personalized health improvement recommendations based on the analysis results. This recommendation method suggests meal plans and exercise plans, providing optimal health guidance to each individual user.

[0036] The terminal acts as a link between the user and the server, transmitting health data entered by the user to the server and presenting the server's output to the user. Specifically, it visually displays generated videos of the user's future body and personalized advice. Information to curb the user's appetite is also provided on the terminal. For example, when faced with the temptation of food, visually showing the user a healthy future self helps them make desirable choices. The terminal also collects user feedback and sends it back to the server, enabling the entire system to provide more personalized support.

[0037] Users input their health data through their device and review the generated content while taking action towards their health goals. Based on the advice provided, they review their daily lifestyle habits and implement healthy weight management. For example, when users decide on specific exercise or dietary adjustments to reach their target weight, they can use this system to obtain a concrete action plan. By providing feedback, more accurate and personalized support becomes continuously available.

[0038] This system thus provides a concrete form of support for users to achieve a healthy lifestyle.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users enter their health data using a device. This includes current weight, height, diet, exercise habits, and target weight. After entering the data, users click a submit button on the device to send this data to the server.

[0042] Step 2:

[0043] The device formats the health data entered by the user into the specified data format and sends it to the server using a secure communication protocol. After the data is sent, a success notification is displayed to the user.

[0044] Step 3:

[0045] The server stores the health data received from the terminal into an analysis platform and begins the analysis. The analysis includes an assessment of the current health status and a prediction of future health status based on the received data.

[0046] Step 4:

[0047] The server uses AI generation based on the analysis results to create a video simulating the user's future physical condition. This video visualizes the user's physique and health status if they achieve their target weight.

[0048] Step 5:

[0049] The server generates personalized health advice based on the analysis results. This includes specific exercise plans, meal plans, and suggestions for behavioral changes.

[0050] Step 6:

[0051] The server sends the generated video and personalized advice to the device. The device then sends a notification when the server has finished processing.

[0052] Step 7:

[0053] The device visually displays a video and advice from the server showing the user's future body shape. The user reviews this and decides on their next action.

[0054] Step 8:

[0055] Users implement their own health management plans based on the displayed content. If necessary, they incorporate the provided advice into their daily routines and record the results.

[0056] Step 9:

[0057] Users input feedback on the results and experiences of the health management measures they have taken into the terminal. The terminal sends this feedback to the server, which is then used to improve the system's response in the future.

[0058] (Example 1)

[0059] 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."

[0060] Traditional health management systems have struggled to comprehensively capture an individual's health status, particularly in providing specific future health predictions and personalized advice. Furthermore, they lacked visualization and adaptive support that reflected feedback, which are crucial for motivating users. Therefore, there is a need for systems that provide specific health predictions based on individual health data, along with individually optimized advice based on these predictions visualized.

[0061] 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.

[0062] In this invention, the server includes an information analysis means that receives personal health-related information and analyzes the information to predict future health status; a generation means that generates a medium that visualizes the future health status based on the analyzed data; and a recommendation means that generates individually optimized suggestions for improving health. This makes it possible to predict and visualize the user's specific future health status and provide personalized health improvement suggestions.

[0063] "Personal health-related information" refers to data such as weight, height, diet, and exercise habits that are necessary to assess the health status of individual users.

[0064] "Information analysis means" refers to a device or process that has the function of analyzing an individual's health-related information received by a server and predicting their future health status based on that information.

[0065] "Generation means" refers to a device or process that has the function of generating a medium for visually representing future health conditions based on analyzed data.

[0066] "Recommendation method" refers to a device or process that has the function of providing health improvement suggestions best suited to each individual user based on analysis results.

[0067] A "display device" refers to a device used to visually present generated visual media and proposals to the user.

[0068] "Reaction processing means" refers to a device or process that has the function of acquiring feedback from the user and adjusting the output content of the system based on that feedback.

[0069] A "satisfaction generation means" refers to a device or process that has the function of generating information that provides alternative satisfaction in order to alleviate the user's desires.

[0070] This invention is a system that supports the promotion of health and weight management for individual users, and mainly consists of three elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[0071] The server receives and analyzes individual health-related information. The data analysis uses the Python data analysis platform, specifically the Pandas library, for data organization and cleaning. The analysis also utilizes the machine learning library TENSORFLOW® to predict the user's future health status. The predicted data is then generated as visual content using 3D graphics software. For example, an open-source 3D modeling tool is used to create a video showing changes in the user's body shape.

[0072] The device transmits health-related information entered by the user to a server and presents the user with content generated from the server. The device features an intuitive user interface, allowing users to easily view generated videos and health improvement suggestions. In addition to visual information, the device also provides lifestyle improvement suggestions to the user through pop-up notifications.

[0073] Users input their health information through their device and take action toward their daily health goals while reviewing personalized improvement suggestions provided by the server. For example, users can use this system to create specific meal plans and exercise plans. They can also send feedback on the results obtained to the server via their device, allowing them to continuously receive more tailored support.

[0074] An example of a prompt to the generating AI model in this system would be, "Generate a predictive video and improvement advice for 12 months from now, based on the user's health data from the past 6 months." Thus, the present invention provides a form that guides users on specific measures necessary to lead a healthy life and assists them in implementing them.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users input their health-related information using a device. Specifically, users record daily health data such as weight, height, diet, type and duration of exercise using a dedicated application. This information is used as input. Users can also automatically collect data via Bluetooth-enabled devices.

[0078] Step 2:

[0079] The device transmits health-related information entered by the user to the server. The entered data is transmitted using a security protocol and protected from eavesdropping. The information is encrypted and uploaded to the server using HTTPS.

[0080] Step 3:

[0081] The server begins processing the received health-related information. First, it removes outliers and missing values ​​through data cleansing using Python. Next, it formats the data using the Pandas library to prepare it for analysis. This is the data processing stage.

[0082] Step 4:

[0083] The server runs a machine learning model using the analyzed data. At this stage, TensorFlow is used to predict the user's future weight trends and health status. The predicted results are output and used in the next step.

[0084] Step 5:

[0085] The server uses a generative AI model to visualize the future state based on the analysis results. Specifically, it uses open-source 3D graphics software to generate a video representing the user's future health condition. The generated product is output as video data.

[0086] Step 6:

[0087] The device displays videos and recommended plans, which are generated from the server, to the user. The device interface is designed for easy user access, allowing users to watch videos and review detailed health suggestions. This enables users to intuitively understand their future health status.

[0088] Step 7:

[0089] Users evaluate the information provided from their devices and input feedback. Users write their opinions, questions, and other comments regarding specific suggestions within the app and send them to the server. This feedback is considered in the next analysis cycle to help generate more appropriate suggestions.

[0090] (Application Example 1)

[0091] 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."

[0092] In modern society, maintaining a healthy weight is a significant challenge for many people. In particular, there is a lack of visual feedback to predict future body shape based on individual health data and to boost motivation. Furthermore, there is no established means to provide an environment that facilitates concrete action, such as offering special prices directly available in stores. Therefore, there is a need for a system that supports a healthy lifestyle through personalized predictive information.

[0093] 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.

[0094] In this invention, the server includes data analysis means for receiving and analyzing personal health data to predict future physical condition, generation means for generating a video to visualize the predicted future physical condition, and proposal means for generating and displaying special price offers at the sales location where the user is located. This makes it possible to promote healthy behaviors by combining personalized health data with special offers within the store.

[0095] "Data analysis means" refers to a function that receives an individual's health information and uses that information to predict their future physical condition.

[0096] "Generative means" refers to the function of creating a video to visually represent the predicted future state of the body.

[0097] "Recommendation tools" refer to functions that generate advice to provide personalized guidance aimed at improving health.

[0098] "Output means" refers to devices or methods for presenting generated videos or advice to the user.

[0099] "Proposal method" refers to a function that generates and presents special price offers at the sales locations visited by users.

[0100] "Feedback processing means" refers to a function that receives opinions and feedback from users and adjusts the system's offerings based on that feedback.

[0101] "Means of generating satisfaction" refers to a function that creates information to suppress the user's appetite and generate alternative satisfaction.

[0102] The system for implementing this invention consists of three main components: a server, a user terminal, and the user. The server collects personal health data and analyzes that data using data analysis means. The analysis includes information such as weight, height, diet, and exercise habits, which is used to predict the future physical condition. Based on the predicted data, the server uses generation means to create a video that visualizes the future body shape.

[0103] The user terminal is equipped with output means for displaying videos and personalized health advice transmitted from the server. This allows users to use it as a guide for daily health management. In addition, a suggestion means can present special price offers at stores where the user is located, allowing the user to use them directly at the store. Furthermore, user feedback is sent to the server through the terminal, and a feedback processing means is used to further personalize the service content.

[0104] This system will utilize Python and video playback libraries to speed up process processing. Machine learning libraries such as scikit-learn will be used for data analysis. Generative AI models will be effective for future visualization of physical conditions. User recommendations will be generated through individual data analysis using historical data and AI models.

[0105] As a concrete example, when a user visits a gym, they scan the store's QR code (registered trademark), which sends their health data to their smartphone. Based on this data, a video predicting their future physique is generated, which the user can use to boost their motivation. In addition, prompts such as "Analyze the user's health data and generate a video of their physique one month from now" or "Personalize meal and exercise plans and suggest special in-store prices" enable the system to make appropriate health improvement suggestions.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server receives health data sent from the user. The input consists of health data such as the user's weight, height, diet, and exercise habits. The received data is preprocessed by data analysis tools, including the imputation of missing values ​​and standardization. The output is health data converted into a format suitable for analysis.

[0109] Step 2:

[0110] The server uses pre-processed health data and a generative AI model to predict future physical conditions. The input is pre-processed health data, and the output is predicted values ​​for future body shape and health indicators. Specifically, by inputting data into the model and performing inference processing, future numerical data is obtained.

[0111] Step 3:

[0112] The server generates a visualization video of body shape using a generation method based on predicted future physical condition. The input is data of predicted health indicators, and the output is a video file showing the future body shape. Video creation is achieved by using video generation software and animating still images.

[0113] Step 4:

[0114] The device receives future body shape videos and personalized recommendations sent from the server and displays them on its screen. Input consists of video files and text-based advice, while output is visual information provided to the user. Operation within the device involves using a viewer application to play the videos, and the text is displayed on the user interface.

[0115] Step 5:

[0116] The terminal displays an interface for receiving user feedback and sends the information entered by the user to the server. Input consists of text data such as user ratings and comments, while output is data sent to the server. Feedback is collected from the text field based on the user's input.

[0117] Step 6:

[0118] The server processes the received feedback and stores it in a database for future system improvements. The input is user feedback, and the output is the information stored in the database. Specifically, the feedback information is analyzed to improve algorithms and refine recommendations.

[0119] Step 7:

[0120] The device generates and presents special price offers for the store where the user is located. Inputs are the user's current location and health data, and output is information about the special offer. The logic operates by obtaining the user's location information via GPS and generating store promotions based on that data.

[0121] 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.

[0122] This invention is a system for improving personal health management, providing personalized support while taking into account the user's emotional state. The system consists of three main components: a server, a terminal, and the user, and further incorporates an emotion engine to enrich the user experience.

[0123] The server first receives and analyzes the user's health data. Based on the analyzed data, it simulates the user's future physical condition and generates a video that visualizes it. This allows the user to visually see themselves after achieving their goals, leading to increased motivation. The server also generates personalized health improvement advice and sends it, along with the video, to the user's device. In this process, the server uses an emotion recognition engine to analyze the user's facial expression data and determine the user's emotional state. Based on this, the generated content is adjusted according to the user's mood. For example, if the user is in a positive emotional state, challenging health advice will be presented, while if they are in a negative emotional state, content emphasizing encouragement and support will be provided.

[0124] The terminal functions as an interface with the user, acting as a tool for inputting health data and as a display device for content received from the server. The terminal's emotion engine captures the user's facial expressions and sends them to the server, allowing for real-time monitoring of the user's emotional state.

[0125] Users input health data through their devices and view future body shape videos and health advice received from the server. This support, customized by an emotion engine, is highly effective in maintaining user motivation. For example, when a user feels hungry and loses willpower, they can view encouraging messages and videos visualizing a positive future through their device. This kind of real-time support guides users towards healthier choices.

[0126] In this way, the system provides a mechanism to support health management in a more individualized and emotionally responsive manner. This approach, which is attentive to the user's physical condition and emotions, encourages the achievement of long-term health goals and increases user satisfaction.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users enter their health data using a device. This includes weight, height, diet, exercise habits, and target weight. After entering the data, the device sends this information to the server.

[0130] Step 2:

[0131] The device captures the user's facial expressions with its built-in camera, analyzes the facial data through an emotion engine, and determines the user's emotional state. This emotional information is also sent to the server.

[0132] Step 3:

[0133] The server receives health data sent by the user and begins predictive analysis of their health status. This analysis includes confirming the user's current health status based on the data and predicting their future body shape.

[0134] Step 4:

[0135] The server uses AI generation based on the analysis results to create a video that simulates the user's future physical condition. This video realistically visualizes the user's body shape once they reach their target weight.

[0136] Step 5:

[0137] The server considers the user's emotional state, as determined by the emotion engine, and customizes the generated videos and health advice accordingly. For example, if the user's emotions are positive, it will provide slightly more challenging advice.

[0138] Step 6:

[0139] The server sends the generated video and customized health advice to the device. The device receives this and prepares to display it to the user.

[0140] Step 7:

[0141] The device displays a video received from the server to the user, simultaneously showing personalized health advice in text and images. The user reviews this and plans their next action.

[0142] Step 8:

[0143] Users will then implement their own health strategies based on the information provided. For example, they might revise their diet and exercise plans according to the advice given and put specific actions into practice.

[0144] Step 9:

[0145] Users input feedback on their daily health management activities into their device. This information is sent to a server and used to adjust future support plans.

[0146] (Example 2)

[0147] 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".

[0148] In modern society, effective individual health management is crucial. However, conventional systems have struggled to provide personalized health support tailored to each user's emotional state, making it difficult to maintain motivation and sustain health improvement. Therefore, a new system is needed that takes into account the user's emotional state and provides health improvement advice and visual content that is appropriately adjusted in real time.

[0149] 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.

[0150] In this invention, the server includes information analysis means for receiving personal health information and analyzing the information to predict the future physical condition; generation means for generating images to visualize the generated future physical condition; and emotion analysis means for analyzing the user's emotional state and adjusting advice and images according to that emotion. This makes it possible to provide personalized health support in real time based on the user's emotional state and promote the maintenance of motivation.

[0151] "Personal health information" refers to health-related information entered by the user, such as weight, exercise level, and diet.

[0152] "Information analysis means" refers to a method of organizing received health information into data frames and other formats, and then using a predictive model to estimate future health conditions.

[0153] "Generation means" refers to a device or software that creates visually easy-to-understand images based on analyzed future health data.

[0154] "Recommendation methods" refer to systems that generate personalized advice and suggestions necessary for improving a user's health.

[0155] "Output mechanism" refers to a display or device used to present generated information or images to the user.

[0156] "Emotional analysis tools" refer to systems that analyze a user's facial expressions and behavior to identify their current emotional state.

[0157] "Input processing means" refers to the process of receiving responses and feedback from the user and adjusting the system's response accordingly.

[0158] "Means of generating satisfaction" refers to means of generating information that provides alternative satisfaction to the user's desires or appetite.

[0159] "Display means" refers to devices or functions that show real-time generated content to users.

[0160] This invention is a system that effectively supports individual health management and consists of three main components: a server, a terminal, and a user. Furthermore, by incorporating an emotion analysis engine, it can provide support tailored to the user's emotional state.

[0161] The server first receives health information sent from the user via their device. This information includes weight, exercise levels, and dietary content. The server converts the health information into a data frame using the Python Pandas library and performs analysis. The analyzed data is then used with machine learning algorithms to predict the user's future physical condition. The predicted data is converted into a visually easy-to-understand video, generated using video editing software such as Adobe After Effects. This allows for a more concrete visualization of the user's health improvement goals.

[0162] Simultaneously, the server analyzes facial expression data sent from the device using Microsoft's Azure Face API to perform emotion analysis. Based on this analysis, a generative AI model is used to generate personalized health recommendations and advice. For example, a user who is feeling down might be given an encouraging message such as, "By taking the next step, you can achieve your ideal state of health."

[0163] The device provides an interface for users to input health information and also has an output function to display generated videos and advice. The device uses a built-in camera to capture the user's facial expressions and transmits the data to the server in real time. This process allows users to receive dynamic and effective health support that responds to their emotional state.

[0164] For example, if a user aims to lose 5 kilograms in the future, the server will provide a video visualizing the achievement of this goal. Furthermore, through sentiment analysis, if the user is losing motivation, it will add an encouraging message. An example of a prompt would be, "Based on my current body data and eating history, please generate a simulation video of my target body shape in 3 months and suggest encouraging messages to help me stay motivated."

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users input health information using a terminal. This information includes weight, exercise level, and diet. The entered data is formatted and converted to CSV format. This is both the input from the terminal and the output to the server.

[0168] Step 2:

[0169] The server receives health information in CSV format from the terminal. Next, it uses the Python Pandas library to convert the CSV data into a DataFrame. This DataFrame becomes the input for analysis, and the analyzed numerical data of the health status is output.

[0170] Step 3:

[0171] The server predicts the future state of the user's physical condition based on the analysis results. This process utilizes machine learning algorithms. For example, a predictive model is built using scikit-learn, and the user's health information is used as input. The output is predictive data indicating the user's future health condition.

[0172] Step 4:

[0173] The server generates videos to visualize the predicted data. For this purpose, it uses video editing software such as Adobe After Effects. Predicted data is used as input, and the output is a video representing the future physical condition.

[0174] Step 5:

[0175] The device uses its built-in camera to capture the user's facial expressions. This allows for the acquisition of facial expression data in real time. The acquired data is sent to a server as input, and the results of the facial expression analysis are output.

[0176] Step 6:

[0177] The server analyzes the received facial expression data. Here, the emotion analysis engine uses Microsoft's Azure Face API to analyze the user's emotional state. In this analysis, facial expression data is used as input, and the user's emotional state is output.

[0178] Step 7:

[0179] Based on the results of sentiment analysis, the server uses a generative AI model to generate personalized health recommendations. This process uses the user's past data and emotional state as input, and outputs appropriate health advice and messages.

[0180] Step 8:

[0181] The device displays generated video and health advice received from the server. The user adjusts their actions based on this information. The input in this step is data from the server, and the output is the visual content and advice received by the user.

[0182] (Application Example 2)

[0183] 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".

[0184] In personal health management, simply analyzing data and providing advice is often insufficient to fully capture the user's feelings and motivation. In particular, the lack of methods to provide personalized support that considers the impact of emotional changes on health behaviors makes it difficult for users to achieve their long-term health goals.

[0185] 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.

[0186] In this invention, the server includes emotion analysis means for analyzing an individual's emotional state and adjusting content for health improvement based on the analyzed emotions; output means for displaying the generated video and advice on an output device; and generation means for using a generation AI model to analyze the user's emotional state and generate appropriate health promotion content. This enables more effective and personalized health support that responds to changes in the user's emotions.

[0187] "Data analysis methods" refer to methods for receiving an individual's health data and analyzing it to predict their future physical condition.

[0188] "Generation method" refers to a method for generating a video for visualization based on the analyzed future state of the body.

[0189] A "recommendation method" is a means of generating personalized advice aimed at improving health.

[0190] "Output means" refers to means for outputting the generated video and advice to a display device.

[0191] "Emotional analysis methods" are means of analyzing an individual's emotional state and adjusting content aimed at improving health based on the analyzed emotions.

[0192] A "generative AI model" is a machine learning model used to generate appropriate health promotion content based on the user's emotional state.

[0193] This invention provides a system for improving individual health management, and in particular, for realizing personalized health support that responds to the user's emotional state.

[0194] The server first receives and analyzes individual health data using data analysis tools. Based on the analysis results, a generation tool generates a video to visualize the future physical condition. At this time, an emotion analysis tool analyzes the user's emotional data received from the terminal and generates content for health improvement in response to changes in emotion. A generation AI model is used for this generation, and appropriate content is generated using prompt sentences based on the user's emotional state as input.

[0195] The device functions as an interface with the user, capturing data on the user's facial expressions and emotions using cameras and sensors. This data is sent to a server for real-time emotion analysis. Furthermore, the generated video and advice are presented to the user via a display device. The displayed advice is tailored based on the user's emotional state, helping them achieve their long-term health goals.

[0196] For example, if a user's "fatigue" is detected via their smartphone, a relaxation video will be displayed, along with a message encouraging them to rest early. An example of this prompt might be: "Generating personalized health advice based on the user's emotions. Emotional state: Fatigue, Recommended action: Relaxation."

[0197] In this way, this system provides health support that is tailored to the user's feelings and makes a significant contribution to improving individual health.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server receives user health data transmitted from the terminal. This data includes heart rate, activity level, and dietary information. The received data is stored in a database and prepared for analysis.

[0201] Step 2:

[0202] The server analyzes the received health data using data analysis tools. This analysis uses statistical models to predict future physical conditions. The output obtained from the analysis is information about predicted health parameters and future health risks.

[0203] Step 3:

[0204] The server generates videos to visualize the future physical condition based on the analysis results, using a generation method. Specifically, it uses an AI model to create digital animations based on the prediction results. The generated videos are prepared as content to help improve user motivation.

[0205] Step 4:

[0206] The device captures the user's facial expressions with a camera and analyzes them in real time. Using emotion analysis technology, it classifies the user's emotional state and sends the results to a server. The input is a camera image, and the output is an emotion label.

[0207] Step 5:

[0208] The server uses a generative AI model based on the received emotional data to generate health improvement advice tailored to the user's emotional state. The generation process utilizes a pre-trained AI model to create appropriate health behavior suggestions from prompt text. For example, it might generate an output such as, "If the user is feeling fatigued, we recommend relaxation."

[0209] Step 6:

[0210] The server sends the generated video and personalized advice to the device. This output is displayed on the user's screen to encourage actual action. The device presents the displayed information to the user and supports healthy behaviors.

[0211] Step 7:

[0212] Users review videos and advice displayed on their devices and provide feedback to the server as needed. This feedback is used to improve the overall accuracy of the system and adjust the content.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] This invention relates to a system that provides personalized support to promote healthy weight management. The system consists of three main components: a server, a terminal, and a user, each performing a specific function to support obese individuals as a whole.

[0230] The server has the function of receiving and analyzing individual health data. This analysis includes weight, height, diet, exercise habits, etc., and uses this data to evaluate the user's current health status. Based on the analysis results, it generates a realistic video of the user's future physical condition. This generation method visualizes the user's future body shape and predicted healthy changes, leading to increased motivation. Furthermore, it generates personalized health improvement recommendations based on the analysis results. This recommendation method suggests meal plans and exercise plans, providing optimal health guidance to each individual user.

[0231] The terminal acts as a link between the user and the server, transmitting health data entered by the user to the server and presenting the server's output to the user. Specifically, it visually displays generated videos of the user's future body and personalized advice. Information to curb the user's appetite is also provided on the terminal. For example, when faced with the temptation of food, visually showing the user a healthy future self helps them make desirable choices. The terminal also collects user feedback and sends it back to the server, enabling the entire system to provide more personalized support.

[0232] Users input their health data through their device and review the generated content while taking action towards their health goals. Based on the advice provided, they review their daily lifestyle habits and implement healthy weight management. For example, when users decide on specific exercise or dietary adjustments to reach their target weight, they can use this system to obtain a concrete action plan. By providing feedback, more accurate and personalized support becomes continuously available.

[0233] This system thus provides a concrete form of support for users to achieve a healthy lifestyle.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] Users enter their health data using a device. This includes current weight, height, diet, exercise habits, and target weight. After entering the data, users click a submit button on the device to send this data to the server.

[0237] Step 2:

[0238] The device formats the health data entered by the user into the specified data format and sends it to the server using a secure communication protocol. After the data is sent, a success notification is displayed to the user.

[0239] Step 3:

[0240] The server stores the health data received from the terminal into an analysis platform and begins the analysis. The analysis includes an assessment of the current health status and a prediction of future health status based on the received data.

[0241] Step 4:

[0242] The server uses AI generation based on the analysis results to create a video simulating the user's future physical condition. This video visualizes the user's physique and health status if they achieve their target weight.

[0243] Step 5:

[0244] The server generates personalized health advice based on the analysis results. This includes specific exercise plans, meal plans, and suggestions for behavioral changes.

[0245] Step 6:

[0246] The server sends the generated video and personalized advice to the device. The device then sends a notification when the server has finished processing.

[0247] Step 7:

[0248] The device visually displays a video and advice from the server showing the user's future body shape. The user reviews this and decides on their next action.

[0249] Step 8:

[0250] Users implement their own health management plans based on the displayed content. If necessary, they incorporate the provided advice into their daily routines and record the results.

[0251] Step 9:

[0252] Users input feedback on the results and experiences of the health management measures they have taken into the terminal. The terminal sends this feedback to the server, which is then used to improve the system's response in the future.

[0253] (Example 1)

[0254] 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."

[0255] Traditional health management systems have struggled to comprehensively capture an individual's health status, particularly in providing specific future health predictions and personalized advice. Furthermore, they lacked visualization and adaptive support that reflected feedback, which are crucial for motivating users. Therefore, there is a need for systems that provide specific health predictions based on individual health data, along with individually optimized advice based on these predictions visualized.

[0256] 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.

[0257] In this invention, the server includes an information analysis means that receives personal health-related information and analyzes the information to predict future health status; a generation means that generates a medium that visualizes the future health status based on the analyzed data; and a recommendation means that generates individually optimized suggestions for improving health. This makes it possible to predict and visualize the user's specific future health status and provide personalized health improvement suggestions.

[0258] "Personal health-related information" refers to data such as weight, height, diet, and exercise habits that are necessary to assess the health status of individual users.

[0259] "Information analysis means" refers to a device or process that has the function of analyzing an individual's health-related information received by a server and predicting their future health status based on that information.

[0260] "Generation means" refers to a device or process that has the function of generating a medium for visually representing future health conditions based on analyzed data.

[0261] "Recommendation method" refers to a device or process that has the function of providing health improvement suggestions best suited to each individual user based on analysis results.

[0262] A "display device" refers to a device used to visually present generated visual media and proposals to the user.

[0263] "Reaction processing means" refers to a device or process that has the function of acquiring feedback from the user and adjusting the output content of the system based on that feedback.

[0264] A "satisfaction generation means" refers to a device or process that has the function of generating information that provides alternative satisfaction in order to alleviate the user's desires.

[0265] This invention is a system that supports the promotion of health and weight management for individual users, and mainly consists of three elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[0266] The server receives and analyzes individual health-related information. The data analysis uses the Python data analysis platform, specifically the Pandas library, for data organization and cleaning. The analysis also utilizes the machine learning library TensorFlow to predict the user's future health status. The predicted data is then generated as visual content using 3D graphics software. For example, an open-source 3D modeling tool is used to create a video showing changes in the user's body shape.

[0267] The device transmits health-related information entered by the user to a server and presents the user with content generated from the server. The device features an intuitive user interface, allowing users to easily view generated videos and health improvement suggestions. In addition to visual information, the device also provides lifestyle improvement suggestions to the user through pop-up notifications.

[0268] Users input their health information through their device and take action toward their daily health goals while reviewing personalized improvement suggestions provided by the server. For example, users can use this system to create specific meal plans and exercise plans. They can also send feedback on the results obtained to the server via their device, allowing them to continuously receive more tailored support.

[0269] An example of a prompt to the generating AI model in this system would be, "Generate a predictive video and improvement advice for 12 months from now, based on the user's health data from the past 6 months." Thus, the present invention provides a form that guides users on specific measures necessary to lead a healthy life and assists them in implementing them.

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

[0271] Step 1:

[0272] Users input their health-related information using a device. Specifically, users record daily health data such as weight, height, diet, type and duration of exercise using a dedicated application. This information is used as input. Users can also automatically collect data via Bluetooth-enabled devices.

[0273] Step 2:

[0274] The device transmits health-related information entered by the user to the server. The entered data is transmitted using a security protocol and protected from eavesdropping. The information is encrypted and uploaded to the server using HTTPS.

[0275] Step 3:

[0276] The server begins processing the received health-related information. First, it removes outliers and missing values ​​through data cleansing using Python. Next, it formats the data using the Pandas library to prepare it for analysis. This is the data processing stage.

[0277] Step 4:

[0278] The server runs a machine learning model using the analyzed data. At this stage, TensorFlow is used to predict the user's future weight trends and health status. The predicted results are output and used in the next step.

[0279] Step 5:

[0280] The server uses the generative AI model to visualize the future state based on the analysis results. Specifically, it uses open-source 3D graphic software to generate a video representing the user's future health state. The generated product is output as video data.

[0281] Step 6:

[0282] The terminal displays the video, which is the generated product sent from the server, and the recommended plan to the user. The interface of the terminal is designed to be easily accessible to the user, allowing for video viewing and detailed confirmation of health proposals. This enables the user to intuitively understand the future state.

[0283] Step 7:

[0284] The user evaluates the information provided by the terminal and enters feedback. The user describes opinions and questions regarding specific proposals in the app and sends them to the server. This feedback is considered in the next analysis cycle and helps generate more appropriate proposals.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] In modern society, continuously maintaining a healthy weight management is a major challenge for many people. In particular, there is a lack of visual feedback for predicting future body shapes based on personal health data and enhancing motivation. Also, there is no established means to provide an environment that facilitates the implementation of actions, such as offering special price proposals available directly at stores. Therefore, there is a need for a system that supports a healthy lifestyle from individualized prediction information.

[0288] 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.

[0289] In this invention, the server includes data analysis means for receiving and analyzing personal health data to predict future physical condition, generation means for generating a video to visualize the predicted future physical condition, and proposal means for generating and displaying special price offers at the sales location where the user is located. This makes it possible to promote healthy behaviors by combining personalized health data with special offers within the store.

[0290] "Data analysis means" refers to a function that receives an individual's health information and uses that information to predict their future physical condition.

[0291] "Generative means" refers to the function of creating a video to visually represent the predicted future state of the body.

[0292] "Recommendation tools" refer to functions that generate advice to provide personalized guidance aimed at improving health.

[0293] "Output means" refers to devices or methods for presenting generated videos or advice to the user.

[0294] "Proposal method" refers to a function that generates and presents special price offers at the sales locations visited by users.

[0295] "Feedback processing means" refers to a function that receives opinions and feedback from users and adjusts the system's offerings based on that feedback.

[0296] "Means of generating satisfaction" refers to a function that creates information to suppress the user's appetite and generate alternative satisfaction.

[0297] The system for implementing this invention consists of three main components: a server, a user terminal, and the user. The server collects personal health data and analyzes that data using data analysis means. The analysis includes information such as weight, height, diet, and exercise habits, which is used to predict the future physical condition. Based on the predicted data, the server uses generation means to create a video that visualizes the future body shape.

[0298] The user terminal is equipped with output means for displaying videos and personalized health advice transmitted from the server. This allows users to use it as a guide for daily health management. In addition, a suggestion means can present special price offers at stores where the user is located, allowing the user to use them directly at the store. Furthermore, user feedback is sent to the server through the terminal, and a feedback processing means is used to further personalize the service content.

[0299] This system will utilize Python and video playback libraries to speed up process processing. Machine learning libraries such as scikit-learn will be used for data analysis. Generative AI models will be effective for future visualization of physical conditions. User recommendations will be generated through individual data analysis using historical data and AI models.

[0300] As a concrete example, when a user visits a gym, they scan a QR code at the gym, which sends their health data to their smartphone. Based on this data, a video predicting their future physique is generated, which the user can use to boost their motivation. Furthermore, prompts such as "Analyze the user's health data and generate a video of their physique one month from now" or "Personalize meal and exercise plans and suggest special in-store prices" enable the system to make appropriate health improvement suggestions.

[0301] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0302] Step 1:

[0303] The server receives the health data sent by the user. The input is health data such as the user's weight, height, diet, and exercise habits. The received data is preprocessed by data analysis means, and missing values are complemented and standardized. The output is health data converted into a form suitable for analysis.

[0304] Step 2:

[0305] The server uses the preprocessed health data to predict the future physical condition by utilizing the generative AI model. The input is the preprocessed health data, and the output is the predicted values of future body shape and health indicators. Specifically, by inputting data into the model and performing inference processing, future numerical data can be obtained.

[0306] Step 3:

[0307] Based on the predicted future physical condition, the server uses the generation means to generate a visualization video of the body shape. The input is the data of the predicted health indicators, and the output is a video file showing the future body shape. For video generation, video generation software is utilized and realized through the process of animating still images.

[0308] Step 4:

[0309] The terminal receives the future body shape video and individualized recommendations sent by the server and displays them on the display. The input is the video file and text-form advice, and the output is the visual information provided to the user. Operations within the terminal use a viewer application for playing the video, and the text is displayed on the user interface.

[0310] Step 5:

[0311] The terminal displays an interface for receiving user feedback and sends the information entered by the user to the server. Input consists of text data such as user ratings and comments, while output is data sent to the server. Feedback is collected from the text field based on the user's input.

[0312] Step 6:

[0313] The server processes the received feedback and stores it in a database for future system improvements. The input is user feedback, and the output is the information stored in the database. Specifically, the feedback information is analyzed to improve algorithms and refine recommendations.

[0314] Step 7:

[0315] The device generates and presents special price offers for the store where the user is located. Inputs are the user's current location and health data, and output is information about the special offer. The logic operates by obtaining the user's location information via GPS and generating store promotions based on that data.

[0316] 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.

[0317] This invention is a system for improving personal health management, providing personalized support while taking into account the user's emotional state. The system consists of three main components: a server, a terminal, and the user, and further incorporates an emotion engine to enrich the user experience.

[0318] The server first receives and analyzes the user's health data. Based on the analyzed data, it simulates the user's future physical condition and generates a video that visualizes it. This allows the user to visually see themselves after achieving their goals, leading to increased motivation. The server also generates personalized health improvement advice and sends it, along with the video, to the user's device. In this process, the server uses an emotion recognition engine to analyze the user's facial expression data and determine the user's emotional state. Based on this, the generated content is adjusted according to the user's mood. For example, if the user is in a positive emotional state, challenging health advice will be presented, while if they are in a negative emotional state, content emphasizing encouragement and support will be provided.

[0319] The terminal functions as an interface with the user, acting as a tool for inputting health data and as a display device for content received from the server. The terminal's emotion engine captures the user's facial expressions and sends them to the server, allowing for real-time monitoring of the user's emotional state.

[0320] Users input health data through their devices and view future body shape videos and health advice received from the server. This support, customized by an emotion engine, is highly effective in maintaining user motivation. For example, when a user feels hungry and loses willpower, they can view encouraging messages and videos visualizing a positive future through their device. This kind of real-time support guides users towards healthier choices.

[0321] In this way, the system provides a mechanism to support health management in a more individualized and emotionally responsive manner. This approach, which is attentive to the user's physical condition and emotions, encourages the achievement of long-term health goals and increases user satisfaction.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] Users enter their health data using a device. This includes weight, height, diet, exercise habits, and target weight. After entering the data, the device sends this information to the server.

[0325] Step 2:

[0326] The device captures the user's facial expressions with its built-in camera, analyzes the facial data through an emotion engine, and determines the user's emotional state. This emotional information is also sent to the server.

[0327] Step 3:

[0328] The server receives health data sent by the user and begins predictive analysis of their health status. This analysis includes confirming the user's current health status based on the data and predicting their future body shape.

[0329] Step 4:

[0330] The server uses AI generation based on the analysis results to create a video that simulates the user's future physical condition. This video realistically visualizes the user's body shape once they reach their target weight.

[0331] Step 5:

[0332] The server considers the user's emotional state, as determined by the emotion engine, and customizes the generated videos and health advice accordingly. For example, if the user's emotions are positive, it will provide slightly more challenging advice.

[0333] Step 6:

[0334] The server sends the generated video and customized health advice to the device. The device receives this and prepares to display it to the user.

[0335] Step 7:

[0336] The device displays a video received from the server to the user, simultaneously showing personalized health advice in text and images. The user reviews this and plans their next action.

[0337] Step 8:

[0338] Users will then implement their own health strategies based on the information provided. For example, they might revise their diet and exercise plans according to the advice given and put specific actions into practice.

[0339] Step 9:

[0340] Users input feedback on their daily health management activities into their device. This information is sent to a server and used to adjust future support plans.

[0341] (Example 2)

[0342] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0343] In modern society, effective individual health management is crucial. However, conventional systems have struggled to provide personalized health support tailored to each user's emotional state, making it difficult to maintain motivation and sustain health improvement. Therefore, a new system is needed that takes into account the user's emotional state and provides health improvement advice and visual content that is appropriately adjusted in real time.

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

[0345] In this invention, the server includes information analysis means for receiving personal health information and analyzing the information to predict the future physical condition; generation means for generating images to visualize the generated future physical condition; and emotion analysis means for analyzing the user's emotional state and adjusting advice and images according to that emotion. This makes it possible to provide personalized health support in real time based on the user's emotional state and promote the maintenance of motivation.

[0346] "Personal health information" refers to health-related information entered by the user, such as weight, exercise level, and diet.

[0347] "Information analysis means" refers to a method of organizing received health information into data frames and other formats, and then using a predictive model to estimate future health conditions.

[0348] "Generation means" refers to a device or software that creates visually easy-to-understand images based on analyzed future health data.

[0349] "Recommendation methods" refer to systems that generate personalized advice and suggestions necessary for improving a user's health.

[0350] "Output mechanism" refers to a display or device used to present generated information or images to the user.

[0351] "Emotional analysis tools" refer to systems that analyze a user's facial expressions and behavior to identify their current emotional state.

[0352] "Input processing means" refers to the process of receiving responses and feedback from the user and adjusting the system's response accordingly.

[0353] "Means of generating satisfaction" refers to means of generating information that provides alternative satisfaction to the user's desires or appetite.

[0354] "Display means" refers to devices or functions that show real-time generated content to users.

[0355] This invention is a system that effectively supports individual health management and consists of three main components: a server, a terminal, and a user. Furthermore, by incorporating an emotion analysis engine, it can provide support tailored to the user's emotional state.

[0356] The server first receives health information sent from the user via their device. This information includes weight, exercise levels, and dietary content. The server converts the health information into a data frame using the Python Pandas library and performs analysis. The analyzed data is then used with machine learning algorithms to predict the user's future physical condition. The predicted data is converted into a visually easy-to-understand video, generated using video editing software such as Adobe After Effects. This allows for a more concrete visualization of the user's health improvement goals.

[0357] Simultaneously, the server uses Microsoft's Azure Face API to analyze facial expression data sent from the device for emotion analysis. Based on this analysis, a generative AI model is used to generate personalized health recommendations and advice. For example, a user who is feeling down might be given an encouraging message such as, "By taking the next step, you can achieve your ideal state of health."

[0358] The device provides an interface for users to input health information and also has an output function to display generated videos and advice. The device uses a built-in camera to capture the user's facial expressions and transmits the data to the server in real time. This process allows users to receive dynamic and effective health support that responds to their emotional state.

[0359] For example, if a user aims to lose 5 kilograms in the future, the server will provide a video visualizing the achievement of this goal. Furthermore, through sentiment analysis, if the user is losing motivation, it will add an encouraging message. An example of a prompt would be, "Based on my current body data and eating history, please generate a simulation video of my target body shape in 3 months and suggest encouraging messages to help me stay motivated."

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

[0361] Step 1:

[0362] Users input health information using a terminal. This information includes weight, exercise level, and diet. The entered data is formatted and converted to CSV format. This is both the input from the terminal and the output to the server.

[0363] Step 2:

[0364] The server receives health information in CSV format from the terminal. Next, it uses the Python Pandas library to convert the CSV data into a DataFrame. This DataFrame becomes the input for analysis, and the analyzed numerical data of the health status is output.

[0365] Step 3:

[0366] The server predicts the future state of the user's physical condition based on the analysis results. This process utilizes machine learning algorithms. For example, a predictive model is built using scikit-learn, and the user's health information is used as input. The output is predictive data indicating the user's future health condition.

[0367] Step 4:

[0368] The server generates videos to visualize the predicted data. For this purpose, it uses video editing software such as Adobe After Effects. Predicted data is used as input, and the output is a video representing the future physical condition.

[0369] Step 5:

[0370] The device uses its built-in camera to capture the user's facial expressions. This allows for the acquisition of facial expression data in real time. The acquired data is sent to a server as input, and the results of the facial expression analysis are output.

[0371] Step 6:

[0372] The server analyzes the received facial expression data. Here, the emotion analysis engine uses Microsoft's Azure Face API to analyze the user's emotional state. In this analysis, facial expression data is used as input, and the user's emotional state is output.

[0373] Step 7:

[0374] Based on the results of sentiment analysis, the server uses a generative AI model to generate personalized health recommendations. This process uses the user's past data and emotional state as input, and outputs appropriate health advice and messages.

[0375] Step 8:

[0376] The device displays generated video and health advice received from the server. The user adjusts their actions based on this information. The input in this step is data from the server, and the output is the visual content and advice received by the user.

[0377] (Application Example 2)

[0378] 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."

[0379] In personal health management, simply analyzing data and providing advice is often insufficient to fully capture the user's feelings and motivation. In particular, the lack of methods to provide personalized support that considers the impact of emotional changes on health behaviors makes it difficult for users to achieve their long-term health goals.

[0380] 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.

[0381] In this invention, the server includes emotion analysis means for analyzing an individual's emotional state and adjusting content for health improvement based on the analyzed emotions; output means for displaying the generated video and advice on an output device; and generation means for using a generation AI model to analyze the user's emotional state and generate appropriate health promotion content. This enables more effective and personalized health support that responds to changes in the user's emotions.

[0382] "Data analysis methods" refer to methods for receiving an individual's health data and analyzing it to predict their future physical condition.

[0383] "Generation method" refers to a method for generating a video for visualization based on the analyzed future state of the body.

[0384] A "recommendation method" is a means of generating personalized advice aimed at improving health.

[0385] "Output means" refers to means for outputting the generated video and advice to a display device.

[0386] "Emotional analysis methods" are means of analyzing an individual's emotional state and adjusting content aimed at improving health based on the analyzed emotions.

[0387] A "generative AI model" is a machine learning model used to generate appropriate health promotion content based on the user's emotional state.

[0388] This invention provides a system for improving individual health management, and in particular, for realizing personalized health support that responds to the user's emotional state.

[0389] The server first receives and analyzes individual health data using data analysis tools. Based on the analysis results, a generation tool generates a video to visualize the future physical condition. At this time, an emotion analysis tool analyzes the user's emotional data received from the terminal and generates content for health improvement in response to changes in emotion. A generation AI model is used for this generation, and appropriate content is generated using prompt sentences based on the user's emotional state as input.

[0390] The device functions as an interface with the user, capturing data on the user's facial expressions and emotions using cameras and sensors. This data is sent to a server for real-time emotion analysis. Furthermore, the generated video and advice are presented to the user via a display device. The displayed advice is tailored based on the user's emotional state, helping them achieve their long-term health goals.

[0391] For example, if a user's "fatigue" is detected via their smartphone, a relaxation video will be displayed, along with a message encouraging them to rest early. An example of this prompt might be: "Generating personalized health advice based on the user's emotions. Emotional state: Fatigue, Recommended action: Relaxation."

[0392] In this way, this system provides health support that is tailored to the user's feelings and makes a significant contribution to improving individual health.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The server receives user health data transmitted from the terminal. This data includes heart rate, activity level, and dietary information. The received data is stored in a database and prepared for analysis.

[0396] Step 2:

[0397] The server analyzes the received health data using data analysis tools. This analysis uses statistical models to predict future physical conditions. The output obtained from the analysis is information about predicted health parameters and future health risks.

[0398] Step 3:

[0399] The server generates videos to visualize the future physical condition based on the analysis results, using a generation method. Specifically, it uses an AI model to create digital animations based on the prediction results. The generated videos are prepared as content to help improve user motivation.

[0400] Step 4:

[0401] The device captures the user's facial expressions with a camera and analyzes them in real time. Using emotion analysis technology, it classifies the user's emotional state and sends the results to a server. The input is a camera image, and the output is an emotion label.

[0402] Step 5:

[0403] The server uses a generative AI model based on the received emotional data to generate health improvement advice tailored to the user's emotional state. The generation process utilizes a pre-trained AI model to create appropriate health behavior suggestions from prompt text. For example, it might generate an output such as, "If the user is feeling fatigued, we recommend relaxation."

[0404] Step 6:

[0405] The server sends the generated video and personalized advice to the device. This output is displayed on the user's screen to encourage actual action. The device presents the displayed information to the user and supports healthy behaviors.

[0406] Step 7:

[0407] Users review videos and advice displayed on their devices and provide feedback to the server as needed. This feedback is used to improve the overall accuracy of the system and adjust the content.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] This invention relates to a system that provides personalized support to promote healthy weight management. The system consists of three main components: a server, a terminal, and a user, each performing a specific function to support obese individuals as a whole.

[0425] The server has the function of receiving and analyzing individual health data. This analysis includes weight, height, diet, exercise habits, etc., and uses this data to evaluate the user's current health status. Based on the analysis results, it generates a realistic video of the user's future physical condition. This generation method visualizes the user's future body shape and predicted healthy changes, leading to increased motivation. Furthermore, it generates personalized health improvement recommendations based on the analysis results. This recommendation method suggests meal plans and exercise plans, providing optimal health guidance to each individual user.

[0426] The terminal acts as a link between the user and the server, transmitting health data entered by the user to the server and presenting the server's output to the user. Specifically, it visually displays generated videos of the user's future body and personalized advice. Information to curb the user's appetite is also provided on the terminal. For example, when faced with the temptation of food, visually showing the user a healthy future self helps them make desirable choices. The terminal also collects user feedback and sends it back to the server, enabling the entire system to provide more personalized support.

[0427] Users input their health data through their device and review the generated content while taking action towards their health goals. Based on the advice provided, they review their daily lifestyle habits and implement healthy weight management. For example, when users decide on specific exercise or dietary adjustments to reach their target weight, they can use this system to obtain a concrete action plan. By providing feedback, more accurate and personalized support becomes continuously available.

[0428] This system thus provides a concrete form of support for users to achieve a healthy lifestyle.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] Users enter their health data using a device. This includes current weight, height, diet, exercise habits, and target weight. After entering the data, users click a submit button on the device to send this data to the server.

[0432] Step 2:

[0433] The device formats the health data entered by the user into the specified data format and sends it to the server using a secure communication protocol. After the data is sent, a success notification is displayed to the user.

[0434] Step 3:

[0435] The server stores the health data received from the terminal into an analysis platform and begins the analysis. The analysis includes an assessment of the current health status and a prediction of future health status based on the received data.

[0436] Step 4:

[0437] The server uses AI generation based on the analysis results to create a video simulating the user's future physical condition. This video visualizes the user's physique and health status if they achieve their target weight.

[0438] Step 5:

[0439] The server generates personalized health advice based on the analysis results. This includes specific exercise plans, meal plans, and suggestions for behavioral changes.

[0440] Step 6:

[0441] The server sends the generated video and personalized advice to the device. The device then sends a notification when the server has finished processing.

[0442] Step 7:

[0443] The device visually displays a video and advice from the server showing the user's future body shape. The user reviews this and decides on their next action.

[0444] Step 8:

[0445] Users implement their own health management plans based on the displayed content. If necessary, they incorporate the provided advice into their daily routines and record the results.

[0446] Step 9:

[0447] Users input feedback on the results and experiences of the health management measures they have taken into the terminal. The terminal sends this feedback to the server, which is then used to improve the system's response in the future.

[0448] (Example 1)

[0449] 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."

[0450] Traditional health management systems have struggled to comprehensively capture an individual's health status, particularly in providing specific future health predictions and personalized advice. Furthermore, they lacked visualization and adaptive support that reflected feedback, which are crucial for motivating users. Therefore, there is a need for systems that provide specific health predictions based on individual health data, along with individually optimized advice based on these predictions visualized.

[0451] 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.

[0452] In this invention, the server includes an information analysis means that receives personal health-related information and analyzes the information to predict future health status; a generation means that generates a medium that visualizes the future health status based on the analyzed data; and a recommendation means that generates individually optimized suggestions for improving health. This makes it possible to predict and visualize the user's specific future health status and provide personalized health improvement suggestions.

[0453] "Personal health-related information" refers to data such as weight, height, diet, and exercise habits that are necessary to assess the health status of individual users.

[0454] "Information analysis means" refers to a device or process that has the function of analyzing an individual's health-related information received by a server and predicting their future health status based on that information.

[0455] "Generation means" refers to a device or process that has the function of generating a medium for visually representing future health conditions based on analyzed data.

[0456] "Recommendation method" refers to a device or process that has the function of providing health improvement suggestions best suited to each individual user based on analysis results.

[0457] A "display device" refers to a device used to visually present generated visual media and proposals to the user.

[0458] "Reaction processing means" refers to a device or process that has the function of acquiring feedback from the user and adjusting the output content of the system based on that feedback.

[0459] A "satisfaction generation means" refers to a device or process that has the function of generating information that provides alternative satisfaction in order to alleviate the user's desires.

[0460] This invention is a system that supports the promotion of health and weight management for individual users, and mainly consists of three elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[0461] The server receives and analyzes individual health-related information. The data analysis uses the Python data analysis platform, specifically the Pandas library, for data organization and cleaning. The analysis also utilizes the machine learning library TensorFlow to predict the user's future health status. The predicted data is then generated as visual content using 3D graphics software. For example, an open-source 3D modeling tool is used to create a video showing changes in the user's body shape.

[0462] The device transmits health-related information entered by the user to a server and presents the user with content generated from the server. The device features an intuitive user interface, allowing users to easily view generated videos and health improvement suggestions. In addition to visual information, the device also provides lifestyle improvement suggestions to the user through pop-up notifications.

[0463] Users input their health information through their device and take action toward their daily health goals while reviewing personalized improvement suggestions provided by the server. For example, users can use this system to create specific meal plans and exercise plans. They can also send feedback on the results obtained to the server via their device, allowing them to continuously receive more tailored support.

[0464] An example of a prompt to the generating AI model in this system would be, "Generate a predictive video and improvement advice for 12 months from now, based on the user's health data from the past 6 months." Thus, the present invention provides a form that guides users on specific measures necessary to lead a healthy life and assists them in implementing them.

[0465] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0466] Step 1:

[0467] Users input their health-related information using a device. Specifically, users record daily health data such as weight, height, diet, type and duration of exercise using a dedicated application. This information is used as input. Users can also automatically collect data via Bluetooth-enabled devices.

[0468] Step 2:

[0469] The device transmits health-related information entered by the user to the server. The entered data is transmitted using a security protocol and protected from eavesdropping. The information is encrypted and uploaded to the server using HTTPS.

[0470] Step 3:

[0471] The server begins processing the received health-related information. First, it removes outliers and missing values ​​through data cleansing using Python. Next, it formats the data using the Pandas library to prepare it for analysis. This is the data processing stage.

[0472] Step 4:

[0473] The server runs a machine learning model using the analyzed data. At this stage, TensorFlow is used to predict the user's future weight trends and health status. The predicted results are output and used in the next step.

[0474] Step 5:

[0475] The server uses a generative AI model to visualize the future state based on the analysis results. Specifically, it uses open-source 3D graphics software to generate a video representing the user's future health condition. The generated product is output as video data.

[0476] Step 6:

[0477] The device displays videos and recommended plans, which are generated from the server, to the user. The device interface is designed for easy user access, allowing users to watch videos and review detailed health suggestions. This enables users to intuitively understand their future health status.

[0478] Step 7:

[0479] Users evaluate the information provided from their devices and input feedback. Users write their opinions, questions, and other comments regarding specific suggestions within the app and send them to the server. This feedback is considered in the next analysis cycle to help generate more appropriate suggestions.

[0480] (Application Example 1)

[0481] 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."

[0482] In modern society, maintaining a healthy weight is a significant challenge for many people. In particular, there is a lack of visual feedback to predict future body shape based on individual health data and to boost motivation. Furthermore, there is no established means to provide an environment that facilitates concrete action, such as offering special prices directly available in stores. Therefore, there is a need for a system that supports a healthy lifestyle through personalized predictive information.

[0483] 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.

[0484] In this invention, the server includes data analysis means for receiving and analyzing personal health data to predict future physical condition, generation means for generating a video to visualize the predicted future physical condition, and proposal means for generating and displaying special price offers at the sales location where the user is located. This makes it possible to promote healthy behaviors by combining personalized health data with special offers within the store.

[0485] "Data analysis means" refers to a function that receives an individual's health information and uses that information to predict their future physical condition.

[0486] "Generative means" refers to the function of creating a video to visually represent the predicted future state of the body.

[0487] "Recommendation tools" refer to functions that generate advice to provide personalized guidance aimed at improving health.

[0488] "Output means" refers to devices or methods for presenting generated videos or advice to the user.

[0489] "Proposal method" refers to a function that generates and presents special price offers at the sales locations visited by users.

[0490] "Feedback processing means" refers to a function that receives opinions and feedback from users and adjusts the system's offerings based on that feedback.

[0491] "Means of generating satisfaction" refers to a function that creates information to suppress the user's appetite and generate alternative satisfaction.

[0492] The system for implementing this invention consists of three main components: a server, a user terminal, and the user. The server collects personal health data and analyzes that data using data analysis means. The analysis includes information such as weight, height, diet, and exercise habits, which is used to predict the future physical condition. Based on the predicted data, the server uses generation means to create a video that visualizes the future body shape.

[0493] The user terminal is equipped with output means for displaying videos and personalized health advice transmitted from the server. This allows users to use it as a guide for daily health management. In addition, a suggestion means can present special price offers at stores where the user is located, allowing the user to use them directly at the store. Furthermore, user feedback is sent to the server through the terminal, and a feedback processing means is used to further personalize the service content.

[0494] This system will utilize Python and video playback libraries to speed up process processing. Machine learning libraries such as scikit-learn will be used for data analysis. Generative AI models will be effective for future visualization of physical conditions. User recommendations will be generated through individual data analysis using historical data and AI models.

[0495] As a concrete example, when a user visits a gym, they scan a QR code at the gym, which sends their health data to their smartphone. Based on this data, a video predicting their future physique is generated, which the user can use to boost their motivation. Furthermore, prompts such as "Analyze the user's health data and generate a video of their physique one month from now" or "Personalize meal and exercise plans and suggest special in-store prices" enable the system to make appropriate health improvement suggestions.

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The server receives health data sent from the user. The input consists of health data such as the user's weight, height, diet, and exercise habits. The received data is preprocessed by data analysis tools, including the imputation of missing values ​​and standardization. The output is health data converted into a format suitable for analysis.

[0499] Step 2:

[0500] The server uses pre-processed health data and a generative AI model to predict future physical conditions. The input is pre-processed health data, and the output is predicted values ​​for future body shape and health indicators. Specifically, by inputting data into the model and performing inference processing, future numerical data is obtained.

[0501] Step 3:

[0502] The server generates a visualization video of body shape using a generation method based on predicted future physical condition. The input is data of predicted health indicators, and the output is a video file showing the future body shape. Video creation is achieved by using video generation software and animating still images.

[0503] Step 4:

[0504] The device receives future body shape videos and personalized recommendations sent from the server and displays them on its screen. Input consists of video files and text-based advice, while output is visual information provided to the user. Operation within the device involves using a viewer application to play the videos, and the text is displayed on the user interface.

[0505] Step 5:

[0506] The terminal displays an interface for receiving user feedback and sends the information entered by the user to the server. Input consists of text data such as user ratings and comments, while output is data sent to the server. Feedback is collected from the text field based on the user's input.

[0507] Step 6:

[0508] The server processes the received feedback and stores it in a database for future system improvements. The input is user feedback, and the output is the information stored in the database. Specifically, the feedback information is analyzed to improve algorithms and refine recommendations.

[0509] Step 7:

[0510] The device generates and presents special price offers for the store where the user is located. Inputs are the user's current location and health data, and output is information about the special offer. The logic operates by obtaining the user's location information via GPS and generating store promotions based on that data.

[0511] 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.

[0512] This invention is a system for improving personal health management, providing personalized support while taking into account the user's emotional state. The system consists of three main components: a server, a terminal, and the user, and further incorporates an emotion engine to enrich the user experience.

[0513] The server first receives and analyzes the user's health data. Based on the analyzed data, it simulates the user's future physical condition and generates a video that visualizes it. This allows the user to visually see themselves after achieving their goals, leading to increased motivation. The server also generates personalized health improvement advice and sends it, along with the video, to the user's device. In this process, the server uses an emotion recognition engine to analyze the user's facial expression data and determine the user's emotional state. Based on this, the generated content is adjusted according to the user's mood. For example, if the user is in a positive emotional state, challenging health advice will be presented, while if they are in a negative emotional state, content emphasizing encouragement and support will be provided.

[0514] The terminal functions as an interface with the user, acting as a tool for inputting health data and as a display device for content received from the server. The terminal's emotion engine captures the user's facial expressions and sends them to the server, allowing for real-time monitoring of the user's emotional state.

[0515] Users input health data through their devices and view future body shape videos and health advice received from the server. This support, customized by an emotion engine, is highly effective in maintaining user motivation. For example, when a user feels hungry and loses willpower, they can view encouraging messages and videos visualizing a positive future through their device. This kind of real-time support guides users towards healthier choices.

[0516] In this way, the system provides a mechanism to support health management in a more individualized and emotionally responsive manner. This approach, which is attentive to the user's physical condition and emotions, encourages the achievement of long-term health goals and increases user satisfaction.

[0517] The following describes the processing flow.

[0518] Step 1:

[0519] Users enter their health data using a device. This includes weight, height, diet, exercise habits, and target weight. After entering the data, the device sends this information to the server.

[0520] Step 2:

[0521] The device captures the user's facial expressions with its built-in camera, analyzes the facial data through an emotion engine, and determines the user's emotional state. This emotional information is also sent to the server.

[0522] Step 3:

[0523] The server receives health data sent by the user and begins predictive analysis of their health status. This analysis includes confirming the user's current health status based on the data and predicting their future body shape.

[0524] Step 4:

[0525] The server uses AI generation based on the analysis results to create a video that simulates the user's future physical condition. This video realistically visualizes the user's body shape once they reach their target weight.

[0526] Step 5:

[0527] The server considers the user's emotional state, as determined by the emotion engine, and customizes the generated videos and health advice accordingly. For example, if the user's emotions are positive, it will provide slightly more challenging advice.

[0528] Step 6:

[0529] The server sends the generated video and customized health advice to the device. The device receives this and prepares to display it to the user.

[0530] Step 7:

[0531] The device displays a video received from the server to the user, simultaneously showing personalized health advice in text and images. The user reviews this and plans their next action.

[0532] Step 8:

[0533] Users will then implement their own health strategies based on the information provided. For example, they might revise their diet and exercise plans according to the advice given and put specific actions into practice.

[0534] Step 9:

[0535] Users input feedback on their daily health management activities into their device. This information is sent to a server and used to adjust future support plans.

[0536] (Example 2)

[0537] 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."

[0538] In modern society, effective individual health management is crucial. However, conventional systems have struggled to provide personalized health support tailored to each user's emotional state, making it difficult to maintain motivation and sustain health improvement. Therefore, a new system is needed that takes into account the user's emotional state and provides health improvement advice and visual content that is appropriately adjusted in real time.

[0539] 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.

[0540] In this invention, the server includes information analysis means for receiving personal health information and analyzing the information to predict the future physical condition; generation means for generating images to visualize the generated future physical condition; and emotion analysis means for analyzing the user's emotional state and adjusting advice and images according to that emotion. This makes it possible to provide personalized health support in real time based on the user's emotional state and promote the maintenance of motivation.

[0541] "Personal health information" refers to health-related information entered by the user, such as weight, exercise level, and diet.

[0542] "Information analysis means" refers to a method of organizing received health information into data frames and other formats, and then using a predictive model to estimate future health conditions.

[0543] "Generation means" refers to a device or software that creates visually easy-to-understand images based on analyzed future health data.

[0544] "Recommendation methods" refer to systems that generate personalized advice and suggestions necessary for improving a user's health.

[0545] "Output mechanism" refers to a display or device used to present generated information or images to the user.

[0546] "Emotional analysis tools" refer to systems that analyze a user's facial expressions and behavior to identify their current emotional state.

[0547] "Input processing means" refers to the process of receiving responses and feedback from the user and adjusting the system's response accordingly.

[0548] "Means of generating satisfaction" refers to means of generating information that provides alternative satisfaction to the user's desires or appetite.

[0549] "Display means" refers to devices or functions that show real-time generated content to users.

[0550] This invention is a system that effectively supports individual health management and consists of three main components: a server, a terminal, and a user. Furthermore, by incorporating an emotion analysis engine, it can provide support tailored to the user's emotional state.

[0551] The server first receives health information sent from the user via their device. This information includes weight, exercise levels, and dietary content. The server converts the health information into a data frame using the Python Pandas library and performs analysis. The analyzed data is then used with machine learning algorithms to predict the user's future physical condition. The predicted data is converted into a visually easy-to-understand video, generated using video editing software such as Adobe After Effects. This allows for a more concrete visualization of the user's health improvement goals.

[0552] Simultaneously, the server uses Microsoft's Azure Face API to analyze facial expression data sent from the device for emotion analysis. Based on this analysis, a generative AI model is used to generate personalized health recommendations and advice. For example, a user who is feeling down might be given an encouraging message such as, "By taking the next step, you can achieve your ideal state of health."

[0553] The device provides an interface for users to input health information and also has an output function to display generated videos and advice. The device uses a built-in camera to capture the user's facial expressions and transmits the data to the server in real time. This process allows users to receive dynamic and effective health support that responds to their emotional state.

[0554] For example, if a user aims to lose 5 kilograms in the future, the server will provide a video visualizing the achievement of this goal. Furthermore, through sentiment analysis, if the user is losing motivation, it will add an encouraging message. An example of a prompt would be, "Based on my current body data and eating history, please generate a simulation video of my target body shape in 3 months and suggest encouraging messages to help me stay motivated."

[0555] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0556] Step 1:

[0557] Users input health information using a terminal. This information includes weight, exercise level, and diet. The entered data is formatted and converted to CSV format. This is both the input from the terminal and the output to the server.

[0558] Step 2:

[0559] The server receives health information in CSV format from the terminal. Next, it uses the Python Pandas library to convert the CSV data into a DataFrame. This DataFrame becomes the input for analysis, and the analyzed numerical data of the health status is output.

[0560] Step 3:

[0561] The server predicts the future state of the user's physical condition based on the analysis results. This process utilizes machine learning algorithms. For example, a predictive model is built using scikit-learn, and the user's health information is used as input. The output is predictive data indicating the user's future health condition.

[0562] Step 4:

[0563] The server generates videos to visualize the predicted data. For this purpose, it uses video editing software such as Adobe After Effects. Predicted data is used as input, and the output is a video representing the future physical condition.

[0564] Step 5:

[0565] The device uses its built-in camera to capture the user's facial expressions. This allows for the acquisition of facial expression data in real time. The acquired data is sent to a server as input, and the results of the facial expression analysis are output.

[0566] Step 6:

[0567] The server analyzes the received facial expression data. Here, the emotion analysis engine uses Microsoft's Azure Face API to analyze the user's emotional state. In this analysis, facial expression data is used as input, and the user's emotional state is output.

[0568] Step 7:

[0569] Based on the results of sentiment analysis, the server uses a generative AI model to generate personalized health recommendations. This process uses the user's past data and emotional state as input, and outputs appropriate health advice and messages.

[0570] Step 8:

[0571] The device displays generated video and health advice received from the server. The user adjusts their actions based on this information. The input in this step is data from the server, and the output is the visual content and advice received by the user.

[0572] (Application Example 2)

[0573] 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."

[0574] In personal health management, simply analyzing data and providing advice is often insufficient to fully capture the user's feelings and motivation. In particular, the lack of methods to provide personalized support that considers the impact of emotional changes on health behaviors makes it difficult for users to achieve their long-term health goals.

[0575] 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.

[0576] In this invention, the server includes emotion analysis means for analyzing an individual's emotional state and adjusting content for health improvement based on the analyzed emotions; output means for displaying the generated video and advice on an output device; and generation means for using a generation AI model to analyze the user's emotional state and generate appropriate health promotion content. This enables more effective and personalized health support that responds to changes in the user's emotions.

[0577] "Data analysis methods" refer to methods for receiving an individual's health data and analyzing it to predict their future physical condition.

[0578] "Generation method" refers to a method for generating a video for visualization based on the analyzed future state of the body.

[0579] A "recommendation method" is a means of generating personalized advice aimed at improving health.

[0580] "Output means" refers to means for outputting the generated video and advice to a display device.

[0581] "Emotional analysis methods" are means of analyzing an individual's emotional state and adjusting content aimed at improving health based on the analyzed emotions.

[0582] A "generative AI model" is a machine learning model used to generate appropriate health promotion content based on the user's emotional state.

[0583] This invention provides a system for improving individual health management, and in particular, for realizing personalized health support that responds to the user's emotional state.

[0584] The server first receives and analyzes individual health data using data analysis tools. Based on the analysis results, a generation tool generates a video to visualize the future physical condition. At this time, an emotion analysis tool analyzes the user's emotional data received from the terminal and generates content for health improvement in response to changes in emotion. A generation AI model is used for this generation, and appropriate content is generated using prompt sentences based on the user's emotional state as input.

[0585] The device functions as an interface with the user, capturing data on the user's facial expressions and emotions using cameras and sensors. This data is sent to a server for real-time emotion analysis. Furthermore, the generated video and advice are presented to the user via a display device. The displayed advice is tailored based on the user's emotional state, helping them achieve their long-term health goals.

[0586] For example, if a user's "fatigue" is detected via their smartphone, a relaxation video will be displayed, along with a message encouraging them to rest early. An example of this prompt might be: "Generating personalized health advice based on the user's emotions. Emotional state: Fatigue, Recommended action: Relaxation."

[0587] In this way, this system provides health support that is tailored to the user's feelings and makes a significant contribution to improving individual health.

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The server receives user health data transmitted from the terminal. This data includes heart rate, activity level, and dietary information. The received data is stored in a database and prepared for analysis.

[0591] Step 2:

[0592] The server analyzes the received health data using data analysis tools. This analysis uses statistical models to predict future physical conditions. The output obtained from the analysis is information about predicted health parameters and future health risks.

[0593] Step 3:

[0594] The server generates videos to visualize the future physical condition based on the analysis results, using a generation method. Specifically, it uses an AI model to create digital animations based on the prediction results. The generated videos are prepared as content to help improve user motivation.

[0595] Step 4:

[0596] The device captures the user's facial expressions with a camera and analyzes them in real time. Using emotion analysis technology, it classifies the user's emotional state and sends the results to a server. The input is a camera image, and the output is an emotion label.

[0597] Step 5:

[0598] The server uses a generative AI model based on the received emotional data to generate health improvement advice tailored to the user's emotional state. The generation process utilizes a pre-trained AI model to create appropriate health behavior suggestions from prompt text. For example, it might generate an output such as, "If the user is feeling fatigued, we recommend relaxation."

[0599] Step 6:

[0600] The server sends the generated video and personalized advice to the device. This output is displayed on the user's screen to encourage actual action. The device presents the displayed information to the user and supports healthy behaviors.

[0601] Step 7:

[0602] Users review videos and advice displayed on their devices and provide feedback to the server as needed. This feedback is used to improve the overall accuracy of the system and adjust the content.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] This invention relates to a system that provides personalized support to promote healthy weight management. The system consists of three main components: a server, a terminal, and a user, each performing a specific function to support obese individuals as a whole.

[0621] The server has the function of receiving and analyzing individual health data. This analysis includes weight, height, diet, exercise habits, etc., and uses this data to evaluate the user's current health status. Based on the analysis results, it generates a realistic video of the user's future physical condition. This generation method visualizes the user's future body shape and predicted healthy changes, leading to increased motivation. Furthermore, it generates personalized health improvement recommendations based on the analysis results. This recommendation method suggests meal plans and exercise plans, providing optimal health guidance to each individual user.

[0622] The terminal acts as a link between the user and the server, transmitting health data entered by the user to the server and presenting the server's output to the user. Specifically, it visually displays generated videos of the user's future body and personalized advice. Information to curb the user's appetite is also provided on the terminal. For example, when faced with the temptation of food, visually showing the user a healthy future self helps them make desirable choices. The terminal also collects user feedback and sends it back to the server, enabling the entire system to provide more personalized support.

[0623] Users input their health data through their device and review the generated content while taking action towards their health goals. Based on the advice provided, they review their daily lifestyle habits and implement healthy weight management. For example, when users decide on specific exercise or dietary adjustments to reach their target weight, they can use this system to obtain a concrete action plan. By providing feedback, more accurate and personalized support becomes continuously available.

[0624] This system thus provides a concrete form of support for users to achieve a healthy lifestyle.

[0625] The following describes the processing flow.

[0626] Step 1:

[0627] Users enter their health data using a device. This includes current weight, height, diet, exercise habits, and target weight. After entering the data, users click a submit button on the device to send this data to the server.

[0628] Step 2:

[0629] The device formats the health data entered by the user into the specified data format and sends it to the server using a secure communication protocol. After the data is sent, a success notification is displayed to the user.

[0630] Step 3:

[0631] The server stores the health data received from the terminal into an analysis platform and begins the analysis. The analysis includes an assessment of the current health status and a prediction of future health status based on the received data.

[0632] Step 4:

[0633] The server uses AI generation based on the analysis results to create a video simulating the user's future physical condition. This video visualizes the user's physique and health status if they achieve their target weight.

[0634] Step 5:

[0635] The server generates personalized health advice based on the analysis results. This includes specific exercise plans, meal plans, and suggestions for behavioral changes.

[0636] Step 6:

[0637] The server sends the generated video and personalized advice to the device. The device then sends a notification when the server has finished processing.

[0638] Step 7:

[0639] The device visually displays a video and advice from the server showing the user's future body shape. The user reviews this and decides on their next action.

[0640] Step 8:

[0641] Users implement their own health management plans based on the displayed content. If necessary, they incorporate the provided advice into their daily routines and record the results.

[0642] Step 9:

[0643] Users input feedback on the results and experiences of the health management measures they have taken into the terminal. The terminal sends this feedback to the server, which is then used to improve the system's response in the future.

[0644] (Example 1)

[0645] 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".

[0646] Traditional health management systems have struggled to comprehensively capture an individual's health status, particularly in providing specific future health predictions and personalized advice. Furthermore, they lacked visualization and adaptive support that reflected feedback, which are crucial for motivating users. Therefore, there is a need for systems that provide specific health predictions based on individual health data, along with individually optimized advice based on these predictions visualized.

[0647] 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.

[0648] In this invention, the server includes an information analysis means that receives personal health-related information and analyzes the information to predict future health status; a generation means that generates a medium that visualizes the future health status based on the analyzed data; and a recommendation means that generates individually optimized suggestions for improving health. This makes it possible to predict and visualize the user's specific future health status and provide personalized health improvement suggestions.

[0649] "Personal health-related information" refers to data such as weight, height, diet, and exercise habits that are necessary to assess the health status of individual users.

[0650] "Information analysis means" refers to a device or process that has the function of analyzing an individual's health-related information received by a server and predicting their future health status based on that information.

[0651] "Generation means" refers to a device or process that has the function of generating a medium for visually representing future health conditions based on analyzed data.

[0652] "Recommendation method" refers to a device or process that has the function of providing health improvement suggestions best suited to each individual user based on analysis results.

[0653] A "display device" refers to a device used to visually present generated visual media and proposals to the user.

[0654] "Reaction processing means" refers to a device or process that has the function of acquiring feedback from the user and adjusting the output content of the system based on that feedback.

[0655] A "satisfaction generation means" refers to a device or process that has the function of generating information that provides alternative satisfaction in order to alleviate the user's desires.

[0656] This invention is a system that supports the promotion of health and weight management for individual users, and mainly consists of three elements: a server, a terminal, and a user. Specific embodiments for carrying out the invention are described below.

[0657] The server receives and analyzes individual health-related information. The data analysis uses the Python data analysis platform, specifically the Pandas library, for data organization and cleaning. The analysis also utilizes the machine learning library TensorFlow to predict the user's future health status. The predicted data is then generated as visual content using 3D graphics software. For example, an open-source 3D modeling tool is used to create a video showing changes in the user's body shape.

[0658] The device transmits health-related information entered by the user to a server and presents the user with content generated from the server. The device features an intuitive user interface, allowing users to easily view generated videos and health improvement suggestions. In addition to visual information, the device also provides lifestyle improvement suggestions to the user through pop-up notifications.

[0659] Users input their health information through their device and take action toward their daily health goals while reviewing personalized improvement suggestions provided by the server. For example, users can use this system to create specific meal plans and exercise plans. They can also send feedback on the results obtained to the server via their device, allowing them to continuously receive more tailored support.

[0660] An example of a prompt to the generating AI model in this system would be, "Generate a predictive video and improvement advice for 12 months from now, based on the user's health data from the past 6 months." Thus, the present invention provides a form that guides users on specific measures necessary to lead a healthy life and assists them in implementing them.

[0661] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0662] Step 1:

[0663] Users input their health-related information using a device. Specifically, users record daily health data such as weight, height, diet, type and duration of exercise using a dedicated application. This information is used as input. Users can also automatically collect data via Bluetooth-enabled devices.

[0664] Step 2:

[0665] The device transmits health-related information entered by the user to the server. The entered data is transmitted using a security protocol and protected from eavesdropping. The information is encrypted and uploaded to the server using HTTPS.

[0666] Step 3:

[0667] The server begins processing the received health-related information. First, it removes outliers and missing values ​​through data cleansing using Python. Next, it formats the data using the Pandas library to prepare it for analysis. This is the data processing stage.

[0668] Step 4:

[0669] The server runs a machine learning model using the analyzed data. At this stage, TensorFlow is used to predict the user's future weight trends and health status. The predicted results are output and used in the next step.

[0670] Step 5:

[0671] The server uses a generative AI model to visualize the future state based on the analysis results. Specifically, it uses open-source 3D graphics software to generate a video representing the user's future health condition. The generated product is output as video data.

[0672] Step 6:

[0673] The device displays videos and recommended plans, which are generated from the server, to the user. The device interface is designed for easy user access, allowing users to watch videos and review detailed health suggestions. This enables users to intuitively understand their future health status.

[0674] Step 7:

[0675] Users evaluate the information provided from their devices and input feedback. Users write their opinions, questions, and other comments regarding specific suggestions within the app and send them to the server. This feedback is considered in the next analysis cycle to help generate more appropriate suggestions.

[0676] (Application Example 1)

[0677] 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".

[0678] In modern society, maintaining a healthy weight is a significant challenge for many people. In particular, there is a lack of visual feedback to predict future body shape based on individual health data and to boost motivation. Furthermore, there is no established means to provide an environment that facilitates concrete action, such as offering special prices directly available in stores. Therefore, there is a need for a system that supports a healthy lifestyle through personalized predictive information.

[0679] 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.

[0680] In this invention, the server includes data analysis means for receiving and analyzing personal health data to predict future physical condition, generation means for generating a video to visualize the predicted future physical condition, and proposal means for generating and displaying special price offers at the sales location where the user is located. This makes it possible to promote healthy behaviors by combining personalized health data with special offers within the store.

[0681] "Data analysis means" refers to a function that receives an individual's health information and uses that information to predict their future physical condition.

[0682] "Generative means" refers to the function of creating a video to visually represent the predicted future state of the body.

[0683] "Recommendation tools" refer to functions that generate advice to provide personalized guidance aimed at improving health.

[0684] "Output means" refers to devices or methods for presenting generated videos or advice to the user.

[0685] "Proposal method" refers to a function that generates and presents special price offers at the sales locations visited by users.

[0686] "Feedback processing means" refers to a function that receives opinions and feedback from users and adjusts the system's offerings based on that feedback.

[0687] "Means of generating satisfaction" refers to a function that creates information to suppress the user's appetite and generate alternative satisfaction.

[0688] The system for implementing this invention consists of three main components: a server, a user terminal, and the user. The server collects personal health data and analyzes that data using data analysis means. The analysis includes information such as weight, height, diet, and exercise habits, which is used to predict the future physical condition. Based on the predicted data, the server uses generation means to create a video that visualizes the future body shape.

[0689] The user terminal is equipped with output means for displaying videos and personalized health advice transmitted from the server. This allows users to use it as a guide for daily health management. In addition, a suggestion means can present special price offers at stores where the user is located, allowing the user to use them directly at the store. Furthermore, user feedback is sent to the server through the terminal, and a feedback processing means is used to further personalize the service content.

[0690] This system will utilize Python and video playback libraries to speed up process processing. Machine learning libraries such as scikit-learn will be used for data analysis. Generative AI models will be effective for future visualization of physical conditions. User recommendations will be generated through individual data analysis using historical data and AI models.

[0691] As a concrete example, when a user visits a gym, they scan a QR code at the gym, which sends their health data to their smartphone. Based on this data, a video predicting their future physique is generated, which the user can use to boost their motivation. Furthermore, prompts such as "Analyze the user's health data and generate a video of their physique one month from now" or "Personalize meal and exercise plans and suggest special in-store prices" enable the system to make appropriate health improvement suggestions.

[0692] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0693] Step 1:

[0694] The server receives health data sent from the user. The input consists of health data such as the user's weight, height, diet, and exercise habits. The received data is preprocessed by data analysis tools, including the imputation of missing values ​​and standardization. The output is health data converted into a format suitable for analysis.

[0695] Step 2:

[0696] The server uses pre-processed health data and a generative AI model to predict future physical conditions. The input is pre-processed health data, and the output is predicted values ​​for future body shape and health indicators. Specifically, by inputting data into the model and performing inference processing, future numerical data is obtained.

[0697] Step 3:

[0698] The server generates a visualization video of body shape using a generation method based on predicted future physical condition. The input is data of predicted health indicators, and the output is a video file showing the future body shape. Video creation is achieved by using video generation software and animating still images.

[0699] Step 4:

[0700] The device receives future body shape videos and personalized recommendations sent from the server and displays them on its screen. Input consists of video files and text-based advice, while output is visual information provided to the user. Operation within the device involves using a viewer application to play the videos, and the text is displayed on the user interface.

[0701] Step 5:

[0702] The terminal displays an interface for receiving user feedback and sends the information entered by the user to the server. Input consists of text data such as user ratings and comments, while output is data sent to the server. Feedback is collected from the text field based on the user's input.

[0703] Step 6:

[0704] The server processes the received feedback and stores it in a database for future system improvements. The input is user feedback, and the output is the information stored in the database. Specifically, the feedback information is analyzed to improve algorithms and refine recommendations.

[0705] Step 7:

[0706] The device generates and presents special price offers for the store where the user is located. Inputs are the user's current location and health data, and output is information about the special offer. The logic operates by obtaining the user's location information via GPS and generating store promotions based on that data.

[0707] 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.

[0708] This invention is a system for improving personal health management, providing personalized support while taking into account the user's emotional state. The system consists of three main components: a server, a terminal, and the user, and further incorporates an emotion engine to enrich the user experience.

[0709] The server first receives and analyzes the user's health data. Based on the analyzed data, it simulates the user's future physical condition and generates a video that visualizes it. This allows the user to visually see themselves after achieving their goals, leading to increased motivation. The server also generates personalized health improvement advice and sends it, along with the video, to the user's device. In this process, the server uses an emotion recognition engine to analyze the user's facial expression data and determine the user's emotional state. Based on this, the generated content is adjusted according to the user's mood. For example, if the user is in a positive emotional state, challenging health advice will be presented, while if they are in a negative emotional state, content emphasizing encouragement and support will be provided.

[0710] The terminal functions as an interface with the user, acting as a tool for inputting health data and as a display device for content received from the server. The terminal's emotion engine captures the user's facial expressions and sends them to the server, allowing for real-time monitoring of the user's emotional state.

[0711] Users input health data through their devices and view future body shape videos and health advice received from the server. This support, customized by an emotion engine, is highly effective in maintaining user motivation. For example, when a user feels hungry and loses willpower, they can view encouraging messages and videos visualizing a positive future through their device. This kind of real-time support guides users towards healthier choices.

[0712] In this way, the system provides a mechanism to support health management in a more individualized and emotionally responsive manner. This approach, which is attentive to the user's physical condition and emotions, encourages the achievement of long-term health goals and increases user satisfaction.

[0713] The following describes the processing flow.

[0714] Step 1:

[0715] Users enter their health data using a device. This includes weight, height, diet, exercise habits, and target weight. After entering the data, the device sends this information to the server.

[0716] Step 2:

[0717] The device captures the user's facial expressions with its built-in camera, analyzes the facial data through an emotion engine, and determines the user's emotional state. This emotional information is also sent to the server.

[0718] Step 3:

[0719] The server receives health data sent by the user and begins predictive analysis of their health status. This analysis includes confirming the user's current health status based on the data and predicting their future body shape.

[0720] Step 4:

[0721] The server uses AI generation based on the analysis results to create a video that simulates the user's future physical condition. This video realistically visualizes the user's body shape once they reach their target weight.

[0722] Step 5:

[0723] The server considers the user's emotional state, as determined by the emotion engine, and customizes the generated videos and health advice accordingly. For example, if the user's emotions are positive, it will provide slightly more challenging advice.

[0724] Step 6:

[0725] The server sends the generated video and customized health advice to the device. The device receives this and prepares to display it to the user.

[0726] Step 7:

[0727] The device displays a video received from the server to the user, simultaneously showing personalized health advice in text and images. The user reviews this and plans their next action.

[0728] Step 8:

[0729] Users will then implement their own health strategies based on the information provided. For example, they might revise their diet and exercise plans according to the advice given and put specific actions into practice.

[0730] Step 9:

[0731] Users input feedback on their daily health management activities into their device. This information is sent to a server and used to adjust future support plans.

[0732] (Example 2)

[0733] 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".

[0734] In modern society, effective individual health management is crucial. However, conventional systems have struggled to provide personalized health support tailored to each user's emotional state, making it difficult to maintain motivation and sustain health improvement. Therefore, a new system is needed that takes into account the user's emotional state and provides health improvement advice and visual content that is appropriately adjusted in real time.

[0735] 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.

[0736] In this invention, the server includes information analysis means for receiving personal health information and analyzing the information to predict the future physical condition; generation means for generating images to visualize the generated future physical condition; and emotion analysis means for analyzing the user's emotional state and adjusting advice and images according to that emotion. This makes it possible to provide personalized health support in real time based on the user's emotional state and promote the maintenance of motivation.

[0737] "Personal health information" refers to health-related information entered by the user, such as weight, exercise level, and diet.

[0738] "Information analysis means" refers to a method of organizing received health information into data frames and other formats, and then using a predictive model to estimate future health conditions.

[0739] "Generation means" refers to a device or software that creates visually easy-to-understand images based on analyzed future health data.

[0740] "Recommendation methods" refer to systems that generate personalized advice and suggestions necessary for improving a user's health.

[0741] "Output mechanism" refers to a display or device used to present generated information or images to the user.

[0742] "Emotional analysis tools" refer to systems that analyze a user's facial expressions and behavior to identify their current emotional state.

[0743] "Input processing means" refers to the process of receiving responses and feedback from the user and adjusting the system's response accordingly.

[0744] "Means of generating satisfaction" refers to means of generating information that provides alternative satisfaction to the user's desires or appetite.

[0745] "Display means" refers to devices or functions that show real-time generated content to users.

[0746] This invention is a system that effectively supports individual health management and consists of three main components: a server, a terminal, and a user. Furthermore, by incorporating an emotion analysis engine, it can provide support tailored to the user's emotional state.

[0747] The server first receives health information sent from the user via their device. This information includes weight, exercise levels, and dietary content. The server converts the health information into a data frame using the Python Pandas library and performs analysis. The analyzed data is then used with machine learning algorithms to predict the user's future physical condition. The predicted data is converted into a visually easy-to-understand video, generated using video editing software such as Adobe After Effects. This allows for a more concrete visualization of the user's health improvement goals.

[0748] Simultaneously, the server uses Microsoft's Azure Face API to analyze facial expression data sent from the device for emotion analysis. Based on this analysis, a generative AI model is used to generate personalized health recommendations and advice. For example, a user who is feeling down might be given an encouraging message such as, "By taking the next step, you can achieve your ideal state of health."

[0749] The device provides an interface for users to input health information and also has an output function to display generated videos and advice. The device uses a built-in camera to capture the user's facial expressions and transmits the data to the server in real time. This process allows users to receive dynamic and effective health support that responds to their emotional state.

[0750] For example, if a user aims to lose 5 kilograms in the future, the server will provide a video visualizing the achievement of this goal. Furthermore, through sentiment analysis, if the user is losing motivation, it will add an encouraging message. An example of a prompt would be, "Based on my current body data and eating history, please generate a simulation video of my target body shape in 3 months and suggest encouraging messages to help me stay motivated."

[0751] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0752] Step 1:

[0753] Users input health information using a terminal. This information includes weight, exercise level, and diet. The entered data is formatted and converted to CSV format. This is both the input from the terminal and the output to the server.

[0754] Step 2:

[0755] The server receives health information in CSV format from the terminal. Next, it uses the Python Pandas library to convert the CSV data into a DataFrame. This DataFrame becomes the input for analysis, and the analyzed numerical data of the health status is output.

[0756] Step 3:

[0757] The server predicts the future state of the user's physical condition based on the analysis results. This process utilizes machine learning algorithms. For example, a predictive model is built using scikit-learn, and the user's health information is used as input. The output is predictive data indicating the user's future health condition.

[0758] Step 4:

[0759] The server generates videos to visualize the predicted data. For this purpose, it uses video editing software such as Adobe After Effects. Predicted data is used as input, and the output is a video representing the future physical condition.

[0760] Step 5:

[0761] The device uses its built-in camera to capture the user's facial expressions. This allows for the acquisition of facial expression data in real time. The acquired data is sent to a server as input, and the results of the facial expression analysis are output.

[0762] Step 6:

[0763] The server analyzes the received facial expression data. Here, the emotion analysis engine uses Microsoft's Azure Face API to analyze the user's emotional state. In this analysis, facial expression data is used as input, and the user's emotional state is output.

[0764] Step 7:

[0765] Based on the results of sentiment analysis, the server uses a generative AI model to generate personalized health recommendations. This process uses the user's past data and emotional state as input, and outputs appropriate health advice and messages.

[0766] Step 8:

[0767] The device displays generated video and health advice received from the server. The user adjusts their actions based on this information. The input in this step is data from the server, and the output is the visual content and advice received by the user.

[0768] (Application Example 2)

[0769] 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".

[0770] In personal health management, simply analyzing data and providing advice is often insufficient to fully capture the user's feelings and motivation. In particular, the lack of methods to provide personalized support that considers the impact of emotional changes on health behaviors makes it difficult for users to achieve their long-term health goals.

[0771] 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.

[0772] In this invention, the server includes emotion analysis means for analyzing an individual's emotional state and adjusting content for health improvement based on the analyzed emotions; output means for displaying the generated video and advice on an output device; and generation means for using a generation AI model to analyze the user's emotional state and generate appropriate health promotion content. This enables more effective and personalized health support that responds to changes in the user's emotions.

[0773] "Data analysis methods" refer to methods for receiving an individual's health data and analyzing it to predict their future physical condition.

[0774] "Generation method" refers to a method for generating a video for visualization based on the analyzed future state of the body.

[0775] A "recommendation method" is a means of generating personalized advice aimed at improving health.

[0776] "Output means" refers to means for outputting the generated video and advice to a display device.

[0777] "Emotional analysis methods" are means of analyzing an individual's emotional state and adjusting content aimed at improving health based on the analyzed emotions.

[0778] A "generative AI model" is a machine learning model used to generate appropriate health promotion content based on the user's emotional state.

[0779] This invention provides a system for improving individual health management, and in particular, for realizing personalized health support that responds to the user's emotional state.

[0780] The server first receives and analyzes individual health data using data analysis tools. Based on the analysis results, a generation tool generates a video to visualize the future physical condition. At this time, an emotion analysis tool analyzes the user's emotional data received from the terminal and generates content for health improvement in response to changes in emotion. A generation AI model is used for this generation, and appropriate content is generated using prompt sentences based on the user's emotional state as input.

[0781] The device functions as an interface with the user, capturing data on the user's facial expressions and emotions using cameras and sensors. This data is sent to a server for real-time emotion analysis. Furthermore, the generated video and advice are presented to the user via a display device. The displayed advice is tailored based on the user's emotional state, helping them achieve their long-term health goals.

[0782] For example, if a user's "fatigue" is detected via their smartphone, a relaxation video will be displayed, along with a message encouraging them to rest early. An example of this prompt might be: "Generating personalized health advice based on the user's emotions. Emotional state: Fatigue, Recommended action: Relaxation."

[0783] In this way, this system provides health support that is tailored to the user's feelings and makes a significant contribution to improving individual health.

[0784] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0785] Step 1:

[0786] The server receives user health data transmitted from the terminal. This data includes heart rate, activity level, and dietary information. The received data is stored in a database and prepared for analysis.

[0787] Step 2:

[0788] The server analyzes the received health data using data analysis tools. This analysis uses statistical models to predict future physical conditions. The output obtained from the analysis is information about predicted health parameters and future health risks.

[0789] Step 3:

[0790] The server generates videos to visualize the future physical condition based on the analysis results, using a generation method. Specifically, it uses an AI model to create digital animations based on the prediction results. The generated videos are prepared as content to help improve user motivation.

[0791] Step 4:

[0792] The device captures the user's facial expressions with a camera and analyzes them in real time. Using emotion analysis technology, it classifies the user's emotional state and sends the results to a server. The input is a camera image, and the output is an emotion label.

[0793] Step 5:

[0794] The server uses a generative AI model based on the received emotional data to generate health improvement advice tailored to the user's emotional state. The generation process utilizes a pre-trained AI model to create appropriate health behavior suggestions from prompt text. For example, it might generate an output such as, "If the user is feeling fatigued, we recommend relaxation."

[0795] Step 6:

[0796] The server sends the generated video and personalized advice to the device. This output is displayed on the user's screen to encourage actual action. The device presents the displayed information to the user and supports healthy behaviors.

[0797] Step 7:

[0798] Users review videos and advice displayed on their devices and provide feedback to the server as needed. This feedback is used to improve the overall accuracy of the system and adjust the content.

[0799] 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.

[0800] 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.

[0801] 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 robot 414.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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."

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] A data analysis means that receives personal health data, analyzes the data, and predicts the future physical condition,

[0823] A means for generating a video to visualize the generated future physical state,

[0824] A recommendation system that generates personalized advice for improving health,

[0825] An output means for displaying the generated video and advice on an output device,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, which includes a feedback processing means for receiving user feedback and adjusting the system's output based on said feedback.

[0829] (Claim 3)

[0830] The system according to claim 1, comprising a satisfaction generation means that generates information providing alternative satisfaction to suppress the appetite experienced by the user.

[0831] "Example 1"

[0832] (Claim 1)

[0833] An information analysis means that receives personal health-related information, analyzes the information, and predicts future health status,

[0834] A generation means that generates a medium that visualizes future health status based on analyzed data,

[0835] A recommendation system that generates personalized suggestions for improving health,

[0836] A display means for presenting the generated visual media and proposals on a display device,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which includes a reaction processing means for obtaining user responses and optimizing the system's output based on those responses.

[0840] (Claim 3)

[0841] The satisfaction generation means according to claim 1, which generates information that provides alternative satisfaction in order to alleviate the desires of the user.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] A data analysis means that receives personal health data, analyzes the data, and predicts the future physical condition,

[0845] A means for generating a video to visualize the generated future physical state,

[0846] A recommendation system that generates personalized advice for improving health,

[0847] An output means for displaying the generated video and advice on an output device,

[0848] A proposal means that generates and displays special price offers for the sales location where the user is located,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, which includes a feedback processing means for receiving user feedback and adjusting the system's output based on said feedback.

[0852] (Claim 3)

[0853] The system according to claim 1, comprising a satisfaction generation means that generates information providing alternative satisfaction to suppress the appetite experienced by the user.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] An information analysis means that receives personal health information, analyzes the information, and predicts the future physical condition,

[0857] A means for generating images to visualize the future physical condition of the generated body,

[0858] A recommendation system for generating personalized advice for improving health,

[0859] An output means for displaying the generated video and advice on an output mechanism,

[0860] An emotion analysis means that analyzes the user's emotional state and adjusts advice and images according to that emotion,

[0861] An input processing means that receives user input information and adjusts the system's output content based on said input information,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The satisfaction generation means according to claim 1, which generates information that provides alternative satisfaction to suppress the appetite experienced by the user.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising a display means for displaying the generated video and advice on a receiving device in real time.

[0867] "Application example 2 when combining with an emotional engine"

[0868] (Claim 1)

[0869] A data analysis means that receives personal health data, analyzes the data, and predicts the future physical condition,

[0870] A means for generating a video to visualize the generated future physical state,

[0871] A recommendation system that generates personalized advice for improving health,

[0872] An output means for displaying the generated video and advice on an output device,

[0873] An emotion analysis tool that analyzes an individual's emotional state and adjusts content for health improvement based on the analyzed emotions,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, which includes a feedback processing means for receiving user feedback and adjusting the system's output based on said feedback.

[0877] (Claim 3)

[0878] The system according to claim 1, which uses a generation AI model to analyze the user's emotional state and generate appropriate health-promoting content. [Explanation of symbols]

[0879] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data analysis means that receives personal health data, analyzes the data, and predicts the future physical condition, A means for generating a video to visualize the generated future physical state, A recommendation system that generates personalized advice for improving health, An output means for displaying the generated video and advice on an output device, A system that includes this.

2. The system according to claim 1, which includes a feedback processing means for receiving user feedback and adjusting the system's output based on said feedback.

3. The system according to claim 1, comprising a satisfaction generation means that generates information providing alternative satisfaction to suppress the appetite experienced by the user.

Images