system

Abstract

We provide the system. [Solution] Means for obtaining individual user information, A means for generating an exercise plan based on the individual information of the user, Means for providing visual content corresponding to the aforementioned movement plan, A means of recording the user's exercise progress and evaluating the degree of goal achievement, A means of offering rewards according to the degree of achievement of the aforementioned goals, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to promote personal weight management and health maintenance, there is a need to provide an effective exercise plan and a suitable reward system. However, existing systems often cannot sufficiently meet the individual preferences and goals of users, and there is also a problem that incentives for promoting the continuation of exercise are insufficient, making it difficult to sustain dieting and health management.

Means for Solving the Problems

[0005] This invention provides a system that acquires individual user information and generates an optimized exercise plan based on that information. Furthermore, this system includes means to maintain motivation by recording the user's exercise progress and offering rewards commensurate with their achievements. Specifically, it effectively promotes exercise by suggesting rewards based on the user's past activity history and preferences, and by streaming visual content.

[0006] "Means for obtaining individual user information" refers to devices or processes for collecting information about individual user characteristics, preferences, and goals.

[0007] "Means for generating exercise plans" refers to a system or algorithm for creating exercise programs tailored to individual health goals based on collected user information.

[0008] "Means of providing visual content" refers to methods or tools for conveying visual information, such as videos and images, aimed at providing appropriate exercise guidance or improving motivation for users.

[0009] "Means for recording exercise progress and evaluating goal achievement" refers to a system or technology that measures the history and effects of exercise performed by a user and calculates the degree of achievement against the set goals.

[0010] "Means of offering rewards" refers to a system that provides incentives or benefits in accordance with the achievement of exercise goals, with the aim of maintaining and improving the user's motivation. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention relates to a system that supports continued health management by providing an optimized exercise plan based on the user's individual information and offering rewards according to the level of achievement. This system operates by exchanging information between a server, a terminal, and the user.

[0033] User Interface

[0034] Users access the system through an application on their device. Initially, users enter personal information and health goals, and select their preferred type of exercise. This information is sent to the server and used as basic data for generating exercise plans.

[0035] Generating an exercise plan

[0036] The server receives user input information and uses a generative AI model to generate an exercise plan tailored to the user's goals and preferences. The AI ​​model constructs an optimal plan based on past data and known exercise effects, and sends it to the device.

[0037] Content provision

[0038] The device provides the user with visual content appropriate to the exercise based on the exercise plan received from the server. This allows the user to visually understand the exercises they should perform in the form of videos and images.

[0039] Progress management and reward system

[0040] The server periodically receives user exercise data and records progress based on it. Different rewards are provided as incentives depending on the user's level of goal achievement. This reward system takes into account the user's past exercise history and usage patterns, supporting the user's continued motivation.

[0041] For example, if a user sets a goal of "losing 5kg in 3 months," the server will create a suitable exercise plan, suggesting three sessions of aerobic exercise plus strength training per week. The device will then deliver relevant training videos to support the user in easily following the plan. The server will also track the user's progress, and upon achieving the goal, will offer rewards such as shopping coupons or points.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user logs into the application using their device and enters personal information, health goals, and preferred exercise type. This information is then sent to the server.

[0045] Step 2:

[0046] The server analyzes the information received from the user and generates an optimal exercise plan using a generative AI model. This plan is customized based on the user's health goals and exercise preferences.

[0047] Step 3:

[0048] The server sends the generated exercise plan to the device. The device receives the exercise plan and displays it as visual content on the screen. The user can then review the suggested exercises.

[0049] Step 4:

[0050] The user performs exercises through a device and records their progress. The device records the duration, type of exercise, calories burned, etc., and sends the data to a server.

[0051] Step 5:

[0052] The server analyzes the received exercise data and tracks the user's progress. It evaluates the degree of goal achievement and reflects the progress in real time on the user's dashboard.

[0053] Step 6:

[0054] When a user achieves a goal they have set, the server proposes an incentive. Using AI, it selects a reward (e.g., coupons, points) based on the user's past history and preferences, and notifies the user's device.

[0055] Step 7:

[0056] Users receive and use the suggested rewards via their devices. User feedback is sent to the server and used to improve future exercise plans and reward selections.

[0057] (Example 1)

[0058] 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."

[0059] In modern society, there is a lack of means for individuals to design exercise plans to maintain their health and achieve their goals, and to efficiently evaluate and manage their progress. Traditional methods are not sufficiently individualized and make it difficult to maintain motivation over the long term.

[0060] 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.

[0061] In this invention, the server includes means for collecting user characteristic information, means for creating an exercise plan using a generative AI model based on the user characteristic information, and means for supplying visual materials according to the exercise plan. This enables the provision of individually optimized exercise plans and the maintenance of continuous motivation.

[0062] "Characteristic information" refers to information about individual attributes or circumstances related to the user, such as age, weight, health goals, and exercise preferences.

[0063] A "generative AI model" refers to an information processing model using artificial intelligence, which is used to create an appropriate exercise plan based on the user's characteristic information.

[0064] An "exercise plan" refers to a plan created by a generating AI model that takes into account the user's characteristics, including the specific content, frequency, and intensity of exercise.

[0065] "Visual materials" refer to visual content such as videos and images provided to users based on their exercise plan, and are materials intended to support the execution of exercises.

[0066] "Behavioral history" refers to records of exercise and achievements that users have performed in the past, and is data used when indicating rewards.

[0067] "Rewards" refer to incentives provided based on the user's achievement of goals, and include coupons and points that can be used to purchase goods.

[0068] This invention is a system for supporting users' health management, specifically by providing personalized exercise plans and rewarding users based on their achievement of those plans. This system operates by exchanging information between a server, a terminal, and the user.

[0069] The server receives characteristic information entered by the user through the application on their device. This characteristic information includes age, weight, health goals, preferred exercise type, etc., and this information is stored in a database. Based on the collected information, the server uses a generative AI model to create prompt messages and generate an exercise plan tailored to the user's characteristics. In this case, an example of a prompt message used is "Please create an optimal exercise plan to lose 5 kg in 3 months."

[0070] The generated exercise plan is sent to the device. Based on the exercise plan received from the server, the device provides the user with visual materials such as videos and images. This allows the user to clearly understand and perform the exercise. In particular, streaming content that matches the user's preferred type of exercise can support them in performing their workout more effectively.

[0071] Furthermore, the server periodically collects users' exercise data and analyzes their progress. Based on this progress data, it evaluates the user's goal achievement and provides rewards. Rewards include incentives such as shopping coupons and points, which are adjusted taking into account the user's past behavioral history. This motivates users to continue managing their health.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] Users enter personal information (e.g., age, weight, health goals) through an application on their device. The entered information is verified on the device and sent to the server. The server records this characteristic information in a database. As part of the input data check, it verifies for inconsistencies and omissions and prompts the user to re-enter the information if necessary.

[0075] Step 2:

[0076] The server generates prompt statements based on characteristic information stored in the database and inputs them into the generating AI model. A specific example of a prompt statement is, "Please create an optimal exercise plan to lose 5 kg in 3 months." Based on this prompt statement, the generating AI model formulates an exercise plan that matches the user's goal. The exercise plan includes recommended exercise types, frequencies, and intensity. The output is the exercise plan proposed by the AI ​​model.

[0077] Step 3:

[0078] The server sends the exercise plan obtained from the generated AI model to the terminal. The terminal selects appropriate visual materials based on the received exercise plan and prepares to stream them. The selected videos and images are displayed on the screen to the user, informing them of the flow of the exercise and specific methods. Specifically, these are provided in the form of video playback or slideshows to help the user perform the exercise smoothly.

[0079] Step 4:

[0080] Users follow the exercise plan presented through the device and record their progress. The device collects data from the user during exercise (exercise time, calories burned, heart rate, etc.) and sends it to the server. Data is collected from sensors built into the device and connected wearable devices. This ensures that an accurate exercise history is recorded.

[0081] Step 5:

[0082] The server analyzes the received exercise data and evaluates the user's progress. Based on the evaluated goal achievement level, an appropriate reward is selected. The selected reward is notified to the user via the terminal. Specifically, shopping coupons or points are provided according to the achievement level, aiming to continuously motivate the user to manage their health.

[0083] (Application Example 1)

[0084] 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."

[0085] Fitness programs often fail to meet the individual needs of many users, making it difficult to stick with them. Furthermore, standard reward systems may be insufficient to support sustained motivation, thus requiring a more personalized approach. Additionally, there is a lack of systems capable of monitoring and appropriately evaluating users' exercise progress in real time.

[0086] 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.

[0087] In this invention, the server includes means for acquiring individual user information, means for generating an exercise plan using a generative model based on the individual user information, and means for providing visual information corresponding to the exercise plan to a display device. This makes it possible to provide an exercise plan optimized for each user, track progress in real time, and provide it as individually customized rewards.

[0088] "Personal user information" refers to information that can identify an individual user, including important data such as their health status and target fitness level.

[0089] A "generative model" is a system that implements an algorithm that automatically creates an optimal exercise plan for each user based on the data it receives.

[0090] An "exercise plan" is a plan that includes specific fitness activities and schedules, designed to suit the individual user's information.

[0091] "Visual information" refers to visual content such as videos and images provided to users in accordance with their exercise plan.

[0092] A "display device" is a device that provides visual information to a user, and includes devices such as smart glasses and head-mounted displays.

[0093] "Movement progress" refers to data that records and evaluates the movements achieved by the user during the process of executing an exercise plan.

[0094] "Goal achievement level" is an indicator that shows how well the user has achieved their exercise goals.

[0095] "Rewards" refer to benefits or incentives provided to users when they achieve their exercise plan goals, including coupons and points that can be used to purchase goods.

[0096] This system is designed to be accessed by users using display devices such as smart glasses. First, the user provides their personal information via voice input or a touch interface using the smart glasses. This information includes health status, fitness goals, and preferred exercise types.

[0097] The device transmits information obtained from the user to a server. This server uses a "generative AI model" to generate an exercise plan tailored to the user's needs. In this process, the optimal plan is constructed based on past data and existing knowledge about the effects of exercise.

[0098] Once an exercise plan is generated, the server provides visual information to the smart glasses based on this information. The visual information displays the exercises the user should perform as videos or images. The visual information is updated in real time and can be controlled by the user using voice recognition.

[0099] Furthermore, the progress of the exercise is recorded using the smart glasses' built-in camera and motion sensors. This allows for accurate tracking of the user's exercise performance. The server receives this progress information and calculates the user's degree of goal achievement. Based on the degree of goal achievement, the server determines the reward for the user and notifies them through the smart glasses, providing the user with incentives such as coupons or points.

[0100] For example, if a user sets a goal of "doing abdominal exercises three times a week for one month," the system provides optimal exercise videos and visually tracks weekly progress. Once the goal is achieved, points are awarded that can be used to purchase fitness equipment.

[0101] An example of a prompt to provide to a generating AI model is: "Generate a 3-day-a-week fitness plan combining 5km runs and strength training. The user has intermediate fitness experience and would like stretching included."

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] Users wear smart glasses and input their personal information and fitness goals into the device using voice or touch input. This information includes their health status, desired achievements, and preferred exercise types. The device receives this input and sends it directly to the server.

[0105] Step 2:

[0106] The server sends prompt messages to the generating AI model based on the individual information received from the terminal. For example, it might use the prompt, "Create an abdominal muscle strengthening plan suitable for an intermediate-level user." The generating AI model uses this prompt to create an exercise plan optimized for the user and returns that information to the server.

[0107] Step 3:

[0108] The server receives the generated exercise plan and selects the corresponding visual information. This visual information includes video content and images that demonstrate the correct form, number of sets, and points to note during the exercise. The server then sends this visual information to the terminal.

[0109] Step 4:

[0110] The device displays visual information transmitted from the server on the smart glasses' screen. The user begins exercising and trains while referring to the visual information. During this time, the smart glasses' motion sensors track the user's movements in real time.

[0111] Step 5:

[0112] The terminal transfers the collected motion data to the server. The server analyzes the received motion data and evaluates the progress. For example, it calculates exactly how much of the set exercise was performed and determines the degree of goal achievement.

[0113] Step 6:

[0114] The server determines rewards for users based on their level of goal achievement. These rewards are provided in the form of coupons, points, etc. The server sends reward information to the device, which then notifies the user of the reward via their smart glasses. This allows the user to receive the reward as feedback.

[0115] 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.

[0116] This invention is an exercise support system that takes the user's emotional state into consideration, aiming to improve the user's motivation and flexibly adjust the exercise plan during the process of achieving their health goals. By incorporating an emotion engine, this system achieves personalization based on the user's emotional characteristics.

[0117] User Interface

[0118] Users can access the application using their device and register their personal information and health goals. They can also periodically input their emotional state or have their emotional state automatically detected using sensors built into their device.

[0119] Exercise plan creation and emotional adjustment

[0120] The server receives the user's individual information and emotional state, and uses a generative AI model to create an optimal exercise plan. The emotion engine adjusts the exercise plan according to the user's emotional changes; for example, if the stress level is high, it might suggest yoga for relaxation.

[0121] Content delivery tailored to emotions

[0122] The device delivers visual content tailored to the user's emotions based on an exercise plan generated by the server. For example, if the user is feeling down, an encouraging video message will be displayed. Furthermore, the selection of visual content based on emotions improves the likelihood of engaging in exercise.

[0123] Progress management and emotionally responsive rewards

[0124] The server analyzes the user's exercise data and emotional state to evaluate progress. Rewards are provided not only based on goal achievement but also on improvements in emotional state. For example, if an improvement in emotional state is observed, additional reward points are provided to support the user's sustained effort.

[0125] Thus, the present invention sensitively captures emotional shifts and dynamically adjusts the exercise plan and reward system accordingly, making it possible to provide an environment in which users can enjoyably and sustainably work towards their health goals.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user launches the application using their device and enters personal information and health goals. Simultaneously, they record their emotional state, or the device's sensors automatically collect emotional data, and this information is sent to the server.

[0129] Step 2:

[0130] The server analyzes the personal information and emotional data it receives and uses a generative AI model to create an optimal exercise plan. The generated exercise plan includes content that takes into account and adjusts the user's emotional state.

[0131] Step 3:

[0132] The server generates an exercise plan and sends it to the device. The device then provides the user with appropriate visual content tailored to their emotions and displays the details of the plan.

[0133] Step 4:

[0134] The user starts exercising according to the instructions on the device, and records activity data (exercise time, calories burned, etc.) and emotional data. The device sends this data to the server.

[0135] Step 5:

[0136] The server tracks progress using received exercise data and emotional data. It evaluates goal achievement, taking into account changes in emotions.

[0137] Step 6:

[0138] Based on the user's progress and emotional state, the server suggests appropriate rewards and incentives. The rewards are selected based on the user's past activity history and emotional changes, choosing those deemed most effective for them.

[0139] Step 7:

[0140] The user reviews the suggested reward on their device and chooses to accept it. The server sends the reward to the device, making it available to the user. User feedback is used to generate future exercise plans and suggest rewards.

[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] This invention aims to solve the challenges of exercise continuity and motivation, which are influenced by the user's emotional state. Conventional exercise support systems often create individual exercise plans based on an individual's physical information, but they do not take into account changes in the user's emotions, which makes it difficult to provide effective support. Furthermore, the lack of appropriate rewards based on emotions necessitates improvements in promoting sustained effort by users.

[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 means for acquiring individual user information, means for detecting the user's emotional state, and means for using a generative AI model that generates an exercise plan based on the user's emotional state and individual information. This enables the provision of an exercise plan tailored to the user's emotional state and continuous exercise support.

[0146] "User's individual information" refers to information unique to an individual, such as name, age, weight, height, and health goals, which the user provides to the exercise support system.

[0147] "Emotional state" refers to information that indicates the mental state a user is experiencing at any given time, and includes emotions such as stress, relaxation, enjoyment, and anxiety.

[0148] A "generative AI model" is an artificial intelligence mechanism that automatically generates an optimized exercise plan based on the user's individual information and emotional state.

[0149] An "exercise plan" is a plan that specifies the particular exercises and schedules that users should perform to achieve their health goals.

[0150] "Visual content" refers to visual information such as videos and images that are provided in accordance with the user's emotional state, and are intended to improve motivation for exercise.

[0151] "Exercise progress" refers to a record showing the extent to which a user has performed the planned exercises, and is used to evaluate the degree of goal achievement.

[0152] "Rewards" refer to incentives and benefits provided to users when they achieve their exercise goals or when their emotional state improves, and are intended to support their continued efforts.

[0153] This invention is a system that takes into account the user's emotional state and provides individually optimized exercise support. The system aims to improve user motivation and flexibly adjust exercise plans during the process of achieving health goals. Its main components include a terminal, a server, and a generative AI model.

[0154] Users can access the application using devices such as smartphones and tablets to input personal information and health goals. The devices have built-in sensors that measure the user's heart rate, skin temperature, and other parameters, enabling automatic detection of their emotional state. Users can also manually input their emotional state.

[0155] The server receives individual user information and emotional state data sent from the device. Based on this information, the server uses a generative AI model and prompts to create an optimal exercise plan. An example of a prompt would be, "Please provide an appropriate exercise plan if the user's emotional state is stressed." This AI model flexibly adjusts the exercise plan according to the user's exercise ability and emotional changes.

[0156] Based on the generated exercise plan, the device provides visual content that responds to the user's emotions. For example, when the user is feeling down, it displays an encouraging video message to help boost their motivation to exercise. Furthermore, the server records the user's exercise progress data and emotional improvement, and evaluates their progress toward achieving their goals. Based on these evaluations, the system provides rewards to the user according to their level of achievement and emotional improvement. In this way, a continuous and effective exercise experience can be achieved for the user.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user enters personal information and health goals using a terminal. The terminal registers the user's name, age, height, weight, and health goals in a database. This information is used as foundational data when the system creates an exercise plan.

[0160] Step 2:

[0161] The device uses built-in sensors to detect the user's emotional state. The measured data includes heart rate and skin temperature, and based on this data, the device infers the user's emotional state. The inferred emotional state is then sent to the server.

[0162] Step 3:

[0163] The server receives individual user information and emotional state sent from the terminal. Based on the received information, the server generates prompts for the AI ​​model. For example, it might use the prompt, "Generate an exercise plan appropriate for the current emotional state." In response to this prompt, the AI ​​model creates an optimal exercise plan and returns the result to the server.

[0164] Step 4:

[0165] The server generates an exercise plan and sends it to the device. Based on the exercise plan, the device selects visual content from its library that is appropriate for the user's mood. The selected content is provided to the user as videos or images, and the visual stimulation enhances motivation for exercise.

[0166] Step 5:

[0167] The user uses a device to perform exercises and records their progress. The device logs the user's exercise volume and frequency, and sends the data to a server for analysis. Based on this data, the server evaluates the degree of goal achievement and emotional improvement, and calculates a reward.

[0168] Step 6:

[0169] The server sends the calculated reward to the device and notifies the user. The reward helps maintain the user's motivation to exercise consistently. The reward is provided as in-app perks or points, encouraging the user to exercise again.

[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] Conventional exercise support systems often fail to consider the user's emotional state when creating exercise plans and food recommendations, leading to decreased motivation and delays in achieving health goals. Furthermore, the lack of mechanisms to provide appropriate rewards based on emotional state makes it difficult for users to consistently work towards their health goals.

[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 means for detecting the user's emotional state and collecting information thereof, means for generating an exercise plan and making food suggestions based on the user's individual information and emotional state, and means for providing visual content corresponding to the exercise plan and food suggestions. This makes it possible to suggest exercises and foods that are appropriate to the user's emotional state, thereby improving motivation and enabling the achievement of sustainable health goals.

[0175] "Users" refers to individuals who use this system to receive exercise plans and food recommendations.

[0176] "Personalized information" refers to information specific to a user, such as their name, age, gender, health status, and lifestyle.

[0177] "Emotional state" refers to data that indicates the emotions and psychological conditions a user is experiencing at a specific point in time.

[0178] An "exercise plan" refers to the content and schedule of exercises that users should perform to effectively achieve their health goals.

[0179] "Food suggestions" refer to recommended meals and food choices based on the user's emotional state and individual information.

[0180] "Visual content" refers to media that includes information and entertainment elements provided to users visually.

[0181] "Progress" refers to an indicator that shows the degree to which a user has achieved their exercise plan or health goals.

[0182] "Rewards" refer to incentives and benefits provided in accordance with the user's progress towards achieving their goals and changes in their emotional state.

[0183] A "server" refers to a central computer system that receives information from users and has the function of generating exercise plans and food recommendations.

[0184] This invention relates to a system that provides exercise support and food recommendations based on the user's emotional state. The system consists of a user's terminal, a server, a generative AI model, and an emotion detection sensor.

[0185] The user's device is a mobile device such as a smartphone or tablet, equipped with a built-in or external emotion detection sensor to capture the user's emotional state. This emotional data, along with individual information, is sent to a server. The server uses a generated AI model based on the user's emotional state and individual information to create a personalized exercise plan and food recommendations.

[0186] The generated exercise plan and food suggestions are provided to the user's device as visual content. This visual content is designed to adapt to the user's emotional state and enhance their motivation. For example, if the user is tired, relaxation-enhancing exercises or snacks can be suggested. A specific example is recommending herbal tea to a stressed user, which promotes relaxation.

[0187] The server also analyzes progress and changes in emotional state, and provides rewards accordingly. This allows users to work more actively towards achieving health goals, including improving their emotional state. For example, users may be awarded points when they overcome stress.

[0188] An example of a prompt from a generative AI model is, "What exercises and foods would you suggest to a user who wants to refresh themselves from a stressful state?" Through such prompts, it is possible to generate suggestions optimized for the user's emotional state.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The user's device uses a built-in emotion detection sensor to detect the user's emotional state. The input is raw data from the sensor, which is analyzed by a data processing algorithm to output the user's emotional state (e.g., stress, happiness, fatigue).

[0192] Step 2:

[0193] The user's device transmits detected emotional states and personal information (e.g., height, weight, favorite foods) to the server. This information is used as foundational data for personalized exercise plans and food recommendations.

[0194] Step 3:

[0195] The server inputs the received emotional state and individual information into a generating AI model, and through the AI ​​model's prompts, generates an optimal exercise plan and food recommendations for the user. Specifically, it processes the data and derives a plan using prompts such as, "If the user's current emotional state is stress, what exercise and food are recommended?"

[0196] Step 4:

[0197] The server provides the generated exercise plan and food suggestions as visual content to the user's device. The visual content is processed and displayed to include colors, images, and recommendation video messages that correspond to the user's emotional state.

[0198] Step 5:

[0199] User progress and emotional state changes are continuously monitored through emotion detection sensors and user activity logs, and the data is sent to the server. The input is a comparison of new and past data from the emotion detection sensors. Analysis evaluates the effectiveness of emotional settings and exercise performance, and the results are recorded in the database.

[0200] Step 6:

[0201] The server determines rewards based on collected progress data and notifies the user. For example, if emotional improvement or goal achievement is confirmed, additional points or rewards are provided. This increases user motivation and encourages continued engagement with health goals.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] This invention relates to a system that supports continued health management by providing an optimized exercise plan based on the user's individual information and offering rewards according to the level of achievement. This system operates by exchanging information between a server, a terminal, and the user.

[0219] User Interface

[0220] Users access the system through an application on their device. Initially, users enter personal information and health goals, and select their preferred type of exercise. This information is sent to the server and used as basic data for generating exercise plans.

[0221] Generating an exercise plan

[0222] The server receives user input information and uses a generative AI model to generate an exercise plan tailored to the user's goals and preferences. The AI ​​model constructs an optimal plan based on past data and known exercise effects, and sends it to the device.

[0223] Content provision

[0224] The device provides the user with visual content appropriate to the exercise based on the exercise plan received from the server. This allows the user to visually understand the exercises they should perform in the form of videos and images.

[0225] Progress management and reward system

[0226] The server periodically receives user exercise data and records progress based on it. Different rewards are provided as incentives depending on the user's level of goal achievement. This reward system takes into account the user's past exercise history and usage patterns, supporting the user's continued motivation.

[0227] For example, if a user sets a goal of "losing 5kg in 3 months," the server will create a suitable exercise plan, suggesting three sessions of aerobic exercise plus strength training per week. The device will then deliver relevant training videos to support the user in easily following the plan. The server will also track the user's progress, and upon achieving the goal, will offer rewards such as shopping coupons or points.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user logs into the application using their device and enters personal information, health goals, and preferred exercise type. This information is then sent to the server.

[0231] Step 2:

[0232] The server analyzes the information received from the user and generates an optimal exercise plan using a generative AI model. This plan is customized based on the user's health goals and exercise preferences.

[0233] Step 3:

[0234] The server sends the generated exercise plan to the device. The device receives the exercise plan and displays it as visual content on the screen. The user can then review the suggested exercises.

[0235] Step 4:

[0236] The user performs exercises through a device and records their progress. The device records the duration, type of exercise, calories burned, etc., and sends the data to a server.

[0237] Step 5:

[0238] The server analyzes the received exercise data and tracks the user's progress. It evaluates the degree of goal achievement and reflects the progress in real time on the user's dashboard.

[0239] Step 6:

[0240] When a user achieves a goal they have set, the server proposes an incentive. Using AI, it selects a reward (e.g., coupons, points) based on the user's past history and preferences, and notifies the user's device.

[0241] Step 7:

[0242] Users receive and use the suggested rewards via their devices. User feedback is sent to the server and used to improve future exercise plans and reward selections.

[0243] (Example 1)

[0244] 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."

[0245] In modern society, there is a lack of means for individuals to design exercise plans to maintain their health and achieve their goals, and to efficiently evaluate and manage their progress. Traditional methods are not sufficiently individualized and make it difficult to maintain motivation over the long term.

[0246] 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.

[0247] In this invention, the server includes means for collecting user characteristic information, means for creating an exercise plan using a generative AI model based on the user characteristic information, and means for supplying visual materials according to the exercise plan. This enables the provision of individually optimized exercise plans and the maintenance of continuous motivation.

[0248] "Characteristic information" refers to information about individual attributes or circumstances related to the user, such as age, weight, health goals, and exercise preferences.

[0249] A "generative AI model" refers to an information processing model using artificial intelligence, which is used to create an appropriate exercise plan based on the user's characteristic information.

[0250] An "exercise plan" refers to a plan created by a generating AI model that takes into account the user's characteristics, including the specific content, frequency, and intensity of exercise.

[0251] "Visual materials" refer to visual content such as videos and images provided to users based on their exercise plan, and are materials intended to support the execution of exercises.

[0252] "Behavioral history" refers to records of exercise and achievements that users have performed in the past, and is data used when indicating rewards.

[0253] "Rewards" refer to incentives provided based on the user's achievement of goals, and include coupons and points that can be used to purchase goods.

[0254] This invention is a system for supporting users' health management, specifically by providing personalized exercise plans and rewarding users based on their achievement of those plans. This system operates by exchanging information between a server, a terminal, and the user.

[0255] The server receives characteristic information entered by the user through the application on their device. This characteristic information includes age, weight, health goals, preferred exercise type, etc., and this information is stored in a database. Based on the collected information, the server uses a generative AI model to create prompt messages and generate an exercise plan tailored to the user's characteristics. In this case, an example of a prompt message used is "Please create an optimal exercise plan to lose 5 kg in 3 months."

[0256] The generated exercise plan is sent to the device. Based on the exercise plan received from the server, the device provides the user with visual materials such as videos and images. This allows the user to clearly understand and perform the exercise. In particular, streaming content that matches the user's preferred type of exercise can support them in performing their workout more effectively.

[0257] Furthermore, the server periodically collects users' exercise data and analyzes their progress. Based on this progress data, it evaluates the user's goal achievement and provides rewards. Rewards include incentives such as shopping coupons and points, which are adjusted taking into account the user's past behavioral history. This motivates users to continue managing their health.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] Users enter personal information (e.g., age, weight, health goals) through an application on their device. The entered information is verified on the device and sent to the server. The server records this characteristic information in a database. As part of the input data check, it verifies for inconsistencies and omissions and prompts the user to re-enter the information if necessary.

[0261] Step 2:

[0262] The server generates prompt statements based on characteristic information stored in the database and inputs them into the generating AI model. A specific example of a prompt statement is, "Please create an optimal exercise plan to lose 5 kg in 3 months." Based on this prompt statement, the generating AI model formulates an exercise plan that matches the user's goal. The exercise plan includes recommended exercise types, frequencies, and intensity. The output is the exercise plan proposed by the AI ​​model.

[0263] Step 3:

[0264] The server sends the exercise plan obtained from the generated AI model to the terminal. The terminal selects appropriate visual materials based on the received exercise plan and prepares to stream them. The selected videos and images are displayed on the screen to the user, informing them of the flow of the exercise and specific methods. Specifically, these are provided in the form of video playback or slideshows to help the user perform the exercise smoothly.

[0265] Step 4:

[0266] Users follow the exercise plan presented through the device and record their progress. The device collects data from the user during exercise (exercise time, calories burned, heart rate, etc.) and sends it to the server. Data is collected from sensors built into the device and connected wearable devices. This ensures that an accurate exercise history is recorded.

[0267] Step 5:

[0268] The server analyzes the received exercise data and evaluates the user's progress. Based on the evaluated goal achievement level, an appropriate reward is selected. The selected reward is notified to the user via the terminal. Specifically, shopping coupons or points are provided according to the achievement level, aiming to continuously motivate the user to manage their health.

[0269] (Application Example 1)

[0270] 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."

[0271] Fitness programs often fail to meet the individual needs of many users, making it difficult to stick with them. Furthermore, standard reward systems may be insufficient to support sustained motivation, thus requiring a more personalized approach. Additionally, there is a lack of systems capable of monitoring and appropriately evaluating users' exercise progress in real time.

[0272] 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.

[0273] In this invention, the server includes means for acquiring individual user information, means for generating an exercise plan using a generative model based on the individual user information, and means for providing visual information corresponding to the exercise plan to a display device. This makes it possible to provide an exercise plan optimized for each user, track progress in real time, and provide it as individually customized rewards.

[0274] "Personal user information" refers to information that can identify an individual user, including important data such as their health status and target fitness level.

[0275] A "generative model" is a system that implements an algorithm that automatically creates an optimal exercise plan for each user based on the data it receives.

[0276] An "exercise plan" is a plan that includes specific fitness activities and schedules, designed to suit the individual user's information.

[0277] "Visual information" refers to visual content such as videos and images provided to users in accordance with their exercise plan.

[0278] A "display device" is a device that provides visual information to a user, and includes devices such as smart glasses and head-mounted displays.

[0279] "Movement progress" refers to data that records and evaluates the movements achieved by the user during the process of executing an exercise plan.

[0280] "Goal achievement level" is an indicator that shows how well the user has achieved their exercise goals.

[0281] "Reward" refers to the benefits and incentives provided when users achieve the goals of their exercise plans, including coupons and points that can be used for purchasing goods.

[0282] This system assumes that users access it using display devices such as smart glasses. First, the user uses the smart glasses to provide their personal information through voice input or a touch interface. This information includes health status, fitness goals, preferred exercise types, etc.

[0283] The terminal sends the information obtained from the user to the server. On this server, a "generative AI model" is utilized to generate an exercise plan tailored to the user's needs. In this process, an optimal plan is constructed based on past data and knowledge regarding existing exercise effects.

[0284] Once the exercise plan is generated, the server provides visual information to the smart glasses based on this information. The visual information displays the exercises the user should perform as videos or images. The visual information is updated in real time and can be operated by the user using voice recognition.

[0285] Furthermore, the progress of the actions is recorded using the built-in camera and motion sensors of the smart glasses. As a result, the implementation status of the user's exercise is accurately tracked. The server receives this progress information and calculates the user's goal achievement rate. Then, according to the goal achievement rate, rewards for the user are determined and notified through the smart glasses, providing incentives such as coupons and points to the user.

[0286] As a specific example, when the user sets a goal of "doing abdominal exercises three times a week for one month", the system provides an optimal exercise video for this purpose and provides visual feedback on the progress every week. When the goal is achieved, points that can be used to purchase fitness goods are awarded.

[0287] An example of a prompt to provide to a generating AI model is: "Generate a 3-day-a-week fitness plan combining 5km runs and strength training. The user has intermediate fitness experience and would like stretching included."

[0288] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0289] Step 1:

[0290] Users wear smart glasses and input their personal information and fitness goals into the device using voice or touch input. This information includes their health status, desired achievements, and preferred exercise types. The device receives this input and sends it directly to the server.

[0291] Step 2:

[0292] The server sends prompt messages to the generating AI model based on the individual information received from the terminal. For example, it might use the prompt, "Create an abdominal muscle strengthening plan suitable for an intermediate-level user." The generating AI model uses this prompt to create an exercise plan optimized for the user and returns that information to the server.

[0293] Step 3:

[0294] The server receives the generated exercise plan and selects the corresponding visual information. This visual information includes video content and images that demonstrate the correct form, number of sets, and points to note during the exercise. The server then sends this visual information to the terminal.

[0295] Step 4:

[0296] The device displays visual information transmitted from the server on the smart glasses' screen. The user begins exercising and trains while referring to the visual information. During this time, the smart glasses' motion sensors track the user's movements in real time.

[0297] Step 5:

[0298] The terminal transfers the collected motion data to the server. The server analyzes the received motion data and evaluates the progress. For example, it calculates exactly how much of the set exercise was performed and determines the degree of goal achievement.

[0299] Step 6:

[0300] The server determines rewards for users based on their level of goal achievement. These rewards are provided in the form of coupons, points, etc. The server sends reward information to the device, which then notifies the user of the reward via their smart glasses. This allows the user to receive the reward as feedback.

[0301] 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.

[0302] This invention is an exercise support system that takes the user's emotional state into consideration, aiming to improve the user's motivation and flexibly adjust the exercise plan during the process of achieving their health goals. By incorporating an emotion engine, this system achieves personalization based on the user's emotional characteristics.

[0303] User Interface

[0304] Users can access the application using their device and register their personal information and health goals. They can also periodically input their emotional state or have their emotional state automatically detected using sensors built into their device.

[0305] Generation of Exercise Plan and Adjustment of Emotional Criteria

[0306] The server receives the user's personal information and emotional state, and creates an optimal exercise plan using a generative AI model. The emotion engine adjusts the exercise plan according to the user's emotional changes. For example, when the stress level is high, yoga for relaxation is proposed.

[0307] Provision of Content According to Emotion

[0308] The terminal distributes visual content according to the user's emotion based on the exercise plan generated by the server. As a specific example, when the user is feeling down, a motivational video message is displayed. Also, the visual content is selected based on emotion, improving the ease of engaging in exercise.

[0309] Progress Management and Emotion - Responsive Rewards

[0310] The server analyzes the user's exercise execution data and emotional state, and evaluates the progress. It provides rewards not only based on the degree of goal achievement but also according to the improvement of emotion. For example, when an improvement in the emotional state is observed, specific reward points are additionally provided to support the user's continuous effort.

[0311] In this way, the present invention sensitively captures the movement of emotions and dynamically adjusts the exercise plan and reward system accordingly, making it possible to provide an environment in which the user can continuously work towards health goals while enjoying themselves.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user launches an application using the terminal and enters personal information and health goals. At the same time, the emotional state is recorded, or the terminal's sensors automatically collect emotional data and send this information to the server.

[0315] Step 2:

[0316] The server analyzes the personal information and emotional data it receives and uses a generative AI model to create an optimal exercise plan. The generated exercise plan includes content that takes into account and adjusts the user's emotional state.

[0317] Step 3:

[0318] The server generates an exercise plan and sends it to the device. The device then provides the user with appropriate visual content tailored to their emotions and displays the details of the plan.

[0319] Step 4:

[0320] The user starts exercising according to the instructions on the device, and records activity data (exercise time, calories burned, etc.) and emotional data. The device sends this data to the server.

[0321] Step 5:

[0322] The server tracks progress using received exercise data and emotional data. It evaluates goal achievement, taking into account changes in emotions.

[0323] Step 6:

[0324] Based on the user's progress and emotional state, the server suggests appropriate rewards and incentives. The rewards are selected based on the user's past activity history and emotional changes, choosing those deemed most effective for them.

[0325] Step 7:

[0326] The user reviews the suggested reward on their device and chooses to accept it. The server sends the reward to the device, making it available to the user. User feedback is used to generate future exercise plans and suggest rewards.

[0327] (Example 2)

[0328] 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".

[0329] This invention aims to solve the challenges of exercise continuity and motivation, which are influenced by the user's emotional state. Conventional exercise support systems often create individual exercise plans based on an individual's physical information, but they do not take into account changes in the user's emotions, which makes it difficult to provide effective support. Furthermore, the lack of appropriate rewards based on emotions necessitates improvements in promoting sustained effort by users.

[0330] 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.

[0331] In this invention, the server includes means for acquiring individual user information, means for detecting the user's emotional state, and means for using a generative AI model that generates an exercise plan based on the user's emotional state and individual information. This enables the provision of an exercise plan tailored to the user's emotional state and continuous exercise support.

[0332] "User's individual information" refers to information unique to an individual, such as name, age, weight, height, and health goals, which the user provides to the exercise support system.

[0333] "Emotional state" refers to information that indicates the mental state a user is experiencing at any given time, and includes emotions such as stress, relaxation, enjoyment, and anxiety.

[0334] A "generative AI model" is an artificial intelligence mechanism that automatically generates an optimized exercise plan based on the user's individual information and emotional state.

[0335] An "exercise plan" is a plan that specifies the particular exercises and schedules that users should perform to achieve their health goals.

[0336] "Visual content" refers to visual information such as videos and images that are provided in accordance with the user's emotional state, and are intended to improve motivation for exercise.

[0337] "Exercise progress" refers to a record showing the extent to which a user has performed the planned exercises, and is used to evaluate the degree of goal achievement.

[0338] "Rewards" refer to incentives and benefits provided to users when they achieve their exercise goals or when their emotional state improves, and are intended to support their continued efforts.

[0339] This invention is a system that provides individually optimized exercise support, taking into account the user's emotional state. The system aims to improve user motivation and flexibly adjust exercise plans during the process of achieving health goals. Its main components include a terminal, a server, and a generative AI model.

[0340] Users can access the application using devices such as smartphones and tablets to input personal information and health goals. The devices have built-in sensors that measure the user's heart rate, skin temperature, and other parameters, enabling automatic detection of their emotional state. Users can also manually input their emotional state.

[0341] The server receives individual user information and emotional state data sent from the device. Based on this information, the server uses a generative AI model and prompts to create an optimal exercise plan. An example of a prompt would be, "Please provide an appropriate exercise plan if the user's emotional state is stressed." This AI model flexibly adjusts the exercise plan according to the user's exercise ability and emotional changes.

[0342] Based on the generated exercise plan, the device provides visual content that responds to the user's emotions. For example, when the user is feeling down, it displays an encouraging video message to help boost their motivation to exercise. Furthermore, the server records the user's exercise progress data and emotional improvement, and evaluates their progress toward achieving their goals. Based on these evaluations, the system provides rewards to the user according to their level of achievement and emotional improvement. In this way, a continuous and effective exercise experience can be achieved for the user.

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

[0344] Step 1:

[0345] The user enters personal information and health goals using a terminal. The terminal registers the user's name, age, height, weight, and health goals in a database. This information is used as foundational data when the system creates an exercise plan.

[0346] Step 2:

[0347] The device uses built-in sensors to detect the user's emotional state. The measured data includes heart rate and skin temperature, and based on this data, the device infers the user's emotional state. The inferred emotional state is then sent to the server.

[0348] Step 3:

[0349] The server receives individual user information and emotional state sent from the terminal. Based on the received information, the server generates prompts for the AI ​​model. For example, it might use the prompt, "Generate an exercise plan appropriate for the current emotional state." In response to this prompt, the AI ​​model creates an optimal exercise plan and returns the result to the server.

[0350] Step 4:

[0351] The server generates an exercise plan and sends it to the device. Based on the exercise plan, the device selects visual content from its library that is appropriate for the user's mood. The selected content is provided to the user as videos or images, and the visual stimulation enhances motivation for exercise.

[0352] Step 5:

[0353] Users perform exercises using a device and record their progress. The device logs the amount and frequency of the user's exercise and sends it to a server for analysis. Based on this data, the server evaluates the degree of goal achievement and emotional improvement, and calculates rewards.

[0354] Step 6:

[0355] The server sends the calculated reward to the device and notifies the user. The reward helps maintain the user's motivation to exercise consistently. The reward is provided as in-app perks or points, encouraging the user to exercise again.

[0356] (Application Example 2)

[0357] 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 as the "terminal".

[0358] Conventional exercise support systems often fail to consider the user's emotional state when creating exercise plans and food recommendations, leading to decreased motivation and delays in achieving health goals. Furthermore, the lack of mechanisms to provide appropriate rewards based on emotional state makes it difficult for users to consistently work towards their health goals.

[0359] 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.

[0360] In this invention, the server includes means for detecting the user's emotional state and collecting information thereof, means for generating an exercise plan and making food suggestions based on the user's individual information and emotional state, and means for providing visual content corresponding to the exercise plan and food suggestions. This makes it possible to suggest exercises and foods that are appropriate to the user's emotional state, thereby improving motivation and enabling the achievement of sustainable health goals.

[0361] "Users" refers to individuals who use this system to receive exercise plans and food recommendations.

[0362] "Personalized information" refers to information specific to a user, such as their name, age, gender, health status, and lifestyle.

[0363] "Emotional state" refers to data that indicates the emotions and psychological conditions a user is experiencing at a specific point in time.

[0364] An "exercise plan" refers to the content and schedule of exercises that users should perform to effectively achieve their health goals.

[0365] "Food suggestions" refer to recommended meals and food choices based on the user's emotional state and individual information.

[0366] "Visual content" refers to media that includes information and entertainment elements provided to users visually.

[0367] "Progress" refers to an indicator that shows the degree to which a user has achieved their exercise plan or health goals.

[0368] "Rewards" refer to incentives and benefits provided in accordance with the user's progress towards achieving their goals and changes in their emotional state.

[0369] A "server" refers to a central computer system that receives information from users and has the function of generating exercise plans and food recommendations.

[0370] This invention relates to a system that provides exercise support and food recommendations based on the user's emotional state. The system consists of a user's terminal, a server, a generative AI model, and an emotion detection sensor.

[0371] The user's device is a mobile device such as a smartphone or tablet, equipped with a built-in or external emotion detection sensor to capture the user's emotional state. This emotional data, along with individual information, is sent to a server. The server uses a generated AI model based on the user's emotional state and individual information to create a personalized exercise plan and food recommendations.

[0372] The generated exercise plan and food suggestions are provided to the user's device as visual content. This visual content is designed to adapt to the user's emotional state and enhance their motivation. For example, if the user is tired, relaxation-enhancing exercises or snacks can be suggested. A specific example is recommending herbal tea to a stressed user, which promotes relaxation.

[0373] The server also analyzes progress and changes in emotional state, and provides rewards accordingly. This allows users to work more actively towards achieving health goals, including improving their emotional state. For example, users may be awarded points when they overcome stress.

[0374] An example of a prompt from a generative AI model is, "What exercises and foods would you suggest to a user who wants to refresh themselves from a stressful state?" Through such prompts, it is possible to generate suggestions optimized for the user's emotional state.

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The user's device uses a built-in emotion detection sensor to detect the user's emotional state. The input is raw data from the sensor, which is analyzed by a data processing algorithm to output the user's emotional state (e.g., stress, happiness, fatigue).

[0378] Step 2:

[0379] The user's device transmits detected emotional states and personal information (e.g., height, weight, favorite foods) to the server. This information is used as foundational data for personalized exercise plans and food recommendations.

[0380] Step 3:

[0381] The server inputs the received emotional state and individual information into a generating AI model, and through the AI ​​model's prompts, generates an optimal exercise plan and food recommendations for the user. Specifically, it processes the data and derives a plan using prompts such as, "If the user's current emotional state is stress, what exercise and food are recommended?"

[0382] Step 4:

[0383] The server provides the generated exercise plan and food suggestions as visual content to the user's device. The visual content is processed and displayed to include colors, images, and recommendation video messages that correspond to the user's emotional state.

[0384] Step 5:

[0385] User progress and emotional state changes are continuously monitored through emotion detection sensors and user activity logs, and the data is sent to the server. The input is a comparison of new and past data from the emotion detection sensors. Analysis evaluates the effectiveness of emotional settings and exercise performance, and the results are recorded in the database.

[0386] Step 6:

[0387] The server determines rewards based on collected progress data and notifies the user. For example, if emotional improvement or goal achievement is confirmed, additional points or rewards are provided. This increases user motivation and encourages continued engagement with health goals.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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".

[0404] This invention relates to a system that supports continued health management by providing an optimized exercise plan based on the user's individual information and offering rewards according to the level of achievement. This system operates by exchanging information between a server, a terminal, and the user.

[0405] User Interface

[0406] Users access the system through an application on their device. Initially, users enter personal information and health goals, and select their preferred type of exercise. This information is sent to the server and used as basic data for generating exercise plans.

[0407] Generating an exercise plan

[0408] The server receives user input information and uses a generative AI model to generate an exercise plan tailored to the user's goals and preferences. The AI ​​model constructs an optimal plan based on past data and known exercise effects, and sends it to the device.

[0409] Content provision

[0410] The device provides the user with visual content appropriate to the exercise based on the exercise plan received from the server. This allows the user to visually understand the exercises they should perform in the form of videos and images.

[0411] Progress management and reward system

[0412] The server periodically receives user exercise data and records progress based on it. Different rewards are provided as incentives depending on the user's level of goal achievement. This reward system takes into account the user's past exercise history and usage patterns, supporting the user's continued motivation.

[0413] For example, if a user sets a goal of "losing 5kg in 3 months," the server will create a suitable exercise plan, suggesting three sessions of aerobic exercise plus strength training per week. The device will then deliver relevant training videos to support the user in easily following the plan. The server will also track the user's progress, and upon achieving the goal, will offer rewards such as shopping coupons or points.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user logs into the application using their device and enters personal information, health goals, and preferred exercise type. This information is then sent to the server.

[0417] Step 2:

[0418] The server analyzes the information received from the user and generates an optimal exercise plan using a generative AI model. This plan is customized based on the user's health goals and exercise preferences.

[0419] Step 3:

[0420] The server sends the generated exercise plan to the device. The device receives the exercise plan and displays it as visual content on the screen. The user can then review the suggested exercises.

[0421] Step 4:

[0422] The user performs exercises through a device and records their progress. The device records the duration, type of exercise, calories burned, etc., and sends the data to a server.

[0423] Step 5:

[0424] The server analyzes the received exercise data and tracks the user's progress. It evaluates the degree of goal achievement and reflects the progress in real time on the user's dashboard.

[0425] Step 6:

[0426] When a user achieves a goal they have set, the server proposes an incentive. Using AI, it selects a reward (e.g., coupons, points) based on the user's past history and preferences, and notifies the user's device.

[0427] Step 7:

[0428] Users receive and use the suggested rewards via their devices. User feedback is sent to the server and used to improve future exercise plans and reward selections.

[0429] (Example 1)

[0430] 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."

[0431] In modern society, there is a lack of means for individuals to design exercise plans to maintain their health and achieve their goals, and to efficiently evaluate and manage their progress. Traditional methods are not sufficiently individualized and make it difficult to maintain motivation over the long term.

[0432] 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.

[0433] In this invention, the server includes means for collecting user characteristic information, means for creating an exercise plan using a generative AI model based on the user characteristic information, and means for supplying visual materials according to the exercise plan. This enables the provision of individually optimized exercise plans and the maintenance of continuous motivation.

[0434] "Characteristic information" refers to information about individual attributes or circumstances related to the user, such as age, weight, health goals, and exercise preferences.

[0435] A "generative AI model" refers to an information processing model using artificial intelligence, which is used to create an appropriate exercise plan based on the user's characteristic information.

[0436] An "exercise plan" refers to a plan created by a generating AI model that takes into account the user's characteristics, including the specific content, frequency, and intensity of exercise.

[0437] "Visual materials" refer to visual content such as videos and images provided to users based on their exercise plan, and are materials intended to support the execution of exercises.

[0438] "Behavioral history" refers to records of exercise and achievements that users have performed in the past, and is data used when indicating rewards.

[0439] "Rewards" refer to incentives provided based on the user's achievement of goals, and include coupons and points that can be used to purchase goods.

[0440] This invention is a system for supporting users' health management, specifically by providing personalized exercise plans and rewarding users based on their achievement of those plans. This system operates by exchanging information between a server, a terminal, and the user.

[0441] The server receives characteristic information entered by the user through the application on their device. This characteristic information includes age, weight, health goals, preferred exercise type, etc., and this information is stored in a database. Based on the collected information, the server uses a generative AI model to create prompt messages and generate an exercise plan tailored to the user's characteristics. In this case, an example of a prompt message used is "Please create an optimal exercise plan to lose 5 kg in 3 months."

[0442] The generated exercise plan is sent to the device. Based on the exercise plan received from the server, the device provides the user with visual materials such as videos and images. This allows the user to clearly understand and perform the exercise. In particular, streaming content that matches the user's preferred type of exercise can support them in performing their workout more effectively.

[0443] Furthermore, the server periodically collects users' exercise data and analyzes their progress. Based on this progress data, it evaluates the user's goal achievement and provides rewards. Rewards include incentives such as shopping coupons and points, which are adjusted taking into account the user's past behavioral history. This motivates users to continue managing their health.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] Users enter personal information (e.g., age, weight, health goals) through an application on their device. The entered information is verified on the device and sent to the server. The server records this characteristic information in a database. As part of the input data check, it verifies for inconsistencies and omissions and prompts the user to re-enter the information if necessary.

[0447] Step 2:

[0448] The server generates prompt statements based on characteristic information stored in the database and inputs them into the generating AI model. A specific example of a prompt statement is, "Please create an optimal exercise plan to lose 5 kg in 3 months." Based on this prompt statement, the generating AI model formulates an exercise plan that matches the user's goal. The exercise plan includes recommended exercise types, frequencies, and intensity. The output is the exercise plan proposed by the AI ​​model.

[0449] Step 3:

[0450] The server sends the exercise plan obtained from the generated AI model to the terminal. The terminal selects appropriate visual materials based on the received exercise plan and prepares to stream them. The selected videos and images are displayed on the screen to the user, informing them of the flow of the exercise and specific methods. Specifically, these are provided in the form of video playback or slideshows to help the user perform the exercise smoothly.

[0451] Step 4:

[0452] Users follow the exercise plan presented through the device and record their progress. The device collects data from the user during exercise (exercise time, calories burned, heart rate, etc.) and sends it to the server. Data is collected from sensors built into the device and connected wearable devices. This ensures that an accurate exercise history is recorded.

[0453] Step 5:

[0454] The server analyzes the received exercise data and evaluates the user's progress. Based on the evaluated goal achievement level, an appropriate reward is selected. The selected reward is notified to the user via the terminal. Specifically, shopping coupons or points are provided according to the achievement level, aiming to continuously motivate the user to manage their health.

[0455] (Application Example 1)

[0456] 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."

[0457] Fitness programs often fail to meet the individual needs of many users, making it difficult to stick with them. Furthermore, standard reward systems may be insufficient to support sustained motivation, thus requiring a more personalized approach. Additionally, there is a lack of systems capable of monitoring and appropriately evaluating users' exercise progress in real time.

[0458] 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.

[0459] In this invention, the server includes means for acquiring individual user information, means for generating an exercise plan using a generative model based on the individual user information, and means for providing visual information corresponding to the exercise plan to a display device. This makes it possible to provide an exercise plan optimized for each user, track progress in real time, and provide it as individually customized rewards.

[0460] "Personal user information" refers to information that can identify an individual user, including important data such as their health status and target fitness level.

[0461] A "generative model" is a system that implements an algorithm that automatically creates an optimal exercise plan for each user based on the data it receives.

[0462] An "exercise plan" is a plan that includes specific fitness activities and schedules, designed to suit the individual user's information.

[0463] "Visual information" refers to visual content such as videos and images provided to users in accordance with their exercise plan.

[0464] A "display device" is a device that provides visual information to a user, and includes devices such as smart glasses and head-mounted displays.

[0465] "Movement progress" refers to data that records and evaluates the movements achieved by the user during the process of executing an exercise plan.

[0466] "Goal achievement level" is an indicator that shows how well the user has achieved their exercise goals.

[0467] "Rewards" refer to benefits or incentives provided to users when they achieve their exercise plan goals, including coupons and points that can be used to purchase goods.

[0468] This system is designed to be accessed by users using display devices such as smart glasses. First, the user provides their personal information via voice input or a touch interface using the smart glasses. This information includes health status, fitness goals, and preferred exercise types.

[0469] The device transmits information obtained from the user to a server. This server uses a "generative AI model" to generate an exercise plan tailored to the user's needs. In this process, the optimal plan is constructed based on past data and existing knowledge about the effects of exercise.

[0470] Once an exercise plan is generated, the server provides visual information to the smart glasses based on this information. The visual information displays the exercises the user should perform as videos or images. The visual information is updated in real time and can be controlled by the user using voice recognition.

[0471] Furthermore, the progress of the exercise is recorded using the smart glasses' built-in camera and motion sensors. This allows for accurate tracking of the user's exercise performance. The server receives this progress information and calculates the user's degree of goal achievement. Based on the degree of goal achievement, the server determines the reward for the user and notifies them through the smart glasses, providing the user with incentives such as coupons or points.

[0472] For example, if a user sets a goal of "doing abdominal exercises three times a week for one month," the system provides optimal exercise videos and visually tracks weekly progress. Once the goal is achieved, points are awarded that can be used to purchase fitness equipment.

[0473] An example of a prompt to provide to a generating AI model is: "Generate a 3-day-a-week fitness plan combining 5km runs and strength training. The user has intermediate fitness experience and would like stretching included."

[0474] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0475] Step 1:

[0476] Users wear smart glasses and input their personal information and fitness goals into the device using voice or touch input. This information includes their health status, desired achievements, and preferred exercise types. The device receives this input and sends it directly to the server.

[0477] Step 2:

[0478] The server sends prompt messages to the generating AI model based on the individual information received from the terminal. For example, it might use the prompt, "Create an abdominal muscle strengthening plan suitable for an intermediate-level user." The generating AI model uses this prompt to create an exercise plan optimized for the user and returns that information to the server.

[0479] Step 3:

[0480] The server receives the generated exercise plan and selects the corresponding visual information. This visual information includes video content and images that demonstrate the correct form, number of sets, and points to note during the exercise. The server then sends this visual information to the terminal.

[0481] Step 4:

[0482] The device displays visual information transmitted from the server on the smart glasses' screen. The user begins exercising and trains while referring to the visual information. During this time, the smart glasses' motion sensors track the user's movements in real time.

[0483] Step 5:

[0484] The terminal transfers the collected motion data to the server. The server analyzes the received motion data and evaluates the progress. For example, it calculates exactly how much of the set exercise was performed and determines the degree of goal achievement.

[0485] Step 6:

[0486] The server determines rewards for users based on their level of goal achievement. These rewards are provided in the form of coupons, points, etc. The server sends reward information to the device, which then notifies the user of the reward via their smart glasses. This allows the user to receive the reward as feedback.

[0487] 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.

[0488] This invention is an exercise support system that takes the user's emotional state into consideration, aiming to improve the user's motivation and flexibly adjust the exercise plan during the process of achieving their health goals. By incorporating an emotion engine, this system achieves personalization based on the user's emotional characteristics.

[0489] User Interface

[0490] Users can access the application using their device and register their personal information and health goals. They can also periodically input their emotional state or have their emotional state automatically detected using sensors built into their device.

[0491] Exercise plan creation and emotional adjustment

[0492] The server receives the user's individual information and emotional state, and uses a generative AI model to create an optimal exercise plan. The emotion engine adjusts the exercise plan according to the user's emotional changes; for example, if the stress level is high, it might suggest yoga for relaxation.

[0493] Content delivery tailored to emotions

[0494] The device delivers visual content tailored to the user's emotions based on an exercise plan generated by the server. For example, if the user is feeling down, an encouraging video message will be displayed. Furthermore, the selection of visual content based on emotions improves the likelihood of engaging in exercise.

[0495] Progress management and emotionally responsive rewards

[0496] The server analyzes the user's exercise data and emotional state to evaluate progress. Rewards are provided not only based on goal achievement but also on improvements in emotional state. For example, if an improvement in emotional state is observed, additional reward points are provided to support the user's sustained effort.

[0497] Thus, the present invention sensitively captures emotional shifts and dynamically adjusts the exercise plan and reward system accordingly, making it possible to provide an environment in which users can enjoyably and sustainably work towards their health goals.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user launches the application using their device and enters personal information and health goals. Simultaneously, they record their emotional state, or the device's sensors automatically collect emotional data, and this information is sent to the server.

[0501] Step 2:

[0502] The server analyzes the personal information and emotional data it receives and uses a generative AI model to create an optimal exercise plan. The generated exercise plan includes content that takes into account and adjusts the user's emotional state.

[0503] Step 3:

[0504] The server generates an exercise plan and sends it to the device. The device then provides the user with appropriate visual content tailored to their emotions and displays the details of the plan.

[0505] Step 4:

[0506] The user starts exercising according to the instructions on the device, and records activity data (exercise time, calories burned, etc.) and emotional data. The device sends this data to the server.

[0507] Step 5:

[0508] The server tracks progress using received exercise data and emotional data. It evaluates goal achievement, taking into account changes in emotions.

[0509] Step 6:

[0510] Based on the user's progress and emotional state, the server suggests appropriate rewards and incentives. The rewards are selected based on the user's past activity history and emotional changes, choosing those deemed most effective for them.

[0511] Step 7:

[0512] The user reviews the suggested reward on their device and chooses to accept it. The server sends the reward to the device, making it available to the user. User feedback is used to generate future exercise plans and suggest rewards.

[0513] (Example 2)

[0514] 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."

[0515] This invention aims to solve the challenges of exercise continuity and motivation, which are influenced by the user's emotional state. Conventional exercise support systems often create individual exercise plans based on an individual's physical information, but they do not take into account changes in the user's emotions, which makes it difficult to provide effective support. Furthermore, the lack of appropriate rewards based on emotions necessitates improvements in promoting sustained effort by users.

[0516] 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.

[0517] In this invention, the server includes means for acquiring individual user information, means for detecting the user's emotional state, and means for using a generative AI model that generates an exercise plan based on the user's emotional state and individual information. This enables the provision of an exercise plan tailored to the user's emotional state and continuous exercise support.

[0518] "User's individual information" refers to information unique to an individual, such as name, age, weight, height, and health goals, which the user provides to the exercise support system.

[0519] "Emotional state" refers to information that indicates the mental state a user is experiencing at any given time, and includes emotions such as stress, relaxation, enjoyment, and anxiety.

[0520] A "generative AI model" is an artificial intelligence mechanism that automatically generates an optimized exercise plan based on the user's individual information and emotional state.

[0521] An "exercise plan" is a plan that specifies the particular exercises and schedules that users should perform to achieve their health goals.

[0522] "Visual content" refers to visual information such as videos and images that are provided in accordance with the user's emotional state, and are intended to improve motivation for exercise.

[0523] "Exercise progress" refers to a record showing the extent to which a user has performed the planned exercises, and is used to evaluate the degree of goal achievement.

[0524] "Rewards" refer to incentives and benefits provided to users when they achieve their exercise goals or when their emotional state improves, and are intended to support their continued efforts.

[0525] This invention is a system that provides individually optimized exercise support, taking into account the user's emotional state. The system aims to improve user motivation and flexibly adjust exercise plans during the process of achieving health goals. Its main components include a terminal, a server, and a generative AI model.

[0526] Users can access the application using devices such as smartphones and tablets to input personal information and health goals. The devices have built-in sensors that measure the user's heart rate, skin temperature, and other parameters, enabling automatic detection of their emotional state. Users can also manually input their emotional state.

[0527] The server receives individual user information and emotional state data sent from the device. Based on this information, the server uses a generative AI model and prompts to create an optimal exercise plan. An example of a prompt would be, "Please provide an appropriate exercise plan if the user's emotional state is stressed." This AI model flexibly adjusts the exercise plan according to the user's exercise ability and emotional changes.

[0528] Based on the generated exercise plan, the device provides visual content that responds to the user's emotions. For example, when the user is feeling down, it displays an encouraging video message to help boost their motivation to exercise. Furthermore, the server records the user's exercise progress data and emotional improvement, and evaluates their progress toward achieving their goals. Based on these evaluations, the system provides rewards to the user according to their level of achievement and emotional improvement. In this way, a continuous and effective exercise experience can be achieved for the user.

[0529] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0530] Step 1:

[0531] The user enters personal information and health goals using a terminal. The terminal registers the user's name, age, height, weight, and health goals in a database. This information is used as foundational data when the system creates an exercise plan.

[0532] Step 2:

[0533] The device uses built-in sensors to detect the user's emotional state. The measured data includes heart rate and skin temperature, and based on this data, the device infers the user's emotional state. The inferred emotional state is then sent to the server.

[0534] Step 3:

[0535] The server receives individual user information and emotional state sent from the terminal. Based on the received information, the server generates prompts for the AI ​​model. For example, it might use the prompt, "Generate an exercise plan appropriate for the current emotional state." In response to this prompt, the AI ​​model creates an optimal exercise plan and returns the result to the server.

[0536] Step 4:

[0537] The server generates an exercise plan and sends it to the device. Based on the exercise plan, the device selects visual content from its library that is appropriate for the user's mood. The selected content is provided to the user as videos or images, and the visual stimulation enhances motivation for exercise.

[0538] Step 5:

[0539] Users perform exercises using a device and record their progress. The device logs the amount and frequency of the user's exercise and sends it to a server for analysis. Based on this data, the server evaluates the degree of goal achievement and emotional improvement, and calculates rewards.

[0540] Step 6:

[0541] The server sends the calculated reward to the device and notifies the user. The reward helps maintain the user's motivation to exercise consistently. The reward is provided as in-app perks or points, encouraging the user to exercise again.

[0542] (Application Example 2)

[0543] 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."

[0544] Conventional exercise support systems often fail to consider the user's emotional state when creating exercise plans and food recommendations, leading to decreased motivation and delays in achieving health goals. Furthermore, the lack of mechanisms to provide appropriate rewards based on emotional state makes it difficult for users to consistently work towards their health goals.

[0545] 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.

[0546] In this invention, the server includes means for detecting the user's emotional state and collecting information thereof, means for generating an exercise plan and making food suggestions based on the user's individual information and emotional state, and means for providing visual content corresponding to the exercise plan and food suggestions. This makes it possible to suggest exercises and foods that are appropriate to the user's emotional state, thereby improving motivation and enabling the achievement of sustainable health goals.

[0547] "Users" refers to individuals who use this system to receive exercise plans and food recommendations.

[0548] "Personalized information" refers to information specific to a user, such as their name, age, gender, health status, and lifestyle.

[0549] "Emotional state" refers to data that indicates the emotions and psychological conditions a user is experiencing at a specific point in time.

[0550] An "exercise plan" refers to the content and schedule of exercises that users should perform to effectively achieve their health goals.

[0551] "Food suggestions" refer to recommended meals and food choices based on the user's emotional state and individual information.

[0552] "Visual content" refers to media that includes information and entertainment elements provided to users visually.

[0553] "Progress" refers to an indicator that shows the degree to which a user has achieved their exercise plan or health goals.

[0554] "Rewards" refer to incentives and benefits provided in accordance with the user's progress towards achieving their goals and changes in their emotional state.

[0555] A "server" refers to a central computer system that receives information from users and has the function of generating exercise plans and food recommendations.

[0556] This invention relates to a system that provides exercise support and food recommendations based on the user's emotional state. The system consists of a user's terminal, a server, a generative AI model, and an emotion detection sensor.

[0557] The user's device is a mobile device such as a smartphone or tablet, equipped with a built-in or external emotion detection sensor to capture the user's emotional state. This emotional data, along with individual information, is sent to a server. The server uses a generated AI model based on the user's emotional state and individual information to create a personalized exercise plan and food recommendations.

[0558] The generated exercise plan and food suggestions are provided to the user's device as visual content. This visual content is designed to adapt to the user's emotional state and enhance their motivation. For example, if the user is tired, relaxation-enhancing exercises or snacks can be suggested. A specific example is recommending herbal tea to a stressed user, which promotes relaxation.

[0559] The server also analyzes progress and changes in emotional state, and provides rewards accordingly. This allows users to work more actively towards achieving health goals, including improving their emotional state. For example, users may be awarded points when they overcome stress.

[0560] An example of a prompt from a generative AI model is, "What exercises and foods would you suggest to a user who wants to refresh themselves from a stressful state?" Through such prompts, it is possible to generate suggestions optimized for the user's emotional state.

[0561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0562] Step 1:

[0563] The user's device uses a built-in emotion detection sensor to detect the user's emotional state. The input is raw data from the sensor, which is analyzed by a data processing algorithm to output the user's emotional state (e.g., stress, happiness, fatigue).

[0564] Step 2:

[0565] The user's device transmits detected emotional states and personal information (e.g., height, weight, favorite foods) to the server. This information is used as foundational data for personalized exercise plans and food recommendations.

[0566] Step 3:

[0567] The server inputs the received emotional state and individual information into a generating AI model, and through the AI ​​model's prompts, generates an optimal exercise plan and food recommendations for the user. Specifically, it processes the data and derives a plan using prompts such as, "If the user's current emotional state is stress, what exercise and food are recommended?"

[0568] Step 4:

[0569] The server provides the generated exercise plan and food suggestions as visual content to the user's device. The visual content is processed and displayed to include colors, images, and recommendation video messages that correspond to the user's emotional state.

[0570] Step 5:

[0571] User progress and emotional state changes are continuously monitored through emotion detection sensors and user activity logs, and the data is sent to the server. The input is a comparison of new and past data from the emotion detection sensors. Analysis evaluates the effectiveness of emotional settings and exercise performance, and the results are recorded in the database.

[0572] Step 6:

[0573] The server determines rewards based on collected progress data and notifies the user. For example, if emotional improvement or goal achievement is confirmed, additional points or rewards are provided. This increases user motivation and encourages continued engagement with health goals.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] [Fourth Embodiment]

[0578] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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).

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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".

[0591] This invention relates to a system that supports continued health management by providing an optimized exercise plan based on the user's individual information and offering rewards according to the level of achievement. This system operates by exchanging information between a server, a terminal, and the user.

[0592] User Interface

[0593] Users access the system through an application on their device. Initially, users enter personal information and health goals, and select their preferred type of exercise. This information is sent to the server and used as basic data for generating exercise plans.

[0594] Generating an exercise plan

[0595] The server receives user input information and uses a generative AI model to generate an exercise plan tailored to the user's goals and preferences. The AI ​​model constructs an optimal plan based on past data and known exercise effects, and sends it to the device.

[0596] Content provision

[0597] The device provides the user with visual content appropriate to the exercise based on the exercise plan received from the server. This allows the user to visually understand the exercises they should perform in the form of videos and images.

[0598] Progress management and reward system

[0599] The server periodically receives user exercise data and records progress based on it. Different rewards are provided as incentives depending on the user's level of goal achievement. This reward system takes into account the user's past exercise history and usage patterns, supporting the user's continued motivation.

[0600] For example, if a user sets a goal of "losing 5kg in 3 months," the server will create a suitable exercise plan, suggesting three sessions of aerobic exercise plus strength training per week. The device will then deliver relevant training videos to support the user in easily following the plan. The server will also track the user's progress, and upon achieving the goal, will offer rewards such as shopping coupons or points.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The user logs into the application using their device and enters personal information, health goals, and preferred exercise type. This information is then sent to the server.

[0604] Step 2:

[0605] The server analyzes the information received from the user and generates an optimal exercise plan using a generative AI model. This plan is customized based on the user's health goals and exercise preferences.

[0606] Step 3:

[0607] The server sends the generated exercise plan to the device. The device receives the exercise plan and displays it as visual content on the screen. The user can then review the suggested exercises.

[0608] Step 4:

[0609] The user performs exercises through a device and records their progress. The device records the duration, type of exercise, calories burned, etc., and sends the data to a server.

[0610] Step 5:

[0611] The server analyzes the received exercise data and tracks the user's progress. It evaluates the degree of goal achievement and reflects the progress in real time on the user's dashboard.

[0612] Step 6:

[0613] When a user achieves a goal they have set, the server proposes an incentive. Using AI, it selects a reward (e.g., coupons, points) based on the user's past history and preferences, and notifies the user's device.

[0614] Step 7:

[0615] Users receive and use the suggested rewards via their devices. User feedback is sent to the server and used to improve future exercise plans and reward selections.

[0616] (Example 1)

[0617] 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".

[0618] In modern society, there is a lack of means for individuals to design exercise plans to maintain their health and achieve their goals, and to efficiently evaluate and manage their progress. Traditional methods are not sufficiently individualized and make it difficult to maintain motivation over the long term.

[0619] 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.

[0620] In this invention, the server includes means for collecting user characteristic information, means for creating an exercise plan using a generative AI model based on the user characteristic information, and means for supplying visual materials according to the exercise plan. This enables the provision of individually optimized exercise plans and the maintenance of continuous motivation.

[0621] "Characteristic information" refers to information about individual attributes or circumstances related to the user, such as age, weight, health goals, and exercise preferences.

[0622] A "generative AI model" refers to an information processing model using artificial intelligence, which is used to create an appropriate exercise plan based on the user's characteristic information.

[0623] An "exercise plan" refers to a plan created by a generating AI model that takes into account the user's characteristics, including the specific content, frequency, and intensity of exercise.

[0624] "Visual materials" refer to visual content such as videos and images provided to users based on their exercise plan, and are materials intended to support the execution of exercises.

[0625] "Behavioral history" refers to records of exercise and achievements that users have performed in the past, and is data used when indicating rewards.

[0626] "Rewards" refer to incentives provided based on the user's achievement of goals, and include coupons and points that can be used to purchase goods.

[0627] This invention is a system for supporting users' health management, specifically by providing personalized exercise plans and rewarding users based on their achievement of those plans. This system operates by exchanging information between a server, a terminal, and the user.

[0628] The server receives characteristic information entered by the user through the application on their device. This characteristic information includes age, weight, health goals, preferred exercise type, etc., and this information is stored in a database. Based on the collected information, the server uses a generative AI model to create prompt messages and generate an exercise plan tailored to the user's characteristics. In this case, an example of a prompt message used is "Please create an optimal exercise plan to lose 5 kg in 3 months."

[0629] The generated exercise plan is sent to the device. Based on the exercise plan received from the server, the device provides the user with visual materials such as videos and images. This allows the user to clearly understand and perform the exercise. In particular, streaming content that matches the user's preferred type of exercise can support them in performing their workout more effectively.

[0630] Furthermore, the server periodically collects users' exercise data and analyzes their progress. Based on this progress data, it evaluates the user's goal achievement and provides rewards. Rewards include incentives such as shopping coupons and points, which are adjusted taking into account the user's past behavioral history. This motivates users to continue managing their health.

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] Users enter personal information (e.g., age, weight, health goals) through an application on their device. The entered information is verified on the device and sent to the server. The server records this characteristic information in a database. As part of the input data check, it verifies for inconsistencies and omissions and prompts the user to re-enter the information if necessary.

[0634] Step 2:

[0635] The server generates prompt statements based on characteristic information stored in the database and inputs them into the generating AI model. A specific example of a prompt statement is, "Please create an optimal exercise plan to lose 5 kg in 3 months." Based on this prompt statement, the generating AI model formulates an exercise plan that matches the user's goal. The exercise plan includes recommended exercise types, frequencies, and intensity. The output is the exercise plan proposed by the AI ​​model.

[0636] Step 3:

[0637] The server sends the exercise plan obtained from the generated AI model to the terminal. The terminal selects appropriate visual materials based on the received exercise plan and prepares to stream them. The selected videos and images are displayed on the screen to the user, informing them of the flow of the exercise and specific methods. Specifically, these are provided in the form of video playback or slideshows to help the user perform the exercise smoothly.

[0638] Step 4:

[0639] Users follow the exercise plan presented through the device and record their progress. The device collects data from the user during exercise (exercise time, calories burned, heart rate, etc.) and sends it to the server. Data is collected from sensors built into the device and connected wearable devices. This ensures that an accurate exercise history is recorded.

[0640] Step 5:

[0641] The server analyzes the received exercise data and evaluates the user's progress. Based on the evaluated goal achievement level, an appropriate reward is selected. The selected reward is notified to the user via the terminal. Specifically, shopping coupons or points are provided according to the achievement level, aiming to continuously motivate the user to manage their health.

[0642] (Application Example 1)

[0643] 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".

[0644] Fitness programs often fail to meet the individual needs of many users, making it difficult to stick with them. Furthermore, standard reward systems may be insufficient to support sustained motivation, thus requiring a more personalized approach. Additionally, there is a lack of systems capable of monitoring and appropriately evaluating users' exercise progress in real time.

[0645] 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.

[0646] In this invention, the server includes means for acquiring individual user information, means for generating an exercise plan using a generative model based on the individual user information, and means for providing visual information corresponding to the exercise plan to a display device. This makes it possible to provide an exercise plan optimized for each user, track progress in real time, and provide it as individually customized rewards.

[0647] "Personal user information" refers to information that can identify an individual user, including important data such as their health status and target fitness level.

[0648] A "generative model" is a system that implements an algorithm that automatically creates an optimal exercise plan for each user based on the data it receives.

[0649] An "exercise plan" is a plan that includes specific fitness activities and schedules, designed to suit the individual user's information.

[0650] "Visual information" refers to visual content such as videos and images provided to users in accordance with their exercise plan.

[0651] A "display device" is a device that provides visual information to a user, and includes devices such as smart glasses and head-mounted displays.

[0652] "Movement progress" refers to data that records and evaluates the movements achieved by the user during the process of executing an exercise plan.

[0653] "Goal achievement level" is an indicator that shows how well the user has achieved their exercise goals.

[0654] "Rewards" refer to benefits or incentives provided to users when they achieve their exercise plan goals, including coupons and points that can be used to purchase goods.

[0655] This system is designed to be accessed by users using display devices such as smart glasses. First, the user provides their personal information via voice input or a touch interface using the smart glasses. This information includes health status, fitness goals, and preferred exercise types.

[0656] The device transmits information obtained from the user to a server. This server uses a "generative AI model" to generate an exercise plan tailored to the user's needs. In this process, the optimal plan is constructed based on past data and existing knowledge about the effects of exercise.

[0657] Once an exercise plan is generated, the server provides visual information to the smart glasses based on this information. The visual information displays the exercises the user should perform as videos or images. The visual information is updated in real time and can be controlled by the user using voice recognition.

[0658] Furthermore, the progress of the exercise is recorded using the smart glasses' built-in camera and motion sensors. This allows for accurate tracking of the user's exercise performance. The server receives this progress information and calculates the user's degree of goal achievement. Based on the degree of goal achievement, the server determines the reward for the user and notifies them through the smart glasses, providing the user with incentives such as coupons or points.

[0659] For example, if a user sets a goal of "doing abdominal exercises three times a week for one month," the system provides optimal exercise videos and visually tracks weekly progress. Once the goal is achieved, points are awarded that can be used to purchase fitness equipment.

[0660] An example of a prompt to provide to a generating AI model is: "Generate a 3-day-a-week fitness plan combining 5km runs and strength training. The user has intermediate fitness experience and would like stretching included."

[0661] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0662] Step 1:

[0663] Users wear smart glasses and input their personal information and fitness goals into the device using voice or touch input. This information includes their health status, desired achievements, and preferred exercise types. The device receives this input and sends it directly to the server.

[0664] Step 2:

[0665] The server sends prompt messages to the generating AI model based on the individual information received from the terminal. For example, it might use the prompt, "Create an abdominal muscle strengthening plan suitable for an intermediate-level user." The generating AI model uses this prompt to create an exercise plan optimized for the user and returns that information to the server.

[0666] Step 3:

[0667] The server receives the generated exercise plan and selects the corresponding visual information. This visual information includes video content and images that demonstrate the correct form, number of sets, and points to note during the exercise. The server then sends this visual information to the terminal.

[0668] Step 4:

[0669] The device displays visual information transmitted from the server on the smart glasses' screen. The user begins exercising and trains while referring to the visual information. During this time, the smart glasses' motion sensors track the user's movements in real time.

[0670] Step 5:

[0671] The terminal transfers the collected motion data to the server. The server analyzes the received motion data and evaluates the progress. For example, it calculates exactly how much of the set exercise was performed and determines the degree of goal achievement.

[0672] Step 6:

[0673] The server determines rewards for users based on their level of goal achievement. These rewards are provided in the form of coupons, points, etc. The server sends reward information to the device, which then notifies the user of the reward via their smart glasses. This allows the user to receive the reward as feedback.

[0674] 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.

[0675] This invention is an exercise support system that takes the user's emotional state into consideration, aiming to improve the user's motivation and flexibly adjust the exercise plan during the process of achieving their health goals. By incorporating an emotion engine, this system achieves personalization based on the user's emotional characteristics.

[0676] User Interface

[0677] Users can access the application using their device and register their personal information and health goals. They can also periodically input their emotional state or have their emotional state automatically detected using sensors built into their device.

[0678] Exercise plan creation and emotional adjustment

[0679] The server receives the user's individual information and emotional state, and uses a generative AI model to create an optimal exercise plan. The emotion engine adjusts the exercise plan according to the user's emotional changes; for example, if the stress level is high, it might suggest yoga for relaxation.

[0680] Content delivery tailored to emotions

[0681] The device delivers visual content tailored to the user's emotions based on an exercise plan generated by the server. For example, if the user is feeling down, an encouraging video message will be displayed. Furthermore, the selection of visual content based on emotions improves the likelihood of engaging in exercise.

[0682] Progress management and emotionally responsive rewards

[0683] The server analyzes the user's exercise data and emotional state to evaluate progress. Rewards are provided not only based on goal achievement but also on improvements in emotional state. For example, if an improvement in emotional state is observed, additional reward points are provided to support the user's sustained effort.

[0684] Thus, the present invention sensitively captures emotional shifts and dynamically adjusts the exercise plan and reward system accordingly, making it possible to provide an environment in which users can enjoyably and sustainably work towards their health goals.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The user launches the application using their device and enters personal information and health goals. Simultaneously, they record their emotional state, or the device's sensors automatically collect emotional data, and this information is sent to the server.

[0688] Step 2:

[0689] The server analyzes the personal information and emotional data it receives and uses a generative AI model to create an optimal exercise plan. The generated exercise plan includes content that takes into account and adjusts the user's emotional state.

[0690] Step 3:

[0691] The server generates an exercise plan and sends it to the device. The device then provides the user with appropriate visual content tailored to their emotions and displays the details of the plan.

[0692] Step 4:

[0693] The user starts exercising according to the instructions on the device, and records activity data (exercise time, calories burned, etc.) and emotional data. The device sends this data to the server.

[0694] Step 5:

[0695] The server tracks progress using received exercise data and emotional data. It evaluates goal achievement, taking into account changes in emotions.

[0696] Step 6:

[0697] Based on the user's progress and emotional state, the server suggests appropriate rewards and incentives. The rewards are selected based on the user's past activity history and emotional changes, choosing those deemed most effective for them.

[0698] Step 7:

[0699] The user reviews the suggested reward on their device and chooses to accept it. The server sends the reward to the device, making it available to the user. User feedback is used to generate future exercise plans and suggest rewards.

[0700] (Example 2)

[0701] 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".

[0702] This invention aims to solve the challenges of exercise continuity and motivation, which are influenced by the user's emotional state. Conventional exercise support systems often create individual exercise plans based on an individual's physical information, but they do not take into account changes in the user's emotions, which makes it difficult to provide effective support. Furthermore, the lack of appropriate rewards based on emotions necessitates improvements in promoting sustained effort by users.

[0703] 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.

[0704] In this invention, the server includes means for acquiring individual user information, means for detecting the user's emotional state, and means for using a generative AI model that generates an exercise plan based on the user's emotional state and individual information. This enables the provision of an exercise plan tailored to the user's emotional state and continuous exercise support.

[0705] "User's individual information" refers to information unique to an individual, such as name, age, weight, height, and health goals, which the user provides to the exercise support system.

[0706] "Emotional state" refers to information that indicates the mental state a user is experiencing at any given time, and includes emotions such as stress, relaxation, enjoyment, and anxiety.

[0707] A "generative AI model" is an artificial intelligence mechanism that automatically generates an optimized exercise plan based on the user's individual information and emotional state.

[0708] An "exercise plan" is a plan that specifies the particular exercises and schedules that users should perform to achieve their health goals.

[0709] "Visual content" refers to visual information such as videos and images that are provided in accordance with the user's emotional state, and are intended to improve motivation for exercise.

[0710] "Exercise progress" refers to a record showing the extent to which a user has performed the planned exercises, and is used to evaluate the degree of goal achievement.

[0711] "Rewards" refer to incentives and benefits provided to users when they achieve their exercise goals or when their emotional state improves, and are intended to support their continued efforts.

[0712] This invention is a system that takes into account the user's emotional state and provides individually optimized exercise support. The system aims to improve user motivation and flexibly adjust exercise plans during the process of achieving health goals. Its main components include a terminal, a server, and a generative AI model.

[0713] Users can access the application using devices such as smartphones and tablets to input personal information and health goals. The devices have built-in sensors that measure the user's heart rate, skin temperature, and other parameters, enabling automatic detection of their emotional state. Users can also manually input their emotional state.

[0714] The server receives individual user information and emotional state data sent from the device. Based on this information, the server uses a generative AI model and prompts to create an optimal exercise plan. An example of a prompt would be, "Please provide an appropriate exercise plan if the user's emotional state is stressed." This AI model flexibly adjusts the exercise plan according to the user's exercise ability and emotional changes.

[0715] Based on the generated exercise plan, the device provides visual content that responds to the user's emotions. For example, when the user is feeling down, it displays an encouraging video message to help boost their motivation to exercise. Furthermore, the server records the user's exercise progress data and emotional improvement, and evaluates their progress toward achieving their goals. Based on these evaluations, the system provides rewards to the user according to their level of achievement and emotional improvement. In this way, a continuous and effective exercise experience can be achieved for the user.

[0716] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0717] Step 1:

[0718] The user enters personal information and health goals using a terminal. The terminal registers the user's name, age, height, weight, and health goals in a database. This information is used as foundational data when the system creates an exercise plan.

[0719] Step 2:

[0720] The device uses built-in sensors to detect the user's emotional state. The measured data includes heart rate and skin temperature, and based on this data, the device infers the user's emotional state. The inferred emotional state is then sent to the server.

[0721] Step 3:

[0722] The server receives individual user information and emotional state sent from the terminal. Based on the received information, the server generates prompts for the AI ​​model. For example, it might use the prompt, "Generate an exercise plan appropriate for the current emotional state." In response to this prompt, the AI ​​model creates an optimal exercise plan and returns the result to the server.

[0723] Step 4:

[0724] The server generates an exercise plan and sends it to the device. Based on the exercise plan, the device selects visual content from its library that is appropriate for the user's mood. The selected content is provided to the user as videos or images, and the visual stimulation enhances motivation for exercise.

[0725] Step 5:

[0726] The user uses a device to perform exercises and records their progress. The device logs the user's exercise volume and frequency, and sends the data to a server for analysis. Based on this data, the server evaluates the degree of goal achievement and emotional improvement, and calculates a reward.

[0727] Step 6:

[0728] The server sends the calculated reward to the device and notifies the user. The reward helps maintain the user's motivation to exercise consistently. The reward is provided as in-app perks or points, encouraging the user to exercise again.

[0729] (Application Example 2)

[0730] 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".

[0731] Conventional exercise support systems often fail to consider the user's emotional state when creating exercise plans and food recommendations, leading to decreased motivation and delays in achieving health goals. Furthermore, the lack of mechanisms to provide appropriate rewards based on emotional state makes it difficult for users to consistently work towards their health goals.

[0732] 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.

[0733] In this invention, the server includes means for detecting the user's emotional state and collecting information thereof, means for generating an exercise plan and making food suggestions based on the user's individual information and emotional state, and means for providing visual content corresponding to the exercise plan and food suggestions. This makes it possible to suggest exercises and foods that are appropriate to the user's emotional state, thereby improving motivation and enabling the achievement of sustainable health goals.

[0734] "Users" refers to individuals who use this system to receive exercise plans and food recommendations.

[0735] "Personalized information" refers to information specific to a user, such as their name, age, gender, health status, and lifestyle.

[0736] "Emotional state" refers to data that indicates the emotions and psychological conditions a user is experiencing at a specific point in time.

[0737] An "exercise plan" refers to the content and schedule of exercises that users should perform to effectively achieve their health goals.

[0738] "Food suggestions" refer to recommended meals and food choices based on the user's emotional state and individual information.

[0739] "Visual content" refers to media that includes information and entertainment elements provided to users visually.

[0740] "Progress" refers to an indicator that shows the degree to which a user has achieved their exercise plan or health goals.

[0741] "Rewards" refer to incentives and benefits provided in accordance with the user's progress towards achieving their goals and changes in their emotional state.

[0742] A "server" refers to a central computer system that receives information from users and has the function of generating exercise plans and food recommendations.

[0743] This invention relates to a system that provides exercise support and food recommendations based on the user's emotional state. The system consists of a user's terminal, a server, a generative AI model, and an emotion detection sensor.

[0744] The user's device is a mobile device such as a smartphone or tablet, equipped with a built-in or external emotion detection sensor to capture the user's emotional state. This emotional data, along with individual information, is sent to a server. The server uses a generated AI model based on the user's emotional state and individual information to create a personalized exercise plan and food recommendations.

[0745] The generated exercise plan and food suggestions are provided to the user's device as visual content. This visual content is designed to adapt to the user's emotional state and enhance their motivation. For example, if the user is tired, relaxation-enhancing exercises or snacks can be suggested. A specific example is recommending herbal tea to a stressed user, which promotes relaxation.

[0746] The server also analyzes progress and changes in emotional state, and provides rewards accordingly. This allows users to work more actively towards achieving health goals, including improving their emotional state. For example, users may be awarded points when they overcome stress.

[0747] An example of a prompt from a generative AI model is, "What exercises and foods would you suggest to a user who wants to refresh themselves from a stressful state?" Through such prompts, it is possible to generate suggestions optimized for the user's emotional state.

[0748] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0749] Step 1:

[0750] The user's device uses a built-in emotion detection sensor to detect the user's emotional state. The input is raw data from the sensor, which is analyzed by a data processing algorithm to output the user's emotional state (e.g., stress, happiness, fatigue).

[0751] Step 2:

[0752] The user's device transmits detected emotional states and personal information (e.g., height, weight, favorite foods) to the server. This information is used as foundational data for personalized exercise plans and food recommendations.

[0753] Step 3:

[0754] The server inputs the received emotional state and individual information into a generating AI model, and through the AI ​​model's prompts, generates an optimal exercise plan and food recommendations for the user. Specifically, it processes the data and derives a plan using prompts such as, "If the user's current emotional state is stress, what exercise and food are recommended?"

[0755] Step 4:

[0756] The server provides the generated exercise plan and food suggestions as visual content to the user's device. The visual content is processed and displayed to include colors, images, and recommendation video messages that correspond to the user's emotional state.

[0757] Step 5:

[0758] User progress and emotional state changes are continuously monitored through emotion detection sensors and user activity logs, and the data is sent to the server. The input is a comparison of new and past data from the emotion detection sensors. Analysis evaluates the effectiveness of emotional settings and exercise performance, and the results are recorded in the database.

[0759] Step 6:

[0760] The server determines rewards based on collected progress data and notifies the user. For example, if emotional improvement or goal achievement is confirmed, additional points or rewards are provided. This increases user motivation and encourages continued engagement with health goals.

[0761] 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.

[0762] 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.

[0763] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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."

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0782] The following is further disclosed regarding the embodiments described above.

[0783] (Claim 1)

[0784] Means for obtaining individual user information,

[0785] A means for generating an exercise plan based on the individual information of the user,

[0786] Means for providing visual content corresponding to the aforementioned movement plan,

[0787] A means of recording the user's exercise progress and evaluating the degree of goal achievement,

[0788] A means of offering rewards according to the degree of achievement of the aforementioned goals,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the reward offering means proposes based on the user's past activity history.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the visual content providing means streams visual content to the user's terminal.

[0794] "Example 1"

[0795] (Claim 1)

[0796] Means for collecting user characteristic information,

[0797] A means for creating an exercise plan using a generated AI model based on the user's characteristic information,

[0798] Means for supplying visual materials in accordance with the aforementioned movement plan,

[0799] A means of recording the user's exercise history and analyzing the degree of goal achievement,

[0800] Based on the degree of achievement of the aforementioned goals, a means of providing rewards as motivation,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, wherein the reward suggestion means is provided taking into consideration the user's past behavioral history.

[0804] (Claim 3)

[0805] The system according to claim 1, wherein the visual material supply means streams visual material to the user's device.

[0806] "Application Example 1"

[0807] (Claim 1)

[0808] Means for obtaining individual user information,

[0809] A means for generating an exercise plan using a generative model based on the individual user information,

[0810] Means for providing visual information corresponding to the aforementioned motion plan to a display device,

[0811] A means of recording the user's progress and evaluating the degree of goal achievement,

[0812] A means for presenting rewards using a device that monitors the user's actions according to the degree of achievement of the aforementioned goal,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the reward offering means proposes based on the user's past activity history and can be notified via a display device.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the visual information providing means provides visual information to the user's display device in real time and is operable using voice recognition.

[0818] "Example 2 of combining an emotion engine"

[0819] (Claim 1)

[0820] Means for obtaining individual user information,

[0821] A means for detecting the emotional state of the user,

[0822] A means of using a generative AI model that generates an exercise plan based on the user's emotional state and individual information,

[0823] A means for providing visual content adjusted based on the user's emotions in accordance with the aforementioned exercise plan,

[0824] A means for recording the user's exercise progress and emotional state, and for evaluating the degree of goal achievement and emotional improvement,

[0825] A means of offering rewards according to the degree of goal achievement and emotional improvement,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the reward offering means proposes based on the user's past activity history and changes in emotional state.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the visual content providing means streams visual content selected according to the user's mood to the user's terminal.

[0831] "Application example 2 when combining with an emotional engine"

[0832] (Claim 1)

[0833] Means for obtaining individual user information,

[0834] A means for detecting the emotional state of the user and collecting information thereof,

[0835] A means for generating an exercise plan and making food suggestions based on the user's individual information and emotional state,

[0836] Means for providing visual content corresponding to the exercise plan and food suggestions,

[0837] A means of recording the user's exercise progress and changes in emotional state, and evaluating the degree of goal achievement,

[0838] A means of offering rewards in accordance with the degree of goal achievement and improvement in emotions,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, wherein the reward offering means proposes based on the user's past activity history and changes in emotional state.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the visual content providing means streams visual content corresponding to the user's emotional state to the user's terminal and makes food suggestions. [Explanation of Symbols]

[0844] 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. Means for obtaining individual user information, A means for generating an exercise plan based on the individual information of the user, Means for providing visual content corresponding to the aforementioned movement plan, A means of recording the user's exercise progress and evaluating the degree of goal achievement, A means of offering rewards according to the degree of achievement of the aforementioned goals, A system that includes this.

2. The system according to claim 1, wherein the reward offering means proposes based on the user's past activity history.

3. The system according to claim 1, wherein the visual content providing means streams visual content to the user's terminal.

Images