System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving user input information and generating an initial fitness plan and meal menu; means for receiving and recording data as a user exercises at a gym; means for taking pictures of meals taken by a user and analyzing the images to generate meal data; means for analyzing user progress data and predicting user goal attainment time; and means for notifying a user of progress data and goal attainment prediction.SELECTED DRAWING: Figure 1

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] With the increase in working from home, many people are facing lack of exercise and difficulties in managing their health. Furthermore, it is difficult to obtain information optimized for individual fitness goals and dietary preferences, making it difficult to maintain sustainable health. Therefore, there is a growing need for a system that provides optimized fitness plans and meal menus for individual users, tracks their progress, and predicts when their goals will be achieved. [Means for solving the problem]

[0005] The present invention solves these problems by providing a system that includes means for receiving user input and generating an initial fitness plan and meal menu, means for receiving and recording data when the user exercises at the gym, means for the user to take photos of their meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goals, and means for notifying the user of the progress data and the predicted goal achievement.

[0006] "User" refers to an individual who uses the system.

[0007] "Input Information" refers to information such as name, age, gender, weight, height, and fitness goals provided by the user during initial setup.

[0008] "Fitness plan" refers to an exercise plan created based on a user's health goals.

[0009] "Meal Menu" refers to a meal plan suggested based on a user's food preferences and health goals.

[0010] "Exercise data" refers to information such as the type, duration, intensity, and calories burned of exercise performed by a user.

[0011] "QR code" refers to a two-dimensional barcode used to read exercise data.

[0012] "Photos of meals" refers to photographic data of meals eaten by the user.

[0013] "Image analysis" refers to the process of using AI to estimate ingredients and calories from photos of meals.

[0014] "Progress Data" refers to the user's daily exercise and dietary summary data.

[0015] "Time to reach goal" refers to the estimated time to reach the goal based on the user's current pace.

[0016] "Notification" refers to the act of informing users of information such as feedback, new plans, and progress reports. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system that provides an individual user with an optimized fitness plan and meal menu, tracks the progress, and predicts when the goal will be achieved. Specific embodiments of the system and its processing method are described below.

[0039] User registration and initial settings

[0040] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[0041] Daily data entry and tracking

[0042] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[0043] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[0044] Personalized advice and progress

[0045] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[0046] Predicting goal achievement

[0047] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[0048] Free and paid versions available

[0049] The system operates on a subscription model with a free version and a paid version: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[0050] Specific examples

[0051] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks.

[0052] In this way, the system helps users achieve their health goals.

[0053] The processing flow will be explained below.

[0054] Specific flow of program processing

[0055] User registration and initial settings

[0056] Step 1:

[0057] The user downloads and installs the app.

[0058] Step 2:

[0059] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[0060] Step 3:

[0061] The device sends the input information to the server via the API.

[0062] Step 4:

[0063] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[0064] Step 5:

[0065] The server sends the generated plan to the terminal in JSON format.

[0066] Step 6:

[0067] The terminal displays the received plan to the user.

[0068] Daily data entry and tracking

[0069] Step 1:

[0070] The user works out at the gym and scans the QR code on the machine with the app.

[0071] Step 2:

[0072] The device parses and formats the exercise data (e.g., time, calories burned, intensity) obtained from the QR code.

[0073] Step 3:

[0074] In some cases, the user manually enters the exercise data.

[0075] Step 4:

[0076] The device sends the exercise data to the server.

[0077] Step 5:

[0078] The server records the received exercise data in the user profile.

[0079] Step 6:

[0080] When users eat, they take a photo of their meal using the app.

[0081] Step 7:

[0082] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[0083] Step 8:

[0084] The user can check the estimated ingredients and calories and make corrections as necessary.

[0085] Step 9:

[0086] The device transmits the corrected meal data to the server.

[0087] Step 10:

[0088] The server records the received meal data in the user profile.

[0089] Personalized advice and progress

[0090] Step 1:

[0091] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[0092] Step 2:

[0093] The server then adjusts new fitness menus and meal plans based on the analysis results.

[0094] Step 3:

[0095] The server sends the generated plan to the terminal in JSON format.

[0096] Step 4:

[0097] The terminal notifies the user of the received plan and advice and displays detailed information.

[0098] Predicting goal achievement

[0099] Step 1:

[0100] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[0101] Step 2:

[0102] The server sends the prediction results in JSON format to the device.

[0103] Step 3:

[0104] The device displays the prediction results and feedback to the user.

[0105] Subscription model available

[0106] Step 1:

[0107] A user creates a free account and accesses basic features.

[0108] Step 2:

[0109] The device sends the registration information for the free version to the server.

[0110] Step 3:

[0111] The server records user data for the free version and provides basic functionality.

[0112] Step 4:

[0113] The user selects to upgrade to the paid version within the app and enters their payment information.

[0114] Step 5:

[0115] The terminal sends the payment information to the server.

[0116] Step 6:

[0117] The server confirms payment and unlocks the paid features.

[0118] Step 7:

[0119] The server provides personalization features and expert support to paid users.

[0120] Example 1

[0121] 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."

[0122] Conventional systems have difficulty providing individual users with optimized exercise and meal plans, efficiently tracking their progress, and accurately predicting when their goals will be achieved. Furthermore, inputting users' exercise and meal data is time-consuming, making them difficult to use. There is a need for a system that can solve these issues and enable users to easily achieve their health goals.

[0123] 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.

[0124] In this invention, the server includes means for receiving user input information and generating an initial exercise menu and meal plan, means for receiving and recording data from the user's training at an exercise facility, means for the user to take photos of their meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, and means for notifying the user of the progress data and the predicted goal achievement. This allows for the provision of a personalized exercise menu and meal plan based on the user's information, enabling easy data entry and accurate progress management.

[0125] "Input Information" is basic information provided by the user, such as name, age, gender, weight, height, and fitness goals.

[0126] An "exercise menu" is an exercise program planned based on fitness goals set by a user.

[0127] A "meal plan" is a list of meals that are nutritionally balanced and planned to meet the user's health goals.

[0128] An "exercise facility" is a place where users train, such as a gym or fitness center.

[0129] "Training data" refers to data such as the content and duration of training the user undertook at an exercise facility, and calories burned.

[0130] "Identification codes" refer to QR codes or barcodes associated with exercise machines, food, etc., and by scanning these, training and dietary information can be quickly entered.

[0131] "Dietary data" refers to photographs of meals consumed by the user and information such as ingredients and calories based on the analysis results.

[0132] "Analysis" is the process by which the server processes the collected data and makes calculations regarding the user's progress and goal achievement.

[0133] The "goal completion date" is the date by which the user plans to achieve the fitness or health goal they set based on their progress data.

[0134] "Progress data" is an ongoing record of the amount of exercise a user is doing and the contents of their diet.

[0135] "Notifications" are messages or alerts generated by the server that are used to communicate progress and goal achievement forecast information to the user's device.

[0136] MODE FOR CARRYING OUT THE INVENTION

[0137] The present invention relates to a system that provides an exercise menu and meal plan optimized for an individual user, tracks the progress, and predicts when the goal will be achieved. Specific embodiments will be described below.

[0138] User registration and initial settings

[0139] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and exercise goals (e.g., weight loss, muscle building, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial exercise menu and meal plan, which it then sends to the device. The device receives this and displays it to the user.

[0140] Daily data entry and tracking

[0141] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[0142] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[0143] Personalized advice and progress

[0144] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new exercise and diet plan is tailored. The server then sends this information to the device, which notifies the user.

[0145] Predicting goal achievement

[0146] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[0147] Free and paid versions available

[0148] The system operates on a two-tier subscription model: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[0149] Specific examples

[0150] For example, if a user sets a goal of losing weight and building muscle, and their current weight is 70 kg and their target weight is 65 kg, the following process will occur: The user exercises on a treadmill for 30 minutes and enters the data by scanning the identification code attached to the gym machine. They also eat a 200 kcal salad and take a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server will suggest a new training plan and meal menu, and notify the user that they will reach their target weight in five weeks.

[0151] Prompt Sentence Examples

[0152] "Please explain in detail how a user trying to lose weight can enter their day's exercise and diet information into the app. How does the server and device process the data?"

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1:

[0155] The user downloads and installs the system's application. On the account creation screen, the user enters basic information such as name, age, gender, weight, height, and exercise goals, and the device sends this information to the server. The entered information is sent to the server and stored in a database. This information becomes the data used to generate an initial exercise menu and meal plan.

[0156] Step 2:

[0157] The server generates an initial exercise menu and meal plan based on the user information stored in the database. The generated exercise menu and meal plan are sent to the terminal, which displays them to the user. The input here is the user information, and the output is the initial exercise menu and meal plan.

[0158] Step 3:

[0159] When a user goes to the gym to work out, they either scan the identification code attached to the gym machine with the app or manually enter their exercise data. The device sends the entered exercise data to the server, which records it in a database. The input is the exercise data, and the output is the recorded database entry.

[0160] Step 4:

[0161] When a user eats a meal, they take a photo of the meal using the app. The device analyzes the captured image of the meal and estimates the ingredients and calories. The estimated data may be reviewed by the user and revised. The revised data is sent to the server, which records it in a database. The input is the meal image and the revised data, and the output is the recorded database entry.

[0162] Step 5:

[0163] The server aggregates the exercise and dietary data recorded in the database and analyzes the user's progress. Based on the results of this analysis, the exercise menu and diet plan are adjusted and a new plan is generated. The generated plan is sent to the device, which notifies the user. The input here is the recorded data, and the output is the adjusted exercise menu and diet plan.

[0164] Step 6:

[0165] The server uses a machine learning algorithm to predict when the goal will be achieved based on the recorded progress data. This prediction result is sent to the device, which then notifies the user of the expected goal achievement date. The input is the progress data, and the output is the predicted goal achievement result.

[0166] Step 7:

[0167] Users can upgrade their subscription from the free version to a paid version as needed, which provides more personalized features and expert support. This upgrade also updates the user's account information and records it on the server. The input here is the subscription information, and the output is the upgraded account information and additional features.

[0168] (Application example 1)

[0169] 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."

[0170] Conventional fitness and diet management systems struggle to provide detailed fitness plans and diet menus tailored to individual users' needs. They also lack the means for users to accurately track their progress and predict when they will reach their goals. It's also difficult for users to receive appropriate training advice in real time during fitness activities. This makes it difficult for users to maintain motivation, often resulting in delays in achieving their goals.

[0171] 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.

[0172] In this invention, the server includes means for receiving user input information and generating an initial fitness plan and meal menu, means for receiving and recording data of the user's fitness activities, means for the user to take images of meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, means for notifying the user of the progress data and the predicted goal achievement, and means for video chatting with the user in real time and providing training advice. This allows the user to accurately understand their own progress through the personalized fitness plan and meal menu and to effectively train and manage their diet to achieve their goal.

[0173] "User input" refers to data such as personal information and fitness goals that a user provides to the system.

[0174] The "initial fitness plan" is a fitness activity plan generated based on the user's input information, and includes exercise content and schedules that are optimal for each individual user.

[0175] A "meal menu" is a meal plan generated based on information input by the user, and includes meal contents that take into consideration the balance of calories and nutrients.

[0176] A "fitness activity" is a series of exercise or training activities undertaken by a user.

[0177] "Means for receiving and recording data" refers to the function of the system receiving data when a user performs a fitness activity and storing that data.

[0178] "Means for analyzing images of meals and generating meal data" refers to a function that analyzes the contents of images of meals taken by the user and estimates the foods, nutrients, and calories contained in the meal.

[0179] "Progress data" is data that indicates the progress status calculated based on the user's fitness activity and diet history.

[0180] "Means for predicting when a goal will be achieved" refers to a function that analyzes a user's progress data and predicts how long it will take to achieve a set fitness goal, weight goal, etc.

[0181] The "notification means" is a means for notifying the user of information such as progress data and a goal achievement forecast.

[0182] "Video chat" is a communication method that allows users to have real-time video calls and receive training advice and guidance.

[0183] A "personalized fitness plan" is a fitness activity plan that is customized to a user's individual circumstances and goals.

[0184] "Appropriate training advice in real time" refers to advice and guidance on exercise methods and form corrections that are provided to the user immediately on the spot while they are training.

[0185] The present invention relates to a system for providing a user with an optimized fitness plan and meal menu, tracking the progress, and predicting when the user will reach their goal. Specific embodiments of the system are described below.

[0186] 1. User registration and initial settings

[0187] The user first downloads and installs the system's application. To use the fitness plan and meal menu, the user provides input information such as name, age, gender, weight, height, and fitness goals. This information is sent by the device to the server. The server receives this input information, generates an initial fitness plan and meal menu, and sends it to the device. The user can view the generated fitness plan and meal menu on the device.

[0188] 2. Daily data entry and tracking

[0189] When a user engages in fitness activities, they are required to input activity data. Users can obtain fitness data by scanning QR codes attached to gym machines, or they can manually enter data if a QR code is not available. This data is sent from the device to a server and recorded. Additionally, when a user eats, they take a photo of the meal using the app. This image is analyzed on the device and meal data is generated. A generative AI model is used for the analysis. The user checks the calories and nutrients of their meal and makes any necessary adjustments. The corrected data is sent to the server and recorded.

[0190] 3. Personalized advice and progress

[0191] The server analyzes the collected fitness and dietary data to evaluate the user's current progress. Based on the analysis results, it adjusts new fitness menus and meal plans. It also provides training advice to the user through a real-time video chat function. This allows users to receive advice on appropriate exercise methods and form corrections in real time.

[0192] 4. Predicting goal achievement

[0193] The server analyzes the user's progress data and uses machine learning algorithms to predict when the goal will be achieved. The calculated goal achievement date is then sent to the user as a notification. For example, if the user's goal is weight loss, the estimated achievement date if the user continues at the current pace of progress is displayed.

[0194] 5. Free and paid versions available

[0195] The system operates on a subscription model with a free version offering basic fitness and diet management features, while upgrading to the paid version offers more in-depth personalization features and expert support.

[0196] Usage example

[0197] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. This data is sent from the device to the server and recorded. The server generates a new training plan and meal menu based on the data so far, and notifies the user that they will reach their target weight in five weeks.

[0198] Prompt Sentence Examples

[0199] Enter your name, age, weight, height, and fitness goal (e.g., lose weight, gain strength, improve endurance).

[0200] This way, users can access a personalized fitness plan at home, receive real-time training advice, and efficiently work towards their goals.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] A user downloads and installs a fitness application. On the account creation screen, the user provides input information such as name, age, gender, weight, height, and fitness goals. The device sends this input information to a server. The server generates an initial fitness plan and meal menu for the user based on the received data. This is done using a generative AI model, which uses the user's basic information as input to provide the optimal plan. The generated plan is sent to the device and displayed to the user.

[0204] Step 2:

[0205] When users perform their daily fitness activities, they can capture data by scanning QR codes attached to gym machines. If a QR code is not available, exercise data can be entered manually. The device sends the captured exercise data to the server, which records it. At this time, the captured data (e.g., exercise time, calories burned) is sent as input to the server and recorded in a database.

[0206] Step 3:

[0207] When a user eats, they take a photo of the meal using the app. The device analyzes this image and generates meal data. This analysis involves preprocessing and then using a generative AI model. Meal data (e.g., food names, calories) is automatically generated, and the user can review and correct this data. The corrected data is sent from the device to the server and recorded.

[0208] Step 4:

[0209] The server analyzes the collected exercise and dietary data to evaluate the user's progress. A machine learning algorithm is used for the analysis, and the user's progress data is used as input to evaluate the current situation. Based on the analysis results, a new fitness manual and diet plan are generated and sent to the device. The user receives the new plan and can proceed to the next step.

[0210] Step 5:

[0211] The server analyzes the user's progress data and uses a machine learning algorithm to predict when the goal will be achieved. Specifically, it evaluates the user's current pace based on past data and calculates the time it will take to achieve the goal. This predicted time is sent to the device and notified to the user. Knowing the specific target date helps users stay motivated.

[0212] Step 6:

[0213] Users can receive training advice through a real-time video chat function. This system allows users to start a video call through an app on their device and receive advice from a trainer on exercise methods and form corrections. By receiving real-time feedback, users can train more effectively.

[0214] Step 7:

[0215] The system offers a subscription model with free and paid versions. Users can use the free version to access basic fitness and diet management functions. By upgrading to the paid version, they can access more detailed personalization features and expert support. The server will provide new modules based on the user's plan and perform further personalization based on that.

[0216] The above are the specific processing steps for carrying out the present invention. This system allows users to efficiently achieve their health goals through fitness plans optimized for their individual needs.

[0217] 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.

[0218] This invention combines a system that provides an individual user with a fitness plan and meal menu optimized for them, tracks their progress, and predicts when they will reach their goals, with an emotion engine that recognizes the user's emotions and adjusts the plan and feedback accordingly. Specific embodiments of the system and its processing method are described below.

[0219] User registration and initial settings

[0220] First, the user downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[0221] Daily data entry and tracking

[0222] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[0223] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[0224] Personalized advice and progress

[0225] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[0226] Predicting goal achievement

[0227] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[0228] Introducing the Emotion Engine

[0229] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions, allowing the server to determine the user's emotional state and adjust the fitness plan, meal menu, and even feedback accordingly.

[0230] For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest appropriate relaxation exercises or meals to relieve stress. The emotion engine can also provide positive feedback to increase the user's motivation.

[0231] Subscription model available

[0232] The system operates on a subscription model with a free and paid version. The free version provides basic exercise and food tracking functions, but users can upgrade to the paid version for more detailed personalization features and expert support. The paid version also includes advanced feedback and support from an emotion engine.

[0233] Specific examples

[0234] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user of a prediction that they will reach their target weight in five weeks.

[0235] Additionally, if the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals, thus supporting the user's health and emotions and helping them achieve their goals.

[0236] The processing flow will be explained below.

[0237] User registration and initial settings

[0238] Step 1:

[0239] The user downloads and installs the app.

[0240] Step 2:

[0241] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[0242] Step 3:

[0243] The device sends the input information to the server via the API.

[0244] Step 4:

[0245] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[0246] Step 5:

[0247] The server sends the generated plan to the terminal in JSON format.

[0248] Step 6:

[0249] The terminal displays the received plan to the user.

[0250] Daily data entry and tracking

[0251] Exercise data entry and recording

[0252] Step 1:

[0253] The user works out at the gym and scans the QR code on the machine with the app.

[0254] Step 2:

[0255] The device analyzes and formats the exercise data (time, calories burned, exercise intensity) obtained from the QR code.

[0256] Step 3:

[0257] In some cases, the user manually enters the exercise data.

[0258] Step 4:

[0259] The device sends the exercise data to the server.

[0260] Step 5:

[0261] The server records the received exercise data in the user profile.

[0262] Meal data entry and recording

[0263] Step 6:

[0264] The user takes a photo of their meal using the app.

[0265] Step 7:

[0266] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[0267] Step 8:

[0268] The user can check the estimated ingredients and calories and make corrections as necessary.

[0269] Step 9:

[0270] The device transmits the corrected meal data to the server.

[0271] Step 10:

[0272] The server records the received meal data in the user profile.

[0273] Personalized advice and progress

[0274] Step 1:

[0275] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[0276] Step 2:

[0277] The server then adjusts new fitness menus and meal plans based on the analysis results.

[0278] Step 3:

[0279] The server sends the generated plan to the terminal in JSON format.

[0280] Step 4:

[0281] The terminal notifies the user of the received plan and advice and displays detailed information.

[0282] Predicting goal achievement

[0283] Step 1:

[0284] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[0285] Step 2:

[0286] The server sends the prediction results in JSON format to the device.

[0287] Step 3:

[0288] The device displays the prediction results and feedback to the user.

[0289] Introducing the Emotion Engine

[0290] Emotion data input and recognition

[0291] Step 1:

[0292] The user uses the app to input voice commands and take a photo of their face.

[0293] Step 2:

[0294] The device sends voice data and facial photo data to the server.

[0295] Step 3:

[0296] The server uses an emotion engine to analyze voice data and facial photo data and recognize the user's emotions.

[0297] Emotion-based plan adjustment and feedback

[0298] Step 4:

[0299] The server uses the recognized emotion data to adjust fitness plans and meal menus.

[0300] Step 5:

[0301] The server sends the adjusted plan and feedback to the device in JSON format.

[0302] Step 6:

[0303] The device notifies the user of the plans and feedback it has received and displays detailed information.

[0304] Subscription model available

[0305] Step 1:

[0306] A user creates a free account and accesses basic features.

[0307] Step 2:

[0308] The device sends the registration information for the free version to the server.

[0309] Step 3:

[0310] The server records user data for the free version and provides basic functionality.

[0311] Step 4:

[0312] The user selects to upgrade to the paid version within the app and enters their payment information.

[0313] Step 5:

[0314] The terminal sends the payment information to the server.

[0315] Step 6:

[0316] The server confirms payment and unlocks the paid features.

[0317] Step 7:

[0318] The server provides personalization features and expert support to paid users.

[0319] Example 2

[0320] 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."

[0321] While traditional fitness and diet management systems provide plans based on a user's basic information, they often fail to take into account the user's emotions and psychological state, making it difficult to maintain user motivation. Furthermore, they often lack the ability to efficiently analyze the data entered by the user and provide appropriate feedback in real time, resulting in ineffective personalization. Furthermore, progress tracking and goal achievement predictions are often inaccurate, and notifications to users are often delayed.

[0322] 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.

[0323] In this invention, the server includes means for receiving user input information and generating an initial exercise plan and meal menu, means for receiving and recording data when the user exercises, means for allowing the user to take photos of meals and analyzing the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goals, means for recognizing the user's emotions and adjusting the plan and feedback, and means for notifying the user of the progress data and goal achievement predictions. This provides a personalized fitness plan and meal plan that takes the user's emotions and psychological state into consideration, enabling effective tracking and goal achievement predictions while maintaining the user's motivation.

[0324] "User" refers to an individual who uses this system, including the entity receiving the fitness plan or meal menu.

[0325] "Input Information" means data provided by a User to the System, including personal information such as name, age, gender, weight, height, and fitness goals.

[0326] "Workout Plan" refers to an exercise or training plan generated based on a user's fitness goals.

[0327] "Meal Menu" refers to a suggested dietary and nutrient-based plan to address a user's fitness goals.

[0328] "Exercise Data" means details of exercises performed by a User at a gym or other exercise facility, including data obtained by scanning a QR code or manually entering it.

[0329] "Dietary Data" refers to information about the diet of a user, such as the types and amounts of food consumed and calories, and includes analysis results of photos taken by the user.

[0330] "Progress Data" refers to data that indicates the current progress of a User toward their fitness goals, as a result of compiling and analyzing the User's exercise and diet data.

[0331] "Goal Timeline" means the estimated time period or specific date by which a user will achieve their fitness goal.

[0332] "Emotion" refers to the user's emotional state, a psychological state determined by the system through voice input and facial expression recognition.

[0333] "Notification methods" refers to the methods by which the system communicates information such as progress, new plans, and feedback to the user, including app push notifications and emails.

[0334] This invention combines a system that provides users with optimized exercise plans and meal menus, tracks their progress, and predicts when they will reach their goals with a function that recognizes the user's emotions and adjusts the plan and feedback. Specific embodiments of the system and its processing method are described below.

[0335] User registration and initial settings

[0336] 1. User behavior:

[0337] First, the user downloads and installs the system's application onto their smartphone, which can be downloaded from the App Store or Google Play, for example.

[0338] Launch the app and enter basic information such as your name, age, gender, weight, height, and fitness goals on the account creation screen.

[0339] 2. Terminal processing:

[0340] The device sends the entered information to a server over the internet, using your internet connection (Wi-Fi or mobile data).

[0341] 3. Server processing:

[0342] The server stores the received information in a database, for example, a relational database such as MySQL, to ensure consistent data storage.

[0343] The server generates an initial exercise plan and meal menu based on the user's basic information and fitness goals, using a Python-based backend program.

[0344] The generated plans and menus are sent back to the terminal and displayed to the user.

[0345] Daily data entry and tracking

[0346] 1. User behavior:

[0347] When a user exercises at the gym, they need to input their exercise data. Users can either scan the QR code attached to the gym machine or input it manually.

[0348] 2. Terminal processing:

[0349] When the QR code is scanned, the device automatically acquires the information and sends the exercise data to the server. Data is also sent to the server when manually entered.

[0350] 3. User Behavior:

[0351] Users take photos of their meals using the app.

[0352] 4. Terminal processing:

[0353] The device uses an image analysis engine (such as OpenCV or TensorFlow) to estimate ingredients and calories from the captured photo. The user can review the estimated results and make corrections as necessary.

[0354] 5. Server Processing:

[0355] The modified data is sent from the terminal and recorded by the server.

[0356] Personalized advice and progress

[0357] 1. Server process:

[0358] The server uses an analytical engine (e.g., the Pandas library or the SciPy library) to analyze the aggregated exercise and dietary data.

[0359] Based on the analysis results, a new exercise plan and meal menu are generated and sent to the device.

[0360] 2. Terminal processing:

[0361] The new plan is received by the terminal and notified to the user.

[0362] Predicting goal achievement

[0363] 1. Server process:

[0364] The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to predict when the goal will be achieved based on the user's progress data.

[0365] The prediction result is sent to the terminal.

[0366] 2. Terminal processing:

[0367] The user is notified of the predicted timeframe for reaching the goal.

[0368] Introducing the Emotion Engine

[0369] 1. User behavior:

[0370] Users input voice and facial expression data into the app.

[0371] 2. Terminal processing:

[0372] The input data is analyzed using a voice recognition engine (e.g., a voice recognition API) or an expression recognition engine (e.g., a face recognition API).

[0373] The analysis results are sent to the server.

[0374] 3. Server processing:

[0375] The server uses an emotion engine to determine the user's emotional state and generates fitness and meal plans and feedback based on this.

[0376] The adjusted feedback is sent to the terminal.

[0377] 4. Terminal processing:

[0378] The adjusted feedback is communicated to the user.

[0379] Subscription model available

[0380] 1. User behavior:

[0381] Users can choose between a free or paid subscription model within the app.

[0382] 2. Terminal processing:

[0383] Depending on the subscription selected, payment is processed through a payment gateway (e.g., a payment service).

[0384] 3. Server processing:

[0385] Once payment is confirmed, the paid version's detailed features, expert support, and feedback from an emotion engine will be unlocked.

[0386] Specific examples

[0387] For example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans the QR code attached to the machine, and eats a 200 kcal salad and takes a photo of it using the app. The device sends the exercise and diet data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks. If the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals. In this way, the system supports the user's health and emotions, helping them achieve their goals.

[0388] Input prompt for generative AI model

[0389] Examples of prompts include:

[0390] I've set my goals to "lose weight" and "increase muscle strength." My current weight is 70 kg, and my target weight is 65 kg. Please suggest me a fitness plan and meal menu for the future. Also, please let me know the date by which I plan to achieve this.

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] System program processing flow

[0393] Step 1:

[0394] User registration and initial settings

[0395] 1. Input:

[0396] Users download and install the system's application, then enter basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen.

[0397] 2. Specific operation:

[0398] The user launches the app and enters their information.

[0399] The terminal receives the input information and transmits it to a server via the Internet.

[0400] 3. Data Processing:

[0401] The server stores the received information (name, age, gender, weight, height, fitness goals) in a database.

[0402] 4. Output:

[0403] The server generates an initial exercise plan and meal menu and sends them to the device.

[0404] Step 2:

[0405] Daily data entry and tracking

[0406] 1. Input:

[0407] Users work out at the gym and either scan a QR code attached to the machine with the app or manually enter their exercise data.

[0408] 2. Specific operation:

[0409] Once the QR code is scanned, the device will automatically obtain relevant information and collect exercise data.

[0410] Manually entered data is handled similarly.

[0411] Users take photos of their meals using the app.

[0412] 3. Data Processing:

[0413] The terminal transmits the collected exercise data to a server via the Internet.

[0414] The device uses an image analysis engine to analyze photos of meals and estimate ingredients and calories.

[0415] The estimated results are displayed to the user, who can then correct them as necessary.

[0416] 4. Output:

[0417] The device transmits the corrected meal data to the server.

[0418] The server records this data in a database.

[0419] Step 3:

[0420] Personalized advice and progress

[0421] 1. Input:

[0422] The collected exercise and dietary data is stored on a server.

[0423] 2. Specific operation:

[0424] The server uses an analysis engine to analyze the exercise and dietary data and calculate the user's progress.

[0425] 3. Data Processing:

[0426] Based on the analysis results, a new exercise plan and meal menu will be generated.

[0427] 4. Output:

[0428] The server sends the generated new plan to the terminal.

[0429] The terminal receives this and notifies the user.

[0430] Step 4:

[0431] Predicting goal achievement

[0432] 1. Input:

[0433] The state in which the user's progress data is accumulated.

[0434] 2. Specific operation:

[0435] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[0436] 3. Data Processing:

[0437] Analyze progress data and apply appropriate algorithms to make predictions.

[0438] 4. Output:

[0439] The server transmits the predicted completion time to the terminal.

[0440] The terminal notifies the user of the prediction result.

[0441] Step 5:

[0442] Introducing the Emotion Engine

[0443] 1. Input:

[0444] The user inputs voice and facial expression data.

[0445] 2. Specific operation:

[0446] The data is analyzed using a voice recognition engine and a facial expression recognition engine.

[0447] 3. Data Processing:

[0448] Based on the analysis results, the user's emotional state is estimated and plans and feedback content are adjusted accordingly.

[0449] 4. Output:

[0450] The server sends the adjusted plan and feedback to the device.

[0451] The terminal notifies the user of the adjustment results.

[0452] Step 6:

[0453] Subscription model available

[0454] 1. Input:

[0455] The user selects a subscription model.

[0456] 2. Specific operation:

[0457] The terminal processes the payment based on the selected subscription.

[0458] 3. Data Processing:

[0459] A payment gateway is used to process the payment, and if successful, the data is stored on the server.

[0460] 4. Output:

[0461] The server will offer users the advanced features and support of the paid version.

[0462] (Application example 2)

[0463] 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."

[0464] Conventional fitness plan and meal menu delivery systems were unable to take into account the user's individual emotional state, resulting in insufficient measures to address user motivation and stress. Furthermore, the manual input method for exercise and dietary data was burdensome, potentially reducing tracking accuracy. Furthermore, there was no system that comprehensively supported fitness activities and dietary management within the gym, resulting in inconsistent progress management for users. These challenges made it difficult for users to achieve their goals.

[0465] 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.

[0466] In the present invention, the server includes the following means:

[0467] means for receiving user input and generating an initial fitness plan and meal menu;

[0468] means for receiving and recording data when a user exercises at a gym;

[0469] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[0470] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[0471] means for notifying the user of progress data and goal achievement projections;

[0472] A means for recognizing the user's emotional state from their voice input and facial expressions, and adjusting the feedback content using an emotion engine;

[0473] A method to scan the QR code on the training machine in the gym and enter exercise data,

[0474] It includes measures to suggest relaxation exercises and stress-relieving meals based on emotional state.

[0475] This allows for feedback and advice tailored to the user's individual emotional state, realizes efficient and precise tracking of exercise and diet data, and provides comprehensive support for users' fitness activities and diet management, helping them achieve their goals.

[0476] "User input information" refers to basic information such as name, age, sex, weight, height, and fitness goals that a user provides to the system.

[0477] A "fitness plan" is an exercise schedule or training menu that is generated based on a user's fitness goals.

[0478] A "meal menu" is a daily meal plan suggested to suit the user's health and fitness goals.

[0479] "Exercise data" refers to data such as the type and duration of exercise performed by the user at the gym, and calories burned.

[0480] "Dietary data" refers to data relating to the contents, calories, ingredients, etc. of meals consumed by the user.

[0481] "Progress data" is data recorded about the exercises a user actually performs and the food they eat, and indicates the user's progress toward achieving their goals.

[0482] The "goal achievement date" is the date and time when the user is expected to achieve the fitness goal they have set.

[0483] "Notification" is the means by which the system provides information to the user, in the form of messages, alerts, etc.

[0484] An "emotion engine" is an algorithm or software that recognizes a user's emotional state from their voice input and facial expressions, and adjusts feedback based on that emotional state.

[0485] A "QR code" is a two-dimensional barcode attached to training machines in the gym, which users can scan with their smartphone to automatically enter exercise data.

[0486] "Relaxation exercises" are relaxation exercises and stretches that users perform to reduce stress and fatigue.

[0487] "Meals for stress relief" are ingredients and menus that are effective in reducing stress and are suggested based on the user's stress level.

[0488] This invention is a system that provides an individual user with an optimized fitness plan and meal menu, tracks their progress, and predicts when they will reach their goals. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the plan and feedback based on those emotions. Specific embodiments of this system and its processing method are described below.

[0489] User registration and initial settings

[0490] First, the user downloads and installs the system's application onto their smartphone. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this information and displays it to the user.

[0491] Daily data entry and tracking

[0492] When a user exercises at the gym, they need to input their exercise data. The user can automatically obtain the exercise data by scanning a QR code attached to the gym's training machines with their smartphone. Meal data is also input by the user taking a photo of the meal using the app. The smartphone analyzes the captured image, and the ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The data is then sent to a server and recorded.

[0493] Personalized advice and progress

[0494] The server analyzes the aggregated exercise and diet data to calculate the user's current progress, and adjusts a new fitness plan and diet menu based on the analysis. The server then sends this information to the smartphone, which notifies the user.

[0495] Goal achievement prediction and notification

[0496] The server uses the user's progress data to predict when the goal will be achieved using a machine learning algorithm. The predicted time is then sent to the user via a smartphone notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[0497] Introducing the Emotion Engine

[0498] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions. Based on the emotional state recognized by the emotion engine, the server can adjust the fitness plan, meal menu, and even feedback content. For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest relaxation exercises or stress-relieving meals to the user. The emotion engine also provides positive feedback to increase the user's motivation.

[0499] Hardware and software used

[0500] Smartphone: where users install apps and use them to enter data and receive notifications.

[0501] Server: Stores and analyzes user data, calculates progress, predicts when goals will be reached, and adjusts fitness plans and meal menus.

[0502] QR Code Reader: A means of reading the QR code on training machines at the gym and automatically obtaining exercise data.

[0503] Image processing library: Used to analyze photos of meals and estimate ingredients and calories.

[0504] Emotion Recognition Library: Used to recognize emotional states by analyzing voice input and facial expressions.

[0505] Examples of concrete examples and prompts

[0506] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill at the gym for 30 minutes and scans a QR code. They also eat a 200 kcal salad and take a photo of it with their smartphone. The server collates this data, suggests a new training plan and meal menu to the user, and notifies them that they will reach their target weight in five weeks. If the user is feeling stressed, the server also suggests relaxing yoga exercises and stress-relieving meals.

[0507] An example prompt is:

[0508] User data registration:

[0509] To register, please enter your name, age, gender, weight, height and fitness goals.

[0510] Exercise Data Entry:

[0511] After your workout, scan the QR code to enter your exercise data.

[0512] Meal Data Entry:

[0513] Take a photo of your meal and check and edit the ingredients and calories.

[0514] Emotional State Check:

[0515] Answer simple questions to identify your current emotional state (e.g., "How are you feeling today?").

[0516] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0517] Step 1:

[0518] A user downloads and installs the system's application on their smartphone, and enters basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen. This information is sent by the device to the server, which receives it and stores it in a database. This process enters the user's initial information into the system, and the server uses this information to generate an initial fitness plan and meal menu.

[0519] Step 2:

[0520] The server generates an initial fitness plan and meal menu based on the user's basic information and fitness goals. The generated plan and menu are sent from the server to the device. The device receives them and displays the plan and menu optimized for the user on the screen. This process allows the user to confirm the fitness plan and meal menu that are best suited to them.

[0521] Step 3:

[0522] When a user works out at the gym, they scan a QR code attached to the training machine with their smartphone to enter their exercise data into the device. The device then sends this data to a server, which records the received data in a database and saves it as the user's exercise history. Through this process, detailed exercise data is automatically collected and recorded.

[0523] Step 4:

[0524] Users take a photo of their meal with their smartphone and enter it into the app. The device's built-in image processing library analyzes the photo and automatically estimates the ingredients and calories. The user can review the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records the received meal data in a database. Through this process, detailed meal data is collected and recorded in the system.

[0525] Step 5:

[0526] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. This analysis is performed using statistical data calculations that combine multiple data points. Based on the analysis results, a new fitness plan and diet menu are generated and sent from the server to the device. The device then notifies the user. This process ensures that the user always receives the latest personalized plan.

[0527] Step 6:

[0528] The server analyzes the user's progress data using a machine learning algorithm to predict when the goal will be achieved. This prediction is sent from the server to the device and presented to the user as a notification. For example, it shows how many weeks it will take to achieve the target weight if the user continues training at the current pace. This process allows the user to get a sense of when they will reach their goal.

[0529] Step 7:

[0530] The device's built-in emotion recognition library analyzes the user's voice input and facial expressions to recognize their emotional state. This emotion data is sent to a server where it is analyzed by an emotion engine. The emotion engine processes the data to adjust fitness plans, meal menus, and feedback content based on the recognized emotions. The server then sends the adjusted plans and feedback to the device and notifies the user. This process provides personalized feedback based on the user's emotional state.

[0531] Step 8:

[0532] The server analyzes the emotional data and, if it determines that the user is feeling stressed or fatigued, suggests relaxation exercises or meals to relieve stress. These suggestions are sent to the device as notifications from the server and presented to the user. For example, if emotion recognition determines that the user is feeling stressed, it will suggest yoga exercises with a relaxing effect or a meal menu to reduce stress. This process allows the user to receive support both physically and mentally.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] [Second embodiment]

[0537] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0538] 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.

[0539] 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).

[0540] 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.

[0541] 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.

[0542] 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).

[0543] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] 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.

[0548] 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."

[0549] The present invention relates to a system that provides an individual user with an optimized fitness plan and meal menu, tracks the progress, and predicts when the goal will be achieved. Specific embodiments of the system and its processing method are described below.

[0550] User registration and initial settings

[0551] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[0552] Daily data entry and tracking

[0553] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[0554] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[0555] Personalized advice and progress

[0556] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[0557] Predicting goal achievement

[0558] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[0559] Free and paid versions available

[0560] The system operates on a subscription model with a free version and a paid version: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[0561] Specific examples

[0562] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks.

[0563] In this way, the system helps users achieve their health goals.

[0564] The processing flow will be explained below.

[0565] Specific flow of program processing

[0566] User registration and initial settings

[0567] Step 1:

[0568] The user downloads and installs the app.

[0569] Step 2:

[0570] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[0571] Step 3:

[0572] The device sends the input information to the server via the API.

[0573] Step 4:

[0574] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[0575] Step 5:

[0576] The server sends the generated plan to the terminal in JSON format.

[0577] Step 6:

[0578] The terminal displays the received plan to the user.

[0579] Daily data entry and tracking

[0580] Step 1:

[0581] The user works out at the gym and scans the QR code on the machine with the app.

[0582] Step 2:

[0583] The device parses and formats the exercise data (e.g., time, calories burned, intensity) obtained from the QR code.

[0584] Step 3:

[0585] In some cases, the user manually enters the exercise data.

[0586] Step 4:

[0587] The device sends the exercise data to the server.

[0588] Step 5:

[0589] The server records the received exercise data in the user profile.

[0590] Step 6:

[0591] When users eat, they take a photo of their meal using the app.

[0592] Step 7:

[0593] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[0594] Step 8:

[0595] The user can check the estimated ingredients and calories and make corrections as necessary.

[0596] Step 9:

[0597] The device transmits the corrected meal data to the server.

[0598] Step 10:

[0599] The server records the received meal data in the user profile.

[0600] Personalized advice and progress

[0601] Step 1:

[0602] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[0603] Step 2:

[0604] The server then adjusts new fitness menus and meal plans based on the analysis results.

[0605] Step 3:

[0606] The server sends the generated plan to the terminal in JSON format.

[0607] Step 4:

[0608] The terminal notifies the user of the received plan and advice and displays detailed information.

[0609] Predicting goal achievement

[0610] Step 1:

[0611] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[0612] Step 2:

[0613] The server sends the prediction results in JSON format to the device.

[0614] Step 3:

[0615] The device displays the prediction results and feedback to the user.

[0616] Subscription model available

[0617] Step 1:

[0618] A user creates a free account and accesses basic features.

[0619] Step 2:

[0620] The device sends the registration information for the free version to the server.

[0621] Step 3:

[0622] The server records user data for the free version and provides basic functionality.

[0623] Step 4:

[0624] The user selects to upgrade to the paid version within the app and enters their payment information.

[0625] Step 5:

[0626] The terminal sends the payment information to the server.

[0627] Step 6:

[0628] The server confirms payment and unlocks the paid features.

[0629] Step 7:

[0630] The server provides personalization features and expert support to paid users.

[0631] Example 1

[0632] 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."

[0633] Conventional systems have difficulty providing individual users with optimized exercise and meal plans, efficiently tracking their progress, and accurately predicting when their goals will be achieved. Furthermore, inputting users' exercise and meal data is time-consuming, making them difficult to use. There is a need for a system that can solve these issues and enable users to easily achieve their health goals.

[0634] 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.

[0635] In this invention, the server includes means for receiving user input information and generating an initial exercise menu and meal plan, means for receiving and recording data from the user's training at an exercise facility, means for the user to take photos of their meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, and means for notifying the user of the progress data and the predicted goal achievement. This allows for the provision of a personalized exercise menu and meal plan based on the user's information, enabling easy data entry and accurate progress management.

[0636] "Input Information" is basic information provided by the user, such as name, age, gender, weight, height, and fitness goals.

[0637] An "exercise menu" is an exercise program planned based on fitness goals set by a user.

[0638] A "meal plan" is a list of meals that are nutritionally balanced and planned to meet the user's health goals.

[0639] An "exercise facility" is a place where users train, such as a gym or fitness center.

[0640] "Training data" refers to data such as the content and duration of training the user undertook at an exercise facility, and calories burned.

[0641] "Identification codes" refer to QR codes or barcodes associated with exercise machines, food, etc., and by scanning these, training and dietary information can be quickly entered.

[0642] "Dietary data" refers to photographs of meals consumed by the user and information such as ingredients and calories based on the analysis results.

[0643] "Analysis" is the process by which the server processes the collected data and makes calculations regarding the user's progress and goal achievement.

[0644] The "goal completion date" is the date by which the user plans to achieve the fitness or health goal they set based on their progress data.

[0645] "Progress data" is an ongoing record of the amount of exercise a user is doing and the contents of their diet.

[0646] "Notifications" are messages or alerts generated by the server that are used to communicate progress and goal achievement forecast information to the user's device.

[0647] MODE FOR CARRYING OUT THE INVENTION

[0648] The present invention relates to a system that provides an exercise menu and meal plan optimized for an individual user, tracks the progress, and predicts when the goal will be achieved. Specific embodiments will be described below.

[0649] User registration and initial settings

[0650] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and exercise goals (e.g., weight loss, muscle building, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial exercise menu and meal plan, which it then sends to the device. The device receives this and displays it to the user.

[0651] Daily data entry and tracking

[0652] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[0653] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[0654] Personalized advice and progress

[0655] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new exercise and diet plan is tailored. The server then sends this information to the device, which notifies the user.

[0656] Predicting goal achievement

[0657] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[0658] Free and paid versions available

[0659] The system operates on a two-tier subscription model: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[0660] Specific examples

[0661] For example, if a user sets a goal of losing weight and building muscle, and their current weight is 70 kg and their target weight is 65 kg, the following process will occur: The user exercises on a treadmill for 30 minutes and enters the data by scanning the identification code attached to the gym machine. They also eat a 200 kcal salad and take a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server will suggest a new training plan and meal menu, and notify the user that they will reach their target weight in five weeks.

[0662] Prompt Sentence Examples

[0663] "Please explain in detail how a user trying to lose weight can enter their day's exercise and diet information into the app. How does the server and device process the data?"

[0664] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0665] Step 1:

[0666] The user downloads and installs the system's application. On the account creation screen, the user enters basic information such as name, age, gender, weight, height, and exercise goals, and the device sends this information to the server. The entered information is sent to the server and stored in a database. This information becomes the data used to generate an initial exercise menu and meal plan.

[0667] Step 2:

[0668] The server generates an initial exercise menu and meal plan based on the user information stored in the database. The generated exercise menu and meal plan are sent to the terminal, which displays them to the user. The input here is the user information, and the output is the initial exercise menu and meal plan.

[0669] Step 3:

[0670] When a user goes to the gym to work out, they either scan the identification code attached to the gym machine with the app or manually enter their exercise data. The device sends the entered exercise data to the server, which records it in a database. The input is the exercise data, and the output is the recorded database entry.

[0671] Step 4:

[0672] When a user eats a meal, they take a photo of the meal using the app. The device analyzes the captured image of the meal and estimates the ingredients and calories. The estimated data may be reviewed by the user and revised. The revised data is sent to the server, which records it in a database. The input is the meal image and the revised data, and the output is the recorded database entry.

[0673] Step 5:

[0674] The server aggregates the exercise and dietary data recorded in the database and analyzes the user's progress. Based on the results of this analysis, the exercise menu and diet plan are adjusted and a new plan is generated. The generated plan is sent to the device, which notifies the user. The input here is the recorded data, and the output is the adjusted exercise menu and diet plan.

[0675] Step 6:

[0676] The server uses a machine learning algorithm to predict when the goal will be achieved based on the recorded progress data. This prediction result is sent to the device, which then notifies the user of the expected goal achievement date. The input is the progress data, and the output is the predicted goal achievement result.

[0677] Step 7:

[0678] Users can upgrade their subscription from the free version to a paid version as needed, which provides more personalized features and expert support. This upgrade also updates the user's account information and records it on the server. The input here is the subscription information, and the output is the upgraded account information and additional features.

[0679] (Application example 1)

[0680] 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."

[0681] Conventional fitness and diet management systems struggle to provide detailed fitness plans and diet menus tailored to individual users' needs. They also lack the means for users to accurately track their progress and predict when they will reach their goals. It's also difficult for users to receive appropriate training advice in real time during fitness activities. This makes it difficult for users to maintain motivation, often resulting in delays in achieving their goals.

[0682] 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.

[0683] In this invention, the server includes means for receiving user input information and generating an initial fitness plan and meal menu, means for receiving and recording data of the user's fitness activities, means for the user to take images of meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, means for notifying the user of the progress data and the predicted goal achievement, and means for video chatting with the user in real time and providing training advice. This allows the user to accurately understand their own progress through the personalized fitness plan and meal menu and to effectively train and manage their diet to achieve their goal.

[0684] "User input" refers to data such as personal information and fitness goals that a user provides to the system.

[0685] The "initial fitness plan" is a fitness activity plan generated based on the user's input information, and includes exercise content and schedules that are optimal for each individual user.

[0686] A "meal menu" is a meal plan generated based on information input by the user, and includes meal contents that take into consideration the balance of calories and nutrients.

[0687] A "fitness activity" is a series of exercise or training activities undertaken by a user.

[0688] "Means for receiving and recording data" refers to the function of the system receiving data when a user performs a fitness activity and storing that data.

[0689] "Means for analyzing images of meals and generating meal data" refers to a function that analyzes the contents of images of meals taken by the user and estimates the foods, nutrients, and calories contained in the meal.

[0690] "Progress data" is data that indicates the progress status calculated based on the user's fitness activity and diet history.

[0691] "Means for predicting when a goal will be achieved" refers to a function that analyzes a user's progress data and predicts how long it will take to achieve a set fitness goal, weight goal, etc.

[0692] The "notification means" is a means for notifying the user of information such as progress data and a goal achievement forecast.

[0693] "Video chat" is a communication method that allows users to have real-time video calls and receive training advice and guidance.

[0694] A "personalized fitness plan" is a fitness activity plan that is customized to a user's individual circumstances and goals.

[0695] "Appropriate training advice in real time" refers to advice and guidance on exercise methods and form corrections that are provided to the user immediately on the spot while they are training.

[0696] The present invention relates to a system for providing a user with an optimized fitness plan and meal menu, tracking the progress, and predicting when the user will reach their goal. Specific embodiments of the system are described below.

[0697] 1. User registration and initial settings

[0698] The user first downloads and installs the system's application. To use the fitness plan and meal menu, the user provides input information such as name, age, gender, weight, height, and fitness goals. This information is sent by the device to the server. The server receives this input information, generates an initial fitness plan and meal menu, and sends it to the device. The user can view the generated fitness plan and meal menu on the device.

[0699] 2. Daily data entry and tracking

[0700] When a user engages in fitness activities, they are required to input activity data. Users can obtain fitness data by scanning QR codes attached to gym machines, or they can manually enter data if a QR code is not available. This data is sent from the device to a server and recorded. Additionally, when a user eats, they take a photo of the meal using the app. This image is analyzed on the device and meal data is generated. A generative AI model is used for the analysis. The user checks the calories and nutrients of their meal and makes any necessary adjustments. The corrected data is sent to the server and recorded.

[0701] 3. Personalized advice and progress

[0702] The server analyzes the collected fitness and dietary data to evaluate the user's current progress. Based on the analysis results, it adjusts new fitness menus and meal plans. It also provides training advice to the user through a real-time video chat function. This allows users to receive advice on appropriate exercise methods and form corrections in real time.

[0703] 4. Predicting goal achievement

[0704] The server analyzes the user's progress data and uses machine learning algorithms to predict when the goal will be achieved. The calculated goal achievement date is then sent to the user as a notification. For example, if the user's goal is weight loss, the estimated achievement date if the user continues at the current pace of progress is displayed.

[0705] 5. Free and paid versions available

[0706] The system operates on a subscription model with a free version offering basic fitness and diet management features, while upgrading to the paid version offers more in-depth personalization features and expert support.

[0707] Usage example

[0708] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. This data is sent from the device to the server and recorded. The server generates a new training plan and meal menu based on the data so far, and notifies the user that they will reach their target weight in five weeks.

[0709] Prompt Sentence Examples

[0710] Enter your name, age, weight, height, and fitness goal (e.g., lose weight, gain strength, improve endurance).

[0711] This way, users can access a personalized fitness plan at home, receive real-time training advice, and efficiently work towards their goals.

[0712] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0713] Step 1:

[0714] A user downloads and installs a fitness application. On the account creation screen, the user provides input information such as name, age, gender, weight, height, and fitness goals. The device sends this input information to a server. The server generates an initial fitness plan and meal menu for the user based on the received data. This is done using a generative AI model, which uses the user's basic information as input to provide the optimal plan. The generated plan is sent to the device and displayed to the user.

[0715] Step 2:

[0716] When users perform their daily fitness activities, they can capture data by scanning QR codes attached to gym machines. If a QR code is not available, exercise data can be entered manually. The device sends the captured exercise data to the server, which records it. At this time, the captured data (e.g., exercise time, calories burned) is sent as input to the server and recorded in a database.

[0717] Step 3:

[0718] When a user eats, they take a photo of the meal using the app. The device analyzes this image and generates meal data. This analysis involves preprocessing and then using a generative AI model. Meal data (e.g., food names, calories) is automatically generated, and the user can review and correct this data. The corrected data is sent from the device to the server and recorded.

[0719] Step 4:

[0720] The server analyzes the collected exercise and dietary data to evaluate the user's progress. A machine learning algorithm is used for the analysis, and the user's progress data is used as input to evaluate the current situation. Based on the analysis results, a new fitness manual and diet plan are generated and sent to the device. The user receives the new plan and can proceed to the next step.

[0721] Step 5:

[0722] The server analyzes the user's progress data and uses a machine learning algorithm to predict when the goal will be achieved. Specifically, it evaluates the user's current pace based on past data and calculates the time it will take to achieve the goal. This predicted time is sent to the device and notified to the user. Knowing the specific target date helps users stay motivated.

[0723] Step 6:

[0724] Users can receive training advice through a real-time video chat function. This system allows users to start a video call through an app on their device and receive advice from a trainer on exercise methods and form corrections. By receiving real-time feedback, users can train more effectively.

[0725] Step 7:

[0726] The system offers a subscription model with free and paid versions. Users can use the free version to access basic fitness and diet management functions. By upgrading to the paid version, they can access more detailed personalization features and expert support. The server will provide new modules based on the user's plan and perform further personalization based on that.

[0727] The above are the specific processing steps for carrying out the present invention. This system allows users to efficiently achieve their health goals through fitness plans optimized for their individual needs.

[0728] 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.

[0729] This invention combines a system that provides an individual user with a fitness plan and meal menu optimized for them, tracks their progress, and predicts when they will reach their goals, with an emotion engine that recognizes the user's emotions and adjusts the plan and feedback accordingly. Specific embodiments of the system and its processing method are described below.

[0730] User registration and initial settings

[0731] First, the user downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[0732] Daily data entry and tracking

[0733] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[0734] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[0735] Personalized advice and progress

[0736] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[0737] Predicting goal achievement

[0738] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[0739] Introducing the Emotion Engine

[0740] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions, allowing the server to determine the user's emotional state and adjust the fitness plan, meal menu, and even feedback accordingly.

[0741] For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest appropriate relaxation exercises or meals to relieve stress. The emotion engine can also provide positive feedback to increase the user's motivation.

[0742] Subscription model available

[0743] The system operates on a subscription model with a free and paid version. The free version provides basic exercise and food tracking functions, but users can upgrade to the paid version for more detailed personalization features and expert support. The paid version also includes advanced feedback and support from an emotion engine.

[0744] Specific examples

[0745] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user of a prediction that they will reach their target weight in five weeks.

[0746] Additionally, if the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals, thus supporting the user's health and emotions and helping them achieve their goals.

[0747] The processing flow will be explained below.

[0748] User registration and initial settings

[0749] Step 1:

[0750] The user downloads and installs the app.

[0751] Step 2:

[0752] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[0753] Step 3:

[0754] The device sends the input information to the server via the API.

[0755] Step 4:

[0756] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[0757] Step 5:

[0758] The server sends the generated plan to the terminal in JSON format.

[0759] Step 6:

[0760] The terminal displays the received plan to the user.

[0761] Daily data entry and tracking

[0762] Exercise data entry and recording

[0763] Step 1:

[0764] The user works out at the gym and scans the QR code on the machine with the app.

[0765] Step 2:

[0766] The device analyzes and formats the exercise data (time, calories burned, exercise intensity) obtained from the QR code.

[0767] Step 3:

[0768] In some cases, the user manually enters the exercise data.

[0769] Step 4:

[0770] The device sends the exercise data to the server.

[0771] Step 5:

[0772] The server records the received exercise data in the user profile.

[0773] Meal data entry and recording

[0774] Step 6:

[0775] The user takes a photo of their meal using the app.

[0776] Step 7:

[0777] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[0778] Step 8:

[0779] The user can check the estimated ingredients and calories and make corrections as necessary.

[0780] Step 9:

[0781] The device transmits the corrected meal data to the server.

[0782] Step 10:

[0783] The server records the received meal data in the user profile.

[0784] Personalized advice and progress

[0785] Step 1:

[0786] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[0787] Step 2:

[0788] The server then adjusts new fitness menus and meal plans based on the analysis results.

[0789] Step 3:

[0790] The server sends the generated plan to the terminal in JSON format.

[0791] Step 4:

[0792] The terminal notifies the user of the received plan and advice and displays detailed information.

[0793] Predicting goal achievement

[0794] Step 1:

[0795] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[0796] Step 2:

[0797] The server sends the prediction results in JSON format to the device.

[0798] Step 3:

[0799] The device displays the prediction results and feedback to the user.

[0800] Introducing the Emotion Engine

[0801] Emotion data input and recognition

[0802] Step 1:

[0803] The user uses the app to input voice commands and take a photo of their face.

[0804] Step 2:

[0805] The device sends voice data and facial photo data to the server.

[0806] Step 3:

[0807] The server uses an emotion engine to analyze voice data and facial photo data and recognize the user's emotions.

[0808] Emotion-based plan adjustment and feedback

[0809] Step 4:

[0810] The server uses the recognized emotion data to adjust fitness plans and meal menus.

[0811] Step 5:

[0812] The server sends the adjusted plan and feedback to the device in JSON format.

[0813] Step 6:

[0814] The device notifies the user of the plans and feedback it has received and displays detailed information.

[0815] Subscription model available

[0816] Step 1:

[0817] A user creates a free account and accesses basic features.

[0818] Step 2:

[0819] The device sends the registration information for the free version to the server.

[0820] Step 3:

[0821] The server records user data for the free version and provides basic functionality.

[0822] Step 4:

[0823] The user selects to upgrade to the paid version within the app and enters their payment information.

[0824] Step 5:

[0825] The terminal sends the payment information to the server.

[0826] Step 6:

[0827] The server confirms payment and unlocks the paid features.

[0828] Step 7:

[0829] The server provides personalization features and expert support to paid users.

[0830] Example 2

[0831] 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."

[0832] While traditional fitness and diet management systems provide plans based on a user's basic information, they often fail to take into account the user's emotions and psychological state, making it difficult to maintain user motivation. Furthermore, they often lack the ability to efficiently analyze the data entered by the user and provide appropriate feedback in real time, resulting in ineffective personalization. Furthermore, progress tracking and goal achievement predictions are often inaccurate, and notifications to users are often delayed.

[0833] 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.

[0834] In this invention, the server includes means for receiving user input information and generating an initial exercise plan and meal menu, means for receiving and recording data when the user exercises, means for allowing the user to take photos of meals and analyzing the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goals, means for recognizing the user's emotions and adjusting the plan and feedback, and means for notifying the user of the progress data and goal achievement predictions. This provides a personalized fitness plan and meal plan that takes the user's emotions and psychological state into consideration, enabling effective tracking and goal achievement predictions while maintaining the user's motivation.

[0835] "User" refers to an individual who uses this system, including the entity receiving the fitness plan or meal menu.

[0836] "Input Information" means data provided by a User to the System, including personal information such as name, age, gender, weight, height, and fitness goals.

[0837] "Workout Plan" refers to an exercise or training plan generated based on a user's fitness goals.

[0838] "Meal Menu" refers to a suggested dietary and nutrient-based plan to address a user's fitness goals.

[0839] "Exercise Data" means details of exercises performed by a User at a gym or other exercise facility, including data obtained by scanning a QR code or manually entering it.

[0840] "Dietary Data" refers to information about the diet of a user, such as the types and amounts of food consumed and calories, and includes analysis results of photos taken by the user.

[0841] "Progress Data" refers to data that indicates the current progress of a User toward their fitness goals, as a result of compiling and analyzing the User's exercise and diet data.

[0842] "Goal Timeline" means the estimated time period or specific date by which a user will achieve their fitness goal.

[0843] "Emotion" refers to the user's emotional state, a psychological state determined by the system through voice input and facial expression recognition.

[0844] "Notification methods" refers to the methods by which the system communicates information such as progress, new plans, and feedback to the user, including app push notifications and emails.

[0845] This invention combines a system that provides users with optimized exercise plans and meal menus, tracks their progress, and predicts when they will reach their goals with a function that recognizes the user's emotions and adjusts the plan and feedback. Specific embodiments of the system and its processing method are described below.

[0846] User registration and initial settings

[0847] 1. User behavior:

[0848] First, the user downloads and installs the system's application onto their smartphone, which can be downloaded from the App Store or Google Play, for example.

[0849] Launch the app and enter basic information such as your name, age, gender, weight, height, and fitness goals on the account creation screen.

[0850] 2. Terminal processing:

[0851] The device sends the entered information to a server over the internet, using your internet connection (Wi-Fi or mobile data).

[0852] 3. Server processing:

[0853] The server stores the received information in a database, for example, a relational database such as MySQL, to ensure consistent data storage.

[0854] The server generates an initial exercise plan and meal menu based on the user's basic information and fitness goals, using a Python-based backend program.

[0855] The generated plans and menus are sent back to the terminal and displayed to the user.

[0856] Daily data entry and tracking

[0857] 1. User behavior:

[0858] When a user exercises at the gym, they need to input their exercise data. Users can either scan the QR code attached to the gym machine or input it manually.

[0859] 2. Terminal processing:

[0860] When the QR code is scanned, the device automatically acquires the information and sends the exercise data to the server. Data is also sent to the server when manually entered.

[0861] 3. User Behavior:

[0862] Users take photos of their meals using the app.

[0863] 4. Terminal processing:

[0864] The device uses an image analysis engine (such as OpenCV or TensorFlow) to estimate ingredients and calories from the captured photo. The user can review the estimated results and make corrections as necessary.

[0865] 5. Server Processing:

[0866] The modified data is sent from the terminal and recorded by the server.

[0867] Personalized advice and progress

[0868] 1. Server process:

[0869] The server uses an analytical engine (e.g., the Pandas library or the SciPy library) to analyze the aggregated exercise and dietary data.

[0870] Based on the analysis results, a new exercise plan and meal menu are generated and sent to the device.

[0871] 2. Terminal processing:

[0872] The new plan is received by the terminal and notified to the user.

[0873] Predicting goal achievement

[0874] 1. Server process:

[0875] The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to predict when the goal will be achieved based on the user's progress data.

[0876] The prediction result is sent to the terminal.

[0877] 2. Terminal processing:

[0878] The user is notified of the predicted timeframe for reaching the goal.

[0879] Introducing the Emotion Engine

[0880] 1. User behavior:

[0881] Users input voice and facial expression data into the app.

[0882] 2. Terminal processing:

[0883] The input data is analyzed using a voice recognition engine (e.g., a voice recognition API) or an expression recognition engine (e.g., a face recognition API).

[0884] The analysis results are sent to the server.

[0885] 3. Server processing:

[0886] The server uses an emotion engine to determine the user's emotional state and generates fitness and meal plans and feedback based on this.

[0887] The adjusted feedback is sent to the terminal.

[0888] 4. Terminal processing:

[0889] The adjusted feedback is communicated to the user.

[0890] Subscription model available

[0891] 1. User behavior:

[0892] Users can choose between a free or paid subscription model within the app.

[0893] 2. Terminal processing:

[0894] Depending on the subscription selected, payment is processed through a payment gateway (e.g., a payment service).

[0895] 3. Server processing:

[0896] Once payment is confirmed, the paid version's detailed features, expert support, and feedback from an emotion engine will be unlocked.

[0897] Specific examples

[0898] For example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans the QR code attached to the machine, and eats a 200 kcal salad and takes a photo of it using the app. The device sends the exercise and diet data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks. If the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals. In this way, the system supports the user's health and emotions, helping them achieve their goals.

[0899] Input prompt for generative AI model

[0900] Examples of prompts include:

[0901] I've set my goals to "lose weight" and "increase muscle strength." My current weight is 70 kg, and my target weight is 65 kg. Please suggest me a fitness plan and meal menu for the future. Also, please let me know the date by which I plan to achieve this.

[0902] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0903] System program processing flow

[0904] Step 1:

[0905] User registration and initial settings

[0906] 1. Input:

[0907] Users download and install the system's application, then enter basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen.

[0908] 2. Specific operation:

[0909] The user launches the app and enters their information.

[0910] The terminal receives the input information and transmits it to a server via the Internet.

[0911] 3. Data Processing:

[0912] The server stores the received information (name, age, gender, weight, height, fitness goals) in a database.

[0913] 4. Output:

[0914] The server generates an initial exercise plan and meal menu and sends them to the device.

[0915] Step 2:

[0916] Daily data entry and tracking

[0917] 1. Input:

[0918] Users work out at the gym and either scan a QR code attached to the machine with the app or manually enter their exercise data.

[0919] 2. Specific operation:

[0920] Once the QR code is scanned, the device will automatically obtain relevant information and collect exercise data.

[0921] Manually entered data is handled similarly.

[0922] Users take photos of their meals using the app.

[0923] 3. Data Processing:

[0924] The terminal transmits the collected exercise data to a server via the Internet.

[0925] The device uses an image analysis engine to analyze photos of meals and estimate ingredients and calories.

[0926] The estimated results are displayed to the user, who can then correct them as necessary.

[0927] 4. Output:

[0928] The device transmits the corrected meal data to the server.

[0929] The server records this data in a database.

[0930] Step 3:

[0931] Personalized advice and progress

[0932] 1. Input:

[0933] The collected exercise and dietary data is stored on a server.

[0934] 2. Specific operation:

[0935] The server uses an analysis engine to analyze the exercise and dietary data and calculate the user's progress.

[0936] 3. Data Processing:

[0937] Based on the analysis results, a new exercise plan and meal menu will be generated.

[0938] 4. Output:

[0939] The server sends the generated new plan to the terminal.

[0940] The terminal receives this and notifies the user.

[0941] Step 4:

[0942] Predicting goal achievement

[0943] 1. Input:

[0944] The state in which the user's progress data is accumulated.

[0945] 2. Specific operation:

[0946] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[0947] 3. Data Processing:

[0948] Analyze progress data and apply appropriate algorithms to make predictions.

[0949] 4. Output:

[0950] The server transmits the predicted completion time to the terminal.

[0951] The terminal notifies the user of the prediction result.

[0952] Step 5:

[0953] Introducing the Emotion Engine

[0954] 1. Input:

[0955] The user inputs voice and facial expression data.

[0956] 2. Specific operation:

[0957] The data is analyzed using a voice recognition engine and a facial expression recognition engine.

[0958] 3. Data Processing:

[0959] Based on the analysis results, the user's emotional state is estimated and plans and feedback content are adjusted accordingly.

[0960] 4. Output:

[0961] The server sends the adjusted plan and feedback to the device.

[0962] The terminal notifies the user of the adjustment results.

[0963] Step 6:

[0964] Subscription model available

[0965] 1. Input:

[0966] The user selects a subscription model.

[0967] 2. Specific operation:

[0968] The terminal processes the payment based on the selected subscription.

[0969] 3. Data Processing:

[0970] A payment gateway is used to process the payment, and if successful, the data is stored on the server.

[0971] 4. Output:

[0972] The server will offer users the advanced features and support of the paid version.

[0973] (Application example 2)

[0974] 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."

[0975] Conventional fitness plan and meal menu delivery systems were unable to take into account the user's individual emotional state, resulting in insufficient measures to address user motivation and stress. Furthermore, the manual input method for exercise and dietary data was burdensome, potentially reducing tracking accuracy. Furthermore, there was no system that comprehensively supported fitness activities and dietary management within the gym, resulting in inconsistent progress management for users. These challenges made it difficult for users to achieve their goals.

[0976] 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.

[0977] In the present invention, the server includes the following means:

[0978] means for receiving user input and generating an initial fitness plan and meal menu;

[0979] means for receiving and recording data when a user exercises at a gym;

[0980] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[0981] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[0982] means for notifying the user of progress data and goal achievement projections;

[0983] A means for recognizing the user's emotional state from their voice input and facial expressions, and adjusting the feedback content using an emotion engine;

[0984] A method to scan the QR code on the training machine in the gym and enter exercise data,

[0985] It includes measures to suggest relaxation exercises and stress-relieving meals based on emotional state.

[0986] This allows for feedback and advice tailored to the user's individual emotional state, realizes efficient and precise tracking of exercise and diet data, and provides comprehensive support for users' fitness activities and diet management, helping them achieve their goals.

[0987] "User input information" refers to basic information such as name, age, sex, weight, height, and fitness goals that a user provides to the system.

[0988] A "fitness plan" is an exercise schedule or training menu that is generated based on a user's fitness goals.

[0989] A "meal menu" is a daily meal plan suggested to suit the user's health and fitness goals.

[0990] "Exercise data" refers to data such as the type and duration of exercise performed by the user at the gym, and calories burned.

[0991] "Dietary data" refers to data relating to the contents, calories, ingredients, etc. of meals consumed by the user.

[0992] "Progress data" is data recorded about the exercises a user actually performs and the food they eat, and indicates the user's progress toward achieving their goals.

[0993] The "goal achievement date" is the date and time when the user is expected to achieve the fitness goal they have set.

[0994] "Notification" is the means by which the system provides information to the user, in the form of messages, alerts, etc.

[0995] An "emotion engine" is an algorithm or software that recognizes a user's emotional state from their voice input and facial expressions, and adjusts feedback based on that emotional state.

[0996] A "QR code" is a two-dimensional barcode attached to training machines in the gym, which users can scan with their smartphone to automatically enter exercise data.

[0997] "Relaxation exercises" are relaxation exercises and stretches that users perform to reduce stress and fatigue.

[0998] "Meals for stress relief" are ingredients and menus that are effective in reducing stress and are suggested based on the user's stress level.

[0999] This invention is a system that provides an individual user with an optimized fitness plan and meal menu, tracks their progress, and predicts when they will reach their goals. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the plan and feedback based on those emotions. Specific embodiments of this system and its processing method are described below.

[1000] User registration and initial settings

[1001] First, the user downloads and installs the system's application onto their smartphone. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this information and displays it to the user.

[1002] Daily data entry and tracking

[1003] When a user exercises at the gym, they need to input their exercise data. The user can automatically obtain the exercise data by scanning a QR code attached to the gym's training machines with their smartphone. Meal data is also input by the user taking a photo of the meal using the app. The smartphone analyzes the captured image, and the ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The data is then sent to a server and recorded.

[1004] Personalized advice and progress

[1005] The server analyzes the aggregated exercise and diet data to calculate the user's current progress, and adjusts a new fitness plan and diet menu based on the analysis. The server then sends this information to the smartphone, which notifies the user.

[1006] Goal achievement prediction and notification

[1007] The server uses the user's progress data to predict when the goal will be achieved using a machine learning algorithm. The predicted time is then sent to the user via a smartphone notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1008] Introducing the Emotion Engine

[1009] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions. Based on the emotional state recognized by the emotion engine, the server can adjust the fitness plan, meal menu, and even feedback content. For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest relaxation exercises or stress-relieving meals to the user. The emotion engine also provides positive feedback to increase the user's motivation.

[1010] Hardware and software used

[1011] Smartphone: where users install apps and use them to enter data and receive notifications.

[1012] Server: Stores and analyzes user data, calculates progress, predicts when goals will be reached, and adjusts fitness plans and meal menus.

[1013] QR Code Reader: A means of reading the QR code on training machines at the gym and automatically obtaining exercise data.

[1014] Image processing library: Used to analyze photos of meals and estimate ingredients and calories.

[1015] Emotion Recognition Library: Used to recognize emotional states by analyzing voice input and facial expressions.

[1016] Examples of concrete examples and prompts

[1017] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill at the gym for 30 minutes and scans a QR code. They also eat a 200 kcal salad and take a photo of it with their smartphone. The server collates this data, suggests a new training plan and meal menu to the user, and notifies them that they will reach their target weight in five weeks. If the user is feeling stressed, the server also suggests relaxing yoga exercises and stress-relieving meals.

[1018] An example prompt is:

[1019] User data registration:

[1020] To register, please enter your name, age, gender, weight, height and fitness goals.

[1021] Exercise Data Entry:

[1022] After your workout, scan the QR code to enter your exercise data.

[1023] Meal Data Entry:

[1024] Take a photo of your meal and check and edit the ingredients and calories.

[1025] Emotional State Check:

[1026] Answer simple questions to identify your current emotional state (e.g., "How are you feeling today?").

[1027] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1028] Step 1:

[1029] A user downloads and installs the system's application on their smartphone, and enters basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen. This information is sent by the device to the server, which receives it and stores it in a database. This process enters the user's initial information into the system, and the server uses this information to generate an initial fitness plan and meal menu.

[1030] Step 2:

[1031] The server generates an initial fitness plan and meal menu based on the user's basic information and fitness goals. The generated plan and menu are sent from the server to the device. The device receives them and displays the plan and menu optimized for the user on the screen. This process allows the user to confirm the fitness plan and meal menu that are best suited to them.

[1032] Step 3:

[1033] When a user works out at the gym, they scan a QR code attached to the training machine with their smartphone to enter their exercise data into the device. The device then sends this data to a server, which records the received data in a database and saves it as the user's exercise history. Through this process, detailed exercise data is automatically collected and recorded.

[1034] Step 4:

[1035] Users take a photo of their meal with their smartphone and enter it into the app. The device's built-in image processing library analyzes the photo and automatically estimates the ingredients and calories. The user can review the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records the received meal data in a database. Through this process, detailed meal data is collected and recorded in the system.

[1036] Step 5:

[1037] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. This analysis is performed using statistical data calculations that combine multiple data points. Based on the analysis results, a new fitness plan and diet menu are generated and sent from the server to the device. The device then notifies the user. This process ensures that the user always receives the latest personalized plan.

[1038] Step 6:

[1039] The server analyzes the user's progress data using a machine learning algorithm to predict when the goal will be achieved. This prediction is sent from the server to the device and presented to the user as a notification. For example, it shows how many weeks it will take to achieve the target weight if the user continues training at the current pace. This process allows the user to get a sense of when they will reach their goal.

[1040] Step 7:

[1041] The device's built-in emotion recognition library analyzes the user's voice input and facial expressions to recognize their emotional state. This emotion data is sent to a server where it is analyzed by an emotion engine. The emotion engine processes the data to adjust fitness plans, meal menus, and feedback content based on the recognized emotions. The server then sends the adjusted plans and feedback to the device and notifies the user. This process provides personalized feedback based on the user's emotional state.

[1042] Step 8:

[1043] The server analyzes the emotional data and, if it determines that the user is feeling stressed or fatigued, suggests relaxation exercises or meals to relieve stress. These suggestions are sent to the device as notifications from the server and presented to the user. For example, if emotion recognition determines that the user is feeling stressed, it will suggest yoga exercises with a relaxing effect or a meal menu to reduce stress. This process allows the user to receive support both physically and mentally.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] [Third embodiment]

[1048] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1049] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1050] 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).

[1051] 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.

[1052] 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.

[1053] 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).

[1054] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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."

[1060] The present invention relates to a system that provides an individual user with an optimized fitness plan and meal menu, tracks the progress, and predicts when the goal will be achieved. Specific embodiments of the system and its processing method are described below.

[1061] User registration and initial settings

[1062] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[1063] Daily data entry and tracking

[1064] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[1065] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[1066] Personalized advice and progress

[1067] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[1068] Predicting goal achievement

[1069] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1070] Free and paid versions available

[1071] The system operates on a subscription model with a free version and a paid version: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[1072] Specific examples

[1073] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks.

[1074] In this way, the system helps users achieve their health goals.

[1075] The processing flow will be explained below.

[1076] Specific flow of program processing

[1077] User registration and initial settings

[1078] Step 1:

[1079] The user downloads and installs the app.

[1080] Step 2:

[1081] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[1082] Step 3:

[1083] The device sends the input information to the server via the API.

[1084] Step 4:

[1085] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[1086] Step 5:

[1087] The server sends the generated plan to the terminal in JSON format.

[1088] Step 6:

[1089] The terminal displays the received plan to the user.

[1090] Daily data entry and tracking

[1091] Step 1:

[1092] The user works out at the gym and scans the QR code on the machine with the app.

[1093] Step 2:

[1094] The device parses and formats the exercise data (e.g., time, calories burned, intensity) obtained from the QR code.

[1095] Step 3:

[1096] In some cases, the user manually enters the exercise data.

[1097] Step 4:

[1098] The device sends the exercise data to the server.

[1099] Step 5:

[1100] The server records the received exercise data in the user profile.

[1101] Step 6:

[1102] When users eat, they take a photo of their meal using the app.

[1103] Step 7:

[1104] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[1105] Step 8:

[1106] The user can check the estimated ingredients and calories and make corrections as necessary.

[1107] Step 9:

[1108] The device transmits the corrected meal data to the server.

[1109] Step 10:

[1110] The server records the received meal data in the user profile.

[1111] Personalized advice and progress

[1112] Step 1:

[1113] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[1114] Step 2:

[1115] The server then adjusts new fitness menus and meal plans based on the analysis results.

[1116] Step 3:

[1117] The server sends the generated plan to the terminal in JSON format.

[1118] Step 4:

[1119] The terminal notifies the user of the received plan and advice and displays detailed information.

[1120] Predicting goal achievement

[1121] Step 1:

[1122] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[1123] Step 2:

[1124] The server sends the prediction results in JSON format to the device.

[1125] Step 3:

[1126] The device displays the prediction results and feedback to the user.

[1127] Subscription model available

[1128] Step 1:

[1129] A user creates a free account and accesses basic features.

[1130] Step 2:

[1131] The device sends the registration information for the free version to the server.

[1132] Step 3:

[1133] The server records user data for the free version and provides basic functionality.

[1134] Step 4:

[1135] The user selects to upgrade to the paid version within the app and enters their payment information.

[1136] Step 5:

[1137] The terminal sends the payment information to the server.

[1138] Step 6:

[1139] The server confirms payment and unlocks the paid features.

[1140] Step 7:

[1141] The server provides personalization features and expert support to paid users.

[1142] Example 1

[1143] 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."

[1144] Conventional systems have difficulty providing individual users with optimized exercise and meal plans, efficiently tracking their progress, and accurately predicting when their goals will be achieved. Furthermore, inputting users' exercise and meal data is time-consuming, making them difficult to use. There is a need for a system that can solve these issues and enable users to easily achieve their health goals.

[1145] 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.

[1146] In this invention, the server includes means for receiving user input information and generating an initial exercise menu and meal plan, means for receiving and recording data from the user's training at an exercise facility, means for the user to take photos of their meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, and means for notifying the user of the progress data and the predicted goal achievement. This allows for the provision of a personalized exercise menu and meal plan based on the user's information, enabling easy data entry and accurate progress management.

[1147] "Input Information" is basic information provided by the user, such as name, age, gender, weight, height, and fitness goals.

[1148] An "exercise menu" is an exercise program planned based on fitness goals set by a user.

[1149] A "meal plan" is a list of meals that are nutritionally balanced and planned to meet the user's health goals.

[1150] An "exercise facility" is a place where users train, such as a gym or fitness center.

[1151] "Training data" refers to data such as the content and duration of training the user undertook at an exercise facility, and calories burned.

[1152] "Identification codes" refer to QR codes or barcodes associated with exercise machines, food, etc., and by scanning these, training and dietary information can be quickly entered.

[1153] "Dietary data" refers to photographs of meals consumed by the user and information such as ingredients and calories based on the analysis results.

[1154] "Analysis" is the process by which the server processes the collected data and makes calculations regarding the user's progress and goal achievement.

[1155] The "goal completion date" is the date by which the user plans to achieve the fitness or health goal they set based on their progress data.

[1156] "Progress data" is an ongoing record of the amount of exercise a user is doing and the contents of their diet.

[1157] "Notifications" are messages or alerts generated by the server that are used to communicate progress and goal achievement forecast information to the user's device.

[1158] MODE FOR CARRYING OUT THE INVENTION

[1159] The present invention relates to a system that provides an exercise menu and meal plan optimized for an individual user, tracks the progress, and predicts when the goal will be achieved. Specific embodiments will be described below.

[1160] User registration and initial settings

[1161] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and exercise goals (e.g., weight loss, muscle building, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial exercise menu and meal plan, which it then sends to the device. The device receives this and displays it to the user.

[1162] Daily data entry and tracking

[1163] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[1164] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[1165] Personalized advice and progress

[1166] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new exercise and diet plan is tailored. The server then sends this information to the device, which notifies the user.

[1167] Predicting goal achievement

[1168] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1169] Free and paid versions available

[1170] The system operates on a two-tier subscription model: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[1171] Specific examples

[1172] For example, if a user sets a goal of losing weight and building muscle, and their current weight is 70 kg and their target weight is 65 kg, the following process will occur: The user exercises on a treadmill for 30 minutes and enters the data by scanning the identification code attached to the gym machine. They also eat a 200 kcal salad and take a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server will suggest a new training plan and meal menu, and notify the user that they will reach their target weight in five weeks.

[1173] Prompt Sentence Examples

[1174] "Please explain in detail how a user trying to lose weight can enter their day's exercise and diet information into the app. How does the server and device process the data?"

[1175] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] The user downloads and installs the system's application. On the account creation screen, the user enters basic information such as name, age, gender, weight, height, and exercise goals, and the device sends this information to the server. The entered information is sent to the server and stored in a database. This information becomes the data used to generate an initial exercise menu and meal plan.

[1178] Step 2:

[1179] The server generates an initial exercise menu and meal plan based on the user information stored in the database. The generated exercise menu and meal plan are sent to the terminal, which displays them to the user. The input here is the user information, and the output is the initial exercise menu and meal plan.

[1180] Step 3:

[1181] When a user goes to the gym to work out, they either scan the identification code attached to the gym machine with the app or manually enter their exercise data. The device sends the entered exercise data to the server, which records it in a database. The input is the exercise data, and the output is the recorded database entry.

[1182] Step 4:

[1183] When a user eats a meal, they take a photo of the meal using the app. The device analyzes the captured image of the meal and estimates the ingredients and calories. The estimated data may be reviewed by the user and revised. The revised data is sent to the server, which records it in a database. The input is the meal image and the revised data, and the output is the recorded database entry.

[1184] Step 5:

[1185] The server aggregates the exercise and dietary data recorded in the database and analyzes the user's progress. Based on the results of this analysis, the exercise menu and diet plan are adjusted and a new plan is generated. The generated plan is sent to the device, which notifies the user. The input here is the recorded data, and the output is the adjusted exercise menu and diet plan.

[1186] Step 6:

[1187] The server uses a machine learning algorithm to predict when the goal will be achieved based on the recorded progress data. This prediction result is sent to the device, which then notifies the user of the expected goal achievement date. The input is the progress data, and the output is the predicted goal achievement result.

[1188] Step 7:

[1189] Users can upgrade their subscription from the free version to a paid version as needed, which provides more personalized features and expert support. This upgrade also updates the user's account information and records it on the server. The input here is the subscription information, and the output is the upgraded account information and additional features.

[1190] (Application example 1)

[1191] 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."

[1192] Conventional fitness and diet management systems struggle to provide detailed fitness plans and diet menus tailored to individual users' needs. They also lack the means for users to accurately track their progress and predict when they will reach their goals. It's also difficult for users to receive appropriate training advice in real time during fitness activities. This makes it difficult for users to maintain motivation, often resulting in delays in achieving their goals.

[1193] 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.

[1194] In this invention, the server includes means for receiving user input information and generating an initial fitness plan and meal menu, means for receiving and recording data of the user's fitness activities, means for the user to take images of meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, means for notifying the user of the progress data and the predicted goal achievement, and means for video chatting with the user in real time and providing training advice. This allows the user to accurately understand their own progress through the personalized fitness plan and meal menu and to effectively train and manage their diet to achieve their goal.

[1195] "User input" refers to data such as personal information and fitness goals that a user provides to the system.

[1196] The "initial fitness plan" is a fitness activity plan generated based on the user's input information, and includes exercise content and schedules that are optimal for each individual user.

[1197] A "meal menu" is a meal plan generated based on information input by the user, and includes meal contents that take into consideration the balance of calories and nutrients.

[1198] A "fitness activity" is a series of exercise or training activities undertaken by a user.

[1199] "Means for receiving and recording data" refers to the function of the system receiving data when a user performs a fitness activity and storing that data.

[1200] "Means for analyzing images of meals and generating meal data" refers to a function that analyzes the contents of images of meals taken by the user and estimates the foods, nutrients, and calories contained in the meal.

[1201] "Progress data" is data that indicates the progress status calculated based on the user's fitness activity and diet history.

[1202] "Means for predicting when a goal will be achieved" refers to a function that analyzes a user's progress data and predicts how long it will take to achieve a set fitness goal, weight goal, etc.

[1203] The "notification means" is a means for notifying the user of information such as progress data and a goal achievement forecast.

[1204] "Video chat" is a communication method that allows users to have real-time video calls and receive training advice and guidance.

[1205] A "personalized fitness plan" is a fitness activity plan that is customized to a user's individual circumstances and goals.

[1206] "Appropriate training advice in real time" refers to advice and guidance on exercise methods and form corrections that are provided to the user immediately on the spot while they are training.

[1207] The present invention relates to a system for providing a user with an optimized fitness plan and meal menu, tracking the progress, and predicting when the user will reach their goal. Specific embodiments of the system are described below.

[1208] 1. User registration and initial settings

[1209] The user first downloads and installs the system's application. To use the fitness plan and meal menu, the user provides input information such as name, age, gender, weight, height, and fitness goals. This information is sent by the device to the server. The server receives this input information, generates an initial fitness plan and meal menu, and sends it to the device. The user can view the generated fitness plan and meal menu on the device.

[1210] 2. Daily data entry and tracking

[1211] When a user engages in fitness activities, they are required to input activity data. Users can obtain fitness data by scanning QR codes attached to gym machines, or they can manually enter data if a QR code is not available. This data is sent from the device to a server and recorded. Additionally, when a user eats, they take a photo of the meal using the app. This image is analyzed on the device and meal data is generated. A generative AI model is used for the analysis. The user checks the calories and nutrients of their meal and makes any necessary adjustments. The corrected data is sent to the server and recorded.

[1212] 3. Personalized advice and progress

[1213] The server analyzes the collected fitness and dietary data to evaluate the user's current progress. Based on the analysis results, it adjusts new fitness menus and meal plans. It also provides training advice to the user through a real-time video chat function. This allows users to receive advice on appropriate exercise methods and form corrections in real time.

[1214] 4. Predicting goal achievement

[1215] The server analyzes the user's progress data and uses machine learning algorithms to predict when the goal will be achieved. The calculated goal achievement date is then sent to the user as a notification. For example, if the user's goal is weight loss, the estimated achievement date if the user continues at the current pace of progress is displayed.

[1216] 5. Free and paid versions available

[1217] The system operates on a subscription model with a free version offering basic fitness and diet management features, while upgrading to the paid version offers more in-depth personalization features and expert support.

[1218] Usage example

[1219] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. This data is sent from the device to the server and recorded. The server generates a new training plan and meal menu based on the data so far, and notifies the user that they will reach their target weight in five weeks.

[1220] Prompt Sentence Examples

[1221] Enter your name, age, weight, height, and fitness goal (e.g., lose weight, gain strength, improve endurance).

[1222] This way, users can access a personalized fitness plan at home, receive real-time training advice, and efficiently work towards their goals.

[1223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1224] Step 1:

[1225] A user downloads and installs a fitness application. On the account creation screen, the user provides input information such as name, age, gender, weight, height, and fitness goals. The device sends this input information to a server. The server generates an initial fitness plan and meal menu for the user based on the received data. This is done using a generative AI model, which uses the user's basic information as input to provide the optimal plan. The generated plan is sent to the device and displayed to the user.

[1226] Step 2:

[1227] When users perform their daily fitness activities, they can capture data by scanning QR codes attached to gym machines. If a QR code is not available, exercise data can be entered manually. The device sends the captured exercise data to the server, which records it. At this time, the captured data (e.g., exercise time, calories burned) is sent as input to the server and recorded in a database.

[1228] Step 3:

[1229] When a user eats, they take a photo of the meal using the app. The device analyzes this image and generates meal data. This analysis involves preprocessing and then using a generative AI model. Meal data (e.g., food names, calories) is automatically generated, and the user can review and correct this data. The corrected data is sent from the device to the server and recorded.

[1230] Step 4:

[1231] The server analyzes the collected exercise and dietary data to evaluate the user's progress. A machine learning algorithm is used for the analysis, and the user's progress data is used as input to evaluate the current situation. Based on the analysis results, a new fitness manual and diet plan are generated and sent to the device. The user receives the new plan and can proceed to the next step.

[1232] Step 5:

[1233] The server analyzes the user's progress data and uses a machine learning algorithm to predict when the goal will be achieved. Specifically, it evaluates the user's current pace based on past data and calculates the time it will take to achieve the goal. This predicted time is sent to the device and notified to the user. Knowing the specific target date helps users stay motivated.

[1234] Step 6:

[1235] Users can receive training advice through a real-time video chat function. This system allows users to start a video call through an app on their device and receive advice from a trainer on exercise methods and form corrections. By receiving real-time feedback, users can train more effectively.

[1236] Step 7:

[1237] The system offers a subscription model with free and paid versions. Users can use the free version to access basic fitness and diet management functions. By upgrading to the paid version, they can access more detailed personalization features and expert support. The server will provide new modules based on the user's plan and perform further personalization based on that.

[1238] The above are the specific processing steps for carrying out the present invention. This system allows users to efficiently achieve their health goals through fitness plans optimized for their individual needs.

[1239] 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.

[1240] This invention combines a system that provides an individual user with a fitness plan and meal menu optimized for them, tracks their progress, and predicts when they will reach their goals, with an emotion engine that recognizes the user's emotions and adjusts the plan and feedback accordingly. Specific embodiments of the system and its processing method are described below.

[1241] User registration and initial settings

[1242] First, the user downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[1243] Daily data entry and tracking

[1244] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[1245] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[1246] Personalized advice and progress

[1247] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[1248] Predicting goal achievement

[1249] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1250] Introducing the Emotion Engine

[1251] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions, allowing the server to determine the user's emotional state and adjust the fitness plan, meal menu, and even feedback accordingly.

[1252] For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest appropriate relaxation exercises or meals to relieve stress. The emotion engine can also provide positive feedback to increase the user's motivation.

[1253] Subscription model available

[1254] The system operates on a subscription model with a free and paid version. The free version provides basic exercise and food tracking functions, but users can upgrade to the paid version for more detailed personalization features and expert support. The paid version also includes advanced feedback and support from an emotion engine.

[1255] Specific examples

[1256] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user of a prediction that they will reach their target weight in five weeks.

[1257] Additionally, if the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals, thus supporting the user's health and emotions and helping them achieve their goals.

[1258] The processing flow will be explained below.

[1259] User registration and initial settings

[1260] Step 1:

[1261] The user downloads and installs the app.

[1262] Step 2:

[1263] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[1264] Step 3:

[1265] The device sends the input information to the server via the API.

[1266] Step 4:

[1267] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[1268] Step 5:

[1269] The server sends the generated plan to the terminal in JSON format.

[1270] Step 6:

[1271] The terminal displays the received plan to the user.

[1272] Daily data entry and tracking

[1273] Exercise data entry and recording

[1274] Step 1:

[1275] The user works out at the gym and scans the QR code on the machine with the app.

[1276] Step 2:

[1277] The device analyzes and formats the exercise data (time, calories burned, exercise intensity) obtained from the QR code.

[1278] Step 3:

[1279] In some cases, the user manually enters the exercise data.

[1280] Step 4:

[1281] The device sends the exercise data to the server.

[1282] Step 5:

[1283] The server records the received exercise data in the user profile.

[1284] Meal data entry and recording

[1285] Step 6:

[1286] The user takes a photo of their meal using the app.

[1287] Step 7:

[1288] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[1289] Step 8:

[1290] The user can check the estimated ingredients and calories and make corrections as necessary.

[1291] Step 9:

[1292] The device transmits the corrected meal data to the server.

[1293] Step 10:

[1294] The server records the received meal data in the user profile.

[1295] Personalized advice and progress

[1296] Step 1:

[1297] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[1298] Step 2:

[1299] The server then adjusts new fitness menus and meal plans based on the analysis results.

[1300] Step 3:

[1301] The server sends the generated plan to the terminal in JSON format.

[1302] Step 4:

[1303] The terminal notifies the user of the received plan and advice and displays detailed information.

[1304] Predicting goal achievement

[1305] Step 1:

[1306] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[1307] Step 2:

[1308] The server sends the prediction results in JSON format to the device.

[1309] Step 3:

[1310] The device displays the prediction results and feedback to the user.

[1311] Introducing the Emotion Engine

[1312] Emotion data input and recognition

[1313] Step 1:

[1314] The user uses the app to input voice commands and take a photo of their face.

[1315] Step 2:

[1316] The device sends voice data and facial photo data to the server.

[1317] Step 3:

[1318] The server uses an emotion engine to analyze voice data and facial photo data and recognize the user's emotions.

[1319] Emotion-based plan adjustment and feedback

[1320] Step 4:

[1321] The server uses the recognized emotion data to adjust fitness plans and meal menus.

[1322] Step 5:

[1323] The server sends the adjusted plan and feedback to the device in JSON format.

[1324] Step 6:

[1325] The device notifies the user of the plans and feedback it has received and displays detailed information.

[1326] Subscription model available

[1327] Step 1:

[1328] A user creates a free account and accesses basic features.

[1329] Step 2:

[1330] The device sends the registration information for the free version to the server.

[1331] Step 3:

[1332] The server records user data for the free version and provides basic functionality.

[1333] Step 4:

[1334] The user selects to upgrade to the paid version within the app and enters their payment information.

[1335] Step 5:

[1336] The terminal sends the payment information to the server.

[1337] Step 6:

[1338] The server confirms payment and unlocks the paid features.

[1339] Step 7:

[1340] The server provides personalization features and expert support to paid users.

[1341] Example 2

[1342] 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."

[1343] While traditional fitness and diet management systems provide plans based on a user's basic information, they often fail to take into account the user's emotions and psychological state, making it difficult to maintain user motivation. Furthermore, they often lack the ability to efficiently analyze the data entered by the user and provide appropriate feedback in real time, resulting in ineffective personalization. Furthermore, progress tracking and goal achievement predictions are often inaccurate, and notifications to users are often delayed.

[1344] 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.

[1345] In this invention, the server includes means for receiving user input information and generating an initial exercise plan and meal menu, means for receiving and recording data when the user exercises, means for allowing the user to take photos of meals and analyzing the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goals, means for recognizing the user's emotions and adjusting the plan and feedback, and means for notifying the user of the progress data and goal achievement predictions. This provides a personalized fitness plan and meal plan that takes the user's emotions and psychological state into consideration, enabling effective tracking and goal achievement predictions while maintaining the user's motivation.

[1346] "User" refers to an individual who uses this system, including the entity receiving the fitness plan or meal menu.

[1347] "Input Information" means data provided by a User to the System, including personal information such as name, age, gender, weight, height, and fitness goals.

[1348] "Workout Plan" refers to an exercise or training plan generated based on a user's fitness goals.

[1349] "Meal Menu" refers to a suggested dietary and nutrient-based plan to address a user's fitness goals.

[1350] "Exercise Data" means details of exercises performed by a User at a gym or other exercise facility, including data obtained by scanning a QR code or manually entering it.

[1351] "Dietary Data" refers to information about the diet of a user, such as the types and amounts of food consumed and calories, and includes analysis results of photos taken by the user.

[1352] "Progress Data" refers to data that indicates the current progress of a User toward their fitness goals, as a result of compiling and analyzing the User's exercise and diet data.

[1353] "Goal Timeline" means the estimated time period or specific date by which a user will achieve their fitness goal.

[1354] "Emotion" refers to the user's emotional state, a psychological state determined by the system through voice input and facial expression recognition.

[1355] "Notification methods" refers to the methods by which the system communicates information such as progress, new plans, and feedback to the user, including app push notifications and emails.

[1356] This invention combines a system that provides users with optimized exercise plans and meal menus, tracks their progress, and predicts when they will reach their goals with a function that recognizes the user's emotions and adjusts the plan and feedback. Specific embodiments of the system and its processing method are described below.

[1357] User registration and initial settings

[1358] 1. User behavior:

[1359] First, the user downloads and installs the system's application onto their smartphone, which can be downloaded from the App Store or Google Play, for example.

[1360] Launch the app and enter basic information such as your name, age, gender, weight, height, and fitness goals on the account creation screen.

[1361] 2. Terminal processing:

[1362] The device sends the entered information to a server over the internet, using your internet connection (Wi-Fi or mobile data).

[1363] 3. Server processing:

[1364] The server stores the received information in a database, for example, a relational database such as MySQL, to ensure consistent data storage.

[1365] The server generates an initial exercise plan and meal menu based on the user's basic information and fitness goals, using a Python-based backend program.

[1366] The generated plans and menus are sent back to the terminal and displayed to the user.

[1367] Daily data entry and tracking

[1368] 1. User behavior:

[1369] When a user exercises at the gym, they need to input their exercise data. Users can either scan the QR code attached to the gym machine or input it manually.

[1370] 2. Terminal processing:

[1371] When the QR code is scanned, the device automatically acquires the information and sends the exercise data to the server. Data is also sent to the server when manually entered.

[1372] 3. User Behavior:

[1373] Users take photos of their meals using the app.

[1374] 4. Terminal processing:

[1375] The device uses an image analysis engine (such as OpenCV or TensorFlow) to estimate ingredients and calories from the captured photo. The user can review the estimated results and make corrections as necessary.

[1376] 5. Server Processing:

[1377] The modified data is sent from the terminal and recorded by the server.

[1378] Personalized advice and progress

[1379] 1. Server process:

[1380] The server uses an analytical engine (e.g., the Pandas library or the SciPy library) to analyze the aggregated exercise and dietary data.

[1381] Based on the analysis results, a new exercise plan and meal menu are generated and sent to the device.

[1382] 2. Terminal processing:

[1383] The new plan is received by the terminal and notified to the user.

[1384] Predicting goal achievement

[1385] 1. Server process:

[1386] The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to predict when the goal will be achieved based on the user's progress data.

[1387] The prediction result is sent to the terminal.

[1388] 2. Terminal processing:

[1389] The user is notified of the predicted timeframe for reaching the goal.

[1390] Introducing the Emotion Engine

[1391] 1. User behavior:

[1392] Users input voice and facial expression data into the app.

[1393] 2. Terminal processing:

[1394] The input data is analyzed using a voice recognition engine (e.g., a voice recognition API) or an expression recognition engine (e.g., a face recognition API).

[1395] The analysis results are sent to the server.

[1396] 3. Server processing:

[1397] The server uses an emotion engine to determine the user's emotional state and generates fitness and meal plans and feedback based on this.

[1398] The adjusted feedback is sent to the terminal.

[1399] 4. Terminal processing:

[1400] The adjusted feedback is communicated to the user.

[1401] Subscription model available

[1402] 1. User behavior:

[1403] Users can choose between a free or paid subscription model within the app.

[1404] 2. Terminal processing:

[1405] Depending on the subscription selected, payment is processed through a payment gateway (e.g., a payment service).

[1406] 3. Server processing:

[1407] Once payment is confirmed, the paid version's detailed features, expert support, and feedback from an emotion engine will be unlocked.

[1408] Specific examples

[1409] For example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans the QR code attached to the machine, and eats a 200 kcal salad and takes a photo of it using the app. The device sends the exercise and diet data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks. If the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals. In this way, the system supports the user's health and emotions, helping them achieve their goals.

[1410] Input prompt for generative AI model

[1411] Examples of prompts include:

[1412] I've set my goals to "lose weight" and "increase muscle strength." My current weight is 70 kg, and my target weight is 65 kg. Please suggest me a fitness plan and meal menu for the future. Also, please let me know the date by which I plan to achieve this.

[1413] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1414] System program processing flow

[1415] Step 1:

[1416] User registration and initial settings

[1417] 1. Input:

[1418] Users download and install the system's application, then enter basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen.

[1419] 2. Specific operation:

[1420] The user launches the app and enters their information.

[1421] The terminal receives the input information and transmits it to a server via the Internet.

[1422] 3. Data Processing:

[1423] The server stores the received information (name, age, gender, weight, height, fitness goals) in a database.

[1424] 4. Output:

[1425] The server generates an initial exercise plan and meal menu and sends them to the device.

[1426] Step 2:

[1427] Daily data entry and tracking

[1428] 1. Input:

[1429] Users work out at the gym and either scan a QR code attached to the machine with the app or manually enter their exercise data.

[1430] 2. Specific operation:

[1431] Once the QR code is scanned, the device will automatically obtain relevant information and collect exercise data.

[1432] Manually entered data is handled similarly.

[1433] Users take photos of their meals using the app.

[1434] 3. Data Processing:

[1435] The terminal transmits the collected exercise data to a server via the Internet.

[1436] The device uses an image analysis engine to analyze photos of meals and estimate ingredients and calories.

[1437] The estimated results are displayed to the user, who can then correct them as necessary.

[1438] 4. Output:

[1439] The device transmits the corrected meal data to the server.

[1440] The server records this data in a database.

[1441] Step 3:

[1442] Personalized advice and progress

[1443] 1. Input:

[1444] The collected exercise and dietary data is stored on a server.

[1445] 2. Specific operation:

[1446] The server uses an analysis engine to analyze the exercise and dietary data and calculate the user's progress.

[1447] 3. Data Processing:

[1448] Based on the analysis results, a new exercise plan and meal menu will be generated.

[1449] 4. Output:

[1450] The server sends the generated new plan to the terminal.

[1451] The terminal receives this and notifies the user.

[1452] Step 4:

[1453] Predicting goal achievement

[1454] 1. Input:

[1455] The state in which the user's progress data is accumulated.

[1456] 2. Specific operation:

[1457] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[1458] 3. Data Processing:

[1459] Analyze progress data and apply appropriate algorithms to make predictions.

[1460] 4. Output:

[1461] The server transmits the predicted completion time to the terminal.

[1462] The terminal notifies the user of the prediction result.

[1463] Step 5:

[1464] Introducing the Emotion Engine

[1465] 1. Input:

[1466] The user inputs voice and facial expression data.

[1467] 2. Specific operation:

[1468] The data is analyzed using a voice recognition engine and a facial expression recognition engine.

[1469] 3. Data Processing:

[1470] Based on the analysis results, the user's emotional state is estimated and plans and feedback content are adjusted accordingly.

[1471] 4. Output:

[1472] The server sends the adjusted plan and feedback to the device.

[1473] The terminal notifies the user of the adjustment results.

[1474] Step 6:

[1475] Subscription model available

[1476] 1. Input:

[1477] The user selects a subscription model.

[1478] 2. Specific operation:

[1479] The terminal processes the payment based on the selected subscription.

[1480] 3. Data Processing:

[1481] A payment gateway is used to process the payment, and if successful, the data is stored on the server.

[1482] 4. Output:

[1483] The server will offer users the advanced features and support of the paid version.

[1484] (Application example 2)

[1485] 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."

[1486] Conventional fitness plan and meal menu delivery systems were unable to take into account the user's individual emotional state, resulting in insufficient measures to address user motivation and stress. Furthermore, the manual input method for exercise and dietary data was burdensome, potentially reducing tracking accuracy. Furthermore, there was no system that comprehensively supported fitness activities and dietary management within the gym, resulting in inconsistent progress management for users. These challenges made it difficult for users to achieve their goals.

[1487] 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.

[1488] In the present invention, the server includes the following means:

[1489] means for receiving user input and generating an initial fitness plan and meal menu;

[1490] means for receiving and recording data when a user exercises at a gym;

[1491] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[1492] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[1493] means for notifying the user of progress data and goal achievement projections;

[1494] A means for recognizing the user's emotional state from their voice input and facial expressions, and adjusting the feedback content using an emotion engine;

[1495] A method to scan the QR code on the training machine in the gym and enter exercise data,

[1496] It includes measures to suggest relaxation exercises and stress-relieving meals based on emotional state.

[1497] This allows for feedback and advice tailored to the user's individual emotional state, realizes efficient and precise tracking of exercise and diet data, and provides comprehensive support for users' fitness activities and diet management, helping them achieve their goals.

[1498] "User input information" refers to basic information such as name, age, sex, weight, height, and fitness goals that a user provides to the system.

[1499] A "fitness plan" is an exercise schedule or training menu that is generated based on a user's fitness goals.

[1500] A "meal menu" is a daily meal plan suggested to suit the user's health and fitness goals.

[1501] "Exercise data" refers to data such as the type and duration of exercise performed by the user at the gym, and calories burned.

[1502] "Dietary data" refers to data relating to the contents, calories, ingredients, etc. of meals consumed by the user.

[1503] "Progress data" is data recorded about the exercises a user actually performs and the food they eat, and indicates the user's progress toward achieving their goals.

[1504] The "goal achievement date" is the date and time when the user is expected to achieve the fitness goal they have set.

[1505] "Notification" is the means by which the system provides information to the user, in the form of messages, alerts, etc.

[1506] An "emotion engine" is an algorithm or software that recognizes a user's emotional state from their voice input and facial expressions, and adjusts feedback based on that emotional state.

[1507] A "QR code" is a two-dimensional barcode attached to training machines in the gym, which users can scan with their smartphone to automatically enter exercise data.

[1508] "Relaxation exercises" are relaxation exercises and stretches that users perform to reduce stress and fatigue.

[1509] "Meals for stress relief" are ingredients and menus that are effective in reducing stress and are suggested based on the user's stress level.

[1510] This invention is a system that provides an individual user with an optimized fitness plan and meal menu, tracks their progress, and predicts when they will reach their goals. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the plan and feedback based on those emotions. Specific embodiments of this system and its processing method are described below.

[1511] User registration and initial settings

[1512] First, the user downloads and installs the system's application onto their smartphone. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this information and displays it to the user.

[1513] Daily data entry and tracking

[1514] When a user exercises at the gym, they need to input their exercise data. The user can automatically obtain the exercise data by scanning a QR code attached to the gym's training machines with their smartphone. Meal data is also input by the user taking a photo of the meal using the app. The smartphone analyzes the captured image, and the ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The data is then sent to a server and recorded.

[1515] Personalized advice and progress

[1516] The server analyzes the aggregated exercise and diet data to calculate the user's current progress, and adjusts a new fitness plan and diet menu based on the analysis. The server then sends this information to the smartphone, which notifies the user.

[1517] Goal achievement prediction and notification

[1518] The server uses the user's progress data to predict when the goal will be achieved using a machine learning algorithm. The predicted time is then sent to the user via a smartphone notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1519] Introducing the Emotion Engine

[1520] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions. Based on the emotional state recognized by the emotion engine, the server can adjust the fitness plan, meal menu, and even feedback content. For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest relaxation exercises or stress-relieving meals to the user. The emotion engine also provides positive feedback to increase the user's motivation.

[1521] Hardware and software used

[1522] Smartphone: where users install apps and use them to enter data and receive notifications.

[1523] Server: Stores and analyzes user data, calculates progress, predicts when goals will be reached, and adjusts fitness plans and meal menus.

[1524] QR Code Reader: A means of reading the QR code on training machines at the gym and automatically obtaining exercise data.

[1525] Image processing library: Used to analyze photos of meals and estimate ingredients and calories.

[1526] Emotion Recognition Library: Used to recognize emotional states by analyzing voice input and facial expressions.

[1527] Examples of concrete examples and prompts

[1528] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill at the gym for 30 minutes and scans a QR code. They also eat a 200 kcal salad and take a photo of it with their smartphone. The server collates this data, suggests a new training plan and meal menu to the user, and notifies them that they will reach their target weight in five weeks. If the user is feeling stressed, the server also suggests relaxing yoga exercises and stress-relieving meals.

[1529] An example prompt is:

[1530] User data registration:

[1531] To register, please enter your name, age, gender, weight, height and fitness goals.

[1532] Exercise Data Entry:

[1533] After your workout, scan the QR code to enter your exercise data.

[1534] Meal Data Entry:

[1535] Take a photo of your meal and check and edit the ingredients and calories.

[1536] Emotional State Check:

[1537] Answer simple questions to identify your current emotional state (e.g., "How are you feeling today?").

[1538] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1539] Step 1:

[1540] A user downloads and installs the system's application on their smartphone, and enters basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen. This information is sent by the device to the server, which receives it and stores it in a database. This process enters the user's initial information into the system, and the server uses this information to generate an initial fitness plan and meal menu.

[1541] Step 2:

[1542] The server generates an initial fitness plan and meal menu based on the user's basic information and fitness goals. The generated plan and menu are sent from the server to the device. The device receives them and displays the plan and menu optimized for the user on the screen. This process allows the user to confirm the fitness plan and meal menu that are best suited to them.

[1543] Step 3:

[1544] When a user works out at the gym, they scan a QR code attached to the training machine with their smartphone to enter their exercise data into the device. The device then sends this data to a server, which records the received data in a database and saves it as the user's exercise history. Through this process, detailed exercise data is automatically collected and recorded.

[1545] Step 4:

[1546] Users take a photo of their meal with their smartphone and enter it into the app. The device's built-in image processing library analyzes the photo and automatically estimates the ingredients and calories. The user can review the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records the received meal data in a database. Through this process, detailed meal data is collected and recorded in the system.

[1547] Step 5:

[1548] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. This analysis is performed using statistical data calculations that combine multiple data points. Based on the analysis results, a new fitness plan and diet menu are generated and sent from the server to the device. The device then notifies the user. This process ensures that the user always receives the latest personalized plan.

[1549] Step 6:

[1550] The server analyzes the user's progress data using a machine learning algorithm to predict when the goal will be achieved. This prediction is sent from the server to the device and presented to the user as a notification. For example, it shows how many weeks it will take to achieve the target weight if the user continues training at the current pace. This process allows the user to get a sense of when they will reach their goal.

[1551] Step 7:

[1552] The device's built-in emotion recognition library analyzes the user's voice input and facial expressions to recognize their emotional state. This emotion data is sent to a server where it is analyzed by an emotion engine. The emotion engine processes the data to adjust fitness plans, meal menus, and feedback content based on the recognized emotions. The server then sends the adjusted plans and feedback to the device and notifies the user. This process provides personalized feedback based on the user's emotional state.

[1553] Step 8:

[1554] The server analyzes the emotional data and, if it determines that the user is feeling stressed or fatigued, suggests relaxation exercises or meals to relieve stress. These suggestions are sent to the device as notifications from the server and presented to the user. For example, if emotion recognition determines that the user is feeling stressed, it will suggest yoga exercises with a relaxing effect or a meal menu to reduce stress. This process allows the user to receive support both physically and mentally.

[1555] 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.

[1556] 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.

[1557] 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.

[1558] [Fourth embodiment]

[1559] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1560] 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.

[1561] 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).

[1562] 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.

[1563] 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.

[1564] 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).

[1565] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] 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.

[1571] 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."

[1572] The present invention relates to a system that provides an individual user with an optimized fitness plan and meal menu, tracks the progress, and predicts when the goal will be achieved. Specific embodiments of the system and its processing method are described below.

[1573] User registration and initial settings

[1574] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[1575] Daily data entry and tracking

[1576] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[1577] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[1578] Personalized advice and progress

[1579] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[1580] Predicting goal achievement

[1581] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1582] Free and paid versions available

[1583] The system operates on a subscription model with a free version and a paid version: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[1584] Specific examples

[1585] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks.

[1586] In this way, the system helps users achieve their health goals.

[1587] The processing flow will be explained below.

[1588] Specific flow of program processing

[1589] User registration and initial settings

[1590] Step 1:

[1591] The user downloads and installs the app.

[1592] Step 2:

[1593] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[1594] Step 3:

[1595] The device sends the input information to the server via the API.

[1596] Step 4:

[1597] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[1598] Step 5:

[1599] The server sends the generated plan to the terminal in JSON format.

[1600] Step 6:

[1601] The terminal displays the received plan to the user.

[1602] Daily data entry and tracking

[1603] Step 1:

[1604] The user works out at the gym and scans the QR code on the machine with the app.

[1605] Step 2:

[1606] The device parses and formats the exercise data (e.g., time, calories burned, intensity) obtained from the QR code.

[1607] Step 3:

[1608] In some cases, the user manually enters the exercise data.

[1609] Step 4:

[1610] The device sends the exercise data to the server.

[1611] Step 5:

[1612] The server records the received exercise data in the user profile.

[1613] Step 6:

[1614] When users eat, they take a photo of their meal using the app.

[1615] Step 7:

[1616] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[1617] Step 8:

[1618] The user can check the estimated ingredients and calories and make corrections as necessary.

[1619] Step 9:

[1620] The device transmits the corrected meal data to the server.

[1621] Step 10:

[1622] The server records the received meal data in the user profile.

[1623] Personalized advice and progress

[1624] Step 1:

[1625] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[1626] Step 2:

[1627] The server then adjusts new fitness menus and meal plans based on the analysis results.

[1628] Step 3:

[1629] The server sends the generated plan to the terminal in JSON format.

[1630] Step 4:

[1631] The terminal notifies the user of the received plan and advice and displays detailed information.

[1632] Predicting goal achievement

[1633] Step 1:

[1634] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[1635] Step 2:

[1636] The server sends the prediction results in JSON format to the device.

[1637] Step 3:

[1638] The device displays the prediction results and feedback to the user.

[1639] Subscription model available

[1640] Step 1:

[1641] A user creates a free account and accesses basic features.

[1642] Step 2:

[1643] The device sends the registration information for the free version to the server.

[1644] Step 3:

[1645] The server records user data for the free version and provides basic functionality.

[1646] Step 4:

[1647] The user selects to upgrade to the paid version within the app and enters their payment information.

[1648] Step 5:

[1649] The terminal sends the payment information to the server.

[1650] Step 6:

[1651] The server confirms payment and unlocks the paid features.

[1652] Step 7:

[1653] The server provides personalization features and expert support to paid users.

[1654] Example 1

[1655] 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."

[1656] Conventional systems have difficulty providing individual users with optimized exercise and meal plans, efficiently tracking their progress, and accurately predicting when their goals will be achieved. Furthermore, inputting users' exercise and meal data is time-consuming, making them difficult to use. There is a need for a system that can solve these issues and enable users to easily achieve their health goals.

[1657] 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.

[1658] In this invention, the server includes means for receiving user input information and generating an initial exercise menu and meal plan, means for receiving and recording data from the user's training at an exercise facility, means for the user to take photos of their meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, and means for notifying the user of the progress data and the predicted goal achievement. This allows for the provision of a personalized exercise menu and meal plan based on the user's information, enabling easy data entry and accurate progress management.

[1659] "Input Information" is basic information provided by the user, such as name, age, gender, weight, height, and fitness goals.

[1660] An "exercise menu" is an exercise program planned based on fitness goals set by a user.

[1661] A "meal plan" is a list of meals that are nutritionally balanced and planned to meet the user's health goals.

[1662] An "exercise facility" is a place where users train, such as a gym or fitness center.

[1663] "Training data" refers to data such as the content and duration of training the user undertook at an exercise facility, and calories burned.

[1664] "Identification codes" refer to QR codes or barcodes associated with exercise machines, food, etc., and by scanning these, training and dietary information can be quickly entered.

[1665] "Dietary data" refers to photographs of meals consumed by the user and information such as ingredients and calories based on the analysis results.

[1666] "Analysis" is the process by which the server processes the collected data and makes calculations regarding the user's progress and goal achievement.

[1667] The "goal completion date" is the date by which the user plans to achieve the fitness or health goal they set based on their progress data.

[1668] "Progress data" is an ongoing record of the amount of exercise a user is doing and the contents of their diet.

[1669] "Notifications" are messages or alerts generated by the server that are used to communicate progress and goal achievement forecast information to the user's device.

[1670] MODE FOR CARRYING OUT THE INVENTION

[1671] The present invention relates to a system that provides an exercise menu and meal plan optimized for an individual user, tracks the progress, and predicts when the goal will be achieved. Specific embodiments will be described below.

[1672] User registration and initial settings

[1673] The user first downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and exercise goals (e.g., weight loss, muscle building, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial exercise menu and meal plan, which it then sends to the device. The device receives this and displays it to the user.

[1674] Daily data entry and tracking

[1675] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[1676] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[1677] Personalized advice and progress

[1678] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new exercise and diet plan is tailored. The server then sends this information to the device, which notifies the user.

[1679] Predicting goal achievement

[1680] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1681] Free and paid versions available

[1682] The system operates on a two-tier subscription model: the free version offers basic exercise and food tracking features, while users can upgrade to the paid version for more in-depth personalization and expert support.

[1683] Specific examples

[1684] For example, if a user sets a goal of losing weight and building muscle, and their current weight is 70 kg and their target weight is 65 kg, the following process will occur: The user exercises on a treadmill for 30 minutes and enters the data by scanning the identification code attached to the gym machine. They also eat a 200 kcal salad and take a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server will suggest a new training plan and meal menu, and notify the user that they will reach their target weight in five weeks.

[1685] Prompt Sentence Examples

[1686] "Please explain in detail how a user trying to lose weight can enter their day's exercise and diet information into the app. How does the server and device process the data?"

[1687] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1688] Step 1:

[1689] The user downloads and installs the system's application. On the account creation screen, the user enters basic information such as name, age, gender, weight, height, and exercise goals, and the device sends this information to the server. The entered information is sent to the server and stored in a database. This information becomes the data used to generate an initial exercise menu and meal plan.

[1690] Step 2:

[1691] The server generates an initial exercise menu and meal plan based on the user information stored in the database. The generated exercise menu and meal plan are sent to the terminal, which displays them to the user. The input here is the user information, and the output is the initial exercise menu and meal plan.

[1692] Step 3:

[1693] When a user goes to the gym to work out, they either scan the identification code attached to the gym machine with the app or manually enter their exercise data. The device sends the entered exercise data to the server, which records it in a database. The input is the exercise data, and the output is the recorded database entry.

[1694] Step 4:

[1695] When a user eats a meal, they take a photo of the meal using the app. The device analyzes the captured image of the meal and estimates the ingredients and calories. The estimated data may be reviewed by the user and revised. The revised data is sent to the server, which records it in a database. The input is the meal image and the revised data, and the output is the recorded database entry.

[1696] Step 5:

[1697] The server aggregates the exercise and dietary data recorded in the database and analyzes the user's progress. Based on the results of this analysis, the exercise menu and diet plan are adjusted and a new plan is generated. The generated plan is sent to the device, which notifies the user. The input here is the recorded data, and the output is the adjusted exercise menu and diet plan.

[1698] Step 6:

[1699] The server uses a machine learning algorithm to predict when the goal will be achieved based on the recorded progress data. This prediction result is sent to the device, which then notifies the user of the expected goal achievement date. The input is the progress data, and the output is the predicted goal achievement result.

[1700] Step 7:

[1701] Users can upgrade their subscription from the free version to a paid version as needed, which provides more personalized features and expert support. This upgrade also updates the user's account information and records it on the server. The input here is the subscription information, and the output is the upgraded account information and additional features.

[1702] (Application example 1)

[1703] 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."

[1704] Conventional fitness and diet management systems struggle to provide detailed fitness plans and diet menus tailored to individual users' needs. They also lack the means for users to accurately track their progress and predict when they will reach their goals. It's also difficult for users to receive appropriate training advice in real time during fitness activities. This makes it difficult for users to maintain motivation, often resulting in delays in achieving their goals.

[1705] 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.

[1706] In this invention, the server includes means for receiving user input information and generating an initial fitness plan and meal menu, means for receiving and recording data of the user's fitness activities, means for the user to take images of meals and analyze the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goal, means for notifying the user of the progress data and the predicted goal achievement, and means for video chatting with the user in real time and providing training advice. This allows the user to accurately understand their own progress through the personalized fitness plan and meal menu and to effectively train and manage their diet to achieve their goal.

[1707] "User input" refers to data such as personal information and fitness goals that a user provides to the system.

[1708] The "initial fitness plan" is a fitness activity plan generated based on the user's input information, and includes exercise content and schedules that are optimal for each individual user.

[1709] A "meal menu" is a meal plan generated based on information input by the user, and includes meal contents that take into consideration the balance of calories and nutrients.

[1710] A "fitness activity" is a series of exercise or training activities undertaken by a user.

[1711] "Means for receiving and recording data" refers to the function of the system receiving data when a user performs a fitness activity and storing that data.

[1712] "Means for analyzing images of meals and generating meal data" refers to a function that analyzes the contents of images of meals taken by the user and estimates the foods, nutrients, and calories contained in the meal.

[1713] "Progress data" is data that indicates the progress status calculated based on the user's fitness activity and diet history.

[1714] "Means for predicting when a goal will be achieved" refers to a function that analyzes a user's progress data and predicts how long it will take to achieve a set fitness goal, weight goal, etc.

[1715] The "notification means" is a means for notifying the user of information such as progress data and a goal achievement forecast.

[1716] "Video chat" is a communication method that allows users to have real-time video calls and receive training advice and guidance.

[1717] A "personalized fitness plan" is a fitness activity plan that is customized to a user's individual circumstances and goals.

[1718] "Appropriate training advice in real time" refers to advice and guidance on exercise methods and form corrections that are provided to the user immediately on the spot while they are training.

[1719] The present invention relates to a system for providing a user with an optimized fitness plan and meal menu, tracking the progress, and predicting when the user will reach their goal. Specific embodiments of the system are described below.

[1720] 1. User registration and initial settings

[1721] The user first downloads and installs the system's application. To use the fitness plan and meal menu, the user provides input information such as name, age, gender, weight, height, and fitness goals. This information is sent by the device to the server. The server receives this input information, generates an initial fitness plan and meal menu, and sends it to the device. The user can view the generated fitness plan and meal menu on the device.

[1722] 2. Daily data entry and tracking

[1723] When a user engages in fitness activities, they are required to input activity data. Users can obtain fitness data by scanning QR codes attached to gym machines, or they can manually enter data if a QR code is not available. This data is sent from the device to a server and recorded. Additionally, when a user eats, they take a photo of the meal using the app. This image is analyzed on the device and meal data is generated. A generative AI model is used for the analysis. The user checks the calories and nutrients of their meal and makes any necessary adjustments. The corrected data is sent to the server and recorded.

[1724] 3. Personalized advice and progress

[1725] The server analyzes the collected fitness and dietary data to evaluate the user's current progress. Based on the analysis results, it adjusts new fitness menus and meal plans. It also provides training advice to the user through a real-time video chat function. This allows users to receive advice on appropriate exercise methods and form corrections in real time.

[1726] 4. Predicting goal achievement

[1727] The server analyzes the user's progress data and uses machine learning algorithms to predict when the goal will be achieved. The calculated goal achievement date is then sent to the user as a notification. For example, if the user's goal is weight loss, the estimated achievement date if the user continues at the current pace of progress is displayed.

[1728] 5. Free and paid versions available

[1729] The system operates on a subscription model with a free version offering basic fitness and diet management features, while upgrading to the paid version offers more in-depth personalization features and expert support.

[1730] Usage example

[1731] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. This data is sent from the device to the server and recorded. The server generates a new training plan and meal menu based on the data so far, and notifies the user that they will reach their target weight in five weeks.

[1732] Prompt Sentence Examples

[1733] Enter your name, age, weight, height, and fitness goal (e.g., lose weight, gain strength, improve endurance).

[1734] This way, users can access a personalized fitness plan at home, receive real-time training advice, and efficiently work towards their goals.

[1735] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1736] Step 1:

[1737] A user downloads and installs a fitness application. On the account creation screen, the user provides input information such as name, age, gender, weight, height, and fitness goals. The device sends this input information to a server. The server generates an initial fitness plan and meal menu for the user based on the received data. This is done using a generative AI model, which uses the user's basic information as input to provide the optimal plan. The generated plan is sent to the device and displayed to the user.

[1738] Step 2:

[1739] When users perform their daily fitness activities, they can capture data by scanning QR codes attached to gym machines. If a QR code is not available, exercise data can be entered manually. The device sends the captured exercise data to the server, which records it. At this time, the captured data (e.g., exercise time, calories burned) is sent as input to the server and recorded in a database.

[1740] Step 3:

[1741] When a user eats, they take a photo of the meal using the app. The device analyzes this image and generates meal data. This analysis involves preprocessing and then using a generative AI model. Meal data (e.g., food names, calories) is automatically generated, and the user can review and correct this data. The corrected data is sent from the device to the server and recorded.

[1742] Step 4:

[1743] The server analyzes the collected exercise and dietary data to evaluate the user's progress. A machine learning algorithm is used for the analysis, and the user's progress data is used as input to evaluate the current situation. Based on the analysis results, a new fitness manual and diet plan are generated and sent to the device. The user receives the new plan and can proceed to the next step.

[1744] Step 5:

[1745] The server analyzes the user's progress data and uses a machine learning algorithm to predict when the goal will be achieved. Specifically, it evaluates the user's current pace based on past data and calculates the time it will take to achieve the goal. This predicted time is sent to the device and notified to the user. Knowing the specific target date helps users stay motivated.

[1746] Step 6:

[1747] Users can receive training advice through a real-time video chat function. This system allows users to start a video call through an app on their device and receive advice from a trainer on exercise methods and form corrections. By receiving real-time feedback, users can train more effectively.

[1748] Step 7:

[1749] The system offers a subscription model with free and paid versions. Users can use the free version to access basic fitness and diet management functions. By upgrading to the paid version, they can access more detailed personalization features and expert support. The server will provide new modules based on the user's plan and perform further personalization based on that.

[1750] The above are the specific processing steps for carrying out the present invention. This system allows users to efficiently achieve their health goals through fitness plans optimized for their individual needs.

[1751] 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.

[1752] This invention combines a system that provides an individual user with a fitness plan and meal menu optimized for them, tracks their progress, and predicts when they will reach their goals, with an emotion engine that recognizes the user's emotions and adjusts the plan and feedback accordingly. Specific embodiments of the system and its processing method are described below.

[1753] User registration and initial settings

[1754] First, the user downloads and installs the system's application. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this and displays it to the user.

[1755] Daily data entry and tracking

[1756] When a user exercises at the gym, they need to input their exercise data. Users can obtain the input data by scanning the QR code attached to the gym machine with the app, or they can input it manually if there is no QR code. The device sends the input exercise data to the server, which records it.

[1757] Meal data is entered by the user taking a photo of the meal using the app. The captured image is analyzed on the device, and ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records it.

[1758] Personalized advice and progress

[1759] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. Based on this analysis, a new fitness menu and meal plan are tailored. The server then sends this information to the device, which notifies the user.

[1760] Predicting goal achievement

[1761] The server uses the user's progress data to predict when the user will achieve their goal using a machine learning algorithm. The predicted time is then provided to the user as a notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[1762] Introducing the Emotion Engine

[1763] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions, allowing the server to determine the user's emotional state and adjust the fitness plan, meal menu, and even feedback accordingly.

[1764] For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest appropriate relaxation exercises or meals to relieve stress. The emotion engine can also provide positive feedback to increase the user's motivation.

[1765] Subscription model available

[1766] The system operates on a subscription model with a free and paid version. The free version provides basic exercise and food tracking functions, but users can upgrade to the paid version for more detailed personalization features and expert support. The paid version also includes advanced feedback and support from an emotion engine.

[1767] Specific examples

[1768] For example, suppose a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg, with a target weight of 65 kg. The user exercises on a treadmill for 30 minutes, scans a QR code, eats a 200 kcal salad, and takes a photo of it using the app. The device sends the exercise and meal data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user of a prediction that they will reach their target weight in five weeks.

[1769] Additionally, if the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals, thus supporting the user's health and emotions and helping them achieve their goals.

[1770] The processing flow will be explained below.

[1771] User registration and initial settings

[1772] Step 1:

[1773] The user downloads and installs the app.

[1774] Step 2:

[1775] A user launches the app and enters their name, age, gender, weight, height, and fitness goals on the account creation screen.

[1776] Step 3:

[1777] The device sends the input information to the server via the API.

[1778] Step 4:

[1779] The server stores the received information in a database and generates an initial fitness plan and meal menu.

[1780] Step 5:

[1781] The server sends the generated plan to the terminal in JSON format.

[1782] Step 6:

[1783] The terminal displays the received plan to the user.

[1784] Daily data entry and tracking

[1785] Exercise data entry and recording

[1786] Step 1:

[1787] The user works out at the gym and scans the QR code on the machine with the app.

[1788] Step 2:

[1789] The device analyzes and formats the exercise data (time, calories burned, exercise intensity) obtained from the QR code.

[1790] Step 3:

[1791] In some cases, the user manually enters the exercise data.

[1792] Step 4:

[1793] The device sends the exercise data to the server.

[1794] Step 5:

[1795] The server records the received exercise data in the user profile.

[1796] Meal data entry and recording

[1797] Step 6:

[1798] The user takes a photo of their meal using the app.

[1799] Step 7:

[1800] The device uses AI to analyze the photos taken and estimate the ingredients and calories.

[1801] Step 8:

[1802] The user can check the estimated ingredients and calories and make corrections as necessary.

[1803] Step 9:

[1804] The device transmits the corrected meal data to the server.

[1805] Step 10:

[1806] The server records the received meal data in the user profile.

[1807] Personalized advice and progress

[1808] Step 1:

[1809] The server analyzes the aggregated exercise and diet data and calculates the user's current progress.

[1810] Step 2:

[1811] The server then adjusts new fitness menus and meal plans based on the analysis results.

[1812] Step 3:

[1813] The server sends the generated plan to the terminal in JSON format.

[1814] Step 4:

[1815] The terminal notifies the user of the received plan and advice and displays detailed information.

[1816] Predicting goal achievement

[1817] Step 1:

[1818] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[1819] Step 2:

[1820] The server sends the prediction results in JSON format to the device.

[1821] Step 3:

[1822] The device displays the prediction results and feedback to the user.

[1823] Introducing the Emotion Engine

[1824] Emotion data input and recognition

[1825] Step 1:

[1826] The user uses the app to input voice commands and take a photo of their face.

[1827] Step 2:

[1828] The device sends voice data and facial photo data to the server.

[1829] Step 3:

[1830] The server uses an emotion engine to analyze voice data and facial photo data and recognize the user's emotions.

[1831] Emotion-based plan adjustment and feedback

[1832] Step 4:

[1833] The server uses the recognized emotion data to adjust fitness plans and meal menus.

[1834] Step 5:

[1835] The server sends the adjusted plan and feedback to the device in JSON format.

[1836] Step 6:

[1837] The device notifies the user of the plans and feedback it has received and displays detailed information.

[1838] Subscription model available

[1839] Step 1:

[1840] A user creates a free account and accesses basic features.

[1841] Step 2:

[1842] The device sends the registration information for the free version to the server.

[1843] Step 3:

[1844] The server records user data for the free version and provides basic functionality.

[1845] Step 4:

[1846] The user selects to upgrade to the paid version within the app and enters their payment information.

[1847] Step 5:

[1848] The terminal sends the payment information to the server.

[1849] Step 6:

[1850] The server confirms payment and unlocks the paid features.

[1851] Step 7:

[1852] The server provides personalization features and expert support to paid users.

[1853] Example 2

[1854] 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."

[1855] While traditional fitness and diet management systems provide plans based on a user's basic information, they often fail to take into account the user's emotions and psychological state, making it difficult to maintain user motivation. Furthermore, they often lack the ability to efficiently analyze the data entered by the user and provide appropriate feedback in real time, resulting in ineffective personalization. Furthermore, progress tracking and goal achievement predictions are often inaccurate, and notifications to users are often delayed.

[1856] 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.

[1857] In this invention, the server includes means for receiving user input information and generating an initial exercise plan and meal menu, means for receiving and recording data when the user exercises, means for allowing the user to take photos of meals and analyzing the images to generate meal data, means for analyzing the user's progress data and predicting when the user will achieve their goals, means for recognizing the user's emotions and adjusting the plan and feedback, and means for notifying the user of the progress data and goal achievement predictions. This provides a personalized fitness plan and meal plan that takes the user's emotions and psychological state into consideration, enabling effective tracking and goal achievement predictions while maintaining the user's motivation.

[1858] "User" refers to an individual who uses this system, including the entity receiving the fitness plan or meal menu.

[1859] "Input Information" means data provided by a User to the System, including personal information such as name, age, gender, weight, height, and fitness goals.

[1860] "Workout Plan" refers to an exercise or training plan generated based on a user's fitness goals.

[1861] "Meal Menu" refers to a suggested dietary and nutrient-based plan to address a user's fitness goals.

[1862] "Exercise Data" means details of exercises performed by a User at a gym or other exercise facility, including data obtained by scanning a QR code or manually entering it.

[1863] "Dietary Data" refers to information about the diet of a user, such as the types and amounts of food consumed and calories, and includes analysis results of photos taken by the user.

[1864] "Progress Data" refers to data that indicates the current progress of a User toward their fitness goals, as a result of compiling and analyzing the User's exercise and diet data.

[1865] "Goal Timeline" means the estimated time period or specific date by which a user will achieve their fitness goal.

[1866] "Emotion" refers to the user's emotional state, a psychological state determined by the system through voice input and facial expression recognition.

[1867] "Notification methods" refers to the methods by which the system communicates information such as progress, new plans, and feedback to the user, including app push notifications and emails.

[1868] This invention combines a system that provides users with optimized exercise plans and meal menus, tracks their progress, and predicts when they will reach their goals with a function that recognizes the user's emotions and adjusts the plan and feedback. Specific embodiments of the system and its processing method are described below.

[1869] User registration and initial settings

[1870] 1. User behavior:

[1871] First, the user downloads and installs the system's application onto their smartphone, which can be downloaded from the App Store or Google Play, for example.

[1872] Launch the app and enter basic information such as your name, age, gender, weight, height, and fitness goals on the account creation screen.

[1873] 2. Terminal processing:

[1874] The device sends the entered information to a server over the internet, using your internet connection (Wi-Fi or mobile data).

[1875] 3. Server processing:

[1876] The server stores the received information in a database, for example, a relational database such as MySQL, to ensure consistent data storage.

[1877] The server generates an initial exercise plan and meal menu based on the user's basic information and fitness goals, using a Python-based backend program.

[1878] The generated plans and menus are sent back to the terminal and displayed to the user.

[1879] Daily data entry and tracking

[1880] 1. User behavior:

[1881] When a user exercises at the gym, they need to input their exercise data. Users can either scan the QR code attached to the gym machine or input it manually.

[1882] 2. Terminal processing:

[1883] When the QR code is scanned, the device automatically acquires the information and sends the exercise data to the server. Data is also sent to the server when manually entered.

[1884] 3. User Behavior:

[1885] Users take photos of their meals using the app.

[1886] 4. Terminal processing:

[1887] The device uses an image analysis engine (such as OpenCV or TensorFlow) to estimate ingredients and calories from the captured photo. The user can review the estimated results and make corrections as necessary.

[1888] 5. Server Processing:

[1889] The modified data is sent from the terminal and recorded by the server.

[1890] Personalized advice and progress

[1891] 1. Server process:

[1892] The server uses an analytical engine (e.g., the Pandas library or the SciPy library) to analyze the aggregated exercise and dietary data.

[1893] Based on the analysis results, a new exercise plan and meal menu are generated and sent to the device.

[1894] 2. Terminal processing:

[1895] The new plan is received by the terminal and notified to the user.

[1896] Predicting goal achievement

[1897] 1. Server process:

[1898] The server uses machine learning algorithms (e.g., Scikit-learn or TensorFlow) to predict when the goal will be achieved based on the user's progress data.

[1899] The prediction result is sent to the terminal.

[1900] 2. Terminal processing:

[1901] The user is notified of the predicted timeframe for reaching the goal.

[1902] Introducing the Emotion Engine

[1903] 1. User behavior:

[1904] Users input voice and facial expression data into the app.

[1905] 2. Terminal processing:

[1906] The input data is analyzed using a voice recognition engine (e.g., a voice recognition API) or an expression recognition engine (e.g., a face recognition API).

[1907] The analysis results are sent to the server.

[1908] 3. Server processing:

[1909] The server uses an emotion engine to determine the user's emotional state and generates fitness and meal plans and feedback based on this.

[1910] The adjusted feedback is sent to the terminal.

[1911] 4. Terminal processing:

[1912] The adjusted feedback is communicated to the user.

[1913] Subscription model available

[1914] 1. User behavior:

[1915] Users can choose between a free or paid subscription model within the app.

[1916] 2. Terminal processing:

[1917] Depending on the subscription selected, payment is processed through a payment gateway (e.g., a payment service).

[1918] 3. Server processing:

[1919] Once payment is confirmed, the paid version's detailed features, expert support, and feedback from an emotion engine will be unlocked.

[1920] Specific examples

[1921] For example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill for 30 minutes, scans the QR code attached to the machine, and eats a 200 kcal salad and takes a photo of it using the app. The device sends the exercise and diet data to the server, which records it. Based on this data, the server proposes a new training plan and meal menu, and notifies the user that they will reach their target weight in five weeks. If the user is feeling stressed, the emotion engine will recognize this and suggest relaxing yoga exercises or stress-relieving meals. In this way, the system supports the user's health and emotions, helping them achieve their goals.

[1922] Input prompt for generative AI model

[1923] Examples of prompts include:

[1924] I've set my goals to "lose weight" and "increase muscle strength." My current weight is 70 kg, and my target weight is 65 kg. Please suggest me a fitness plan and meal menu for the future. Also, please let me know the date by which I plan to achieve this.

[1925] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1926] System program processing flow

[1927] Step 1:

[1928] User registration and initial settings

[1929] 1. Input:

[1930] Users download and install the system's application, then enter basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen.

[1931] 2. Specific operation:

[1932] The user launches the app and enters their information.

[1933] The terminal receives the input information and transmits it to a server via the Internet.

[1934] 3. Data Processing:

[1935] The server stores the received information (name, age, gender, weight, height, fitness goals) in a database.

[1936] 4. Output:

[1937] The server generates an initial exercise plan and meal menu and sends them to the device.

[1938] Step 2:

[1939] Daily data entry and tracking

[1940] 1. Input:

[1941] Users work out at the gym and either scan a QR code attached to the machine with the app or manually enter their exercise data.

[1942] 2. Specific operation:

[1943] Once the QR code is scanned, the device will automatically obtain relevant information and collect exercise data.

[1944] Manually entered data is handled similarly.

[1945] Users take photos of their meals using the app.

[1946] 3. Data Processing:

[1947] The terminal transmits the collected exercise data to a server via the Internet.

[1948] The device uses an image analysis engine to analyze photos of meals and estimate ingredients and calories.

[1949] The estimated results are displayed to the user, who can then correct them as necessary.

[1950] 4. Output:

[1951] The device transmits the corrected meal data to the server.

[1952] The server records this data in a database.

[1953] Step 3:

[1954] Personalized advice and progress

[1955] 1. Input:

[1956] The collected exercise and dietary data is stored on a server.

[1957] 2. Specific operation:

[1958] The server uses an analysis engine to analyze the exercise and dietary data and calculate the user's progress.

[1959] 3. Data Processing:

[1960] Based on the analysis results, a new exercise plan and meal menu will be generated.

[1961] 4. Output:

[1962] The server sends the generated new plan to the terminal.

[1963] The terminal receives this and notifies the user.

[1964] Step 4:

[1965] Predicting goal achievement

[1966] 1. Input:

[1967] The state in which the user's progress data is accumulated.

[1968] 2. Specific operation:

[1969] The server uses machine learning algorithms to predict when the goal will be achieved based on the progress data.

[1970] 3. Data Processing:

[1971] Analyze progress data and apply appropriate algorithms to make predictions.

[1972] 4. Output:

[1973] The server transmits the predicted completion time to the terminal.

[1974] The terminal notifies the user of the prediction result.

[1975] Step 5:

[1976] Introducing the Emotion Engine

[1977] 1. Input:

[1978] The user inputs voice and facial expression data.

[1979] 2. Specific operation:

[1980] The data is analyzed using a voice recognition engine and a facial expression recognition engine.

[1981] 3. Data Processing:

[1982] Based on the analysis results, the user's emotional state is estimated and plans and feedback content are adjusted accordingly.

[1983] 4. Output:

[1984] The server sends the adjusted plan and feedback to the device.

[1985] The terminal notifies the user of the adjustment results.

[1986] Step 6:

[1987] Subscription model available

[1988] 1. Input:

[1989] The user selects a subscription model.

[1990] 2. Specific operation:

[1991] The terminal processes the payment based on the selected subscription.

[1992] 3. Data Processing:

[1993] A payment gateway is used to process the payment, and if successful, the data is stored on the server.

[1994] 4. Output:

[1995] The server will offer users the advanced features and support of the paid version.

[1996] (Application example 2)

[1997] 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."

[1998] Conventional fitness plan and meal menu delivery systems were unable to take into account the user's individual emotional state, resulting in insufficient measures to address user motivation and stress. Furthermore, the manual input method for exercise and dietary data was burdensome, potentially reducing tracking accuracy. Furthermore, there was no system that comprehensively supported fitness activities and dietary management within the gym, resulting in inconsistent progress management for users. These challenges made it difficult for users to achieve their goals.

[1999] 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.

[2000] In the present invention, the server includes the following means:

[2001] means for receiving user input and generating an initial fitness plan and meal menu;

[2002] means for receiving and recording data when a user exercises at a gym;

[2003] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[2004] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[2005] means for notifying the user of progress data and goal achievement projections;

[2006] A means for recognizing the user's emotional state from their voice input and facial expressions, and adjusting the feedback content using an emotion engine;

[2007] A method to scan the QR code on the training machine in the gym and enter exercise data,

[2008] It includes measures to suggest relaxation exercises and stress-relieving meals based on emotional state.

[2009] This allows for feedback and advice tailored to the user's individual emotional state, realizes efficient and precise tracking of exercise and diet data, and provides comprehensive support for users' fitness activities and diet management, helping them achieve their goals.

[2010] "User input information" refers to basic information such as name, age, sex, weight, height, and fitness goals that a user provides to the system.

[2011] A "fitness plan" is an exercise schedule or training menu that is generated based on a user's fitness goals.

[2012] A "meal menu" is a daily meal plan suggested to suit the user's health and fitness goals.

[2013] "Exercise data" refers to data such as the type and duration of exercise performed by the user at the gym, and calories burned.

[2014] "Dietary data" refers to data relating to the contents, calories, ingredients, etc. of meals consumed by the user.

[2015] "Progress data" is data recorded about the exercises a user actually performs and the food they eat, and indicates the user's progress toward achieving their goals.

[2016] The "goal achievement date" is the date and time when the user is expected to achieve the fitness goal they have set.

[2017] "Notification" is the means by which the system provides information to the user, in the form of messages, alerts, etc.

[2018] An "emotion engine" is an algorithm or software that recognizes a user's emotional state from their voice input and facial expressions, and adjusts feedback based on that emotional state.

[2019] A "QR code" is a two-dimensional barcode attached to training machines in the gym, which users can scan with their smartphone to automatically enter exercise data.

[2020] "Relaxation exercises" are relaxation exercises and stretches that users perform to reduce stress and fatigue.

[2021] "Meals for stress relief" are ingredients and menus that are effective in reducing stress and are suggested based on the user's stress level.

[2022] This invention is a system that provides an individual user with an optimized fitness plan and meal menu, tracks their progress, and predicts when they will reach their goals. It also incorporates an emotion engine that recognizes the user's emotions and adjusts the plan and feedback based on those emotions. Specific embodiments of this system and its processing method are described below.

[2023] User registration and initial settings

[2024] First, the user downloads and installs the system's application onto their smartphone. On the account creation screen, the user enters their name, age, gender, weight, height, and fitness goals (e.g., weight loss, muscle gain, endurance improvement). Once this information is entered, the device sends it to the server. The server stores the received information in a database and generates an initial fitness plan and meal menu, which it then sends to the device. The device receives this information and displays it to the user.

[2025] Daily data entry and tracking

[2026] When a user exercises at the gym, they need to input their exercise data. The user can automatically obtain the exercise data by scanning a QR code attached to the gym's training machines with their smartphone. Meal data is also input by the user taking a photo of the meal using the app. The smartphone analyzes the captured image, and the ingredients and calories are automatically estimated. The user can check the estimated results and make corrections as necessary. The data is then sent to a server and recorded.

[2027] Personalized advice and progress

[2028] The server analyzes the aggregated exercise and diet data to calculate the user's current progress, and adjusts a new fitness plan and diet menu based on the analysis. The server then sends this information to the smartphone, which notifies the user.

[2029] Goal achievement prediction and notification

[2030] The server uses the user's progress data to predict when the goal will be achieved using a machine learning algorithm. The predicted time is then sent to the user via a smartphone notification. For example, if the user's goal is to lose weight, the server calculates the expected date of achievement if the user continues at their current pace.

[2031] Introducing the Emotion Engine

[2032] The emotion engine includes a means for recognizing emotions from the user's voice input and facial expressions. Based on the emotional state recognized by the emotion engine, the server can adjust the fitness plan, meal menu, and even feedback content. For example, if the user is feeling tired or stressed, the emotion engine can detect this and the server can suggest relaxation exercises or stress-relieving meals to the user. The emotion engine also provides positive feedback to increase the user's motivation.

[2033] Hardware and software used

[2034] Smartphone: where users install apps and use them to enter data and receive notifications.

[2035] Server: Stores and analyzes user data, calculates progress, predicts when goals will be reached, and adjusts fitness plans and meal menus.

[2036] QR Code Reader: A means of reading the QR code on training machines at the gym and automatically obtaining exercise data.

[2037] Image processing library: Used to analyze photos of meals and estimate ingredients and calories.

[2038] Emotion Recognition Library: Used to recognize emotional states by analyzing voice input and facial expressions.

[2039] Examples of concrete examples and prompts

[2040] As a concrete example, consider a case where a user sets the goals of "weight loss" and "muscle gain," and their current weight is 70 kg and their target weight is 65 kg. The user exercises on a treadmill at the gym for 30 minutes and scans a QR code. They also eat a 200 kcal salad and take a photo of it with their smartphone. The server collates this data, suggests a new training plan and meal menu to the user, and notifies them that they will reach their target weight in five weeks. If the user is feeling stressed, the server also suggests relaxing yoga exercises and stress-relieving meals.

[2041] An example prompt is:

[2042] User data registration:

[2043] To register, please enter your name, age, gender, weight, height and fitness goals.

[2044] Exercise Data Entry:

[2045] After your workout, scan the QR code to enter your exercise data.

[2046] Meal Data Entry:

[2047] Take a photo of your meal and check and edit the ingredients and calories.

[2048] Emotional State Check:

[2049] Answer simple questions to identify your current emotional state (e.g., "How are you feeling today?").

[2050] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2051] Step 1:

[2052] A user downloads and installs the system's application on their smartphone, and enters basic information such as name, age, gender, weight, height, and fitness goals on the account creation screen. This information is sent by the device to the server, which receives it and stores it in a database. This process enters the user's initial information into the system, and the server uses this information to generate an initial fitness plan and meal menu.

[2053] Step 2:

[2054] The server generates an initial fitness plan and meal menu based on the user's basic information and fitness goals. The generated plan and menu are sent from the server to the device. The device receives them and displays the plan and menu optimized for the user on the screen. This process allows the user to confirm the fitness plan and meal menu that are best suited to them.

[2055] Step 3:

[2056] When a user works out at the gym, they scan a QR code attached to the training machine with their smartphone to enter their exercise data into the device. The device then sends this data to a server, which records the received data in a database and saves it as the user's exercise history. Through this process, detailed exercise data is automatically collected and recorded.

[2057] Step 4:

[2058] Users take a photo of their meal with their smartphone and enter it into the app. The device's built-in image processing library analyzes the photo and automatically estimates the ingredients and calories. The user can review the estimated results and make corrections as necessary. The device then sends the corrected data to the server, which records the received meal data in a database. Through this process, detailed meal data is collected and recorded in the system.

[2059] Step 5:

[2060] The server analyzes the aggregated exercise and diet data to calculate the user's current progress. This analysis is performed using statistical data calculations that combine multiple data points. Based on the analysis results, a new fitness plan and diet menu are generated and sent from the server to the device. The device then notifies the user. This process ensures that the user always receives the latest personalized plan.

[2061] Step 6:

[2062] The server analyzes the user's progress data using a machine learning algorithm to predict when the goal will be achieved. This prediction is sent from the server to the device and presented to the user as a notification. For example, it shows how many weeks it will take to achieve the target weight if the user continues training at the current pace. This process allows the user to get a sense of when they will reach their goal.

[2063] Step 7:

[2064] The device's built-in emotion recognition library analyzes the user's voice input and facial expressions to recognize their emotional state. This emotion data is sent to a server where it is analyzed by an emotion engine. The emotion engine processes the data to adjust fitness plans, meal menus, and feedback content based on the recognized emotions. The server then sends the adjusted plans and feedback to the device and notifies the user. This process provides personalized feedback based on the user's emotional state.

[2065] Step 8:

[2066] The server analyzes the emotional data and, if it determines that the user is feeling stressed or fatigued, suggests relaxation exercises or meals to relieve stress. These suggestions are sent to the device as notifications from the server and presented to the user. For example, if emotion recognition determines that the user is feeling stressed, it will suggest yoga exercises with a relaxing effect or a meal menu to reduce stress. This process allows the user to receive support both physically and mentally.

[2067] 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.

[2068] 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.

[2069] 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.

[2070] 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.

[2071] FIG. 9 is a diagram illustrating 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 actions 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.

[2072] 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.

[2073] 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).

[2074] 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.

[2075] 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."

[2076] 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.

[2077] 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).

[2078] 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.

[2079] 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.

[2080] 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.

[2081] 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.

[2082] 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.

[2083] 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.

[2084] 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.

[2085] 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.

[2086] 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.

[2087] 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.

[2088] The following is further disclosed regarding the above embodiment.

[2089] (Claim 1)

[2090] means for receiving user input and generating an initial fitness plan and meal menu;

[2091] means for receiving and recording data when a user exercises at a gym;

[2092] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[2093] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[2094] The system includes a means for notifying the user of progress data and goal achievement projections.

[2095] (Claim 2)

[2096] 10. The system of claim 1, wherein an initial fitness plan and meal menu is generated based on the user's basic information and fitness goals.

[2097] (Claim 3)

[2098] 10. The system of claim 1, wherein the exercise data is entered by scanning a QR code or by manual entry.

[2099] "Example 1"

[2100] (Claim 1)

[2101] means for receiving user input information and generating an initial exercise menu and meal plan;

[2102] A means for receiving and recording data when a user trains at an exercise facility;

[2103] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[2104] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[2105] The system includes a means for notifying the user of progress data and goal achievement projections.

[2106] (Claim 2)

[2107] 10. The system of claim 1, wherein an initial exercise menu and meal plan is generated based on the user's basic information and exercise goals.

[2108] (Claim 3)

[2109] 2. The system of claim 1, wherein the training data is input by reading an identification code or by manual input.

[2110] "Application Example 1"

[2111] (Claim 1)

[2112] means for receiving user input and generating an initial fitness plan and meal menu;

[2113] means for receiving and recording data when a user engages in fitness activities;

[2114] A means for a user to take an image of a meal and analyze the image to generate meal data;

[2115] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[2116] means for notifying the user of progress data and goal achievement projections;

[2117] The system includes a means for conducting real-time video chat with users and providing training advice.

[2118] (Claim 2)

[2119] 10. The system of claim 1, wherein an initial fitness plan and meal menu are generated based on the user's basic information and fitness goals, providing the user with a personalized fitness plan and meal menu.

[2120] (Claim 3)

[2121] The system of claim 1, wherein exercise data is entered by scanning a QR code or by manual entry, and wherein a generative AI model is used to analyze food images.

[2122] "Example 2: Combining Emotion Engines"

[2123] (Claim 1)

[2124] means for receiving user input information and generating an initial exercise plan and meal menu;

[2125] means for receiving and recording data when a user exercises;

[2126] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[2127] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[2128] A way to recognize user emotions and adjust plans and feedback;

[2129] The system includes a means for notifying the user of progress data and goal achievement projections.

[2130] (Claim 2)

[2131] 10. The system of claim 1, wherein an initial exercise plan and meal menu are generated based on the user's basic information and exercise goals.

[2132] (Claim 3)

[2133] 2. The system of claim 1, wherein the exercise data is input by scanning an image code or by manual input.

[2134] "Application example 2 when combining emotion engines"

[2135] (Claim 1)

[2136] means for receiving user input and generating an initial fitness plan and meal menu;

[2137] means for receiving and recording data when a user exercises at a gym;

[2138] A means for a user to take a photo of a meal and analyze the image to generate meal data;

[2139] a means for analyzing the user's progress data and predicting when the user will achieve their goal;

[2140] means for notifying the user of progress data and goal achievement projections;

[2141] A means for recognizing the user's emotional state from their voice input and facial expressions, and adjusting the feedback content using an emotion engine;

[2142] A method to scan the QR code on the training machine in the gym and enter exercise data,

[2143] A system including a means for suggesting relaxation exercises and stress-relieving meals based on emotional state.

[2144] (Claim 2)

[2145] 10. The system of claim 1, wherein an initial fitness plan and meal menu is generated based on the user's basic information and fitness goals.

[2146] (Claim 3)

[2147] 10. The system of claim 1, wherein the exercise data is entered by scanning a QR code or by manual entry. [Explanation of symbols]

[2148] 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. means for receiving user input and generating an initial fitness plan and meal menu; means for receiving and recording data when a user exercises at a gym; A means for a user to take a photo of a meal and analyze the image to generate meal data; a means for analyzing the user's progress data and predicting when the user will achieve their goal; The system includes a means for notifying the user of progress data and goal achievement projections.

2. The system of claim 1 , wherein an initial fitness plan and meal menu is generated based on the user's basic information and fitness goals.

3. 10. The system of claim 1, wherein the exercise data is input by scanning a QR code or by manual input.

Images