System

Abstract

A system is provided.SOLUTION: This system is provided with a means for allowing a user to input his or her own body data, a means for allowing a server to generate an avatar based on received body data, and a means for allowing a terminal to display generated avatar data.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] In the fields of dieting and muscle training, many people start using gyms and fitness centers, but many don't continue. This results in problems such as not being able to achieve their diet or muscle-building goals. Furthermore, self-management is difficult, and results are hard to see, which often leads to a loss of motivation. In addition, managing training menus and meals can be complicated, which also becomes an obstacle to continuing. [Means for solving the problem]

[0005] This invention provides a system in which a user inputs their own physical data and generates an avatar based on that data. The user's goal data is sent to a server, which then uses AI to generate a training menu based on the goal. The server also receives the user's training results, calculates points, and adds and saves them to the user's account. Furthermore, when the user uploads photos of their meals, the server analyzes the photos, calculates calories, and sends the analysis results and improvement advice to the device. A system with these functions allows users to enjoy training in a game-like manner, effectively manage their training and diet, and maintain motivation.

[0006] "User" refers to a user of the diet and muscle training support application.

[0007] "Terminal" refers to a device used by a user, such as a smartphone or tablet.

[0008] "Server" refers to a computer system that processes requests from users and manages and provides data.

[0009] "Avatar" refers to a virtual character generated based on a user's physical data.

[0010] "Physical Data" refers to height, weight, and other physical data entered by a user.

[0011] "Training menu" refers to an exercise program generated by AI based on the user's goals.

[0012] "Goal data" refers to data relating to training and diet goals set by the user.

[0013] "Training results" refers to the training content and progress information of the user.

[0014] "Points" refer to the numerical rewards that users receive when they complete a workout.

[0015] An "account" refers to a record in a database that manages a user's personal information, points, etc.

[0016] "Meal photos" refer to images uploaded by users to record the meals they have eaten.

[0017] A "calorie" is a unit that indicates the amount of energy contained in food.

[0018] "Analysis" refers to the process by which the server processes the photos and data to extract meaningful information.

[0019] "Improvement Advice" refers to health and training suggestions provided to users based on the analysis results.

[0020] "Event" means a special user-participation activity or competition.

[0021] "Notification" refers to a message sent from the server to inform the user of information.

[0022] "Progress" refers to the results a user achieves in an event or training.

[0023] "Management" refers to the process by which a system organizes and controls information.

[0024] "Display" refers to the operation of a terminal to visually present information to a user. [Brief explanation of the drawings]

[0025] [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

[0026] 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.

[0027] First, the terms used in the following description will be explained.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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."

[0046] The present invention relates to a system for enabling a user to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user and uses the avatar to manage training and diet. A specific embodiment of this system is described below.

[0047] 1. Avatar creation function

[0048] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[0049] 2. Training menu generation function

[0050] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[0051] 3. Points System

[0052] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[0053] 4. Calorie calculation and advice function

[0054] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[0055] 5. Event function

[0056] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[0057] Specific examples

[0058] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[0059] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[0060] The processing flow will be explained below.

[0061] 1. Avatar creation process steps

[0062] Step 1:

[0063] The user installs and launches the app.

[0064] Step 2:

[0065] Users enter their height, weight, and other physical data within the app.

[0066] Step 3:

[0067] The terminal transmits the input data to the server.

[0068] Step 4:

[0069] The server processes the request to generate an avatar based on the received data.

[0070] Step 5:

[0071] The server passes the data to an avatar generation system, which generates an avatar that resembles the user.

[0072] Step 6:

[0073] The server returns the generated avatar data to the device.

[0074] Step 7:

[0075] The device displays an avatar on the screen based on the avatar data received.

[0076] Step 8:

[0077] The user checks the displayed avatar and makes adjustments if necessary.

[0078] 2. Training Menu Processing Steps

[0079] Step 1:

[0080] The user enters their goal (e.g., gaining muscle, losing weight) into the app.

[0081] Step 2:

[0082] The terminal transmits the input target data to the server.

[0083] Step 3:

[0084] The server receives the target data.

[0085] Step 4:

[0086] The server uses an AI system to generate the optimal training menu based on the goal.

[0087] Step 5:

[0088] The server returns the generated training menu to the terminal.

[0089] Step 6:

[0090] The terminal displays the received training menu to the user.

[0091] Step 7:

[0092] The user performs the training and enters their progress into the app.

[0093] Step 8:

[0094] The device sends progress data to the server.

[0095] 3. Point system processing steps

[0096] Step 1:

[0097] The terminal transmits the user's training results to the server.

[0098] Step 2:

[0099] The server calculates points based on the received training results.

[0100] Step 3:

[0101] The server adds the calculated points to the user's account and stores the information.

[0102] Step 4:

[0103] The server returns the updated point information to the terminal.

[0104] Step 5:

[0105] The terminal displays the latest points to the user in real time.

[0106] Step 6:

[0107] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[0108] 4. Calorie Count and Advice Function Processing Steps

[0109] Step 1:

[0110] Users upload photos of their meals to the app.

[0111] Step 2:

[0112] The device sends the photo to the server.

[0113] Step 3:

[0114] The server passes the received photos to an AI analysis system, which calculates calories.

[0115] Step 4:

[0116] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[0117] Step 5:

[0118] The server sends the calorie calculation results and improvement advice back to the device.

[0119] Step 6:

[0120] The terminal displays the received information to the user.

[0121] Step 7:

[0122] Users can understand areas for improvement in their diet and reflect this in their next meal.

[0123] 5. Event function processing steps

[0124] Step 1:

[0125] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[0126] Step 2:

[0127] The terminal displays detailed information about the event to the user.

[0128] Step 3:

[0129] When a user wants to participate in an event, he or she registers for participation through the terminal.

[0130] Step 4:

[0131] The terminal transmits the participation registration information to the server.

[0132] Step 5:

[0133] The server registers the user in the event and begins tracking the progress of the competition.

[0134] Step 6:

[0135] The device will display the user's progress and ranking information in real time during the event.

[0136] Step 7:

[0137] Users can participate in events, track their progress, and compete with other users.

[0138] Example 1

[0139] 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."

[0140] Conventional diet and strength training support systems have struggled to maintain user motivation and lacked the means to support continuous training and dietary management. Furthermore, it has been difficult to provide a wide range of functions, such as training menus tailored to individual goals, calorie calculations, and real-time progress management, all in one place. This has resulted in users being unable to receive consistent support, making it difficult to achieve effective diet and strength training.

[0141] 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.

[0142] In this invention, the server includes means for the user to input their own physical data, means for the server to generate a fitness tracker based on the received physical data, means for the terminal to display the generated data, means for the user to input a training record, means for the terminal to transmit the input training record to the server, means for the server to calculate points based on the received training record and add them to the user's account, and means for the terminal to display the latest point information. This allows the user to effectively continue their diet and muscle training while receiving consistent support.

[0143] "User" refers to an individual who uses this system to input their own physical data and goal data and manage their training and diet.

[0144] A "server" refers to a computer system that processes data received from a user and returns the results to the user's terminal.

[0145] "Terminal" refers to a device that allows a user to input data and receives and displays information from a server.

[0146] "Physical data" is information about the user's own body that is input by the user, and specifically includes height, weight, and the like.

[0147] An "avatar" is a virtual character generated based on the user's physical data, which acts as the user's representative in training and diet management.

[0148] "Training history" refers to detailed information about the training the user has done, including the type of training, time, calories burned, and so on.

[0149] "Points" are evaluation values ​​calculated by the server based on the user's training results, and are used as a means to maintain the user's motivation.

[0150] "Goal data" refers to information about training and diet goals set by the user, including specific goals, time periods, and what the user wants to achieve.

[0151] "Training menu" refers to exercise instructions and programs that the server generates based on the user's goal data.

[0152] "Calories" refers to the amount of energy in the food a user takes in, and is a numerical value that serves as an indicator when managing diet.

[0153] "Advice" refers to suggestions for improving diet and training that the server generates based on the calorie calculation results.

[0154] "Events" refer to server-sponsored activities and competitions that users can participate in, providing an opportunity to stay motivated and have fun.

[0155] "Progress information" refers to information that displays a user's progress and achievements in training or events in real time.

[0156] The present invention relates to a system that allows users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on the user's physical data and uses the avatar to manage training and diet, thereby supporting the user in achieving their goals.

[0157] First, the user installs and launches the app. The user uses the device to enter their own physical data (e.g., height, weight, etc.). This data is sent from the device to the server. Specifically, the data is sent using an HTTP request. Based on the received data, the server uses an avatar generation system (e.g., Blender or Unity) to generate an avatar that corresponds to the user's physical data. The generated avatar data is then sent back to the device, where the user can review it and make any necessary adjustments.

[0158] Next, the user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app. This goal data is sent from the device to the server. The server uses an AI system (e.g., TensorFlow or PyTorch) to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the displayed training menu and enters their progress into the app.

[0159] When a user completes a workout, the device sends the training data to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated points are sent back to the device and displayed to the user in real time, allowing the user to track their progress and stay motivated.

[0160] Furthermore, when a user uploads a photo of a meal to the app, the photo is sent from the device to the server. The server uses an AI analysis system (e.g., image recognition API) to calculate the calories of the meal from the photo. Along with the calculation results, advice on how to improve based on nutritional balance is generated and sent to the device. The user can then check the calorie calculation results and advice and incorporate them into their daily diet.

[0161] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[0162] Specific examples

[0163] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[0164] Prompt Sentence Examples

[0165] "Please describe a scenario in which a user inputs their height and weight and sets a goal of increasing muscle strength. Also, please detail how the resulting workout menu and point system works."

[0166] These procedures allow users to effectively maintain their diet and strength training regimen with consistent support.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1:

[0169] A user installs and launches the app. On the initial screen of the app, they enter their physical data (e.g., height, weight, etc.). This data is entered based on the app's form.

[0170] Input: Physical data such as height and weight

[0171] Output: User data in JSON format

[0172] Specific behavior:

[0173] The user enters their physical data into the app's form and clicks the "Submit" button.

[0174] Step 2:

[0175] The terminal sends the entered user data to the server using an HTTP POST request.

[0176] Input: User data in JSON format

[0177] Output: HTTP response (confirmation of data reception)

[0178] Specific behavior:

[0179] The device serializes the user data in JSON format and sends it to the server via an HTTP POST request.

[0180] Step 3:

[0181] The server generates an avatar based on the received user data. For example, an avatar generation system such as Blender or Unity is used to generate the avatar.

[0182] Input: User data in JSON format

[0183] Output: Avatar data (3D model or 2D image)

[0184] Specific behavior:

[0185] The server calls a Blender script to input data such as height and weight and generate a corresponding avatar.

[0186] Step 4:

[0187] The server returns the generated avatar data to the device using an HTTP response.

[0188] Input: Avatar data

[0189] Output: HTTP response (avatar data)

[0190] Specific behavior:

[0191] The server sends the generated avatar data to the terminal in JSON format as an HTTP response.

[0192] Step 5:

[0193] The device displays the received avatar data to the user using the app's UI (user interface).

[0194] Input: Avatar data

[0195] Output: Avatar image or 3D model display

[0196] Specific behavior:

[0197] The device parses (analyzes) the received avatar data and displays it on the app's UI.

[0198] Step 6:

[0199] The user inputs training or diet goal data into the app (e.g., muscle gain, weight loss, etc.).

[0200] Input: Goal data (training and diet goals)

[0201] Output: Goal data in JSON format

[0202] Specific behavior:

[0203] The user enters goal data on the app's goal setting screen and clicks the submit button.

[0204] Step 7:

[0205] The terminal sends the target data to the server using an HTTP POST request.

[0206] Input: Goal data in JSON format

[0207] Output: HTTP response (confirmation of data reception)

[0208] Specific behavior:

[0209] The device serializes the target data into JSON format and sends it to the server via an HTTP POST request.

[0210] Step 8:

[0211] The server uses an AI system to generate a training menu based on the user's goals. For example, TensorFlow and PyTorch are used as the AI ​​system.

[0212] Input: Goal data in JSON format

[0213] Output: Training menu

[0214] Specific behavior:

[0215] The server inputs the received goal data into the model and uses AI to generate the optimal training menu.

[0216] Step 9:

[0217] The server sends the generated training menu to the device using HTTP responses.

[0218] Input: Training Menu

[0219] Output: HTTP response (Training menu)

[0220] Specific behavior:

[0221] The server sends the generated training menu to the terminal in JSON format as an HTTP response.

[0222] Step 10:

[0223] The device displays the training menu received to the user using the app's UI (user interface).

[0224] Input: Training Menu

[0225] Output: Training menu display

[0226] Specific behavior:

[0227] The device parses (analyzes) the received training menu and displays it on the app's UI.

[0228] Step 11:

[0229] The user performs the training and enters performance data into the app.

[0230] Input: Training performance data (e.g., type of training, time, calories burned, etc.)

[0231] Output: Performance data in JSON format

[0232] Specific behavior:

[0233] After completing the training, the user enters their results in the app and clicks the submit button.

[0234] Step 12:

[0235] The device sends the training results entered to the server using an HTTP POST request.

[0236] Input: Achievement data in JSON format

[0237] Output: HTTP response (confirmation of data reception)

[0238] Specific behavior:

[0239] The device serializes the training results in JSON format and sends it to the server via an HTTP POST request.

[0240] Step 13:

[0241] The server calculates points based on the received training results and adds them to the user's account.

[0242] Input: Achievement data in JSON format

[0243] Output: Points added to the user account

[0244] Specific behavior:

[0245] The server analyzes the received performance data, calculates points based on the point calculation logic, and adds the points to the user's account.

[0246] Step 14:

[0247] The server returns the updated point information to the terminal using an HTTP response.

[0248] Input: Points added to user account

[0249] Output: HTTP response (latest point information)

[0250] Specific behavior:

[0251] The server sends the latest point information to the terminal in JSON format.

[0252] Step 15:

[0253] The latest point information received by the terminal is displayed to the user.

[0254] Input: Latest point information

[0255] Output: Real-time display of point information

[0256] Specific behavior:

[0257] The device analyzes the received point information and displays it in real time on the app's UI.

[0258] Step 16:

[0259] Users upload photos of their meals to the app and enter meal information.

[0260] Input: Meal photos, meal information

[0261] Output: Meal data in JSON format

[0262] Specific behavior:

[0263] Users use the app's camera function to take a photo of their meal, enter meal information, and click the submit button.

[0264] Step 17:

[0265] The device sends the meal photo and information to the server using an HTTP POST request.

[0266] Input: JSON formatted meal data

[0267] Output: HTTP response (confirmation of data reception)

[0268] Specific behavior:

[0269] The device serializes the meal data in JSON format and sends it to the server via an HTTP POST request.

[0270] Step 18:

[0271] The server uses an AI analysis system to calculate the calorie content of meals and generate advice on improving nutritional balance. For example, an image recognition API is used for the AI ​​analysis system.

[0272] Input: JSON formatted meal data

[0273] Output: Calorie calculation results and improvement advice

[0274] Specific behavior:

[0275] The server analyzes the received meal data using an image recognition API, calculates calories, and generates improvement advice that takes nutritional balance into consideration.

[0276] Step 19:

[0277] The server sends the calorie calculation results and improvement advice to the device using HTTP responses.

[0278] Input: Calorie calculation results and improvement advice

[0279] Output: HTTP response (calorie calculation results and improvement advice)

[0280] Specific behavior:

[0281] The server serializes the calorie calculation results and advice into JSON format and sends them to the device.

[0282] Step 20:

[0283] The device displays the calorie calculation results and advice for improvement to the user.

[0284] Input: Calorie calculation results and improvement advice

[0285] Output: Display of calorie calculation results and improvement advice

[0286] Specific behavior:

[0287] The device analyzes the calorie calculation results and advice received and displays them on the app's UI.

[0288] These processing steps allow the user to effectively continue their diet and strength training with consistent support.

[0289] (Application example 1)

[0290] 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."

[0291] Conventional diet and strength training support systems make it difficult for users to effectively utilize their free time, and in particular, do not adequately consider how to efficiently use time while traveling. Furthermore, when users manage their diet, there is a lack of easy-to-use functions that allow them to take photos of their meals and calculate calories. Furthermore, there is a need for a method to improve users' motivation to continue exercising by linking exercise status and point management in real time while in the car.

[0292] 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.

[0293] In this invention, the server includes a means for the user to input their own physical data, a means for generating an avatar based on the physical data received by the server, a means for the terminal to display the generated avatar data, a means for operating in conjunction with the infotainment system of the autonomous vehicle, and a means for taking photos of meals with a camera installed in the vehicle and calculating calories. This allows users to effectively use their time while traveling to diet and strength train, and easily manage their diet. Furthermore, real-time display of training results and point information can increase motivation to continue.

[0294] "User" refers to an individual who uses the system to manage and execute their own diet and strength training.

[0295] "Server" refers to a central processing unit that receives data from a user, processes it, and sends the results to the user's terminal.

[0296] A "terminal" is a device used for data communication between a user and a server, including a smartphone or a vehicle infotainment system.

[0297] An "infotainment system" is a system installed in an autonomous vehicle that provides information and entertainment functions.

[0298] "Physical data" is numerical data that indicates the user's physical characteristics such as height and weight.

[0299] An "avatar" is a virtual character generated based on the user's physical data, and visually represents the user's training status.

[0300] "Training menu" refers to a set of exercise and strength training plans and detailed instructions that the server generates according to the user's goals.

[0301] The "point system" is a system that calculates and manages points based on the user's training performance, and adds those points to the user's account.

[0302] "Calorie counting" refers to the process of calculating the energy content of a meal from a photo taken by a camera installed inside the vehicle.

[0303] "Photo of meal" is image data of a photograph of a meal that the user plans to eat.

[0304] "In motion" refers to the time the self-driving vehicle is in motion and the time the user spends inside the vehicle.

[0305] The present invention is a system for enabling users to continue dieting and strength training effectively and enjoyably, and is intended for use in autonomous vehicles. This system is implemented in the following steps.

[0306] First, the user inputs their physical data (e.g., height, weight, etc.) through the infotainment system of the autonomous vehicle or their smartphone. The input data is then sent to a server via the infotainment system or smartphone. The server then generates an avatar for the user based on the physical data. This avatar is then displayed on the display of the vehicle's infotainment system or on the smartphone.

[0307] Next, when the user sets their training goals (such as building muscle or losing weight) in the vehicle, the goal data is sent to the server. The server uses AI to generate an optimal training menu based on the user's goals. The generated training menu is displayed on the infotainment system display or on a smartphone, and the user follows the instructions to exercise.

[0308] Furthermore, the user can take photos of their daily meals using a camera installed in the vehicle. The images are sent to a server, which uses an AI analysis system to calculate the calories in each meal. This calorie information and advice on how to improve are displayed on the infotainment system or smartphone, allowing the user to reflect this information in their daily diet management. The user's training results are also sent to a server, which calculates points based on this data and adds them to the user's account. The points information is displayed in real time on the infotainment system or smartphone.

[0309] Examples of hardware and software used in these steps include the infotainment system display, smartphone, in-vehicle camera (required for image analysis for calorie calculation), Python, OpenCV (image processing library), and AI models (for training menu generation and calorie calculation).

[0310] As a specific example, if a user enters their height of 160 cm and weight of 60 kg into the in-car display and sets the goal of increasing muscle strength, the generated avatar will be displayed in the car's interior mirror. The user can also take a photo of their breakfast with the in-car camera, which sends the image to the server. The server uses AI to analyze the image and provides the user with calorie calculation results and advice on how to improve based on nutritional balance. The server receives the training results, calculates points, and adds them to the user's account. Point information is displayed in real time on the in-car display and smartphone, helping to maintain the user's motivation.

[0311] Examples of prompts to input to a generative AI model:

[0312] "The user enters their height (160cm) and weight (60kg) on ​​the display of the self-driving vehicle. Next, they set their goal of increasing muscle strength, and the generated avatar is displayed in the car's interior mirror. Running records taken in the car are registered, and the AI ​​model provides a training menu based on that data. In addition, calories are calculated from an image of breakfast taken by the in-car camera, and detailed advice is sent to the smartphone in real time."

[0313] In this way, users can make effective use of their travel time and continue dieting and strength training in a fun and effective way.

[0314] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0315] Step 1:

[0316] The user inputs their own physical data (e.g., height, weight) through the infotainment system of the autonomous vehicle or a smartphone. The input data is sent to the server via the infotainment system or smartphone. The input data is sent to the server using a format such as JSON.

[0317] Input: User's physical data such as height and weight

[0318] Output: Sending body data to the server

[0319] Step 2:

[0320] The server generates an avatar based on the received physical data. Using a generative AI model, it generates a virtual character (avatar) based on the user's physical data. The generated avatar data is then sent to the device.

[0321] Input: User's physical data

[0322] Output: Generated avatar data

[0323] Step 3:

[0324] The device displays the generated avatar data on the display. When the user checks the displayed avatar, they can make fine adjustments as necessary.

[0325] Input: Generated avatar data

[0326] Output: Avatar displayed on the screen

[0327] Step 4:

[0328] The user inputs their training goals (e.g., muscle gain, weight loss, etc.) into the infotainment system or smartphone and sends them to the server. The input goal data is then sent to the server.

[0329] Input: User's training goal

[0330] Output: Sends target data to the server

[0331] Step 5:

[0332] The server uses an AI system to generate an optimal training menu based on the received goal data. The generative AI model is used to create a training menu that matches the user's goals. The generated training menu is then sent to the device.

[0333] Input: User goal data

[0334] Output: Generated training menu

[0335] Step 6:

[0336] The terminal displays the generated training menu, and the user performs training according to the displayed menu.

[0337] Input: Generated training menu

[0338] Output: Training menu displayed on the screen

[0339] Step 7:

[0340] The user takes a photo of the meal using a camera installed in the vehicle and sends it to the server via the infotainment system or smartphone. The image data is then sent to the server.

[0341] Input: A photo of the food taken

[0342] Output: Sending image data to the server

[0343] Step 8:

[0344] The server analyzes the received image data and calculates the calorie content of the meal. It uses a generative AI model to analyze the image and generate calorie calculation results. The calculation results and nutritional advice are then sent to the device.

[0345] Input: Food image data

[0346] Output: Calculated calorie information and nutrition advice

[0347] Step 9:

[0348] The device displays the calculated calorie information and nutrition advice, and the user manages their diet based on the advice received.

[0349] Input: Calorie information and nutrition advice

[0350] Output: Calorie information and nutrition advice displayed on the display

[0351] Step 10:

[0352] The user inputs their training results into the infotainment system or smartphone and sends them to the server. The inputted results data is then sent to the server.

[0353] Input: User's training record

[0354] Output: Sending performance data to the server

[0355] Step 11:

[0356] The server calculates points based on the received training results and adds them to the user's account. The server then transmits the point information to the terminal.

[0357] Input: User's training record

[0358] Output: Calculated point information

[0359] Step 12:

[0360] The terminal displays the latest point information on the display, allowing the user to check progress in real time.

[0361] Input: Calculated point information

[0362] Output: Point information displayed on the display

[0363] Through these steps, users can effectively utilize their travel time in an autonomous vehicle while dieting, strength training, and managing their diet.

[0364] 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.

[0365] The present invention relates to a system for enabling users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user, and further combines it with an emotion engine to recognize the user's emotions and provide training and advice based on those emotions. A specific embodiment of this system is described below.

[0366] 1. Avatar creation function

[0367] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[0368] 2. Training menu generation function

[0369] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[0370] 3. Points System

[0371] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[0372] 4. Calorie calculation and advice function

[0373] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[0374] 5. Event function

[0375] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[0376] 6. Emotion engine integration

[0377] One of the features of this invention is the integration of an emotion engine. While a user is using the app, the emotion engine analyzes the user's emotions in real time using facial recognition technology and voice analysis. For example, it can read emotions from the user's facial expressions and tone of voice using a camera and microphone.

[0378] Adjusting your training routine based on your emotions

[0379] The server uses the data obtained from the emotion engine to adjust the training menu according to the user's emotional state. For example, if the user is tired, it can suggest a lighter training menu. If the user is highly motivated, it can offer a more challenging menu.

[0380] Emotion-based advice

[0381] The emotion engine provides motivational advice based on the user's emotions. For example, if the user is feeling stressed, it can suggest relaxation exercises or meditation. Conversely, if the user is feeling very positive, it can provide advice to encourage further challenges.

[0382] Specific examples

[0383] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a muscle training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time.

[0384] When users upload photos of their meals, the server calculates the calories and provides advice on how to improve. Furthermore, the system uses an emotion engine to recognize the user's emotions and adjusts training menus and advice based on their emotional state. For example, if the user is feeling tired, the emotion engine will detect this and the server will suggest a lighter training menu.

[0385] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[0386] The processing flow will be explained below.

[0387] 1. Avatar creation process steps

[0388] Step 1:

[0389] The user installs and launches the app.

[0390] Step 2:

[0391] Users enter their height, weight, and other physical data within the app.

[0392] Step 3:

[0393] The terminal transmits the input data to the server.

[0394] Step 4:

[0395] The server starts an avatar generation system based on the received data and generates an avatar that resembles the user.

[0396] Step 5:

[0397] The server sends the generated avatar data to the device.

[0398] Step 6:

[0399] The avatar data received by the terminal is displayed on the screen.

[0400] Step 7:

[0401] The user checks the displayed avatar and makes minor adjustments as necessary.

[0402] 2. Training Menu Processing Steps

[0403] Step 1:

[0404] The user enters their training goal (e.g., gaining muscle, losing weight) into the app.

[0405] Step 2:

[0406] The terminal transmits the input target data to the server.

[0407] Step 3:

[0408] The server receives the goal data and uses an AI system to generate an optimal training menu.

[0409] Step 4:

[0410] The server transmits the generated training menu to the terminal.

[0411] Step 5:

[0412] The terminal displays the received training menu to the user.

[0413] Step 6:

[0414] The user performs the workout and enters their progress into the app.

[0415] Step 7:

[0416] The device sends progress data to the server.

[0417] 3. Point system processing steps

[0418] Step 1:

[0419] The terminal transmits the user's training results to the server.

[0420] Step 2:

[0421] The server calculates points based on the received training results.

[0422] Step 3:

[0423] The server adds the calculated points to the user's account and stores them.

[0424] Step 4:

[0425] The server transmits the updated point information to the terminal.

[0426] Step 5:

[0427] The terminal displays the latest points to the user in real time.

[0428] Step 6:

[0429] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[0430] 4. Calorie Count and Advice Function Processing Steps

[0431] Step 1:

[0432] Users upload photos of their meals to the app.

[0433] Step 2:

[0434] The device sends the photo to the server.

[0435] Step 3:

[0436] The server passes the received photos to an AI analysis system, which calculates calories.

[0437] Step 4:

[0438] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[0439] Step 5:

[0440] The server sends the calorie calculation results and improvement advice to the terminal.

[0441] Step 6:

[0442] The terminal displays the received information to the user.

[0443] Step 7:

[0444] Users can understand areas for improvement in their diet and reflect this in their next meal.

[0445] 5. Event function processing steps

[0446] Step 1:

[0447] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[0448] Step 2:

[0449] The terminal displays detailed information about the event to the user.

[0450] Step 3:

[0451] If a user wishes to participate in an event, he or she registers for participation through the terminal.

[0452] Step 4:

[0453] The terminal transmits the participation registration information to the server.

[0454] Step 5:

[0455] The server registers the user in the event and begins tracking the progress of the competition.

[0456] Step 6:

[0457] The device will display the user's progress and ranking information in real time during the event.

[0458] Step 7:

[0459] Users can participate in events, track their progress, and compete with other users.

[0460] 6. Emotion Engine Processing Steps

[0461] Step 1:

[0462] When a user starts using the app, the camera and microphone are automatically turned on.

[0463] Step 2:

[0464] The device captures the user's facial expressions and voice in real time and extracts emotional data.

[0465] Step 3:

[0466] The device transmits the emotion data to the server.

[0467] Step 4:

[0468] The emotion data received by the server is passed to the emotion engine for analysis.

[0469] Step 5:

[0470] The server receives the analysis results and understands the user's emotional state.

[0471] Step 6:

[0472] The server adjusts the training menu and generates advice based on the emotional state.

[0473] Step 7:

[0474] The server sends the adjusted training menu and advice to the device.

[0475] Step 8:

[0476] The training menu and advice received by the terminal are displayed to the user.

[0477] For example, if a user is using a camera and microphone while training and the emotion engine detects fatigue from the user's facial expression, the server can instantly generate a lighter training menu and suggest it to the user through the device.Also, if the user is in a positive emotional state, it can provide a more challenging training menu.

[0478] Example 2

[0479] 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."

[0480] Conventional diet and strength training support systems have difficulty generating personalized training programs based on the user's individual physical data and goals, and lack the ability to adjust training content according to the user's emotions and motivation. Furthermore, they do not include complex elements such as diet management and events, making it difficult to improve the overall experience.

[0481] 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.

[0482] a means for the user to input their physical data;

[0483] A means for generating an avatar based on the received body data by the server;

[0484] A means for displaying the generated avatar data on the terminal;

[0485] a means for a user to input target data;

[0486] A means for the server to use AI to generate a training menu according to the goal,

[0487] a means for a user to input performance data;

[0488] A means for the server to receive the performance data and calculate points;

[0489] A means for the terminal to display point information;

[0490] A means for a user to upload image data of a meal;

[0491] A means for the server to analyze the image data and calculate calories;

[0492] a means for generating improvement advice;

[0493] A means for the server to analyze the user's emotional data and adjust the training menu;

[0494] a means for the server to notify event information and register users for the event;

[0495] A means for the terminal to collect user emotion data and transmit it to a server;

[0496] This makes it possible to provide a training program based on the user's individual physical data and goals, and also to adjust the program according to the user's emotions and motivation, thereby realizing comprehensive and effective support for dieting and muscle training.

[0497] "User" refers to a person who uses the system to input their own physical data and training goals and receive feedback and services.

[0498] A "server" refers to a computer system that receives data sent by a user, processes it, and returns the results.

[0499] "Terminal" refers to a device operated by a user that provides an interface for inputting and displaying data.

[0500] An "avatar" refers to a virtual character generated based on a user's physical data.

[0501] "Physical data" refers to personal physical information such as height and weight entered by the user.

[0502] "Training menu" refers to an exercise program generated by the server in accordance with the user's goal data.

[0503] "Performance data" refers to data entered by a user regarding the results and progress of training.

[0504] "Points" refers to units of evaluation calculated by the server based on a user's training performance and added to the user's account.

[0505] "Image data" refers to digital data that users upload to the app, such as photos of their meals.

[0506] "Calories" is a unit that indicates the amount of energy contained in a meal, and is calculated by the server by analyzing image data.

[0507] "Improvement advice" refers to health management suggestions provided by the server based on the calorie analysis results and the user's nutritional balance.

[0508] "Emotion data" refers to information that indicates the emotional state of a user, as analyzed from facial expressions, tone of voice, and the like.

[0509] "Event" refers to a competition or activity such as a bodybuilding competition or marathon that is planned by the server and provided to users.

[0510] "AI" refers to artificial intelligence, including technology that generates optimal training menus based on user input data.

[0511] "Analysis" refers to the process by which the server analyzes the data received from the user and extracts meaningful information.

[0512] The present invention relates to a system for enabling users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user, and further combines it with an emotion engine to recognize the user's emotions and provide training and advice based on those emotions. Detailed embodiments of the present invention are described below.

[0513] Launching the app and entering physical data

[0514] A user installs and launches an application on a device such as a smartphone or tablet. When the application is launched for the first time, the user enters physical data such as height and weight. The device used for this is a general mobile device.

[0515] Sending data and generating avatars

[0516] The physical data entered by the user is sent from the device to a server. The server is equipped with a system that generates an avatar based on the physical data. For example, 3D modeling software (e.g., Unity 3D or Mixamo) is used to generate a virtual character that corresponds to the user's physical proportions. The generated avatar data is then sent back from the server to the device, where the user can review it and make any necessary adjustments.

[0517] Training menu generation

[0518] The user enters their training or diet goal data (e.g., muscle gain, weight loss, etc.) into the app. The goal data is sent from the device to the server, and the server uses an AI system (e.g., TensorFlow or PyTorch) to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the displayed training menu and enters their progress into the app.

[0519] Training progress record and points calculation

[0520] When a user completes a workout, they enter their training results into the app. The results data is sent from the device to the server, and the server calculates points based on the received data. The calculated points are added to the user's account and sent back to the device. Updated point information is displayed in real time, allowing users to track their progress and stay motivated.

[0521] Calorie counting and advice provided

[0522] When a user uploads a photo of their meal to the app, the image data is sent from the device to a server. The server is equipped with an AI system (e.g., Clarifai or Google Cloud Vision) that analyzes the image data and calculates calories. It also generates and provides improvement advice based on nutritional balance to the user. The user can then incorporate the calorie calculation results and improvement advice into their daily diet.

[0523] Event Features

[0524] The server periodically plans events using avatars (e.g., bodybuilding competitions or marathons) and notifies users of these events. Detailed information about the event is displayed on the device, and users who wish to participate can register through the app. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[0525] Emotion engine integration

[0526] While the user is using the app, an emotion engine (e.g., Affectiva or Microsoft's Emotion API) analyzes the user's emotions in real time using facial recognition technology and voice analysis. The server collects the user's emotional data and adjusts the training menu and advice based on the analysis results. For example, if the user is tired, the server can suggest a lighter training menu.

[0527] Specific examples

[0528] For example, suppose a user enters their height (160 cm) and weight (60 kg) into the app and sets their goal as increasing muscle strength. The device sends this data to the server, which then uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu tailored to the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters their completed training results into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. Updated points are returned to the device and displayed in real time. When the user uploads a photo of a meal, the server calculates calories and provides improvement advice. Furthermore, the emotion engine recognizes the user's emotions and adjusts the training menu and advice based on their emotional state. For example, if the user is feeling tired, the emotion engine detects this and the server suggests a lighter training menu. This provides an environment where users can continue dieting and muscle training in a fun and effective manner.

[0529] Example prompts for generative AI models

[0530] "A user is 160cm tall and weighs 60kg and wants to improve her muscle mass. Please generate a training menu for her. Also, if she uploads photos of her meals, please provide calorie calculations and advice on how to improve."

[0531] This prompt provides accurate input to the AI ​​model, providing instructions for generating appropriate training and dietary advice.

[0532] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0533] Step 1:

[0534] The user installs and launches the app. The user enters their physical data (e.g., height, weight) on the screen that appears when the app is launched for the first time. The entered data is temporarily saved on the device. Input: User's physical data. Output: Temporarily saved data.

[0535] Step 2:

[0536] The device sends the inputted physical data to the server. Specifically, the device's communication module encrypts the data and establishes a secure connection to the server. Input: Temporarily stored user's physical data. Output: Data sent to the server.

[0537] Step 3:

[0538] The server generates an avatar using an avatar generation system (e.g., Unity 3D or Mixamo) based on the received body data. The server generates a 3D model using the user's data as parameters, creating a virtual character. Input: User's body data sent to the server. Output: Generated avatar data.

[0539] Step 4:

[0540] The server returns the generated avatar data to the device. The server's sending module is used to convert the generated data into the required format and send it to the device. Input: Generated avatar data. Output: Avatar data returned to the device.

[0541] Step 5:

[0542] The device displays the received avatar data to the user. Specifically, the avatar is displayed as a 3D model through the application's user interface so that the user can check it. Input: Avatar data returned to the device. Output: Displayed avatar.

[0543] Step 6:

[0544] The user inputs training and diet goal data into the application. For example, they set specific goals such as increasing muscle strength or losing weight. Input: Goal data set by the user. Output: Temporarily saved goal data.

[0545] Step 7:

[0546] The device sends the entered goal data to the server. Processing is carried out over a secure connection, similar to the transmission of body data. Input: Temporarily saved goal data. Output: Goal data sent to the server.

[0547] Step 8:

[0548] The server uses an AI system (e.g. TensorFlow or PyTorch) to generate an optimal training menu based on the input goal data. The server analyzes the user's goals and uses historical data and models to create the most suitable menu. Input: Goal data sent to the server. Output: Generated training menu.

[0549] Step 9:

[0550] The server sends the generated training menu to the terminal. The server converts it into the required format and sends it to the terminal via a secure connection. Input: Generated training menu. Output: Training menu sent to the terminal.

[0551] Step 10:

[0552] The terminal displays the received training menu to the user. The training menu is displayed clearly through the user interface. Input: Training menu sent to the terminal. Output: Training menu displayed to the user.

[0553] Step 11:

[0554] The user performs training and enters performance data into the app after completion. Performance data includes the type of exercise, time, calories burned, etc. Input: User performance data. Output: Temporarily saved performance data.

[0555] Step 12:

[0556] The terminal sends the entered performance data to the server. The process is the same as previous data transmissions. Input: Temporarily saved performance data. Output: Performance data sent to the server.

[0557] Step 13:

[0558] The server calculates points based on the performance data received. The point calculation system awards points based on the type and amount of training. Input: Performance data sent to the server. Output: Calculated points.

[0559] Step 14:

[0560] The server adds the calculated points to the user's account and saves the information. The saving is done in a database system. Input: Calculated points. Output: Information on points added to the user's account.

[0561] Step 15:

[0562] The server returns the latest point information to the device, converts it into the required format, and sends it to the device. Input: Point information stored in the user's account. Output: Point information sent to the device.

[0563] Step 16:

[0564] The point information received by the terminal is displayed to the user in real time. The user can check the displayed points. Input: Point information sent to the terminal. Output: Point information displayed to the user.

[0565] Step 17:

[0566] The user uploads a photo of their meal to the app. The photo is temporarily saved on the device. Input: A photo of the meal uploaded by the user. Output: A photo of the meal that has been saved.

[0567] Step 18:

[0568] The device sends the photo data to the server. The process is the same as previous data transmissions. Input: Temporarily saved photo of the meal. Output: Photo data sent to the server.

[0569] Step 19:

[0570] The server analyzes the image data and calculates the calories of the meal. An AI analysis system (e.g., Clarifai or Google Cloud Vision) is used to calculate calories based on the image data of the meal. Input: Photo data sent to the server. Output: Calculated calories.

[0571] Step 20:

[0572] The server generates improvement advice based on nutritional balance. The generated advice takes into account the user's eating habits. Input: Calculated calories. Output: Generated improvement advice.

[0573] Step 21:

[0574] The server sends the calorie results and improvement advice to the terminal. It is converted into a format and sent. Input: Calculated calories and improvement advice. Output: Calorie results and improvement advice sent to the terminal.

[0575] Step 22:

[0576] The terminal displays the calorie results and improvement advice to the user. The information is displayed clearly through the user interface. Input: Calorie results and improvement advice sent to the terminal. Output: Calorie results and improvement advice displayed to the user.

[0577] Step 23:

[0578] The server plans the event information and notifies the user. The details of the event are sent to the terminal. Input: Planned event information. Output: Event information sent to the terminal.

[0579] Step 24:

[0580] The device displays detailed information about the event to the user. The user checks the displayed information and registers if they wish to participate. Input: Event information sent to the device. Output: Event details displayed to the user.

[0581] Step 25:

[0582] A user registers to participate in an event. Participation request data is sent from the device to the server. Input: Event participation request data. Output: Participation request data sent to the server.

[0583] Step 26:

[0584] The server receives the registration information and registers the user for the event. The registered information is saved in a database. Input: Participation request data. Output: Event registration information.

[0585] Step 27:

[0586] During the event, the device will display the user's progress in real time. The progress of other users will also be displayed, encouraging competition. Input: Event progress data. Output: Displayed progress information.

[0587] Step 28:

[0588] The emotion engine runs while the user is using the app. It collects the user's emotion data using the camera and microphone. Input: User's facial and voice data. Output: Collected emotion data.

[0589] Step 29:

[0590] The device sends the collected emotion data to the server. It is sent via a secure connection. Input: Collected emotion data. Output: Emotion data sent to the server.

[0591] Step 30:

[0592] The server analyzes the emotional data and adjusts the training menu and advice according to the user's emotional state. The emotion analysis system is used to generate appropriate menus and advice. Input: Emotional data sent to the server. Output: Adjusted training menu and advice.

[0593] Step 31:

[0594] The server sends the adjusted training menu and advice to the terminal. It converts it into the required format and sends it. Input: Adjusted training menu and advice. Output: Menu and advice sent to the terminal.

[0595] Step 32:

[0596] The device displays the adjusted training menu and advice to the user. Specifically, the adjustments are displayed clearly through the user interface. Input: The adjusted menu and advice sent to the device. Output: The adjustments displayed to the user.

[0597] (Application example 2)

[0598] 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."

[0599] Conventional fitness systems only provide training menus based on a user's physical data and training goals, but lack real-time feedback and advice that takes into account the user's emotional state. Furthermore, point systems for maintaining training motivation are ineffective. Furthermore, there is a need for easy and accurate calorie calculation and advice for dietary management. The present invention aims to solve these problems.

[0600] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input their own physical data, means for generating an avatar based on the received physical data, means for capturing the user's facial expressions and voice and analyzing their emotions, means for adjusting the difficulty level of the training menu in real time based on the emotion analysis results, means for recording the user's training progress and awarding points upon completion, and means for using the generated points in a shop within the store. As a result, the user receives an optimal training menu and feedback based on their emotional state, enabling them to continue training effectively while maintaining their motivation.

[0601] "Physical data" refers to physical information such as the user's height, weight, and age.

[0602] An "avatar" is a virtual character generated based on a user's physical data.

[0603] The "server" refers to a data processing device that processes user data and generates various training menus and feedback.

[0604] The term "terminal" refers to a mobile information terminal such as a smartphone or tablet used by a user.

[0605] "Emotion analysis" refers to a technology that uses a smartphone's camera, microphone, etc. to analyze a user's emotional state from their facial expressions and voice.

[0606] A "training menu" is an exercise program that is generated according to the user's goals.

[0607] "Points" are a type of reward given to users when they complete a workout.

[0608] "In-gym shop" refers to a store or virtual store located within a fitness gym that sells items.

[0609] "Calorie counting" refers to the process of calculating the energy content of the food a user consumes.

[0610] "Advice" refers to guidance and suggestions provided to help users train and manage their diet more effectively.

[0611] "Training progress" refers to data that indicates how much training a user has completed.

[0612] The present invention is a system for supporting fitness and dietary management according to a user's emotional state, and is mainly comprised of a smartphone, a server, and an AI analysis engine.

[0613] System Configuration

[0614] The system consists of the following hardware and software:

[0615] 1. Hardware

[0616] Smartphones (iPhone, Android smartphones)

[0617] Built-in camera and microphone

[0618] 2. Software

[0619] Smartphone application (iOS / Android)

[0620] Server-side programs (Node.js, Python Django)

[0621] AI analysis engine (TensorFlow, PyTorch)

[0622] System Operation

[0623] 1. Avatar creation function

[0624] Users use a smartphone application to input their own physical data (height, weight, etc.). This data is sent from the smartphone to a server. Based on the received data, the server uses an AI analysis engine to generate a virtual character (avatar) that corresponds to the user's physical data and sends it back to the smartphone. The user can then check the generated avatar and make any necessary adjustments.

[0625] 2. Training menu generation function

[0626] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent to the server. The server uses an AI analysis engine to generate the optimal training menu based on the goal. The generated menu is sent back to the smartphone and displayed to the user. The user then exercises according to the displayed training menu.

[0627] 3. Sentiment Analysis and Real-Time Feedback

[0628] The smartphone's camera and microphone are used to capture the user's facial expressions and voice. The captured data is sent to a server, where emotions are analyzed using an AI analysis engine. Based on the analysis results, the server generates appropriate training menu adjustments and advice in real time and sends them back to the smartphone. This provides feedback and advice according to the user's emotional state.

[0629] 4. Training Progress and Points System

[0630] When a user completes a workout, their smartphone sends their workout record to the server, which calculates points based on their workout record and adds them to the user's account. The updated points information is sent back to the smartphone and displayed to the user. The points can then be used at the gym's shop.

[0631] 5. Food calorie calculation and advice

[0632] Users upload photos of their meals to a smartphone application. The photos are sent to a server, where calorie calculations are performed using an AI analysis engine. Advice based on the calculation results and nutritional balance is generated and sent back to the smartphone. This allows users to receive calorie information and improvement advice for their daily meals.

[0633] Specific examples

[0634] For example, consider the case of generating the optimal training menu for a 24-year-old female user to lose weight. The user's current weight is 60 kg and her height is 160 cm. The user enters this information into a smartphone application and sets training goals. The server receives the information and generates the optimal training menu using an AI analysis engine.

[0635] When users upload photos of their meals, the server analyzes them to calculate calories and provide dietary advice. Furthermore, the system uses the smartphone's camera and microphone to perform emotion analysis during training, providing real-time feedback based on the user's emotional state.

[0636] Prompt Sentence Examples

[0637] "Generate the optimal training menu for a 25-year-old male looking to improve muscle mass. The user's current weight is 75 kg and height is 180 cm. Provide necessary feedback based on sentiment analysis and reward using a points system."

[0638] As described above, the present invention is a system that comprehensively supports fitness and diet management based on the user's physical data and emotional state.

[0639] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0640] Step 1:

[0641] The user enters physical data (e.g., height, weight, age) into a smartphone application. The entered data is sent from the smartphone to a server. This data processing provides the basic information needed to generate an avatar.

[0642] Step 2:

[0643] The server uses an AI analysis engine to generate an avatar based on the received physical data. The generated avatar data is then sent back to the smartphone. This output provides an avatar image that the user can view.

[0644] Step 3:

[0645] The user checks the generated avatar on their smartphone and makes any necessary adjustments. The adjusted data is then sent back to the server. This step is the user's way of making changes to their virtual character to create the look they desire.

[0646] Step 4:

[0647] The user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into a smartphone application. The goal data is sent from the smartphone to a server, which then uses an AI analysis engine to automatically generate an optimal training menu based on the user's goals. The generated menu is then sent back from the server to the smartphone and displayed to the user.

[0648] Step 5:

[0649] When a user trains, their facial expressions and voice are captured using the smartphone's camera and microphone. The captured data is sent from the smartphone to a server, where the server's AI analysis engine performs emotional analysis. Based on the results of the emotional analysis, appropriate training menu adjustments and advice are generated. The analysis results and advice are then sent back from the server to the smartphone.

[0650] Step 6:

[0651] When a user completes a workout, the smartphone sends the workout results to the server, which calculates points based on the results and adds them to the user's account. The updated points information is sent back from the server to the smartphone and displayed to the user. This step allows the user to check the rewards they have earned in real time.

[0652] Step 7:

[0653] Users upload photos of their meals to a smartphone application. The photo data is sent from the smartphone to a server, where the server's AI analysis engine analyzes the photos and generates advice based on calorie calculations and nutritional balance. The calculation results and advice are sent back from the server to the smartphone and displayed to the user. This step allows users to obtain detailed information about their meals and suggestions for improvement.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] [Second embodiment]

[0658] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0659] 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.

[0660] 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).

[0661] 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.

[0662] 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.

[0663] 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).

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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."

[0670] The present invention relates to a system for enabling a user to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user and uses the avatar to manage training and diet. A specific embodiment of this system is described below.

[0671] 1. Avatar creation function

[0672] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[0673] 2. Training menu generation function

[0674] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[0675] 3. Points System

[0676] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[0677] 4. Calorie calculation and advice function

[0678] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[0679] 5. Event function

[0680] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[0681] Specific examples

[0682] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[0683] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[0684] The processing flow will be explained below.

[0685] 1. Avatar creation process steps

[0686] Step 1:

[0687] The user installs and launches the app.

[0688] Step 2:

[0689] Users enter their height, weight, and other physical data within the app.

[0690] Step 3:

[0691] The terminal transmits the input data to the server.

[0692] Step 4:

[0693] The server processes the request to generate an avatar based on the received data.

[0694] Step 5:

[0695] The server passes the data to an avatar generation system, which generates an avatar that resembles the user.

[0696] Step 6:

[0697] The server returns the generated avatar data to the device.

[0698] Step 7:

[0699] The device displays an avatar on the screen based on the avatar data received.

[0700] Step 8:

[0701] The user checks the displayed avatar and makes adjustments if necessary.

[0702] 2. Training Menu Processing Steps

[0703] Step 1:

[0704] The user enters their goal (e.g., gaining muscle, losing weight) into the app.

[0705] Step 2:

[0706] The terminal transmits the input target data to the server.

[0707] Step 3:

[0708] The server receives the target data.

[0709] Step 4:

[0710] The server uses an AI system to generate the optimal training menu based on the goal.

[0711] Step 5:

[0712] The server returns the generated training menu to the terminal.

[0713] Step 6:

[0714] The terminal displays the received training menu to the user.

[0715] Step 7:

[0716] The user performs the training and enters their progress into the app.

[0717] Step 8:

[0718] The device sends progress data to the server.

[0719] 3. Point system processing steps

[0720] Step 1:

[0721] The terminal transmits the user's training results to the server.

[0722] Step 2:

[0723] The server calculates points based on the received training results.

[0724] Step 3:

[0725] The server adds the calculated points to the user's account and stores the information.

[0726] Step 4:

[0727] The server returns the updated point information to the terminal.

[0728] Step 5:

[0729] The terminal displays the latest points to the user in real time.

[0730] Step 6:

[0731] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[0732] 4. Calorie Count and Advice Function Processing Steps

[0733] Step 1:

[0734] Users upload photos of their meals to the app.

[0735] Step 2:

[0736] The device sends the photo to the server.

[0737] Step 3:

[0738] The server passes the received photos to an AI analysis system, which calculates calories.

[0739] Step 4:

[0740] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[0741] Step 5:

[0742] The server sends the calorie calculation results and improvement advice back to the device.

[0743] Step 6:

[0744] The terminal displays the received information to the user.

[0745] Step 7:

[0746] Users can understand areas for improvement in their diet and reflect this in their next meal.

[0747] 5. Event function processing steps

[0748] Step 1:

[0749] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[0750] Step 2:

[0751] The terminal displays detailed information about the event to the user.

[0752] Step 3:

[0753] When a user wants to participate in an event, he or she registers for participation through the terminal.

[0754] Step 4:

[0755] The terminal transmits the participation registration information to the server.

[0756] Step 5:

[0757] The server registers the user in the event and begins tracking the progress of the competition.

[0758] Step 6:

[0759] The device will display the user's progress and ranking information in real time during the event.

[0760] Step 7:

[0761] Users can participate in events, track their progress, and compete with other users.

[0762] Example 1

[0763] 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."

[0764] Conventional diet and strength training support systems have struggled to maintain user motivation and lacked the means to support continuous training and dietary management. Furthermore, it has been difficult to provide a wide range of functions, such as training menus tailored to individual goals, calorie calculations, and real-time progress management, all in one place. This has resulted in users being unable to receive consistent support, making it difficult to achieve effective diet and strength training.

[0765] 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.

[0766] In this invention, the server includes means for the user to input their own physical data, means for the server to generate a fitness tracker based on the received physical data, means for the terminal to display the generated data, means for the user to input a training record, means for the terminal to transmit the input training record to the server, means for the server to calculate points based on the received training record and add them to the user's account, and means for the terminal to display the latest point information. This allows the user to effectively continue their diet and muscle training while receiving consistent support.

[0767] "User" refers to an individual who uses this system to input their own physical data and goal data and manage their training and diet.

[0768] A "server" refers to a computer system that processes data received from a user and returns the results to the user's terminal.

[0769] "Terminal" refers to a device that allows a user to input data and receives and displays information from a server.

[0770] "Physical data" is information about the user's own body that is input by the user, and specifically includes height, weight, and the like.

[0771] An "avatar" is a virtual character generated based on the user's physical data, which acts as the user's representative in training and diet management.

[0772] "Training history" refers to detailed information about the training the user has done, including the type of training, time, calories burned, and so on.

[0773] "Points" are evaluation values ​​calculated by the server based on the user's training results, and are used as a means to maintain the user's motivation.

[0774] "Goal data" refers to information about training and diet goals set by the user, including specific goals, time periods, and what the user wants to achieve.

[0775] "Training menu" refers to exercise instructions and programs that the server generates based on the user's goal data.

[0776] "Calories" refers to the amount of energy in the food a user takes in, and is a numerical value that serves as an indicator when managing diet.

[0777] "Advice" refers to suggestions for improving diet and training that the server generates based on the calorie calculation results.

[0778] "Events" refer to server-sponsored activities and competitions that users can participate in, providing an opportunity to stay motivated and have fun.

[0779] "Progress information" refers to information that displays a user's progress and achievements in training or events in real time.

[0780] The present invention relates to a system that allows users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on the user's physical data and uses the avatar to manage training and diet, thereby supporting the user in achieving their goals.

[0781] First, the user installs and launches the app. The user uses the device to enter their own physical data (e.g., height, weight, etc.). This data is sent from the device to the server. Specifically, the data is sent using an HTTP request. Based on the received data, the server uses an avatar generation system (e.g., Blender or Unity) to generate an avatar that corresponds to the user's physical data. The generated avatar data is then sent back to the device, where the user can review it and make any necessary adjustments.

[0782] Next, the user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app. This goal data is sent from the device to the server. The server uses an AI system (e.g., TensorFlow or PyTorch) to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the displayed training menu and enters their progress into the app.

[0783] When a user completes a workout, the device sends the training data to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated points are sent back to the device and displayed to the user in real time, allowing the user to track their progress and stay motivated.

[0784] Furthermore, when a user uploads a photo of a meal to the app, the photo is sent from the device to the server. The server uses an AI analysis system (e.g., image recognition API) to calculate the calories of the meal from the photo. Along with the calculation results, advice on how to improve based on nutritional balance is generated and sent to the device. The user can then check the calorie calculation results and advice and incorporate them into their daily diet.

[0785] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[0786] Specific examples

[0787] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[0788] Prompt Sentence Examples

[0789] "Please describe a scenario in which a user inputs their height and weight and sets a goal of increasing muscle strength. Also, please detail how the resulting workout menu and point system works."

[0790] These procedures allow users to effectively maintain their diet and strength training regimen with consistent support.

[0791] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0792] Step 1:

[0793] A user installs and launches the app. On the initial screen of the app, they enter their physical data (e.g., height, weight, etc.). This data is entered based on the app's form.

[0794] Input: Physical data such as height and weight

[0795] Output: User data in JSON format

[0796] Specific behavior:

[0797] The user enters their physical data into the app's form and clicks the "Submit" button.

[0798] Step 2:

[0799] The terminal sends the entered user data to the server using an HTTP POST request.

[0800] Input: User data in JSON format

[0801] Output: HTTP response (confirmation of data reception)

[0802] Specific behavior:

[0803] The device serializes the user data in JSON format and sends it to the server via an HTTP POST request.

[0804] Step 3:

[0805] The server generates an avatar based on the received user data. For example, an avatar generation system such as Blender or Unity is used to generate the avatar.

[0806] Input: User data in JSON format

[0807] Output: Avatar data (3D model or 2D image)

[0808] Specific behavior:

[0809] The server calls a Blender script to input data such as height and weight and generate a corresponding avatar.

[0810] Step 4:

[0811] The server returns the generated avatar data to the device using an HTTP response.

[0812] Input: Avatar data

[0813] Output: HTTP response (avatar data)

[0814] Specific behavior:

[0815] The server sends the generated avatar data to the terminal in JSON format as an HTTP response.

[0816] Step 5:

[0817] The device displays the received avatar data to the user using the app's UI (user interface).

[0818] Input: Avatar data

[0819] Output: Avatar image or 3D model display

[0820] Specific behavior:

[0821] The device parses (analyzes) the received avatar data and displays it on the app's UI.

[0822] Step 6:

[0823] The user inputs training or diet goal data into the app (e.g., muscle gain, weight loss, etc.).

[0824] Input: Goal data (training and diet goals)

[0825] Output: Goal data in JSON format

[0826] Specific behavior:

[0827] The user enters goal data on the app's goal setting screen and clicks the submit button.

[0828] Step 7:

[0829] The terminal sends the target data to the server using an HTTP POST request.

[0830] Input: Goal data in JSON format

[0831] Output: HTTP response (confirmation of data reception)

[0832] Specific behavior:

[0833] The device serializes the target data into JSON format and sends it to the server via an HTTP POST request.

[0834] Step 8:

[0835] The server uses an AI system to generate a training menu based on the user's goals. For example, TensorFlow and PyTorch are used as the AI ​​system.

[0836] Input: Goal data in JSON format

[0837] Output: Training menu

[0838] Specific behavior:

[0839] The server inputs the received goal data into the model and uses AI to generate the optimal training menu.

[0840] Step 9:

[0841] The server sends the generated training menu to the device using HTTP responses.

[0842] Input: Training Menu

[0843] Output: HTTP response (Training menu)

[0844] Specific behavior:

[0845] The server sends the generated training menu to the terminal in JSON format as an HTTP response.

[0846] Step 10:

[0847] The device displays the training menu received to the user using the app's UI (user interface).

[0848] Input: Training Menu

[0849] Output: Training menu display

[0850] Specific behavior:

[0851] The device parses (analyzes) the received training menu and displays it on the app's UI.

[0852] Step 11:

[0853] The user performs the training and enters performance data into the app.

[0854] Input: Training performance data (e.g., type of training, time, calories burned, etc.)

[0855] Output: Performance data in JSON format

[0856] Specific behavior:

[0857] After completing the training, the user enters their results in the app and clicks the submit button.

[0858] Step 12:

[0859] The device sends the training results entered to the server using an HTTP POST request.

[0860] Input: Achievement data in JSON format

[0861] Output: HTTP response (confirmation of data reception)

[0862] Specific behavior:

[0863] The device serializes the training results in JSON format and sends it to the server via an HTTP POST request.

[0864] Step 13:

[0865] The server calculates points based on the received training results and adds them to the user's account.

[0866] Input: Achievement data in JSON format

[0867] Output: Points added to the user account

[0868] Specific behavior:

[0869] The server analyzes the received performance data, calculates points based on the point calculation logic, and adds the points to the user's account.

[0870] Step 14:

[0871] The server returns the updated point information to the terminal using an HTTP response.

[0872] Input: Points added to user account

[0873] Output: HTTP response (latest point information)

[0874] Specific behavior:

[0875] The server sends the latest point information to the terminal in JSON format.

[0876] Step 15:

[0877] The latest point information received by the terminal is displayed to the user.

[0878] Input: Latest point information

[0879] Output: Real-time display of point information

[0880] Specific behavior:

[0881] The device analyzes the received point information and displays it in real time on the app's UI.

[0882] Step 16:

[0883] Users upload photos of their meals to the app and enter meal information.

[0884] Input: Meal photos, meal information

[0885] Output: Meal data in JSON format

[0886] Specific behavior:

[0887] Users use the app's camera function to take a photo of their meal, enter meal information, and click the submit button.

[0888] Step 17:

[0889] The device sends the meal photo and information to the server using an HTTP POST request.

[0890] Input: JSON formatted meal data

[0891] Output: HTTP response (confirmation of data reception)

[0892] Specific behavior:

[0893] The device serializes the meal data in JSON format and sends it to the server via an HTTP POST request.

[0894] Step 18:

[0895] The server uses an AI analysis system to calculate the calorie content of meals and generate advice on improving nutritional balance. For example, an image recognition API is used for the AI ​​analysis system.

[0896] Input: JSON formatted meal data

[0897] Output: Calorie calculation results and improvement advice

[0898] Specific behavior:

[0899] The server analyzes the received meal data using an image recognition API, calculates calories, and generates improvement advice that takes nutritional balance into consideration.

[0900] Step 19:

[0901] The server sends the calorie calculation results and improvement advice to the device using HTTP responses.

[0902] Input: Calorie calculation results and improvement advice

[0903] Output: HTTP response (calorie calculation results and improvement advice)

[0904] Specific behavior:

[0905] The server serializes the calorie calculation results and advice into JSON format and sends them to the device.

[0906] Step 20:

[0907] The device displays the calorie calculation results and advice for improvement to the user.

[0908] Input: Calorie calculation results and improvement advice

[0909] Output: Display of calorie calculation results and improvement advice

[0910] Specific behavior:

[0911] The device analyzes the calorie calculation results and advice received and displays them on the app's UI.

[0912] These processing steps allow the user to effectively continue their diet and strength training with consistent support.

[0913] (Application example 1)

[0914] 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."

[0915] Conventional diet and strength training support systems make it difficult for users to effectively utilize their free time, and in particular, do not adequately consider how to efficiently use time while traveling. Furthermore, when users manage their diet, there is a lack of easy-to-use functions that allow them to take photos of their meals and calculate calories. Furthermore, there is a need for a method to improve users' motivation to continue exercising by linking exercise status and point management in real time while in the car.

[0916] 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.

[0917] In this invention, the server includes a means for the user to input their own physical data, a means for generating an avatar based on the physical data received by the server, a means for the terminal to display the generated avatar data, a means for operating in conjunction with the infotainment system of the autonomous vehicle, and a means for taking photos of meals with a camera installed in the vehicle and calculating calories. This allows users to effectively use their time while traveling to diet and strength train, and easily manage their diet. Furthermore, real-time display of training results and point information can increase motivation to continue.

[0918] "User" refers to an individual who uses the system to manage and execute their own diet and strength training.

[0919] "Server" refers to a central processing unit that receives data from a user, processes it, and sends the results to the user's terminal.

[0920] A "terminal" is a device used for data communication between a user and a server, including a smartphone or a vehicle infotainment system.

[0921] An "infotainment system" is a system installed in an autonomous vehicle that provides information and entertainment functions.

[0922] "Physical data" is numerical data that indicates the user's physical characteristics such as height and weight.

[0923] An "avatar" is a virtual character generated based on the user's physical data, and visually represents the user's training status.

[0924] "Training menu" refers to a set of exercise and strength training plans and detailed instructions that the server generates according to the user's goals.

[0925] The "point system" is a system that calculates and manages points based on the user's training performance, and adds those points to the user's account.

[0926] "Calorie counting" refers to the process of calculating the energy content of a meal from a photo taken by a camera installed inside the vehicle.

[0927] "Photo of meal" is image data of a photograph of a meal that the user plans to eat.

[0928] "In motion" refers to the time the self-driving vehicle is in motion and the time the user spends inside the vehicle.

[0929] The present invention is a system for enabling users to continue dieting and strength training effectively and enjoyably, and is intended for use in autonomous vehicles. This system is implemented in the following steps.

[0930] First, the user inputs their physical data (e.g., height, weight, etc.) through the infotainment system of the autonomous vehicle or their smartphone. The input data is then sent to a server via the infotainment system or smartphone. The server then generates an avatar for the user based on the physical data. This avatar is then displayed on the display of the vehicle's infotainment system or on the smartphone.

[0931] Next, when the user sets their training goals (such as building muscle or losing weight) in the vehicle, the goal data is sent to the server. The server uses AI to generate an optimal training menu based on the user's goals. The generated training menu is displayed on the infotainment system display or on a smartphone, and the user follows the instructions to exercise.

[0932] Furthermore, the user can take photos of their daily meals using a camera installed in the vehicle. The images are sent to a server, which uses an AI analysis system to calculate the calories in each meal. This calorie information and advice on how to improve are displayed on the infotainment system or smartphone, allowing the user to reflect this information in their daily diet management. The user's training results are also sent to a server, which calculates points based on this data and adds them to the user's account. The points information is displayed in real time on the infotainment system or smartphone.

[0933] Examples of hardware and software used in these steps include the infotainment system display, smartphone, in-vehicle camera (required for image analysis for calorie calculation), Python, OpenCV (image processing library), and AI models (for training menu generation and calorie calculation).

[0934] As a specific example, if a user enters their height of 160 cm and weight of 60 kg into the in-car display and sets the goal of increasing muscle strength, the generated avatar will be displayed in the car's interior mirror. The user can also take a photo of their breakfast with the in-car camera, which sends the image to the server. The server uses AI to analyze the image and provides the user with calorie calculation results and advice on how to improve based on nutritional balance. The server receives the training results, calculates points, and adds them to the user's account. Point information is displayed in real time on the in-car display and smartphone, helping to maintain the user's motivation.

[0935] Examples of prompts to input to a generative AI model:

[0936] "The user enters their height (160cm) and weight (60kg) on ​​the display of the self-driving vehicle. Next, they set their goal of increasing muscle strength, and the generated avatar is displayed in the car's interior mirror. Running records taken in the car are registered, and the AI ​​model provides a training menu based on that data. In addition, calories are calculated from an image of breakfast taken by the in-car camera, and detailed advice is sent to the smartphone in real time."

[0937] In this way, users can make effective use of their travel time and continue dieting and strength training in a fun and effective way.

[0938] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0939] Step 1:

[0940] The user inputs their own physical data (e.g., height, weight) through the infotainment system of the autonomous vehicle or a smartphone. The input data is sent to the server via the infotainment system or smartphone. The input data is sent to the server using a format such as JSON.

[0941] Input: User's physical data such as height and weight

[0942] Output: Sending body data to the server

[0943] Step 2:

[0944] The server generates an avatar based on the received physical data. Using a generative AI model, it generates a virtual character (avatar) based on the user's physical data. The generated avatar data is then sent to the device.

[0945] Input: User's physical data

[0946] Output: Generated avatar data

[0947] Step 3:

[0948] The device displays the generated avatar data on the display. When the user checks the displayed avatar, they can make fine adjustments as necessary.

[0949] Input: Generated avatar data

[0950] Output: Avatar displayed on the screen

[0951] Step 4:

[0952] The user inputs their training goals (e.g., muscle gain, weight loss, etc.) into the infotainment system or smartphone and sends them to the server. The input goal data is then sent to the server.

[0953] Input: User's training goal

[0954] Output: Sends target data to the server

[0955] Step 5:

[0956] The server uses an AI system to generate an optimal training menu based on the received goal data. The generative AI model is used to create a training menu that matches the user's goals. The generated training menu is then sent to the device.

[0957] Input: User goal data

[0958] Output: Generated training menu

[0959] Step 6:

[0960] The terminal displays the generated training menu, and the user performs training according to the displayed menu.

[0961] Input: Generated training menu

[0962] Output: Training menu displayed on the screen

[0963] Step 7:

[0964] The user takes a photo of the meal using a camera installed in the vehicle and sends it to the server via the infotainment system or smartphone. The image data is then sent to the server.

[0965] Input: A photo of the food taken

[0966] Output: Sending image data to the server

[0967] Step 8:

[0968] The server analyzes the received image data and calculates the calorie content of the meal. It uses a generative AI model to analyze the image and generate calorie calculation results. The calculation results and nutritional advice are then sent to the device.

[0969] Input: Food image data

[0970] Output: Calculated calorie information and nutrition advice

[0971] Step 9:

[0972] The device displays the calculated calorie information and nutrition advice, and the user manages their diet based on the advice received.

[0973] Input: Calorie information and nutrition advice

[0974] Output: Calorie information and nutrition advice displayed on the display

[0975] Step 10:

[0976] The user inputs their training results into the infotainment system or smartphone and sends them to the server. The inputted results data is then sent to the server.

[0977] Input: User's training record

[0978] Output: Sending performance data to the server

[0979] Step 11:

[0980] The server calculates points based on the received training results and adds them to the user's account. The server then transmits the point information to the terminal.

[0981] Input: User's training record

[0982] Output: Calculated point information

[0983] Step 12:

[0984] The terminal displays the latest point information on the display, allowing the user to check progress in real time.

[0985] Input: Calculated point information

[0986] Output: Point information displayed on the display

[0987] Through these steps, users can effectively utilize their travel time in an autonomous vehicle while dieting, strength training, and managing their diet.

[0988] 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.

[0989] The present invention relates to a system for enabling users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user, and further combines it with an emotion engine to recognize the user's emotions and provide training and advice based on those emotions. A specific embodiment of this system is described below.

[0990] 1. Avatar creation function

[0991] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[0992] 2. Training menu generation function

[0993] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[0994] 3. Points System

[0995] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[0996] 4. Calorie calculation and advice function

[0997] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[0998] 5. Event function

[0999] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[1000] 6. Emotion engine integration

[1001] One of the features of this invention is the integration of an emotion engine. While a user is using the app, the emotion engine analyzes the user's emotions in real time using facial recognition technology and voice analysis. For example, it can read emotions from the user's facial expressions and tone of voice using a camera and microphone.

[1002] Adjusting your training routine based on your emotions

[1003] The server uses the data obtained from the emotion engine to adjust the training menu according to the user's emotional state. For example, if the user is tired, it can suggest a lighter training menu. If the user is highly motivated, it can offer a more challenging menu.

[1004] Emotion-based advice

[1005] The emotion engine provides motivational advice based on the user's emotions. For example, if the user is feeling stressed, it can suggest relaxation exercises or meditation. Conversely, if the user is feeling very positive, it can provide advice to encourage further challenges.

[1006] Specific examples

[1007] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a muscle training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time.

[1008] When users upload photos of their meals, the server calculates the calories and provides advice on how to improve. Furthermore, the system uses an emotion engine to recognize the user's emotions and adjusts training menus and advice based on their emotional state. For example, if the user is feeling tired, the emotion engine will detect this and the server will suggest a lighter training menu.

[1009] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[1010] The processing flow will be explained below.

[1011] 1. Avatar creation process steps

[1012] Step 1:

[1013] The user installs and launches the app.

[1014] Step 2:

[1015] Users enter their height, weight, and other physical data within the app.

[1016] Step 3:

[1017] The terminal transmits the input data to the server.

[1018] Step 4:

[1019] The server starts an avatar generation system based on the received data and generates an avatar that resembles the user.

[1020] Step 5:

[1021] The server sends the generated avatar data to the device.

[1022] Step 6:

[1023] The avatar data received by the terminal is displayed on the screen.

[1024] Step 7:

[1025] The user checks the displayed avatar and makes minor adjustments as necessary.

[1026] 2. Training Menu Processing Steps

[1027] Step 1:

[1028] The user enters their training goal (e.g., gaining muscle, losing weight) into the app.

[1029] Step 2:

[1030] The terminal transmits the input target data to the server.

[1031] Step 3:

[1032] The server receives the goal data and uses an AI system to generate an optimal training menu.

[1033] Step 4:

[1034] The server transmits the generated training menu to the terminal.

[1035] Step 5:

[1036] The terminal displays the received training menu to the user.

[1037] Step 6:

[1038] The user performs the workout and enters their progress into the app.

[1039] Step 7:

[1040] The device sends progress data to the server.

[1041] 3. Point system processing steps

[1042] Step 1:

[1043] The terminal transmits the user's training results to the server.

[1044] Step 2:

[1045] The server calculates points based on the received training results.

[1046] Step 3:

[1047] The server adds the calculated points to the user's account and stores them.

[1048] Step 4:

[1049] The server transmits the updated point information to the terminal.

[1050] Step 5:

[1051] The terminal displays the latest points to the user in real time.

[1052] Step 6:

[1053] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[1054] 4. Calorie Count and Advice Function Processing Steps

[1055] Step 1:

[1056] Users upload photos of their meals to the app.

[1057] Step 2:

[1058] The device sends the photo to the server.

[1059] Step 3:

[1060] The server passes the received photos to an AI analysis system, which calculates calories.

[1061] Step 4:

[1062] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[1063] Step 5:

[1064] The server sends the calorie calculation results and improvement advice to the terminal.

[1065] Step 6:

[1066] The terminal displays the received information to the user.

[1067] Step 7:

[1068] Users can understand areas for improvement in their diet and reflect this in their next meal.

[1069] 5. Event function processing steps

[1070] Step 1:

[1071] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[1072] Step 2:

[1073] The terminal displays detailed information about the event to the user.

[1074] Step 3:

[1075] If a user wishes to participate in an event, he or she registers for participation through the terminal.

[1076] Step 4:

[1077] The terminal transmits the participation registration information to the server.

[1078] Step 5:

[1079] The server registers the user in the event and begins tracking the progress of the competition.

[1080] Step 6:

[1081] The device will display the user's progress and ranking information in real time during the event.

[1082] Step 7:

[1083] Users can participate in events, track their progress, and compete with other users.

[1084] 6. Emotion Engine Processing Steps

[1085] Step 1:

[1086] When a user starts using the app, the camera and microphone are automatically turned on.

[1087] Step 2:

[1088] The device captures the user's facial expressions and voice in real time and extracts emotional data.

[1089] Step 3:

[1090] The device transmits the emotion data to the server.

[1091] Step 4:

[1092] The emotion data received by the server is passed to the emotion engine for analysis.

[1093] Step 5:

[1094] The server receives the analysis results and understands the user's emotional state.

[1095] Step 6:

[1096] The server adjusts the training menu and generates advice based on the emotional state.

[1097] Step 7:

[1098] The server sends the adjusted training menu and advice to the device.

[1099] Step 8:

[1100] The training menu and advice received by the terminal are displayed to the user.

[1101] For example, if a user is using a camera and microphone while training and the emotion engine detects fatigue from the user's facial expression, the server can instantly generate a lighter training menu and suggest it to the user through the device.Also, if the user is in a positive emotional state, it can provide a more challenging training menu.

[1102] Example 2

[1103] 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."

[1104] Conventional diet and strength training support systems have difficulty generating personalized training programs based on the user's individual physical data and goals, and lack the ability to adjust training content according to the user's emotions and motivation. Furthermore, they do not include complex elements such as diet management and events, making it difficult to improve the overall experience.

[1105] 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.

[1106] a means for the user to input their physical data;

[1107] A means for generating an avatar based on the received body data by the server;

[1108] A means for displaying the generated avatar data on the terminal;

[1109] a means for a user to input target data;

[1110] A means for the server to use AI to generate a training menu according to the goal,

[1111] a means for a user to input performance data;

[1112] A means for the server to receive the performance data and calculate points;

[1113] A means for the terminal to display point information;

[1114] A means for a user to upload image data of a meal;

[1115] A means for the server to analyze the image data and calculate calories;

[1116] a means for generating improvement advice;

[1117] A means for the server to analyze the user's emotional data and adjust the training menu;

[1118] a means for the server to notify event information and register users for the event;

[1119] A means for the terminal to collect user emotion data and transmit it to a server;

[1120] This makes it possible to provide a training program based on the user's individual physical data and goals, and also to adjust the program according to the user's emotions and motivation, thereby realizing comprehensive and effective support for dieting and muscle training.

[1121] "User" refers to a person who uses the system to input their own physical data and training goals and receive feedback and services.

[1122] A "server" refers to a computer system that receives data sent by a user, processes it, and returns the results.

[1123] "Terminal" refers to a device operated by a user that provides an interface for inputting and displaying data.

[1124] An "avatar" refers to a virtual character generated based on a user's physical data.

[1125] "Physical data" refers to personal physical information such as height and weight entered by the user.

[1126] "Training menu" refers to an exercise program generated by the server in accordance with the user's goal data.

[1127] "Performance data" refers to data entered by a user regarding the results and progress of training.

[1128] "Points" refers to units of evaluation calculated by the server based on a user's training performance and added to the user's account.

[1129] "Image data" refers to digital data that users upload to the app, such as photos of their meals.

[1130] "Calories" is a unit that indicates the amount of energy contained in a meal, and is calculated by the server by analyzing image data.

[1131] "Improvement advice" refers to health management suggestions provided by the server based on the calorie analysis results and the user's nutritional balance.

[1132] "Emotion data" refers to information that indicates the emotional state of a user, as analyzed from facial expressions, tone of voice, and the like.

[1133] "Event" refers to a competition or activity such as a bodybuilding competition or marathon that is planned by the server and provided to users.

[1134] "AI" refers to artificial intelligence, including technology that generates optimal training menus based on user input data.

[1135] "Analysis" refers to the process by which the server analyzes the data received from the user and extracts meaningful information.

[1136] The present invention relates to a system for enabling users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user, and further combines it with an emotion engine to recognize the user's emotions and provide training and advice based on those emotions. Detailed embodiments of the present invention are described below.

[1137] Launching the app and entering physical data

[1138] A user installs and launches an application on a device such as a smartphone or tablet. When the application is launched for the first time, the user enters physical data such as height and weight. The device used for this is a general mobile device.

[1139] Sending data and generating avatars

[1140] The physical data entered by the user is sent from the device to a server. The server is equipped with a system that generates an avatar based on the physical data. For example, 3D modeling software (e.g., Unity 3D or Mixamo) is used to generate a virtual character that corresponds to the user's physical proportions. The generated avatar data is then sent back from the server to the device, where the user can review it and make any necessary adjustments.

[1141] Training menu generation

[1142] The user enters their training or diet goal data (e.g., muscle gain, weight loss, etc.) into the app. The goal data is sent from the device to the server, and the server uses an AI system (e.g., TensorFlow or PyTorch) to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the displayed training menu and enters their progress into the app.

[1143] Training progress record and points calculation

[1144] When a user completes a workout, they enter their training results into the app. The results data is sent from the device to the server, and the server calculates points based on the received data. The calculated points are added to the user's account and sent back to the device. Updated point information is displayed in real time, allowing users to track their progress and stay motivated.

[1145] Calorie counting and advice provided

[1146] When a user uploads a photo of their meal to the app, the image data is sent from the device to a server. The server is equipped with an AI system (e.g., Clarifai or Google Cloud Vision) that analyzes the image data and calculates calories. It also generates and provides improvement advice based on nutritional balance to the user. The user can then incorporate the calorie calculation results and improvement advice into their daily diet.

[1147] Event Features

[1148] The server periodically plans events using avatars (e.g., bodybuilding competitions or marathons) and notifies users of these events. Detailed information about the event is displayed on the device, and users who wish to participate can register through the app. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[1149] Emotion engine integration

[1150] While the user is using the app, an emotion engine (e.g., Affectiva or Microsoft's Emotion API) analyzes the user's emotions in real time using facial recognition technology and voice analysis. The server collects the user's emotional data and adjusts the training menu and advice based on the analysis results. For example, if the user is tired, the server can suggest a lighter training menu.

[1151] Specific examples

[1152] For example, suppose a user enters their height (160 cm) and weight (60 kg) into the app and sets their goal as increasing muscle strength. The device sends this data to the server, which then uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu tailored to the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters their completed training results into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. Updated points are returned to the device and displayed in real time. When the user uploads a photo of a meal, the server calculates calories and provides improvement advice. Furthermore, the emotion engine recognizes the user's emotions and adjusts the training menu and advice based on their emotional state. For example, if the user is feeling tired, the emotion engine detects this and the server suggests a lighter training menu. This provides an environment where users can continue dieting and muscle training in a fun and effective manner.

[1153] Example prompts for generative AI models

[1154] "A user is 160cm tall and weighs 60kg and wants to improve her muscle mass. Please generate a training menu for her. Also, if she uploads photos of her meals, please provide calorie calculations and advice on how to improve."

[1155] This prompt provides accurate input to the AI ​​model, providing instructions for generating appropriate training and dietary advice.

[1156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1157] Step 1:

[1158] The user installs and launches the app. The user enters their physical data (e.g., height, weight) on the screen that appears when the app is launched for the first time. The entered data is temporarily saved on the device. Input: User's physical data. Output: Temporarily saved data.

[1159] Step 2:

[1160] The device sends the inputted physical data to the server. Specifically, the device's communication module encrypts the data and establishes a secure connection to the server. Input: Temporarily stored user's physical data. Output: Data sent to the server.

[1161] Step 3:

[1162] The server generates an avatar using an avatar generation system (e.g., Unity 3D or Mixamo) based on the received body data. The server generates a 3D model using the user's data as parameters, creating a virtual character. Input: User's body data sent to the server. Output: Generated avatar data.

[1163] Step 4:

[1164] The server returns the generated avatar data to the device. The server's sending module is used to convert the generated data into the required format and send it to the device. Input: Generated avatar data. Output: Avatar data returned to the device.

[1165] Step 5:

[1166] The device displays the received avatar data to the user. Specifically, the avatar is displayed as a 3D model through the application's user interface so that the user can check it. Input: Avatar data returned to the device. Output: Displayed avatar.

[1167] Step 6:

[1168] The user inputs training and diet goal data into the application. For example, they set specific goals such as increasing muscle strength or losing weight. Input: Goal data set by the user. Output: Temporarily saved goal data.

[1169] Step 7:

[1170] The device sends the entered goal data to the server. Processing is carried out over a secure connection, similar to the transmission of body data. Input: Temporarily saved goal data. Output: Goal data sent to the server.

[1171] Step 8:

[1172] The server uses an AI system (e.g. TensorFlow or PyTorch) to generate an optimal training menu based on the input goal data. The server analyzes the user's goals and uses historical data and models to create the most suitable menu. Input: Goal data sent to the server. Output: Generated training menu.

[1173] Step 9:

[1174] The server sends the generated training menu to the terminal. The server converts it into the required format and sends it to the terminal via a secure connection. Input: Generated training menu. Output: Training menu sent to the terminal.

[1175] Step 10:

[1176] The terminal displays the received training menu to the user. The training menu is displayed clearly through the user interface. Input: Training menu sent to the terminal. Output: Training menu displayed to the user.

[1177] Step 11:

[1178] The user performs training and enters performance data into the app after completion. Performance data includes the type of exercise, time, calories burned, etc. Input: User performance data. Output: Temporarily saved performance data.

[1179] Step 12:

[1180] The terminal sends the entered performance data to the server. The process is the same as previous data transmissions. Input: Temporarily saved performance data. Output: Performance data sent to the server.

[1181] Step 13:

[1182] The server calculates points based on the performance data received. The point calculation system awards points based on the type and amount of training. Input: Performance data sent to the server. Output: Calculated points.

[1183] Step 14:

[1184] The server adds the calculated points to the user's account and saves the information. The saving is done in a database system. Input: Calculated points. Output: Information on points added to the user's account.

[1185] Step 15:

[1186] The server returns the latest point information to the device, converts it into the required format, and sends it to the device. Input: Point information stored in the user's account. Output: Point information sent to the device.

[1187] Step 16:

[1188] The point information received by the terminal is displayed to the user in real time. The user can check the displayed points. Input: Point information sent to the terminal. Output: Point information displayed to the user.

[1189] Step 17:

[1190] The user uploads a photo of their meal to the app. The photo is temporarily saved on the device. Input: A photo of the meal uploaded by the user. Output: A photo of the meal that has been saved.

[1191] Step 18:

[1192] The device sends the photo data to the server. The process is the same as previous data transmissions. Input: Temporarily saved photo of the meal. Output: Photo data sent to the server.

[1193] Step 19:

[1194] The server analyzes the image data and calculates the calories of the meal. An AI analysis system (e.g., Clarifai or Google Cloud Vision) is used to calculate calories based on the image data of the meal. Input: Photo data sent to the server. Output: Calculated calories.

[1195] Step 20:

[1196] The server generates improvement advice based on nutritional balance. The generated advice takes into account the user's eating habits. Input: Calculated calories. Output: Generated improvement advice.

[1197] Step 21:

[1198] The server sends the calorie results and improvement advice to the terminal. It is converted into a format and sent. Input: Calculated calories and improvement advice. Output: Calorie results and improvement advice sent to the terminal.

[1199] Step 22:

[1200] The terminal displays the calorie results and improvement advice to the user. The information is displayed clearly through the user interface. Input: Calorie results and improvement advice sent to the terminal. Output: Calorie results and improvement advice displayed to the user.

[1201] Step 23:

[1202] The server plans the event information and notifies the user. The details of the event are sent to the terminal. Input: Planned event information. Output: Event information sent to the terminal.

[1203] Step 24:

[1204] The device displays detailed information about the event to the user. The user checks the displayed information and registers if they wish to participate. Input: Event information sent to the device. Output: Event details displayed to the user.

[1205] Step 25:

[1206] A user registers to participate in an event. Participation request data is sent from the device to the server. Input: Event participation request data. Output: Participation request data sent to the server.

[1207] Step 26:

[1208] The server receives the registration information and registers the user for the event. The registered information is saved in a database. Input: Participation request data. Output: Event registration information.

[1209] Step 27:

[1210] During the event, the device will display the user's progress in real time. The progress of other users will also be displayed, encouraging competition. Input: Event progress data. Output: Displayed progress information.

[1211] Step 28:

[1212] The emotion engine runs while the user is using the app. It collects the user's emotion data using the camera and microphone. Input: User's facial and voice data. Output: Collected emotion data.

[1213] Step 29:

[1214] The device sends the collected emotion data to the server. It is sent via a secure connection. Input: Collected emotion data. Output: Emotion data sent to the server.

[1215] Step 30:

[1216] The server analyzes the emotional data and adjusts the training menu and advice according to the user's emotional state. The emotion analysis system is used to generate appropriate menus and advice. Input: Emotional data sent to the server. Output: Adjusted training menu and advice.

[1217] Step 31:

[1218] The server sends the adjusted training menu and advice to the terminal. It converts it into the required format and sends it. Input: Adjusted training menu and advice. Output: Menu and advice sent to the terminal.

[1219] Step 32:

[1220] The device displays the adjusted training menu and advice to the user. Specifically, the adjustments are displayed clearly through the user interface. Input: The adjusted menu and advice sent to the device. Output: The adjustments displayed to the user.

[1221] (Application example 2)

[1222] 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."

[1223] Conventional fitness systems only provide training menus based on a user's physical data and training goals, but lack real-time feedback and advice that takes into account the user's emotional state. Furthermore, point systems for maintaining training motivation are ineffective. Furthermore, there is a need for easy and accurate calorie calculation and advice for dietary management. The present invention aims to solve these problems.

[1224] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input their own physical data, means for generating an avatar based on the received physical data, means for capturing the user's facial expressions and voice and analyzing their emotions, means for adjusting the difficulty level of the training menu in real time based on the emotion analysis results, means for recording the user's training progress and awarding points upon completion, and means for using the generated points in a shop within the store. As a result, the user receives an optimal training menu and feedback based on their emotional state, enabling them to continue training effectively while maintaining their motivation.

[1225] "Physical data" refers to physical information such as the user's height, weight, and age.

[1226] An "avatar" is a virtual character generated based on a user's physical data.

[1227] The "server" refers to a data processing device that processes user data and generates various training menus and feedback.

[1228] The term "terminal" refers to a mobile information terminal such as a smartphone or tablet used by a user.

[1229] "Emotion analysis" refers to a technology that uses a smartphone's camera, microphone, etc. to analyze a user's emotional state from their facial expressions and voice.

[1230] A "training menu" is an exercise program that is generated according to the user's goals.

[1231] "Points" are a type of reward given to users when they complete a workout.

[1232] "In-gym shop" refers to a store or virtual store located within a fitness gym that sells items.

[1233] "Calorie counting" refers to the process of calculating the energy content of the food a user consumes.

[1234] "Advice" refers to guidance and suggestions provided to help users train and manage their diet more effectively.

[1235] "Training progress" refers to data that indicates how much training a user has completed.

[1236] The present invention is a system for supporting fitness and dietary management according to a user's emotional state, and is mainly comprised of a smartphone, a server, and an AI analysis engine.

[1237] System Configuration

[1238] The system consists of the following hardware and software:

[1239] 1. Hardware

[1240] Smartphones (iPhone, Android smartphones)

[1241] Built-in camera and microphone

[1242] 2. Software

[1243] Smartphone application (iOS / Android)

[1244] Server-side programs (Node.js, Python Django)

[1245] AI analysis engine (TensorFlow, PyTorch)

[1246] System Operation

[1247] 1. Avatar creation function

[1248] Users use a smartphone application to input their own physical data (height, weight, etc.). This data is sent from the smartphone to a server. Based on the received data, the server uses an AI analysis engine to generate a virtual character (avatar) that corresponds to the user's physical data and sends it back to the smartphone. The user can then check the generated avatar and make any necessary adjustments.

[1249] 2. Training menu generation function

[1250] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent to the server. The server uses an AI analysis engine to generate the optimal training menu based on the goal. The generated menu is sent back to the smartphone and displayed to the user. The user then exercises according to the displayed training menu.

[1251] 3. Sentiment Analysis and Real-Time Feedback

[1252] The smartphone's camera and microphone are used to capture the user's facial expressions and voice. The captured data is sent to a server, where emotions are analyzed using an AI analysis engine. Based on the analysis results, the server generates appropriate training menu adjustments and advice in real time and sends them back to the smartphone. This provides feedback and advice according to the user's emotional state.

[1253] 4. Training Progress and Points System

[1254] When a user completes a workout, their smartphone sends their workout record to the server, which calculates points based on their workout record and adds them to the user's account. The updated points information is sent back to the smartphone and displayed to the user. The points can then be used at the gym's shop.

[1255] 5. Food calorie calculation and advice

[1256] Users upload photos of their meals to a smartphone application. The photos are sent to a server, where calorie calculations are performed using an AI analysis engine. Advice based on the calculation results and nutritional balance is generated and sent back to the smartphone. This allows users to receive calorie information and improvement advice for their daily meals.

[1257] Specific examples

[1258] For example, consider the case of generating the optimal training menu for a 24-year-old female user to lose weight. The user's current weight is 60 kg and her height is 160 cm. The user enters this information into a smartphone application and sets training goals. The server receives the information and generates the optimal training menu using an AI analysis engine.

[1259] When users upload photos of their meals, the server analyzes them to calculate calories and provide dietary advice. Furthermore, the system uses the smartphone's camera and microphone to perform emotion analysis during training, providing real-time feedback based on the user's emotional state.

[1260] Prompt Sentence Examples

[1261] "Generate the optimal training menu for a 25-year-old male looking to improve muscle mass. The user's current weight is 75 kg and height is 180 cm. Provide necessary feedback based on sentiment analysis and reward using a points system."

[1262] As described above, the present invention is a system that comprehensively supports fitness and diet management based on the user's physical data and emotional state.

[1263] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1264] Step 1:

[1265] The user enters physical data (e.g., height, weight, age) into a smartphone application. The entered data is sent from the smartphone to a server. This data processing provides the basic information needed to generate an avatar.

[1266] Step 2:

[1267] The server uses an AI analysis engine to generate an avatar based on the received physical data. The generated avatar data is then sent back to the smartphone. This output provides an avatar image that the user can view.

[1268] Step 3:

[1269] The user checks the generated avatar on their smartphone and makes any necessary adjustments. The adjusted data is then sent back to the server. This step is the user's way of making changes to their virtual character to create the look they desire.

[1270] Step 4:

[1271] The user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into a smartphone application. The goal data is sent from the smartphone to a server, which then uses an AI analysis engine to automatically generate an optimal training menu based on the user's goals. The generated menu is then sent back from the server to the smartphone and displayed to the user.

[1272] Step 5:

[1273] When a user trains, their facial expressions and voice are captured using the smartphone's camera and microphone. The captured data is sent from the smartphone to a server, where the server's AI analysis engine performs emotional analysis. Based on the results of the emotional analysis, appropriate training menu adjustments and advice are generated. The analysis results and advice are then sent back from the server to the smartphone.

[1274] Step 6:

[1275] When a user completes a workout, the smartphone sends the workout results to the server, which calculates points based on the results and adds them to the user's account. The updated points information is sent back from the server to the smartphone and displayed to the user. This step allows the user to check the rewards they have earned in real time.

[1276] Step 7:

[1277] Users upload photos of their meals to a smartphone application. The photo data is sent from the smartphone to a server, where the server's AI analysis engine analyzes the photos and generates advice based on calorie calculations and nutritional balance. The calculation results and advice are sent back from the server to the smartphone and displayed to the user. This step allows users to obtain detailed information about their meals and suggestions for improvement.

[1278] 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.

[1279] 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.

[1280] 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.

[1281] [Third embodiment]

[1282] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1283] 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.

[1284] 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).

[1285] 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.

[1286] 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.

[1287] 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).

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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.

[1292] 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.

[1293] 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."

[1294] The present invention relates to a system for enabling a user to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user and uses the avatar to manage training and diet. A specific embodiment of this system is described below.

[1295] 1. Avatar creation function

[1296] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[1297] 2. Training menu generation function

[1298] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[1299] 3. Points System

[1300] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[1301] 4. Calorie calculation and advice function

[1302] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[1303] 5. Event function

[1304] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[1305] Specific examples

[1306] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[1307] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[1308] The processing flow will be explained below.

[1309] 1. Avatar creation process steps

[1310] Step 1:

[1311] The user installs and launches the app.

[1312] Step 2:

[1313] Users enter their height, weight, and other physical data within the app.

[1314] Step 3:

[1315] The terminal transmits the input data to the server.

[1316] Step 4:

[1317] The server processes the request to generate an avatar based on the received data.

[1318] Step 5:

[1319] The server passes the data to an avatar generation system, which generates an avatar that resembles the user.

[1320] Step 6:

[1321] The server returns the generated avatar data to the device.

[1322] Step 7:

[1323] The device displays an avatar on the screen based on the avatar data received.

[1324] Step 8:

[1325] The user checks the displayed avatar and makes adjustments if necessary.

[1326] 2. Training Menu Processing Steps

[1327] Step 1:

[1328] The user enters their goal (e.g., gaining muscle, losing weight) into the app.

[1329] Step 2:

[1330] The terminal transmits the input target data to the server.

[1331] Step 3:

[1332] The server receives the target data.

[1333] Step 4:

[1334] The server uses an AI system to generate the optimal training menu based on the goal.

[1335] Step 5:

[1336] The server returns the generated training menu to the terminal.

[1337] Step 6:

[1338] The terminal displays the received training menu to the user.

[1339] Step 7:

[1340] The user performs the training and enters their progress into the app.

[1341] Step 8:

[1342] The device sends progress data to the server.

[1343] 3. Point system processing steps

[1344] Step 1:

[1345] The terminal transmits the user's training results to the server.

[1346] Step 2:

[1347] The server calculates points based on the received training results.

[1348] Step 3:

[1349] The server adds the calculated points to the user's account and stores the information.

[1350] Step 4:

[1351] The server returns the updated point information to the terminal.

[1352] Step 5:

[1353] The terminal displays the latest points to the user in real time.

[1354] Step 6:

[1355] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[1356] 4. Calorie Count and Advice Function Processing Steps

[1357] Step 1:

[1358] Users upload photos of their meals to the app.

[1359] Step 2:

[1360] The device sends the photo to the server.

[1361] Step 3:

[1362] The server passes the received photos to an AI analysis system, which calculates calories.

[1363] Step 4:

[1364] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[1365] Step 5:

[1366] The server sends the calorie calculation results and improvement advice back to the device.

[1367] Step 6:

[1368] The terminal displays the received information to the user.

[1369] Step 7:

[1370] Users can understand areas for improvement in their diet and reflect this in their next meal.

[1371] 5. Event function processing steps

[1372] Step 1:

[1373] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[1374] Step 2:

[1375] The terminal displays detailed information about the event to the user.

[1376] Step 3:

[1377] When a user wants to participate in an event, he or she registers for participation through the terminal.

[1378] Step 4:

[1379] The terminal transmits the participation registration information to the server.

[1380] Step 5:

[1381] The server registers the user in the event and begins tracking the progress of the competition.

[1382] Step 6:

[1383] The device will display the user's progress and ranking information in real time during the event.

[1384] Step 7:

[1385] Users can participate in events, track their progress, and compete with other users.

[1386] Example 1

[1387] 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."

[1388] Conventional diet and strength training support systems have struggled to maintain user motivation and lacked the means to support continuous training and dietary management. Furthermore, it has been difficult to provide a wide range of functions, such as training menus tailored to individual goals, calorie calculations, and real-time progress management, all in one place. This has resulted in users being unable to receive consistent support, making it difficult to achieve effective diet and strength training.

[1389] 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.

[1390] In this invention, the server includes means for the user to input their own physical data, means for the server to generate a fitness tracker based on the received physical data, means for the terminal to display the generated data, means for the user to input a training record, means for the terminal to transmit the input training record to the server, means for the server to calculate points based on the received training record and add them to the user's account, and means for the terminal to display the latest point information. This allows the user to effectively continue their diet and muscle training while receiving consistent support.

[1391] "User" refers to an individual who uses this system to input their own physical data and goal data and manage their training and diet.

[1392] A "server" refers to a computer system that processes data received from a user and returns the results to the user's terminal.

[1393] "Terminal" refers to a device that allows a user to input data and receives and displays information from a server.

[1394] "Physical data" is information about the user's own body that is input by the user, and specifically includes height, weight, and the like.

[1395] An "avatar" is a virtual character generated based on the user's physical data, which acts as the user's representative in training and diet management.

[1396] "Training history" refers to detailed information about the training the user has done, including the type of training, time, calories burned, and so on.

[1397] "Points" are evaluation values ​​calculated by the server based on the user's training results, and are used as a means to maintain the user's motivation.

[1398] "Goal data" refers to information about training and diet goals set by the user, including specific goals, time periods, and what the user wants to achieve.

[1399] "Training menu" refers to exercise instructions and programs that the server generates based on the user's goal data.

[1400] "Calories" refers to the amount of energy in the food a user takes in, and is a numerical value that serves as an indicator when managing diet.

[1401] "Advice" refers to suggestions for improving diet and training that the server generates based on the calorie calculation results.

[1402] "Events" refer to server-sponsored activities and competitions that users can participate in, providing an opportunity to stay motivated and have fun.

[1403] "Progress information" refers to information that displays a user's progress and achievements in training or events in real time.

[1404] The present invention relates to a system that allows users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on the user's physical data and uses the avatar to manage training and diet, thereby supporting the user in achieving their goals.

[1405] First, the user installs and launches the app. The user uses the device to enter their own physical data (e.g., height, weight, etc.). This data is sent from the device to the server. Specifically, the data is sent using an HTTP request. Based on the received data, the server uses an avatar generation system (e.g., Blender or Unity) to generate an avatar that corresponds to the user's physical data. The generated avatar data is then sent back to the device, where the user can review it and make any necessary adjustments.

[1406] Next, the user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app. This goal data is sent from the device to the server. The server uses an AI system (e.g., TensorFlow or PyTorch) to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the displayed training menu and enters their progress into the app.

[1407] When a user completes a workout, the device sends the training data to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated points are sent back to the device and displayed to the user in real time, allowing the user to track their progress and stay motivated.

[1408] Furthermore, when a user uploads a photo of a meal to the app, the photo is sent from the device to the server. The server uses an AI analysis system (e.g., image recognition API) to calculate the calories of the meal from the photo. Along with the calculation results, advice on how to improve based on nutritional balance is generated and sent to the device. The user can then check the calorie calculation results and advice and incorporate them into their daily diet.

[1409] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[1410] Specific examples

[1411] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[1412] Prompt Sentence Examples

[1413] "Please describe a scenario in which a user inputs their height and weight and sets a goal of increasing muscle strength. Also, please detail how the resulting workout menu and point system works."

[1414] These procedures allow users to effectively maintain their diet and strength training regimen with consistent support.

[1415] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1416] Step 1:

[1417] A user installs and launches the app. On the initial screen of the app, they enter their physical data (e.g., height, weight, etc.). This data is entered based on the app's form.

[1418] Input: Physical data such as height and weight

[1419] Output: User data in JSON format

[1420] Specific behavior:

[1421] The user enters their physical data into the app's form and clicks the "Submit" button.

[1422] Step 2:

[1423] The terminal sends the entered user data to the server using an HTTP POST request.

[1424] Input: User data in JSON format

[1425] Output: HTTP response (confirmation of data reception)

[1426] Specific behavior:

[1427] The device serializes the user data in JSON format and sends it to the server via an HTTP POST request.

[1428] Step 3:

[1429] The server generates an avatar based on the received user data. For example, an avatar generation system such as Blender or Unity is used to generate the avatar.

[1430] Input: User data in JSON format

[1431] Output: Avatar data (3D model or 2D image)

[1432] Specific behavior:

[1433] The server calls a Blender script to input data such as height and weight and generate a corresponding avatar.

[1434] Step 4:

[1435] The server returns the generated avatar data to the device using an HTTP response.

[1436] Input: Avatar data

[1437] Output: HTTP response (avatar data)

[1438] Specific behavior:

[1439] The server sends the generated avatar data to the terminal in JSON format as an HTTP response.

[1440] Step 5:

[1441] The device displays the received avatar data to the user using the app's UI (user interface).

[1442] Input: Avatar data

[1443] Output: Avatar image or 3D model display

[1444] Specific behavior:

[1445] The device parses (analyzes) the received avatar data and displays it on the app's UI.

[1446] Step 6:

[1447] The user inputs training or diet goal data into the app (e.g., muscle gain, weight loss, etc.).

[1448] Input: Goal data (training and diet goals)

[1449] Output: Goal data in JSON format

[1450] Specific behavior:

[1451] The user enters goal data on the app's goal setting screen and clicks the submit button.

[1452] Step 7:

[1453] The terminal sends the target data to the server using an HTTP POST request.

[1454] Input: Goal data in JSON format

[1455] Output: HTTP response (confirmation of data reception)

[1456] Specific behavior:

[1457] The device serializes the target data into JSON format and sends it to the server via an HTTP POST request.

[1458] Step 8:

[1459] The server uses an AI system to generate a training menu based on the user's goals. For example, TensorFlow and PyTorch are used as the AI ​​system.

[1460] Input: Goal data in JSON format

[1461] Output: Training menu

[1462] Specific behavior:

[1463] The server inputs the received goal data into the model and uses AI to generate the optimal training menu.

[1464] Step 9:

[1465] The server sends the generated training menu to the device using HTTP responses.

[1466] Input: Training Menu

[1467] Output: HTTP response (Training menu)

[1468] Specific behavior:

[1469] The server sends the generated training menu to the terminal in JSON format as an HTTP response.

[1470] Step 10:

[1471] The device displays the training menu received to the user using the app's UI (user interface).

[1472] Input: Training Menu

[1473] Output: Training menu display

[1474] Specific behavior:

[1475] The device parses (analyzes) the received training menu and displays it on the app's UI.

[1476] Step 11:

[1477] The user performs the training and enters performance data into the app.

[1478] Input: Training performance data (e.g., type of training, time, calories burned, etc.)

[1479] Output: Performance data in JSON format

[1480] Specific behavior:

[1481] After completing the training, the user enters their results in the app and clicks the submit button.

[1482] Step 12:

[1483] The device sends the training results entered to the server using an HTTP POST request.

[1484] Input: Achievement data in JSON format

[1485] Output: HTTP response (confirmation of data reception)

[1486] Specific behavior:

[1487] The device serializes the training results in JSON format and sends it to the server via an HTTP POST request.

[1488] Step 13:

[1489] The server calculates points based on the received training results and adds them to the user's account.

[1490] Input: Achievement data in JSON format

[1491] Output: Points added to the user account

[1492] Specific behavior:

[1493] The server analyzes the received performance data, calculates points based on the point calculation logic, and adds the points to the user's account.

[1494] Step 14:

[1495] The server returns the updated point information to the terminal using an HTTP response.

[1496] Input: Points added to user account

[1497] Output: HTTP response (latest point information)

[1498] Specific behavior:

[1499] The server sends the latest point information to the terminal in JSON format.

[1500] Step 15:

[1501] The latest point information received by the terminal is displayed to the user.

[1502] Input: Latest point information

[1503] Output: Real-time display of point information

[1504] Specific behavior:

[1505] The device analyzes the received point information and displays it in real time on the app's UI.

[1506] Step 16:

[1507] Users upload photos of their meals to the app and enter meal information.

[1508] Input: Meal photos, meal information

[1509] Output: Meal data in JSON format

[1510] Specific behavior:

[1511] Users use the app's camera function to take a photo of their meal, enter meal information, and click the submit button.

[1512] Step 17:

[1513] The device sends the meal photo and information to the server using an HTTP POST request.

[1514] Input: JSON formatted meal data

[1515] Output: HTTP response (confirmation of data reception)

[1516] Specific behavior:

[1517] The device serializes the meal data in JSON format and sends it to the server via an HTTP POST request.

[1518] Step 18:

[1519] The server uses an AI analysis system to calculate the calorie content of meals and generate advice on improving nutritional balance. For example, an image recognition API is used for the AI ​​analysis system.

[1520] Input: JSON formatted meal data

[1521] Output: Calorie calculation results and improvement advice

[1522] Specific behavior:

[1523] The server analyzes the received meal data using an image recognition API, calculates calories, and generates improvement advice that takes nutritional balance into consideration.

[1524] Step 19:

[1525] The server sends the calorie calculation results and improvement advice to the device using HTTP responses.

[1526] Input: Calorie calculation results and improvement advice

[1527] Output: HTTP response (calorie calculation results and improvement advice)

[1528] Specific behavior:

[1529] The server serializes the calorie calculation results and advice into JSON format and sends them to the device.

[1530] Step 20:

[1531] The device displays the calorie calculation results and advice for improvement to the user.

[1532] Input: Calorie calculation results and improvement advice

[1533] Output: Display of calorie calculation results and improvement advice

[1534] Specific behavior:

[1535] The device analyzes the calorie calculation results and advice received and displays them on the app's UI.

[1536] These processing steps allow the user to effectively continue their diet and strength training with consistent support.

[1537] (Application example 1)

[1538] 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."

[1539] Conventional diet and strength training support systems make it difficult for users to effectively utilize their free time, and in particular, do not adequately consider how to efficiently use time while traveling. Furthermore, when users manage their diet, there is a lack of easy-to-use functions that allow them to take photos of their meals and calculate calories. Furthermore, there is a need for a method to improve users' motivation to continue exercising by linking exercise status and point management in real time while in the car.

[1540] 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.

[1541] In this invention, the server includes a means for the user to input their own physical data, a means for generating an avatar based on the physical data received by the server, a means for the terminal to display the generated avatar data, a means for operating in conjunction with the infotainment system of the autonomous vehicle, and a means for taking photos of meals with a camera installed in the vehicle and calculating calories. This allows users to effectively use their time while traveling to diet and strength train, and easily manage their diet. Furthermore, real-time display of training results and point information can increase motivation to continue.

[1542] "User" refers to an individual who uses the system to manage and execute their own diet and strength training.

[1543] "Server" refers to a central processing unit that receives data from a user, processes it, and sends the results to the user's terminal.

[1544] A "terminal" is a device used for data communication between a user and a server, including a smartphone or a vehicle infotainment system.

[1545] An "infotainment system" is a system installed in an autonomous vehicle that provides information and entertainment functions.

[1546] "Physical data" is numerical data that indicates the user's physical characteristics such as height and weight.

[1547] An "avatar" is a virtual character generated based on the user's physical data, and visually represents the user's training status.

[1548] "Training menu" refers to a set of exercise and strength training plans and detailed instructions that the server generates according to the user's goals.

[1549] The "point system" is a system that calculates and manages points based on the user's training performance, and adds those points to the user's account.

[1550] "Calorie counting" refers to the process of calculating the energy content of a meal from a photo taken by a camera installed inside the vehicle.

[1551] "Photo of meal" is image data of a photograph of a meal that the user plans to eat.

[1552] "In motion" refers to the time the self-driving vehicle is in motion and the time the user spends inside the vehicle.

[1553] The present invention is a system for enabling users to continue dieting and strength training effectively and enjoyably, and is intended for use in autonomous vehicles. This system is implemented in the following steps.

[1554] First, the user inputs their physical data (e.g., height, weight, etc.) through the infotainment system of the autonomous vehicle or their smartphone. The input data is then sent to a server via the infotainment system or smartphone. The server then generates an avatar for the user based on the physical data. This avatar is then displayed on the display of the vehicle's infotainment system or on the smartphone.

[1555] Next, when the user sets their training goals (such as building muscle or losing weight) in the vehicle, the goal data is sent to the server. The server uses AI to generate an optimal training menu based on the user's goals. The generated training menu is displayed on the infotainment system display or on a smartphone, and the user follows the instructions to exercise.

[1556] Furthermore, the user can take photos of their daily meals using a camera installed in the vehicle. The images are sent to a server, which uses an AI analysis system to calculate the calories in each meal. This calorie information and advice on how to improve are displayed on the infotainment system or smartphone, allowing the user to reflect this information in their daily diet management. The user's training results are also sent to a server, which calculates points based on this data and adds them to the user's account. The points information is displayed in real time on the infotainment system or smartphone.

[1557] Examples of hardware and software used in these steps include the infotainment system display, smartphone, in-vehicle camera (required for image analysis for calorie calculation), Python, OpenCV (image processing library), and AI models (for training menu generation and calorie calculation).

[1558] As a specific example, if a user enters their height of 160 cm and weight of 60 kg into the in-car display and sets the goal of increasing muscle strength, the generated avatar will be displayed in the car's interior mirror. The user can also take a photo of their breakfast with the in-car camera, which sends the image to the server. The server uses AI to analyze the image and provides the user with calorie calculation results and advice on how to improve based on nutritional balance. The server receives the training results, calculates points, and adds them to the user's account. Point information is displayed in real time on the in-car display and smartphone, helping to maintain the user's motivation.

[1559] Examples of prompts to input to a generative AI model:

[1560] "The user enters their height (160cm) and weight (60kg) on ​​the display of the self-driving vehicle. Next, they set their goal of increasing muscle strength, and the generated avatar is displayed in the car's interior mirror. Running records taken in the car are registered, and the AI ​​model provides a training menu based on that data. In addition, calories are calculated from an image of breakfast taken by the in-car camera, and detailed advice is sent to the smartphone in real time."

[1561] In this way, users can make effective use of their travel time and continue dieting and strength training in a fun and effective way.

[1562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1563] Step 1:

[1564] The user inputs their own physical data (e.g., height, weight) through the infotainment system of the autonomous vehicle or a smartphone. The input data is sent to the server via the infotainment system or smartphone. The input data is sent to the server using a format such as JSON.

[1565] Input: User's physical data such as height and weight

[1566] Output: Sending body data to the server

[1567] Step 2:

[1568] The server generates an avatar based on the received physical data. Using a generative AI model, it generates a virtual character (avatar) based on the user's physical data. The generated avatar data is then sent to the device.

[1569] Input: User's physical data

[1570] Output: Generated avatar data

[1571] Step 3:

[1572] The device displays the generated avatar data on the display. When the user checks the displayed avatar, they can make fine adjustments as necessary.

[1573] Input: Generated avatar data

[1574] Output: Avatar displayed on the screen

[1575] Step 4:

[1576] The user inputs their training goals (e.g., muscle gain, weight loss, etc.) into the infotainment system or smartphone and sends them to the server. The input goal data is then sent to the server.

[1577] Input: User's training goal

[1578] Output: Sends target data to the server

[1579] Step 5:

[1580] The server uses an AI system to generate an optimal training menu based on the received goal data. The generative AI model is used to create a training menu that matches the user's goals. The generated training menu is then sent to the device.

[1581] Input: User goal data

[1582] Output: Generated training menu

[1583] Step 6:

[1584] The terminal displays the generated training menu, and the user performs training according to the displayed menu.

[1585] Input: Generated training menu

[1586] Output: Training menu displayed on the screen

[1587] Step 7:

[1588] The user takes a photo of the meal using a camera installed in the vehicle and sends it to the server via the infotainment system or smartphone. The image data is then sent to the server.

[1589] Input: A photo of the food taken

[1590] Output: Sending image data to the server

[1591] Step 8:

[1592] The server analyzes the received image data and calculates the calorie content of the meal. It uses a generative AI model to analyze the image and generate calorie calculation results. The calculation results and nutritional advice are then sent to the device.

[1593] Input: Food image data

[1594] Output: Calculated calorie information and nutrition advice

[1595] Step 9:

[1596] The device displays the calculated calorie information and nutrition advice, and the user manages their diet based on the advice received.

[1597] Input: Calorie information and nutrition advice

[1598] Output: Calorie information and nutrition advice displayed on the display

[1599] Step 10:

[1600] The user inputs their training results into the infotainment system or smartphone and sends them to the server. The inputted results data is then sent to the server.

[1601] Input: User's training record

[1602] Output: Sending performance data to the server

[1603] Step 11:

[1604] The server calculates points based on the received training results and adds them to the user's account. The server then transmits the point information to the terminal.

[1605] Input: User's training record

[1606] Output: Calculated point information

[1607] Step 12:

[1608] The terminal displays the latest point information on the display, allowing the user to check progress in real time.

[1609] Input: Calculated point information

[1610] Output: Point information displayed on the display

[1611] Through these steps, users can effectively utilize their travel time in an autonomous vehicle while dieting, strength training, and managing their diet.

[1612] 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.

[1613] The present invention relates to a system for enabling users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user, and further combines it with an emotion engine to recognize the user's emotions and provide training and advice based on those emotions. A specific embodiment of this system is described below.

[1614] 1. Avatar creation function

[1615] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[1616] 2. Training menu generation function

[1617] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[1618] 3. Points System

[1619] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[1620] 4. Calorie calculation and advice function

[1621] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[1622] 5. Event function

[1623] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[1624] 6. Emotion engine integration

[1625] One of the features of this invention is the integration of an emotion engine. While a user is using the app, the emotion engine analyzes the user's emotions in real time using facial recognition technology and voice analysis. For example, it can read emotions from the user's facial expressions and tone of voice using a camera and microphone.

[1626] Adjusting your training routine based on your emotions

[1627] The server uses the data obtained from the emotion engine to adjust the training menu according to the user's emotional state. For example, if the user is tired, it can suggest a lighter training menu. If the user is highly motivated, it can offer a more challenging menu.

[1628] Emotion-based advice

[1629] The emotion engine provides motivational advice based on the user's emotions. For example, if the user is feeling stressed, it can suggest relaxation exercises or meditation. Conversely, if the user is feeling very positive, it can provide advice to encourage further challenges.

[1630] Specific examples

[1631] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a muscle training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time.

[1632] When users upload photos of their meals, the server calculates the calories and provides advice on how to improve. Furthermore, the system uses an emotion engine to recognize the user's emotions and adjusts training menus and advice based on their emotional state. For example, if the user is feeling tired, the emotion engine will detect this and the server will suggest a lighter training menu.

[1633] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[1634] The processing flow will be explained below.

[1635] 1. Avatar creation process steps

[1636] Step 1:

[1637] The user installs and launches the app.

[1638] Step 2:

[1639] Users enter their height, weight, and other physical data within the app.

[1640] Step 3:

[1641] The terminal transmits the input data to the server.

[1642] Step 4:

[1643] The server starts an avatar generation system based on the received data and generates an avatar that resembles the user.

[1644] Step 5:

[1645] The server sends the generated avatar data to the device.

[1646] Step 6:

[1647] The avatar data received by the terminal is displayed on the screen.

[1648] Step 7:

[1649] The user checks the displayed avatar and makes minor adjustments as necessary.

[1650] 2. Training Menu Processing Steps

[1651] Step 1:

[1652] The user enters their training goal (e.g., gaining muscle, losing weight) into the app.

[1653] Step 2:

[1654] The terminal transmits the input target data to the server.

[1655] Step 3:

[1656] The server receives the goal data and uses an AI system to generate an optimal training menu.

[1657] Step 4:

[1658] The server transmits the generated training menu to the terminal.

[1659] Step 5:

[1660] The terminal displays the received training menu to the user.

[1661] Step 6:

[1662] The user performs the workout and enters their progress into the app.

[1663] Step 7:

[1664] The device sends progress data to the server.

[1665] 3. Point system processing steps

[1666] Step 1:

[1667] The terminal transmits the user's training results to the server.

[1668] Step 2:

[1669] The server calculates points based on the received training results.

[1670] Step 3:

[1671] The server adds the calculated points to the user's account and stores them.

[1672] Step 4:

[1673] The server transmits the updated point information to the terminal.

[1674] Step 5:

[1675] The terminal displays the latest points to the user in real time.

[1676] Step 6:

[1677] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[1678] 4. Calorie Count and Advice Function Processing Steps

[1679] Step 1:

[1680] Users upload photos of their meals to the app.

[1681] Step 2:

[1682] The device sends the photo to the server.

[1683] Step 3:

[1684] The server passes the received photos to an AI analysis system, which calculates calories.

[1685] Step 4:

[1686] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[1687] Step 5:

[1688] The server sends the calorie calculation results and improvement advice to the terminal.

[1689] Step 6:

[1690] The terminal displays the received information to the user.

[1691] Step 7:

[1692] Users can understand areas for improvement in their diet and reflect this in their next meal.

[1693] 5. Event function processing steps

[1694] Step 1:

[1695] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[1696] Step 2:

[1697] The terminal displays detailed information about the event to the user.

[1698] Step 3:

[1699] If a user wishes to participate in an event, he or she registers for participation through the terminal.

[1700] Step 4:

[1701] The terminal transmits the participation registration information to the server.

[1702] Step 5:

[1703] The server registers the user in the event and begins tracking the progress of the competition.

[1704] Step 6:

[1705] The device will display the user's progress and ranking information in real time during the event.

[1706] Step 7:

[1707] Users can participate in events, track their progress, and compete with other users.

[1708] 6. Emotion Engine Processing Steps

[1709] Step 1:

[1710] When a user starts using the app, the camera and microphone are automatically turned on.

[1711] Step 2:

[1712] The device captures the user's facial expressions and voice in real time and extracts emotional data.

[1713] Step 3:

[1714] The device transmits the emotion data to the server.

[1715] Step 4:

[1716] The emotion data received by the server is passed to the emotion engine for analysis.

[1717] Step 5:

[1718] The server receives the analysis results and understands the user's emotional state.

[1719] Step 6:

[1720] The server adjusts the training menu and generates advice based on the emotional state.

[1721] Step 7:

[1722] The server sends the adjusted training menu and advice to the device.

[1723] Step 8:

[1724] The training menu and advice received by the terminal are displayed to the user.

[1725] For example, if a user is using a camera and microphone while training and the emotion engine detects fatigue from the user's facial expression, the server can instantly generate a lighter training menu and suggest it to the user through the device.Also, if the user is in a positive emotional state, it can provide a more challenging training menu.

[1726] Example 2

[1727] 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."

[1728] Conventional diet and strength training support systems have difficulty generating personalized training programs based on the user's individual physical data and goals, and lack the ability to adjust training content according to the user's emotions and motivation. Furthermore, they do not include complex elements such as diet management and events, making it difficult to improve the overall experience.

[1729] 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.

[1730] a means for the user to input their physical data;

[1731] A means for generating an avatar based on the received body data by the server;

[1732] A means for displaying the generated avatar data on the terminal;

[1733] a means for a user to input target data;

[1734] A means for the server to use AI to generate a training menu according to the goal,

[1735] a means for a user to input performance data;

[1736] A means for the server to receive the performance data and calculate points;

[1737] A means for the terminal to display point information;

[1738] A means for a user to upload image data of a meal;

[1739] A means for the server to analyze the image data and calculate calories;

[1740] a means for generating improvement advice;

[1741] A means for the server to analyze the user's emotional data and adjust the training menu;

[1742] a means for the server to notify event information and register users for the event;

[1743] A means for the terminal to collect user emotion data and transmit it to a server;

[1744] This makes it possible to provide a training program based on the user's individual physical data and goals, and also to adjust the program according to the user's emotions and motivation, thereby realizing comprehensive and effective support for dieting and muscle training.

[1745] "User" refers to a person who uses the system to input their own physical data and training goals and receive feedback and services.

[1746] A "server" refers to a computer system that receives data sent by a user, processes it, and returns the results.

[1747] "Terminal" refers to a device operated by a user that provides an interface for inputting and displaying data.

[1748] An "avatar" refers to a virtual character generated based on a user's physical data.

[1749] "Physical data" refers to personal physical information such as height and weight entered by the user.

[1750] "Training menu" refers to an exercise program generated by the server in accordance with the user's goal data.

[1751] "Performance data" refers to data entered by a user regarding the results and progress of training.

[1752] "Points" refers to units of evaluation calculated by the server based on a user's training performance and added to the user's account.

[1753] "Image data" refers to digital data that users upload to the app, such as photos of their meals.

[1754] "Calories" is a unit that indicates the amount of energy contained in a meal, and is calculated by the server by analyzing image data.

[1755] "Improvement advice" refers to health management suggestions provided by the server based on the calorie analysis results and the user's nutritional balance.

[1756] "Emotion data" refers to information that indicates the emotional state of a user, as analyzed from facial expressions, tone of voice, and the like.

[1757] "Event" refers to a competition or activity such as a bodybuilding competition or marathon that is planned by the server and provided to users.

[1758] "AI" refers to artificial intelligence, including technology that generates optimal training menus based on user input data.

[1759] "Analysis" refers to the process by which the server analyzes the data received from the user and extracts meaningful information.

[1760] The present invention relates to a system for enabling users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user, and further combines it with an emotion engine to recognize the user's emotions and provide training and advice based on those emotions. Detailed embodiments of the present invention are described below.

[1761] Launching the app and entering physical data

[1762] A user installs and launches an application on a device such as a smartphone or tablet. When the application is launched for the first time, the user enters physical data such as height and weight. The device used for this is a general mobile device.

[1763] Sending data and generating avatars

[1764] The physical data entered by the user is sent from the device to a server. The server is equipped with a system that generates an avatar based on the physical data. For example, 3D modeling software (e.g., Unity 3D or Mixamo) is used to generate a virtual character that corresponds to the user's physical proportions. The generated avatar data is then sent back from the server to the device, where the user can review it and make any necessary adjustments.

[1765] Training menu generation

[1766] The user enters their training or diet goal data (e.g., muscle gain, weight loss, etc.) into the app. The goal data is sent from the device to the server, and the server uses an AI system (e.g., TensorFlow or PyTorch) to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the displayed training menu and enters their progress into the app.

[1767] Training progress record and points calculation

[1768] When a user completes a workout, they enter their training results into the app. The results data is sent from the device to the server, and the server calculates points based on the received data. The calculated points are added to the user's account and sent back to the device. Updated point information is displayed in real time, allowing users to track their progress and stay motivated.

[1769] Calorie counting and advice provided

[1770] When a user uploads a photo of their meal to the app, the image data is sent from the device to a server. The server is equipped with an AI system (e.g., Clarifai or Google Cloud Vision) that analyzes the image data and calculates calories. It also generates and provides improvement advice based on nutritional balance to the user. The user can then incorporate the calorie calculation results and improvement advice into their daily diet.

[1771] Event Features

[1772] The server periodically plans events using avatars (e.g., bodybuilding competitions or marathons) and notifies users of these events. Detailed information about the event is displayed on the device, and users who wish to participate can register through the app. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[1773] Emotion engine integration

[1774] While the user is using the app, an emotion engine (e.g., Affectiva or Microsoft's Emotion API) analyzes the user's emotions in real time using facial recognition technology and voice analysis. The server collects the user's emotional data and adjusts the training menu and advice based on the analysis results. For example, if the user is tired, the server can suggest a lighter training menu.

[1775] Specific examples

[1776] For example, suppose a user enters their height (160 cm) and weight (60 kg) into the app and sets their goal as increasing muscle strength. The device sends this data to the server, which then uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu tailored to the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters their completed training results into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. Updated points are returned to the device and displayed in real time. When the user uploads a photo of a meal, the server calculates calories and provides improvement advice. Furthermore, the emotion engine recognizes the user's emotions and adjusts the training menu and advice based on their emotional state. For example, if the user is feeling tired, the emotion engine detects this and the server suggests a lighter training menu. This provides an environment where users can continue dieting and muscle training in a fun and effective manner.

[1777] Example prompts for generative AI models

[1778] "A user is 160cm tall and weighs 60kg and wants to improve her muscle mass. Please generate a training menu for her. Also, if she uploads photos of her meals, please provide calorie calculations and advice on how to improve."

[1779] This prompt provides accurate input to the AI ​​model, providing instructions for generating appropriate training and dietary advice.

[1780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1781] Step 1:

[1782] The user installs and launches the app. The user enters their physical data (e.g., height, weight) on the screen that appears when the app is launched for the first time. The entered data is temporarily saved on the device. Input: User's physical data. Output: Temporarily saved data.

[1783] Step 2:

[1784] The device sends the inputted physical data to the server. Specifically, the device's communication module encrypts the data and establishes a secure connection to the server. Input: Temporarily stored user's physical data. Output: Data sent to the server.

[1785] Step 3:

[1786] The server generates an avatar using an avatar generation system (e.g., Unity 3D or Mixamo) based on the received body data. The server generates a 3D model using the user's data as parameters, creating a virtual character. Input: User's body data sent to the server. Output: Generated avatar data.

[1787] Step 4:

[1788] The server returns the generated avatar data to the device. The server's sending module is used to convert the generated data into the required format and send it to the device. Input: Generated avatar data. Output: Avatar data returned to the device.

[1789] Step 5:

[1790] The device displays the received avatar data to the user. Specifically, the avatar is displayed as a 3D model through the application's user interface so that the user can check it. Input: Avatar data returned to the device. Output: Displayed avatar.

[1791] Step 6:

[1792] The user inputs training and diet goal data into the application. For example, they set specific goals such as increasing muscle strength or losing weight. Input: Goal data set by the user. Output: Temporarily saved goal data.

[1793] Step 7:

[1794] The device sends the entered goal data to the server. Processing is carried out over a secure connection, similar to the transmission of body data. Input: Temporarily saved goal data. Output: Goal data sent to the server.

[1795] Step 8:

[1796] The server uses an AI system (e.g. TensorFlow or PyTorch) to generate an optimal training menu based on the input goal data. The server analyzes the user's goals and uses historical data and models to create the most suitable menu. Input: Goal data sent to the server. Output: Generated training menu.

[1797] Step 9:

[1798] The server sends the generated training menu to the terminal. The server converts it into the required format and sends it to the terminal via a secure connection. Input: Generated training menu. Output: Training menu sent to the terminal.

[1799] Step 10:

[1800] The terminal displays the received training menu to the user. The training menu is displayed clearly through the user interface. Input: Training menu sent to the terminal. Output: Training menu displayed to the user.

[1801] Step 11:

[1802] The user performs training and enters performance data into the app after completion. Performance data includes the type of exercise, time, calories burned, etc. Input: User performance data. Output: Temporarily saved performance data.

[1803] Step 12:

[1804] The terminal sends the entered performance data to the server. The process is the same as previous data transmissions. Input: Temporarily saved performance data. Output: Performance data sent to the server.

[1805] Step 13:

[1806] The server calculates points based on the performance data received. The point calculation system awards points based on the type and amount of training. Input: Performance data sent to the server. Output: Calculated points.

[1807] Step 14:

[1808] The server adds the calculated points to the user's account and saves the information. The saving is done in a database system. Input: Calculated points. Output: Information on points added to the user's account.

[1809] Step 15:

[1810] The server returns the latest point information to the device, converts it into the required format, and sends it to the device. Input: Point information stored in the user's account. Output: Point information sent to the device.

[1811] Step 16:

[1812] The point information received by the terminal is displayed to the user in real time. The user can check the displayed points. Input: Point information sent to the terminal. Output: Point information displayed to the user.

[1813] Step 17:

[1814] The user uploads a photo of their meal to the app. The photo is temporarily saved on the device. Input: A photo of the meal uploaded by the user. Output: A photo of the meal that has been saved.

[1815] Step 18:

[1816] The device sends the photo data to the server. The process is the same as previous data transmissions. Input: Temporarily saved photo of the meal. Output: Photo data sent to the server.

[1817] Step 19:

[1818] The server analyzes the image data and calculates the calories of the meal. An AI analysis system (e.g., Clarifai or Google Cloud Vision) is used to calculate calories based on the image data of the meal. Input: Photo data sent to the server. Output: Calculated calories.

[1819] Step 20:

[1820] The server generates improvement advice based on nutritional balance. The generated advice takes into account the user's eating habits. Input: Calculated calories. Output: Generated improvement advice.

[1821] Step 21:

[1822] The server sends the calorie results and improvement advice to the terminal. It is converted into a format and sent. Input: Calculated calories and improvement advice. Output: Calorie results and improvement advice sent to the terminal.

[1823] Step 22:

[1824] The terminal displays the calorie results and improvement advice to the user. The information is displayed clearly through the user interface. Input: Calorie results and improvement advice sent to the terminal. Output: Calorie results and improvement advice displayed to the user.

[1825] Step 23:

[1826] The server plans the event information and notifies the user. The details of the event are sent to the terminal. Input: Planned event information. Output: Event information sent to the terminal.

[1827] Step 24:

[1828] The device displays detailed information about the event to the user. The user checks the displayed information and registers if they wish to participate. Input: Event information sent to the device. Output: Event details displayed to the user.

[1829] Step 25:

[1830] A user registers to participate in an event. Participation request data is sent from the device to the server. Input: Event participation request data. Output: Participation request data sent to the server.

[1831] Step 26:

[1832] The server receives the registration information and registers the user for the event. The registered information is saved in a database. Input: Participation request data. Output: Event registration information.

[1833] Step 27:

[1834] During the event, the device will display the user's progress in real time. The progress of other users will also be displayed, encouraging competition. Input: Event progress data. Output: Displayed progress information.

[1835] Step 28:

[1836] The emotion engine runs while the user is using the app. It collects the user's emotion data using the camera and microphone. Input: User's facial and voice data. Output: Collected emotion data.

[1837] Step 29:

[1838] The device sends the collected emotion data to the server. It is sent via a secure connection. Input: Collected emotion data. Output: Emotion data sent to the server.

[1839] Step 30:

[1840] The server analyzes the emotional data and adjusts the training menu and advice according to the user's emotional state. The emotion analysis system is used to generate appropriate menus and advice. Input: Emotional data sent to the server. Output: Adjusted training menu and advice.

[1841] Step 31:

[1842] The server sends the adjusted training menu and advice to the terminal. It converts it into the required format and sends it. Input: Adjusted training menu and advice. Output: Menu and advice sent to the terminal.

[1843] Step 32:

[1844] The device displays the adjusted training menu and advice to the user. Specifically, the adjustments are displayed clearly through the user interface. Input: The adjusted menu and advice sent to the device. Output: The adjustments displayed to the user.

[1845] (Application example 2)

[1846] 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."

[1847] Conventional fitness systems only provide training menus based on a user's physical data and training goals, but lack real-time feedback and advice that takes into account the user's emotional state. Furthermore, point systems for maintaining training motivation are ineffective. Furthermore, there is a need for easy and accurate calorie calculation and advice for dietary management. The present invention aims to solve these problems.

[1848] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for the user to input their own physical data, means for generating an avatar based on the received physical data, means for capturing the user's facial expressions and voice and analyzing their emotions, means for adjusting the difficulty level of the training menu in real time based on the emotion analysis results, means for recording the user's training progress and awarding points upon completion, and means for using the generated points in a shop within the store. As a result, the user receives an optimal training menu and feedback based on their emotional state, enabling them to continue training effectively while maintaining their motivation.

[1849] "Physical data" refers to physical information such as the user's height, weight, and age.

[1850] An "avatar" is a virtual character generated based on a user's physical data.

[1851] The "server" refers to a data processing device that processes user data and generates various training menus and feedback.

[1852] The term "terminal" refers to a mobile information terminal such as a smartphone or tablet used by a user.

[1853] "Emotion analysis" refers to a technology that uses a smartphone's camera, microphone, etc. to analyze a user's emotional state from their facial expressions and voice.

[1854] A "training menu" is an exercise program that is generated according to the user's goals.

[1855] "Points" are a type of reward given to users when they complete a workout.

[1856] "In-gym shop" refers to a store or virtual store located within a fitness gym that sells items.

[1857] "Calorie counting" refers to the process of calculating the energy content of the food a user consumes.

[1858] "Advice" refers to guidance and suggestions provided to help users train and manage their diet more effectively.

[1859] "Training progress" refers to data that indicates how much training a user has completed.

[1860] The present invention is a system for supporting fitness and dietary management according to a user's emotional state, and is mainly comprised of a smartphone, a server, and an AI analysis engine.

[1861] System Configuration

[1862] The system consists of the following hardware and software:

[1863] 1. Hardware

[1864] Smartphones (iPhone, Android smartphones)

[1865] Built-in camera and microphone

[1866] 2. Software

[1867] Smartphone application (iOS / Android)

[1868] Server-side programs (Node.js, Python Django)

[1869] AI analysis engine (TensorFlow, PyTorch)

[1870] System Operation

[1871] 1. Avatar creation function

[1872] Users use a smartphone application to input their own physical data (height, weight, etc.). This data is sent from the smartphone to a server. Based on the received data, the server uses an AI analysis engine to generate a virtual character (avatar) that corresponds to the user's physical data and sends it back to the smartphone. The user can then check the generated avatar and make any necessary adjustments.

[1873] 2. Training menu generation function

[1874] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent to the server. The server uses an AI analysis engine to generate the optimal training menu based on the goal. The generated menu is sent back to the smartphone and displayed to the user. The user then exercises according to the displayed training menu.

[1875] 3. Sentiment Analysis and Real-Time Feedback

[1876] The smartphone's camera and microphone are used to capture the user's facial expressions and voice. The captured data is sent to a server, where emotions are analyzed using an AI analysis engine. Based on the analysis results, the server generates appropriate training menu adjustments and advice in real time and sends them back to the smartphone. This provides feedback and advice according to the user's emotional state.

[1877] 4. Training Progress and Points System

[1878] When a user completes a workout, their smartphone sends their workout record to the server, which calculates points based on their workout record and adds them to the user's account. The updated points information is sent back to the smartphone and displayed to the user. The points can then be used at the gym's shop.

[1879] 5. Food calorie calculation and advice

[1880] Users upload photos of their meals to a smartphone application. The photos are sent to a server, where calorie calculations are performed using an AI analysis engine. Advice based on the calculation results and nutritional balance is generated and sent back to the smartphone. This allows users to receive calorie information and improvement advice for their daily meals.

[1881] Specific examples

[1882] For example, consider the case of generating the optimal training menu for a 24-year-old female user to lose weight. The user's current weight is 60 kg and her height is 160 cm. The user enters this information into a smartphone application and sets training goals. The server receives the information and generates the optimal training menu using an AI analysis engine.

[1883] When users upload photos of their meals, the server analyzes them to calculate calories and provide dietary advice. Furthermore, the system uses the smartphone's camera and microphone to perform emotion analysis during training, providing real-time feedback based on the user's emotional state.

[1884] Prompt Sentence Examples

[1885] "Generate the optimal training menu for a 25-year-old male looking to improve muscle mass. The user's current weight is 75 kg and height is 180 cm. Provide necessary feedback based on sentiment analysis and reward using a points system."

[1886] As described above, the present invention is a system that comprehensively supports fitness and diet management based on the user's physical data and emotional state.

[1887] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1888] Step 1:

[1889] The user enters physical data (e.g., height, weight, age) into a smartphone application. The entered data is sent from the smartphone to a server. This data processing provides the basic information needed to generate an avatar.

[1890] Step 2:

[1891] The server uses an AI analysis engine to generate an avatar based on the received physical data. The generated avatar data is then sent back to the smartphone. This output provides an avatar image that the user can view.

[1892] Step 3:

[1893] The user checks the generated avatar on their smartphone and makes any necessary adjustments. The adjusted data is then sent back to the server. This step is the user's way of making changes to their virtual character to create the look they desire.

[1894] Step 4:

[1895] The user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into a smartphone application. The goal data is sent from the smartphone to a server, which then uses an AI analysis engine to automatically generate an optimal training menu based on the user's goals. The generated menu is then sent back from the server to the smartphone and displayed to the user.

[1896] Step 5:

[1897] When a user trains, their facial expressions and voice are captured using the smartphone's camera and microphone. The captured data is sent from the smartphone to a server, where the server's AI analysis engine performs emotional analysis. Based on the results of the emotional analysis, appropriate training menu adjustments and advice are generated. The analysis results and advice are then sent back from the server to the smartphone.

[1898] Step 6:

[1899] When a user completes a workout, the smartphone sends the workout results to the server, which calculates points based on the results and adds them to the user's account. The updated points information is sent back from the server to the smartphone and displayed to the user. This step allows the user to check the rewards they have earned in real time.

[1900] Step 7:

[1901] Users upload photos of their meals to a smartphone application. The photo data is sent from the smartphone to a server, where the server's AI analysis engine analyzes the photos and generates advice based on calorie calculations and nutritional balance. The calculation results and advice are sent back from the server to the smartphone and displayed to the user. This step allows users to obtain detailed information about their meals and suggestions for improvement.

[1902] 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.

[1903] 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.

[1904] 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.

[1905] [Fourth embodiment]

[1906] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1907] 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.

[1908] 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).

[1909] 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.

[1910] 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.

[1911] 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).

[1912] 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.

[1913] 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.

[1914] 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.

[1915] 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.

[1916] 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.

[1917] 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.

[1918] 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."

[1919] The present invention relates to a system for enabling a user to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user and uses the avatar to manage training and diet. A specific embodiment of this system is described below.

[1920] 1. Avatar creation function

[1921] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[1922] 2. Training menu generation function

[1923] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[1924] 3. Points System

[1925] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[1926] 4. Calorie calculation and advice function

[1927] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[1928] 5. Event function

[1929] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[1930] Specific examples

[1931] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[1932] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[1933] The processing flow will be explained below.

[1934] 1. Avatar creation process steps

[1935] Step 1:

[1936] The user installs and launches the app.

[1937] Step 2:

[1938] Users enter their height, weight, and other physical data within the app.

[1939] Step 3:

[1940] The terminal transmits the input data to the server.

[1941] Step 4:

[1942] The server processes the request to generate an avatar based on the received data.

[1943] Step 5:

[1944] The server passes the data to an avatar generation system, which generates an avatar that resembles the user.

[1945] Step 6:

[1946] The server returns the generated avatar data to the device.

[1947] Step 7:

[1948] The device displays an avatar on the screen based on the avatar data received.

[1949] Step 8:

[1950] The user checks the displayed avatar and makes adjustments if necessary.

[1951] 2. Training Menu Processing Steps

[1952] Step 1:

[1953] The user enters their goal (e.g., gaining muscle, losing weight) into the app.

[1954] Step 2:

[1955] The terminal transmits the input target data to the server.

[1956] Step 3:

[1957] The server receives the target data.

[1958] Step 4:

[1959] The server uses an AI system to generate the optimal training menu based on the goal.

[1960] Step 5:

[1961] The server returns the generated training menu to the terminal.

[1962] Step 6:

[1963] The terminal displays the received training menu to the user.

[1964] Step 7:

[1965] The user performs the training and enters their progress into the app.

[1966] Step 8:

[1967] The device sends progress data to the server.

[1968] 3. Point system processing steps

[1969] Step 1:

[1970] The terminal transmits the user's training results to the server.

[1971] Step 2:

[1972] The server calculates points based on the received training results.

[1973] Step 3:

[1974] The server adds the calculated points to the user's account and stores the information.

[1975] Step 4:

[1976] The server returns the updated point information to the terminal.

[1977] Step 5:

[1978] The terminal displays the latest points to the user in real time.

[1979] Step 6:

[1980] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[1981] 4. Calorie Count and Advice Function Processing Steps

[1982] Step 1:

[1983] Users upload photos of their meals to the app.

[1984] Step 2:

[1985] The device sends the photo to the server.

[1986] Step 3:

[1987] The server passes the received photos to an AI analysis system, which calculates calories.

[1988] Step 4:

[1989] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[1990] Step 5:

[1991] The server sends the calorie calculation results and improvement advice back to the device.

[1992] Step 6:

[1993] The terminal displays the received information to the user.

[1994] Step 7:

[1995] Users can understand areas for improvement in their diet and reflect this in their next meal.

[1996] 5. Event function processing steps

[1997] Step 1:

[1998] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[1999] Step 2:

[2000] The terminal displays detailed information about the event to the user.

[2001] Step 3:

[2002] When a user wants to participate in an event, he or she registers for participation through the terminal.

[2003] Step 4:

[2004] The terminal transmits the participation registration information to the server.

[2005] Step 5:

[2006] The server registers the user in the event and begins tracking the progress of the competition.

[2007] Step 6:

[2008] The device will display the user's progress and ranking information in real time during the event.

[2009] Step 7:

[2010] Users can participate in events, track their progress, and compete with other users.

[2011] Example 1

[2012] 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."

[2013] Conventional diet and strength training support systems have struggled to maintain user motivation and lacked the means to support continuous training and dietary management. Furthermore, it has been difficult to provide a wide range of functions, such as training menus tailored to individual goals, calorie calculations, and real-time progress management, all in one place. This has resulted in users being unable to receive consistent support, making it difficult to achieve effective diet and strength training.

[2014] 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.

[2015] In this invention, the server includes means for the user to input their own physical data, means for the server to generate a fitness tracker based on the received physical data, means for the terminal to display the generated data, means for the user to input a training record, means for the terminal to transmit the input training record to the server, means for the server to calculate points based on the received training record and add them to the user's account, and means for the terminal to display the latest point information. This allows the user to effectively continue their diet and muscle training while receiving consistent support.

[2016] "User" refers to an individual who uses this system to input their own physical data and goal data and manage their training and diet.

[2017] A "server" refers to a computer system that processes data received from a user and returns the results to the user's terminal.

[2018] "Terminal" refers to a device that allows a user to input data and receives and displays information from a server.

[2019] "Physical data" is information about the user's own body that is input by the user, and specifically includes height, weight, and the like.

[2020] An "avatar" is a virtual character generated based on the user's physical data, which acts as the user's representative in training and diet management.

[2021] "Training history" refers to detailed information about the training the user has done, including the type of training, time, calories burned, and so on.

[2022] "Points" are evaluation values ​​calculated by the server based on the user's training results, and are used as a means to maintain the user's motivation.

[2023] "Goal data" refers to information about training and diet goals set by the user, including specific goals, time periods, and what the user wants to achieve.

[2024] "Training menu" refers to exercise instructions and programs that the server generates based on the user's goal data.

[2025] "Calories" refers to the amount of energy in the food a user takes in, and is a numerical value that serves as an indicator when managing diet.

[2026] "Advice" refers to suggestions for improving diet and training that the server generates based on the calorie calculation results.

[2027] "Events" refer to server-sponsored activities and competitions that users can participate in, providing an opportunity to stay motivated and have fun.

[2028] "Progress information" refers to information that displays a user's progress and achievements in training or events in real time.

[2029] The present invention relates to a system that allows users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on the user's physical data and uses the avatar to manage training and diet, thereby supporting the user in achieving their goals.

[2030] First, the user installs and launches the app. The user uses the device to enter their own physical data (e.g., height, weight, etc.). This data is sent from the device to the server. Specifically, the data is sent using an HTTP request. Based on the received data, the server uses an avatar generation system (e.g., Blender or Unity) to generate an avatar that corresponds to the user's physical data. The generated avatar data is then sent back to the device, where the user can review it and make any necessary adjustments.

[2031] Next, the user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app. This goal data is sent from the device to the server. The server uses an AI system (e.g., TensorFlow or PyTorch) to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the displayed training menu and enters their progress into the app.

[2032] When a user completes a workout, the device sends the training data to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated points are sent back to the device and displayed to the user in real time, allowing the user to track their progress and stay motivated.

[2033] Furthermore, when a user uploads a photo of a meal to the app, the photo is sent from the device to the server. The server uses an AI analysis system (e.g., image recognition API) to calculate the calories of the meal from the photo. Along with the calculation results, advice on how to improve based on nutritional balance is generated and sent to the device. The user can then check the calorie calculation results and advice and incorporate them into their daily diet.

[2034] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[2035] Specific examples

[2036] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a strength training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device then sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time. Alternatively, if the user uploads a photo of their breakfast, the server will calculate the calories and provide improvement advice.

[2037] Prompt Sentence Examples

[2038] "Please describe a scenario in which a user inputs their height and weight and sets a goal of increasing muscle strength. Also, please detail how the resulting workout menu and point system works."

[2039] These procedures allow users to effectively maintain their diet and strength training regimen with consistent support.

[2040] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2041] Step 1:

[2042] A user installs and launches the app. On the initial screen of the app, they enter their physical data (e.g., height, weight, etc.). This data is entered based on the app's form.

[2043] Input: Physical data such as height and weight

[2044] Output: User data in JSON format

[2045] Specific behavior:

[2046] The user enters their physical data into the app's form and clicks the "Submit" button.

[2047] Step 2:

[2048] The terminal sends the entered user data to the server using an HTTP POST request.

[2049] Input: User data in JSON format

[2050] Output: HTTP response (confirmation of data reception)

[2051] Specific behavior:

[2052] The device serializes the user data in JSON format and sends it to the server via an HTTP POST request.

[2053] Step 3:

[2054] The server generates an avatar based on the received user data. For example, an avatar generation system such as Blender or Unity is used to generate the avatar.

[2055] Input: User data in JSON format

[2056] Output: Avatar data (3D model or 2D image)

[2057] Specific behavior:

[2058] The server calls a Blender script to input data such as height and weight and generate a corresponding avatar.

[2059] Step 4:

[2060] The server returns the generated avatar data to the device using an HTTP response.

[2061] Input: Avatar data

[2062] Output: HTTP response (avatar data)

[2063] Specific behavior:

[2064] The server sends the generated avatar data to the terminal in JSON format as an HTTP response.

[2065] Step 5:

[2066] The device displays the received avatar data to the user using the app's UI (user interface).

[2067] Input: Avatar data

[2068] Output: Avatar image or 3D model display

[2069] Specific behavior:

[2070] The device parses (analyzes) the received avatar data and displays it on the app's UI.

[2071] Step 6:

[2072] The user inputs training or diet goal data into the app (e.g., muscle gain, weight loss, etc.).

[2073] Input: Goal data (training and diet goals)

[2074] Output: Goal data in JSON format

[2075] Specific behavior:

[2076] The user enters goal data on the app's goal setting screen and clicks the submit button.

[2077] Step 7:

[2078] The terminal sends the target data to the server using an HTTP POST request.

[2079] Input: Goal data in JSON format

[2080] Output: HTTP response (confirmation of data reception)

[2081] Specific behavior:

[2082] The device serializes the target data into JSON format and sends it to the server via an HTTP POST request.

[2083] Step 8:

[2084] The server uses an AI system to generate a training menu based on the user's goals. For example, TensorFlow and PyTorch are used as the AI ​​system.

[2085] Input: Goal data in JSON format

[2086] Output: Training menu

[2087] Specific behavior:

[2088] The server inputs the received goal data into the model and uses AI to generate the optimal training menu.

[2089] Step 9:

[2090] The server sends the generated training menu to the device using HTTP responses.

[2091] Input: Training Menu

[2092] Output: HTTP response (Training menu)

[2093] Specific behavior:

[2094] The server sends the generated training menu to the terminal in JSON format as an HTTP response.

[2095] Step 10:

[2096] The device displays the training menu received to the user using the app's UI (user interface).

[2097] Input: Training Menu

[2098] Output: Training menu display

[2099] Specific behavior:

[2100] The device parses (analyzes) the received training menu and displays it on the app's UI.

[2101] Step 11:

[2102] The user performs the training and enters performance data into the app.

[2103] Input: Training performance data (e.g., type of training, time, calories burned, etc.)

[2104] Output: Performance data in JSON format

[2105] Specific behavior:

[2106] After completing the training, the user enters their results in the app and clicks the submit button.

[2107] Step 12:

[2108] The device sends the training results entered to the server using an HTTP POST request.

[2109] Input: Achievement data in JSON format

[2110] Output: HTTP response (confirmation of data reception)

[2111] Specific behavior:

[2112] The device serializes the training results in JSON format and sends it to the server via an HTTP POST request.

[2113] Step 13:

[2114] The server calculates points based on the received training results and adds them to the user's account.

[2115] Input: Achievement data in JSON format

[2116] Output: Points added to the user account

[2117] Specific behavior:

[2118] The server analyzes the received performance data, calculates points based on the point calculation logic, and adds the points to the user's account.

[2119] Step 14:

[2120] The server returns the updated point information to the terminal using an HTTP response.

[2121] Input: Points added to user account

[2122] Output: HTTP response (latest point information)

[2123] Specific behavior:

[2124] The server sends the latest point information to the terminal in JSON format.

[2125] Step 15:

[2126] The latest point information received by the terminal is displayed to the user.

[2127] Input: Latest point information

[2128] Output: Real-time display of point information

[2129] Specific behavior:

[2130] The device analyzes the received point information and displays it in real time on the app's UI.

[2131] Step 16:

[2132] Users upload photos of their meals to the app and enter meal information.

[2133] Input: Meal photos, meal information

[2134] Output: Meal data in JSON format

[2135] Specific behavior:

[2136] Users use the app's camera function to take a photo of their meal, enter meal information, and click the submit button.

[2137] Step 17:

[2138] The device sends the meal photo and information to the server using an HTTP POST request.

[2139] Input: JSON formatted meal data

[2140] Output: HTTP response (confirmation of data reception)

[2141] Specific behavior:

[2142] The device serializes the meal data in JSON format and sends it to the server via an HTTP POST request.

[2143] Step 18:

[2144] The server uses an AI analysis system to calculate the calorie content of meals and generate advice on improving nutritional balance. For example, an image recognition API is used for the AI ​​analysis system.

[2145] Input: JSON formatted meal data

[2146] Output: Calorie calculation results and improvement advice

[2147] Specific behavior:

[2148] The server analyzes the received meal data using an image recognition API, calculates calories, and generates improvement advice that takes nutritional balance into consideration.

[2149] Step 19:

[2150] The server sends the calorie calculation results and improvement advice to the device using HTTP responses.

[2151] Input: Calorie calculation results and improvement advice

[2152] Output: HTTP response (calorie calculation results and improvement advice)

[2153] Specific behavior:

[2154] The server serializes the calorie calculation results and advice into JSON format and sends them to the device.

[2155] Step 20:

[2156] The device displays the calorie calculation results and advice for improvement to the user.

[2157] Input: Calorie calculation results and improvement advice

[2158] Output: Display of calorie calculation results and improvement advice

[2159] Specific behavior:

[2160] The device analyzes the calorie calculation results and advice received and displays them on the app's UI.

[2161] These processing steps allow the user to effectively continue their diet and strength training with consistent support.

[2162] (Application example 1)

[2163] 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."

[2164] Conventional diet and strength training support systems make it difficult for users to effectively utilize their free time, and in particular, do not adequately consider how to efficiently use time while traveling. Furthermore, when users manage their diet, there is a lack of easy-to-use functions that allow them to take photos of their meals and calculate calories. Furthermore, there is a need for a method to improve users' motivation to continue exercising by linking exercise status and point management in real time while in the car.

[2165] 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.

[2166] In this invention, the server includes a means for the user to input their own physical data, a means for generating an avatar based on the physical data received by the server, a means for the terminal to display the generated avatar data, a means for operating in conjunction with the infotainment system of the autonomous vehicle, and a means for taking photos of meals with a camera installed in the vehicle and calculating calories. This allows users to effectively use their time while traveling to diet and strength train, and easily manage their diet. Furthermore, real-time display of training results and point information can increase motivation to continue.

[2167] "User" refers to an individual who uses the system to manage and execute their own diet and strength training.

[2168] "Server" refers to a central processing unit that receives data from a user, processes it, and sends the results to the user's terminal.

[2169] A "terminal" is a device used for data communication between a user and a server, including a smartphone or a vehicle infotainment system.

[2170] An "infotainment system" is a system installed in an autonomous vehicle that provides information and entertainment functions.

[2171] "Physical data" is numerical data that indicates the user's physical characteristics such as height and weight.

[2172] An "avatar" is a virtual character generated based on the user's physical data, and visually represents the user's training status.

[2173] "Training menu" refers to a set of exercise and strength training plans and detailed instructions that the server generates according to the user's goals.

[2174] The "point system" is a system that calculates and manages points based on the user's training performance, and adds those points to the user's account.

[2175] "Calorie counting" refers to the process of calculating the energy content of a meal from a photo taken by a camera installed inside the vehicle.

[2176] "Photo of meal" is image data of a photograph of a meal that the user plans to eat.

[2177] "In motion" refers to the time the self-driving vehicle is in motion and the time the user spends inside the vehicle.

[2178] The present invention is a system for enabling users to continue dieting and strength training effectively and enjoyably, and is intended for use in autonomous vehicles. This system is implemented in the following steps.

[2179] First, the user inputs their physical data (e.g., height, weight, etc.) through the infotainment system of the autonomous vehicle or their smartphone. The input data is then sent to a server via the infotainment system or smartphone. The server then generates an avatar for the user based on the physical data. This avatar is then displayed on the display of the vehicle's infotainment system or on the smartphone.

[2180] Next, when the user sets their training goals (such as building muscle or losing weight) in the vehicle, the goal data is sent to the server. The server uses AI to generate an optimal training menu based on the user's goals. The generated training menu is displayed on the infotainment system display or on a smartphone, and the user follows the instructions to exercise.

[2181] Furthermore, the user can take photos of their daily meals using a camera installed in the vehicle. The images are sent to a server, which uses an AI analysis system to calculate the calories in each meal. This calorie information and advice on how to improve are displayed on the infotainment system or smartphone, allowing the user to reflect this information in their daily diet management. The user's training results are also sent to a server, which calculates points based on this data and adds them to the user's account. The points information is displayed in real time on the infotainment system or smartphone.

[2182] Examples of hardware and software used in these steps include the infotainment system display, smartphone, in-vehicle camera (required for image analysis for calorie calculation), Python, OpenCV (image processing library), and AI models (for training menu generation and calorie calculation).

[2183] As a specific example, if a user enters their height of 160 cm and weight of 60 kg into the in-car display and sets the goal of increasing muscle strength, the generated avatar will be displayed in the car's interior mirror. The user can also take a photo of their breakfast with the in-car camera, which sends the image to the server. The server uses AI to analyze the image and provides the user with calorie calculation results and advice on how to improve based on nutritional balance. The server receives the training results, calculates points, and adds them to the user's account. Point information is displayed in real time on the in-car display and smartphone, helping to maintain the user's motivation.

[2184] Examples of prompts to input to a generative AI model:

[2185] "The user enters their height (160cm) and weight (60kg) on ​​the display of the self-driving vehicle. Next, they set their goal of increasing muscle strength, and the generated avatar is displayed in the car's interior mirror. Running records taken in the car are registered, and the AI ​​model provides a training menu based on that data. In addition, calories are calculated from an image of breakfast taken by the in-car camera, and detailed advice is sent to the smartphone in real time."

[2186] In this way, users can make effective use of their travel time and continue dieting and strength training in a fun and effective way.

[2187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2188] Step 1:

[2189] The user inputs their own physical data (e.g., height, weight) through the infotainment system of the autonomous vehicle or a smartphone. The input data is sent to the server via the infotainment system or smartphone. The input data is sent to the server using a format such as JSON.

[2190] Input: User's physical data such as height and weight

[2191] Output: Sending body data to the server

[2192] Step 2:

[2193] The server generates an avatar based on the received physical data. Using a generative AI model, it generates a virtual character (avatar) based on the user's physical data. The generated avatar data is then sent to the device.

[2194] Input: User's physical data

[2195] Output: Generated avatar data

[2196] Step 3:

[2197] The device displays the generated avatar data on the display. When the user checks the displayed avatar, they can make fine adjustments as necessary.

[2198] Input: Generated avatar data

[2199] Output: Avatar displayed on the screen

[2200] Step 4:

[2201] The user inputs their training goals (e.g., muscle gain, weight loss, etc.) into the infotainment system or smartphone and sends them to the server. The input goal data is then sent to the server.

[2202] Input: User's training goal

[2203] Output: Sends target data to the server

[2204] Step 5:

[2205] The server uses an AI system to generate an optimal training menu based on the received goal data. The generative AI model is used to create a training menu that matches the user's goals. The generated training menu is then sent to the device.

[2206] Input: User goal data

[2207] Output: Generated training menu

[2208] Step 6:

[2209] The terminal displays the generated training menu, and the user performs training according to the displayed menu.

[2210] Input: Generated training menu

[2211] Output: Training menu displayed on the screen

[2212] Step 7:

[2213] The user takes a photo of the meal using a camera installed in the vehicle and sends it to the server via the infotainment system or smartphone. The image data is then sent to the server.

[2214] Input: A photo of the food taken

[2215] Output: Sending image data to the server

[2216] Step 8:

[2217] The server analyzes the received image data and calculates the calorie content of the meal. It uses a generative AI model to analyze the image and generate calorie calculation results. The calculation results and nutritional advice are then sent to the device.

[2218] Input: Food image data

[2219] Output: Calculated calorie information and nutrition advice

[2220] Step 9:

[2221] The device displays the calculated calorie information and nutrition advice, and the user manages their diet based on the advice received.

[2222] Input: Calorie information and nutrition advice

[2223] Output: Calorie information and nutrition advice displayed on the display

[2224] Step 10:

[2225] The user inputs their training results into the infotainment system or smartphone and sends them to the server. The inputted results data is then sent to the server.

[2226] Input: User's training record

[2227] Output: Sending performance data to the server

[2228] Step 11:

[2229] The server calculates points based on the received training results and adds them to the user's account. The server then transmits the point information to the terminal.

[2230] Input: User's training record

[2231] Output: Calculated point information

[2232] Step 12:

[2233] The terminal displays the latest point information on the display, allowing the user to check progress in real time.

[2234] Input: Calculated point information

[2235] Output: Point information displayed on the display

[2236] Through these steps, users can effectively utilize their travel time in an autonomous vehicle while dieting, strength training, and managing their diet.

[2237] 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.

[2238] The present invention relates to a system for enabling users to continue dieting and strength training effectively and enjoyably. This system generates an avatar based on physical data entered by the user, and further combines it with an emotion engine to recognize the user's emotions and provide training and advice based on those emotions. A specific embodiment of this system is described below.

[2239] 1. Avatar creation function

[2240] When a user installs and launches the app, they are first shown a screen where they can enter their physical data (height, weight, etc.). The user enters this data and the device sends it to the server. Based on the received data, the server uses an avatar generation system to generate a virtual character (avatar) that corresponds to the user's physical data. The generated avatar data is sent back to the device, where the user can review it and make any necessary adjustments.

[2241] 2. Training menu generation function

[2242] When a user enters their training or diet goals (e.g., muscle building, weight loss, etc.) into the app, the goal data is sent from the device to the server. The server uses an AI system to generate the optimal training menu based on the goal. The generated training menu is sent to the device and displayed to the user. The user exercises according to the training menu displayed in the app and enters their progress into the app.

[2243] 3. Points System

[2244] When a user completes a training session, the device sends the results to the server. The server calculates points based on the training results and adds and saves the information to the user's account. Updated point information is sent back to the device and displayed to the user in real time. This allows users to check their progress and stay motivated. Users can also use the points they earn to purchase training gear and apparel from the app's virtual store.

[2245] 4. Calorie calculation and advice function

[2246] When a user uploads a photo of a meal to the app, the device sends the photo to a server. The server uses an AI analysis system to calculate the calories in the meal from the photo. The calculation results are sent to the device along with advice on how to improve nutritional balance. The user can then review the calorie calculation results and advice and incorporate them into their daily diet.

[2247] 5. Event function

[2248] The server periodically plans events using avatars, such as bodybuilding competitions and marathons, and notifies users of these events. The device displays detailed information about the event to the user, and users who wish to participate can register through their device. The server receives the registration information and registers the user for the event. During the event, the device displays the user's progress in real time, allowing them to enjoy competing with other users.

[2249] 6. Emotion engine integration

[2250] One of the features of this invention is the integration of an emotion engine. While a user is using the app, the emotion engine analyzes the user's emotions in real time using facial recognition technology and voice analysis. For example, it can read emotions from the user's facial expressions and tone of voice using a camera and microphone.

[2251] Adjusting your training routine based on your emotions

[2252] The server uses the data obtained from the emotion engine to adjust the training menu according to the user's emotional state. For example, if the user is tired, it can suggest a lighter training menu. If the user is highly motivated, it can offer a more challenging menu.

[2253] Emotion-based advice

[2254] The emotion engine provides motivational advice based on the user's emotions. For example, if the user is feeling stressed, it can suggest relaxation exercises or meditation. Conversely, if the user is feeling very positive, it can provide advice to encourage further challenges.

[2255] Specific examples

[2256] For example, suppose a user enters their height of 160 cm and weight of 60 kg into the app and sets the goal of increasing muscle strength. The device sends this data to the server, which uses the avatar generation system to generate an avatar with the proportions of 160 cm and 60 kg and sends it back to the device. The server then uses the AI ​​system to generate a muscle training menu based on the user's goal and sends it to the device. The user exercises according to the training menu displayed in the app and enters the results of the completed training into the app. The device sends the results to the server, which calculates points and adds them to the user's account. The updated point information is sent back to the device and displayed in real time.

[2257] When users upload photos of their meals, the server calculates the calories and provides advice on how to improve. Furthermore, the system uses an emotion engine to recognize the user's emotions and adjusts training menus and advice based on their emotional state. For example, if the user is feeling tired, the emotion engine will detect this and the server will suggest a lighter training menu.

[2258] This provides an environment where dieting and muscle training can be continued in a fun and effective manner.

[2259] The processing flow will be explained below.

[2260] 1. Avatar creation process steps

[2261] Step 1:

[2262] The user installs and launches the app.

[2263] Step 2:

[2264] Users enter their height, weight, and other physical data within the app.

[2265] Step 3:

[2266] The terminal transmits the input data to the server.

[2267] Step 4:

[2268] The server starts an avatar generation system based on the received data and generates an avatar that resembles the user.

[2269] Step 5:

[2270] The server sends the generated avatar data to the device.

[2271] Step 6:

[2272] The avatar data received by the terminal is displayed on the screen.

[2273] Step 7:

[2274] The user checks the displayed avatar and makes minor adjustments as necessary.

[2275] 2. Training Menu Processing Steps

[2276] Step 1:

[2277] The user enters their training goal (e.g., gaining muscle, losing weight) into the app.

[2278] Step 2:

[2279] The terminal transmits the input target data to the server.

[2280] Step 3:

[2281] The server receives the goal data and uses an AI system to generate an optimal training menu.

[2282] Step 4:

[2283] The server transmits the generated training menu to the terminal.

[2284] Step 5:

[2285] The terminal displays the received training menu to the user.

[2286] Step 6:

[2287] The user performs the workout and enters their progress into the app.

[2288] Step 7:

[2289] The device sends progress data to the server.

[2290] 3. Point system processing steps

[2291] Step 1:

[2292] The terminal transmits the user's training results to the server.

[2293] Step 2:

[2294] The server calculates points based on the received training results.

[2295] Step 3:

[2296] The server adds the calculated points to the user's account and stores them.

[2297] Step 4:

[2298] The server transmits the updated point information to the terminal.

[2299] Step 5:

[2300] The terminal displays the latest points to the user in real time.

[2301] Step 6:

[2302] Users can check the points they have earned and purchase training gear and apparel from the in-app store.

[2303] 4. Calorie Count and Advice Function Processing Steps

[2304] Step 1:

[2305] Users upload photos of their meals to the app.

[2306] Step 2:

[2307] The device sends the photo to the server.

[2308] Step 3:

[2309] The server passes the received photos to an AI analysis system, which calculates calories.

[2310] Step 4:

[2311] The server obtains the calculation results (calories and nutritional balance) and generates improvement advice.

[2312] Step 5:

[2313] The server sends the calorie calculation results and improvement advice to the terminal.

[2314] Step 6:

[2315] The terminal displays the received information to the user.

[2316] Step 7:

[2317] Users can understand areas for improvement in their diet and reflect this in their next meal.

[2318] 5. Event function processing steps

[2319] Step 1:

[2320] The server periodically plans events (e.g., marathons and bodybuilding competitions) and notifies users.

[2321] Step 2:

[2322] The terminal displays detailed information about the event to the user.

[2323] Step 3:

[2324] If a user wishes to participate in an event, he or she registers for participation through the terminal.

[2325] Step 4:

[2326] The terminal transmits the participation registration information to the server.

[2327] Step 5:

[2328] The server registers ...

Claims

1. a means for the user to input their physical data; A means for generating an avatar based on the received body data by the server; A means for displaying the generated avatar data on the terminal; A system including:

2. means for transmitting user goal data to a server; A means for the server to use AI to generate a training menu according to the goal, a means for displaying the generated training menu on the terminal; The system of claim 1 , comprising:

3. A server receives the user's training results and calculates points; a means for adding and storing the calculated points in the user's account; A means for the terminal to display the latest point information; 3. The system according to claim 1 or 2, comprising:

4. a means for users to upload photos of their meals; A server analyzes the photos and calculates calories. A means for transmitting analysis results and improvement advice to the terminal; means for displaying the information received by the terminal; 3. The system according to claim 1 or 2, comprising:

5. a means by which the server organizes events and notifies users; a means for the terminal to display detailed information about the event; a means for users to register to attend an event; A means for the server to manage and display the progress of participating users; 5. The system of claim 1, further comprising:

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