system

Abstract

Provide a system. A means for inputting a user's physical information and goals; means for generating training and meal plans based on the user's physical information and goals; means for providing the generated training and meal plans to the user; means for collecting user training and dietary progress data; A means of visualizing the collected progress data; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Recently, the workload of teachers in the educational field has increased, and the shortage of club activity instructors has become a particularly serious problem. This shortage of instructors means that students do not receive appropriate support and are unable to fully enjoy opportunities to improve their physical strength and skills. There is also a shortage of local instructors, which is one of the reasons why local migration has not progressed. Furthermore, in order to maintain students' motivation, they need a way to visually confirm their own growth and achievements. Appropriate tools to solve these issues are needed. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting a user's physical information and goals, a means for generating a training plan and a meal plan based on the user's physical information and goals, a means for providing the generated training plan and meal plan to the user, a means for collecting the user's training and meal progress data, and a means for visualizing the collected progress data. This system automatically provides appropriate training and meal guidance even when a teacher or instructor is not present, supporting students in improving their physical strength and skills. Visualizing the progress data also makes it easier for students to check their own progress and maintain motivation. Furthermore, the system also includes a means for providing points for self-study and improvement, providing an environment in which students can develop independently. This system thus contributes to reducing the burden on teachers and resolving the shortage of instructors in the region.

[0006] "User's physical information" refers to information about the user's physical attributes, such as the user's name, age, height, weight, and training goals.

[0007] A "goal" is a specific outcome, such as a particular workout or improved health, that a user aims to achieve.

[0008] A "training plan" is a specific plan of exercises or movements that a user should perform based on the user's physical information and goals.

[0009] A "meal plan" is a plan of foods and nutrients a user should consume based on the user's physical information and goals.

[0010] "Progress data" refers to data relating to the performance status and results that a user records as a result of their daily training and diet.

[0011] A "means of collection" is the interface or application's ability to receive data input from a user.

[0012] "Visualization means" is a function that displays collected progress data in the form of graphs, charts, tables, etc., allowing users to visually confirm their own growth and achievements.

[0013] "Self-study" is a learning method in which users identify areas for improvement and self-reflection based on their own training and diet records. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[0036] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[0037] The server then uses the AI ​​model to generate a training plan and a meal plan based on the user's physical information and goals. The generated plan is sent to the device to be provided to the user. The user then performs daily training and eating according to the received training plan and meal plan.

[0038] Users input their daily training results and dietary records into the device, which then sends this progress data to the server, which receives the data and stores it in a database.

[0039] The server then visualizes the collected progress data using graphs and charts, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[0040] Furthermore, the server uses AI to analyze the user's training data and provide suggestions for improvement. This self-study material is sent to the user, who can use it to adjust their future training plans.

[0041] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week and a specific meal plan. These plans are then sent to Tanaka Taro's device. Tanaka Taro trains and eats according to the provided plan and records the results on his device. The recorded data is sent to the server, which uses this data to visualize his progress and provide it to Tanaka Taro. The server then analyzes his training data and suggests areas for improvement, thereby supporting Tanaka Taro's self-learning.

[0042] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills.

[0043] The processing flow will be explained below.

[0044] Step 1:

[0045] The user enters their profile information (name, age, height, weight, training goals) into the device.

[0046] Step 2:

[0047] The terminal transmits the input user information to the server.

[0048] Step 3:

[0049] The server receives the transmitted user data and stores it internally.

[0050] Step 4:

[0051] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[0052] Step 5:

[0053] The server transmits the generated training plan to the terminal.

[0054] Step 6:

[0055] The device displays the training plan to the user.

[0056] Step 7:

[0057] The user inputs daily training results and meal records into the terminal.

[0058] Step 8:

[0059] The terminal transmits the user's progress data to the server.

[0060] Step 9:

[0061] The server receives the submitted progress data and stores it in a database.

[0062] Step 10:

[0063] The server visualizes the collected progress data.

[0064] Step 11:

[0065] The server transmits the visualized progress data to the terminal.

[0066] Step 12:

[0067] The terminal displays the visualized progress data to the user.

[0068] Step 13:

[0069] The server uses AI to analyze the user's training data and generate points for reflection and improvement.

[0070] Step 14:

[0071] The server sends the generated points for reflection and improvement to the terminal.

[0072] Step 15:

[0073] The device displays points for improvement and reflection to the user.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] Conventional training and diet management systems have had the problem of being difficult to generate plans based on individual users' physical information and goals, and lacking means to sustain user motivation. Furthermore, there are no effective means to visualize collected progress data and improve user motivation, making it difficult for users to continue self-management.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes a means for inputting the user's physical information and goals, a means for generating a training plan and a meal plan based on the user's physical information and goals, and a means for providing points for improvement and reflection using a generative AI model, thereby enabling the generation and provision of individually tailored training plans and meal plans, visualization of progress data, and increased user motivation.

[0079] "User's physical information" refers to data relating to an individual's physiological or anatomical characteristics, such as the user's height, weight, age, sex, and body fat percentage.

[0080] A "training plan" is a plan that indicates the specific schedule and content of exercise and fitness activities created based on the user's physical information and goals.

[0081] A "meal plan" is a plan that indicates the specific contents and schedule of a nutritionally balanced meal that is created based on the user's physical information and goals.

[0082] "User Goal" means a specific health, fitness or strength-related goal that a user wishes to achieve.

[0083] "Progress data" refers to data recorded by the user regarding the results of their daily training and diet, and specifically includes the time and distance exercised, the contents of their meals, etc.

[0084] "Visualization" means presenting collected data in a visual format such as charts and graphs so that it is easy for users to understand.

[0085] "Generative AI model" means a model that uses artificial intelligence algorithms to generate optimal training and meal plans based on a user's data and goals.

[0086] "Points for reflection and improvement" is information that analyzes the user's training and diet progress data and points out points that should be improved or taken into consideration for the next activity.

[0087] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[0088] First, the user enters their profile information (name, age, height, weight, training goals) into the device. The device then sends this information to the server. The server receives the user data and stores it internally. The specific storage operation is performed using a database system such as MySQL (registered trademark) or PostgreSQL.

[0089] Next, the server uses a generative AI model (e.g., TENSORFLOW® or PyTorch) to generate a training plan and a meal plan based on the user's physical information and goals. For example, it generates prompt sentences like the following and inputs them to the AI ​​model:

[0090] Taro Tanaka's profile information is as follows:

[0091] Age: 30

[0092] Height: 175cm

[0093] Weight: 70kg

[0094] Goal: Increased endurance

[0095] Based on this information, generate a training plan that includes jogging three times a week and exercising twice a week, as well as a balanced, protein-rich meal plan.

[0096] The generated plan is sent to the device to be provided to the user. The device visually displays it and provides the user with training and dietary guidelines. For example, a specific plan including jogging (30 minutes) three times a week and exercising (15 minutes) twice a week is provided, along with recommendations such as eating a balanced diet and high-protein foods.

[0097] Users enter their daily training results and food records into the device, which then sends this progress data to the server. The server receives the progress data and stores it in a database. The server also visualizes the collected progress data. Tools such as Matplotlib and D3.js are used for visualization. For example, graphs showing progress over time and distance of jogging or charts showing changes in weight can be generated.

[0098] The server then uses AI to analyze the training data and provide suggestions for improvement. This is done using AI tools such as scikit-learn. The server then sends the generated feedback to the device, allowing the user to use it to adjust their future training plan. For example, the server might provide advice such as, "It would be more effective if you increased your jogging pace a little."

[0099] Through these procedures, users can continuously train and manage their diet, and effectively advance self-study. In addition, the server uses collected data to provide a means for users to improve their motivation, enabling them to achieve their goals while maintaining a high level of motivation.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1: Entering User Profile Information

[0102] Users enter their name, age, height, weight, and training goals into the device.

[0103] The information entered is structured as follows:

[0104] Name: "Yamada Taro"

[0105] Age: 35

[0106] Height: 180cm

[0107] Weight: 75

[0108] Training goal: "Improve muscle strength"

[0109] The device converts this information into JSON format and sends it to the server.

[0110] Step 2: Receiving and storing user data

[0111] The server receives the user data in JSON format sent from the device.

[0112] Validate whether the received data is in the correct format.

[0113] Data that has been successfully validated is saved in a database such as MySQL or PostgreSQL.

[0114] Example SQL query:

[0115] sql

[0116] INSERT INTO users (name, age, height, weight, goal)

[0117] VALUES ('Yamada Taro', 35, 180, 75, 'Improve Muscle Strength')

[0118] Step 3: Generate a training and meal plan

[0119] The server generates prompt sentences for a generative AI model (e.g., TensorFlow) based on the stored user data.

[0120] Specific prompt examples:

[0121] Taro Yamada's profile information is as follows:

[0122] Age: 35

[0123] Height: 180cm

[0124] Weight: 75kg

[0125] Goal: Strength

[0126] Use this information to generate a training plan that includes weight training three times a week and cardio twice a week, along with a balanced, protein-rich meal plan.

[0127] The generative AI model generates training and meal plans based on the prompt text and returns them to the server.

[0128] The server parses the generated plan and formats it into the following format:

[0129] Training plan: "Weight training (30 minutes) three times a week, cardio (20 minutes) two times a week."

[0130] Meal plan: "Eat a balanced diet, high in protein foods"

[0131] Step 4: Providing training and meal plans

[0132] The server transmits the generated plan to the terminal.

[0133] The terminal visually displays the received plan to the user.

[0134] Specifically, the app screen will display the following:

[0135] Training plan: 30 minutes of weight training 3 times a week, 20 minutes of cardio 2 times a week

[0136] Meal plan: Eat a balanced diet, high in protein

[0137] Step 5: Enter and submit progress data

[0138] Users input their daily training results and dietary information into the device.

[0139] For example, "Do 30 minutes of weight training" or "Breakfast: 3 eggs, lunch: salad, dinner: chicken steak."

[0140] The device sends the recorded information to the server. The format of the data sent is:

[0141] json

[0142] {

[0143] "user_id": 1,

[0144] "training_result": "30 minutes of weight training",

[0145] "diet_record": "Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak"

[0146] }

[0147] Step 6: Receiving and saving progress data

[0148] The server receives the progress data sent from the terminal.

[0149] Performs necessary validation and saves successful data to the database.

[0150] Example SQL query:

[0151] sql

[0152] INSERT INTO progress (user_id, training_result, diet_record)

[0153] VALUES (1, '30 minutes of weight training', 'Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak')

[0154] Step 7: Visualize progress data

[0155] The server visualizes the collected progress data, for example by generating graphs using Matplotlib or D3.js.

[0156] A graph showing jogging distance and time progress and a chart showing weight changes are generated.

[0157] The server saves the generated graphs and charts in data format and sends them to the terminal.

[0158] Step 8: Analyze the data and provide feedback

[0159] The server uses AI to analyze the training data, for example, using scikit-learn to analyze the training data and extract areas for improvement.

[0160] The server will provide feedback on areas for improvement and reflection based on the analysis results.

[0161] Examples of feedback provided include:

[0162] json

[0163] {

[0164] "feedback": "Increasing the number of weight training sessions to four times a week will be even more effective."

[0165] }

[0166] The device will provide visual feedback to the user, specifically by displaying advice and improvements in text format on the app screen.

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] Machines and robots operating in factories require continuous maintenance and efficient training. However, there are currently no systems that can centrally manage these tasks and automatically provide optimal plans. There is also a lack of systems that can visualize progress based on each machine's work history and have self-learning capabilities. The purpose of this invention is to solve these issues and support the efficient operation and maintenance of machines in factories.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a meal plan based on the user's physical information and goals, means for providing the generated training plan and meal plan to the user, means for collecting the user's training and meal progress data, means for visualizing the collected progress data, means for collecting operation data and work history of machines in the factory, means for generating a training plan and a maintenance plan for the machine based on the collected machine data, and means for providing the generated training plan and maintenance plan to the machine. This makes it possible to achieve efficient training and maintenance while maintaining optimal operating conditions for the factory machines.

[0172] "User" refers to a person who uses this system.

[0173] "Physical information" refers to basic data about a user's body, such as weight, height, age, and gender.

[0174] "Goal" refers to the purpose or goal that a user wants to achieve, such as a specific expectation such as weight loss or muscle gain.

[0175] "Training Plan" refers to an exercise or fitness plan created based on a user's physical information and goals.

[0176] "Meal Plan" refers to a nutritionally balanced meal plan created based on a user's physical information and goals.

[0177] "Progress data" refers to data recorded by a user regarding the results of their daily training and diet.

[0178] "Visualization" refers to visually displaying collected progress data using graphs, charts, etc.

[0179] "Server" refers to a central computer for receiving, processing, and storing data from users.

[0180] "Machinery" refers to robots and automated equipment operating in factories.

[0181] "Operational data" refers to data relating to the movement and operating status of a machine.

[0182] "Work history" refers to a record of the actions and work that a machine has performed in the past.

[0183] A "maintenance plan" refers to a maintenance and inspection plan created based on a machine's operating data and work history.

[0184] This invention is a system that uses a server as a central location to generate and provide optimal training and meal plans based on a user's physical information and goals. Furthermore, this system provides similar training and maintenance plans for machinery in factories, supporting efficient operation of the machinery.

[0185] Hardware and software used

[0186] Hardware

[0187] Sensors (IMU, camera, etc.)

[0188] Robots and automation equipment used in factories

[0189] Smartphones / Tablets

[0190] software

[0191] Python (a programming language for data collection and analysis)

[0192] TensorFlow (Building and training AI models)

[0193] Database (MySQL or PostgreSQL)

[0194] Flask (backend framework)

[0195] Frontend (React or Vue.js)

[0196] Program processing description

[0197] Data Entry

[0198] Users enter their physical information (age, height, weight, gender, goals, etc.) into a smartphone or tablet. This data is sent from the device to a server in real time. In addition, the machines in the factory also send their operating data and work history to the server via sensors.

[0199] Data storage

[0200] The server stores the received data in a database, such as physical information and machine operation data.

[0201] Plan generation using AI models

[0202] The server uses TensorFlow to generate training and meal plans for the user, as well as training and maintenance plans for factory machinery, based on the received data. Prompt statements can be used to generate the plans.

[0203] Serving the generated plan

[0204] The generated training plans, meal plans, and maintenance plans are provided to the user or factory system in real time, allowing the user or factory to act in accordance with the plans provided.

[0205] Recording and collecting progress

[0206] Users enter their daily training results and dietary records into their smartphones or tablets. Similarly, factory machines periodically send their operation results to a server, and this data is then stored in a database.

[0207] Progress data visualization

[0208] The server visualizes progress based on the collected data, for example by providing it to users and factories using graphs and charts, allowing users and managers to visually confirm growth and areas for improvement.

[0209] Feedback and self-learning

[0210] The server uses AI to analyze the collected data and provide users and machines with suggestions for improvement. Based on this feedback, users and factory systems update their future plans and strive for continuous improvement.

[0211] Specific examples

[0212] For example, if a robot operating in a factory is becoming less efficient at a particular operation, the system will collect data to identify the cause and automatically generate an appropriate training plan.It will also detect areas that require maintenance and notify the operator immediately, minimizing machine downtime.

[0213] Prompt Sentence Examples

[0214] By using the following prompt sentences, the AI ​​model will generate an appropriate plan.

[0215] "Joint 2 of the robot may be experiencing excessive wear. Please generate a regular maintenance and training plan."

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The user inputs physical information (age, height, weight, gender, goals, etc.) and machine operation information (operating time, failure history, etc.) into the terminal. The terminal sends this input data to the server. The input data includes raw data.

[0219] Step 2:

[0220] The server stores the received user's physical information and machine operation information in a database. Specifically, it stores the information in the database using Python and a database management tool (MySQL or PostgreSQL). The output here is structured data stored in the database.

[0221] Step 3:

[0222] The server uses TensorFlow to generate optimal training and maintenance plans for users and machines based on the stored data. The AI ​​model uses the structured data in the database as input and obtains the generated plans as output.

[0223] Step 4:

[0224] The generated training and maintenance plans are sent from the server to the device and provided to users and machine managers in real time. Users can view the plans on their smartphones or tablets. The plans are also provided to the factory system in the same way.

[0225] Step 5:

[0226] Users input their daily training results and dietary records into the terminal, and factory machines periodically send their operation results to the server. The input data is the training results and work history, and the output data is updated progress data.

[0227] Step 6:

[0228] The server saves the collected progress data back to the database, converting it from raw data into an organized data format, and keeping the database updated.

[0229] Step 7:

[0230] The server uses visualization tools (such as matplotlib or Plotly) to create graphs and charts based on the collected progress data and provides them to users and machine administrators. Specifically, the progress data is used as input data, and visualized graphs and charts are generated as output data.

[0231] Step 8:

[0232] The server uses AI to analyze progress data and provide feedback to users and machines on areas for improvement and reflection. It also generates prompts and suggests areas for improvement. Based on this feedback, the user or factory system obtains input data for updating future plans. The output data is suggestions for improvements and new plans.

[0233] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0234] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, adaptively adjusting the plan and supporting motivation improvement based on the user's emotions.

[0235] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[0236] The server then uses the AI ​​model to generate training and meal plans based on the user's physical information and goals. Furthermore, an emotion engine analyzes the user's emotion data and adaptively adjusts the generated plans. The generated plans are then sent to the device for provision to the user. The user then performs daily training and eating according to the received training and meal plans.

[0237] The user enters their daily training results and dietary records into the device. The device then sends this progress data to the server. In addition, the user's emotions are periodically entered or measured, and this data is also sent to the server. The server receives this data and stores it in a database.

[0238] The server then analyzes the collected progress data and emotion data to generate visualized progress data. Graphs and charts are used for visualization, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[0239] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing points for reflection and improvement. This self-study material is sent to the user. Based on the emotional data, the server also provides appropriate messages and support to improve the user's motivation.

[0240] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week, as well as a specific meal plan. The emotion engine also analyzes Tanaka Taro's emotional data and adaptively adjusts the plan. The generated plan is sent to Tanaka Taro's device, and he trains and eats accordingly. Tanaka Taro records the results on his device, and also sends progress data and emotional data to the server. The server analyzes this data, visualizes his progress, and provides it to the device. Based on the training data and emotional data, the server also provides points for reflection and improvement, and even sends motivational messages.

[0241] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills. Furthermore, the emotion engine realizes support that takes into consideration the user's emotions, contributing to improving user motivation.

[0242] The processing flow will be explained below.

[0243] Step 1:

[0244] The user enters their profile information (name, age, height, weight, training goals) into the device.

[0245] Step 2:

[0246] The terminal transmits the input user information to the server.

[0247] Step 3:

[0248] The server receives the transmitted user data and stores it internally.

[0249] Step 4:

[0250] The user inputs their mood and emotions for the day into the device, or emotion data is collected using an emotion sensor.

[0251] Step 5:

[0252] The terminal transmits the emotion data to the server.

[0253] Step 6:

[0254] The server receives the emotion data and sends it to the emotion engine for analysis.

[0255] Step 7:

[0256] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[0257] Step 8:

[0258] A server receives the analysis results from the emotion engine and adaptively adjusts the training and meal plans.

[0259] Step 9:

[0260] The server transmits the generated training plan and meal plan to the terminal.

[0261] Step 10:

[0262] The terminal displays the training plan and meal plan to the user.

[0263] Step 11:

[0264] The user inputs daily training results and meal records into the terminal.

[0265] Step 12:

[0266] The terminal transmits the user's progress data to the server.

[0267] Step 13:

[0268] The server receives the submitted progress data and stores it in a database.

[0269] Step 14:

[0270] The server analyzes the collected progress data and emotion data and generates visualized progress data.

[0271] Step 15:

[0272] The server transmits the visualized progress data to the terminal.

[0273] Step 16:

[0274] The terminal displays the visualized progress data to the user.

[0275] Step 17:

[0276] The server uses AI to analyze the user's training data and emotional data, generating points for reflection and improvement.

[0277] Step 18:

[0278] The server sends the generated points for reflection and improvement to the terminal.

[0279] Step 19:

[0280] The device displays points for improvement and reflection to the user.

[0281] Step 20:

[0282] The server generates appropriate messages and support to improve the user's motivation based on the emotion data.

[0283] Step 21:

[0284] The server sends the generated message and support to the terminal.

[0285] Step 22:

[0286] The device displays motivational messages and support to the user.

[0287] In this way, this system, which combines an emotion engine, not only provides training and meal plans based on the user's physical information and goals, but also recognizes the user's emotions, adaptively adjusts the plans, and supports increased motivation.

[0288] Example 2

[0289] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] Conventional methods for providing training and meal plans often set uniform plans based on the user's profile information and goals, making it difficult to provide appropriate plans based on individual physical information and emotional data. Furthermore, there was a lack of feedback to properly understand the user's progress and maintain motivation. Another problem was the lack of a mechanism for adaptively adjusting the plan based on the user's emotions.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0292] In this invention, the server includes means for inputting a user's profile information and goals, means for transmitting the input user information to the server, means for utilizing a generative AI model that generates training plans and meal plans based on the user's physical information and goals, means for adaptively adjusting the training plans and meal plans generated by the generative AI model based on emotional data, means for transmitting the generated training plans and meal plans to a user terminal, means for recording the user's daily training results and meal contents and transmitting this to the server, and means for analyzing the collected progress data and emotional data and visualizing it using graphs, charts, etc. This makes it possible to provide optimal training plans and meal plans based on individual physical information and emotional data, allowing the user's progress to be properly understood and appropriate support to maintain motivation.

[0293] "Profile Information" is basic information about an individual, such as a user's name, age, height, weight, and training goals.

[0294] A "generative AI model" is a program or system that uses artificial intelligence techniques to generate a plan based on a user's physical information and goals.

[0295] "Emotion data" is information relating to the user's emotional state, such as data indicating emotions such as "satisfied" or "tired."

[0296] A "training plan" is an exercise or training plan that is individually created based on the user's physical information and goals.

[0297] A "meal plan" is a personalized meal plan based on a user's physical information and goals.

[0298] A "terminal" is a device through which a user inputs information, and includes smartphones, tablets, personal computers, etc.

[0299] A "server" is a computer system that receives, processes, stores, and analyzes information sent by users.

[0300] "Visualization" means displaying progress data and emotional data in a visually easy-to-understand format, such as graphs or charts.

[0301] "Motivational messages" are messages of encouragement and support to increase the user's motivation and enthusiasm.

[0302] "Progress data" is data that indicates the progress of the plan, such as the user's daily training results and dietary details.

[0303] A "user" is an individual who utilizes the system and needs workout and meal plans.

[0304] This invention relates to a system that generates training and meal plans optimized for individual users, and adaptively adjusts the plans based on the user's progress and emotions. Furthermore, the system analyzes the user's progress and emotions and provides messages to improve motivation.

[0305] First, the user enters their profile information (name, age, height, weight, and training goals) into the device. The device then sends this information to the server. The server stores the received user information in a database and generates training and meal plans using a generative AI model (e.g., TensorFlow or PyTorch). The generated plans are adaptively adjusted based on the user's emotional data by an emotion engine (e.g., Watson® Emotion Analysis).

[0306] The generated plan is sent from the server to the user's device. The user then carries out their daily training and diet according to the plan received through the device. The training results and meal contents are recorded on the device and sent to the server. At this time, the user's emotional data is also sent. The server stores this data in a database and uses it for analysis.

[0307] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is sent to the user's device, allowing them to check their own progress and achievements.

[0308] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing suggestions for reflection and improvement. Based on the emotional data, the server also generates appropriate messages to motivate the user and sends them to the device, providing support for the user to effectively achieve their goals.

[0309] As a specific example, a user aiming to improve their endurance enters their physical information and goals into their device and sends them to the system. Based on the received information, the server generates a training plan including jogging and exercise three times a week and a meal plan that takes into account a specific nutritional balance. The emotion engine analyzes the user's emotion data and adaptively adjusts the plan. This generated plan is sent to the user's device, and the user trains and eats accordingly. The server continuously receives the user's progress and emotion data, and based on that, generates visualized data and motivational messages and sends them to the device.

[0310] Example prompt for a generative AI model:

[0311] "Enter the user's profile information, such as name, age, height, weight, and training goals. Generate personalized training and meal plans based on this information. Additionally, analyze the user's emotional data and adaptively adjust the plans."

[0312] The system not only uses individual user data and emotional information to provide optimal training and meal plans, but also adaptively adjusts based on progress and emotions, helping users achieve their goals efficiently and effectively.

[0313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0314] Step 1:

[0315] The user enters their profile information (name, age, height, weight, training goals) into the device. This information is collected by the device and used in the next step. Specifically, the user enters information into the application's input form and clicks the "Submit" button.

[0316] Input: User profile information

[0317] Output: User information stored on the device

[0318] Step 2:

[0319] The terminal sends the entered user profile information to the server using an HTTP POST request.

[0320] Input: User information stored on the device

[0321] Output: User information sent to the server

[0322] Step 3:

[0323] The server stores the received user information in a database. It then uses a generative AI model (e.g., TensorFlow or PyTorch) to generate training and meal plans based on the user's physical information and goals. Specifically, the server provides the stored data to the AI ​​model, which then generates the plans.

[0324] Input: User information sent to the server

[0325] Output: Generated training and meal plans

[0326] Step 4:

[0327] The generated plan is adaptively adjusted based on the user's emotional data by an emotion engine (e.g., "Watson Emotion Analysis"). The emotional data is analyzed and each part of the plan is adjusted.

[0328] Input: Generated training and meal plans

[0329] Output: Adaptively adjusted training and meal plans

[0330] Step 5:

[0331] The server sends the adaptively adjusted plan to the user's terminal using an HTTP response.

[0332] Input: Adaptively adjusted training and meal plans

[0333] Output: Training and meal plans sent to your device

[0334] Step 6:

[0335] The user performs daily training and meals according to the plan received on the device. The training results and meal contents are recorded on the device and sent to the server. Specifically, the user enters the training results and meal contents into the application and clicks the "Save" button.

[0336] Input: User's training results and dietary information

[0337] Output: Progress and emotion data recorded on the device

[0338] Step 7:

[0339] The device sends the recorded progress data and emotion data to the server using an HTTP POST request.

[0340] Input: Progress data and emotion data recorded on the device

[0341] Output: Progress and emotion data sent to the server

[0342] Step 8:

[0343] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is then sent to the user's device.

[0344] Input: Progress and emotion data sent to the server

[0345] Output: Visualized progress data

[0346] Step 9:

[0347] The server uses AI to analyze the user's training data and emotional data, providing suggestions for improvement and suggestions for reflection. It also generates appropriate messages based on the emotional data to motivate the user and sends them to the device.

[0348] Input: Progress data and emotion data stored on the server

[0349] Output: Reflection, improvement, and motivational messages sent to the device

[0350] (Application example 2)

[0351] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0352] Conventional fitness and nutrition plan providing systems are limited to generating plans based on a user's physical information and goals, and lack the ability to adapt to changes in the user's emotions and motivation. As a result, if a user is unable to maintain their motivation to continue the plan, the effectiveness of the plan decreases. To address these issues, the present invention aims to provide a system that adapts plans and improves motivation based on progress data and emotional data.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0354] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a nutrition plan based on the user's physical information and goals, means for providing the generated training plan and nutrition plan to the user, means for collecting the user's training and diet progress data, means for visualizing the collected progress data, means for collecting and analyzing emotional data and adaptively adjusting the training plan and nutrition plan based on the collected emotional data, and means for providing messages and support to increase the user's motivation based on the emotional data. This makes it possible to adapt the plan based on both the progress data and the emotional data, thereby maintaining and increasing the user's motivation.

[0355] "User's physical information" refers to data regarding the user's biological attributes, including bone structure, muscle mass, body fat percentage, height, weight, age, and gender.

[0356] A "goal" refers to a specific outcome, such as a level of fitness, weight loss, strength gain, or increased endurance, that a user wishes to achieve.

[0357] A "training plan" is a plan that includes specific content and schedule of an exercise program, designed based on the user's physical information and goals.

[0358] A "nutritional plan" is a plan for the types, amounts, and timing of meals designed based on a user's physical information and goals.

[0359] "Progress data" refers to record and evaluation data regarding the results of the training and dietary habits of the user.

[0360] "Visualization" refers to displaying progress data in a visual format such as a graph or chart so that the user can easily understand it.

[0361] "Emotion data" refers to data relating to the user's psychological state and emotions, including stress level, motivation, satisfaction level, and the like.

[0362] "Adaptively adjusting" means dynamically changing and optimizing the training and nutrition plans based on the user's progress and emotional data.

[0363] "Motivation" refers to support and encouragement to motivate a user to continue with their training and diet plan.

[0364] The present invention is a system that generates and provides personalized training and nutrition plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, enabling adaptive plan adjustments and motivational support based on emotions. This will be described in more detail below.

[0365] Entering user's physical information and goals

[0366] First, the user uses a smartphone application to input their profile information (name, age, height, weight, training goals), which is then sent to the server via the application and stored.

[0367] Generate training and nutrition plans

[0368] The server uses a generative AI model to generate training and nutrition plans based on the user's physical information and goals. This generative AI model uses Python and TensorFlow, and implements advanced machine learning algorithms. Specific examples of prompts include:

[0369] Generate a training and meal plan including jogging 3 times a week and specific exercises based on the following user profile:

[0370] Name: Username

[0371] Age: 30

[0372] Height: 170cm

[0373] Weight: 70kg

[0374] Training goal: Improve endurance

[0375] Progress data collection and visualization

[0376] Users enter their daily training results and food records into a smartphone application, and the data is sent to a server in real time. The collected data is stored in a database and used for analysis.

[0377] The server collects progress data and visualizes it in graphs and charts (using Matplotlib), allowing users to view their progress on their smartphones.

[0378] Emotional Data Analysis and Adaptive Adjustment

[0379] The server uses an emotion engine to analyze the user's emotional data. The emotional data is collected from the user's self-reporting or wearable device. A specialized algorithm is used to analyze the emotional data and accurately analyze the user's psychological state. For example, if the user is feeling stressed or unmotivated, the system will adaptively adjust their training and nutrition plans based on that data.

[0380] Motivation support

[0381] Based on the collected and analyzed emotional data, the server generates support messages to improve the user's motivation, including encouraging messages and advice on how to achieve the goal, which helps the user to maintain their motivation to continue with the plan.

[0382] With this system configuration, users can obtain optimal training and nutrition plans based on their physical information and goals, and visualize and check their progress. Furthermore, adaptive plan adjustments based on emotional data and support for increased motivation improve the user's fitness experience, enabling them to achieve sustainable results.

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] The user launches the smartphone application and enters their profile information (name, age, height, weight, and training goals).

[0386] Input: Name, Age, Height, Weight, Training Goal

[0387] Output: User data entered

[0388] Operation: When a user enters information on the input screen and presses the send button, the device sends this data to the server.

[0389] Step 2:

[0390] The server stores the received user data.

[0391] Input: User data (name, age, height, weight, training goal)

[0392] Output: User data stored in the database

[0393] Operation: The server saves the received data in the database and confirms that the data has been saved.

[0394] Step 3:

[0395] The server generates training and nutrition plans using generative AI models based on the stored data.

[0396] Input: User data in the database

[0397] Output: Generated training and nutrition plans

[0398] How it works: The server uses Python and TensorFlow to convert user data into prompts, which are then fed into a generative AI model. The generated plans are then stored in a database.

[0399] Step 4:

[0400] The server transmits the generated training plan and nutrition plan to the user's terminal.

[0401] Input: Generated training and nutrition plans

[0402] Output: The plan displayed on the user's terminal

[0403] How it works: The server sends planning data to the user's smartphone application, which displays it on the device.

[0404] Step 5:

[0405] The user enters daily training results and food records into a smartphone application.

[0406] Input: Training results, food records

[0407] Output: The progress data entered

[0408] Operation: When the user enters data into the specified input form and presses the send button, the terminal sends the data to the server.

[0409] Step 6:

[0410] The server stores the progress data in a database and analyzes it.

[0411] Input: Daily progress data

[0412] Output: Parsed progress data

[0413] How it works: The server stores progress data and performs data analysis using Python.

[0414] Step 7:

[0415] The server visualizes the progress data in graphs and charts and sends it to the user's device.

[0416] Input: Parsed progress data

[0417] Output: Visualized progress data (graphs, charts)

[0418] How it works: The server uses Matplotlib to turn progress data into graphs and charts and sends them to the user's device.

[0419] Step 8:

[0420] The server collects and analyzes the emotional data and adaptively adjusts training and nutrition plans as needed.

[0421] Input: Emotion data

[0422] Output: Adaptively adjusted training and nutrition plans

[0423] Operation: The server analyzes the emotional data collected from the user using the emotion engine, readjusts the plan based on the user's psychological state, saves it in a database, and sends it to the device.

[0424] Step 9:

[0425] The server generates messages and support based on the user's emotions and sends them to the user's device.

[0426] Input: Emotion data

[0427] Output: Motivational messages and support

[0428] Operation: Based on the emotional data, the server generates appropriate messages and support content and sends them to the user's device.

[0429] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0430] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0431] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0432] [Second embodiment]

[0433] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0434] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0435] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0436] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0437] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0438] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0439] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0440] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0441] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0442] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0443] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0444] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0445] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[0446] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[0447] The server then uses the AI ​​model to generate a training plan and a meal plan based on the user's physical information and goals. The generated plan is sent to the device to be provided to the user. The user then performs daily training and eating according to the received training plan and meal plan.

[0448] Users input their daily training results and dietary records into the device, which then sends this progress data to the server, which receives the data and stores it in a database.

[0449] The server then visualizes the collected progress data using graphs and charts, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[0450] Furthermore, the server uses AI to analyze the user's training data and provide suggestions for improvement. This self-study material is sent to the user, who can use it to adjust their future training plans.

[0451] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week and a specific meal plan. These plans are then sent to Tanaka Taro's device. Tanaka Taro trains and eats according to the provided plan and records the results on his device. The recorded data is sent to the server, which uses this data to visualize his progress and provide it to Tanaka Taro. The server then analyzes his training data and suggests areas for improvement, thereby supporting Tanaka Taro's self-learning.

[0452] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills.

[0453] The processing flow will be explained below.

[0454] Step 1:

[0455] The user enters their profile information (name, age, height, weight, training goals) into the device.

[0456] Step 2:

[0457] The terminal transmits the input user information to the server.

[0458] Step 3:

[0459] The server receives the transmitted user data and stores it internally.

[0460] Step 4:

[0461] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[0462] Step 5:

[0463] The server transmits the generated training plan to the terminal.

[0464] Step 6:

[0465] The device displays the training plan to the user.

[0466] Step 7:

[0467] The user inputs daily training results and meal records into the terminal.

[0468] Step 8:

[0469] The terminal transmits the user's progress data to the server.

[0470] Step 9:

[0471] The server receives the submitted progress data and stores it in a database.

[0472] Step 10:

[0473] The server visualizes the collected progress data.

[0474] Step 11:

[0475] The server transmits the visualized progress data to the terminal.

[0476] Step 12:

[0477] The terminal displays the visualized progress data to the user.

[0478] Step 13:

[0479] The server uses AI to analyze the user's training data and generate points for reflection and improvement.

[0480] Step 14:

[0481] The server sends the generated points for reflection and improvement to the terminal.

[0482] Step 15:

[0483] The device displays points for improvement and reflection to the user.

[0484] Example 1

[0485] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0486] Conventional training and diet management systems have had the problem of being difficult to generate plans based on individual users' physical information and goals, and lacking means to sustain user motivation. Furthermore, there are no effective means to visualize collected progress data and improve user motivation, making it difficult for users to continue self-management.

[0487] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0488] In this invention, the server includes a means for inputting the user's physical information and goals, a means for generating a training plan and a meal plan based on the user's physical information and goals, and a means for providing points for improvement and reflection using a generative AI model, thereby enabling the generation and provision of individually tailored training plans and meal plans, visualization of progress data, and increased user motivation.

[0489] "User's physical information" refers to data relating to an individual's physiological or anatomical characteristics, such as the user's height, weight, age, sex, and body fat percentage.

[0490] A "training plan" is a plan that indicates the specific schedule and content of exercise and fitness activities created based on the user's physical information and goals.

[0491] A "meal plan" is a plan that indicates the specific contents and schedule of a nutritionally balanced meal that is created based on the user's physical information and goals.

[0492] "User Goal" means a specific health, fitness or strength-related goal that a user wishes to achieve.

[0493] "Progress data" refers to data recorded by the user regarding the results of their daily training and diet, and specifically includes the time and distance exercised, the contents of their meals, etc.

[0494] "Visualization" means presenting collected data in a visual format such as charts and graphs so that it is easy for users to understand.

[0495] "Generative AI model" means a model that uses artificial intelligence algorithms to generate optimal training and meal plans based on a user's data and goals.

[0496] "Points for reflection and improvement" is information that analyzes the user's training and diet progress data and points out points that should be improved or taken into consideration for the next activity.

[0497] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[0498] First, the user enters their profile information (name, age, height, weight, training goals) into the device. The device then sends this information to the server. The server receives the user data and stores it internally. The specific storage operation is performed using a database system such as MySQL or PostgreSQL.

[0499] The server then uses a generative AI model (e.g., TensorFlow or PyTorch) to generate a training plan and meal plan based on the user's physical information and goals. For example, it generates prompts like the following and inputs them into the AI ​​model:

[0500] Taro Tanaka's profile information is as follows:

[0501] Age: 30

[0502] Height: 175cm

[0503] Weight: 70kg

[0504] Goal: Increased endurance

[0505] Based on this information, generate a training plan that includes jogging three times a week and exercising twice a week, as well as a balanced, protein-rich meal plan.

[0506] The generated plan is sent to the device to be provided to the user. The device visually displays it and provides the user with training and dietary guidelines. For example, a specific plan including jogging (30 minutes) three times a week and exercising (15 minutes) twice a week is provided, along with recommendations such as eating a balanced diet and high-protein foods.

[0507] Users enter their daily training results and food records into the device, which then sends this progress data to the server. The server receives the progress data and stores it in a database. The server also visualizes the collected progress data. Tools such as Matplotlib and D3.js are used for visualization. For example, graphs showing progress over time and distance of jogging or charts showing changes in weight can be generated.

[0508] The server then uses AI to analyze the training data and provide suggestions for improvement. This is done using AI tools such as scikit-learn. The server then sends the generated feedback to the device, allowing the user to use it to adjust their future training plan. For example, the server might provide advice such as, "It would be more effective if you increased your jogging pace a little."

[0509] Through these procedures, users can continuously train and manage their diet, and effectively advance self-study. In addition, the server uses collected data to provide a means for users to improve their motivation, enabling them to achieve their goals while maintaining a high level of motivation.

[0510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0511] Step 1: Entering User Profile Information

[0512] Users enter their name, age, height, weight, and training goals into the device.

[0513] The information entered is structured as follows:

[0514] Name: "Yamada Taro"

[0515] Age: 35

[0516] Height: 180cm

[0517] Weight: 75

[0518] Training goal: "Improve muscle strength"

[0519] The device converts this information into JSON format and sends it to the server.

[0520] Step 2: Receiving and storing user data

[0521] The server receives the user data in JSON format sent from the device.

[0522] Validate whether the received data is in the correct format.

[0523] Data that has been successfully validated is saved in a database such as MySQL or PostgreSQL.

[0524] Example SQL query:

[0525] sql

[0526] INSERT INTO users (name, age, height, weight, goal)

[0527] VALUES ('Yamada Taro', 35, 180, 75, 'Improve Muscle Strength')

[0528] Step 3: Generate a training and meal plan

[0529] The server generates prompt sentences for a generative AI model (e.g., TensorFlow) based on the stored user data.

[0530] Specific prompt examples:

[0531] Taro Yamada's profile information is as follows:

[0532] Age: 35

[0533] Height: 180cm

[0534] Weight: 75kg

[0535] Goal: Strength

[0536] Use this information to generate a training plan that includes weight training three times a week and cardio twice a week, along with a balanced, protein-rich meal plan.

[0537] The generative AI model generates training and meal plans based on the prompt text and returns them to the server.

[0538] The server parses the generated plan and formats it into the following format:

[0539] Training plan: "Weight training (30 minutes) three times a week, cardio (20 minutes) two times a week."

[0540] Meal plan: "Eat a balanced diet, high in protein foods"

[0541] Step 4: Providing training and meal plans

[0542] The server transmits the generated plan to the terminal.

[0543] The terminal visually displays the received plan to the user.

[0544] Specifically, the app screen will display the following:

[0545] Training plan: 30 minutes of weight training 3 times a week, 20 minutes of cardio 2 times a week

[0546] Meal plan: Eat a balanced diet, high in protein

[0547] Step 5: Enter and submit progress data

[0548] Users input their daily training results and dietary information into the device.

[0549] For example, "Do 30 minutes of weight training" or "Breakfast: 3 eggs, lunch: salad, dinner: chicken steak."

[0550] The device sends the recorded information to the server. The format of the data sent is:

[0551] json

[0552] {

[0553] "user_id": 1,

[0554] "training_result": "30 minutes of weight training",

[0555] "diet_record": "Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak"

[0556] }

[0557] Step 6: Receiving and saving progress data

[0558] The server receives the progress data sent from the terminal.

[0559] Performs necessary validation and saves successful data to the database.

[0560] Example SQL query:

[0561] sql

[0562] INSERT INTO progress (user_id, training_result, diet_record)

[0563] VALUES (1, '30 minutes of weight training', 'Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak')

[0564] Step 7: Visualize progress data

[0565] The server visualizes the collected progress data, for example by generating graphs using Matplotlib or D3.js.

[0566] A graph showing jogging distance and time progress and a chart showing weight changes are generated.

[0567] The server saves the generated graphs and charts in data format and sends them to the terminal.

[0568] Step 8: Analyze the data and provide feedback

[0569] The server uses AI to analyze the training data, for example, using scikit-learn to analyze the training data and extract areas for improvement.

[0570] The server will provide feedback on areas for improvement and reflection based on the analysis results.

[0571] Examples of feedback provided include:

[0572] json

[0573] {

[0574] "feedback": "Increasing the number of weight training sessions to four times a week will be even more effective."

[0575] }

[0576] The device will provide visual feedback to the user, specifically by displaying advice and improvements in text format on the app screen.

[0577] (Application example 1)

[0578] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] Machines and robots operating in factories require continuous maintenance and efficient training. However, there are currently no systems that can centrally manage these tasks and automatically provide optimal plans. There is also a lack of systems that can visualize progress based on each machine's work history and have self-learning capabilities. The purpose of this invention is to solve these issues and support the efficient operation and maintenance of machines in factories.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0581] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a meal plan based on the user's physical information and goals, means for providing the generated training plan and meal plan to the user, means for collecting the user's training and meal progress data, means for visualizing the collected progress data, means for collecting operation data and work history of machines in the factory, means for generating a training plan and a maintenance plan for the machine based on the collected machine data, and means for providing the generated training plan and maintenance plan to the machine. This makes it possible to achieve efficient training and maintenance while maintaining optimal operating conditions for the factory machines.

[0582] "User" refers to a person who uses this system.

[0583] "Physical information" refers to basic data about a user's body, such as weight, height, age, and gender.

[0584] "Goal" refers to the purpose or goal that a user wants to achieve, such as a specific expectation such as weight loss or muscle gain.

[0585] "Training Plan" refers to an exercise or fitness plan created based on a user's physical information and goals.

[0586] "Meal Plan" refers to a nutritionally balanced meal plan created based on a user's physical information and goals.

[0587] "Progress data" refers to data recorded by a user regarding the results of their daily training and diet.

[0588] "Visualization" refers to visually displaying collected progress data using graphs, charts, etc.

[0589] "Server" refers to a central computer for receiving, processing, and storing data from users.

[0590] "Machinery" refers to robots and automated equipment operating in factories.

[0591] "Operational data" refers to data relating to the movement and operating status of a machine.

[0592] "Work history" refers to a record of the actions and work that a machine has performed in the past.

[0593] A "maintenance plan" refers to a maintenance and inspection plan created based on a machine's operating data and work history.

[0594] This invention is a system that uses a server as a central location to generate and provide optimal training and meal plans based on a user's physical information and goals. Furthermore, this system provides similar training and maintenance plans for machinery in factories, supporting efficient operation of the machinery.

[0595] Hardware and software used

[0596] Hardware

[0597] Sensors (IMU, camera, etc.)

[0598] Robots and automation equipment used in factories

[0599] Smartphones / Tablets

[0600] software

[0601] Python (a programming language for data collection and analysis)

[0602] TensorFlow (Building and training AI models)

[0603] Database (MySQL or PostgreSQL)

[0604] Flask (backend framework)

[0605] Frontend (React or Vue.js)

[0606] Program processing description

[0607] Data Entry

[0608] Users enter their physical information (age, height, weight, gender, goals, etc.) into a smartphone or tablet. This data is sent from the device to a server in real time. In addition, the machines in the factory also send their operating data and work history to the server via sensors.

[0609] Data storage

[0610] The server stores the received data in a database, such as physical information and machine operation data.

[0611] Plan generation using AI models

[0612] The server uses TensorFlow to generate training and meal plans for the user, as well as training and maintenance plans for factory machinery, based on the received data. Prompt statements can be used to generate the plans.

[0613] Serving the generated plan

[0614] The generated training plans, meal plans, and maintenance plans are provided to the user or factory system in real time, allowing the user or factory to act in accordance with the plans provided.

[0615] Recording and collecting progress

[0616] Users enter their daily training results and dietary records into their smartphones or tablets. Similarly, factory machines periodically send their operation results to a server, and this data is then stored in a database.

[0617] Progress data visualization

[0618] The server visualizes progress based on the collected data, for example by providing it to users and factories using graphs and charts, allowing users and managers to visually confirm growth and areas for improvement.

[0619] Feedback and self-learning

[0620] The server uses AI to analyze the collected data and provide users and machines with suggestions for improvement. Based on this feedback, users and factory systems update their future plans and strive for continuous improvement.

[0621] Specific examples

[0622] For example, if a robot operating in a factory is becoming less efficient at a particular operation, the system will collect data to identify the cause and automatically generate an appropriate training plan.It will also detect areas that require maintenance and notify the operator immediately, minimizing machine downtime.

[0623] Prompt Sentence Examples

[0624] By using the following prompt sentences, the AI ​​model will generate an appropriate plan.

[0625] "Joint 2 of the robot may be experiencing excessive wear. Please generate a regular maintenance and training plan."

[0626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0627] Step 1:

[0628] The user inputs physical information (age, height, weight, gender, goals, etc.) and machine operation information (operating time, failure history, etc.) into the terminal. The terminal sends this input data to the server. The input data includes raw data.

[0629] Step 2:

[0630] The server stores the received user's physical information and machine operation information in a database. Specifically, it stores the information in the database using Python and a database management tool (MySQL or PostgreSQL). The output here is structured data stored in the database.

[0631] Step 3:

[0632] The server uses TensorFlow to generate optimal training and maintenance plans for users and machines based on the stored data. The AI ​​model uses the structured data in the database as input and obtains the generated plans as output.

[0633] Step 4:

[0634] The generated training and maintenance plans are sent from the server to the device and provided to users and machine managers in real time. Users can view the plans on their smartphones or tablets. The plans are also provided to the factory system in the same way.

[0635] Step 5:

[0636] Users input their daily training results and dietary records into the terminal, and factory machines periodically send their operation results to the server. The input data is the training results and work history, and the output data is updated progress data.

[0637] Step 6:

[0638] The server saves the collected progress data back to the database, converting it from raw data into an organized data format, and keeping the database updated.

[0639] Step 7:

[0640] The server uses visualization tools (such as matplotlib or Plotly) to create graphs and charts based on the collected progress data and provides them to users and machine administrators. Specifically, the progress data is used as input data, and visualized graphs and charts are generated as output data.

[0641] Step 8:

[0642] The server uses AI to analyze progress data and provide feedback to users and machines on areas for improvement and reflection. It also generates prompts and suggests areas for improvement. Based on this feedback, the user or factory system obtains input data for updating future plans. The output data is suggestions for improvements and new plans.

[0643] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0644] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, adaptively adjusting the plan and supporting motivation improvement based on the user's emotions.

[0645] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[0646] The server then uses the AI ​​model to generate training and meal plans based on the user's physical information and goals. Furthermore, an emotion engine analyzes the user's emotion data and adaptively adjusts the generated plans. The generated plans are then sent to the device for provision to the user. The user then performs daily training and eating according to the received training and meal plans.

[0647] The user enters their daily training results and dietary records into the device. The device then sends this progress data to the server. In addition, the user's emotions are periodically entered or measured, and this data is also sent to the server. The server receives this data and stores it in a database.

[0648] The server then analyzes the collected progress data and emotion data to generate visualized progress data. Graphs and charts are used for visualization, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[0649] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing points for reflection and improvement. This self-study material is sent to the user. Based on the emotional data, the server also provides appropriate messages and support to improve the user's motivation.

[0650] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week, as well as a specific meal plan. The emotion engine also analyzes Tanaka Taro's emotional data and adaptively adjusts the plan. The generated plan is sent to Tanaka Taro's device, and he trains and eats accordingly. Tanaka Taro records the results on his device, and also sends progress data and emotional data to the server. The server analyzes this data, visualizes his progress, and provides it to the device. Based on the training data and emotional data, the server also provides points for reflection and improvement, and even sends motivational messages.

[0651] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills. Furthermore, the emotion engine realizes support that takes into consideration the user's emotions, contributing to improving user motivation.

[0652] The processing flow will be explained below.

[0653] Step 1:

[0654] The user enters their profile information (name, age, height, weight, training goals) into the device.

[0655] Step 2:

[0656] The terminal transmits the input user information to the server.

[0657] Step 3:

[0658] The server receives the transmitted user data and stores it internally.

[0659] Step 4:

[0660] The user inputs their mood and emotions for the day into the device, or emotion data is collected using an emotion sensor.

[0661] Step 5:

[0662] The terminal transmits the emotion data to the server.

[0663] Step 6:

[0664] The server receives the emotion data and sends it to the emotion engine for analysis.

[0665] Step 7:

[0666] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[0667] Step 8:

[0668] A server receives the analysis results from the emotion engine and adaptively adjusts the training and meal plans.

[0669] Step 9:

[0670] The server transmits the generated training plan and meal plan to the terminal.

[0671] Step 10:

[0672] The terminal displays the training plan and meal plan to the user.

[0673] Step 11:

[0674] The user inputs daily training results and meal records into the terminal.

[0675] Step 12:

[0676] The terminal transmits the user's progress data to the server.

[0677] Step 13:

[0678] The server receives the submitted progress data and stores it in a database.

[0679] Step 14:

[0680] The server analyzes the collected progress data and emotion data and generates visualized progress data.

[0681] Step 15:

[0682] The server transmits the visualized progress data to the terminal.

[0683] Step 16:

[0684] The terminal displays the visualized progress data to the user.

[0685] Step 17:

[0686] The server uses AI to analyze the user's training data and emotional data, generating points for reflection and improvement.

[0687] Step 18:

[0688] The server sends the generated points for reflection and improvement to the terminal.

[0689] Step 19:

[0690] The device displays points for improvement and reflection to the user.

[0691] Step 20:

[0692] The server generates appropriate messages and support to improve the user's motivation based on the emotion data.

[0693] Step 21:

[0694] The server sends the generated message and support to the terminal.

[0695] Step 22:

[0696] The device displays motivational messages and support to the user.

[0697] In this way, this system, which combines an emotion engine, not only provides training and meal plans based on the user's physical information and goals, but also recognizes the user's emotions, adaptively adjusts the plans, and supports increased motivation.

[0698] Example 2

[0699] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] Conventional methods for providing training and meal plans often set uniform plans based on the user's profile information and goals, making it difficult to provide appropriate plans based on individual physical information and emotional data. Furthermore, there was a lack of feedback to properly understand the user's progress and maintain motivation. Another problem was the lack of a mechanism for adaptively adjusting the plan based on the user's emotions.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0702] In this invention, the server includes means for inputting a user's profile information and goals, means for transmitting the input user information to the server, means for utilizing a generative AI model that generates training plans and meal plans based on the user's physical information and goals, means for adaptively adjusting the training plans and meal plans generated by the generative AI model based on emotional data, means for transmitting the generated training plans and meal plans to a user terminal, means for recording the user's daily training results and meal contents and transmitting this to the server, and means for analyzing the collected progress data and emotional data and visualizing it using graphs, charts, etc. This makes it possible to provide optimal training plans and meal plans based on individual physical information and emotional data, allowing the user's progress to be properly understood and appropriate support to maintain motivation.

[0703] "Profile Information" is basic information about an individual, such as a user's name, age, height, weight, and training goals.

[0704] A "generative AI model" is a program or system that uses artificial intelligence techniques to generate a plan based on a user's physical information and goals.

[0705] "Emotion data" is information relating to the user's emotional state, such as data indicating emotions such as "satisfied" or "tired."

[0706] A "training plan" is an exercise or training plan that is individually created based on the user's physical information and goals.

[0707] A "meal plan" is a personalized meal plan based on a user's physical information and goals.

[0708] A "terminal" is a device through which a user inputs information, and includes smartphones, tablets, personal computers, etc.

[0709] A "server" is a computer system that receives, processes, stores, and analyzes information sent by users.

[0710] "Visualization" means displaying progress data and emotional data in a visually easy-to-understand format, such as graphs or charts.

[0711] "Motivational messages" are messages of encouragement and support to increase the user's motivation and enthusiasm.

[0712] "Progress data" is data that indicates the progress of the plan, such as the user's daily training results and dietary details.

[0713] A "user" is an individual who utilizes the system and needs workout and meal plans.

[0714] This invention relates to a system that generates training and meal plans optimized for individual users, and adaptively adjusts the plans based on the user's progress and emotions. Furthermore, the system analyzes the user's progress and emotions and provides messages to improve motivation.

[0715] First, the user enters their profile information (name, age, height, weight, and training goals) into the device. The device then sends this information to the server. The server stores the received user information in a database and generates training and meal plans using a generative AI model (e.g., TensorFlow or PyTorch). The generated plans are adaptively adjusted based on the user's emotional data using an emotion engine (e.g., Watson Emotion Analysis).

[0716] The generated plan is sent from the server to the user's device. The user then carries out their daily training and diet according to the plan received through the device. The training results and meal contents are recorded on the device and sent to the server. At this time, the user's emotional data is also sent. The server stores this data in a database and uses it for analysis.

[0717] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is sent to the user's device, allowing them to check their own progress and achievements.

[0718] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing suggestions for reflection and improvement. Based on the emotional data, the server also generates appropriate messages to motivate the user and sends them to the device, providing support for the user to effectively achieve their goals.

[0719] As a specific example, a user aiming to improve their endurance enters their physical information and goals into their device and sends them to the system. Based on the received information, the server generates a training plan including jogging and exercise three times a week and a meal plan that takes into account a specific nutritional balance. The emotion engine analyzes the user's emotion data and adaptively adjusts the plan. This generated plan is sent to the user's device, and the user trains and eats accordingly. The server continuously receives the user's progress and emotion data, and based on that, generates visualized data and motivational messages and sends them to the device.

[0720] Example prompt for a generative AI model:

[0721] "Enter the user's profile information, such as name, age, height, weight, and training goals. Generate personalized training and meal plans based on this information. Additionally, analyze the user's emotional data and adaptively adjust the plans."

[0722] The system not only uses individual user data and emotional information to provide optimal training and meal plans, but also adaptively adjusts based on progress and emotions, helping users achieve their goals efficiently and effectively.

[0723] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0724] Step 1:

[0725] The user enters their profile information (name, age, height, weight, training goals) into the device. This information is collected by the device and used in the next step. Specifically, the user enters information into the application's input form and clicks the "Submit" button.

[0726] Input: User profile information

[0727] Output: User information stored on the device

[0728] Step 2:

[0729] The terminal sends the entered user profile information to the server using an HTTP POST request.

[0730] Input: User information stored on the device

[0731] Output: User information sent to the server

[0732] Step 3:

[0733] The server stores the received user information in a database. It then uses a generative AI model (e.g., TensorFlow or PyTorch) to generate training and meal plans based on the user's physical information and goals. Specifically, the server provides the stored data to the AI ​​model, which then generates the plans.

[0734] Input: User information sent to the server

[0735] Output: Generated training and meal plans

[0736] Step 4:

[0737] The generated plan is adaptively adjusted based on the user's emotional data by an emotion engine (e.g., "Watson Emotion Analysis"). The emotional data is analyzed and each part of the plan is adjusted.

[0738] Input: Generated training and meal plans

[0739] Output: Adaptively adjusted training and meal plans

[0740] Step 5:

[0741] The server sends the adaptively adjusted plan to the user's terminal using an HTTP response.

[0742] Input: Adaptively adjusted training and meal plans

[0743] Output: Training and meal plans sent to your device

[0744] Step 6:

[0745] The user performs daily training and meals according to the plan received on the device. The training results and meal contents are recorded on the device and sent to the server. Specifically, the user enters the training results and meal contents into the application and clicks the "Save" button.

[0746] Input: User's training results and dietary information

[0747] Output: Progress and emotion data recorded on the device

[0748] Step 7:

[0749] The device sends the recorded progress data and emotion data to the server using an HTTP POST request.

[0750] Input: Progress data and emotion data recorded on the device

[0751] Output: Progress and emotion data sent to the server

[0752] Step 8:

[0753] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is then sent to the user's device.

[0754] Input: Progress and emotion data sent to the server

[0755] Output: Visualized progress data

[0756] Step 9:

[0757] The server uses AI to analyze the user's training data and emotional data, providing suggestions for improvement and suggestions for reflection. It also generates appropriate messages based on the emotional data to motivate the user and sends them to the device.

[0758] Input: Progress data and emotion data stored on the server

[0759] Output: Reflection, improvement, and motivational messages sent to the device

[0760] (Application example 2)

[0761] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0762] Conventional fitness and nutrition plan providing systems are limited to generating plans based on a user's physical information and goals, and lack the ability to adapt to changes in the user's emotions and motivation. As a result, if a user is unable to maintain their motivation to continue the plan, the effectiveness of the plan decreases. To address these issues, the present invention aims to provide a system that adapts plans and improves motivation based on progress data and emotional data.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0764] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a nutrition plan based on the user's physical information and goals, means for providing the generated training plan and nutrition plan to the user, means for collecting the user's training and diet progress data, means for visualizing the collected progress data, means for collecting and analyzing emotional data and adaptively adjusting the training plan and nutrition plan based on the collected emotional data, and means for providing messages and support to increase the user's motivation based on the emotional data. This makes it possible to adapt the plan based on both the progress data and the emotional data, thereby maintaining and increasing the user's motivation.

[0765] "User's physical information" refers to data regarding the user's biological attributes, including bone structure, muscle mass, body fat percentage, height, weight, age, and gender.

[0766] A "goal" refers to a specific outcome, such as a level of fitness, weight loss, strength gain, or increased endurance, that a user wishes to achieve.

[0767] A "training plan" is a plan that includes specific content and schedule of an exercise program, designed based on the user's physical information and goals.

[0768] A "nutritional plan" is a plan for the types, amounts, and timing of meals designed based on a user's physical information and goals.

[0769] "Progress data" refers to record and evaluation data regarding the results of the training and dietary habits of the user.

[0770] "Visualization" refers to displaying progress data in a visual format such as a graph or chart so that the user can easily understand it.

[0771] "Emotion data" refers to data relating to the user's psychological state and emotions, including stress level, motivation, satisfaction level, and the like.

[0772] "Adaptively adjusting" means dynamically changing and optimizing the training and nutrition plans based on the user's progress and emotional data.

[0773] "Motivation" refers to support and encouragement to motivate a user to continue with their training and diet plan.

[0774] The present invention is a system that generates and provides personalized training and nutrition plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, enabling adaptive plan adjustments and motivational support based on emotions. This will be described in more detail below.

[0775] Entering user's physical information and goals

[0776] First, the user uses a smartphone application to input their profile information (name, age, height, weight, training goals), which is then sent to the server via the application and stored.

[0777] Generate training and nutrition plans

[0778] The server uses a generative AI model to generate training and nutrition plans based on the user's physical information and goals. This generative AI model uses Python and TensorFlow, and implements advanced machine learning algorithms. Specific examples of prompts include:

[0779] Generate a training and meal plan including jogging 3 times a week and specific exercises based on the following user profile:

[0780] Name: Username

[0781] Age: 30

[0782] Height: 170cm

[0783] Weight: 70kg

[0784] Training goal: Improve endurance

[0785] Progress data collection and visualization

[0786] Users enter their daily training results and food records into a smartphone application, and the data is sent to a server in real time. The collected data is stored in a database and used for analysis.

[0787] The server collects progress data and visualizes it in graphs and charts (using Matplotlib), allowing users to view their progress on their smartphones.

[0788] Emotional Data Analysis and Adaptive Adjustment

[0789] The server uses an emotion engine to analyze the user's emotional data. The emotional data is collected from the user's self-reporting or wearable device. A specialized algorithm is used to analyze the emotional data and accurately analyze the user's psychological state. For example, if the user is feeling stressed or unmotivated, the system will adaptively adjust their training and nutrition plans based on that data.

[0790] Motivation support

[0791] Based on the collected and analyzed emotional data, the server generates support messages to improve the user's motivation, including encouraging messages and advice on how to achieve the goal, which helps the user to maintain their motivation to continue with the plan.

[0792] With this system configuration, users can obtain optimal training and nutrition plans based on their physical information and goals, and visualize and check their progress. Furthermore, adaptive plan adjustments based on emotional data and support for increased motivation improve the user's fitness experience, enabling them to achieve sustainable results.

[0793] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0794] Step 1:

[0795] The user launches the smartphone application and enters their profile information (name, age, height, weight, and training goals).

[0796] Input: Name, Age, Height, Weight, Training Goal

[0797] Output: User data entered

[0798] Operation: When a user enters information on the input screen and presses the send button, the device sends this data to the server.

[0799] Step 2:

[0800] The server stores the received user data.

[0801] Input: User data (name, age, height, weight, training goal)

[0802] Output: User data stored in the database

[0803] Operation: The server saves the received data in the database and confirms that the data has been saved.

[0804] Step 3:

[0805] The server generates training and nutrition plans using generative AI models based on the stored data.

[0806] Input: User data in the database

[0807] Output: Generated training and nutrition plans

[0808] How it works: The server uses Python and TensorFlow to convert user data into prompts, which are then fed into a generative AI model. The generated plans are then stored in a database.

[0809] Step 4:

[0810] The server transmits the generated training plan and nutrition plan to the user's terminal.

[0811] Input: Generated training and nutrition plans

[0812] Output: The plan displayed on the user's terminal

[0813] How it works: The server sends planning data to the user's smartphone application, which displays it on the device.

[0814] Step 5:

[0815] The user enters daily training results and food records into a smartphone application.

[0816] Input: Training results, food records

[0817] Output: The progress data entered

[0818] Operation: When the user enters data into the specified input form and presses the send button, the terminal sends the data to the server.

[0819] Step 6:

[0820] The server stores the progress data in a database and analyzes it.

[0821] Input: Daily progress data

[0822] Output: Parsed progress data

[0823] How it works: The server stores progress data and performs data analysis using Python.

[0824] Step 7:

[0825] The server visualizes the progress data in graphs and charts and sends it to the user's device.

[0826] Input: Parsed progress data

[0827] Output: Visualized progress data (graphs, charts)

[0828] How it works: The server uses Matplotlib to turn progress data into graphs and charts and sends them to the user's device.

[0829] Step 8:

[0830] The server collects and analyzes the emotional data and adaptively adjusts training and nutrition plans as needed.

[0831] Input: Emotion data

[0832] Output: Adaptively adjusted training and nutrition plans

[0833] Operation: The server analyzes the emotional data collected from the user using the emotion engine, readjusts the plan based on the user's psychological state, saves it in a database, and sends it to the device.

[0834] Step 9:

[0835] The server generates messages and support based on the user's emotions and sends them to the user's device.

[0836] Input: Emotion data

[0837] Output: Motivational messages and support

[0838] Operation: Based on the emotional data, the server generates appropriate messages and support content and sends them to the user's device.

[0839] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0840] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0841] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0842] [Third embodiment]

[0843] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0844] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0845] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0846] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0847] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0848] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0849] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0850] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0851] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0852] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0853] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0854] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0855] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[0856] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[0857] The server then uses the AI ​​model to generate a training plan and a meal plan based on the user's physical information and goals. The generated plan is sent to the device to be provided to the user. The user then performs daily training and eating according to the received training plan and meal plan.

[0858] Users input their daily training results and dietary records into the device, which then sends this progress data to the server, which receives the data and stores it in a database.

[0859] The server then visualizes the collected progress data using graphs and charts, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[0860] Furthermore, the server uses AI to analyze the user's training data and provide suggestions for improvement. This self-study material is sent to the user, who can use it to adjust their future training plans.

[0861] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week and a specific meal plan. These plans are then sent to Tanaka Taro's device. Tanaka Taro trains and eats according to the provided plan and records the results on his device. The recorded data is sent to the server, which uses this data to visualize his progress and provide it to Tanaka Taro. The server then analyzes his training data and suggests areas for improvement, thereby supporting Tanaka Taro's self-learning.

[0862] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills.

[0863] The processing flow will be explained below.

[0864] Step 1:

[0865] The user enters their profile information (name, age, height, weight, training goals) into the device.

[0866] Step 2:

[0867] The terminal transmits the input user information to the server.

[0868] Step 3:

[0869] The server receives the transmitted user data and stores it internally.

[0870] Step 4:

[0871] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[0872] Step 5:

[0873] The server transmits the generated training plan to the terminal.

[0874] Step 6:

[0875] The device displays the training plan to the user.

[0876] Step 7:

[0877] The user inputs daily training results and meal records into the terminal.

[0878] Step 8:

[0879] The terminal transmits the user's progress data to the server.

[0880] Step 9:

[0881] The server receives the submitted progress data and stores it in a database.

[0882] Step 10:

[0883] The server visualizes the collected progress data.

[0884] Step 11:

[0885] The server transmits the visualized progress data to the terminal.

[0886] Step 12:

[0887] The terminal displays the visualized progress data to the user.

[0888] Step 13:

[0889] The server uses AI to analyze the user's training data and generate points for reflection and improvement.

[0890] Step 14:

[0891] The server sends the generated points for reflection and improvement to the terminal.

[0892] Step 15:

[0893] The device displays points for improvement and reflection to the user.

[0894] Example 1

[0895] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0896] Conventional training and diet management systems have had the problem of being difficult to generate plans based on individual users' physical information and goals, and lacking means to sustain user motivation. Furthermore, there are no effective means to visualize collected progress data and improve user motivation, making it difficult for users to continue self-management.

[0897] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0898] In this invention, the server includes a means for inputting the user's physical information and goals, a means for generating a training plan and a meal plan based on the user's physical information and goals, and a means for providing points for improvement and reflection using a generative AI model, thereby enabling the generation and provision of individually tailored training plans and meal plans, visualization of progress data, and increased user motivation.

[0899] "User's physical information" refers to data relating to an individual's physiological or anatomical characteristics, such as the user's height, weight, age, sex, and body fat percentage.

[0900] A "training plan" is a plan that indicates the specific schedule and content of exercise and fitness activities created based on the user's physical information and goals.

[0901] A "meal plan" is a plan that indicates the specific contents and schedule of a nutritionally balanced meal that is created based on the user's physical information and goals.

[0902] "User Goal" means a specific health, fitness or strength-related goal that a user wishes to achieve.

[0903] "Progress data" refers to data recorded by the user regarding the results of their daily training and diet, and specifically includes the time and distance exercised, the contents of their meals, etc.

[0904] "Visualization" means presenting collected data in a visual format such as charts and graphs so that it is easy for users to understand.

[0905] "Generative AI model" means a model that uses artificial intelligence algorithms to generate optimal training and meal plans based on a user's data and goals.

[0906] "Points for reflection and improvement" is information that analyzes the user's training and diet progress data and points out points that should be improved or taken into consideration for the next activity.

[0907] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[0908] First, the user enters their profile information (name, age, height, weight, training goals) into the device. The device then sends this information to the server. The server receives the user data and stores it internally. The specific storage operation is performed using a database system such as MySQL or PostgreSQL.

[0909] The server then uses a generative AI model (e.g., TensorFlow or PyTorch) to generate a training plan and meal plan based on the user's physical information and goals. For example, it generates prompts like the following and inputs them into the AI ​​model:

[0910] Taro Tanaka's profile information is as follows:

[0911] Age: 30

[0912] Height: 175cm

[0913] Weight: 70kg

[0914] Goal: Increased endurance

[0915] Based on this information, generate a training plan that includes jogging three times a week and exercising twice a week, as well as a balanced, protein-rich meal plan.

[0916] The generated plan is sent to the device to be provided to the user. The device visually displays it and provides the user with training and dietary guidelines. For example, a specific plan including jogging (30 minutes) three times a week and exercising (15 minutes) twice a week is provided, along with recommendations such as eating a balanced diet and high-protein foods.

[0917] Users enter their daily training results and food records into the device, which then sends this progress data to the server. The server receives the progress data and stores it in a database. The server also visualizes the collected progress data. Tools such as Matplotlib and D3.js are used for visualization. For example, graphs showing progress over time and distance of jogging or charts showing changes in weight can be generated.

[0918] The server then uses AI to analyze the training data and provide suggestions for improvement. This is done using AI tools such as scikit-learn. The server then sends the generated feedback to the device, allowing the user to use it to adjust their future training plan. For example, the server might provide advice such as, "It would be more effective if you increased your jogging pace a little."

[0919] Through these procedures, users can continuously train and manage their diet, and effectively advance self-study. In addition, the server uses collected data to provide a means for users to improve their motivation, enabling them to achieve their goals while maintaining a high level of motivation.

[0920] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0921] Step 1: Entering User Profile Information

[0922] Users enter their name, age, height, weight, and training goals into the device.

[0923] The information entered is structured as follows:

[0924] Name: "Yamada Taro"

[0925] Age: 35

[0926] Height: 180cm

[0927] Weight: 75

[0928] Training goal: "Improve muscle strength"

[0929] The device converts this information into JSON format and sends it to the server.

[0930] Step 2: Receiving and storing user data

[0931] The server receives the user data in JSON format sent from the device.

[0932] Validate whether the received data is in the correct format.

[0933] Data that has been successfully validated is saved in a database such as MySQL or PostgreSQL.

[0934] Example SQL query:

[0935] sql

[0936] INSERT INTO users (name, age, height, weight, goal)

[0937] VALUES ('Yamada Taro', 35, 180, 75, 'Improve Muscle Strength')

[0938] Step 3: Generate a training and meal plan

[0939] The server generates prompt sentences for a generative AI model (e.g., TensorFlow) based on the stored user data.

[0940] Specific prompt examples:

[0941] Taro Yamada's profile information is as follows:

[0942] Age: 35

[0943] Height: 180cm

[0944] Weight: 75kg

[0945] Goal: Strength

[0946] Use this information to generate a training plan that includes weight training three times a week and cardio twice a week, along with a balanced, protein-rich meal plan.

[0947] The generative AI model generates training and meal plans based on the prompt text and returns them to the server.

[0948] The server parses the generated plan and formats it into the following format:

[0949] Training plan: "Weight training (30 minutes) three times a week, cardio (20 minutes) two times a week."

[0950] Meal plan: "Eat a balanced diet, high in protein foods"

[0951] Step 4: Providing training and meal plans

[0952] The server transmits the generated plan to the terminal.

[0953] The terminal visually displays the received plan to the user.

[0954] Specifically, the app screen will display the following:

[0955] Training plan: 30 minutes of weight training 3 times a week, 20 minutes of cardio 2 times a week

[0956] Meal plan: Eat a balanced diet, high in protein

[0957] Step 5: Enter and submit progress data

[0958] Users input their daily training results and dietary information into the device.

[0959] For example, "Do 30 minutes of weight training" or "Breakfast: 3 eggs, lunch: salad, dinner: chicken steak."

[0960] The device sends the recorded information to the server. The format of the data sent is:

[0961] json

[0962] {

[0963] "user_id": 1,

[0964] "training_result": "30 minutes of weight training",

[0965] "diet_record": "Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak"

[0966] }

[0967] Step 6: Receiving and saving progress data

[0968] The server receives the progress data sent from the terminal.

[0969] Performs necessary validation and saves successful data to the database.

[0970] Example SQL query:

[0971] sql

[0972] INSERT INTO progress (user_id, training_result, diet_record)

[0973] VALUES (1, '30 minutes of weight training', 'Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak')

[0974] Step 7: Visualize progress data

[0975] The server visualizes the collected progress data, for example by generating graphs using Matplotlib or D3.js.

[0976] A graph showing jogging distance and time progress and a chart showing weight changes are generated.

[0977] The server saves the generated graphs and charts in data format and sends them to the terminal.

[0978] Step 8: Analyze the data and provide feedback

[0979] The server uses AI to analyze the training data, for example, using scikit-learn to analyze the training data and extract areas for improvement.

[0980] The server will provide feedback on areas for improvement and reflection based on the analysis results.

[0981] Examples of feedback provided include:

[0982] json

[0983] {

[0984] "feedback": "Increasing the number of weight training sessions to four times a week will be even more effective."

[0985] }

[0986] The device will provide visual feedback to the user, specifically by displaying advice and improvements in text format on the app screen.

[0987] (Application example 1)

[0988] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0989] Machines and robots operating in factories require continuous maintenance and efficient training. However, there are currently no systems that can centrally manage these tasks and automatically provide optimal plans. There is also a lack of systems that can visualize progress based on each machine's work history and have self-learning capabilities. The purpose of this invention is to solve these issues and support the efficient operation and maintenance of machines in factories.

[0990] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0991] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a meal plan based on the user's physical information and goals, means for providing the generated training plan and meal plan to the user, means for collecting the user's training and meal progress data, means for visualizing the collected progress data, means for collecting operation data and work history of machines in the factory, means for generating a training plan and a maintenance plan for the machine based on the collected machine data, and means for providing the generated training plan and maintenance plan to the machine. This makes it possible to achieve efficient training and maintenance while maintaining optimal operating conditions for the factory machines.

[0992] "User" refers to a person who uses this system.

[0993] "Physical information" refers to basic data about a user's body, such as weight, height, age, and gender.

[0994] "Goal" refers to the purpose or goal that a user wants to achieve, such as a specific expectation such as weight loss or muscle gain.

[0995] "Training Plan" refers to an exercise or fitness plan created based on a user's physical information and goals.

[0996] "Meal Plan" refers to a nutritionally balanced meal plan created based on a user's physical information and goals.

[0997] "Progress data" refers to data recorded by a user regarding the results of their daily training and diet.

[0998] "Visualization" refers to visually displaying collected progress data using graphs, charts, etc.

[0999] "Server" refers to a central computer for receiving, processing, and storing data from users.

[1000] "Machinery" refers to robots and automated equipment operating in factories.

[1001] "Operational data" refers to data relating to the movement and operating status of a machine.

[1002] "Work history" refers to a record of the actions and work that a machine has performed in the past.

[1003] A "maintenance plan" refers to a maintenance and inspection plan created based on a machine's operating data and work history.

[1004] This invention is a system that uses a server as a central location to generate and provide optimal training and meal plans based on a user's physical information and goals. Furthermore, this system provides similar training and maintenance plans for machinery in factories, supporting efficient operation of the machinery.

[1005] Hardware and software used

[1006] Hardware

[1007] Sensors (IMU, camera, etc.)

[1008] Robots and automation equipment used in factories

[1009] Smartphones / Tablets

[1010] software

[1011] Python (a programming language for data collection and analysis)

[1012] TensorFlow (Building and training AI models)

[1013] Database (MySQL or PostgreSQL)

[1014] Flask (backend framework)

[1015] Frontend (React or Vue.js)

[1016] Program processing description

[1017] Data Entry

[1018] Users enter their physical information (age, height, weight, gender, goals, etc.) into a smartphone or tablet. This data is sent from the device to a server in real time. In addition, the machines in the factory also send their operating data and work history to the server via sensors.

[1019] Data storage

[1020] The server stores the received data in a database, such as physical information and machine operation data.

[1021] Plan generation using AI models

[1022] The server uses TensorFlow to generate training and meal plans for the user, as well as training and maintenance plans for factory machinery, based on the received data. Prompt statements can be used to generate the plans.

[1023] Serving the generated plan

[1024] The generated training plans, meal plans, and maintenance plans are provided to the user or factory system in real time, allowing the user or factory to act in accordance with the plans provided.

[1025] Recording and collecting progress

[1026] Users enter their daily training results and dietary records into their smartphones or tablets. Similarly, factory machines periodically send their operation results to a server, and this data is then stored in a database.

[1027] Progress data visualization

[1028] The server visualizes progress based on the collected data, for example by providing it to users and factories using graphs and charts, allowing users and managers to visually confirm growth and areas for improvement.

[1029] Feedback and self-learning

[1030] The server uses AI to analyze the collected data and provide users and machines with suggestions for improvement. Based on this feedback, users and factory systems update their future plans and strive for continuous improvement.

[1031] Specific examples

[1032] For example, if a robot operating in a factory is becoming less efficient at a particular operation, the system will collect data to identify the cause and automatically generate an appropriate training plan.It will also detect areas that require maintenance and notify the operator immediately, minimizing machine downtime.

[1033] Prompt Sentence Examples

[1034] By using the following prompt sentences, the AI ​​model will generate an appropriate plan.

[1035] "Joint 2 of the robot may be experiencing excessive wear. Please generate a regular maintenance and training plan."

[1036] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1037] Step 1:

[1038] The user inputs physical information (age, height, weight, gender, goals, etc.) and machine operation information (operating time, failure history, etc.) into the terminal. The terminal sends this input data to the server. The input data includes raw data.

[1039] Step 2:

[1040] The server stores the received user's physical information and machine operation information in a database. Specifically, it stores the information in the database using Python and a database management tool (MySQL or PostgreSQL). The output here is structured data stored in the database.

[1041] Step 3:

[1042] The server uses TensorFlow to generate optimal training and maintenance plans for users and machines based on the stored data. The AI ​​model uses the structured data in the database as input and obtains the generated plans as output.

[1043] Step 4:

[1044] The generated training and maintenance plans are sent from the server to the device and provided to users and machine managers in real time. Users can view the plans on their smartphones or tablets. The plans are also provided to the factory system in the same way.

[1045] Step 5:

[1046] Users input their daily training results and dietary records into the terminal, and factory machines periodically send their operation results to the server. The input data is the training results and work history, and the output data is updated progress data.

[1047] Step 6:

[1048] The server saves the collected progress data back to the database, converting it from raw data into an organized data format, and keeping the database updated.

[1049] Step 7:

[1050] The server uses visualization tools (such as matplotlib or Plotly) to create graphs and charts based on the collected progress data and provides them to users and machine administrators. Specifically, the progress data is used as input data, and visualized graphs and charts are generated as output data.

[1051] Step 8:

[1052] The server uses AI to analyze progress data and provide feedback to users and machines on areas for improvement and reflection. It also generates prompts and suggests areas for improvement. Based on this feedback, the user or factory system obtains input data for updating future plans. The output data is suggestions for improvements and new plans.

[1053] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1054] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, adaptively adjusting the plan and supporting motivation improvement based on the user's emotions.

[1055] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[1056] The server then uses the AI ​​model to generate training and meal plans based on the user's physical information and goals. Furthermore, an emotion engine analyzes the user's emotion data and adaptively adjusts the generated plans. The generated plans are then sent to the device for provision to the user. The user then performs daily training and eating according to the received training and meal plans.

[1057] The user enters their daily training results and dietary records into the device. The device then sends this progress data to the server. In addition, the user's emotions are periodically entered or measured, and this data is also sent to the server. The server receives this data and stores it in a database.

[1058] The server then analyzes the collected progress data and emotion data to generate visualized progress data. Graphs and charts are used for visualization, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[1059] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing points for reflection and improvement. This self-study material is sent to the user. Based on the emotional data, the server also provides appropriate messages and support to improve the user's motivation.

[1060] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week, as well as a specific meal plan. The emotion engine also analyzes Tanaka Taro's emotional data and adaptively adjusts the plan. The generated plan is sent to Tanaka Taro's device, and he trains and eats accordingly. Tanaka Taro records the results on his device, and also sends progress data and emotional data to the server. The server analyzes this data, visualizes his progress, and provides it to the device. Based on the training data and emotional data, the server also provides points for reflection and improvement, and even sends motivational messages.

[1061] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills. Furthermore, the emotion engine realizes support that takes into consideration the user's emotions, contributing to improving user motivation.

[1062] The processing flow will be explained below.

[1063] Step 1:

[1064] The user enters their profile information (name, age, height, weight, training goals) into the device.

[1065] Step 2:

[1066] The terminal transmits the input user information to the server.

[1067] Step 3:

[1068] The server receives the transmitted user data and stores it internally.

[1069] Step 4:

[1070] The user inputs their mood and emotions for the day into the device, or emotion data is collected using an emotion sensor.

[1071] Step 5:

[1072] The terminal transmits the emotion data to the server.

[1073] Step 6:

[1074] The server receives the emotion data and sends it to the emotion engine for analysis.

[1075] Step 7:

[1076] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[1077] Step 8:

[1078] A server receives the analysis results from the emotion engine and adaptively adjusts the training and meal plans.

[1079] Step 9:

[1080] The server transmits the generated training plan and meal plan to the terminal.

[1081] Step 10:

[1082] The terminal displays the training plan and meal plan to the user.

[1083] Step 11:

[1084] The user inputs daily training results and meal records into the terminal.

[1085] Step 12:

[1086] The terminal transmits the user's progress data to the server.

[1087] Step 13:

[1088] The server receives the submitted progress data and stores it in a database.

[1089] Step 14:

[1090] The server analyzes the collected progress data and emotion data and generates visualized progress data.

[1091] Step 15:

[1092] The server transmits the visualized progress data to the terminal.

[1093] Step 16:

[1094] The terminal displays the visualized progress data to the user.

[1095] Step 17:

[1096] The server uses AI to analyze the user's training data and emotional data, generating points for reflection and improvement.

[1097] Step 18:

[1098] The server sends the generated points for reflection and improvement to the terminal.

[1099] Step 19:

[1100] The device displays points for improvement and reflection to the user.

[1101] Step 20:

[1102] The server generates appropriate messages and support to improve the user's motivation based on the emotion data.

[1103] Step 21:

[1104] The server sends the generated message and support to the terminal.

[1105] Step 22:

[1106] The device displays motivational messages and support to the user.

[1107] In this way, this system, which combines an emotion engine, not only provides training and meal plans based on the user's physical information and goals, but also recognizes the user's emotions, adaptively adjusts the plans, and supports increased motivation.

[1108] Example 2

[1109] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1110] Conventional methods for providing training and meal plans often set uniform plans based on the user's profile information and goals, making it difficult to provide appropriate plans based on individual physical information and emotional data. Furthermore, there was a lack of feedback to properly understand the user's progress and maintain motivation. Another problem was the lack of a mechanism for adaptively adjusting the plan based on the user's emotions.

[1111] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1112] In this invention, the server includes means for inputting a user's profile information and goals, means for transmitting the input user information to the server, means for utilizing a generative AI model that generates training plans and meal plans based on the user's physical information and goals, means for adaptively adjusting the training plans and meal plans generated by the generative AI model based on emotional data, means for transmitting the generated training plans and meal plans to a user terminal, means for recording the user's daily training results and meal contents and transmitting this to the server, and means for analyzing the collected progress data and emotional data and visualizing it using graphs, charts, etc. This makes it possible to provide optimal training plans and meal plans based on individual physical information and emotional data, allowing the user's progress to be properly understood and appropriate support to maintain motivation.

[1113] "Profile Information" is basic information about an individual, such as a user's name, age, height, weight, and training goals.

[1114] A "generative AI model" is a program or system that uses artificial intelligence techniques to generate a plan based on a user's physical information and goals.

[1115] "Emotion data" is information relating to the user's emotional state, such as data indicating emotions such as "satisfied" or "tired."

[1116] A "training plan" is an exercise or training plan that is individually created based on the user's physical information and goals.

[1117] A "meal plan" is a personalized meal plan based on a user's physical information and goals.

[1118] A "terminal" is a device through which a user inputs information, and includes smartphones, tablets, personal computers, etc.

[1119] A "server" is a computer system that receives, processes, stores, and analyzes information sent by users.

[1120] "Visualization" means displaying progress data and emotional data in a visually easy-to-understand format, such as graphs or charts.

[1121] "Motivational messages" are messages of encouragement and support to increase the user's motivation and enthusiasm.

[1122] "Progress data" is data that indicates the progress of the plan, such as the user's daily training results and dietary details.

[1123] A "user" is an individual who utilizes the system and needs workout and meal plans.

[1124] This invention relates to a system that generates training and meal plans optimized for individual users, and adaptively adjusts the plans based on the user's progress and emotions. Furthermore, the system analyzes the user's progress and emotions and provides messages to improve motivation.

[1125] First, the user enters their profile information (name, age, height, weight, and training goals) into the device. The device then sends this information to the server. The server stores the received user information in a database and generates training and meal plans using a generative AI model (e.g., TensorFlow or PyTorch). The generated plans are adaptively adjusted based on the user's emotional data using an emotion engine (e.g., Watson Emotion Analysis).

[1126] The generated plan is sent from the server to the user's device. The user then carries out their daily training and diet according to the plan received through the device. The training results and meal contents are recorded on the device and sent to the server. At this time, the user's emotional data is also sent. The server stores this data in a database and uses it for analysis.

[1127] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is sent to the user's device, allowing them to check their own progress and achievements.

[1128] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing suggestions for reflection and improvement. Based on the emotional data, the server also generates appropriate messages to motivate the user and sends them to the device, providing support for the user to effectively achieve their goals.

[1129] As a specific example, a user aiming to improve their endurance enters their physical information and goals into their device and sends them to the system. Based on the received information, the server generates a training plan including jogging and exercise three times a week and a meal plan that takes into account a specific nutritional balance. The emotion engine analyzes the user's emotion data and adaptively adjusts the plan. This generated plan is sent to the user's device, and the user trains and eats accordingly. The server continuously receives the user's progress and emotion data, and based on that, generates visualized data and motivational messages and sends them to the device.

[1130] Example prompt for a generative AI model:

[1131] "Enter the user's profile information, such as name, age, height, weight, and training goals. Generate personalized training and meal plans based on this information. Additionally, analyze the user's emotional data and adaptively adjust the plans."

[1132] The system not only uses individual user data and emotional information to provide optimal training and meal plans, but also adaptively adjusts based on progress and emotions, helping users achieve their goals efficiently and effectively.

[1133] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1134] Step 1:

[1135] The user enters their profile information (name, age, height, weight, training goals) into the device. This information is collected by the device and used in the next step. Specifically, the user enters information into the application's input form and clicks the "Submit" button.

[1136] Input: User profile information

[1137] Output: User information stored on the device

[1138] Step 2:

[1139] The terminal sends the entered user profile information to the server using an HTTP POST request.

[1140] Input: User information stored on the device

[1141] Output: User information sent to the server

[1142] Step 3:

[1143] The server stores the received user information in a database. It then uses a generative AI model (e.g., TensorFlow or PyTorch) to generate training and meal plans based on the user's physical information and goals. Specifically, the server provides the stored data to the AI ​​model, which then generates the plans.

[1144] Input: User information sent to the server

[1145] Output: Generated training and meal plans

[1146] Step 4:

[1147] The generated plan is adaptively adjusted based on the user's emotional data by an emotion engine (e.g., "Watson Emotion Analysis"). The emotional data is analyzed and each part of the plan is adjusted.

[1148] Input: Generated training and meal plans

[1149] Output: Adaptively adjusted training and meal plans

[1150] Step 5:

[1151] The server sends the adaptively adjusted plan to the user's terminal using an HTTP response.

[1152] Input: Adaptively adjusted training and meal plans

[1153] Output: Training and meal plans sent to your device

[1154] Step 6:

[1155] The user performs daily training and meals according to the plan received on the device. The training results and meal contents are recorded on the device and sent to the server. Specifically, the user enters the training results and meal contents into the application and clicks the "Save" button.

[1156] Input: User's training results and dietary information

[1157] Output: Progress and emotion data recorded on the device

[1158] Step 7:

[1159] The device sends the recorded progress data and emotion data to the server using an HTTP POST request.

[1160] Input: Progress data and emotion data recorded on the device

[1161] Output: Progress and emotion data sent to the server

[1162] Step 8:

[1163] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is then sent to the user's device.

[1164] Input: Progress and emotion data sent to the server

[1165] Output: Visualized progress data

[1166] Step 9:

[1167] The server uses AI to analyze the user's training data and emotional data, providing suggestions for improvement and suggestions for reflection. It also generates appropriate messages based on the emotional data to motivate the user and sends them to the device.

[1168] Input: Progress data and emotion data stored on the server

[1169] Output: Reflection, improvement, and motivational messages sent to the device

[1170] (Application example 2)

[1171] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1172] Conventional fitness and nutrition plan providing systems are limited to generating plans based on a user's physical information and goals, and lack the ability to adapt to changes in the user's emotions and motivation. As a result, if a user is unable to maintain their motivation to continue the plan, the effectiveness of the plan decreases. To address these issues, the present invention aims to provide a system that adapts plans and improves motivation based on progress data and emotional data.

[1173] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1174] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a nutrition plan based on the user's physical information and goals, means for providing the generated training plan and nutrition plan to the user, means for collecting the user's training and diet progress data, means for visualizing the collected progress data, means for collecting and analyzing emotional data and adaptively adjusting the training plan and nutrition plan based on the collected emotional data, and means for providing messages and support to increase the user's motivation based on the emotional data. This makes it possible to adapt the plan based on both the progress data and the emotional data, thereby maintaining and increasing the user's motivation.

[1175] "User's physical information" refers to data regarding the user's biological attributes, including bone structure, muscle mass, body fat percentage, height, weight, age, and gender.

[1176] A "goal" refers to a specific outcome, such as a level of fitness, weight loss, strength gain, or increased endurance, that a user wishes to achieve.

[1177] A "training plan" is a plan that includes specific content and schedule of an exercise program, designed based on the user's physical information and goals.

[1178] A "nutritional plan" is a plan for the types, amounts, and timing of meals designed based on a user's physical information and goals.

[1179] "Progress data" refers to record and evaluation data regarding the results of the training and dietary habits of the user.

[1180] "Visualization" refers to displaying progress data in a visual format such as a graph or chart so that the user can easily understand it.

[1181] "Emotion data" refers to data relating to the user's psychological state and emotions, including stress level, motivation, satisfaction level, and the like.

[1182] "Adaptively adjusting" means dynamically changing and optimizing the training and nutrition plans based on the user's progress and emotional data.

[1183] "Motivation" refers to support and encouragement to motivate a user to continue with their training and diet plan.

[1184] The present invention is a system that generates and provides personalized training and nutrition plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, enabling adaptive plan adjustments and motivational support based on emotions. This will be described in more detail below.

[1185] Entering user's physical information and goals

[1186] First, the user uses a smartphone application to input their profile information (name, age, height, weight, training goals), which is then sent to the server via the application and stored.

[1187] Generate training and nutrition plans

[1188] The server uses a generative AI model to generate training and nutrition plans based on the user's physical information and goals. This generative AI model uses Python and TensorFlow, and implements advanced machine learning algorithms. Specific examples of prompts include:

[1189] Generate a training and meal plan including jogging 3 times a week and specific exercises based on the following user profile:

[1190] Name: Username

[1191] Age: 30

[1192] Height: 170cm

[1193] Weight: 70kg

[1194] Training goal: Improve endurance

[1195] Progress data collection and visualization

[1196] Users enter their daily training results and food records into a smartphone application, and the data is sent to a server in real time. The collected data is stored in a database and used for analysis.

[1197] The server collects progress data and visualizes it in graphs and charts (using Matplotlib), allowing users to view their progress on their smartphones.

[1198] Emotional Data Analysis and Adaptive Adjustment

[1199] The server uses an emotion engine to analyze the user's emotional data. The emotional data is collected from the user's self-reporting or wearable device. A specialized algorithm is used to analyze the emotional data and accurately analyze the user's psychological state. For example, if the user is feeling stressed or unmotivated, the system will adaptively adjust their training and nutrition plans based on that data.

[1200] Motivation support

[1201] Based on the collected and analyzed emotional data, the server generates support messages to improve the user's motivation, including encouraging messages and advice on how to achieve the goal, which helps the user to maintain their motivation to continue with the plan.

[1202] With this system configuration, users can obtain optimal training and nutrition plans based on their physical information and goals, and visualize and check their progress. Furthermore, adaptive plan adjustments based on emotional data and support for increased motivation improve the user's fitness experience, enabling them to achieve sustainable results.

[1203] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1204] Step 1:

[1205] The user launches the smartphone application and enters their profile information (name, age, height, weight, and training goals).

[1206] Input: Name, Age, Height, Weight, Training Goal

[1207] Output: User data entered

[1208] Operation: When a user enters information on the input screen and presses the send button, the device sends this data to the server.

[1209] Step 2:

[1210] The server stores the received user data.

[1211] Input: User data (name, age, height, weight, training goal)

[1212] Output: User data stored in the database

[1213] Operation: The server saves the received data in the database and confirms that the data has been saved.

[1214] Step 3:

[1215] The server generates training and nutrition plans using generative AI models based on the stored data.

[1216] Input: User data in the database

[1217] Output: Generated training and nutrition plans

[1218] How it works: The server uses Python and TensorFlow to convert user data into prompts, which are then fed into a generative AI model. The generated plans are then stored in a database.

[1219] Step 4:

[1220] The server transmits the generated training plan and nutrition plan to the user's terminal.

[1221] Input: Generated training and nutrition plans

[1222] Output: The plan displayed on the user's terminal

[1223] How it works: The server sends planning data to the user's smartphone application, which displays it on the device.

[1224] Step 5:

[1225] The user enters daily training results and food records into a smartphone application.

[1226] Input: Training results, food records

[1227] Output: The progress data entered

[1228] Operation: When the user enters data into the specified input form and presses the send button, the terminal sends the data to the server.

[1229] Step 6:

[1230] The server stores the progress data in a database and analyzes it.

[1231] Input: Daily progress data

[1232] Output: Parsed progress data

[1233] How it works: The server stores progress data and performs data analysis using Python.

[1234] Step 7:

[1235] The server visualizes the progress data in graphs and charts and sends it to the user's device.

[1236] Input: Parsed progress data

[1237] Output: Visualized progress data (graphs, charts)

[1238] How it works: The server uses Matplotlib to turn progress data into graphs and charts and sends them to the user's device.

[1239] Step 8:

[1240] The server collects and analyzes the emotional data and adaptively adjusts training and nutrition plans as needed.

[1241] Input: Emotion data

[1242] Output: Adaptively adjusted training and nutrition plans

[1243] Operation: The server analyzes the emotional data collected from the user using the emotion engine, readjusts the plan based on the user's psychological state, saves it in a database, and sends it to the device.

[1244] Step 9:

[1245] The server generates messages and support based on the user's emotions and sends them to the user's device.

[1246] Input: Emotion data

[1247] Output: Motivational messages and support

[1248] Operation: Based on the emotional data, the server generates appropriate messages and support content and sends them to the user's device.

[1249] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1250] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1251] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1252] [Fourth embodiment]

[1253] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1254] 7, a 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.

[1255] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1256] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1257] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1258] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1259] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1260] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1261] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1262] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1263] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1264] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1265] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1266] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[1267] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[1268] The server then uses the AI ​​model to generate a training plan and a meal plan based on the user's physical information and goals. The generated plan is sent to the device to be provided to the user. The user then performs daily training and eating according to the received training plan and meal plan.

[1269] Users input their daily training results and dietary records into the device, which then sends this progress data to the server, which receives the data and stores it in a database.

[1270] The server then visualizes the collected progress data using graphs and charts, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[1271] Furthermore, the server uses AI to analyze the user's training data and provide suggestions for improvement. This self-study material is sent to the user, who can use it to adjust their future training plans.

[1272] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week and a specific meal plan. These plans are then sent to Tanaka Taro's device. Tanaka Taro trains and eats according to the provided plan and records the results on his device. The recorded data is sent to the server, which uses this data to visualize his progress and provide it to Tanaka Taro. The server then analyzes his training data and suggests areas for improvement, thereby supporting Tanaka Taro's self-learning.

[1273] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills.

[1274] The processing flow will be explained below.

[1275] Step 1:

[1276] The user enters their profile information (name, age, height, weight, training goals) into the device.

[1277] Step 2:

[1278] The terminal transmits the input user information to the server.

[1279] Step 3:

[1280] The server receives the transmitted user data and stores it internally.

[1281] Step 4:

[1282] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[1283] Step 5:

[1284] The server transmits the generated training plan to the terminal.

[1285] Step 6:

[1286] The device displays the training plan to the user.

[1287] Step 7:

[1288] The user inputs daily training results and meal records into the terminal.

[1289] Step 8:

[1290] The terminal transmits the user's progress data to the server.

[1291] Step 9:

[1292] The server receives the submitted progress data and stores it in a database.

[1293] Step 10:

[1294] The server visualizes the collected progress data.

[1295] Step 11:

[1296] The server transmits the visualized progress data to the terminal.

[1297] Step 12:

[1298] The terminal displays the visualized progress data to the user.

[1299] Step 13:

[1300] The server uses AI to analyze the user's training data and generate points for reflection and improvement.

[1301] Step 14:

[1302] The server sends the generated points for reflection and improvement to the terminal.

[1303] Step 15:

[1304] The device displays points for improvement and reflection to the user.

[1305] Example 1

[1306] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1307] Conventional training and diet management systems have had the problem of being difficult to generate plans based on individual users' physical information and goals, and lacking means to sustain user motivation. Furthermore, there are no effective means to visualize collected progress data and improve user motivation, making it difficult for users to continue self-management.

[1308] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1309] In this invention, the server includes a means for inputting the user's physical information and goals, a means for generating a training plan and a meal plan based on the user's physical information and goals, and a means for providing points for improvement and reflection using a generative AI model, thereby enabling the generation and provision of individually tailored training plans and meal plans, visualization of progress data, and increased user motivation.

[1310] "User's physical information" refers to data relating to an individual's physiological or anatomical characteristics, such as the user's height, weight, age, sex, and body fat percentage.

[1311] A "training plan" is a plan that indicates the specific schedule and content of exercise and fitness activities created based on the user's physical information and goals.

[1312] A "meal plan" is a plan that indicates the specific contents and schedule of a nutritionally balanced meal that is created based on the user's physical information and goals.

[1313] "User Goal" means a specific health, fitness or strength-related goal that a user wishes to achieve.

[1314] "Progress data" refers to data recorded by the user regarding the results of their daily training and diet, and specifically includes the time and distance exercised, the contents of their meals, etc.

[1315] "Visualization" means presenting collected data in a visual format such as charts and graphs so that it is easy for users to understand.

[1316] "Generative AI model" means a model that uses artificial intelligence algorithms to generate optimal training and meal plans based on a user's data and goals.

[1317] "Points for reflection and improvement" is information that analyzes the user's training and diet progress data and points out points that should be improved or taken into consideration for the next activity.

[1318] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals, and includes means for collecting and visualizing the user's progress data and enabling the user to self-study.

[1319] First, the user enters their profile information (name, age, height, weight, training goals) into the device. The device then sends this information to the server. The server receives the user data and stores it internally. The specific storage operation is performed using a database system such as MySQL or PostgreSQL.

[1320] The server then uses a generative AI model (e.g., TensorFlow or PyTorch) to generate a training plan and meal plan based on the user's physical information and goals. For example, it generates prompts like the following and inputs them into the AI ​​model:

[1321] Taro Tanaka's profile information is as follows:

[1322] Age: 30

[1323] Height: 175cm

[1324] Weight: 70kg

[1325] Goal: Increased endurance

[1326] Based on this information, generate a training plan that includes jogging three times a week and exercising twice a week, as well as a balanced, protein-rich meal plan.

[1327] The generated plan is sent to the device to be provided to the user. The device visually displays it and provides the user with training and dietary guidelines. For example, a specific plan including jogging (30 minutes) three times a week and exercising (15 minutes) twice a week is provided, along with recommendations such as eating a balanced diet and high-protein foods.

[1328] Users enter their daily training results and food records into the device, which then sends this progress data to the server. The server receives the progress data and stores it in a database. The server also visualizes the collected progress data. Tools such as Matplotlib and D3.js are used for visualization. For example, graphs showing progress over time and distance of jogging or charts showing changes in weight can be generated.

[1329] The server then uses AI to analyze the training data and provide suggestions for improvement. This is done using AI tools such as scikit-learn. The server then sends the generated feedback to the device, allowing the user to use it to adjust their future training plan. For example, the server might provide advice such as, "It would be more effective if you increased your jogging pace a little."

[1330] Through these procedures, users can continuously train and manage their diet, and effectively advance self-study. In addition, the server uses collected data to provide a means for users to improve their motivation, enabling them to achieve their goals while maintaining a high level of motivation.

[1331] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1332] Step 1: Entering User Profile Information

[1333] Users enter their name, age, height, weight, and training goals into the device.

[1334] The information entered is structured as follows:

[1335] Name: "Yamada Taro"

[1336] Age: 35

[1337] Height: 180cm

[1338] Weight: 75

[1339] Training goal: "Improve muscle strength"

[1340] The device converts this information into JSON format and sends it to the server.

[1341] Step 2: Receiving and storing user data

[1342] The server receives the user data in JSON format sent from the device.

[1343] Validate whether the received data is in the correct format.

[1344] Data that has been successfully validated is saved in a database such as MySQL or PostgreSQL.

[1345] Example SQL query:

[1346] sql

[1347] INSERT INTO users (name, age, height, weight, goal)

[1348] VALUES ('Yamada Taro', 35, 180, 75, 'Improve Muscle Strength')

[1349] Step 3: Generate a training and meal plan

[1350] The server generates prompt sentences for a generative AI model (e.g., TensorFlow) based on the stored user data.

[1351] Specific prompt examples:

[1352] Taro Yamada's profile information is as follows:

[1353] Age: 35

[1354] Height: 180cm

[1355] Weight: 75kg

[1356] Goal: Strength

[1357] Use this information to generate a training plan that includes weight training three times a week and cardio twice a week, along with a balanced, protein-rich meal plan.

[1358] The generative AI model generates training and meal plans based on the prompt text and returns them to the server.

[1359] The server parses the generated plan and formats it into the following format:

[1360] Training plan: "Weight training (30 minutes) three times a week, cardio (20 minutes) two times a week."

[1361] Meal plan: "Eat a balanced diet, high in protein foods"

[1362] Step 4: Providing training and meal plans

[1363] The server transmits the generated plan to the terminal.

[1364] The terminal visually displays the received plan to the user.

[1365] Specifically, the app screen will display the following:

[1366] Training plan: 30 minutes of weight training 3 times a week, 20 minutes of cardio 2 times a week

[1367] Meal plan: Eat a balanced diet, high in protein

[1368] Step 5: Enter and submit progress data

[1369] Users input their daily training results and dietary information into the device.

[1370] For example, "Do 30 minutes of weight training" or "Breakfast: 3 eggs, lunch: salad, dinner: chicken steak."

[1371] The device sends the recorded information to the server. The format of the data sent is:

[1372] json

[1373] {

[1374] "user_id": 1,

[1375] "training_result": "30 minutes of weight training",

[1376] "diet_record": "Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak"

[1377] }

[1378] Step 6: Receiving and saving progress data

[1379] The server receives the progress data sent from the terminal.

[1380] Performs necessary validation and saves successful data to the database.

[1381] Example SQL query:

[1382] sql

[1383] INSERT INTO progress (user_id, training_result, diet_record)

[1384] VALUES (1, '30 minutes of weight training', 'Breakfast: 3 eggs, Lunch: Salad, Dinner: Chicken steak')

[1385] Step 7: Visualize progress data

[1386] The server visualizes the collected progress data, for example by generating graphs using Matplotlib or D3.js.

[1387] A graph showing jogging distance and time progress and a chart showing weight changes are generated.

[1388] The server saves the generated graphs and charts in data format and sends them to the terminal.

[1389] Step 8: Analyze the data and provide feedback

[1390] The server uses AI to analyze the training data, for example, using scikit-learn to analyze the training data and extract areas for improvement.

[1391] The server will provide feedback on areas for improvement and reflection based on the analysis results.

[1392] Examples of feedback provided include:

[1393] json

[1394] {

[1395] "feedback": "Increasing the number of weight training sessions to four times a week will be even more effective."

[1396] }

[1397] The device will provide visual feedback to the user, specifically by displaying advice and improvements in text format on the app screen.

[1398] (Application example 1)

[1399] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1400] Machines and robots operating in factories require continuous maintenance and efficient training. However, there are currently no systems that can centrally manage these tasks and automatically provide optimal plans. There is also a lack of systems that can visualize progress based on each machine's work history and have self-learning capabilities. The purpose of this invention is to solve these issues and support the efficient operation and maintenance of machines in factories.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1402] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a meal plan based on the user's physical information and goals, means for providing the generated training plan and meal plan to the user, means for collecting the user's training and meal progress data, means for visualizing the collected progress data, means for collecting operation data and work history of machines in the factory, means for generating a training plan and a maintenance plan for the machine based on the collected machine data, and means for providing the generated training plan and maintenance plan to the machine. This makes it possible to achieve efficient training and maintenance while maintaining optimal operating conditions for the factory machines.

[1403] "User" refers to a person who uses this system.

[1404] "Physical information" refers to basic data about a user's body, such as weight, height, age, and gender.

[1405] "Goal" refers to the purpose or goal that a user wants to achieve, such as a specific expectation such as weight loss or muscle gain.

[1406] "Training Plan" refers to an exercise or fitness plan created based on a user's physical information and goals.

[1407] "Meal Plan" refers to a nutritionally balanced meal plan created based on a user's physical information and goals.

[1408] "Progress data" refers to data recorded by a user regarding the results of their daily training and diet.

[1409] "Visualization" refers to visually displaying collected progress data using graphs, charts, etc.

[1410] "Server" refers to a central computer for receiving, processing, and storing data from users.

[1411] "Machinery" refers to robots and automated equipment operating in factories.

[1412] "Operational data" refers to data relating to the movement and operating status of a machine.

[1413] "Work history" refers to a record of the actions and work that a machine has performed in the past.

[1414] A "maintenance plan" refers to a maintenance and inspection plan created based on a machine's operating data and work history.

[1415] This invention is a system that uses a server as a central location to generate and provide optimal training and meal plans based on a user's physical information and goals. Furthermore, this system provides similar training and maintenance plans for machinery in factories, supporting efficient operation of the machinery.

[1416] Hardware and software used

[1417] Hardware

[1418] Sensors (IMU, camera, etc.)

[1419] Robots and automation equipment used in factories

[1420] Smartphones / Tablets

[1421] software

[1422] Python (a programming language for data collection and analysis)

[1423] TensorFlow (Building and training AI models)

[1424] Database (MySQL or PostgreSQL)

[1425] Flask (backend framework)

[1426] Frontend (React or Vue.js)

[1427] Program processing description

[1428] Data Entry

[1429] Users enter their physical information (age, height, weight, gender, goals, etc.) into a smartphone or tablet. This data is sent from the device to a server in real time. In addition, the machines in the factory also send their operating data and work history to the server via sensors.

[1430] Data storage

[1431] The server stores the received data in a database, such as physical information and machine operation data.

[1432] Plan generation using AI models

[1433] The server uses TensorFlow to generate training and meal plans for the user, as well as training and maintenance plans for factory machinery, based on the received data. Prompt statements can be used to generate the plans.

[1434] Serving the generated plan

[1435] The generated training plans, meal plans, and maintenance plans are provided to the user or factory system in real time, allowing the user or factory to act in accordance with the plans provided.

[1436] Recording and collecting progress

[1437] Users enter their daily training results and dietary records into their smartphones or tablets. Similarly, factory machines periodically send their operation results to a server, and this data is then stored in a database.

[1438] Progress data visualization

[1439] The server visualizes progress based on the collected data, for example by providing it to users and factories using graphs and charts, allowing users and managers to visually confirm growth and areas for improvement.

[1440] Feedback and self-learning

[1441] The server uses AI to analyze the collected data and provide users and machines with suggestions for improvement. Based on this feedback, users and factory systems update their future plans and strive for continuous improvement.

[1442] Specific examples

[1443] For example, if a robot operating in a factory is becoming less efficient at a particular operation, the system will collect data to identify the cause and automatically generate an appropriate training plan.It will also detect areas that require maintenance and notify the operator immediately, minimizing machine downtime.

[1444] Prompt Sentence Examples

[1445] By using the following prompt sentences, the AI ​​model will generate an appropriate plan.

[1446] "Joint 2 of the robot may be experiencing excessive wear. Please generate a regular maintenance and training plan."

[1447] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1448] Step 1:

[1449] The user inputs physical information (age, height, weight, gender, goals, etc.) and machine operation information (operating time, failure history, etc.) into the terminal. The terminal sends this input data to the server. The input data includes raw data.

[1450] Step 2:

[1451] The server stores the received user's physical information and machine operation information in a database. Specifically, it stores the information in the database using Python and a database management tool (MySQL or PostgreSQL). The output here is structured data stored in the database.

[1452] Step 3:

[1453] The server uses TensorFlow to generate optimal training and maintenance plans for users and machines based on the stored data. The AI ​​model uses the structured data in the database as input and obtains the generated plans as output.

[1454] Step 4:

[1455] The generated training and maintenance plans are sent from the server to the device and provided to users and machine managers in real time. Users can view the plans on their smartphones or tablets. The plans are also provided to the factory system in the same way.

[1456] Step 5:

[1457] Users input their daily training results and dietary records into the terminal, and factory machines periodically send their operation results to the server. The input data is the training results and work history, and the output data is updated progress data.

[1458] Step 6:

[1459] The server saves the collected progress data back to the database, converting it from raw data into an organized data format, and keeping the database updated.

[1460] Step 7:

[1461] The server uses visualization tools (such as matplotlib or Plotly) to create graphs and charts based on the collected progress data and provides them to users and machine administrators. Specifically, the progress data is used as input data, and visualized graphs and charts are generated as output data.

[1462] Step 8:

[1463] The server uses AI to analyze progress data and provide feedback to users and machines on areas for improvement and reflection. It also generates prompts and suggests areas for improvement. Based on this feedback, the user or factory system obtains input data for updating future plans. The output data is suggestions for improvements and new plans.

[1464] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1465] The present invention is a system that generates and provides personalized training and meal plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, adaptively adjusting the plan and supporting motivation improvement based on the user's emotions.

[1466] First, the user enters their profile information (name, age, height, weight, training goals) into the device, which then sends this information to the server, which then receives the user data and stores it internally.

[1467] The server then uses the AI ​​model to generate training and meal plans based on the user's physical information and goals. Furthermore, an emotion engine analyzes the user's emotion data and adaptively adjusts the generated plans. The generated plans are then sent to the device for provision to the user. The user then performs daily training and eating according to the received training and meal plans.

[1468] The user enters their daily training results and dietary records into the device. The device then sends this progress data to the server. In addition, the user's emotions are periodically entered or measured, and this data is also sent to the server. The server receives this data and stores it in a database.

[1469] The server then analyzes the collected progress data and emotion data to generate visualized progress data. Graphs and charts are used for visualization, allowing users to visually confirm their progress and achievements. The visualized progress data is then sent to the device and provided to the user.

[1470] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing points for reflection and improvement. This self-study material is sent to the user. Based on the emotional data, the server also provides appropriate messages and support to improve the user's motivation.

[1471] For example, suppose a user named Tanaka Taro aims to improve his endurance. Tanaka Taro enters his physical information and goals into his device and sends them to the system. Based on the received information, the server generates a training plan including appropriate jogging and exercise three times a week, as well as a specific meal plan. The emotion engine also analyzes Tanaka Taro's emotional data and adaptively adjusts the plan. The generated plan is sent to Tanaka Taro's device, and he trains and eats accordingly. Tanaka Taro records the results on his device, and also sends progress data and emotional data to the server. The server analyzes this data, visualizes his progress, and provides it to the device. Based on the training data and emotional data, the server also provides points for reflection and improvement, and even sends motivational messages.

[1472] In this way, the system of the present invention has the effect of reducing the burden on teachers and supporting students in improving their physical strength and skills. Furthermore, the emotion engine realizes support that takes into consideration the user's emotions, contributing to improving user motivation.

[1473] The processing flow will be explained below.

[1474] Step 1:

[1475] The user enters their profile information (name, age, height, weight, training goals) into the device.

[1476] Step 2:

[1477] The terminal transmits the input user information to the server.

[1478] Step 3:

[1479] The server receives the transmitted user data and stores it internally.

[1480] Step 4:

[1481] The user inputs their mood and emotions for the day into the device, or emotion data is collected using an emotion sensor.

[1482] Step 5:

[1483] The terminal transmits the emotion data to the server.

[1484] Step 6:

[1485] The server receives the emotion data and sends it to the emotion engine for analysis.

[1486] Step 7:

[1487] The server uses an AI model to generate a training plan based on the user's physical information and goals.

[1488] Step 8:

[1489] A server receives the analysis results from the emotion engine and adaptively adjusts the training and meal plans.

[1490] Step 9:

[1491] The server transmits the generated training plan and meal plan to the terminal.

[1492] Step 10:

[1493] The terminal displays the training plan and meal plan to the user.

[1494] Step 11:

[1495] The user inputs daily training results and meal records into the terminal.

[1496] Step 12:

[1497] The terminal transmits the user's progress data to the server.

[1498] Step 13:

[1499] The server receives the submitted progress data and stores it in a database.

[1500] Step 14:

[1501] The server analyzes the collected progress data and emotion data and generates visualized progress data.

[1502] Step 15:

[1503] The server transmits the visualized progress data to the terminal.

[1504] Step 16:

[1505] The terminal displays the visualized progress data to the user.

[1506] Step 17:

[1507] The server uses AI to analyze the user's training data and emotional data, generating points for reflection and improvement.

[1508] Step 18:

[1509] The server sends the generated points for reflection and improvement to the terminal.

[1510] Step 19:

[1511] The device displays points for improvement and reflection to the user.

[1512] Step 20:

[1513] The server generates appropriate messages and support to improve the user's motivation based on the emotion data.

[1514] Step 21:

[1515] The server sends the generated message and support to the terminal.

[1516] Step 22:

[1517] The device displays motivational messages and support to the user.

[1518] In this way, this system, which combines an emotion engine, not only provides training and meal plans based on the user's physical information and goals, but also recognizes the user's emotions, adaptively adjusts the plans, and supports increased motivation.

[1519] Example 2

[1520] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1521] Conventional methods for providing training and meal plans often set uniform plans based on the user's profile information and goals, making it difficult to provide appropriate plans based on individual physical information and emotional data. Furthermore, there was a lack of feedback to properly understand the user's progress and maintain motivation. Another problem was the lack of a mechanism for adaptively adjusting the plan based on the user's emotions.

[1522] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1523] In this invention, the server includes means for inputting a user's profile information and goals, means for transmitting the input user information to the server, means for utilizing a generative AI model that generates training plans and meal plans based on the user's physical information and goals, means for adaptively adjusting the training plans and meal plans generated by the generative AI model based on emotional data, means for transmitting the generated training plans and meal plans to a user terminal, means for recording the user's daily training results and meal contents and transmitting this to the server, and means for analyzing the collected progress data and emotional data and visualizing it using graphs, charts, etc. This makes it possible to provide optimal training plans and meal plans based on individual physical information and emotional data, allowing the user's progress to be properly understood and appropriate support to maintain motivation.

[1524] "Profile Information" is basic information about an individual, such as a user's name, age, height, weight, and training goals.

[1525] A "generative AI model" is a program or system that uses artificial intelligence techniques to generate a plan based on a user's physical information and goals.

[1526] "Emotion data" is information relating to the user's emotional state, such as data indicating emotions such as "satisfied" or "tired."

[1527] A "training plan" is an exercise or training plan that is individually created based on the user's physical information and goals.

[1528] A "meal plan" is a personalized meal plan based on a user's physical information and goals.

[1529] A "terminal" is a device through which a user inputs information, and includes smartphones, tablets, personal computers, etc.

[1530] A "server" is a computer system that receives, processes, stores, and analyzes information sent by users.

[1531] "Visualization" means displaying progress data and emotional data in a visually easy-to-understand format, such as graphs or charts.

[1532] "Motivational messages" are messages of encouragement and support to increase the user's motivation and enthusiasm.

[1533] "Progress data" is data that indicates the progress of the plan, such as the user's daily training results and dietary details.

[1534] A "user" is an individual who utilizes the system and needs workout and meal plans.

[1535] This invention relates to a system that generates training and meal plans optimized for individual users, and adaptively adjusts the plans based on the user's progress and emotions. Furthermore, the system analyzes the user's progress and emotions and provides messages to improve motivation.

[1536] First, the user enters their profile information (name, age, height, weight, and training goals) into the device. The device then sends this information to the server. The server stores the received user information in a database and generates training and meal plans using a generative AI model (e.g., TensorFlow or PyTorch). The generated plans are adaptively adjusted based on the user's emotional data using an emotion engine (e.g., Watson Emotion Analysis).

[1537] The generated plan is sent from the server to the user's device. The user then carries out their daily training and diet according to the plan received through the device. The training results and meal contents are recorded on the device and sent to the server. At this time, the user's emotional data is also sent. The server stores this data in a database and uses it for analysis.

[1538] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is sent to the user's device, allowing them to check their own progress and achievements.

[1539] Furthermore, the server uses AI to analyze the user's training data and emotional data, providing suggestions for reflection and improvement. Based on the emotional data, the server also generates appropriate messages to motivate the user and sends them to the device, providing support for the user to effectively achieve their goals.

[1540] As a specific example, a user aiming to improve their endurance enters their physical information and goals into their device and sends them to the system. Based on the received information, the server generates a training plan including jogging and exercise three times a week and a meal plan that takes into account a specific nutritional balance. The emotion engine analyzes the user's emotion data and adaptively adjusts the plan. This generated plan is sent to the user's device, and the user trains and eats accordingly. The server continuously receives the user's progress and emotion data, and based on that, generates visualized data and motivational messages and sends them to the device.

[1541] Example prompt for a generative AI model:

[1542] "Enter the user's profile information, such as name, age, height, weight, and training goals. Generate personalized training and meal plans based on this information. Additionally, analyze the user's emotional data and adaptively adjust the plans."

[1543] The system not only uses individual user data and emotional information to provide optimal training and meal plans, but also adaptively adjusts based on progress and emotions, helping users achieve their goals efficiently and effectively.

[1544] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1545] Step 1:

[1546] The user enters their profile information (name, age, height, weight, training goals) into the device. This information is collected by the device and used in the next step. Specifically, the user enters information into the application's input form and clicks the "Submit" button.

[1547] Input: User profile information

[1548] Output: User information stored on the device

[1549] Step 2:

[1550] The terminal sends the entered user profile information to the server using an HTTP POST request.

[1551] Input: User information stored on the device

[1552] Output: User information sent to the server

[1553] Step 3:

[1554] The server stores the received user information in a database. It then uses a generative AI model (e.g., TensorFlow or PyTorch) to generate training and meal plans based on the user's physical information and goals. Specifically, the server provides the stored data to the AI ​​model, which then generates the plans.

[1555] Input: User information sent to the server

[1556] Output: Generated training and meal plans

[1557] Step 4:

[1558] The generated plan is adaptively adjusted based on the user's emotional data by an emotion engine (e.g., "Watson Emotion Analysis"). The emotional data is analyzed and each part of the plan is adjusted.

[1559] Input: Generated training and meal plans

[1560] Output: Adaptively adjusted training and meal plans

[1561] Step 5:

[1562] The server sends the adaptively adjusted plan to the user's terminal using an HTTP response.

[1563] Input: Adaptively adjusted training and meal plans

[1564] Output: Training and meal plans sent to your device

[1565] Step 6:

[1566] The user performs daily training and meals according to the plan received on the device. The training results and meal contents are recorded on the device and sent to the server. Specifically, the user enters the training results and meal contents into the application and clicks the "Save" button.

[1567] Input: User's training results and dietary information

[1568] Output: Progress and emotion data recorded on the device

[1569] Step 7:

[1570] The device sends the recorded progress data and emotion data to the server using an HTTP POST request.

[1571] Input: Progress data and emotion data recorded on the device

[1572] Output: Progress and emotion data sent to the server

[1573] Step 8:

[1574] The server analyzes the collected progress and emotion data and visualizes it as easy-to-understand graphs and charts using tools such as Tableau and D3.js. This visualized data is then sent to the user's device.

[1575] Input: Progress and emotion data sent to the server

[1576] Output: Visualized progress data

[1577] Step 9:

[1578] The server uses AI to analyze the user's training data and emotional data, providing suggestions for improvement and suggestions for reflection. It also generates appropriate messages based on the emotional data to motivate the user and sends them to the device.

[1579] Input: Progress data and emotion data stored on the server

[1580] Output: Reflection, improvement, and motivational messages sent to the device

[1581] (Application example 2)

[1582] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1583] Conventional fitness and nutrition plan providing systems are limited to generating plans based on a user's physical information and goals, and lack the ability to adapt to changes in the user's emotions and motivation. As a result, if a user is unable to maintain their motivation to continue the plan, the effectiveness of the plan decreases. To address these issues, the present invention aims to provide a system that adapts plans and improves motivation based on progress data and emotional data.

[1584] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1585] In this invention, the server includes means for inputting a user's physical information and goals, means for generating a training plan and a nutrition plan based on the user's physical information and goals, means for providing the generated training plan and nutrition plan to the user, means for collecting the user's training and diet progress data, means for visualizing the collected progress data, means for collecting and analyzing emotional data and adaptively adjusting the training plan and nutrition plan based on the collected emotional data, and means for providing messages and support to increase the user's motivation based on the emotional data. This makes it possible to adapt the plan based on both the progress data and the emotional data, thereby maintaining and increasing the user's motivation.

[1586] "User's physical information" refers to data regarding the user's biological attributes, including bone structure, muscle mass, body fat percentage, height, weight, age, and gender.

[1587] A "goal" refers to a specific outcome, such as a level of fitness, weight loss, strength gain, or increased endurance, that a user wishes to achieve.

[1588] A "training plan" is a plan that includes specific content and schedule of an exercise program, designed based on the user's physical information and goals.

[1589] A "nutritional plan" is a plan for the types, amounts, and timing of meals designed based on a user's physical information and goals.

[1590] "Progress data" refers to record and evaluation data regarding the results of the training and dietary habits of the user.

[1591] "Visualization" refers to displaying progress data in a visual format such as a graph or chart so that the user can easily understand it.

[1592] "Emotion data" refers to data relating to the user's psychological state and emotions, including stress level, motivation, satisfaction level, and the like.

[1593] "Adaptively adjusting" means dynamically changing and optimizing the training and nutrition plans based on the user's progress and emotional data.

[1594] "Motivation" refers to support and encouragement to motivate a user to continue with their training and diet plan.

[1595] The present invention is a system that generates and provides personalized training and nutrition plans based on a user's physical information and goals. The system also includes a means for collecting and visualizing the user's progress data, allowing the user to advance self-study. It also incorporates an emotion engine that recognizes the user's emotions, enabling adaptive plan adjustments and motivational support based on emotions. This will be described in more detail below.

[1596] Entering user's physical information and goals

[1597] First, the user uses a smartphone application to input their profile information (name, age, height, weight, training goals), which is then sent to the server via the application and stored.

[1598] Generate training and nutrition plans

[1599] The server uses a generative AI model to generate training and nutrition plans based on the user's physical information and goals. This generative AI model uses Python and TensorFlow, and implements advanced machine learning algorithms. Specific examples of prompts include:

[1600] Generate a training and meal plan including jogging 3 times a week and specific exercises based on the following user profile:

[1601] Name: Username

[1602] Age: 30

[1603] Height: 170cm

[1604] Weight: 70kg

[1605] Training goal: Improve endurance

[1606] Progress data collection and visualization

[1607] Users enter their daily training results and food records into a smartphone application, and the data is sent to a server in real time. The collected data is stored in a database and used for analysis.

[1608] The server collects progress data and visualizes it in graphs and charts (using Matplotlib), allowing users to view their progress on their smartphones.

[1609] Emotional Data Analysis and Adaptive Adjustment

[1610] The server uses an emotion engine to analyze the user's emotional data. The emotional data is collected from the user's self-reporting or wearable device. A specialized algorithm is used to analyze the emotional data and accurately analyze the user's psychological state. For example, if the user is feeling stressed or unmotivated, the system will adaptively adjust their training and nutrition plans based on that data.

[1611] Motivation support

[1612] Based on the collected and analyzed emotional data, the server generates support messages to improve the user's motivation, including encouraging messages and advice on how to achieve the goal, which helps the user to maintain their motivation to continue with the plan.

[1613] With this system configuration, users can obtain optimal training and nutrition plans based on their physical information and goals, and visualize and check their progress. Furthermore, adaptive plan adjustments based on emotional data and support for increased motivation improve the user's fitness experience, enabling them to achieve sustainable results.

[1614] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1615] Step 1:

[1616] The user launches the smartphone application and enters their profile information (name, age, height, weight, and training goals).

[1617] Input: Name, Age, Height, Weight, Training Goal

[1618] Output: User data entered

[1619] Operation: When a user enters information on the input screen and presses the send button, the device sends this data to the server.

[1620] Step 2:

[1621] The server stores the received user data.

[1622] Input: User data (name, age, height, weight, training goal)

[1623] Output: User data stored in the database

[1624] Operation: The server saves the received data in the database and confirms that the data has been saved.

[1625] Step 3:

[1626] The server generates training and nutrition plans using generative AI models based on the stored data.

[1627] Input: User data in the database

[1628] Output: Generated training and nutrition plans

[1629] How it works: The server uses Python and TensorFlow to convert user data into prompts, which are then fed into a generative AI model. The generated plans are then stored in a database.

[1630] Step 4:

[1631] The server transmits the generated training plan and nutrition plan to the user's terminal.

[1632] Input: Generated training and nutrition plans

[1633] Output: The plan displayed on the user's terminal

[1634] How it works: The server sends planning data to the user's smartphone application, which displays it on the device.

[1635] Step 5:

[1636] The user enters daily training results and food records into a smartphone application.

[1637] Input: Training results, food records

[1638] Output: The progress data entered

[1639] Operation: When the user enters data into the specified input form and presses the send button, the terminal sends the data to the server.

[1640] Step 6:

[1641] The server stores the progress data in a database and analyzes it.

[1642] Input: Daily progress data

[1643] Output: Parsed progress data

[1644] How it works: The server stores progress data and performs data analysis using Python.

[1645] Step 7:

[1646] The server visualizes the progress data in graphs and charts and sends it to the user's device.

[1647] Input: Parsed progress data

[1648] Output: Visualized progress data (graphs, charts)

[1649] How it works: The server uses Matplotlib to turn progress data into graphs and charts and sends them to the user's device.

[1650] Step 8:

[1651] The server collects and analyzes the emotional data and adaptively adjusts training and nutrition plans as needed.

[1652] Input: Emotion data

[1653] Output: Adaptively adjusted training and nutrition plans

[1654] Operation: The server analyzes the emotional data collected from the user using the emotion engine, readjusts the plan based on the user's psychological state, saves it in a database, and sends it to the device.

[1655] Step 9:

[1656] The server generates messages and support based on the user's emotions and sends them to the user's device.

[1657] Input: Emotion data

[1658] Output: Motivational messages and support

[1659] Operation: Based on the emotional data, the server generates appropriate messages and support content and sends them to the user's device.

[1660] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1661] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1662] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1663] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1664] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1665] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1666] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1667] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1668] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1669] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1670] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1671] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1672] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1673] 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.

[1674] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1675] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1676] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1677] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1678] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1679] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1680] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1681] The following is further disclosed regarding the above embodiment.

[1682] (Claim 1)

[1683] a means for inputting the user's physical information and goals;

[1684] means for generating training and meal plans based on the user's physical information and goals;

[1685] means for providing the generated training and meal plans to the user;

[1686] means for collecting user training and dietary progress data;

[1687] A means of visualizing the collected progress data;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, further comprising means for analyzing the user's training data and providing suggestions for improvement.

[1691] (Claim 3)

[1692] 2. The system according to claim 1, further comprising means for improving a user's motivation based on the visualized progress data.

[1693] "Example 1"

[1694] (Claim 1)

[1695] a means for inputting the user's physical information and goals;

[1696] means for generating training and meal plans based on the user's physical information and goals;

[1697] means for providing the generated training and meal plans to the user;

[1698] means for collecting user training and dietary progress data;

[1699] A means of visualizing the collected progress data;

[1700] A means to improve user motivation based on visualized progress data;

[1701] A system including:

[1702] (Claim 2)

[1703] 10. The system of claim 1, further comprising means for analyzing a user's training data and providing lessons learned and improvements using the generative AI model.

[1704] (Claim 3)

[1705] 10. The system of claim 1, further comprising means for storing the collected progress data in a database.

[1706] "Application Example 1"

[1707] (Claim 1)

[1708] a means for inputting the user's physical information and goals;

[1709] means for generating training and meal plans based on the user's physical information and goals;

[1710] means for providing the generated training and meal plans to the user;

[1711] means for collecting user training and dietary progress data;

[1712] A means of visualizing the collected progress data;

[1713] A means for collecting machine operation data and work history in a factory;

[1714] means for generating machine training and maintenance plans based on the collected machine data;

[1715] means for providing the generated training and maintenance plans to the machine;

[1716] A system including:

[1717] (Claim 2)

[1718] 10. The system of claim 1, further comprising means for analyzing the user's training data and providing suggestions for improvement.

[1719] (Claim 3)

[1720] 2. The system according to claim 1, further comprising means for improving a user's motivation based on the visualized progress data.

[1721] "Example 2: Combining Emotion Engines"

[1722] (Claim 1)

[1723] a means for inputting user profile information and goals;

[1724] means for transmitting the input user information to a server;

[1725] a means for utilizing a generative AI model to generate training and meal plans based on the user's physical information and goals;

[1726] means for adaptively adjusting the training plan and meal plan generated by the generative AI model based on the emotion data;

[1727] means for transmitting the generated training plan and meal plan to a user terminal;

[1728] A means for recording the user's daily training results and dietary details and transmitting the results to a server;

[1729] A means of analyzing the collected progress data and emotion data and visualizing it using graphs and charts, etc.

[1730] A system including:

[1731] (Claim 2)

[1732] 10. The system of claim 1, further comprising means for analyzing the user's training data and emotion data and providing suggestions for reflection and improvement.

[1733] (Claim 3)

[1734] 10. The system according to claim 1, further comprising means for generating adaptive messages based on the visualized progress data and emotion data, thereby improving the motivation of the user.

[1735] "Application example 2 when combining emotion engines"

[1736] (Claim 1)

[1737] a means for inputting the user's physical information and goals;

[1738] means for generating a training plan and a nutrition plan based on the user's physical information and goals;

[1739] means for providing the generated training and nutrition plans to a user;

[1740] means for collecting user training and dietary progress data;

[1741] A means of visualizing the collected progress data;

[1742] means for collecting and analyzing emotional data and adaptively adjusting training and nutrition plans based on the collected emotional data;

[1743] A means for providing messages and support that improve the user's motivation based on emotional data;

[1744] A system including:

[1745] (Claim 2)

[1746] 10. The system of claim 1, further comprising means for analyzing the user's training data and emotion data and providing suggestions for reflection and improvement.

[1747] (Claim 3)

[1748] 10. The system of claim 1, further comprising means for improving a user's motivation based on the visualized progress data and emotion data. [Explanation of symbols]

[1749] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting the user's physical information and goals; means for generating training and meal plans based on the user's physical information and goals; means for providing the generated training and meal plans to the user; means for collecting user training and dietary progress data; A means of visualizing the collected progress data; A system including:

2. 10. The system of claim 1, further comprising means for analyzing the user's training data and providing suggestions for improvement.

3. The system according to claim 1 , further comprising means for improving the motivation of the user based on the visualized progress data.

Images