system

Abstract

We provide the system. [Solution] A data input means for collecting user information, A storage means for storing user information obtained from the data input means, An analysis means that generates training and meal menus based on user information stored in the aforementioned storage means, A display means for providing generated training and meal menus, A learning means that analyzes the feedback received via the aforementioned display means and improves the training and meal menus, A robot control system that monitors the user's physical activity and provides exercise guidance, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 modern society, while many people recognize the importance of health management, it is difficult for them to find a training or diet plan suitable for themselves. As a result, the situation where appropriate exercise is not performed is increasing. In addition, since there is a lack of means to consistently provide training and diet plans suitable for individuals' health conditions and lifestyles, there is a problem that it is difficult to effectively maintain and improve health.

Means for Solving the Problems

[0005] This invention provides a system that collects user information and generates training and meal plans based on that information. Specifically, it stores and analyzes information obtained from the user to create a plan optimized for the user's health condition and lifestyle. It also includes a learning mechanism that receives feedback from the user and continuously improves the plan, thereby supporting health management that is tailored to each individual user.

[0006] "User information" includes information such as the user's age, gender, height, weight, exercise experience, health status, daily activity level, and dietary preferences.

[0007] "Data input means" refers to an interface or device used to collect user information.

[0008] "Memory means" refers to digital storage or a database for storing user information obtained from data input means.

[0009] "Analysis means" refers to a system or algorithm that performs processing to generate training and meal menus based on user information stored in memory means.

[0010] "Display means" refers to a display device or interface for providing the generated training and meal menus to the user.

[0011] "Feedback" refers to data on evaluations and comments about training and meal plans provided by users through display methods.

[0012] A "learning tool" is an algorithm or process for analyzing feedback and improving training and meal plans.

[0013] A "training and meal plan" is a plan of exercise and nutritional intake generated based on the user's health status, exercise history, and activity level. [Brief explanation of the drawing]

[0014] [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 the data processing device and 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, when an emotion engine is combined. [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]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is constructed as a system for providing users with optimized training and meal plans. The following describes how this system can be implemented.

[0036] First, the user uses a device to input basic information, health status, exercise experience, daily activity levels, and dietary preferences. The device then sends this data to a server. The server stores the transmitted information in a database and generates individual user profiles.

[0037] The server uses an AI model to analyze user profiles. Based on the collected data, the AI ​​model generates optimal training and meal plans tailored to the user's health, goals, and lifestyle. This analysis process also utilizes statistical data derived from past training history and successful plan examples from other users.

[0038] The generated training and meal plans are sent back to the device and provided to the user. The user can then train according to this plan and record their daily activity. After each training session, the user enters feedback into the device regarding the difficulty of the training and their satisfaction with the meals.

[0039] The device sends this feedback to the server, which analyzes it. This analysis allows the server to update its AI model and continuously optimize training and meal plans. For example, if a user feels the training load is too high, the load can be adjusted in the next plan.

[0040] Specific example

[0041] Let's consider a scenario where user A uses this system. User A has a goal of losing weight and inputs basic information such as age 30, height 175cm, and weight 85kg. The server analyzes this information and proposes a training menu consisting of three sessions per week, focusing on aerobic exercise, and a low-calorie, high-protein meal plan to user A.

[0042] After user A completes a training session using their device, they send feedback indicating that the training was somewhat burdensome. The server uses this data to adjust the training plan for the following week, slightly reducing the workload, and sends the adjustment back to the device. In this way, user A continues to receive the optimal training plan.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user inputs basic information, health status, exercise experience, daily activity level, and dietary preferences from their device. The device then sends the entered data to the server.

[0046] Step 2:

[0047] The server stores the received user information in a database and creates individual user profiles. These profiles serve as the basis for generating training and meal plans.

[0048] Step 3:

[0049] The server uses an AI model to analyze stored user profiles. This analysis includes generating optimal training and meal plans tailored to the user's goals, health status, and lifestyle.

[0050] Step 4:

[0051] The server sends the generated training and meal plans to the device. The device then displays this to the user, providing it as a guide for their daily activities.

[0052] Step 5:

[0053] Users complete the training according to the provided plan and, after completion, input feedback on the difficulty level and menu of the training into their device. The device then sends this feedback to the server.

[0054] Step 6:

[0055] The server analyzes user feedback and updates the AI ​​model based on the results. This improves future training and meal plans to better suit the user.

[0056] Step 7:

[0057] The server regenerates the improved plan and sends the updated content to the device. By continuing this process, the server continues to provide users with optimized training and dietary guidance.

[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] Creating and providing training and meal plans tailored to each user's individual health condition and lifestyle is difficult with existing systems. In particular, there is a lack of processes to dynamically optimize plans based on user feedback, so there is a need for a system that can provide more personalized guidance.

[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 terminal means for inputting user information, storage means for storing user information, and analysis means for generating training and meal plans using an AI model. This enables the provision of an optimal plan tailored to the user's health condition and lifestyle, and dynamic optimization of the plan based on the feedback received.

[0063] "Terminal means" refers to a device or system for which a user inputs information, thereby acquiring the user's individual attributes and health data.

[0064] "Memory means" refers to a device or system that has the function of saving acquired user information, and is used to store information in a database or similar.

[0065] "Analysis means" refers to a device or process for creating training and meal plans based on user information stored using AI models or the like.

[0066] "Means of delivery" refers to a device or system for presenting the generated plan to the user, and includes display devices and notification systems.

[0067] "Learning tools" refer to devices or algorithms that analyze user feedback and optimize plans based on the data obtained.

[0068] A "generative AI model" refers to an algorithm or program that uses machine learning technology to create training and meal plans based on user information.

[0069] A "prompt statement" refers to a specific input statement used to give instructions to an AI model, and it acts as a trigger to encourage the model to process data.

[0070] This invention is specifically implemented as a system for providing users with optimal training and meal plans. First, the user inputs information such as their basic information, health status, exercise experience, daily activity level, and dietary preferences using a terminal. This terminal is an input / output device that is easy for the user to use, such as a smartphone or computer. The data entered on the terminal is transmitted to a server via the internet.

[0071] The server stores the received data in a storage device. This storage device is a cloud server or database system, used to organize and manage user information. Next, the server utilizes a generative AI model to generate training and meal plans based on the stored user data. This AI model is an algorithm that applies machine learning techniques, constructing an appropriate plan by inputting a large amount of historical data and individual user information as prompts.

[0072] For example, one possible prompt statement is: "For user A, who is 30 years old, 175cm tall, and weighs 85kg, please propose a plan for weight loss consisting of three training sessions per week and a low-calorie, high-protein diet." This prompt statement provides specific instructions to the AI ​​model, enabling the creation of a plan tailored to each user.

[0073] The generated plan is sent back to the user's device and provided to them. The user can then train and eat according to this plan. They can also input feedback on their training and diet through their device. This feedback is sent to the server, which updates the AI ​​model and incorporates it into the next plan. In this way, the plan provided to the user is constantly optimized based on their individual circumstances and needs.

[0074] This system will allow users to easily obtain training and meal plans tailored to their own health status and goals, enabling more effective health management.

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

[0076] Step 1:

[0077] Users use a device to input data such as their basic information, health status, exercise history, daily activity level, and dietary preferences. This data is checked in real time on the device, and a step is included to verify that there are no input errors. If there are no problems, the data is sent to the server via the internet.

[0078] Step 2:

[0079] The server stores the received user data in a storage device. This storage device uses a database, and user information is organized and managed within it. In this step, the input user information is structured and formatted so that it can be easily used in subsequent processing.

[0080] Step 3:

[0081] The server generates training and meal plans using an AI model based on stored user data. Here, the AI ​​model receives instructions via prompts and constructs an optimal plan tailored to the user's health condition and lifestyle. Input consists of user data and prompts, and the output is a individually optimized plan.

[0082] Step 4:

[0083] The server sends the generated plan to the device and provides it to the user. The device receives this data and presents it to the user visually. The user can then use this information to implement their training and diet.

[0084] Step 5:

[0085] Users input feedback into the device regarding their training and dietary performance, as well as their perceived training difficulty and satisfaction with their meals. The device then organizes this information and sends it to the server.

[0086] Step 6:

[0087] The server analyzes the received feedback information and updates the generating AI model. Here, the feedback data is analyzed, the AI ​​model is trained to help generate the next plan, and the plan provided to the user is improved to be more effective. This ensures the continuous optimization of the plan to meet the user's needs.

[0088] (Application Example 1)

[0089] 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."

[0090] In modern society, providing fitness and nutrition plans optimized for individual users is crucial for maintaining good health. However, current systems struggle to monitor users' specific activity levels in real time and provide effective exercise guidance. Furthermore, mechanisms for dynamically improving plans based on user feedback are insufficient. In this context, there is a need for a system that provides personalized support tailored to each user's health condition and lifestyle.

[0091] 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.

[0092] In this invention, the server includes data input means for collecting user information, storage means for storing user information obtained from the data input means, analysis means for generating training and meal menus based on the user information stored in the storage means, display means for providing the generated training and meal menus, learning means for analyzing feedback received via the display means and improving the training and meal menus, and robot control means for monitoring the user's physical activity and providing exercise guidance. This enables the provision of individually optimized fitness and nutrition plans to users, real-time exercise guidance, and dynamic adjustment of plans based on feedback.

[0093] A "data input means for collecting user information" is a device that provides an interface for users to input personal information, such as their health status, exercise experience, and dietary preferences.

[0094] A "storage device" is a data recording device for storing and managing collected user information.

[0095] "Analysis means" refers to a device or program that has the function of generating training and meal plans optimized for the user based on data stored in a memory means.

[0096] "Display means" refers to a system that visually communicates the generated training and meal menus to the user.

[0097] A "learning tool" is a system equipped with the function to analyze user feedback and continuously improve and optimize the training and meal plans it provides.

[0098] A "robot control system" is a mechanism that operates and controls a robot to support the user's movements and provide appropriate guidance.

[0099] This invention is designed as a system to support users in maintaining their health and fitness. This system allows users to input personal health information, exercise history, and dietary preferences using devices such as smartphones or home robots, and then provides optimized training and meal plans based on that information.

[0100] The server receives input data from the user and stores this data in a cloud database. The stored data is analyzed using an AI model to generate a specific plan tailored to the user's health status and goals. The AI ​​model is implemented using frameworks such as TENSORFLOW® and utilizes machine learning algorithms based on the user's profile data.

[0101] Furthermore, the generated plan is deployed to the user's device, and the robot control system supports the actual movement of the home robot. This system incorporates a function that reflects real-time feedback; the feedback entered by the user is analyzed by the server and used to improve the plan for the next time.

[0102] As a concrete example, suppose a user provides the following input: "I am 30 years old, 175cm tall, weigh 85kg, and my goal is weight loss. I can exercise three times a week and prefer a low-calorie, high-protein diet. Please suggest the optimal training and meal plan under these conditions." Based on this information, an AI model will create a plan, and exercise guidance will be provided by a home robot.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user enters personal information from their device. This includes health status, exercise history, and dietary preferences. The device transmits this information to the server through a data entry mechanism. The entered data is converted into a structured format and stored in a cloud database by the server.

[0106] Step 2:

[0107] The server applies a generative AI model based on stored user information to generate training and meal plans. By analyzing the input data, it generates prompts that provide appropriate fitness plans and nutritional intake guidelines. The AI ​​model uses historical data and learning algorithms to formulate a plan that is optimal for the user's individual profile.

[0108] Step 3:

[0109] The server generates training and meal plans and sends them to the device, displaying them on the user's screen. The plans include specific exercises and meal recipes, which the user can incorporate into their daily activities.

[0110] Step 4:

[0111] Users perform activities based on the provided plan and send feedback to the server via their device. This feedback includes things like their impressions of the training and their satisfaction with the meals. The input feedback data is stored on the server and used as the basis for subsequent data analysis.

[0112] Step 5:

[0113] The server readjusts the generated AI model based on the collected feedback, optimizing the training and meal plans. This provides a new plan that matches the user's progress. Specifically, the load is adjusted and new exercises are suggested based on the feedback, and this is output as a new prompt message.

[0114] Step 6:

[0115] The optimized plan is sent back to the device and provided to the user. This ensures that the user is always presented with a continuously improved health and fitness plan.

[0116] 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.

[0117] This invention incorporates a novel approach to a system that provides user-optimized training and meal plans, taking into account the user's emotional state. This system incorporates an emotion engine that can grasp the user's psychological state in real time and present a more appropriate plan based on that information.

[0118] The user uses a device to input basic information, health status, exercise experience, and daily activity levels. The device sends this information to a server, which stores it in a database to generate the user's profile. In addition to this basic process, the device's emotion recognition sensor detects the user's voice, facial expressions, and behavioral patterns, and an emotion engine analyzes this in real time. The analysis results are recognized as the user's temporary emotional state, such as "feeling stressed" or "highly motivated."

[0119] The server analyzes this emotional data along with the user's basic profile to generate the most suitable training and meal plan for each individual user. This plan can change daily depending on the user's emotional state; it recommends less strenuous exercise when the user is feeling more stressed and suggests a more challenging plan when they are highly motivated.

[0120] The generated plan is sent to the device and presented to the user. After the user performs activities according to the plan and enters feedback on the device, the feedback is sent to the server. The server continuously analyzes the feedback along with emotion recognition data to improve the accuracy of the plan.

[0121] For example, if user B is using the system and the emotion engine detects that they are feeling stressed after a negative experience, the server can suggest low-intensity exercise for relaxation and a meal plan to reduce stress. When the user's emotions improve, the system can offer a different plan, demonstrating dynamic responses tailored to each user's individual situation. In this way, the system can provide flexible health management that is tailored to the user's needs.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] Users input information such as basic information, health status, exercise experience, and daily activity levels through their device. The device then transmits this information to the server.

[0125] Step 2:

[0126] The server stores the received user information in a database and generates a user profile. This profile includes the user's basic information and past training data.

[0127] Step 3:

[0128] The device uses a built-in emotion recognition sensor to capture the user's voice and facial expressions in real time. This data is initially processed within the device and then sent to the emotion engine.

[0129] Step 4:

[0130] The emotion engine analyzes the user's voice and facial expression data to evaluate their current emotional state. This evaluation result is recognized as "stressed" or "highly motivated," etc. The device then sends this information to the server.

[0131] Step 5:

[0132] The server comprehensively analyzes user profiles and data from the emotion engine to generate training and meal plans optimized for the user's current situation. Specifically, it suggests plans that include relaxing exercises if the user is highly stressed, and challenging training if the user is highly motivated.

[0133] Step 6:

[0134] The server sends the generated plan to the terminal. The terminal provides this information to the user and supports the user in carrying out activities according to it.

[0135] Step 7:

[0136] The user enters feedback on the training they performed into the device. This feedback includes their impressions of the training and their evaluation of the meal. The device then sends the feedback to the server.

[0137] Step 8:

[0138] The server analyzes the feedback received and the emotion engine data received in real time to update the AI ​​model. This update enables the generation of even more accurate plans.

[0139] Step 9:

[0140] The server generates an improved plan and sends it to the terminal. This process is repeated to continuously provide the user with the most suitable training and meal plan.

[0141] (Example 2)

[0142] 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".

[0143] Traditional health management systems have struggled to provide personalized training and meal plans that take into account the emotional state of individual users. Therefore, they lacked the flexibility to respond to users' psychological needs, resulting in shortcomings in terms of maintaining user motivation and promoting health.

[0144] 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.

[0145] In this invention, the server includes an input device for collecting user information, a storage device for storing user information and emotional data obtained from the input device, and a computing device for generating training and meal menus based on the user information and emotional data stored in the storage device. This makes it possible to provide optimal training and meal menus that take into account the user's emotional state.

[0146] "User information" refers to data that includes the user's basic attribute information, health status, exercise experience, and daily activity level.

[0147] An "input device" is a device that provides a means for a user to input information.

[0148] A "memory device" is a device used to store user information and emotional data that has been entered.

[0149] A "calculating device" is a device that generates training and meal plans based on input and stored data.

[0150] A "display device" is a device that visually presents the generated training and meal menus to the user.

[0151] A "learning device" is a device that analyzes feedback and emotional data and uses that analysis to improve the generation process.

[0152] An "emotion recognition sensor" is a sensor that detects a user's voice, facial expressions, and behavioral patterns to recognize their emotional state.

[0153] This invention is a system for providing users with optimized training and meal plans. Its main feature is its ability to grasp the user's emotional state in real time and generate a personalized plan based on that information.

[0154] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The terminal functions as a data input device and transmits this information to the server. The server stores the transmitted user information in its storage device and generates individual profiles based on this information. A large-capacity database can be used for storage.

[0155] Furthermore, the device is equipped with an emotion recognition sensor that detects the user's voice, facial expressions, and behavioral patterns. This allows the device to collect emotional data in real time and send it to a server. The server then uses an emotion engine to analyze this data and determine the user's emotional state, such as "stressed" or "highly motivated."

[0156] The server also functions as a computing device, using a generative AI model to integrate user profiles and emotional data to generate the most suitable training and meal plans for each individual user. This generated plan is sent to a terminal, which acts as a display device, and presented visually to the user.

[0157] When a user performs activities according to a plan and inputs the results as feedback into their device, the server receives this feedback and uses a learning device to improve the menu generation algorithm based on the feedback in the database and ongoing sentiment data. At this stage, the generative AI model plays a crucial role in improving the accuracy of training and meal menus.

[0158] As a concrete example, a user might enter a prompt message into their terminal saying, "Please suggest a diet and exercise menu that would be suitable for someone who has been feeling stressed recently." This information is sent to the server, which analyzes past data and emotional information to generate a menu tailored to the user's needs. This allows the user to receive training and meal plans that are appropriate for their individual psychological state.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The entered data is sent from the terminal to the server. The server stores the received user information in its storage device and generates a user profile. This profile aggregates the user's attribute data and serves as the basis for analysis.

[0162] Step 2:

[0163] The device uses emotion recognition sensors to detect the user's voice, facial expressions, and actions in real time. This data measures the user's instantaneous emotions, and the device sends this data to a server. The server uses an emotion engine to analyze this emotional data and determine emotional states such as "stressed" or "highly motivated." This output is added to the profile as numerical data or categories of emotional states.

[0164] Step 3:

[0165] The server integrates user profiles and emotional data using a generative AI model. This process generates training and meal plans based on the user's attribute data and emotional state, which are the inputs. The server uses historical data and the generative AI model's algorithms to generate an optimized plan using a computing device. This output is a personalized training and meal plan.

[0166] Step 4:

[0167] The server sends the generated training and meal plans to the terminal. The terminal functions as a display device, visually presenting this data to the user. The user begins activities based on the provided plan and inputs the results as feedback into the terminal.

[0168] Step 5:

[0169] The device sends continuous sentiment data collected simultaneously with feedback to the server. The server uses a learning device to improve the plan generation algorithm based on the feedback and sentiment data in the database. The learning algorithm of the generative AI model uses this data to improve the accuracy of subsequent plans. The output at this stage is the updated generative algorithm.

[0170] (Application Example 2)

[0171] 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".

[0172] In today's advanced living environment, there is a demand for personalized health management tailored to people's physical and emotional states. However, conventional systems have struggled to accurately grasp emotional states and quickly provide care plans that take that information into account. This invention aims to solve these problems by recognizing users' emotions in real time and utilizing that data to provide optimal training and meal menus.

[0173] 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.

[0174] In this invention, the server includes data acquisition means for collecting user information, recording means for storing user information obtained from the data acquisition means, and analysis means for generating individual care plans based on the user information and emotional state stored in the recording means. This makes it possible to provide care plans that reflect the emotional state in real time.

[0175] "User information" refers to information about individual users collected by the system, including health status, exercise history, and activity level.

[0176] "Data acquisition means" refers to a device or method for accurately collecting user information.

[0177] "Recording means" refers to a device or method for storing acquired user information, such as a database or storage.

[0178] "Analysis means" refers to a device or method that has the function of generating an optimal plan for the user using information stored in a recording means.

[0179] A "display device" refers to a screen or device used to visually present the generated plan to the user.

[0180] A "learning device" is a device or method for improving the accuracy of a generation plan based on the feedback received.

[0181] "Emotion recognition means" refers to a device or method that analyzes voice and facial expressions in order to detect the emotional state of a user.

[0182] "Suggested means" refers to a device or method that has the function of generating an individual care plan from the results of the analysis and recommending actions based on that plan.

[0183] The system for carrying out this invention aims to provide a health management plan that takes into account the user's emotional state. This system mainly consists of data acquisition means, recording means, analysis means, display device, learning device, emotion recognition means, and proposal means.

[0184] First, the device uses data acquisition means to collect basic health information, exercise history, and activity status from the user. This information is stored in a database by recording means. In addition, emotion recognition means detect the user's facial expressions and voice data and analyze their emotional state in real time. The hardware uses a device with an emotion recognition sensor (e.g., smart glasses), and the software uses an AI model for emotion analysis (e.g., Amazon Rekognition).

[0185] The server uses stored information and emotional states to generate a training and nutrition plan tailored to the user through analytical means. The generated plan is presented to the user via a display device. The user acts according to the plan and inputs feedback into the terminal. The feedback is sent to the server and continuously analyzed by the learning device. As a result, the accuracy of the plan improves over time.

[0186] For example, if the emotion recognition system detects that a user has a quiet and depressed expression after a day's activities, the server uses analysis tools to generate a relaxation plan. The user will then be shown suggestions such as "listen to relaxation music" or "take a walk."

[0187] As an example of a prompt message when using a generative AI model, a question in the format of, "The user may be experiencing stress. What relaxation activities can you suggest?" can be used to automatically generate more appropriate suggestions.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The terminal collects the user's basic information, health status, exercise history, and activity status through data input means. This includes manual input and data acquisition from wearable devices. This information is transmitted as input data to a recording means and stored in a database.

[0191] Step 2:

[0192] The device uses an emotion recognition sensor to collect the user's voice and facial expressions in real time. The collected data is analyzed by an emotion recognition device and output as the user's emotional state (e.g., "feeling stressed"). This emotional data is also stored by a recording device.

[0193] Step 3:

[0194] The server sends stored user information and sentiment data as input to the analysis system. The analysis system generates individual training and meal plans based on this data. Machine learning algorithms are used for data processing to output the optimal plan.

[0195] Step 4:

[0196] The server presents the generated training and meal plans to the user via a display device. This information is transmitted directly to the terminal and displayed on a smartphone or smart glasses.

[0197] Step 5:

[0198] The user performs activities according to the presented plan and inputs the results and feedback into the device. The input feedback is then sent from the device to the server.

[0199] Step 6:

[0200] The server sends feedback and emotion recognition data as input to the learning device. The learning device uses this data to learn how to improve the accuracy of the plan and outputs an improved plan generation algorithm as a result of the calculation.

[0201] Step 7:

[0202] The server uses analysis tools to generate a new plan based on the user's updated emotional state and feedback, and then presents it to the user again via the display device. This enables the provision of dynamic plans tailored to the user's state.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention is constructed as a system for providing users with optimized training and meal plans. The following describes how this system can be implemented.

[0220] First, the user uses a device to input basic information, health status, exercise experience, daily activity levels, and dietary preferences. The device then sends this data to a server. The server stores the transmitted information in a database and generates individual user profiles.

[0221] The server uses an AI model to analyze user profiles. Based on the collected data, the AI ​​model generates optimal training and meal plans tailored to the user's health, goals, and lifestyle. This analysis process also utilizes statistical data derived from past training history and successful plan examples from other users.

[0222] The generated training and meal plans are sent back to the device and provided to the user. The user can then train according to this plan and record their daily activity. After each training session, the user enters feedback into the device regarding the difficulty of the training and their satisfaction with the meals.

[0223] The device sends this feedback to the server, which analyzes it. This analysis allows the server to update its AI model and continuously optimize training and meal plans. For example, if a user feels the training load is too high, the load can be adjusted in the next plan.

[0224] Specific example

[0225] Let's consider a scenario where user A uses this system. User A has a goal of losing weight and inputs basic information such as age 30, height 175cm, and weight 85kg. The server analyzes this information and proposes a training menu consisting of three sessions per week, focusing on aerobic exercise, and a low-calorie, high-protein meal plan to user A.

[0226] After user A completes a training session using their device, they send feedback indicating that the training was somewhat burdensome. The server uses this data to adjust the training plan for the following week, slightly reducing the workload, and sends the adjustment back to the device. In this way, user A continues to receive the optimal training plan.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The user inputs basic information, health status, exercise experience, daily activity level, and dietary preferences from their device. The device then sends the entered data to the server.

[0230] Step 2:

[0231] The server stores the received user information in a database and creates individual user profiles. These profiles serve as the basis for generating training and meal plans.

[0232] Step 3:

[0233] The server uses an AI model to analyze stored user profiles. This analysis includes generating optimal training and meal plans tailored to the user's goals, health status, and lifestyle.

[0234] Step 4:

[0235] The server sends the generated training and meal plans to the device. The device then displays this to the user, providing it as a guide for their daily activities.

[0236] Step 5:

[0237] Users complete the training according to the provided plan and, after completion, input feedback on the difficulty level and menu of the training into their device. The device then sends this feedback to the server.

[0238] Step 6:

[0239] The server analyzes user feedback and updates the AI ​​model based on the results. This improves future training and meal plans to better suit the user.

[0240] Step 7:

[0241] The server regenerates the improved plan and sends the updated content to the device. By continuing this process, the server continues to provide users with optimized training and dietary guidance.

[0242] (Example 1)

[0243] 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."

[0244] Creating and providing training and meal plans tailored to each user's individual health condition and lifestyle is difficult with existing systems. In particular, there is a lack of processes to dynamically optimize plans based on user feedback, so there is a need for a system that can provide more personalized guidance.

[0245] 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.

[0246] In this invention, the server includes terminal means for inputting user information, storage means for storing user information, and analysis means for generating training and meal plans using an AI model. This enables the provision of an optimal plan tailored to the user's health condition and lifestyle, and dynamic optimization of the plan based on the feedback received.

[0247] "Terminal means" refers to a device or system for which a user inputs information, thereby acquiring the user's individual attributes and health data.

[0248] "Memory means" refers to a device or system that has the function of saving acquired user information, and is used to store information in a database or similar.

[0249] "Analysis means" refers to a device or process for creating training and meal plans based on user information stored using AI models or the like.

[0250] "Means of delivery" refers to a device or system for presenting the generated plan to the user, and includes display devices and notification systems.

[0251] "Learning method" refers to a device or algorithm that analyzes user feedback and optimizes the plan based on the data obtained.

[0252] A "generative AI model" refers to an algorithm or program that uses machine learning technology to construct training and meal plans based on user information.

[0253] A "prompt statement" refers to a specific input statement used to give instructions to an AI model, and it acts as a trigger to encourage the model to process data.

[0254] This invention is specifically implemented as a system for providing users with optimal training and meal plans. First, the user inputs information such as their basic information, health status, exercise experience, daily activity level, and dietary preferences using a terminal. This terminal is an input / output device that is easy for the user to use, such as a smartphone or computer. The data entered on the terminal is transmitted to a server via the internet.

[0255] The server stores the received data in a storage device. This storage device is a cloud server or database system, used to organize and manage user information. Next, the server utilizes a generative AI model to generate training and meal plans based on the stored user data. This AI model is an algorithm that applies machine learning techniques, constructing an appropriate plan by inputting a large amount of historical data and individual user information as prompts.

[0256] For example, one possible prompt statement is: "For user A, who is 30 years old, 175cm tall, and weighs 85kg, please propose a plan for weight loss consisting of three training sessions per week and a low-calorie, high-protein diet." This prompt statement provides specific instructions to the AI ​​model, enabling the creation of a plan tailored to each user.

[0257] The generated plan is sent back to the user's device and provided to them. The user can then train and eat according to this plan. They can also input feedback on their training and diet through their device. This feedback is sent to the server, which updates the AI ​​model and incorporates it into the next plan. In this way, the plan provided to the user is constantly optimized based on their individual circumstances and needs.

[0258] This system will allow users to easily obtain training and meal plans tailored to their own health status and goals, enabling more effective health management.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] Users use a device to input data such as their basic information, health status, exercise history, daily activity level, and dietary preferences. This data is checked in real time on the device, and a step is included to verify that there are no input errors. If there are no problems, the data is sent to the server via the internet.

[0262] Step 2:

[0263] The server stores the received user data in a storage device. This storage device uses a database, and user information is organized and managed within it. In this step, the input user information is structured and formatted so that it can be easily used in subsequent processing.

[0264] Step 3:

[0265] The server generates training and meal plans using an AI model based on stored user data. Here, the AI ​​model receives instructions via prompts and constructs an optimal plan tailored to the user's health condition and lifestyle. Input consists of user data and prompts, and the output is a individually optimized plan.

[0266] Step 4:

[0267] The server sends the generated plan to the device and provides it to the user. The device receives this data and presents it to the user visually. The user can then use this information to implement their training and diet.

[0268] Step 5:

[0269] Users input feedback into the device regarding their training and dietary performance, as well as their perceived training difficulty and satisfaction with their meals. The device then organizes this information and sends it to the server.

[0270] Step 6:

[0271] The server analyzes the received feedback information and updates the generating AI model. Here, the feedback data is analyzed, the AI ​​model is trained to help generate the next plan, and the plan provided to the user is improved to be more effective. This ensures the continuous optimization of the plan to meet the user's needs.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] In modern society, providing fitness and nutrition plans optimized for individual users is crucial for maintaining good health. However, current systems struggle to monitor users' specific activity levels in real time and provide effective exercise guidance. Furthermore, mechanisms for dynamically improving plans based on user feedback are insufficient. In this context, there is a need for a system that provides personalized support tailored to each user's health condition and lifestyle.

[0275] 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.

[0276] In this invention, the server includes data input means for collecting user information, storage means for storing user information obtained from the data input means, analysis means for generating training and meal menus based on the user information stored in the storage means, display means for providing the generated training and meal menus, learning means for analyzing feedback received via the display means and improving the training and meal menus, and robot control means for monitoring the user's physical activity and providing exercise guidance. This enables the provision of individually optimized fitness and nutrition plans to users, real-time exercise guidance, and dynamic adjustment of plans based on feedback.

[0277] A "data input means for collecting user information" is a device that provides an interface for users to input personal information, such as their health status, exercise experience, and dietary preferences.

[0278] A "storage device" is a data recording device for storing and managing collected user information.

[0279] The "analysis means" is a device or program having a function of generating a training and diet plan optimized for the user based on the data stored in the storage means.

[0280] The "display means" is a mechanism for visually transmitting the generated training and diet menus to the user.

[0281] The "learning means" is a system equipped with a function of analyzing feedback from the user and continuously improving and optimizing the provided training and diet plans.

[0282] The "robot control means" is a mechanism for operating and controlling a robot to support the user's movement and provide appropriate guidance.

[0283] The present invention is constructed as a system for supporting the user's health maintenance and fitness. This system enables the user to input personal health information, exercise history, and dietary preferences using a terminal such as a smartphone or a household robot, and provides an optimized training and diet plan based on this information.

[0284] The server receives input data from the user and stores this data in a cloud database. The stored data is analyzed using an AI model to generate a specific plan suitable for the user's health condition and goals. The AI model is implemented using a framework such as TensorFlow and utilizes machine learning algorithms based on the user's profile data.

[0285] Furthermore, the generated plan is deployed to the user's terminal, and the household robot supports the actual movement by the robot control means. This system incorporates a function of reflecting real-time feedback, and the feedback input by the user is analyzed by the server and utilized for improving the next plan.

[0286] As a specific example, assume that a user provides input information such as "30 years old, 175 cm tall, 85 kg in weight, goal is weight loss. Can exercise three times a week and prefers a low-calorie, high-protein diet. Please propose an optimal training and diet plan under these conditions." Based on this information, the AI model creates a plan, and exercise guidance by a household robot is to be implemented.

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The user inputs personal information from the terminal. This includes health status, exercise history, and dietary preferences. The terminal sends this information to the server through the data input means. The input data is converted into a structured format and stored in the cloud database by the server.

[0290] Step 2:

[0291] The server applies the generated AI model based on the stored user information to generate a training and diet plan. By analyzing the input data, a prompt sentence that provides appropriate fitness plan and nutrition intake guidelines is generated. The AI model uses past data and learning algorithms to formulate an optimal plan for the user's individual profile.

[0292] Step 3:

[0293] The training and diet plan generated by the server is sent to the terminal and displayed on the user's screen. The plan includes specific exercises and diet recipes, and the user can incorporate this into their daily activities.

[0294] Step 4:

[0295] Users perform activities based on the provided plan and send feedback to the server via their device. This feedback includes things like their impressions of the training and their satisfaction with the meals. The entered feedback data is stored on the server and used as the basis for subsequent data analysis.

[0296] Step 5:

[0297] The server readjusts the generated AI model based on the collected feedback, optimizing the training and meal plans. This provides a new plan that matches the user's progress. Specifically, the load is adjusted and new exercises are suggested based on the feedback, and this is output as a new prompt message.

[0298] Step 6:

[0299] The optimized plan is sent back to the device and provided to the user. This ensures that the user is always presented with a continuously improved health and fitness plan.

[0300] 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.

[0301] This invention incorporates a novel approach to a system that provides user-optimized training and meal plans, taking into account the user's emotional state. This system incorporates an emotion engine that can grasp the user's psychological state in real time and present a more appropriate plan based on that information.

[0302] The user uses the terminal to input basic information, health status, exercise experience, and daily activity levels. The terminal sends this information to the server, which stores it in a database and generates a user profile. In addition to this basic process, the terminal's emotion recognition sensor detects the user's voice, expression, and behavior patterns, and the emotion engine analyzes this in real time. The analysis results are recognized as the user's temporary emotional state, such as "feeling stressed" or "highly motivated".

[0303] The server analyzes this emotion data together with the user's basic profile to generate a training and diet plan that is most suitable for each individual user. This plan can change daily according to the user's emotional state, recommending less strenuous exercises when the user is feeling more stressed and proposing a more challenging plan when the user is highly motivated.

[0304] The generated plan is sent to the terminal and presented to the user. After the user performs activities according to the plan and inputs feedback to the terminal, the feedback is sent to the server. The server continuously analyzes the feedback together with the emotion recognition data to improve the accuracy of the plan.

[0305] For example, if User B is using the system and the emotion engine detects that the user is feeling stressed after a failure experience, the server can propose low-intensity exercises for relaxation and a diet menu to reduce stress. When the user's emotions improve, dynamic responses according to the individual situation of the user, such as presenting another plan, are possible. Thus, this system can provide flexible health management that is tailored to the user.

[0306] The following describes the processing flow.

[0307] Step 1:

[0308] Users input information such as basic information, health status, exercise experience, and daily activity levels through their device. The device then transmits this information to the server.

[0309] Step 2:

[0310] The server stores the received user information in a database and generates a user profile. This profile includes the user's basic information and past training data.

[0311] Step 3:

[0312] The device uses a built-in emotion recognition sensor to capture the user's voice and facial expressions in real time. This data is initially processed within the device and then sent to the emotion engine.

[0313] Step 4:

[0314] The emotion engine analyzes the user's voice and facial expression data to evaluate their current emotional state. This evaluation result is recognized as "stressed" or "highly motivated," etc. The device then sends this information to the server.

[0315] Step 5:

[0316] The server comprehensively analyzes user profiles and data from the emotion engine to generate training and meal plans optimized for the user's current situation. Specifically, it suggests plans that include relaxing exercises if the user is highly stressed, and challenging training if the user is highly motivated.

[0317] Step 6:

[0318] The server sends the generated plan to the terminal. The terminal provides this information to the user and supports the user in carrying out activities according to it.

[0319] Step 7:

[0320] The user enters feedback on the training they performed into the device. This feedback includes their impressions of the training and their evaluation of the meal. The device then sends the feedback to the server.

[0321] Step 8:

[0322] The server analyzes the feedback received and the emotion engine data received in real time to update the AI ​​model. This update enables the generation of even more accurate plans.

[0323] Step 9:

[0324] The server generates an improved plan and sends it to the terminal. This process is repeated to continuously provide the user with the most suitable training and meal plan.

[0325] (Example 2)

[0326] 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".

[0327] Traditional health management systems have struggled to provide personalized training and meal plans that take into account the emotional state of individual users. Therefore, they lacked the flexibility to respond to users' psychological needs, resulting in shortcomings in terms of maintaining user motivation and promoting health.

[0328] 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.

[0329] In this invention, the server includes an input device for collecting user information, a storage device for storing user information and emotional data obtained from the input device, and a computing device for generating training and meal menus based on the user information and emotional data stored in the storage device. This makes it possible to provide optimal training and meal menus that take into account the user's emotional state.

[0330] "User information" refers to data that includes the user's basic attribute information, health status, exercise experience, and daily activity level.

[0331] An "input device" is a device that provides a means for a user to input information.

[0332] A "memory device" is a device used to store user information and emotional data that has been entered.

[0333] A "calculating device" is a device that generates training and meal plans based on input and stored data.

[0334] A "display device" is a device that visually presents the generated training and meal menus to the user.

[0335] A "learning device" is a device that analyzes feedback and emotional data and uses that analysis to improve the generation process.

[0336] An "emotion recognition sensor" is a sensor that detects a user's voice, facial expressions, and behavioral patterns to recognize their emotional state.

[0337] This invention is a system for providing users with optimized training and meal plans. Its main feature is its ability to grasp the user's emotional state in real time and generate a personalized plan based on that information.

[0338] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The terminal functions as a data input device and transmits this information to the server. The server stores the transmitted user information in its storage device and generates individual profiles based on this information. A large-capacity database can be used for storage.

[0339] Furthermore, the device is equipped with an emotion recognition sensor that detects the user's voice, facial expressions, and behavioral patterns. This allows the device to collect emotional data in real time and send it to a server. The server then uses an emotion engine to analyze this data and determine the user's emotional state, such as "stressed" or "highly motivated."

[0340] The server also functions as a computing device, using a generative AI model to integrate user profiles and emotional data to generate the most suitable training and meal plans for each individual user. This generated plan is sent to a terminal, which acts as a display device, and presented visually to the user.

[0341] When a user performs activities according to a plan and inputs the results as feedback into their device, the server receives this feedback and uses a learning device to improve the menu generation algorithm based on the feedback in the database and ongoing sentiment data. At this stage, the generative AI model plays a crucial role in improving the accuracy of training and meal menus.

[0342] As a concrete example, a user might enter a prompt message into their terminal saying, "Please suggest a diet and exercise menu that would be suitable for someone who has been feeling stressed recently." This information is sent to the server, which analyzes past data and emotional information to generate a menu tailored to the user's needs. This allows the user to receive training and meal plans that are appropriate for their individual psychological state.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The entered data is sent from the terminal to the server. The server stores the received user information in its storage device and generates a user profile. This profile aggregates the user's attribute data and serves as the basis for analysis.

[0346] Step 2:

[0347] The device uses emotion recognition sensors to detect the user's voice, facial expressions, and actions in real time. This data measures the user's instantaneous emotions, and the device sends this data to a server. The server uses an emotion engine to analyze this emotional data and determine emotional states such as "stressed" or "highly motivated." This output is added to the profile as numerical data or categories of emotional states.

[0348] Step 3:

[0349] The server integrates user profiles and emotional data using a generative AI model. This process generates training and meal plans based on the user's attribute data and emotional state, which are the inputs. The server uses historical data and the generative AI model's algorithms to generate an optimized plan using a computing device. This output is a personalized training and meal plan.

[0350] Step 4:

[0351] The server sends the generated training and meal plans to the terminal. The terminal functions as a display device, visually presenting this data to the user. The user begins activities based on the provided plan and inputs the results as feedback into the terminal.

[0352] Step 5:

[0353] The device sends continuous sentiment data collected simultaneously with feedback to the server. The server uses a learning device to improve the plan generation algorithm based on the feedback and sentiment data in the database. The learning algorithm of the generative AI model uses this data to improve the accuracy of subsequent plans. The output at this stage is the updated generative algorithm.

[0354] (Application Example 2)

[0355] 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."

[0356] In today's advanced living environment, there is a demand for personalized health management tailored to people's physical and emotional states. However, conventional systems have struggled to accurately grasp emotional states and quickly provide care plans that take that information into account. This invention aims to solve these problems by recognizing users' emotions in real time and utilizing that data to provide optimal training and meal menus.

[0357] 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.

[0358] In this invention, the server includes data acquisition means for collecting user information, recording means for storing user information obtained from the data acquisition means, and analysis means for generating individual care plans based on the user information and emotional state stored in the recording means. This makes it possible to provide care plans that reflect the emotional state in real time.

[0359] "User information" refers to information about individual users collected by the system, including health status, exercise history, and activity level.

[0360] "Data acquisition means" refers to a device or method for accurately collecting user information.

[0361] "Recording means" refers to a device or method for storing acquired user information, such as a database or storage.

[0362] "Analysis means" refers to a device or method that has the function of generating an optimal plan for the user using information stored in a recording means.

[0363] A "display device" refers to a screen or device used to visually present the generated plan to the user.

[0364] A "learning device" is a device or method for improving the accuracy of a generation plan based on the feedback received.

[0365] "Emotion recognition means" refers to a device or method that analyzes voice and facial expressions in order to detect the emotional state of a user.

[0366] "Suggested means" refers to a device or method that has the function of generating an individual care plan from the results of the analysis and recommending actions based on that plan.

[0367] The system for carrying out this invention aims to provide a health management plan that takes into account the user's emotional state. This system mainly consists of data acquisition means, recording means, analysis means, display device, learning device, emotion recognition means, and suggestion means.

[0368] First, the device uses data acquisition means to collect basic health information, exercise history, and activity status from the user. This information is stored in a database by recording means. In addition, emotion recognition means detect the user's facial expressions and voice data and analyze their emotional state in real time. The hardware uses a device with an emotion recognition sensor (e.g., smart glasses), and the software uses an AI model for emotion analysis (e.g., Amazon Rekognition).

[0369] The server uses stored information and emotional states to generate a training and nutrition plan tailored to the user through analytical means. The generated plan is presented to the user via a display device. The user acts according to the plan and inputs feedback into the terminal. The feedback is sent to the server and continuously analyzed by the learning device. As a result, the accuracy of the plan improves over time.

[0370] For example, if the emotion recognition system detects that a user has a quiet and depressed expression after a day's activities, the server uses analysis tools to generate a relaxation plan. The user will then be shown suggestions such as "listen to relaxation music" or "take a walk."

[0371] As an example of a prompt message when using a generative AI model, a question in the format of, "The user may be experiencing stress. What relaxation activities can you suggest?" can be used to automatically generate more appropriate suggestions.

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] The terminal collects the user's basic information, health status, exercise history, and activity status through data input means. This includes manual input and data acquisition from wearable devices. This information is transmitted as input data to a recording means and stored in a database.

[0375] Step 2:

[0376] The device uses an emotion recognition sensor to collect the user's voice and facial expressions in real time. The collected data is analyzed by an emotion recognition device and output as the user's emotional state (e.g., "feeling stressed"). This emotional data is also stored by a recording device.

[0377] Step 3:

[0378] The server sends stored user information and sentiment data as input to the analysis system. The analysis system generates individual training and meal plans based on this data. Machine learning algorithms are used for data processing to output the optimal plan.

[0379] Step 4:

[0380] The server presents the generated training and meal plans to the user via a display device. This information is transmitted directly to the terminal and displayed on a smartphone or smart glasses.

[0381] Step 5:

[0382] The user performs activities according to the presented plan and inputs the results and feedback into the device. The input feedback is then sent from the device to the server.

[0383] Step 6:

[0384] The server sends feedback and emotion recognition data as input to the learning device. The learning device uses this data to learn how to improve the accuracy of the plan and outputs an improved plan generation algorithm as a result of the calculation.

[0385] Step 7:

[0386] The server uses analysis tools to generate a new plan based on the user's updated emotional state and feedback, and then presents it to the user again via the display device. This enables the provision of dynamic plans tailored to the user's state.

[0387] 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.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention is constructed as a system for providing users with optimized training and meal plans. The following describes how this system can be implemented.

[0404] First, the user uses a device to input basic information, health status, exercise experience, daily activity levels, and dietary preferences. The device then sends this data to a server. The server stores the transmitted information in a database and generates individual user profiles.

[0405] The server uses an AI model to analyze user profiles. Based on the collected data, the AI ​​model generates optimal training and meal plans tailored to the user's health, goals, and lifestyle. This analysis process also utilizes statistical data derived from past training history and successful plan examples from other users.

[0406] The generated training and meal plans are sent back to the device and provided to the user. The user can then train according to this plan and record their daily activity. After each training session, the user enters feedback into the device regarding the difficulty of the training and their satisfaction with the meals.

[0407] The device sends this feedback to the server, which analyzes it. This analysis allows the server to update its AI model and continuously optimize training and meal plans. For example, if a user feels the training load is too high, the load can be adjusted in the next plan.

[0408] Specific example

[0409] Let's consider a scenario where user A uses this system. User A has a goal of losing weight and inputs basic information such as age 30, height 175cm, and weight 85kg. The server analyzes this information and proposes a training menu consisting of three sessions per week, focusing on aerobic exercise, and a low-calorie, high-protein meal plan to user A.

[0410] After user A completes a training session using their device, they send feedback indicating that the training was somewhat burdensome. The server uses this data to adjust the training plan for the following week, slightly reducing the workload, and sends the adjustment back to the device. In this way, user A continues to receive the optimal training plan.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The user inputs basic information, health status, exercise experience, daily activity level, and dietary preferences from their device. The device then sends the entered data to the server.

[0414] Step 2:

[0415] The server stores the received user information in a database and creates individual user profiles. These profiles serve as the basis for generating training and meal plans.

[0416] Step 3:

[0417] The server uses an AI model to analyze stored user profiles. This analysis includes generating optimal training and meal plans tailored to the user's goals, health status, and lifestyle.

[0418] Step 4:

[0419] The server sends the generated training and meal plans to the device. The device then displays this to the user, providing it as a guide for their daily activities.

[0420] Step 5:

[0421] Users complete the training according to the provided plan and, after completion, input feedback on the difficulty level and menu of the training into their device. The device then sends this feedback to the server.

[0422] Step 6:

[0423] The server analyzes user feedback and updates the AI ​​model based on the results. This improves future training and meal plans to better suit the user.

[0424] Step 7:

[0425] The server regenerates the improved plan and sends the updated content to the device. By continuing this process, the server continues to provide users with optimized training and dietary guidance.

[0426] (Example 1)

[0427] 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."

[0428] Creating and providing training and meal plans tailored to each user's individual health condition and lifestyle is difficult with existing systems. In particular, there is a lack of processes to dynamically optimize plans based on user feedback, so there is a need for a system that can provide more personalized guidance.

[0429] 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.

[0430] In this invention, the server includes terminal means for inputting user information, storage means for storing user information, and analysis means for generating training and meal plans using an AI model. This enables the provision of an optimal plan tailored to the user's health condition and lifestyle, and dynamic optimization of the plan based on the feedback received.

[0431] "Terminal means" refers to a device or system for which a user inputs information, thereby acquiring the user's individual attributes and health data.

[0432] "Memory means" refers to a device or system that has the function of saving acquired user information, and is used to store information in a database or similar.

[0433] "Analysis means" refers to a device or process for creating training and meal plans based on user information stored using AI models or the like.

[0434] "Means of delivery" refers to a device or system for presenting the generated plan to the user, and includes display devices and notification systems.

[0435] "Learning method" refers to a device or algorithm that analyzes user feedback and optimizes the plan based on the data obtained.

[0436] A "generative AI model" refers to an algorithm or program that uses machine learning technology to construct training and meal plans based on user information.

[0437] A "prompt statement" refers to a specific input statement used to give instructions to an AI model, and it acts as a trigger to encourage the model to process data.

[0438] This invention is specifically implemented as a system for providing users with optimal training and meal plans. First, the user inputs information such as their basic information, health status, exercise experience, daily activity level, and dietary preferences using a terminal. This terminal is an input / output device that is easy for the user to use, such as a smartphone or computer. The data entered on the terminal is transmitted to a server via the internet.

[0439] The server stores the received data in a storage device. This storage device is a cloud server or database system, used to organize and manage user information. Next, the server utilizes a generative AI model to generate training and meal plans based on the stored user data. This AI model is an algorithm that applies machine learning techniques, constructing an appropriate plan by inputting a large amount of historical data and individual user information as prompts.

[0440] For example, one possible prompt statement is: "For user A, who is 30 years old, 175cm tall, and weighs 85kg, please propose a plan for weight loss consisting of three training sessions per week and a low-calorie, high-protein diet." This prompt statement provides specific instructions to the AI ​​model, enabling the creation of a plan tailored to each user.

[0441] The generated plan is sent back to the user's device and provided to them. The user can then train and eat according to this plan. They can also input feedback on their training and diet through their device. This feedback is sent to the server, which updates the AI ​​model and incorporates it into the next plan. In this way, the plan provided to the user is constantly optimized based on their individual circumstances and needs.

[0442] This system will allow users to easily obtain training and meal plans tailored to their own health status and goals, enabling more effective health management.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] Users use a device to input data such as their basic information, health status, exercise history, daily activity level, and dietary preferences. This data is checked in real time on the device, and a step is included to verify that there are no input errors. If there are no problems, the data is sent to the server via the internet.

[0446] Step 2:

[0447] The server stores the received user data in a storage device. This storage device uses a database, and user information is organized and managed within it. In this step, the input user information is structured and formatted so that it can be easily used in subsequent processing.

[0448] Step 3:

[0449] The server generates training and meal plans using an AI model based on stored user data. Here, the AI ​​model receives instructions via prompts and constructs an optimal plan tailored to the user's health condition and lifestyle. Input consists of user data and prompts, and the output is a individually optimized plan.

[0450] Step 4:

[0451] The server sends the generated plan to the device and provides it to the user. The device receives this data and presents it to the user visually. The user can then use this information to implement their training and diet.

[0452] Step 5:

[0453] Users input feedback into the device regarding their training and dietary performance, as well as their perceived training difficulty and satisfaction with their meals. The device then organizes this information and sends it to the server.

[0454] Step 6:

[0455] The server analyzes the received feedback information and updates the generating AI model. Here, the feedback data is analyzed, the AI ​​model is trained to help generate the next plan, and the plan provided to the user is improved to be more effective. This ensures the continuous optimization of the plan to meet the user's needs.

[0456] (Application Example 1)

[0457] 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."

[0458] In modern society, providing fitness and nutrition plans optimized for individual users is crucial for maintaining good health. However, current systems struggle to monitor users' specific activity levels in real time and provide effective exercise guidance. Furthermore, mechanisms for dynamically improving plans based on user feedback are insufficient. In this context, there is a need for a system that provides personalized support tailored to each user's health condition and lifestyle.

[0459] 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.

[0460] In this invention, the server includes data input means for collecting user information, storage means for storing user information obtained from the data input means, analysis means for generating training and meal menus based on the user information stored in the storage means, display means for providing the generated training and meal menus, learning means for analyzing feedback received via the display means and improving the training and meal menus, and robot control means for monitoring the user's physical activity and providing exercise guidance. This enables the provision of individually optimized fitness and nutrition plans to users, real-time exercise guidance, and dynamic adjustment of plans based on feedback.

[0461] A "data input means for collecting user information" is a device that provides an interface for users to input personal information, such as their health status, exercise experience, and dietary preferences.

[0462] A "storage device" is a data recording device for storing and managing collected user information.

[0463] "Analysis means" refers to a device or program that has the function of generating training and meal plans optimized for the user based on data stored in a memory means.

[0464] "Display means" refers to a system that visually communicates the generated training and meal menus to the user.

[0465] A "learning tool" is a system equipped with the function to analyze user feedback and continuously improve and optimize the training and meal plans it provides.

[0466] A "robot control system" is a mechanism that operates and controls a robot to support the user's movements and provide appropriate guidance.

[0467] This invention is designed as a system to support users in maintaining their health and fitness. This system allows users to input personal health information, exercise history, and dietary preferences using devices such as smartphones or home robots, and then provides optimized training and meal plans based on that information.

[0468] The server receives input data from the user and stores this data in a cloud database. The stored data is analyzed using an AI model to generate a specific plan tailored to the user's health status and goals. The AI ​​model is implemented using frameworks such as TensorFlow and utilizes machine learning algorithms based on the user's profile data.

[0469] Furthermore, the generated plan is deployed to the user's device, and the robot control system supports the actual movement of the home robot. This system incorporates a function that reflects real-time feedback; the feedback entered by the user is analyzed by the server and used to improve the plan for the next time.

[0470] As a concrete example, suppose a user provides the following input: "I am 30 years old, 175cm tall, weigh 85kg, and my goal is weight loss. I can exercise three times a week and prefer a low-calorie, high-protein diet. Please suggest the optimal training and meal plan under these conditions." Based on this information, an AI model will create a plan, and exercise guidance will be provided by a home robot.

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The user enters personal information from their device. This includes health status, exercise history, and dietary preferences. The device transmits this information to the server through a data entry mechanism. The entered data is converted into a structured format and stored in a cloud database by the server.

[0474] Step 2:

[0475] The server applies a generative AI model based on stored user information to generate training and meal plans. By analyzing the input data, it generates prompts that provide appropriate fitness plans and nutritional intake guidelines. The AI ​​model uses historical data and learning algorithms to formulate a plan that is optimal for the user's individual profile.

[0476] Step 3:

[0477] The server generates training and meal plans and sends them to the device, displaying them on the user's screen. The plans include specific exercises and meal recipes, which the user can incorporate into their daily activities.

[0478] Step 4:

[0479] Users perform activities based on the provided plan and send feedback to the server via their device. This feedback includes things like their impressions of the training and their satisfaction with the meals. The input feedback data is stored on the server and used as the basis for subsequent data analysis.

[0480] Step 5:

[0481] The server readjusts the generated AI model based on the collected feedback, optimizing the training and meal plans. This provides a new plan that matches the user's progress. Specifically, the load is adjusted and new exercises are suggested based on the feedback, and this is output as a new prompt message.

[0482] Step 6:

[0483] The optimized plan is sent back to the device and provided to the user. This ensures that the user is always presented with a continuously improved health and fitness plan.

[0484] 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.

[0485] This invention incorporates a novel approach to a system that provides user-optimized training and meal plans, taking into account the user's emotional state. This system incorporates an emotion engine that can grasp the user's psychological state in real time and present a more appropriate plan based on that information.

[0486] The user uses a device to input basic information, health status, exercise experience, and daily activity levels. The device sends this information to a server, which stores it in a database to generate the user's profile. In addition to this basic process, the device's emotion recognition sensor detects the user's voice, facial expressions, and behavioral patterns, and an emotion engine analyzes this in real time. The analysis results are recognized as the user's temporary emotional state, such as "feeling stressed" or "highly motivated."

[0487] The server analyzes this emotional data along with the user's basic profile to generate the most suitable training and meal plan for each individual user. This plan can change daily depending on the user's emotional state; it recommends less strenuous exercise when the user is feeling more stressed and suggests a more challenging plan when they are highly motivated.

[0488] The generated plan is sent to the device and presented to the user. After the user performs activities according to the plan and enters feedback on the device, the feedback is sent to the server. The server continuously analyzes the feedback along with emotion recognition data to improve the accuracy of the plan.

[0489] For example, if user B is using the system and the emotion engine detects that they are feeling stressed after a negative experience, the server can suggest low-intensity exercise for relaxation and a meal plan to reduce stress. When the user's emotions improve, the system can offer a different plan, demonstrating dynamic responses tailored to each user's individual situation. In this way, the system can provide flexible health management that is tailored to the user's needs.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] Users input information such as basic information, health status, exercise experience, and daily activity levels through their device. The device then transmits this information to the server.

[0493] Step 2:

[0494] The server stores the received user information in a database and generates a user profile. This profile includes the user's basic information and past training data.

[0495] Step 3:

[0496] The device uses a built-in emotion recognition sensor to capture the user's voice and facial expressions in real time. This data is initially processed within the device and then sent to the emotion engine.

[0497] Step 4:

[0498] The emotion engine analyzes the user's voice and facial expression data to evaluate their current emotional state. This evaluation result is recognized as "stressed" or "highly motivated," etc. The device then sends this information to the server.

[0499] Step 5:

[0500] The server comprehensively analyzes user profiles and data from the emotion engine to generate training and meal plans optimized for the user's current situation. Specifically, it suggests plans that include relaxing exercises if the user is highly stressed, and challenging training if the user is highly motivated.

[0501] Step 6:

[0502] The server sends the generated plan to the terminal. The terminal provides this information to the user and supports the user in carrying out activities according to it.

[0503] Step 7:

[0504] The user enters feedback on the training they performed into the device. This feedback includes their impressions of the training and their evaluation of the meal. The device then sends the feedback to the server.

[0505] Step 8:

[0506] The server analyzes the feedback received and the emotion engine data received in real time to update the AI ​​model. This update enables the generation of even more accurate plans.

[0507] Step 9:

[0508] The server generates an improved plan and sends it to the terminal. This process is repeated to continuously provide the user with the most suitable training and meal plan.

[0509] (Example 2)

[0510] 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."

[0511] Traditional health management systems have struggled to provide personalized training and meal plans that take into account the emotional state of individual users. Therefore, they lacked the flexibility to respond to users' psychological needs, resulting in shortcomings in terms of maintaining user motivation and promoting health.

[0512] 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.

[0513] In this invention, the server includes an input device for collecting user information, a storage device for storing user information and emotional data obtained from the input device, and a computing device for generating training and meal menus based on the user information and emotional data stored in the storage device. This makes it possible to provide optimal training and meal menus that take into account the user's emotional state.

[0514] "User information" refers to data that includes the user's basic attribute information, health status, exercise experience, and daily activity level.

[0515] An "input device" is a device that provides a means for a user to input information.

[0516] A "memory device" is a device used to store user information and emotional data that has been entered.

[0517] A "calculating device" is a device that generates training and meal plans based on input and stored data.

[0518] A "display device" is a device that visually presents the generated training and meal menus to the user.

[0519] A "learning device" is a device that analyzes feedback and emotional data and uses that analysis to improve the generation process.

[0520] An "emotion recognition sensor" is a sensor that detects a user's voice, facial expressions, and behavioral patterns to recognize their emotional state.

[0521] This invention is a system for providing users with optimized training and meal plans. Its main feature is its ability to grasp the user's emotional state in real time and generate a personalized plan based on that information.

[0522] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The terminal functions as a data input device and transmits this information to the server. The server stores the transmitted user information in its storage device and generates individual profiles based on this information. A large-capacity database can be used for storage.

[0523] Furthermore, the device is equipped with an emotion recognition sensor that detects the user's voice, facial expressions, and behavioral patterns. This allows the device to collect emotional data in real time and send it to a server. The server then uses an emotion engine to analyze this data and determine the user's emotional state, such as "stressed" or "highly motivated."

[0524] The server also functions as a computing device, using a generative AI model to integrate user profiles and emotional data to generate the most suitable training and meal plans for each individual user. This generated plan is sent to a terminal, which acts as a display device, and presented visually to the user.

[0525] When a user performs activities according to a plan and inputs the results as feedback into their device, the server receives this feedback and uses a learning device to improve the menu generation algorithm based on the feedback in the database and ongoing sentiment data. At this stage, the generative AI model plays a crucial role in improving the accuracy of training and meal menus.

[0526] As a concrete example, a user might enter a prompt message into their terminal saying, "Please suggest a diet and exercise menu that would be suitable for someone who has been feeling stressed recently." This information is sent to the server, which analyzes past data and emotional information to generate a menu tailored to the user's needs. This allows the user to receive training and meal plans that are appropriate for their individual psychological state.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The entered data is sent from the terminal to the server. The server stores the received user information in its storage device and generates a user profile. This profile aggregates the user's attribute data and serves as the basis for analysis.

[0530] Step 2:

[0531] The device uses emotion recognition sensors to detect the user's voice, facial expressions, and actions in real time. This data measures the user's instantaneous emotions, and the device sends this data to a server. The server uses an emotion engine to analyze this emotional data and determine emotional states such as "stressed" or "highly motivated." This output is added to the profile as numerical data or categories of emotional states.

[0532] Step 3:

[0533] The server integrates user profiles and emotional data using a generative AI model. This process generates training and meal plans based on the user's attribute data and emotional state, which are the inputs. The server uses historical data and the generative AI model's algorithms to generate an optimized plan using a computing device. This output is a personalized training and meal plan.

[0534] Step 4:

[0535] The server sends the generated training and meal plans to the terminal. The terminal functions as a display device, visually presenting this data to the user. The user begins activities based on the provided plan and inputs the results as feedback into the terminal.

[0536] Step 5:

[0537] The device sends continuous sentiment data collected simultaneously with feedback to the server. The server uses a learning device to improve the plan generation algorithm based on the feedback and sentiment data in the database. The learning algorithm of the generative AI model uses this data to improve the accuracy of subsequent plans. The output at this stage is the updated generative algorithm.

[0538] (Application Example 2)

[0539] 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."

[0540] In today's advanced living environment, there is a demand for personalized health management tailored to people's physical and emotional states. However, conventional systems have struggled to accurately grasp emotional states and quickly provide care plans that take that information into account. This invention aims to solve these problems by recognizing users' emotions in real time and utilizing that data to provide optimal training and meal menus.

[0541] 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.

[0542] In this invention, the server includes data acquisition means for collecting user information, recording means for storing user information obtained from the data acquisition means, and analysis means for generating individual care plans based on the user information and emotional state stored in the recording means. This makes it possible to provide care plans that reflect the emotional state in real time.

[0543] "User information" refers to information about individual users collected by the system, including health status, exercise history, and activity level.

[0544] "Data acquisition means" refers to a device or method for accurately collecting user information.

[0545] "Recording means" refers to a device or method for storing acquired user information, such as a database or storage.

[0546] "Analysis means" refers to a device or method that has the function of generating an optimal plan for the user using information stored in a recording means.

[0547] A "display device" refers to a screen or device used to visually present the generated plan to the user.

[0548] A "learning device" is a device or method for improving the accuracy of a generation plan based on the feedback received.

[0549] "Emotion recognition means" refers to a device or method that analyzes voice and facial expressions in order to detect the emotional state of a user.

[0550] "Suggested means" refers to a device or method that has the function of generating an individual care plan from the results of the analysis and recommending actions based on that plan.

[0551] The system for carrying out this invention aims to provide a health management plan that takes into account the user's emotional state. This system mainly consists of data acquisition means, recording means, analysis means, display device, learning device, emotion recognition means, and proposal means.

[0552] First, the device uses data acquisition means to collect basic health information, exercise history, and activity status from the user. This information is stored in a database by recording means. In addition, emotion recognition means detect the user's facial expressions and voice data and analyze their emotional state in real time. The hardware uses a device with an emotion recognition sensor (e.g., smart glasses), and the software uses an AI model for emotion analysis (e.g., Amazon Rekognition).

[0553] The server uses stored information and emotional states to generate a training and nutrition plan tailored to the user through analytical means. The generated plan is presented to the user via a display device. The user acts according to the plan and inputs feedback into the terminal. The feedback is sent to the server and continuously analyzed by the learning device. As a result, the accuracy of the plan improves over time.

[0554] For example, if the emotion recognition system detects that a user has a quiet and depressed expression after a day's activities, the server uses analysis tools to generate a relaxation plan. The user will then be shown suggestions such as "listen to relaxation music" or "take a walk."

[0555] As an example of a prompt message when using a generative AI model, a question in the format of, "The user may be experiencing stress. What relaxation activities can you suggest?" can be used to automatically generate more appropriate suggestions.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The terminal collects the user's basic information, health status, exercise history, and activity status through data input means. This includes manual input and data acquisition from wearable devices. This information is transmitted as input data to a recording means and stored in a database.

[0559] Step 2:

[0560] The device uses an emotion recognition sensor to collect the user's voice and facial expressions in real time. The collected data is analyzed by an emotion recognition device and output as the user's emotional state (e.g., "feeling stressed"). This emotional data is also stored by a recording device.

[0561] Step 3:

[0562] The server sends stored user information and sentiment data as input to the analysis system. The analysis system generates individual training and meal plans based on this data. Machine learning algorithms are used for data processing to output the optimal plan.

[0563] Step 4:

[0564] The server presents the generated training and meal plans to the user via a display device. This information is transmitted directly to the terminal and displayed on a smartphone or smart glasses.

[0565] Step 5:

[0566] The user performs activities according to the presented plan and inputs the results and feedback into the device. The input feedback is then sent from the device to the server.

[0567] Step 6:

[0568] The server sends feedback and emotion recognition data as input to the learning device. The learning device uses this data to learn how to improve the accuracy of the plan and outputs an improved plan generation algorithm as a result of the calculation.

[0569] Step 7:

[0570] The server uses analysis tools to generate a new plan based on the user's updated emotional state and feedback, and then presents it to the user again via the display device. This enables the provision of dynamic plans tailored to the user's state.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention is constructed as a system for providing users with optimized training and meal plans. The following describes how this system can be implemented.

[0589] First, the user uses a device to input basic information, health status, exercise experience, daily activity levels, and dietary preferences. The device then sends this data to a server. The server stores the transmitted information in a database and generates individual user profiles.

[0590] The server uses an AI model to analyze user profiles. Based on the collected data, the AI ​​model generates optimal training and meal plans tailored to the user's health, goals, and lifestyle. This analysis process also utilizes statistical data derived from past training history and successful plan examples from other users.

[0591] The generated training and meal plans are sent back to the device and provided to the user. The user can then train according to this plan and record their daily activity. After each training session, the user enters feedback into the device regarding the difficulty of the training and their satisfaction with the meals.

[0592] The device sends this feedback to the server, which analyzes it. This analysis allows the server to update its AI model and continuously optimize training and meal plans. For example, if a user feels the training load is too high, the load can be adjusted in the next plan.

[0593] Specific example

[0594] Let's consider a scenario where user A uses this system. User A has a goal of losing weight and inputs basic information such as age 30, height 175cm, and weight 85kg. The server analyzes this information and proposes a training menu consisting of three sessions per week, focusing on aerobic exercise, and a low-calorie, high-protein meal plan to user A.

[0595] After user A completes a training session using their device, they send feedback indicating that the training was somewhat burdensome. The server uses this data to adjust the training plan for the following week, slightly reducing the workload, and sends the adjustment back to the device. In this way, user A continues to receive the optimal training plan.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The user inputs basic information, health status, exercise experience, daily activity level, and dietary preferences from their device. The device then sends the entered data to the server.

[0599] Step 2:

[0600] The server stores the received user information in a database and creates individual user profiles. These profiles serve as the basis for generating training and meal plans.

[0601] Step 3:

[0602] The server uses an AI model to analyze stored user profiles. This analysis includes generating optimal training and meal plans tailored to the user's goals, health status, and lifestyle.

[0603] Step 4:

[0604] The server sends the generated training and meal plans to the device. The device then displays this to the user, providing it as a guide for their daily activities.

[0605] Step 5:

[0606] Users complete the training according to the provided plan and, after completion, input feedback on the difficulty level and menu of the training into their device. The device then sends this feedback to the server.

[0607] Step 6:

[0608] The server analyzes user feedback and updates the AI ​​model based on the results. This improves future training and meal plans to better suit the user.

[0609] Step 7:

[0610] The server regenerates the improved plan and sends the updated content to the device. By continuing this process, the server continues to provide users with optimized training and dietary guidance.

[0611] (Example 1)

[0612] 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".

[0613] Creating and providing training and meal plans tailored to each user's individual health condition and lifestyle is difficult with existing systems. In particular, there is a lack of processes to dynamically optimize plans based on user feedback, so there is a need for a system that can provide more personalized guidance.

[0614] 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.

[0615] In this invention, the server includes terminal means for inputting user information, storage means for storing user information, and analysis means for generating training and meal plans using an AI model. This enables the provision of an optimal plan tailored to the user's health condition and lifestyle, and dynamic optimization of the plan based on the feedback received.

[0616] "Terminal means" refers to a device or system for which a user inputs information, thereby acquiring the user's individual attributes and health data.

[0617] "Memory means" refers to a device or system that has the function of saving acquired user information, and is used to store information in a database or similar.

[0618] "Analysis means" refers to a device or process for creating training and meal plans based on user information stored using AI models or the like.

[0619] "Means of delivery" refers to a device or system for presenting the generated plan to the user, and includes display devices and notification systems.

[0620] "Learning method" refers to a device or algorithm that analyzes user feedback and optimizes the plan based on the data obtained.

[0621] A "generative AI model" refers to an algorithm or program that uses machine learning technology to construct training and meal plans based on user information.

[0622] A "prompt statement" refers to a specific input statement used to give instructions to an AI model, and it acts as a trigger to encourage the model to process data.

[0623] This invention is specifically implemented as a system for providing users with optimal training and meal plans. First, the user inputs information such as their basic information, health status, exercise experience, daily activity level, and dietary preferences using a terminal. This terminal is an input / output device that is easy for the user to use, such as a smartphone or computer. The data entered on the terminal is transmitted to a server via the internet.

[0624] The server stores the received data in a storage device. This storage device is a cloud server or database system, used to organize and manage user information. Next, the server utilizes a generative AI model to generate training and meal plans based on the stored user data. This AI model is an algorithm that applies machine learning techniques, constructing an appropriate plan by inputting a large amount of historical data and individual user information as prompts.

[0625] For example, one possible prompt statement is: "For user A, who is 30 years old, 175cm tall, and weighs 85kg, please propose a plan for weight loss consisting of three training sessions per week and a low-calorie, high-protein diet." This prompt statement provides specific instructions to the AI ​​model, enabling the creation of a plan tailored to each user.

[0626] The generated plan is sent back to the user's device and provided to them. The user can then train and eat according to this plan. They can also input feedback on their training and diet through their device. This feedback is sent to the server, which updates the AI ​​model and incorporates it into the next plan. In this way, the plan provided to the user is constantly optimized based on their individual circumstances and needs.

[0627] This system will allow users to easily obtain training and meal plans tailored to their own health status and goals, enabling more effective health management.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] Users use a device to input data such as their basic information, health status, exercise history, daily activity level, and dietary preferences. This data is checked in real time on the device, and a step is included to verify that there are no input errors. If there are no problems, the data is sent to the server via the internet.

[0631] Step 2:

[0632] The server stores the received user data in a storage device. This storage device uses a database, and user information is organized and managed within it. In this step, the input user information is structured and formatted so that it can be easily used in subsequent processing.

[0633] Step 3:

[0634] The server generates training and meal plans using an AI model based on stored user data. Here, the AI ​​model receives instructions via prompts and constructs an optimal plan tailored to the user's health condition and lifestyle. Input consists of user data and prompts, and the output is a individually optimized plan.

[0635] Step 4:

[0636] The server sends the generated plan to the device and provides it to the user. The device receives this data and presents it to the user visually. The user can then use this information to implement their training and diet.

[0637] Step 5:

[0638] Users input feedback into the device regarding their training and dietary performance, as well as their perceived training difficulty and satisfaction with their meals. The device then organizes this information and sends it to the server.

[0639] Step 6:

[0640] The server analyzes the received feedback information and updates the generating AI model. Here, the feedback data is analyzed, the AI ​​model is trained to help generate the next plan, and the plan provided to the user is improved to be more effective. This ensures the continuous optimization of the plan to meet the user's needs.

[0641] (Application Example 1)

[0642] 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".

[0643] In modern society, providing fitness and nutrition plans optimized for individual users is crucial for maintaining good health. However, current systems struggle to monitor users' specific activity levels in real time and provide effective exercise guidance. Furthermore, mechanisms for dynamically improving plans based on user feedback are insufficient. In this context, there is a need for a system that provides personalized support tailored to each user's health condition and lifestyle.

[0644] 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.

[0645] In this invention, the server includes data input means for collecting user information, storage means for storing user information obtained from the data input means, analysis means for generating training and meal menus based on the user information stored in the storage means, display means for providing the generated training and meal menus, learning means for analyzing feedback received via the display means and improving the training and meal menus, and robot control means for monitoring the user's physical activity and providing exercise guidance. This enables the provision of individually optimized fitness and nutrition plans to users, real-time exercise guidance, and dynamic adjustment of plans based on feedback.

[0646] A "data input means for collecting user information" is a device that provides an interface for users to input personal information, such as their health status, exercise experience, and dietary preferences.

[0647] A "storage device" is a data recording device for storing and managing collected user information.

[0648] "Analysis means" refers to a device or program that has the function of generating training and meal plans optimized for the user based on data stored in a memory means.

[0649] "Display means" refers to a system that visually communicates the generated training and meal menus to the user.

[0650] A "learning tool" is a system equipped with the function to analyze user feedback and continuously improve and optimize the training and meal plans it provides.

[0651] A "robot control system" is a mechanism that operates and controls a robot to support the user's movements and provide appropriate guidance.

[0652] This invention is designed as a system to support users in maintaining their health and fitness. This system allows users to input personal health information, exercise history, and dietary preferences using devices such as smartphones or home robots, and then provides optimized training and meal plans based on that information.

[0653] The server receives input data from the user and stores this data in a cloud database. The stored data is analyzed using an AI model to generate a specific plan tailored to the user's health status and goals. The AI ​​model is implemented using frameworks such as TensorFlow and utilizes machine learning algorithms based on the user's profile data.

[0654] Furthermore, the generated plan is deployed to the user's device, and the robot control system supports the actual movement of the home robot. This system incorporates a function that reflects real-time feedback; the feedback entered by the user is analyzed by the server and used to improve the plan for the next time.

[0655] As a concrete example, suppose a user provides the following input: "I am 30 years old, 175cm tall, weigh 85kg, and my goal is weight loss. I can exercise three times a week and prefer a low-calorie, high-protein diet. Please suggest the optimal training and meal plan under these conditions." Based on this information, an AI model will create a plan, and exercise guidance will be provided by a home robot.

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The user enters personal information from their device. This includes health status, exercise history, and dietary preferences. The device transmits this information to the server through a data entry mechanism. The entered data is converted into a structured format and stored in a cloud database by the server.

[0659] Step 2:

[0660] The server applies a generative AI model based on stored user information to generate training and meal plans. By analyzing the input data, it generates prompts that provide appropriate fitness plans and nutritional intake guidelines. The AI ​​model uses historical data and learning algorithms to formulate a plan that is optimal for the user's individual profile.

[0661] Step 3:

[0662] The server generates training and meal plans and sends them to the device, displaying them on the user's screen. The plans include specific exercises and meal recipes, which the user can incorporate into their daily activities.

[0663] Step 4:

[0664] Users perform activities based on the provided plan and send feedback to the server via their device. This feedback includes things like their impressions of the training and their satisfaction with the meals. The input feedback data is stored on the server and used as the basis for subsequent data analysis.

[0665] Step 5:

[0666] The server readjusts the generated AI model based on the collected feedback, optimizing the training and meal plans. This provides a new plan that matches the user's progress. Specifically, the load is adjusted and new exercises are suggested based on the feedback, and this is output as a new prompt message.

[0667] Step 6:

[0668] The optimized plan is sent back to the device and provided to the user. This ensures that the user is always presented with a continuously improved health and fitness plan.

[0669] 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.

[0670] This invention incorporates a novel approach to a system that provides user-optimized training and meal plans, taking into account the user's emotional state. This system incorporates an emotion engine that can grasp the user's psychological state in real time and present a more appropriate plan based on that information.

[0671] The user uses a device to input basic information, health status, exercise experience, and daily activity levels. The device sends this information to a server, which stores it in a database to generate the user's profile. In addition to this basic process, the device's emotion recognition sensor detects the user's voice, facial expressions, and behavioral patterns, and an emotion engine analyzes this in real time. The analysis results are recognized as the user's temporary emotional state, such as "feeling stressed" or "highly motivated."

[0672] The server analyzes this emotional data along with the user's basic profile to generate the most suitable training and meal plan for each individual user. This plan can change daily depending on the user's emotional state; it recommends less strenuous exercise when the user is feeling more stressed and suggests a more challenging plan when they are highly motivated.

[0673] The generated plan is sent to the device and presented to the user. After the user performs activities according to the plan and enters feedback on the device, the feedback is sent to the server. The server continuously analyzes the feedback along with emotion recognition data to improve the accuracy of the plan.

[0674] For example, if user B is using the system and the emotion engine detects that they are feeling stressed after a negative experience, the server can suggest low-intensity exercise for relaxation and a meal plan to reduce stress. When the user's emotions improve, the system can offer a different plan, demonstrating dynamic responses tailored to each user's individual situation. In this way, the system can provide flexible health management that is tailored to the user's needs.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] Users input information such as basic information, health status, exercise experience, and daily activity levels through their device. The device then transmits this information to the server.

[0678] Step 2:

[0679] The server stores the received user information in a database and generates a user profile. This profile includes the user's basic information and past training data.

[0680] Step 3:

[0681] The device uses a built-in emotion recognition sensor to capture the user's voice and facial expressions in real time. This data is initially processed within the device and then sent to the emotion engine.

[0682] Step 4:

[0683] The emotion engine analyzes the user's voice and facial expression data to evaluate their current emotional state. This evaluation result is recognized as "stressed" or "highly motivated," etc. The device then sends this information to the server.

[0684] Step 5:

[0685] The server comprehensively analyzes user profiles and data from the emotion engine to generate training and meal plans optimized for the user's current situation. Specifically, it suggests plans that include relaxing exercises if the user is highly stressed, and challenging training if the user is highly motivated.

[0686] Step 6:

[0687] The server sends the generated plan to the terminal. The terminal provides this information to the user and supports the user in carrying out activities according to it.

[0688] Step 7:

[0689] The user enters feedback on the training they performed into the device. This feedback includes their impressions of the training and their evaluation of the meal. The device then sends the feedback to the server.

[0690] Step 8:

[0691] The server analyzes the feedback received and the emotion engine data received in real time to update the AI ​​model. This update enables the generation of even more accurate plans.

[0692] Step 9:

[0693] The server generates an improved plan and sends it to the terminal. This process is repeated to continuously provide the user with the most suitable training and meal plan.

[0694] (Example 2)

[0695] 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".

[0696] Traditional health management systems have struggled to provide personalized training and meal plans that take into account the emotional state of individual users. Therefore, they lacked the flexibility to respond to users' psychological needs, resulting in shortcomings in terms of maintaining user motivation and promoting health.

[0697] 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.

[0698] In this invention, the server includes an input device for collecting user information, a storage device for storing user information and emotional data obtained from the input device, and a computing device for generating training and meal menus based on the user information and emotional data stored in the storage device. This makes it possible to provide optimal training and meal menus that take into account the user's emotional state.

[0699] "User information" refers to data that includes the user's basic attribute information, health status, exercise experience, and daily activity level.

[0700] An "input device" is a device that provides a means for a user to input information.

[0701] A "memory device" is a device used to store user information and emotional data that has been entered.

[0702] A "calculating device" is a device that generates training and meal plans based on input and stored data.

[0703] A "display device" is a device that visually presents the generated training and meal menus to the user.

[0704] A "learning device" is a device that analyzes feedback and emotional data and uses that analysis to improve the generation process.

[0705] An "emotion recognition sensor" is a sensor that detects a user's voice, facial expressions, and behavioral patterns to recognize their emotional state.

[0706] This invention is a system for providing users with optimized training and meal plans. Its main feature is its ability to grasp the user's emotional state in real time and generate a personalized plan based on that information.

[0707] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The terminal functions as a data input device and transmits this information to the server. The server stores the transmitted user information in its storage device and generates individual profiles based on this information. A large-capacity database can be used for storage.

[0708] Furthermore, the device is equipped with an emotion recognition sensor that detects the user's voice, facial expressions, and behavioral patterns. This allows the device to collect emotional data in real time and send it to a server. The server then uses an emotion engine to analyze this data and determine the user's emotional state, such as "stressed" or "highly motivated."

[0709] The server also functions as a computing device, using a generative AI model to integrate user profiles and emotional data to generate the most suitable training and meal plans for each individual user. This generated plan is sent to a terminal, which acts as a display device, and presented visually to the user.

[0710] When a user performs activities according to a plan and inputs the results as feedback into their device, the server receives this feedback and uses a learning device to improve the menu generation algorithm based on the feedback in the database and ongoing sentiment data. At this stage, the generative AI model plays a crucial role in improving the accuracy of training and meal menus.

[0711] As a concrete example, a user might enter a prompt message into their terminal saying, "Please suggest a diet and exercise menu that would be suitable for someone who has been feeling stressed recently." This information is sent to the server, which analyzes past data and emotional information to generate a menu tailored to the user's needs. This allows the user to receive training and meal plans that are appropriate for their individual psychological state.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] Users input basic information (age, gender, weight, etc.), health status, exercise experience, and daily activity levels using a terminal. The entered data is sent from the terminal to the server. The server stores the received user information in its storage device and generates a user profile. This profile aggregates the user's attribute data and serves as the basis for analysis.

[0715] Step 2:

[0716] The device uses emotion recognition sensors to detect the user's voice, facial expressions, and actions in real time. This data measures the user's instantaneous emotions, and the device sends this data to a server. The server uses an emotion engine to analyze this emotional data and determine emotional states such as "stressed" or "highly motivated." This output is added to the profile as numerical data or categories of emotional states.

[0717] Step 3:

[0718] The server integrates user profiles and emotional data using a generative AI model. This process generates training and meal plans based on the user's attribute data and emotional state, which are the inputs. The server uses historical data and the generative AI model's algorithms to generate an optimized plan using a computing device. This output is a personalized training and meal plan.

[0719] Step 4:

[0720] The server sends the generated training and meal plans to the terminal. The terminal functions as a display device, visually presenting this data to the user. The user begins activities based on the provided plan and inputs the results as feedback into the terminal.

[0721] Step 5:

[0722] The device sends continuous sentiment data collected simultaneously with feedback to the server. The server uses a learning device to improve the plan generation algorithm based on the feedback and sentiment data in the database. The learning algorithm of the generative AI model uses this data to improve the accuracy of subsequent plans. The output at this stage is the updated generative algorithm.

[0723] (Application Example 2)

[0724] 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".

[0725] In today's advanced living environment, there is a demand for personalized health management tailored to people's physical and emotional states. However, conventional systems have struggled to accurately grasp emotional states and quickly provide care plans that take that information into account. This invention aims to solve these problems by recognizing users' emotions in real time and utilizing that data to provide optimal training and meal menus.

[0726] 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.

[0727] In this invention, the server includes data acquisition means for collecting user information, recording means for storing user information obtained from the data acquisition means, and analysis means for generating individual care plans based on the user information and emotional state stored in the recording means. This makes it possible to provide care plans that reflect the emotional state in real time.

[0728] "User information" refers to information about individual users collected by the system, including health status, exercise history, and activity level.

[0729] "Data acquisition means" refers to a device or method for accurately collecting user information.

[0730] "Recording means" refers to a device or method for storing acquired user information, such as a database or storage.

[0731] "Analysis means" refers to a device or method that has the function of generating an optimal plan for the user using information stored in a recording means.

[0732] A "display device" refers to a screen or device used to visually present the generated plan to the user.

[0733] A "learning device" is a device or method for improving the accuracy of a generation plan based on the feedback received.

[0734] "Emotion recognition means" refers to a device or method that analyzes voice and facial expressions in order to detect the emotional state of a user.

[0735] "Suggested means" refers to a device or method that has the function of generating an individual care plan from the results of the analysis and recommending actions based on that plan.

[0736] The system for carrying out this invention aims to provide a health management plan that takes into account the user's emotional state. This system mainly consists of data acquisition means, recording means, analysis means, display device, learning device, emotion recognition means, and proposal means.

[0737] First, the device uses data acquisition means to collect basic health information, exercise history, and activity status from the user. This information is stored in a database by recording means. In addition, emotion recognition means detect the user's facial expressions and voice data and analyze their emotional state in real time. The hardware uses a device with an emotion recognition sensor (e.g., smart glasses), and the software uses an AI model for emotion analysis (e.g., Amazon Rekognition).

[0738] The server uses stored information and emotional states to generate a training and nutrition plan tailored to the user through analytical means. The generated plan is presented to the user via a display device. The user acts according to the plan and inputs feedback into the terminal. The feedback is sent to the server and continuously analyzed by the learning device. As a result, the accuracy of the plan improves over time.

[0739] For example, if the emotion recognition system detects that a user has a quiet and depressed expression after a day's activities, the server uses analysis tools to generate a relaxation plan. The user will then be shown suggestions such as "listen to relaxation music" or "take a walk."

[0740] As an example of a prompt message when using a generative AI model, a question in the format of, "The user may be experiencing stress. What relaxation activities can you suggest?" can be used to automatically generate more appropriate suggestions.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] The terminal collects the user's basic information, health status, exercise history, and activity status through data input means. This includes manual input and data acquisition from wearable devices. This information is transmitted as input data to a recording means and stored in a database.

[0744] Step 2:

[0745] The device uses an emotion recognition sensor to collect the user's voice and facial expressions in real time. The collected data is analyzed by an emotion recognition device and output as the user's emotional state (e.g., "feeling stressed"). This emotional data is also stored by a recording device.

[0746] Step 3:

[0747] The server sends stored user information and sentiment data as input to the analysis system. The analysis system generates individual training and meal plans based on this data. Machine learning algorithms are used for data processing to output the optimal plan.

[0748] Step 4:

[0749] The server presents the generated training and meal plans to the user via a display device. This information is transmitted directly to the terminal and displayed on a smartphone or smart glasses.

[0750] Step 5:

[0751] The user performs activities according to the presented plan and inputs the results and feedback into the device. The input feedback is then sent from the device to the server.

[0752] Step 6:

[0753] The server sends feedback and emotion recognition data as input to the learning device. The learning device uses this data to learn how to improve the accuracy of the plan and outputs an improved plan generation algorithm as a result of the calculation.

[0754] Step 7:

[0755] The server uses analysis tools to generate a new plan based on the user's updated emotional state and feedback, and then presents it to the user again via the display device. This enables the provision of dynamic plans tailored to the user's state.

[0756] 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.

[0757] 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.

[0758] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A data input means for collecting user information,

[0780] A storage means for storing user information obtained from the data input means,

[0781] An analysis means that generates training and meal menus based on user information stored in the aforementioned storage means,

[0782] A display means for providing generated training and meal menus,

[0783] A learning means that analyzes the feedback received via the aforementioned display means and improves the training and meal menus,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, characterized in that the learning means includes a learning algorithm for improving the generation of training and meal menus based on feedback stored in a database.

[0787] (Claim 3)

[0788] The system according to claim 1, characterized in that the analysis means generates an optimal training and meal plan based on the user's health status, exercise history, and activity level.

[0789] "Example 1"

[0790] (Claim 1)

[0791] A terminal device for inputting user information,

[0792] A storage means for storing user information obtained by the terminal means,

[0793] Based on the user information stored in the aforementioned storage means, an analysis means for generating training and meal plans using an AI model,

[0794] A means for displaying the generated training and meal plan,

[0795] A means of receiving user feedback on the provided plan and using that feedback to learn how to optimize the plan,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, characterized in that the learning means includes an algorithm that analyzes feedback data accumulated in a database and uses a generated AI model to improve training and meal plans.

[0799] (Claim 3)

[0800] The system according to claim 1, characterized in that the analysis means generates an optimal training and meal plan based on the user's health status, past exercise history, and lifestyle activity information.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A data input means for collecting user information,

[0804] A storage means for storing user information obtained from the data input means,

[0805] An analysis means that generates training and meal menus based on user information stored in the aforementioned storage means,

[0806] A display means for providing generated training and meal menus,

[0807] A learning means that analyzes the feedback received via the aforementioned display means and improves the training and meal menus,

[0808] A robot control system that monitors the user's physical activity and provides exercise guidance,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, characterized in that the learning means includes a learning algorithm for improving the generation of training and meal menus based on feedback stored in a database.

[0812] (Claim 3)

[0813] The system according to claim 1, characterized in that the analysis means generates an optimal training and meal plan based on the user's health status, exercise history, and activity level.

[0814] "Example 2 of combining an emotion engine"

[0815] (Claim 1)

[0816] An input device for collecting user information,

[0817] A storage device for storing user information and emotion data obtained from the input device,

[0818] A computing device that generates training and meal menus based on user information and emotional data stored in the aforementioned storage device,

[0819] A display device for providing generated training and meal menus,

[0820] A learning device that analyzes feedback and continuous emotional data received via the aforementioned display device to improve training and meal menus,

[0821] A means for detecting the user's emotional state in real time using an emotion recognition sensor and analyzing it using the aforementioned computing device,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, characterized in that the learning device includes a learning algorithm for improving the generation of training and meal menus using a generative AI model based on feedback and emotional data stored in a database.

[0825] (Claim 3)

[0826] The system according to claim 1, characterized in that the computing device generates an optimal training and meal plan based on the user's health status, exercise history, activity level, and emotional state.

[0827] "Application example 2 when combining with an emotional engine"

[0828] (Claim 1)

[0829] A means of acquiring data for collecting user information,

[0830] A recording means for storing user information obtained from the data acquisition means,

[0831] An analysis means that generates training and nutrition plans based on user information stored in the recording means,

[0832] A display device for providing generated training and nutrition plans,

[0833] A learning device that analyzes feedback received via the aforementioned display device and improves training and nutrition plans,

[0834] An emotion recognition means for detecting the user's emotional state,

[0835] A proposal means that analyzes the information from the aforementioned emotion recognition means and generates an individual care plan,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, characterized in that the learning device includes a learning method for improving the generation of training and nutrition plans based on feedback stored in the information infrastructure.

[0839] (Claim 3)

[0840] The system according to claim 1, characterized in that the analysis means generates an optimal training and nutrition plan based on the user's health status, exercise history, emotional state, and activity level. [Explanation of Symbols]

[0841] 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 input means for collecting user information, A storage means for storing user information obtained from the data input means, An analysis means that generates training and meal menus based on user information stored in the aforementioned storage means, A display means for providing generated training and meal menus, A learning means that analyzes the feedback received via the aforementioned display means and improves the training and meal menus, A robot control system that monitors the user's physical activity and provides exercise guidance, A system that includes this.

2. The system according to claim 1, characterized in that the learning means includes a learning algorithm for improving the generation of training and meal menus based on feedback stored in a database.

3. The system according to claim 1, characterized in that the analysis means generates an optimal training and meal plan based on the user's health status, exercise history, and activity level.

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