System

Abstract

To provide a diet support system adjusted to needs of a user.SOLUTION: The system includes: means for recording the current figure of a user in image data; means for presenting ideal figures in a selectable manner; means for registering information on the exercise routine of the user; means for generating information on at least one of a meal menu and an exercise program for losing weight on the basis of the current figure of the user, an image of an ideal figure selected by the user, and the exercise routine of the user, and also generating information for that period of time; means for collecting exercise data; means for analyzing the collected exercise data; generating a prompt sentence which generates a voice instruction including an exercise instruction to the user in real time on the basis of the result of the analysis; means for generating a voice instruction by using the generated prompt sentence and a generative AI model; and means for regenerating the generated voice instruction to a terminal of the user.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] Conventional diet support systems have difficulty providing individualized meal menus and exercise programs based on the user's body shape, exercise habits, and desired body image, and they also provide insufficient support during exercise. [Means for solving the problem]

[0005] This invention generates diet meal menus, exercises, and durations by registering an image of the user's body shape, using a generation AI to select an image that closely resembles the desired body shape, and registering the user's exercise habits. Furthermore, the generated menus can be edited by the user, and audio support is provided during exercise. In particular, for strength training, the system reads out the next menu to be performed, and for running, it checks the user's pace distribution and suggests speeding up or slowing down. This allows for diet support tailored to the individual needs of the user. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In the system of the present invention, the user takes a photo of their own body shape using a digital camera or smartphone camera and registers the image in the system. Next, from multiple body shape images provided in the system, the user selects the one that most closely resembles the body image they are aiming for. In addition, the user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes daily, etc.) in the system. Based on this information, the system uses generative AI to generate a diet meal menu, exercise program, and duration that are optimal for the user. The generated menu can be edited by the user as needed.

[0029] "Example 2"

[0030] The system of the present invention provides audio support when the user is exercising. For example, when doing strength training, the system reads out the next exercise to be done (e.g., "Next, do 20 sit-ups"). When running, the system checks the user's pacing and suggests speeding up or slowing down as necessary (e.g., "You may not be able to reach your target time at your current pace. Try picking up the pace a little"). This allows the user to continue exercising appropriately.

[0031] The processing flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: The user takes a photo of their body using a digital camera or smartphone camera. The image is then uploaded to the system.

[0034] Step 2: From the body shape images provided in the system, the user selects the one that most closely matches the desired body image. This selection is performed on the system's user interface.

[0035] Step 3: The user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system. This registration is also done through the system's user interface.

[0036] Step 4: Based on this information, the system uses generative AI to generate the optimal diet menu, exercise program, and duration for the user. The generated menu can be edited by the user as needed.

[0037] "Example 2"

[0038] Step 1: When the user starts exercising, the system provides audio support.

[0039] Step 2: When performing strength training, the system will read out what to do next (e.g., "Next, do 20 sit-ups").

[0040] Step 3: When you run, the system monitors your pacing and suggests speeding up or slowing down as needed (e.g., "Your current pace may not meet your target time. Try picking up the pace a bit.").

[0041] Example 1

[0042] Next, a description will be given of Example 1 of Form 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."

[0043] Conventional diet systems have difficulty generating individual diet plans based on a user's body shape and exercise habits. Furthermore, they lack the ability to edit the generated plan or support functions during exercise, making it difficult for users to continue dieting.

[0044] 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.

[0045] In this invention, the server includes a means for registering a user's body shape as an image, a means for presenting a plurality of body shape images and allowing the user to select a desired body shape, a means for registering the user's exercise habits, a means for generating a diet meal menu, an exercise program, and its duration using a generative AI model based on the user's body shape images, desired body shape, and exercise habits, and a means for editing the generated menu, which enables the generation and editing of a diet plan tailored to the individual needs of the user.

[0046] "Means for registering the user's body shape as an image" refers to a function that allows the user to take a photo of their body shape using a digital camera or a smartphone camera and upload the image to the system.

[0047] The "means for presenting a plurality of body shape images and allowing the user to select the body shape they are aiming for" is a function that presents a plurality of body shape images prepared within the system to the user and allows the user to select the one that is closest to the body shape they are aiming for.

[0048] "Means for registering user's exercise habits" refers to a function that allows users to input their own exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system and register that information.

[0049] "Means for generating diet meal menus, exercise programs, and their duration using a generative AI model" refers to a function that uses a generative AI model to generate optimal diet meal menus, exercise programs, and their duration based on the user's body image, desired body image, and exercise habits.

[0050] The "means for editing the generated menu" is a function that allows the user to check the generated diet plan and edit the meal menu and exercise program as necessary.

[0051] This invention begins when a user takes a photo of their body using a digital camera or smartphone camera and registers the image in the system. The user launches the smartphone's camera app and takes a photo of their body. The captured image is uploaded from the device to the system, and the server receives the uploaded image and stores it in a database. At this time, the quality of the image is checked using an image processing library (e.g., OpenCV).

[0052] Next, the server presents the user with multiple body shape images prepared in the system. The user selects the one that most closely resembles the desired body shape from the presented body shape images. The ID of the selected body shape image is sent from the device to the server, and the server stores the ID in a database.

[0053] Users input their exercise habits on their device. For example, they can enter information such as "I jog three times a week" or "I walk 30 minutes every day." The entered exercise data is sent from the device to a server, which then stores it in a database.

[0054] The server obtains information about the user's body image, desired body shape, and exercise habits, and inputs a prompt into a generative AI model (e.g., GPT-4 (registered trademark)) to request the generation of a diet plan. An example of a prompt is, "Based on the user's body image, desired body shape, and exercise habits, please generate an optimal diet meal menu, exercise program, and duration." Based on the prompt, the generative AI model generates the optimal meal menu, exercise program, and duration. The generated diet plan is stored in a database by the server.

[0055] The user can check the generated diet plan on their device and edit the meal menu and exercise program as needed. For example, they can change the breakfast menu from oatmeal to yogurt. The edited content is sent from the device to the server, which updates the database.

[0056] In this way, the system allows users to easily create a diet plan tailored to their goals and edit it as needed.

[0057] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0058] Step 1: Registering a user's body shape image

[0059] Users simply open the camera app on their smartphone and take a photo of their body shape.

[0060] Input: Body image taken by the user

[0061] The device displays the captured image on the system's upload screen, and the user presses the "Upload" button.

[0062] The server receives the uploaded images and checks the quality of the images using an image processing library (e.g., OpenCV).

[0063] Output: Quality-confirmed body shape images

[0064] The server stores the verified images in a database.

[0065] Step 2: Choose your desired body image

[0066] The server retrieves multiple body shape images from the database and displays them on the user's terminal.

[0067] Input: Multiple body shape images stored in a database

[0068] The user taps to select the desired body shape from the displayed body shape images.

[0069] The terminal transmits the ID of the selected body shape image to the server.

[0070] Output: ID of the body shape image selected by the user

[0071] The server stores the ID of the selected body shape image in a database.

[0072] Step 3: Register your exercise habits

[0073] Users enter their exercise habits into an input form on the device (e.g., "I jog three times a week").

[0074] Input: Exercise habit data entered by the user

[0075] The device transmits the entered exercise habit data to the server.

[0076] Output: Exercise habits data

[0077] The server stores exercise habit data in a database.

[0078] Step 4: Generate an optimal diet plan

[0079] The server retrieves information about the user's body image, desired body image, and exercise habits from a database.

[0080] Input: User's body image, desired body image, exercise habits

[0081] The server inputs the following prompt to the generative AI model (e.g., GPT-4):

[0082] "Generate the optimal diet menu, exercise program, and duration based on the user's body image, desired body image, and exercise habits."

[0083] The generative AI model generates optimal meal plans, exercise programs, and their duration based on the prompt text.

[0084] Output: Generated diet plan (meal menu, exercise program, duration)

[0085] The server stores the generated diet plan in a database.

[0086] Step 5: Edit the menu

[0087] The user can check the generated diet plan on their device.

[0088] Input: Generated diet plan

[0089] Users can edit their meal menus and exercise programs as needed (e.g., change oatmeal to yogurt for breakfast).

[0090] The terminal transmits the edited content to the server.

[0091] Output: Edited diet plan

[0092] The server updates the edited diet plan to the database.

[0093] (Application example 1)

[0094] Next, a description will be given of Application Example 1 of Embodiment 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."

[0095] Traditional diet and fitness programs often offer generic menus and exercise plans, making it difficult to provide programs optimized for each user's body shape and exercise habits. Providing individually optimized training programs and meal menus also places a heavy burden on trainers at fitness facilities. Furthermore, the lack of real-time feedback makes it difficult for users to train effectively.

[0096] 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.

[0097] In this invention, the server includes a means for registering an image of the user's body shape, a means for selecting an image that is close to the desired body shape using a generation AI, and a means for registering the user's exercise habits, which makes it possible to provide individually optimized training programs and meal menus.

[0098] "Means for registering an image of the user's body shape" refers to a means for a user to take a photo of their body shape using a digital camera or a smartphone camera and register that image in the system.

[0099] "Means for using generative AI to select an image that is closest to the desired body shape" refers to a means for using generative AI to select the image that is closest to the desired body shape from multiple body shape images prepared within the system.

[0100] "Means for registering user's exercise habits" refers to the means by which a user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system.

[0101] "Means for generating diet meal menus, exercise programs, and durations" refers to means for generating optimal diet meal menus, exercise programs, and durations for a user using a generative AI based on the user's body image and exercise habits.

[0102] The "means for editing the generated menu" is a means for the user to edit the generated diet meal menu and exercise program as needed.

[0103] "A means for providing individually optimized training programs and meal menus to fitness facility members by allowing them to take a photo of their own body shape, select the body image they are aiming for, and register their exercise habits" is a means for providing individually optimized training programs and meal menus to fitness facility members by allowing them to take a photo of their own body shape, select the body image they are aiming for, and register their exercise habits.

[0104] The "means of collaborating with in-facility trainers to receive real-time feedback" refers to a means of collaborating with in-facility trainers to receive real-time feedback on training and diet.

[0105] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating diet meal menus, exercises, and durations, means for editing the generated menus, means for providing individually optimized training programs and meal menus by allowing members of a fitness facility to take a photo of their own body shape, select the desired body image, and register their exercise habits, and means for receiving real-time feedback in collaboration with trainers within the facility.

[0106] 1. System Program

[0107] The program of this system performs the following processing.

[0108] 2. Explanation of program processing

[0109] The server receives body images taken by users using their smartphone cameras and processes them. Specifically, it uses Python's PIL library to read the images and preprocess them as needed. It then uses a generative AI model to select the image that most closely resembles the user's desired body shape. This generative AI model uses a pre-trained Keras model.

[0110] The user's exercise habits are registered in text format on a server. This includes information such as how many times a week the user exercises and what type of exercise they do. Based on this information, the generative AI model generates the optimal diet menu, exercise program, and duration for the user.

[0111] The generated menu can be viewed and edited as needed through a smartphone app, and users can also connect with trainers at the fitness facility to receive real-time feedback, including training progress and dietary advice.

[0112] 3. Examples of concrete examples and prompts

[0113] As a concrete example, consider a user who jogs three times a week. Based on this information, the generative AI model generates the optimal meal plan and exercise program for the user. Below is an example of a prompt to input to the generative AI model.

[0114] User body image: user_image.jpg

[0115] Target body shape: target_shape.jpg

[0116] Exercise habits: Jogging three times a week

[0117] Use this information to create the perfect diet and exercise program for you.

[0118] This prompt sentence can be used as input into a generative AI model to generate the optimal menu for the user.

[0119] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0120] Step 1:

[0121] The user takes a photo of their body shape using the smartphone camera and saves the image on the device.

[0122] Input: User's body image

[0123] Output: Body shape image saved on the device

[0124] Specific operation: The user launches the camera app on their smartphone, takes a photo of their body, and then saves the image on their device.

[0125] Step 2:

[0126] The terminal uploads the saved body shape image to the server.

[0127] Input: Body shape image saved on the device

[0128] Output: Body shape image uploaded to the server

[0129] How it works: The device uploads the stored body image to the server via an Internet connection, using an HTTP request.

[0130] Step 3:

[0131] The server receives the uploaded body shape images and processes the images.

[0132] Input: Body image uploaded to the server

[0133] Output: Preprocessed body shape image

[0134] What it does: The server uses Python's PIL library to load the image and perform preprocessing such as resizing and noise removal if necessary.

[0135] Step 4:

[0136] The server uses a generative AI model to select the image that most closely resembles the user's desired body image.

[0137] Input: Preprocessed body shape image

[0138] Output: Image that is closest to the desired body image

[0139] How it works: The server uses a pre-trained Keras model to compare images of the user's body shape with images of the desired body shape and selects the image that is closest.

[0140] Step 5:

[0141] The user inputs their exercise habits into the terminal and sends them to the server.

[0142] Input: User's exercise habits

[0143] Output: Exercise habit information sent to the server

[0144] Specific operation: The user enters their exercise habits (e.g., jogging three times a week) through a smartphone app and sends that information to the server.

[0145] Step 6:

[0146] The server uses a generative AI model to generate optimal diet meal menus and exercise programs based on the user's body image and exercise habit information.

[0147] Input: User's body image, exercise habits information

[0148] Output: Optimal diet and exercise program

[0149] Specific operation: The server inputs prompt sentences into the generative AI model and generates optimal diet meal menus and exercise programs for the user. Examples of prompt sentences are as follows:

[0150] User body image: user_image.jpg

[0151] Target body shape: target_shape.jpg

[0152] Exercise habits: Jogging three times a week

[0153] Use this information to create the perfect diet and exercise program for you.

[0154] Step 7:

[0155] The server sends the generated menu to the user's terminal, where the user edits it as needed.

[0156] Input: Optimal diet and exercise program

[0157] Output: The menu sent to the user's device.

[0158] Specific operation: The server sends the generated menu to the user's device, and the user can check the menu through the smartphone app and edit it as necessary.

[0159] Step 8:

[0160] Users train in fitness facilities and connect with trainers to receive real-time feedback.

[0161] Input: Training progress, dietary advice

[0162] Output: Real-time feedback

[0163] How it works: Users train in fitness facilities, and trainers monitor users' progress through a dedicated application and provide real-time feedback.

[0164] Example 2

[0165] Next, a description will be given of Example 2 of Form 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."

[0166] Conventional exercise support systems have difficulty collecting and analyzing users' exercise data in real time and providing appropriate voice instructions. Furthermore, they lack the ability to generate specific exercise and meal plans to help users achieve their desired body shape and provide support for exercising based on those plans. This makes it difficult for users to continue exercising effectively.

[0167] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for registering the user's body shape as an image, means for selecting an image that resembles the desired body shape using a generative AI model, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for collecting the user's exercise data, means for analyzing the collected exercise data, means for generating voice instructions based on the analysis results, means for transmitting the generated voice instructions to the user's terminal, and means for playing the voice instructions on the user's terminal. This allows the user to receive appropriate exercise instructions in real time and effectively continue exercising to get closer to their desired body shape.

[0168] "Means for registering the user's body shape as an image" refers to a function for recording the user's current body shape as image data using a device such as a camera or scanner.

[0169] "Means of using a generative AI model to select an image that is close to the desired body shape" is a function that utilizes a generative AI model to present images that are close to the ideal body shape that the user is aiming for, allowing the user to select from among them.

[0170] "Means for registering a user's exercise habits" refers to a function that records information such as the type, frequency, and duration of the exercise a user engages in on a daily basis in a database.

[0171] "Means for generating diet meal menus, exercise plans, and duration" refers to a function that automatically creates an appropriate meal menu, exercise plan, and duration based on the user's goals, current body shape, and exercise habits.

[0172] "Means for editing the generated menu" refers to a function that allows users to manually modify and adjust the automatically generated meal menu and exercise plan.

[0173] "Means for collecting user exercise data" refers to a function that uses a wearable device, smartphone, etc. to obtain exercise data such as the user's heart rate, speed, and distance in real time.

[0174] "Means for analyzing collected exercise data" refers to a function that evaluates and analyzes the user's exercise status and performance based on the collected exercise data.

[0175] The "means for generating voice instructions based on the analysis results" is a function that generates instructions to provide appropriate exercise instructions and advice to the user by voice based on the results of data analysis.

[0176] "Means for transmitting the generated voice instructions to the user's terminal" refers to a function for transmitting the generated voice instructions to the user's smartphone or tablet via a communication means such as the Internet or Bluetooth.

[0177] "Means for playing voice instructions on the user's device" refers to a function that plays voice instructions received by the user's smartphone or tablet through a speaker or earphones.

[0178] This invention is a system that provides audio support to users while exercising. The system registers the user's body shape as an image, uses a generative AI model to select an image that is similar to the desired body shape, and registers the user's exercise habits. Furthermore, the system includes a function to generate diet meal menus, exercises, and durations, and to edit the generated menus.

[0179] The server collects and analyzes the user's exercise data in real time. The hardware used includes wearable devices (e.g., smartwatches and fitness trackers) for collecting the user's exercise data. The software includes algorithms for data analysis and generative AI models (e.g., OpenAI's GPT-4) for voice synthesis.

[0180] The device (user's smartphone or tablet) conveys the voice instructions sent from the server to the user. Specifically, the device plays back the voice data received from the server and provides the user with advice on the next exercise menu and pacing.

[0181] The user receives voice instructions from the device while exercising and continues exercising accordingly. For example, when doing strength training, the user may receive instructions such as "Next, do 20 sit-ups," and then perform sit-ups. When running, if the current pace is not reaching the target time, the user may receive advice such as "Try to increase your pace a little," and adjust the pace accordingly.

[0182] As a concrete example, consider the case where a user is running. The user puts on a smartwatch, picks up their smartphone, and starts running. The smartwatch collects data such as the user's heart rate and speed, and sends it to a server. The server analyzes this data and detects that the user's current pace is not enough to reach their target time. The server uses a generative AI model to generate a voice instruction saying, "Your current pace may not reach your target time. Try picking up the pace a little," and sends it to the device. The device then plays back this voice instruction and relays it to the user.

[0183] Example prompt sentence:

[0184] "When a user is running, if their current pace is not enough to reach their target time, generate voice prompts suggesting they speed up."

[0185] This system allows users to continue to exercise appropriately.

[0186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0187] Step 1:

[0188] Before starting exercise, the user puts on a wearable device (e.g., a smartwatch) and launches a dedicated smartphone app. The wearable device collects exercise data such as the user's heart rate, speed, and distance in real time. This data is sent to the user's smartphone via Bluetooth or Wi-Fi. The input is the user's exercise data, and the output is the exercise data sent to the smartphone.

[0189] Step 2:

[0190] The server receives the exercise data sent from the user's smartphone. The server uses a data analysis algorithm to evaluate the user's exercise status in real time. For example, it determines whether the user's pace while running is within the target time. The input is the exercise data sent from the smartphone, and the output is the analysis result.

[0191] Step 3:

[0192] The server uses a generative AI model (e.g., generative AI model) to generate appropriate voice instructions for the user. For example, if the user's pace is slow, the server generates a voice instruction such as, "You may not reach your target time at your current pace. Try picking up the pace a little." The input is the analysis result, and the output is the generated voice instruction.

[0193] Step 4:

[0194] The server sends the generated voice instructions to the user's smartphone via an internet connection. The input is the generated voice instructions, and the output is the voice instructions sent to the smartphone.

[0195] Step 5:

[0196] The terminal (user's smartphone) plays the voice instructions received from the server. The user can hear the voice instructions through the smartphone's speaker or earphones. The input is the voice instructions received from the server, and the output is the played voice instructions.

[0197] Step 6:

[0198] The user continues exercising by following the voice instructions from the device. For example, if the user receives the instruction "Next, do 20 sit-ups," the user will do 20 sit-ups. If the user receives the instruction "Try to increase your pace a little," the user will increase the pace of their running. The input is the played voice instructions, and the output is the user's exercise behavior.

[0199] (Application example 2)

[0200] Next, a description will be given of Application Example 2 of Form 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."

[0201] While conventional exercise support systems can provide appropriate exercise menus based on the user's exercise habits and body shape, they have issues with being unable to provide efficient route guidance or time management for delivery work.In addition, there is a lack of means to provide appropriate advice when delivery times are likely to be delayed, so there is a need to improve the efficiency of delivery work.

[0202] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0203] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating diet meal menus, exercises, and periods, means for editing the generated menus, means for registering delivery destination information, means for generating delivery routes, means for managing delivery times, means for providing voice guidance on delivery destinations and routes, and means for encouraging the delivery driver to speed up if the delivery time is likely to be delayed. This enables efficient route guidance and time management in delivery work, and can provide appropriate advice if the delivery time is likely to be delayed.

[0204] "Means for registering the user's body shape as an image" refers to a method for saving the shape and size of the user's body as image data.

[0205] "Method of using generative AI to select an image that is close to the desired body" is a method that uses artificial intelligence to allow users to select an image of the body they are aiming for.

[0206] "Means for registering a user's exercise habits" refers to a method for recording the type and frequency of exercise that a user does on a daily basis.

[0207] The "means for generating diet meal menus, exercise programs, and durations" is a method for creating meal plans, exercise programs, and durations that match the user's goals.

[0208] The "means for editing the generated menu" is a method for modifying the generated meal plan or exercise program according to the user's wishes.

[0209] The "means for registering delivery destination information" is a method for recording information such as the address and contact details of the delivery location.

[0210] The "means for generating a delivery route" is a method for calculating the optimal route for efficiently visiting multiple delivery destinations.

[0211] "Means for managing delivery times" refers to a method for recording and managing the estimated arrival time and actual arrival time at each delivery destination.

[0212] "A means for providing delivery destination and route guidance by voice" is a method for providing voice instructions to delivery personnel regarding the next delivery destination and the optimal route.

[0213] The "means for encouraging delivery staff to speed up when delivery time is likely to be delayed" is a method for instructing delivery staff by voice to speed up when delivery is behind schedule.

[0214] The system for implementing this invention has the functions of registering an image of the user's body shape, using a generation AI to select an image that closely resembles the desired body shape, and registering the user's exercise habits. It also includes functions to generate diet meal menus, exercises, and durations, and to edit the generated menus. It also has functions to register delivery destination information, generate delivery routes, and manage delivery times. It also includes functions to provide voice guidance on delivery destinations and routes, and to encourage the delivery driver to speed up if they are likely to be late.

[0215] System program processing

[0216] The server acquires image data using a smartphone or camera to register the user's body shape as an image and stores it in a database. Using a generative AI model, the server generates and presents prompts to the user to select an image that best represents the body they desire. To register the user's exercise habits, the server collects exercise data from smartphone apps and wearable devices and stores it in a database.

[0217] To generate a diet menu, exercise plan, and duration, the server generates an optimal plan based on the user's goals, current body shape, and exercise habits. To edit the generated menu, the user can modify it using a smartphone app.

[0218] To register delivery destination information, the server stores the delivery destination address and contact information in a database. To generate delivery routes, the server uses map data to calculate the optimal route. To manage delivery times, the server records and manages the estimated and actual arrival times at each delivery destination.

[0219] The server uses voice synthesis technology to provide voice guidance on delivery destinations and routes to the delivery person, and if delivery time is likely to be delayed, the server will give voice instructions to the delivery person to speed up.

[0220] Specific examples

[0221] For example, if a delivery person is delivering to "1-1-1, Shibuya-ku, Tokyo," the server will do the following:

[0222] 1. From the list of delivery addresses, select "1-1-1, Shibuya-ku, Tokyo."

[0223] 2. A voice will say, "Your next delivery destination is 1-1-1, Shibuya-ku, Tokyo. We will guide you to the best route."

[0224] 3. If the delivery time is likely to be delayed, the system will provide a voice message saying, "At the current pace, the delivery time is likely to be delayed. Please speed up the pace a little."

[0225] Example prompts to input to a generative AI model:

[0226] "Your next delivery is to 1-1-1, Shibuya-ku, Tokyo. We'll guide you to the best route. At your current pace, your delivery time will likely be delayed. Please speed up a bit."

[0227] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0228] Step 1:

[0229] The server registers the user's body shape as an image. The user takes a photo of their body using a smartphone or camera and sends the image data to the server. The server stores the received image data in a database. The input is the user's body shape image, and the output is the image data stored in the database.

[0230] Step 2:

[0231] The server uses a generative AI model to generate a prompt to select an image that best suits the desired body shape. When the user inputs the desired body features, the server uses the generative AI model to generate an image based on those features and presents it to the user. The input is the user's desired body features, and the output is the generated image.

[0232] Step 3:

[0233] The server registers the user's exercise habits. The user inputs daily exercise data using a smartphone app or wearable device and sends the data to the server. The server stores the received exercise data in a database. The input is the user's exercise data, and the output is the exercise data stored in the database.

[0234] Step 4:

[0235] The server generates diet meal menus, exercises, and durations. Based on the user's goals, current body shape, and exercise habits, the server generates an optimal diet plan and exercise program and presents it to the user. The input is the user's goals, body shape, and exercise habits, and the output is the generated diet plan and exercise program.

[0236] Step 5:

[0237] The user edits the generated menu. Using a smartphone app, the user modifies the generated meal plan and exercise program and sends the modified data to the server. The server stores the received modified data in a database. The input is the user's modified data, and the output is the modified data stored in the database.

[0238] Step 6:

[0239] The server registers the delivery address information. The user enters the delivery address and contact information and sends that information to the server. The server saves the received delivery address information in a database. The input is the delivery address and contact information, and the output is the delivery address information saved in the database.

[0240] Step 7:

[0241] The server generates a delivery route. Based on the information of the delivery destination, the server uses map data to calculate the optimal route and presents it to the delivery person. The input is the delivery destination information, and the output is the optimal delivery route.

[0242] Step 8:

[0243] The server manages delivery times. It records and manages the estimated and actual arrival times at each delivery destination. The input is delivery destination information and current location information, and the output is the estimated and actual arrival times.

[0244] Step 9:

[0245] The server provides voice guidance on delivery destinations and routes. It uses speech synthesis technology to provide voice instructions to the delivery person on the next delivery destination and the optimal route. The input is delivery route information, and the output is voice guidance.

[0246] Step 10:

[0247] The server prompts the delivery person to speed up if the delivery time is likely to be delayed. If the delivery is behind schedule, the server gives a voice command to the delivery person to speed up. The input is the current location information and the estimated arrival time, and the output is a voice command to speed up.

[0248] 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.

[0249] "Example 1"

[0250] In one embodiment of the present invention, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, and movements during exercise. For example, if the user shows signs of pain while exercising, the emotion engine will capture this information and determine that the user is experiencing excessive stress. This information is fed back to the system, which then adjusts the content and tone of the voice support. Specifically, the system may provide encouraging messages to the user or automatically reduce the intensity of the exercise.

[0251] "Example 2"

[0252] The emotion engine also adjusts diet meal menus and exercise programs based on the user's emotional state. For example, if a user shows a happy expression after exercising, the emotion engine will pick up on that information and determine that the user likes that exercise. This information is fed back to the system, and that exercise will be more frequently incorporated into recommended exercise programs. Also, if a user shows a satisfied expression after eating, that meal menu will be determined to be favorable to the user, and similar meal menus will be recommended.

[0253] The processing flow of each embodiment will be described below.

[0254] "Example 1"

[0255] Step 1: The user begins exercising.

[0256] Step 2: The emotion engine estimates the user's emotions based on their facial expressions, tone of voice, and movements during exercise.

[0257] Step 3: If the emotion engine determines that the user is experiencing excessive stress, it feeds that information back into the system.

[0258] Step 4: The system adjusts the content and tone of the audio support, such as providing encouraging messages or automatically reducing the intensity of the exercise.

[0259] "Example 2"

[0260] Step 1: The user finishes the exercise.

[0261] Step 2: The emotion engine infers emotions from the user's facial expressions.

[0262] Step 3: If the emotion engine determines that the user likes the exercise, it feeds that information back into the system.

[0263] Step 4: Adjust the system to include the exercise frequently in your recommended exercise program.

[0264] Step 5: The user finishes eating.

[0265] Step 6: The emotion engine infers emotions from the user's facial expressions.

[0266] Step 7: If the emotion engine determines that the meal menu is favorable to the user, it feeds that information back into the system.

[0267] Step 8: The system adjusts to recommend similar meals.

[0268] Example 1

[0269] Next, a description will be given of Example 1 of Form 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."

[0270] Conventional diet support systems have had issues with providing effective diet support due to the difficulty of providing individualized menus based on the user's body shape and exercise habits, and a lack of feedback that takes into account the user's emotions and stress levels. Furthermore, it is not possible to detect the stress and fatigue the user feels during exercise in real time and provide appropriate support, making it difficult to maintain the user's motivation.

[0271] 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.

[0272] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means including an emotion engine for recognizing the user's emotions, means for the emotion engine to analyze the user's facial expressions, tone of voice, and movements during exercise to estimate emotions, means for adjusting the content and tone of audio support based on emotions, and means for automatically adjusting exercise intensity. This makes it possible to not only provide an individualized menu based on the user's body shape and exercise habits, but also to detect the user's emotions and stress level in real time and provide support accordingly.

[0273] "Means for registering a user's body shape using an image" refers to a function that allows a user to take a photo of their body shape using a digital camera or a smartphone camera and upload the image to the system.

[0274] "Means of using generative AI to select an image that is closest to the desired body shape" is a function that uses generative AI to select the image that is closest to the body shape the user is aiming for from multiple body shape images prepared within the system.

[0275] "Means for registering user's exercise habits" refers to a function that allows a user to input their own exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system and save that information.

[0276] "Means for generating diet meal menus, exercise programs, and duration" refers to a function that uses a generative AI model to create optimal diet meal menus, exercise programs, and durations based on the user's body image, desired body image, and exercise habits.

[0277] The "means for editing the generated menu" is a function that allows the user to check the generated diet meal menu and exercise program, and make changes or additions as necessary.

[0278] "Means including an emotion engine that recognizes the user's emotions" refers to a function that incorporates an emotion engine into the system to estimate the user's emotions from their facial expressions, tone of voice, movements during exercise, etc.

[0279] "Means for the emotion engine to analyze the user's facial expressions, tone of voice, and movements during exercise to infer emotions" refers to a function in which the emotion engine analyzes data collected through the camera and microphone to infer the user's emotional state.

[0280] "Means for adjusting the content and tone of voice support based on emotions" refers to a function that changes the content and tone of voice support according to the emotional state of the user estimated by the emotion engine, and provides messages of encouragement to the user.

[0281] "Means for automatically adjusting exercise intensity" is a function that allows the emotion engine to appropriately change the intensity of exercise based on the user's emotional state, thereby reducing the user's stress and fatigue.

[0282] MODE FOR CARRYING OUT THE INVENTION

[0283] The present invention begins with the user taking a photo of their own body shape using a digital camera or smartphone camera and registering the image in the system. Next, the user selects the body shape image that most closely resembles their desired body shape from the multiple body shape images available in the system. The user also registers their exercise habits (e.g., jogging three times a week, walking 30 minutes daily, etc.) in the system. Based on this information, the system uses a generative AI model to generate a diet meal menu, exercise program, and duration that are optimal for the user. The generated menu can be edited by the user as needed.

[0284] In one embodiment of the present invention, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, and movements during exercise. For example, if the user shows signs of pain while exercising, the emotion engine will capture this information and determine that the user is experiencing excessive stress. This information is fed back to the system, which then adjusts the content and tone of the voice support. Specifically, the system may provide encouraging messages to the user or automatically reduce the intensity of the exercise.

[0285] Hardware and software used

[0286] This system uses the following hardware and software:

[0287] Digital camera or smartphone camera: Used to take a picture of the user's body shape.

[0288] Terminal (PC, smartphone, tablet, etc.): Used by users to access the system and register images and exercise habits.

[0289] Server: Stores the data sent by the user and generates diet menus and exercise programs using generative AI models.

[0290] Generative AI model: Generates optimal diet menus and exercise programs based on the user's body image, desired body image, and exercise habits.

[0291] Emotion engine: Analyzes the user's facial expressions, tone of voice, and movements during exercise to estimate emotions.

[0292] Specific examples

[0293] Example 1: Registering a body shape image

[0294] Users take a photo of their body with their smartphone camera and upload the image to the system, where the server receives the image and stores it in the user's profile.

[0295] Example 2: Choosing the body image you want to achieve

[0296] The user selects the body shape image they want to achieve from the body shape images in the system and sends that information to the server, which then stores the selected image as the user's goal.

[0297] Example 3: Registering exercise habits

[0298] A user enters their exercise habits, such as "I jog three times a week," into the system and sends them to the server, which adds this information to the user's profile.

[0299] Example 4: Generating a diet menu

[0300] The server uses a generative AI model to generate a one-week diet menu based on the user's body image, desired body shape, and exercise habits. The generated menu is sent to the user's device, where the user can edit it as needed.

[0301] Example 5: Emotional Engine Feedback

[0302] If the user shows any facial expression of pain during exercise, the emotion engine captures that information and sends it back to the server, which then generates an encouraging message and sends it to the device. The intensity of the exercise is also automatically reduced.

[0303] Prompt Sentence Examples

[0304] "Upload a photo of your body and select the body image you want to achieve. Next, enter your exercise habits and we'll generate the optimal diet menu for you. We'll also recognize your emotions during exercise and provide feedback."

[0305] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0306] Step 1:

[0307] Users take a photo of their body shape using a digital camera or smartphone camera.

[0308] Input: User's body image

[0309] Output: Photographed body shape image file

[0310] Specific operation: The user launches the camera app and takes a photo from the front so that the entire body is captured. After taking the photo, the image file is saved.

[0311] Step 2:

[0312] The user logs in to the system from their terminal and uploads the images they have taken.

[0313] Input: Body shape image file

[0314] Output: Body shape image saved on the server

[0315] Specific operation: The user accesses the system login screen, enters their user ID and password to log in. After logging in, they move to the image upload screen, select the saved image file, and click the upload button.

[0316] Step 3:

[0317] The server stores the received image and associates it with the user's profile.

[0318] Input: Uploaded body shape image file

[0319] Output: Body image associated with the user's profile

[0320] Specific operation: The server receives the uploaded image file and stores it in the database. The stored image is associated with the user ID.

[0321] Step 4:

[0322] The user browses through multiple body shape images prepared within the system.

[0323] Input: Body shape image data in the system

[0324] Output: A list of body shape images to be viewed by the user

[0325] Specific operation: The user accesses the body shape image selection screen and views the body shape images displayed in slideshow format.

[0326] Step 5:

[0327] Select the image that most closely resembles the body image the user is aiming for.

[0328] Input: User-selected body shape image

[0329] Output: Selected body shape image data

[0330] Specific actions: The user selects the image that most closely resembles the desired body image and clicks the Select button.

[0331] Step 6:

[0332] The server stores the selected image as the user's goal.

[0333] Input: Selected body shape image data

[0334] Output: Target body shape image saved in the user's profile

[0335] Specific operation: The server receives the selected image data and stores it in the user's profile as a target body shape image.

[0336] Step 7:

[0337] Users enter their exercise habits into the system via their device.

[0338] Input: User's exercise habits (e.g., jogging three times a week)

[0339] Output: Input exercise habit data

[0340] Specific operation: The user accesses the exercise habit input screen, enters their exercise habit in the text box, and then clicks the save button.

[0341] Step 8:

[0342] The server receives the entered exercise habits information and adds it to the user's profile.

[0343] Input: Entered exercise habit data

[0344] Output: Exercise habits added to the user's profile

[0345] Specific operation: The server receives the entered exercise habit data and stores it in a database. The stored data is linked to the user ID.

[0346] Step 9:

[0347] The server uses a generative AI model to generate the optimal diet meal menu, exercise program, and duration based on the user's body image, desired body shape, and exercise habits.

[0348] Input: User's body image, desired body image, exercise habits

[0349] Output: Generated diet menu and exercise program

[0350] Specific operation: The server runs the generative AI model and creates a menu based on the input data, taking into account calorie calculations and nutritional balance.

[0351] Step 10:

[0352] The server sends the generated menu to the user's device.

[0353] Input: Generated diet meal menu and exercise program

[0354] Output: The menu sent to the user's device

[0355] Specific operation: The server sends the generated menu to the user's terminal in PDF format or similar.

[0356] Step 11:

[0357] Review the menu the user receives and edit it if necessary.

[0358] Input: Generated diet meal menu and exercise program

[0359] Output: Edited menu

[0360] Specific actions: The user accesses the menu editing screen, changes ingredients and adds exercise, and then clicks the save button after editing.

[0361] Step 12:

[0362] The user uses the system while exercising.

[0363] Input: User's exercise data (e.g. facial expressions, tone of voice, movements during exercise)

[0364] Output: Real-time collected exercise data

[0365] Specific operation: The user exercises while holding the smartphone, and data is collected via the camera and microphone.

[0366] Step 13:

[0367] The emotion engine analyzes the user's facial expressions, tone of voice, and movements during exercise in real time.

[0368] Input: Exercise data collected in real time

[0369] Output: Estimated user emotional state

[0370] Specific operation: The emotion engine analyzes the collected data and estimates the user's emotional state.

[0371] Step 14:

[0372] The emotion engine estimates the user's emotions and, if it determines that the user is experiencing excessive stress, it feeds that information back to the server.

[0373] Input: Estimated user emotional state

[0374] Output: Emotion information fed back to the server

[0375] Specific operation: The emotion engine determines the user's emotional state, and if the user is feeling excessive stress, it sends that information to the server.

[0376] Step 15:

[0377] The server adjusts the content and tone of the voice support based on the feedback and generates encouraging messages for the user.

[0378] Input: Feedback emotional information

[0379] Output: Generated encouraging message

[0380] Specific operation: The server changes the content and tone of the voice support based on the emotional information, generating messages such as "Hang in there! You're almost there!"

[0381] Step 16:

[0382] The server automatically adjusts the intensity of the exercise and sends it to the user's device.

[0383] Input: Feedback emotional information

[0384] Output: Adjusted exercise intensity instructions

[0385] Specific operation: The server appropriately adjusts the exercise intensity based on the emotional information and sends instructions to the user's device, such as instructing them to slow down their exercise pace.

[0386] (Application example 1)

[0387] Next, a description will be given of Application Example 1 of Embodiment 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."

[0388] While conventional diet support systems can provide personalized menus based on a user's body shape and exercise habits, they have the problem of not being able to provide feedback that takes into account the user's emotional state, which can lead to excessive stress and a loss of motivation.

[0389] 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.

[0390] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for recognizing the user's emotions, and means for providing feedback based on the user's emotional state. This makes it possible to not only provide an optimal menu based on the user's individual body shape and exercise habits, but also to provide feedback according to the user's emotional state.

[0391] "Means for registering an image of the user's body shape" refers to a means for a user to take a photo of their body shape using a digital camera or a smartphone camera and register that image in the system.

[0392] "Means for using generative AI to select an image that is closest to the desired body shape" refers to a means for using generative AI to select the image that is closest to the body shape the user is aiming for from multiple body shape images prepared within the system.

[0393] "Means for registering user's exercise habits" refers to the means by which a user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system.

[0394] "Means for generating diet meal menus, exercise programs, and durations" refers to means for using a generative AI to generate optimal diet meal menus, exercise programs, and durations for a user based on the user's body image and exercise habits.

[0395] The "means for editing the generated menu" is a means for the user to edit the generated diet meal menu and exercise program as needed.

[0396] "Means for recognizing the user's emotions" refers to means for estimating emotions from the user's facial expressions, tone of voice, movements during exercise, etc.

[0397] "Means for providing feedback based on the user's emotional state" refers to means for adjusting the content and tone of the audio support according to the user's emotional state, providing encouraging messages to the user, or automatically reducing the intensity of exercise.

[0398] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means for recognizing the user's emotions, and means for providing feedback based on the user's emotional state.

[0399] Hardware and Software Configuration

[0400] Hardware:

[0401] Smartphone (camera, microphone, sensor)

[0402] Server (data processing, execution of generative AI models)

[0403] software:

[0404] Image processing library (OpenCV)

[0405] Emotion Recognition Engine (Microsoft® Azure® Cognitive Services)

[0406] Generative AI model (GPT-4)

[0407] Database (MySQL (registered trademark))

[0408] Data processing and calculation

[0409] Registering a user's body image:

[0410] Users take a photo of their body shape using their smartphone camera and upload the image to the system. Body shape data is analyzed from the image using an image processing library (OpenCV).

[0411] Select an image that best represents the body you want to achieve:

[0412] Using a generative AI model (GPT-4), the system selects the body shape image that is closest to the user's desired one from multiple body shape images provided within the system.

[0413] Registering your exercise habits:

[0414] Users enter their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system as text data.

[0415] Generate diet menus and exercise programs:

[0416] Enter the following prompt into the generative AI model to generate an optimal diet meal menu and exercise program.

[0417] Based on your body image and exercise habits, please provide the following information:

[0418] 1. The body image that most closely resembles the user's desired body image

[0419] 2. The best diet meal menu (breakfast, lunch, dinner)

[0420] 3. An optimal exercise program (e.g., jogging three times a week, walking 30 minutes daily, etc.)

[0421] 4. Recommended duration

[0422] Edit the generated menu:

[0423] The user can edit the generated diet meal menu and exercise program as needed.

[0424] Emotion recognition and feedback:

[0425] Using an emotion recognition engine (Microsoft Azure Cognitive Services), the system analyzes the user's facial expressions and tone of voice to estimate their emotional state. If the user shows signs of distress during exercise, the system will capture this information and adjust the content and tone of the voice support. Specifically, it will provide an encouraging message to the user or automatically reduce the intensity of the exercise.

[0426] Specific examples

[0427] The user launches the app and takes a photo of their body with their smartphone camera. The app analyzes the image and obtains the user's body data. The user enters their exercise habits (e.g., jogging three times a week). The emotion recognition engine analyzes the user's facial expressions and tone of voice. The generative AI model generates an optimal meal menu and exercise program based on the prompt text. The app displays the generated menu to the user, who can edit it as needed. If the user shows signs of pain while exercising, the emotion engine detects this and provides feedback (e.g., displaying an encouraging message or reducing the intensity of the exercise).

[0428] In this way, users receive an individually tailored diet plan and support according to their emotional state.

[0429] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0430] Step 1:

[0431] The user takes a photo of their own body shape using the smartphone camera.

[0432] Input: User's body image

[0433] Output: Body shape image data

[0434] Specific operation: The user activates the smartphone camera and takes a full-body photo, which is then saved in the application.

[0435] Step 2:

[0436] The device uses an image processing library (OpenCV) to analyze the body shape image.

[0437] Input: Body shape image data

[0438] Output: Body shape analysis data

[0439] Specific operation: The device analyzes the image using OpenCV and extracts the user's body characteristics (e.g., height, weight, body fat percentage, etc.).

[0440] Step 3:

[0441] The user inputs his or her exercise habits as text data.

[0442] Input: Exercise habits data (e.g., jogging three times a week)

[0443] Output: Exercise habits text data

[0444] Specific operation: The user enters their exercise habits into the application's input form and presses the submit button.

[0445] Step 4:

[0446] The server uses a generative AI model (GPT-4) to select an image that is closest to the desired body type.

[0447] Input: Body shape analysis data, exercise habits text data

[0448] Output: Image data that is close to the desired body shape

[0449] Specific operation: The server inputs body shape analysis data and exercise habit text data into a generative AI model based on the prompt text, and generates an image that closely resembles the desired body shape.

[0450] Step 5:

[0451] The server uses the generated AI model to generate diet meal menus and exercise programs.

[0452] Input: Body shape analysis data, exercise habits text data, image data similar to the body you want to achieve

[0453] Output: Diet meal plan, exercise program, recommended duration

[0454] Specific operation: The server inputs the following prompt sentence into the generative AI model to generate an optimal meal menu and exercise program.

[0455] Based on your body image and exercise habits, please provide the following information:

[0456] 1. The body image that most closely resembles the user's desired body image

[0457] 2. The best diet meal menu (breakfast, lunch, dinner)

[0458] 3. An optimal exercise program (e.g., jogging three times a week, walking 30 minutes daily, etc.)

[0459] 4. Recommended duration

[0460] Step 6:

[0461] The user reviews the generated menu and edits it if necessary.

[0462] Input: Diet meal plan, exercise program, recommended duration

[0463] Output: The edited menu

[0464] What happens: The user reviews the menu generated within the application and edits the meal menu and exercise program as needed.

[0465] Step 7:

[0466] The device uses an emotion recognition engine (Microsoft Azure Cognitive Services) to recognize the user's emotions.

[0467] Input: User's facial expression image, voice tone data

[0468] Output: Emotional state data

[0469] What it does: The device uses the camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed by an emotion recognition engine.

[0470] Step 8:

[0471] The server provides feedback based on the user's emotional state.

[0472] Input: Emotional state data

[0473] Output: Feedback messages, exercise intensity adjustment

[0474] Specific operation: The server generates encouraging messages for the user based on the emotional state data and automatically reduces the intensity of exercise as needed.

[0475] Example 2

[0476] Next, a description will be given of Example 2 of Form 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."

[0477] Conventional exercise support systems have difficulty providing individualized exercise programs that fully consider the user's exercise habits and emotional state. Furthermore, they lack real-time audio support during exercise and functionality that provides feedback based on the user's emotions, making it difficult for users to maintain their motivation to exercise continuously.

[0478] 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.

[0479] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for collecting the user's exercise data, means for analyzing the collected exercise data, means for collecting the user's emotional data, means for analyzing the collected emotional data and adjusting the exercise program, and means for providing voice instructions for the exercise program. This makes it possible to provide an exercise program based on the user's individual exercise habits and emotional state, thereby maintaining the user's motivation to exercise continuously.

[0480] "Means for registering the user's body shape as an image" refers to devices or software for recording the shape and size of the user's body as image data.

[0481] "Generative AI" refers to a system that uses artificial intelligence technology to generate data and information for specific purposes.

[0482] "Means for selecting an image that is close to the desired body shape" refers to an interface or algorithm that allows the user to select an image that is close to the image of the desired body shape.

[0483] "Means for registering a user's exercise habits" refers to devices or software for recording a user's daily exercise patterns and frequency.

[0484] "Means for generating diet meal menus, exercise programs, and duration" refers to a system that automatically creates meal plans and exercise programs according to the user's goals.

[0485] "Means for editing the generated menu" refers to an interface that allows a user to manually modify or adjust the automatically generated meal menu or exercise program.

[0486] "Means for collecting user exercise data" refers to devices or software for recording data such as the type, number, and intensity of exercise performed by the user.

[0487] "Means for analyzing collected athletic data" refers to algorithms or systems used to analyze collected athletic data and evaluate a user's athletic performance and progress.

[0488] "Means for collecting user emotional data" refers to devices or software for recording a user's emotional state from facial expressions and voice.

[0489] "Means for analyzing collected emotional data and adjusting exercise programs" refers to algorithms or systems for analyzing collected emotional data and optimizing exercise programs according to the user's emotional state.

[0490] "Means for providing audio instructions on an exercise menu" refers to a device or software that provides audio guidance to the user on the next exercise menu that should be performed.

[0491] The present invention is a system that provides audio support to a user while exercising and adjusts an exercise program based on the user's emotional state. Specific embodiments of this system are described below.

[0492] First, the user launches an exercise app using a smartphone or wearable device. The smartphone's camera or the wearable device's sensor is used to register an image of the user's body shape. This records the user's body shape and size as image data.

[0493] Next, generative AI is used to select images that are closest to the body the user is aiming for. The generative AI model generates images that are close to the image of the body the user is aiming for, and the user can choose from them.

[0494] To register a user's exercise habits, an application on a smartphone or wearable device is used, which records the user's daily exercise patterns and frequency.

[0495] To generate diet menus, exercises, and durations, the server uses data analysis tools such as Python and TENSORFLOW®, which automatically creates meal plans and exercise programs tailored to the user's goals.

[0496] To edit the generated menu, users use a smartphone application, which allows them to manually modify and adjust the automatically generated meal menu and exercise program.

[0497] To collect user exercise data, sensors in smartphones and wearable devices are used, such as accelerometers and heart rate monitors, to record data such as the type, frequency, and intensity of exercise.

[0498] The server uses Python and TensorFlow to analyze the collected exercise data and evaluate the user's exercise performance and progress.

[0499] To collect user emotional data, the smartphone's camera and microphone are used to record the user's emotional state from their facial expressions and voice.

[0500] The server uses an emotion engine to analyze the collected emotion data and adjust the exercise program, which analyzes the collected emotion data and optimizes the exercise program according to the user's emotional state.

[0501] To provide voice instructions for the exercise menu, the device uses the Google® Text-to-Speech API, which provides the user with a voice prompt for the next exercise they should do.

[0502] Examples:

[0503] A user launches a strength training app on their smartphone. The app gives a voice instruction such as "Next, do 20 sit-ups." After the user does sit-ups, the device uses an accelerometer to record the number of exercises. The server analyzes the data and determines the next exercise menu as "Next, do 15 squats," which is sent to the device. The device then reads the instruction aloud. If the user smiles after the exercise, the device uses a camera to record their facial expression and sends it to the server. The server analyzes the data and incorporates sit-ups more frequently into future exercise programs.

[0504] Example prompt sentence:

[0505] "When the user is doing strength training, create a program that gives voice instructions on what to do next. For example, it could say, 'Next, do 20 sit-ups.' Also, analyze the user's emotional state. If they show a happy expression, incorporate that exercise more frequently into future programs."

[0506] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0507] Step 1:

[0508] The user begins exercising.

[0509] Input: A user launches an exercise app using a smartphone or wearable device.

[0510] Specific action: The user taps the smartphone app and selects "Strength Training."

[0511] Output: A signal to start exercise is sent to the terminal.

[0512] Step 2:

[0513] The device will provide voice instructions for the exercise menu.

[0514] Input: Signal to start exercise and exercise menu sent from the server.

[0515] What it does: The device uses the Google Text-to-Speech API to read aloud, "Next, do 20 sit-ups."

[0516] Output: Recognize the exercise menu that the user should do next.

[0517] Step 3:

[0518] The user performs an exercise.

[0519] Input: Voice commands from the device.

[0520] Specific action: The user performs 20 sit-ups.

[0521] Output: Performing exercise.

[0522] Step 4:

[0523] The terminal collects the user's exercise data.

[0524] Input: User's exercise performance.

[0525] How it works: The device uses an accelerometer and heart rate monitor to record the number and intensity of your exercise.

[0526] Output: Exercise data is saved on the device.

[0527] Step 5:

[0528] The server analyzes the exercise data.

[0529] Input: Exercise data sent from the device.

[0530] Specific operation: The server uses Python and TensorFlow to analyze exercise data and evaluate the user's exercise performance.

[0531] Output: The analysis results are generated.

[0532] Step 6:

[0533] The server determines the next exercise menu and sends it to the terminal.

[0534] Input: Analysis results of movement data.

[0535] Specific operation: Based on the analysis results, the server determines the next exercise menu to be performed and sends that information to the device. For example, it may decide, "Next, do 15 squats."

[0536] Output: The following exercise menu is sent to the device.

[0537] Step 7:

[0538] The device will then provide voice instructions for the next exercise menu.

[0539] Input: Next exercise menu sent from the server.

[0540] What happens: The device again uses the Google Text-to-Speech API to read aloud, "Now do 15 squats."

[0541] Output: Recognize the exercise menu that the user should do next.

[0542] Step 8:

[0543] The user finishes the exercise.

[0544] Input: User indicates intent to end exercise.

[0545] Specific behavior: The user taps the "Exit" button in the app.

[0546] Output: A signal indicating the end of exercise is sent to the terminal.

[0547] Step 9:

[0548] The device collects the user's emotional data.

[0549] Input: Signal to end exercise.

[0550] Specific operation: The device uses a camera and microphone to collect emotional data from the user's facial expressions and voice.

[0551] Output: Emotion data is saved on the device.

[0552] Step 10:

[0553] The server analyzes the emotional data and adjusts the exercise program.

[0554] Input: Emotion data sent from the device.

[0555] Specific operation: The server uses the emotion engine to analyze the collected emotion data and optimize the exercise program according to the user's emotional state.

[0556] Output: A tailored exercise program is generated.

[0557] (Application example 2)

[0558] Next, a description will be given of Application Example 2 of Form 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."

[0559] Conventional exercise support systems and diet support systems only provide menus based on the user's exercise habits and body shape, but they lack the ability to propose meal menus that take into account the user's emotional state or to link the proposed menus with delivery services. This makes it difficult for users to maintain their motivation to exercise and eat consistently.

[0560] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0561] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for analyzing the user's emotional state, means for suggesting a meal menu based on the emotional state, and means for delivering the suggested meal menu in cooperation with a delivery service. This enables the system to suggest an optimal meal menu according to the user's emotional state and to quickly deliver the menu.

[0562] "Means for registering the user's body shape as an image" refers to a device or software for recording the shape and dimensions of the user's body as image data.

[0563] "Means for using generative AI to select an image that is close to the desired body shape" refers to devices or software that use artificial intelligence technology to allow users to select an image of the desired body shape.

[0564] "Means for registering a user's exercise habits" refers to devices or software for recording the type and frequency of a user's daily exercise.

[0565] "Means for generating diet meal menus, exercise programs, and duration" refers to devices or software that automatically create appropriate meal menus, exercise programs, and durations based on the user's goals and current condition.

[0566] "Means for editing the generated menu" refers to devices or software that allow the user to manually modify or adjust the generated meal menu or exercise program.

[0567] "Means for analyzing the user's emotional state" refers to devices or software that analyze the user's facial expressions and feedback to determine their emotional state.

[0568] The "means for proposing a meal menu based on an emotional state" refers to a device or software that takes into account the emotional state of the user and proposes an optimal meal menu.

[0569] The "means for delivering the proposed meal menu in cooperation with a delivery service" refers to a device or software for delivering the proposed meal menu to the user in cooperation with a delivery service.

[0570] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means for analyzing the user's emotional state, means for suggesting a meal menu based on the emotional state, and means for delivering the suggested meal menu in cooperation with a delivery service.

[0571] Hardware and software used

[0572] Hardware

[0573] Smartphone

[0574] server

[0575] Delivery service terminal

[0576] software

[0577] Python

[0578] Speech recognition libraries (e.g. Google Speech-to-Text)

[0579] Sentiment analysis library (e.g., IBM Watson®)

[0580] Generative AI models (e.g., GPT-3 (registered trademark))

[0581] Data processing and calculation

[0582] A means of registering the user's body shape using an image

[0583] Users take a photo of their body using their smartphone camera and send the image data to a server, which stores the image data and uses it for later analysis.

[0584] A method for using generative AI to select an image that is close to the desired body type

[0585] The server uses a generative AI model to generate images of the body the user wants to achieve, and the user selects the desired body shape from the generated images via a smartphone application.

[0586] A means of registering users' exercise habits

[0587] Users use a smartphone application to input the type and frequency of their daily exercise, and this data is sent to a server and stored.

[0588] A means of generating diet menus, exercises, and durations

[0589] The server automatically generates an appropriate meal menu, exercise program, and duration based on the user's body shape data, target body image, and exercise habit data using a generative AI model.

[0590] A way to edit the generated menu

[0591] Users can manually modify and adjust the generated meal menus and exercise programs using a smartphone application, and the edited data is stored on the server.

[0592] A means of analyzing the user's emotional state

[0593] Feedback from users after exercise or meals is collected using the smartphone's camera and microphone, and the server uses an emotion analysis library to analyze this feedback and determine the user's emotional state.

[0594] A method for suggesting meal menus based on emotional state

[0595] Based on the results of the emotion analysis, the server proposes the optimal meal menu according to the user's emotional state. The proposals are automatically generated using a generative AI model.

[0596] A means of delivering the proposed meal menu in collaboration with a delivery service

[0597] The server sends the suggested meal menu to the delivery service's terminal and instructs it to be delivered to the user, who then promptly delivers the suggested menu.

[0598] Examples of concrete examples and prompts

[0599] Specific examples

[0600] When the user is running, they will receive a voice notification if their current pace is not reaching their target time and will be suggested to speed up.

[0601] If the user gives feedback after exercising that it was "fun," the emotion engine will determine this as "happy" and suggest salad and grilled chicken.

[0602] Prompt Sentence Examples

[0603] "When the user is running, analyze their current pace and suggest speeding up if they are not reaching their target time."

[0604] "Analyze user feedback after exercise and suggest appropriate meal plans based on their emotional state."

[0605] The above is an embodiment of the present invention.

[0606] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0607] Step 1:

[0608] The user takes a picture of their body using the smartphone camera and sends the image data to the server. The server stores this image data and uses it for later analysis. The input is the user's body image, and the output is the image data stored on the server.

[0609] Step 2:

[0610] The server uses a generative AI model to generate an image of the body the user desires. The user selects the desired body shape from the generated images through a smartphone application. The input is the user's body shape image and the desired body characteristics, and the output is an image of the desired body shape selected by the user.

[0611] Step 3:

[0612] Users use a smartphone application to input the type and frequency of their daily exercise. This data is sent to and stored on a server. The input is the user's exercise habit data, and the output is the exercise habit data stored on the server.

[0613] Step 4:

[0614] The server generates an appropriate meal menu, exercise program, and duration based on the user's body shape data, image of the target body, and exercise habit data. These menus are automatically created using a generative AI model. The input is the user's body shape data, image of the target body, and exercise habit data, and the output is the generated meal menu and exercise program.

[0615] Step 5:

[0616] Users can manually modify and adjust the generated meal menu and exercise program using a smartphone application. The edited data is saved on the server. The input is the user's edited content, and the output is the edited menu saved on the server.

[0617] Step 6:

[0618] The system collects feedback from users after exercise or meals using the smartphone's camera and microphone. The server uses an emotion analysis library to analyze this feedback and determine the user's emotional state. The input is the user's feedback data, and the output is the analyzed emotional state.

[0619] Step 7:

[0620] The server uses the results of emotion analysis to suggest optimal meal plans based on the user's emotional state. The suggestions are automatically generated using a generative AI model. The input is the analyzed emotional state, and the output is a suggested meal plan.

[0621] Step 8:

[0622] The server sends the suggested meal menu to the delivery service terminal and instructs it to be delivered to the user. The delivery service then promptly delivers the suggested meal. The input is the suggested meal menu, and the output is the meal delivered to the user.

[0623] 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.

[0624] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0625] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0626] 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.

[0627] [Second embodiment]

[0628] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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).

[0634] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0640] "Example 1"

[0641] In the system of the present invention, the user takes a photo of their own body shape using a digital camera or smartphone camera and registers the image in the system. Next, from multiple body shape images provided in the system, the user selects the one that most closely resembles the body image they are aiming for. In addition, the user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes daily, etc.) in the system. Based on this information, the system uses generative AI to generate a diet meal menu, exercise program, and duration that are optimal for the user. The generated menu can be edited by the user as needed.

[0642] "Example 2"

[0643] The system of the present invention provides audio support when the user is exercising. For example, when doing strength training, the system reads out the next exercise to be done (e.g., "Next, do 20 sit-ups"). When running, the system checks the user's pacing and suggests speeding up or slowing down as necessary (e.g., "You may not be able to reach your target time at your current pace. Try picking up the pace a little"). This allows the user to continue exercising appropriately.

[0644] The processing flow of each embodiment will be described below.

[0645] "Example 1"

[0646] Step 1: The user takes a photo of their body using a digital camera or smartphone camera. The image is then uploaded to the system.

[0647] Step 2: From the body shape images provided in the system, the user selects the one that most closely matches the desired body image. This selection is performed on the system's user interface.

[0648] Step 3: The user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system. This registration is also done through the system's user interface.

[0649] Step 4: Based on this information, the system uses generative AI to generate the optimal diet menu, exercise program, and duration for the user. The generated menu can be edited by the user as needed.

[0650] "Example 2"

[0651] Step 1: When the user starts exercising, the system provides audio support.

[0652] Step 2: When performing strength training, the system will read out what to do next (e.g., "Next, do 20 sit-ups").

[0653] Step 3: When you run, the system monitors your pacing and suggests speeding up or slowing down as needed (e.g., "Your current pace may not meet your target time. Try picking up the pace a bit.").

[0654] Example 1

[0655] Next, a description will be given of Example 1 of Form 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."

[0656] Conventional diet systems have difficulty generating individual diet plans based on a user's body shape and exercise habits. Furthermore, they lack the ability to edit the generated plan or support functions during exercise, making it difficult for users to continue dieting.

[0657] 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.

[0658] In this invention, the server includes a means for registering a user's body shape as an image, a means for presenting a plurality of body shape images and allowing the user to select a desired body shape, a means for registering the user's exercise habits, a means for generating a diet meal menu, an exercise program, and its duration using a generative AI model based on the user's body shape images, desired body shape, and exercise habits, and a means for editing the generated menu, which enables the generation and editing of a diet plan tailored to the individual needs of the user.

[0659] "Means for registering the user's body shape as an image" refers to a function that allows the user to take a photo of their body shape using a digital camera or a smartphone camera and upload the image to the system.

[0660] The "means for presenting a plurality of body shape images and allowing the user to select the body shape they are aiming for" is a function that presents a plurality of body shape images prepared within the system to the user and allows the user to select the one that is closest to the body shape they are aiming for.

[0661] "Means for registering user's exercise habits" refers to a function that allows users to input their own exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system and register that information.

[0662] "Means for generating diet meal menus, exercise programs, and their duration using a generative AI model" refers to a function that uses a generative AI model to generate optimal diet meal menus, exercise programs, and their duration based on the user's body image, desired body image, and exercise habits.

[0663] The "means for editing the generated menu" is a function that allows the user to check the generated diet plan and edit the meal menu and exercise program as necessary.

[0664] This invention begins when a user takes a photo of their body using a digital camera or smartphone camera and registers the image in the system. The user launches the smartphone's camera app and takes a photo of their body. The captured image is uploaded from the device to the system, and the server receives the uploaded image and stores it in a database. At this time, the quality of the image is checked using an image processing library (e.g., OpenCV).

[0665] Next, the server presents the user with multiple body shape images prepared in the system. The user selects the one that most closely resembles the desired body shape from the presented body shape images. The ID of the selected body shape image is sent from the device to the server, and the server stores the ID in a database.

[0666] Users input their exercise habits on their device. For example, they can enter information such as "I jog three times a week" or "I walk 30 minutes every day." The entered exercise data is sent from the device to a server, which then stores it in a database.

[0667] The server obtains information about the user's body image, desired body shape, and exercise habits, and inputs a prompt into a generative AI model (e.g., GPT-4) to request the generation of a diet plan. An example of a prompt is, "Based on the user's body image, desired body shape, and exercise habits, please generate the optimal diet meal menu, exercise program, and duration." Based on the prompt, the generative AI model generates the optimal meal menu, exercise program, and duration. The generated diet plan is then stored in a database by the server.

[0668] The user can check the generated diet plan on their device and edit the meal menu and exercise program as needed. For example, they can change the breakfast menu from oatmeal to yogurt. The edited content is sent from the device to the server, which updates the database.

[0669] In this way, the system allows users to easily create a diet plan tailored to their goals and edit it as needed.

[0670] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0671] Step 1: Registering a user's body shape image

[0672] Users simply open the camera app on their smartphone and take a photo of their body shape.

[0673] Input: Body image taken by the user

[0674] The device displays the captured image on the system's upload screen, and the user presses the "Upload" button.

[0675] The server receives the uploaded images and checks the quality of the images using an image processing library (e.g., OpenCV).

[0676] Output: Quality-confirmed body shape images

[0677] The server stores the verified images in a database.

[0678] Step 2: Choose your desired body image

[0679] The server retrieves multiple body shape images from the database and displays them on the user's terminal.

[0680] Input: Multiple body shape images stored in a database

[0681] The user taps to select the desired body shape from the displayed body shape images.

[0682] The terminal transmits the ID of the selected body shape image to the server.

[0683] Output: ID of the body shape image selected by the user

[0684] The server stores the ID of the selected body shape image in a database.

[0685] Step 3: Register your exercise habits

[0686] Users enter their exercise habits into an input form on the device (e.g., "I jog three times a week").

[0687] Input: Exercise habit data entered by the user

[0688] The device transmits the entered exercise habit data to the server.

[0689] Output: Exercise habits data

[0690] The server stores exercise habit data in a database.

[0691] Step 4: Generate an optimal diet plan

[0692] The server retrieves information about the user's body image, desired body image, and exercise habits from a database.

[0693] Input: User's body image, desired body image, exercise habits

[0694] The server inputs the following prompt to the generative AI model (e.g., GPT-4):

[0695] "Generate the optimal diet menu, exercise program, and duration based on the user's body image, desired body image, and exercise habits."

[0696] The generative AI model generates optimal meal plans, exercise programs, and their duration based on the prompt text.

[0697] Output: Generated diet plan (meal menu, exercise program, duration)

[0698] The server stores the generated diet plan in a database.

[0699] Step 5: Edit the menu

[0700] The user can check the generated diet plan on their device.

[0701] Input: Generated diet plan

[0702] Users can edit their meal menus and exercise programs as needed (e.g., change oatmeal to yogurt for breakfast).

[0703] The terminal transmits the edited content to the server.

[0704] Output: Edited diet plan

[0705] The server updates the edited diet plan to the database.

[0706] (Application example 1)

[0707] Next, a description will be given of Application Example 1 of Form 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."

[0708] Traditional diet and fitness programs often offer generic menus and exercise plans, making it difficult to provide programs optimized for each user's body shape and exercise habits. Providing individually optimized training programs and meal menus also places a heavy burden on trainers at fitness facilities. Furthermore, the lack of real-time feedback makes it difficult for users to train effectively.

[0709] 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.

[0710] In this invention, the server includes a means for registering an image of the user's body shape, a means for selecting an image that is close to the desired body shape using a generation AI, and a means for registering the user's exercise habits, which makes it possible to provide individually optimized training programs and meal menus.

[0711] "Means for registering an image of the user's body shape" refers to a means for a user to take a photo of their body shape using a digital camera or a smartphone camera and register that image in the system.

[0712] "Means for using generative AI to select an image that is closest to the desired body shape" refers to a means for using generative AI to select the image that is closest to the desired body shape from multiple body shape images prepared within the system.

[0713] "Means for registering user's exercise habits" refers to the means by which a user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system.

[0714] "Means for generating diet meal menus, exercise programs, and durations" refers to means for generating optimal diet meal menus, exercise programs, and durations for a user using a generative AI based on the user's body image and exercise habits.

[0715] The "means for editing the generated menu" is a means for the user to edit the generated diet meal menu and exercise program as needed.

[0716] "A means for providing individually optimized training programs and meal menus to fitness facility members by allowing them to take a photo of their own body shape, select the body image they are aiming for, and register their exercise habits" is a means for providing individually optimized training programs and meal menus to fitness facility members by allowing them to take a photo of their own body shape, select the body image they are aiming for, and register their exercise habits.

[0717] The "means of collaborating with in-facility trainers to receive real-time feedback" refers to a means of collaborating with in-facility trainers to receive real-time feedback on training and diet.

[0718] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating diet meal menus, exercises, and durations, means for editing the generated menus, means for providing individually optimized training programs and meal menus by allowing members of a fitness facility to take a photo of their own body shape, select the desired body image, and register their exercise habits, and means for receiving real-time feedback in collaboration with trainers within the facility.

[0719] 1. System Program

[0720] The program of this system performs the following processing.

[0721] 2. Explanation of program processing

[0722] The server receives body images taken by users using their smartphone cameras and processes them. Specifically, it uses Python's PIL library to read the images and preprocess them as needed. It then uses a generative AI model to select the image that most closely resembles the user's desired body shape. This generative AI model uses a pre-trained Keras model.

[0723] The user's exercise habits are registered in text format on a server. This includes information such as how many times a week the user exercises and what type of exercise they do. Based on this information, the generative AI model generates the optimal diet menu, exercise program, and duration for the user.

[0724] The generated menu can be viewed and edited as needed through a smartphone app, and users can also connect with trainers at the fitness facility to receive real-time feedback, including training progress and dietary advice.

[0725] 3. Examples of concrete examples and prompts

[0726] As a concrete example, consider a user who jogs three times a week. Based on this information, the generative AI model generates the optimal meal plan and exercise program for the user. Below is an example of a prompt to input to the generative AI model.

[0727] User body image: user_image.jpg

[0728] Target body shape: target_shape.jpg

[0729] Exercise habits: Jogging three times a week

[0730] Use this information to create the perfect diet and exercise program for you.

[0731] This prompt sentence can be used as input into a generative AI model to generate the optimal menu for the user.

[0732] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0733] Step 1:

[0734] The user takes a photo of their body shape using the smartphone camera and saves the image on the device.

[0735] Input: User's body image

[0736] Output: Body shape image saved on the device

[0737] Specific operation: The user launches the camera app on their smartphone, takes a photo of their body, and then saves the image on their device.

[0738] Step 2:

[0739] The terminal uploads the saved body shape image to the server.

[0740] Input: Body shape image saved on the device

[0741] Output: Body shape image uploaded to the server

[0742] How it works: The device uploads the stored body image to the server via an Internet connection, using an HTTP request.

[0743] Step 3:

[0744] The server receives the uploaded body shape images and processes the images.

[0745] Input: Body image uploaded to the server

[0746] Output: Preprocessed body shape image

[0747] What it does: The server uses Python's PIL library to load the image and perform preprocessing such as resizing and noise removal if necessary.

[0748] Step 4:

[0749] The server uses a generative AI model to select the image that most closely resembles the user's desired body image.

[0750] Input: Preprocessed body shape image

[0751] Output: Image that is closest to the desired body image

[0752] How it works: The server uses a pre-trained Keras model to compare images of the user's body shape with images of the desired body shape and selects the image that is closest.

[0753] Step 5:

[0754] The user inputs their exercise habits into the terminal and sends them to the server.

[0755] Input: User's exercise habits

[0756] Output: Exercise habit information sent to the server

[0757] Specific operation: The user enters their exercise habits (e.g., jogging three times a week) through a smartphone app and sends that information to the server.

[0758] Step 6:

[0759] The server uses a generative AI model to generate optimal diet meal menus and exercise programs based on the user's body image and exercise habit information.

[0760] Input: User's body image, exercise habits information

[0761] Output: Optimal diet and exercise program

[0762] Specific operation: The server inputs prompt sentences into the generative AI model and generates optimal diet meal menus and exercise programs for the user. Examples of prompt sentences are as follows:

[0763] User body image: user_image.jpg

[0764] Target body shape: target_shape.jpg

[0765] Exercise habits: Jogging three times a week

[0766] Use this information to create the perfect diet and exercise program for you.

[0767] Step 7:

[0768] The server sends the generated menu to the user's terminal, where the user edits it as needed.

[0769] Input: Optimal diet and exercise program

[0770] Output: The menu sent to the user's device.

[0771] Specific operation: The server sends the generated menu to the user's device, and the user can check the menu through the smartphone app and edit it as necessary.

[0772] Step 8:

[0773] Users train in fitness facilities and connect with trainers to receive real-time feedback.

[0774] Input: Training progress, dietary advice

[0775] Output: Real-time feedback

[0776] How it works: Users train in fitness facilities, and trainers monitor users' progress through a dedicated application and provide real-time feedback.

[0777] Example 2

[0778] Next, a description will be given of Example 2 of Form 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."

[0779] Conventional exercise support systems have difficulty collecting and analyzing users' exercise data in real time and providing appropriate voice instructions. They also lack the ability to generate specific exercise and meal plans to help users achieve their desired body shape and provide support for exercising based on those plans. This makes it difficult for users to continue exercising effectively.

[0780] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for registering the user's body shape as an image, means for selecting an image that resembles the desired body shape using a generative AI model, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for collecting the user's exercise data, means for analyzing the collected exercise data, means for generating voice instructions based on the analysis results, means for transmitting the generated voice instructions to the user's terminal, and means for playing the voice instructions on the user's terminal. This allows the user to receive appropriate exercise instructions in real time and effectively continue exercising to get closer to their desired body shape.

[0781] "Means for registering the user's body shape as an image" refers to a function for recording the user's current body shape as image data using a device such as a camera or scanner.

[0782] "Means of using a generative AI model to select an image that is close to the desired body shape" is a function that utilizes a generative AI model to present images that are close to the ideal body shape that the user is aiming for, allowing the user to select from among them.

[0783] "Means for registering a user's exercise habits" refers to a function that records information such as the type, frequency, and duration of the exercise a user engages in on a daily basis in a database.

[0784] "Means for generating diet meal menus, exercise plans, and duration" refers to a function that automatically creates an appropriate meal menu, exercise plan, and duration based on the user's goals, current body shape, and exercise habits.

[0785] "Means for editing the generated menu" refers to a function that allows users to manually modify and adjust the automatically generated meal menu and exercise plan.

[0786] "Means for collecting user exercise data" refers to a function that uses a wearable device, smartphone, etc. to obtain exercise data such as the user's heart rate, speed, and distance in real time.

[0787] "Means for analyzing collected exercise data" refers to a function that evaluates and analyzes the user's exercise status and performance based on the collected exercise data.

[0788] The "means for generating voice instructions based on the analysis results" is a function that generates instructions to provide appropriate exercise instructions and advice to the user by voice based on the results of data analysis.

[0789] "Means for transmitting the generated voice instructions to the user's terminal" refers to a function for transmitting the generated voice instructions to the user's smartphone or tablet via a communication means such as the Internet or Bluetooth.

[0790] "Means for playing voice instructions on the user's device" refers to a function that plays voice instructions received by the user's smartphone or tablet through a speaker or earphones.

[0791] This invention is a system that provides audio support to users while exercising. The system registers the user's body shape as an image, uses a generative AI model to select an image that is similar to the desired body shape, and registers the user's exercise habits. Furthermore, the system includes a function to generate diet meal menus, exercises, and durations, and to edit the generated menus.

[0792] The server collects and analyzes the user's exercise data in real time. The hardware used includes wearable devices (e.g., smartwatches and fitness trackers) for collecting the user's exercise data. The software includes algorithms for data analysis and generative AI models (e.g., OpenAI's GPT-4) for voice synthesis.

[0793] The device (user's smartphone or tablet) conveys the voice instructions sent from the server to the user. Specifically, the device plays back the voice data received from the server and provides the user with advice on the next exercise menu and pacing.

[0794] The user receives voice instructions from the device while exercising and continues exercising accordingly. For example, when doing strength training, the user may receive instructions such as "Next, do 20 sit-ups," and then perform sit-ups. When running, if the current pace is not reaching the target time, the user may receive advice such as "Try to increase your pace a little," and adjust the pace accordingly.

[0795] As a concrete example, consider the case where a user is running. The user puts on a smartwatch, picks up their smartphone, and starts running. The smartwatch collects data such as the user's heart rate and speed, and sends it to a server. The server analyzes this data and detects that the user's current pace is not enough to reach their target time. The server uses a generative AI model to generate a voice instruction saying, "Your current pace may not reach your target time. Try picking up the pace a little," and sends it to the device. The device then plays back this voice instruction and relays it to the user.

[0796] Example prompt sentence:

[0797] "When a user is running, if their current pace is not enough to reach their target time, generate voice prompts suggesting they speed up."

[0798] This system allows users to continue to exercise appropriately.

[0799] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0800] Step 1:

[0801] Before starting exercise, the user puts on a wearable device (e.g., a smartwatch) and launches a dedicated smartphone app. The wearable device collects exercise data such as the user's heart rate, speed, and distance in real time. This data is sent to the user's smartphone via Bluetooth or Wi-Fi. The input is the user's exercise data, and the output is the exercise data sent to the smartphone.

[0802] Step 2:

[0803] The server receives the exercise data sent from the user's smartphone. The server uses a data analysis algorithm to evaluate the user's exercise status in real time. For example, it determines whether the user's pace while running is within the target time. The input is the exercise data sent from the smartphone, and the output is the analysis result.

[0804] Step 3:

[0805] The server uses a generative AI model (e.g., generative AI model) to generate appropriate voice instructions for the user. For example, if the user's pace is slow, the server generates a voice instruction such as, "You may not reach your target time at your current pace. Try picking up the pace a little." The input is the analysis result, and the output is the generated voice instruction.

[0806] Step 4:

[0807] The server sends the generated voice instructions to the user's smartphone via an internet connection. The input is the generated voice instructions, and the output is the voice instructions sent to the smartphone.

[0808] Step 5:

[0809] The terminal (user's smartphone) plays the voice instructions received from the server. The user can hear the voice instructions through the smartphone's speaker or earphones. The input is the voice instructions received from the server, and the output is the played voice instructions.

[0810] Step 6:

[0811] The user continues exercising by following the voice instructions from the device. For example, if the user receives the instruction "Next, do 20 sit-ups," the user will do 20 sit-ups. If the user receives the instruction "Try to increase your pace a little," the user will increase the pace of their running. The input is the played voice instructions, and the output is the user's exercise behavior.

[0812] (Application example 2)

[0813] Next, a description will be given of Application Example 2 of Form 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."

[0814] While conventional exercise support systems can provide appropriate exercise menus based on the user's exercise habits and body shape, they have issues with being unable to provide efficient route guidance or time management for delivery work.In addition, there is a lack of means to provide appropriate advice when delivery times are likely to be delayed, so there is a need to improve the efficiency of delivery work.

[0815] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0816] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating diet meal menus, exercises, and periods, means for editing the generated menus, means for registering delivery destination information, means for generating delivery routes, means for managing delivery times, means for providing voice guidance on delivery destinations and routes, and means for encouraging the delivery driver to speed up if the delivery time is likely to be delayed. This enables efficient route guidance and time management in delivery work, and can provide appropriate advice if the delivery time is likely to be delayed.

[0817] "Means for registering the user's body shape as an image" refers to a method for saving the shape and size of the user's body as image data.

[0818] "Method of using generative AI to select an image that is close to the desired body" is a method that uses artificial intelligence to allow users to select an image of the body they are aiming for.

[0819] "Means for registering a user's exercise habits" refers to a method for recording the type and frequency of exercise that a user does on a daily basis.

[0820] The "means for generating diet meal menus, exercise programs, and durations" is a method for creating meal plans, exercise programs, and durations that match the user's goals.

[0821] The "means for editing the generated menu" is a method for modifying the generated meal plan or exercise program according to the user's wishes.

[0822] The "means for registering delivery destination information" is a method for recording information such as the address and contact details of the delivery location.

[0823] The "means for generating a delivery route" is a method for calculating the optimal route for efficiently visiting multiple delivery destinations.

[0824] "Means for managing delivery times" refers to a method for recording and managing the estimated arrival time and actual arrival time at each delivery destination.

[0825] "A means for providing delivery destination and route guidance by voice" is a method for providing voice instructions to delivery personnel regarding the next delivery destination and the optimal route.

[0826] The "means for encouraging delivery staff to speed up when delivery time is likely to be delayed" is a method for instructing delivery staff by voice to speed up when delivery is behind schedule.

[0827] The system for implementing this invention has the functions of registering an image of the user's body shape, using a generation AI to select an image that closely resembles the desired body shape, and registering the user's exercise habits. It also includes functions to generate diet meal menus, exercises, and durations, and to edit the generated menus. It also has functions to register delivery destination information, generate delivery routes, and manage delivery times. It also includes functions to provide voice guidance on delivery destinations and routes, and to encourage the delivery driver to speed up if they are likely to be late.

[0828] System program processing

[0829] The server acquires image data using a smartphone or camera to register the user's body shape as an image and stores it in a database. Using a generative AI model, the server generates and presents prompts to the user to select an image that best represents the body they desire. To register the user's exercise habits, the server collects exercise data from smartphone apps and wearable devices and stores it in a database.

[0830] To generate a diet menu, exercise plan, and duration, the server generates an optimal plan based on the user's goals, current body shape, and exercise habits. To edit the generated menu, the user can modify it using a smartphone app.

[0831] To register delivery destination information, the server stores the delivery destination address and contact information in a database. To generate delivery routes, the server uses map data to calculate the optimal route. To manage delivery times, the server records and manages the estimated and actual arrival times at each delivery destination.

[0832] The server uses voice synthesis technology to provide voice guidance on delivery destinations and routes to the delivery person, and if delivery time is likely to be delayed, the server will give voice instructions to the delivery person to speed up.

[0833] Specific examples

[0834] For example, if a delivery person is delivering to "1-1-1, Shibuya-ku, Tokyo," the server will do the following:

[0835] 1. From the list of delivery addresses, select "1-1-1, Shibuya-ku, Tokyo."

[0836] 2. A voice will say, "Your next delivery destination is 1-1-1, Shibuya-ku, Tokyo. We will guide you to the best route."

[0837] 3. If the delivery time is likely to be delayed, the system will provide a voice message saying, "At the current pace, the delivery time is likely to be delayed. Please speed up the pace a little."

[0838] Example prompts to input to a generative AI model:

[0839] "Your next delivery is to 1-1-1, Shibuya-ku, Tokyo. We'll guide you to the best route. At your current pace, your delivery time will likely be delayed. Please speed up a bit."

[0840] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0841] Step 1:

[0842] The server registers the user's body shape as an image. The user takes a photo of their body using a smartphone or camera and sends the image data to the server. The server stores the received image data in a database. The input is the user's body shape image, and the output is the image data stored in the database.

[0843] Step 2:

[0844] The server uses a generative AI model to generate a prompt to select an image that best suits the desired body shape. When the user inputs the desired body features, the server uses the generative AI model to generate an image based on those features and presents it to the user. The input is the user's desired body features, and the output is the generated image.

[0845] Step 3:

[0846] The server registers the user's exercise habits. The user inputs daily exercise data using a smartphone app or wearable device and sends the data to the server. The server stores the received exercise data in a database. The input is the user's exercise data, and the output is the exercise data stored in the database.

[0847] Step 4:

[0848] The server generates diet meal menus, exercises, and durations. Based on the user's goals, current body shape, and exercise habits, the server generates an optimal diet plan and exercise program and presents it to the user. The input is the user's goals, body shape, and exercise habits, and the output is the generated diet plan and exercise program.

[0849] Step 5:

[0850] The user edits the generated menu. Using a smartphone app, the user modifies the generated meal plan and exercise program and sends the modified data to the server. The server stores the received modified data in a database. The input is the user's modified data, and the output is the modified data stored in the database.

[0851] Step 6:

[0852] The server registers the delivery address information. The user enters the delivery address and contact information and sends that information to the server. The server saves the received delivery address information in a database. The input is the delivery address and contact information, and the output is the delivery address information saved in the database.

[0853] Step 7:

[0854] The server generates a delivery route. Based on the information of the delivery destination, the server uses map data to calculate the optimal route and presents it to the delivery person. The input is the delivery destination information, and the output is the optimal delivery route.

[0855] Step 8:

[0856] The server manages delivery times. It records and manages the estimated and actual arrival times at each delivery destination. The input is delivery destination information and current location information, and the output is the estimated and actual arrival times.

[0857] Step 9:

[0858] The server provides voice guidance on delivery destinations and routes. It uses speech synthesis technology to provide voice instructions to the delivery person on the next delivery destination and the optimal route. The input is delivery route information, and the output is voice guidance.

[0859] Step 10:

[0860] The server prompts the delivery person to speed up if the delivery time is likely to be delayed. If the delivery is behind schedule, the server gives a voice command to the delivery person to speed up. The input is the current location information and the estimated arrival time, and the output is a voice command to speed up.

[0861] 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.

[0862] "Example 1"

[0863] In one embodiment of the present invention, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, and movements during exercise. For example, if the user shows signs of pain while exercising, the emotion engine will capture this information and determine that the user is experiencing excessive stress. This information is fed back to the system, which then adjusts the content and tone of the voice support. Specifically, the system may provide encouraging messages to the user or automatically reduce the intensity of the exercise.

[0864] "Example 2"

[0865] The emotion engine also adjusts diet meal menus and exercise programs based on the user's emotional state. For example, if a user shows a happy expression after exercising, the emotion engine will pick up on that information and determine that the user likes that exercise. This information is fed back to the system, and that exercise will be more frequently incorporated into recommended exercise programs. Also, if a user shows a satisfied expression after eating, that meal menu will be determined to be favorable to the user, and similar meal menus will be recommended.

[0866] The processing flow of each embodiment will be described below.

[0867] "Example 1"

[0868] Step 1: The user begins exercising.

[0869] Step 2: The emotion engine estimates the user's emotions based on their facial expressions, tone of voice, and movements during exercise.

[0870] Step 3: If the emotion engine determines that the user is experiencing excessive stress, it feeds that information back into the system.

[0871] Step 4: The system adjusts the content and tone of the audio support, such as providing encouraging messages or automatically reducing the intensity of the exercise.

[0872] "Example 2"

[0873] Step 1: The user finishes the exercise.

[0874] Step 2: The emotion engine infers emotions from the user's facial expressions.

[0875] Step 3: If the emotion engine determines that the user likes the exercise, it feeds that information back into the system.

[0876] Step 4: Adjust the system to include the exercise frequently in your recommended exercise program.

[0877] Step 5: The user finishes eating.

[0878] Step 6: The emotion engine infers emotions from the user's facial expressions.

[0879] Step 7: If the emotion engine determines that the meal menu is favorable to the user, it feeds that information back into the system.

[0880] Step 8: The system adjusts to recommend similar meals.

[0881] Example 1

[0882] Next, a description will be given of Example 1 of Form 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."

[0883] Conventional diet support systems have had issues with providing effective diet support due to the difficulty of providing individualized menus based on the user's body shape and exercise habits, and a lack of feedback that takes into account the user's emotions and stress levels. Furthermore, it is not possible to detect the stress and fatigue the user feels during exercise in real time and provide appropriate support, making it difficult to maintain the user's motivation.

[0884] 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.

[0885] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means including an emotion engine for recognizing the user's emotions, means for the emotion engine to analyze the user's facial expressions, tone of voice, and movements during exercise to estimate emotions, means for adjusting the content and tone of audio support based on emotions, and means for automatically adjusting exercise intensity. This makes it possible to not only provide an individualized menu based on the user's body shape and exercise habits, but also to detect the user's emotions and stress level in real time and provide support accordingly.

[0886] "Means for registering a user's body shape using an image" refers to a function that allows a user to take a photo of their body shape using a digital camera or a smartphone camera and upload the image to the system.

[0887] "Means of using generative AI to select an image that is closest to the desired body shape" is a function that uses generative AI to select the image that is closest to the body shape the user is aiming for from multiple body shape images prepared within the system.

[0888] "Means for registering user's exercise habits" refers to a function that allows a user to input their own exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system and save that information.

[0889] "Means for generating diet meal menus, exercise programs, and duration" refers to a function that uses a generative AI model to create optimal diet meal menus, exercise programs, and durations based on the user's body image, desired body image, and exercise habits.

[0890] The "means for editing the generated menu" is a function that allows the user to check the generated diet meal menu and exercise program, and make changes or additions as necessary.

[0891] "Means including an emotion engine that recognizes the user's emotions" refers to a function that incorporates an emotion engine into the system to estimate the user's emotions from their facial expressions, tone of voice, movements during exercise, etc.

[0892] "Means for the emotion engine to analyze the user's facial expressions, tone of voice, and movements during exercise to infer emotions" refers to a function in which the emotion engine analyzes data collected through the camera and microphone to infer the user's emotional state.

[0893] "Means for adjusting the content and tone of voice support based on emotions" refers to a function that changes the content and tone of voice support according to the emotional state of the user estimated by the emotion engine, and provides messages of encouragement to the user.

[0894] "Means for automatically adjusting exercise intensity" is a function that allows the emotion engine to appropriately change the intensity of exercise based on the user's emotional state, thereby reducing the user's stress and fatigue.

[0895] MODE FOR CARRYING OUT THE INVENTION

[0896] The present invention begins with the user taking a photo of their own body shape using a digital camera or smartphone camera and registering the image in the system. Next, the user selects the body shape image that most closely resembles their desired body shape from the multiple body shape images available in the system. The user also registers their exercise habits (e.g., jogging three times a week, walking 30 minutes daily, etc.) in the system. Based on this information, the system uses a generative AI model to generate a diet meal menu, exercise program, and duration that are optimal for the user. The generated menu can be edited by the user as needed.

[0897] In one embodiment of the present invention, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, and movements during exercise. For example, if the user shows signs of pain while exercising, the emotion engine will capture this information and determine that the user is experiencing excessive stress. This information is fed back to the system, which then adjusts the content and tone of the voice support. Specifically, the system may provide encouraging messages to the user or automatically reduce the intensity of the exercise.

[0898] Hardware and software used

[0899] This system uses the following hardware and software:

[0900] Digital camera or smartphone camera: Used to take a picture of the user's body shape.

[0901] Terminal (PC, smartphone, tablet, etc.): Used by users to access the system and register images and exercise habits.

[0902] Server: Stores the data sent by the user and generates diet menus and exercise programs using generative AI models.

[0903] Generative AI model: Generates optimal diet menus and exercise programs based on the user's body image, desired body image, and exercise habits.

[0904] Emotion engine: Analyzes the user's facial expressions, tone of voice, and movements during exercise to estimate emotions.

[0905] Specific examples

[0906] Example 1: Registering a body shape image

[0907] Users take a photo of their body with their smartphone camera and upload the image to the system, where the server receives the image and stores it in the user's profile.

[0908] Example 2: Choosing the body image you want to achieve

[0909] The user selects the body shape image they want to achieve from the body shape images in the system and sends that information to the server, which then stores the selected image as the user's goal.

[0910] Example 3: Registering exercise habits

[0911] A user enters their exercise habits, such as "I jog three times a week," into the system and sends them to the server, which adds this information to the user's profile.

[0912] Example 4: Generating a diet menu

[0913] The server uses a generative AI model to generate a one-week diet menu based on the user's body image, desired body shape, and exercise habits. The generated menu is sent to the user's device, where the user can edit it as needed.

[0914] Example 5: Emotional Engine Feedback

[0915] If the user shows any facial expression of pain during exercise, the emotion engine captures that information and sends it back to the server, which then generates an encouraging message and sends it to the device. The intensity of the exercise is also automatically reduced.

[0916] Prompt Sentence Examples

[0917] "Upload a photo of your body and select the body image you want to achieve. Next, enter your exercise habits and we'll generate the optimal diet menu for you. We'll also recognize your emotions during exercise and provide feedback."

[0918] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0919] Step 1:

[0920] Users take a photo of their body shape using a digital camera or smartphone camera.

[0921] Input: User's body image

[0922] Output: Photographed body shape image file

[0923] Specific operation: The user launches the camera app and takes a photo from the front so that the entire body is captured. After taking the photo, the image file is saved.

[0924] Step 2:

[0925] The user logs in to the system from their terminal and uploads the images they have taken.

[0926] Input: Body shape image file

[0927] Output: Body shape image saved on the server

[0928] Specific operation: The user accesses the system login screen, enters their user ID and password to log in. After logging in, they move to the image upload screen, select the saved image file, and click the upload button.

[0929] Step 3:

[0930] The server stores the received image and associates it with the user's profile.

[0931] Input: Uploaded body shape image file

[0932] Output: Body image associated with the user's profile

[0933] Specific operation: The server receives the uploaded image file and stores it in the database. The stored image is associated with the user ID.

[0934] Step 4:

[0935] The user browses through multiple body shape images prepared within the system.

[0936] Input: Body shape image data in the system

[0937] Output: A list of body shape images to be viewed by the user

[0938] Specific operation: The user accesses the body shape image selection screen and views the body shape images displayed in slideshow format.

[0939] Step 5:

[0940] Select the image that most closely resembles the body image the user is aiming for.

[0941] Input: User-selected body shape image

[0942] Output: Selected body shape image data

[0943] Specific actions: The user selects the image that most closely resembles the desired body image and clicks the Select button.

[0944] Step 6:

[0945] The server stores the selected image as the user's goal.

[0946] Input: Selected body shape image data

[0947] Output: Target body shape image saved in the user's profile

[0948] Specific operation: The server receives the selected image data and stores it in the user's profile as a target body shape image.

[0949] Step 7:

[0950] Users enter their exercise habits into the system via their device.

[0951] Input: User's exercise habits (e.g., jogging three times a week)

[0952] Output: Input exercise habit data

[0953] Specific operation: The user accesses the exercise habit input screen, enters their exercise habit in the text box, and then clicks the save button.

[0954] Step 8:

[0955] The server receives the entered exercise habits information and adds it to the user's profile.

[0956] Input: Entered exercise habit data

[0957] Output: Exercise habits added to the user's profile

[0958] Specific operation: The server receives the entered exercise habit data and stores it in a database. The stored data is linked to the user ID.

[0959] Step 9:

[0960] The server uses a generative AI model to generate the optimal diet meal menu, exercise program, and duration based on the user's body image, desired body shape, and exercise habits.

[0961] Input: User's body image, desired body image, exercise habits

[0962] Output: Generated diet menu and exercise program

[0963] Specific operation: The server runs the generative AI model and creates a menu based on the input data, taking into account calorie calculations and nutritional balance.

[0964] Step 10:

[0965] The server sends the generated menu to the user's device.

[0966] Input: Generated diet meal menu and exercise program

[0967] Output: The menu sent to the user's device

[0968] Specific operation: The server sends the generated menu to the user's terminal in PDF format or similar.

[0969] Step 11:

[0970] Review the menu the user receives and edit it if necessary.

[0971] Input: Generated diet meal menu and exercise program

[0972] Output: Edited menu

[0973] Specific actions: The user accesses the menu editing screen, changes ingredients and adds exercise, and then clicks the save button after editing.

[0974] Step 12:

[0975] The user uses the system while exercising.

[0976] Input: User's exercise data (e.g. facial expressions, tone of voice, movements during exercise)

[0977] Output: Real-time collected exercise data

[0978] Specific operation: The user exercises while holding the smartphone, and data is collected via the camera and microphone.

[0979] Step 13:

[0980] The emotion engine analyzes the user's facial expressions, tone of voice, and movements during exercise in real time.

[0981] Input: Exercise data collected in real time

[0982] Output: Estimated user emotional state

[0983] Specific operation: The emotion engine analyzes the collected data and estimates the user's emotional state.

[0984] Step 14:

[0985] The emotion engine estimates the user's emotions and, if it determines that the user is experiencing excessive stress, it feeds that information back to the server.

[0986] Input: Estimated user emotional state

[0987] Output: Emotion information fed back to the server

[0988] Specific operation: The emotion engine determines the user's emotional state, and if the user is feeling excessive stress, it sends that information to the server.

[0989] Step 15:

[0990] The server adjusts the content and tone of the voice support based on the feedback and generates encouraging messages for the user.

[0991] Input: Feedback emotional information

[0992] Output: Generated encouraging message

[0993] Specific operation: The server changes the content and tone of the voice support based on the emotional information, generating messages such as "Hang in there! You're almost there!"

[0994] Step 16:

[0995] The server automatically adjusts the intensity of the exercise and sends it to the user's device.

[0996] Input: Feedback emotional information

[0997] Output: Adjusted exercise intensity instructions

[0998] Specific operation: The server appropriately adjusts the exercise intensity based on the emotional information and sends instructions to the user's device, such as instructing them to slow down their exercise pace.

[0999] (Application example 1)

[1000] Next, a description will be given of Application Example 1 of Form 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."

[1001] While conventional diet support systems can provide personalized menus based on a user's body shape and exercise habits, they have the problem of not being able to provide feedback that takes into account the user's emotional state, which can lead to excessive stress and a loss of motivation.

[1002] 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.

[1003] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for recognizing the user's emotions, and means for providing feedback based on the user's emotional state. This makes it possible to not only provide an optimal menu based on the user's individual body shape and exercise habits, but also to provide feedback according to the user's emotional state.

[1004] "Means for registering an image of the user's body shape" refers to a means for a user to take a photo of their body shape using a digital camera or a smartphone camera and register that image in the system.

[1005] "Means for using generative AI to select an image that is closest to the desired body shape" refers to a means for using generative AI to select the image that is closest to the body shape the user is aiming for from multiple body shape images prepared within the system.

[1006] "Means for registering user's exercise habits" refers to the means by which a user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system.

[1007] "Means for generating diet meal menus, exercise programs, and durations" refers to means for using a generative AI to generate optimal diet meal menus, exercise programs, and durations for a user based on the user's body image and exercise habits.

[1008] The "means for editing the generated menu" is a means for the user to edit the generated diet meal menu and exercise program as needed.

[1009] "Means for recognizing the user's emotions" refers to means for estimating emotions from the user's facial expressions, tone of voice, movements during exercise, etc.

[1010] "Means for providing feedback based on the user's emotional state" refers to means for adjusting the content and tone of the audio support according to the user's emotional state, providing encouraging messages to the user, or automatically reducing the intensity of exercise.

[1011] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means for recognizing the user's emotions, and means for providing feedback based on the user's emotional state.

[1012] Hardware and Software Configuration

[1013] Hardware:

[1014] Smartphone (camera, microphone, sensor)

[1015] Server (data processing, execution of generative AI models)

[1016] software:

[1017] Image processing library (OpenCV)

[1018] Emotion Recognition Engine (Microsoft Azure Cognitive Services)

[1019] Generative AI model (GPT-4)

[1020] Database (MySQL)

[1021] Data processing and calculation

[1022] Registering a user's body image:

[1023] Users take a photo of their body shape using their smartphone camera and upload the image to the system. Body shape data is analyzed from the image using an image processing library (OpenCV).

[1024] Select an image that best represents the body you want to achieve:

[1025] Using a generative AI model (GPT-4), the system selects the body shape image that is closest to the user's desired one from multiple body shape images provided within the system.

[1026] Registering your exercise habits:

[1027] Users enter their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system as text data.

[1028] Generate diet menus and exercise programs:

[1029] Enter the following prompt into the generative AI model to generate an optimal diet meal menu and exercise program.

[1030] Based on your body image and exercise habits, please provide the following information:

[1031] 1. The body image that most closely resembles the user's desired body image

[1032] 2. The best diet meal menu (breakfast, lunch, dinner)

[1033] 3. An optimal exercise program (e.g., jogging three times a week, walking 30 minutes daily, etc.)

[1034] 4. Recommended duration

[1035] Edit the generated menu:

[1036] The user can edit the generated diet meal menu and exercise program as needed.

[1037] Emotion recognition and feedback:

[1038] Using an emotion recognition engine (Microsoft Azure Cognitive Services), the system analyzes the user's facial expressions and tone of voice to estimate their emotional state. If the user shows signs of distress during exercise, the system will capture this information and adjust the content and tone of the voice support. Specifically, it will provide an encouraging message to the user or automatically reduce the intensity of the exercise.

[1039] Specific examples

[1040] The user launches the app and takes a photo of their body with their smartphone camera. The app analyzes the image and obtains the user's body data. The user enters their exercise habits (e.g., jogging three times a week). The emotion recognition engine analyzes the user's facial expressions and tone of voice. The generative AI model generates an optimal meal menu and exercise program based on the prompt text. The app displays the generated menu to the user, who can edit it as needed. If the user shows signs of pain while exercising, the emotion engine detects this and provides feedback (e.g., displaying an encouraging message or reducing the intensity of the exercise).

[1041] In this way, users receive an individually tailored diet plan and support according to their emotional state.

[1042] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1043] Step 1:

[1044] The user takes a photo of their own body shape using the smartphone camera.

[1045] Input: User's body image

[1046] Output: Body shape image data

[1047] Specific operation: The user activates the smartphone camera and takes a full-body photo, which is then saved in the application.

[1048] Step 2:

[1049] The device uses an image processing library (OpenCV) to analyze the body shape image.

[1050] Input: Body shape image data

[1051] Output: Body shape analysis data

[1052] Specific operation: The device analyzes the image using OpenCV and extracts the user's body characteristics (e.g., height, weight, body fat percentage, etc.).

[1053] Step 3:

[1054] The user inputs his or her exercise habits as text data.

[1055] Input: Exercise habits data (e.g., jogging three times a week)

[1056] Output: Exercise habits text data

[1057] Specific operation: The user enters their exercise habits into the application's input form and presses the submit button.

[1058] Step 4:

[1059] The server uses a generative AI model (GPT-4) to select an image that is closest to the desired body type.

[1060] Input: Body shape analysis data, exercise habits text data

[1061] Output: Image data that is close to the desired body shape

[1062] Specific operation: The server inputs body shape analysis data and exercise habit text data into a generative AI model based on the prompt text, and generates an image that closely resembles the desired body shape.

[1063] Step 5:

[1064] The server uses the generated AI model to generate diet meal menus and exercise programs.

[1065] Input: Body shape analysis data, exercise habits text data, image data similar to the body you want to achieve

[1066] Output: Diet meal plan, exercise program, recommended duration

[1067] Specific operation: The server inputs the following prompt sentence into the generative AI model to generate an optimal meal menu and exercise program.

[1068] Based on your body image and exercise habits, please provide the following information:

[1069] 1. The body image that most closely resembles the user's desired body image

[1070] 2. The best diet meal menu (breakfast, lunch, dinner)

[1071] 3. An optimal exercise program (e.g., jogging three times a week, walking 30 minutes daily, etc.)

[1072] 4. Recommended duration

[1073] Step 6:

[1074] The user reviews the generated menu and edits it if necessary.

[1075] Input: Diet meal plan, exercise program, recommended duration

[1076] Output: The edited menu

[1077] What happens: The user reviews the menu generated within the application and edits the meal menu and exercise program as needed.

[1078] Step 7:

[1079] The device uses an emotion recognition engine (Microsoft Azure Cognitive Services) to recognize the user's emotions.

[1080] Input: User's facial expression image, voice tone data

[1081] Output: Emotional state data

[1082] What it does: The device uses the camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed by an emotion recognition engine.

[1083] Step 8:

[1084] The server provides feedback based on the user's emotional state.

[1085] Input: Emotional state data

[1086] Output: Feedback messages, exercise intensity adjustment

[1087] Specific operation: The server generates encouraging messages for the user based on the emotional state data and automatically reduces the intensity of exercise as needed.

[1088] Example 2

[1089] Next, a description will be given of Example 2 of Form 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."

[1090] Conventional exercise support systems have difficulty providing individualized exercise programs that fully consider the user's exercise habits and emotional state. Furthermore, they lack real-time audio support during exercise and functionality that provides feedback based on the user's emotions, making it difficult for users to maintain their motivation to exercise continuously.

[1091] 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.

[1092] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for collecting the user's exercise data, means for analyzing the collected exercise data, means for collecting the user's emotional data, means for analyzing the collected emotional data and adjusting the exercise program, and means for providing voice instructions for the exercise program. This makes it possible to provide an exercise program based on the user's individual exercise habits and emotional state, thereby maintaining the user's motivation to exercise continuously.

[1093] "Means for registering the user's body shape as an image" refers to devices or software for recording the shape and size of the user's body as image data.

[1094] "Generative AI" refers to a system that uses artificial intelligence technology to generate data and information for specific purposes.

[1095] "Means for selecting an image that is close to the desired body shape" refers to an interface or algorithm that allows the user to select an image that is close to the image of the desired body shape.

[1096] "Means for registering a user's exercise habits" refers to devices or software for recording a user's daily exercise patterns and frequency.

[1097] "Means for generating diet meal menus, exercise programs, and duration" refers to a system that automatically creates meal plans and exercise programs according to the user's goals.

[1098] "Means for editing the generated menu" refers to an interface that allows a user to manually modify or adjust the automatically generated meal menu or exercise program.

[1099] "Means for collecting user exercise data" refers to devices or software for recording data such as the type, number, and intensity of exercise performed by the user.

[1100] "Means for analyzing collected athletic data" refers to algorithms or systems used to analyze collected athletic data and evaluate a user's athletic performance and progress.

[1101] "Means for collecting user emotional data" refers to devices or software for recording a user's emotional state from facial expressions and voice.

[1102] "Means for analyzing collected emotional data and adjusting exercise programs" refers to algorithms or systems for analyzing collected emotional data and optimizing exercise programs according to the user's emotional state.

[1103] "Means for providing audio instructions on an exercise menu" refers to a device or software that provides audio guidance to the user on the next exercise menu that should be performed.

[1104] The present invention is a system that provides audio support to a user while exercising and adjusts an exercise program based on the user's emotional state. Specific embodiments of this system are described below.

[1105] First, the user launches an exercise app using a smartphone or wearable device. The smartphone's camera or the wearable device's sensor is used to register an image of the user's body shape. This records the user's body shape and size as image data.

[1106] Next, generative AI is used to select images that are closest to the body the user is aiming for. The generative AI model generates images that are close to the image of the body the user is aiming for, and the user can choose from them.

[1107] To register a user's exercise habits, an application on a smartphone or wearable device is used, which records the user's daily exercise patterns and frequency.

[1108] To generate diet menus, exercises, and durations, the server uses data analysis tools such as Python and TensorFlow, which automatically creates meal plans and exercise programs tailored to the user's goals.

[1109] To edit the generated menu, users use a smartphone application, which allows them to manually modify and adjust the automatically generated meal menu and exercise program.

[1110] To collect user exercise data, sensors in smartphones and wearable devices are used, such as accelerometers and heart rate monitors, to record data such as the type, frequency, and intensity of exercise.

[1111] The server uses Python and TensorFlow to analyze the collected exercise data and evaluate the user's exercise performance and progress.

[1112] To collect user emotional data, the smartphone's camera and microphone are used to record the user's emotional state from their facial expressions and voice.

[1113] The server uses an emotion engine to analyze the collected emotion data and adjust the exercise program, which analyzes the collected emotion data and optimizes the exercise program according to the user's emotional state.

[1114] To provide voice instructions for the exercise menu, the device uses the Google Text-to-Speech API, which provides the user with a voice prompt for the next exercise they should do.

[1115] Examples:

[1116] A user launches a strength training app on their smartphone. The app gives a voice instruction such as "Next, do 20 sit-ups." After the user does sit-ups, the device uses an accelerometer to record the number of exercises. The server analyzes the data and determines the next exercise menu as "Next, do 15 squats," which is sent to the device. The device then reads the instruction aloud. If the user smiles after the exercise, the device uses a camera to record their facial expression and sends it to the server. The server analyzes the data and incorporates sit-ups more frequently into future exercise programs.

[1117] Example prompt sentence:

[1118] "When the user is doing strength training, create a program that gives voice instructions on what to do next. For example, it could say, 'Next, do 20 sit-ups.' Also, analyze the user's emotional state. If they show a happy expression, incorporate that exercise more frequently into future programs."

[1119] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1120] Step 1:

[1121] The user begins exercising.

[1122] Input: A user launches an exercise app using a smartphone or wearable device.

[1123] Specific action: The user taps the smartphone app and selects "Strength Training."

[1124] Output: A signal to start exercise is sent to the terminal.

[1125] Step 2:

[1126] The device will provide voice instructions for the exercise menu.

[1127] Input: Signal to start exercise and exercise menu sent from the server.

[1128] What it does: The device uses the Google Text-to-Speech API to read aloud, "Next, do 20 sit-ups."

[1129] Output: Recognize the exercise menu that the user should do next.

[1130] Step 3:

[1131] The user performs an exercise.

[1132] Input: Voice commands from the device.

[1133] Specific action: The user performs 20 sit-ups.

[1134] Output: Performing exercise.

[1135] Step 4:

[1136] The terminal collects the user's exercise data.

[1137] Input: User's exercise performance.

[1138] How it works: The device uses an accelerometer and heart rate monitor to record the number and intensity of your exercise.

[1139] Output: Exercise data is saved on the device.

[1140] Step 5:

[1141] The server analyzes the exercise data.

[1142] Input: Exercise data sent from the device.

[1143] Specific operation: The server uses Python and TensorFlow to analyze exercise data and evaluate the user's exercise performance.

[1144] Output: The analysis results are generated.

[1145] Step 6:

[1146] The server determines the next exercise menu and sends it to the terminal.

[1147] Input: Analysis results of movement data.

[1148] Specific operation: Based on the analysis results, the server determines the next exercise menu to be performed and sends that information to the device. For example, it may decide, "Next, do 15 squats."

[1149] Output: The following exercise menu is sent to the device.

[1150] Step 7:

[1151] The device will then provide voice instructions for the next exercise menu.

[1152] Input: Next exercise menu sent from the server.

[1153] What happens: The device again uses the Google Text-to-Speech API to read aloud, "Now do 15 squats."

[1154] Output: Recognize the exercise menu that the user should do next.

[1155] Step 8:

[1156] The user finishes the exercise.

[1157] Input: User indicates intent to end exercise.

[1158] Specific behavior: The user taps the "Exit" button in the app.

[1159] Output: A signal indicating the end of exercise is sent to the terminal.

[1160] Step 9:

[1161] The device collects the user's emotional data.

[1162] Input: Signal to end exercise.

[1163] Specific operation: The device uses a camera and microphone to collect emotional data from the user's facial expressions and voice.

[1164] Output: Emotion data is saved on the device.

[1165] Step 10:

[1166] The server analyzes the emotional data and adjusts the exercise program.

[1167] Input: Emotion data sent from the device.

[1168] Specific operation: The server uses the emotion engine to analyze the collected emotion data and optimize the exercise program according to the user's emotional state.

[1169] Output: A tailored exercise program is generated.

[1170] (Application example 2)

[1171] Next, a description will be given of Application Example 2 of Form 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."

[1172] Conventional exercise support systems and diet support systems only provide menus based on the user's exercise habits and body shape, but they lack the ability to propose meal menus that take into account the user's emotional state or to link the proposed menus with delivery services. This makes it difficult for users to maintain their motivation to exercise and eat consistently.

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

[1174] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for analyzing the user's emotional state, means for suggesting a meal menu based on the emotional state, and means for delivering the suggested meal menu in cooperation with a delivery service. This enables the system to suggest an optimal meal menu according to the user's emotional state and to quickly deliver the menu.

[1175] "Means for registering the user's body shape as an image" refers to a device or software for recording the shape and dimensions of the user's body as image data.

[1176] "Means for using generative AI to select an image that is close to the desired body shape" refers to devices or software that use artificial intelligence technology to allow users to select an image of the desired body shape.

[1177] "Means for registering a user's exercise habits" refers to devices or software for recording the type and frequency of a user's daily exercise.

[1178] "Means for generating diet meal menus, exercise programs, and duration" refers to devices or software that automatically create appropriate meal menus, exercise programs, and durations based on the user's goals and current condition.

[1179] "Means for editing the generated menu" refers to devices or software that allow the user to manually modify or adjust the generated meal menu or exercise program.

[1180] "Means for analyzing the user's emotional state" refers to devices or software that analyze the user's facial expressions and feedback to determine their emotional state.

[1181] The "means for proposing a meal menu based on an emotional state" refers to a device or software that takes into account the emotional state of the user and proposes an optimal meal menu.

[1182] The "means for delivering the proposed meal menu in cooperation with a delivery service" refers to a device or software for delivering the proposed meal menu to the user in cooperation with a delivery service.

[1183] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means for analyzing the user's emotional state, means for suggesting a meal menu based on the emotional state, and means for delivering the suggested meal menu in cooperation with a delivery service.

[1184] Hardware and software used

[1185] Hardware

[1186] Smartphone

[1187] server

[1188] Delivery service terminal

[1189] software

[1190] Python

[1191] Speech recognition libraries (e.g. Google Speech-to-Text)

[1192] Sentiment analysis library (e.g. IBM Watson)

[1193] Generative AI models (e.g., GPT-3)

[1194] Data processing and calculation

[1195] A means of registering the user's body shape using an image

[1196] Users take a photo of their body using their smartphone camera and send the image data to a server, which stores the image data and uses it for later analysis.

[1197] A method for using generative AI to select an image that is close to the desired body type

[1198] The server uses a generative AI model to generate images of the body the user wants to achieve, and the user selects the desired body shape from the generated images via a smartphone application.

[1199] A means of registering users' exercise habits

[1200] Users use a smartphone application to input the type and frequency of their daily exercise, and this data is sent to a server and stored.

[1201] A means of generating diet menus, exercises, and durations

[1202] The server automatically generates an appropriate meal menu, exercise program, and duration based on the user's body shape data, target body image, and exercise habit data using a generative AI model.

[1203] A way to edit the generated menu

[1204] Users can manually modify and adjust the generated meal menus and exercise programs using a smartphone application, and the edited data is stored on the server.

[1205] A means of analyzing the user's emotional state

[1206] Feedback from users after exercise or meals is collected using the smartphone's camera and microphone, and the server uses an emotion analysis library to analyze this feedback and determine the user's emotional state.

[1207] A method for suggesting meal menus based on emotional state

[1208] Based on the results of the emotion analysis, the server proposes the optimal meal menu according to the user's emotional state. The proposals are automatically generated using a generative AI model.

[1209] A means of delivering the proposed meal menu in collaboration with a delivery service

[1210] The server sends the suggested meal menu to the delivery service's terminal and instructs it to be delivered to the user, who then promptly delivers the suggested menu.

[1211] Examples of concrete examples and prompts

[1212] Specific examples

[1213] When the user is running, they will receive a voice notification if their current pace is not reaching their target time and will be suggested to speed up.

[1214] If the user gives feedback after exercising that it was "fun," the emotion engine will determine this as "happy" and suggest salad and grilled chicken.

[1215] Prompt Sentence Examples

[1216] "When the user is running, analyze their current pace and suggest speeding up if they are not reaching their target time."

[1217] "Analyze user feedback after exercise and suggest appropriate meal plans based on their emotional state."

[1218] The above is an embodiment of the present invention.

[1219] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1220] Step 1:

[1221] The user takes a picture of their body using the smartphone camera and sends the image data to the server. The server stores this image data and uses it for later analysis. The input is the user's body image, and the output is the image data stored on the server.

[1222] Step 2:

[1223] The server uses a generative AI model to generate an image of the body the user desires. The user selects the desired body shape from the generated images through a smartphone application. The input is the user's body shape image and the desired body characteristics, and the output is an image of the desired body shape selected by the user.

[1224] Step 3:

[1225] Users use a smartphone application to input the type and frequency of their daily exercise. This data is sent to and stored on a server. The input is the user's exercise habit data, and the output is the exercise habit data stored on the server.

[1226] Step 4:

[1227] The server generates an appropriate meal menu, exercise program, and duration based on the user's body shape data, image of the target body, and exercise habit data. These menus are automatically created using a generative AI model. The input is the user's body shape data, image of the target body, and exercise habit data, and the output is the generated meal menu and exercise program.

[1228] Step 5:

[1229] Users can manually modify and adjust the generated meal menu and exercise program using a smartphone application. The edited data is saved on the server. The input is the user's edited content, and the output is the edited menu saved on the server.

[1230] Step 6:

[1231] The system collects feedback from users after exercise or meals using the smartphone's camera and microphone. The server uses an emotion analysis library to analyze this feedback and determine the user's emotional state. The input is the user's feedback data, and the output is the analyzed emotional state.

[1232] Step 7:

[1233] The server uses the results of emotion analysis to suggest optimal meal plans based on the user's emotional state. The suggestions are automatically generated using a generative AI model. The input is the analyzed emotional state, and the output is a suggested meal plan.

[1234] Step 8:

[1235] The server sends the suggested meal menu to the delivery service terminal and instructs it to be delivered to the user. The delivery service then promptly delivers the suggested meal. The input is the suggested meal menu, and the output is the meal delivered to the user.

[1236] 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.

[1237] 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> ) 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.

[1238] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1239] 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.

[1240] [Third embodiment]

[1241] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1242] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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).

[1247] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1253] "Example 1"

[1254] In the system of the present invention, the user takes a photo of their own body shape using a digital camera or smartphone camera and registers the image in the system. Next, from multiple body shape images provided in the system, the user selects the one that most closely resembles the body image they are aiming for. In addition, the user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes daily, etc.) in the system. Based on this information, the system uses generative AI to generate a diet meal menu, exercise program, and duration that are optimal for the user. The generated menu can be edited by the user as needed.

[1255] "Example 2"

[1256] The system of the present invention provides audio support when the user is exercising. For example, when doing strength training, the system reads out the next exercise to be done (e.g., "Next, do 20 sit-ups"). When running, the system checks the user's pacing and suggests speeding up or slowing down as necessary (e.g., "You may not be able to reach your target time at your current pace. Try picking up the pace a little"). This allows the user to continue exercising appropriately.

[1257] The processing flow of each embodiment will be described below.

[1258] "Example 1"

[1259] Step 1: The user takes a photo of their body using a digital camera or smartphone camera. The image is then uploaded to the system.

[1260] Step 2: From the body shape images provided in the system, the user selects the one that most closely matches the desired body image. This selection is performed on the system's user interface.

[1261] Step 3: The user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system. This registration is also done through the system's user interface.

[1262] Step 4: Based on this information, the system uses generative AI to generate the optimal diet menu, exercise program, and duration for the user. The generated menu can be edited by the user as needed.

[1263] "Example 2"

[1264] Step 1: When the user starts exercising, the system provides audio support.

[1265] Step 2: When performing strength training, the system will read out what to do next (e.g., "Next, do 20 sit-ups").

[1266] Step 3: When you run, the system monitors your pacing and suggests speeding up or slowing down as needed (e.g., "Your current pace may not meet your target time. Try picking up the pace a bit.").

[1267] Example 1

[1268] Next, a description will be given of Example 1 of Form 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."

[1269] Conventional diet systems have difficulty generating individual diet plans based on a user's body shape and exercise habits. Furthermore, they lack the ability to edit the generated plan or support functions during exercise, making it difficult for users to continue dieting.

[1270] 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.

[1271] In this invention, the server includes a means for registering a user's body shape as an image, a means for presenting a plurality of body shape images and allowing the user to select a desired body shape, a means for registering the user's exercise habits, a means for generating a diet meal menu, an exercise program, and its duration using a generative AI model based on the user's body shape images, desired body shape, and exercise habits, and a means for editing the generated menu, which enables the generation and editing of a diet plan tailored to the individual needs of the user.

[1272] "Means for registering the user's body shape as an image" refers to a function that allows the user to take a photo of their body shape using a digital camera or a smartphone camera and upload the image to the system.

[1273] The "means for presenting a plurality of body shape images and allowing the user to select the body shape they are aiming for" is a function that presents a plurality of body shape images prepared within the system to the user and allows the user to select the one that is closest to the body shape they are aiming for.

[1274] "Means for registering user's exercise habits" refers to a function that allows users to input their own exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system and register that information.

[1275] "Means for generating diet meal menus, exercise programs, and their duration using a generative AI model" refers to a function that uses a generative AI model to generate optimal diet meal menus, exercise programs, and their duration based on the user's body image, desired body image, and exercise habits.

[1276] The "means for editing the generated menu" is a function that allows the user to check the generated diet plan and edit the meal menu and exercise program as necessary.

[1277] This invention begins when a user takes a photo of their body using a digital camera or smartphone camera and registers the image in the system. The user launches the smartphone's camera app and takes a photo of their body. The captured image is uploaded from the device to the system, and the server receives the uploaded image and stores it in a database. At this time, the quality of the image is checked using an image processing library (e.g., OpenCV).

[1278] Next, the server presents the user with multiple body shape images prepared in the system. The user selects the one that most closely resembles the desired body shape from the presented body shape images. The ID of the selected body shape image is sent from the device to the server, and the server stores the ID in a database.

[1279] Users input their exercise habits on their device. For example, they can enter information such as "I jog three times a week" or "I walk 30 minutes every day." The entered exercise data is sent from the device to a server, which then stores it in a database.

[1280] The server obtains information about the user's body image, desired body shape, and exercise habits, and inputs a prompt into a generative AI model (e.g., GPT-4) to request the generation of a diet plan. An example of a prompt is, "Based on the user's body image, desired body shape, and exercise habits, please generate the optimal diet meal menu, exercise program, and duration." Based on the prompt, the generative AI model generates the optimal meal menu, exercise program, and duration. The generated diet plan is then stored in a database by the server.

[1281] The user can check the generated diet plan on their device and edit the meal menu and exercise program as needed. For example, they can change the breakfast menu from oatmeal to yogurt. The edited content is sent from the device to the server, which updates the database.

[1282] In this way, the system allows users to easily create a diet plan tailored to their goals and edit it as needed.

[1283] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1284] Step 1: Registering a user's body shape image

[1285] Users simply open the camera app on their smartphone and take a photo of their body shape.

[1286] Input: Body image taken by the user

[1287] The device displays the captured image on the system's upload screen, and the user presses the "Upload" button.

[1288] The server receives the uploaded images and checks the quality of the images using an image processing library (e.g., OpenCV).

[1289] Output: Quality-confirmed body shape images

[1290] The server stores the verified images in a database.

[1291] Step 2: Choose your desired body image

[1292] The server retrieves multiple body shape images from the database and displays them on the user's terminal.

[1293] Input: Multiple body shape images stored in a database

[1294] The user taps to select the desired body shape from the displayed body shape images.

[1295] The terminal transmits the ID of the selected body shape image to the server.

[1296] Output: ID of the body shape image selected by the user

[1297] The server stores the ID of the selected body shape image in a database.

[1298] Step 3: Register your exercise habits

[1299] Users enter their exercise habits into an input form on the device (e.g., "I jog three times a week").

[1300] Input: Exercise habit data entered by the user

[1301] The device transmits the entered exercise habit data to the server.

[1302] Output: Exercise habits data

[1303] The server stores exercise habit data in a database.

[1304] Step 4: Generate an optimal diet plan

[1305] The server retrieves information about the user's body image, desired body image, and exercise habits from a database.

[1306] Input: User's body image, desired body image, exercise habits

[1307] The server inputs the following prompt to the generative AI model (e.g., GPT-4):

[1308] "Generate the optimal diet menu, exercise program, and duration based on the user's body image, desired body image, and exercise habits."

[1309] The generative AI model generates optimal meal plans, exercise programs, and their duration based on the prompt text.

[1310] Output: Generated diet plan (meal menu, exercise program, duration)

[1311] The server stores the generated diet plan in a database.

[1312] Step 5: Edit the menu

[1313] The user can check the generated diet plan on their device.

[1314] Input: Generated diet plan

[1315] Users can edit their meal menus and exercise programs as needed (e.g., change oatmeal to yogurt for breakfast).

[1316] The terminal transmits the edited content to the server.

[1317] Output: Edited diet plan

[1318] The server updates the edited diet plan to the database.

[1319] (Application example 1)

[1320] Next, a description will be given of Application Example 1 of Form 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."

[1321] Traditional diet and fitness programs often offer generic menus and exercise plans, making it difficult to provide programs optimized for each user's body shape and exercise habits. Providing individually optimized training programs and meal menus also places a heavy burden on trainers at fitness facilities. Furthermore, the lack of real-time feedback makes it difficult for users to train effectively.

[1322] 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.

[1323] In this invention, the server includes a means for registering an image of the user's body shape, a means for selecting an image that is close to the desired body shape using a generation AI, and a means for registering the user's exercise habits, which makes it possible to provide individually optimized training programs and meal menus.

[1324] "Means for registering an image of the user's body shape" refers to a means for a user to take a photo of their body shape using a digital camera or a smartphone camera and register that image in the system.

[1325] "Means for using generative AI to select an image that is closest to the desired body shape" refers to a means for using generative AI to select the image that is closest to the desired body shape from multiple body shape images prepared within the system.

[1326] "Means for registering user's exercise habits" refers to the means by which a user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system.

[1327] "Means for generating diet meal menus, exercise programs, and durations" refers to means for generating optimal diet meal menus, exercise programs, and durations for a user using a generative AI based on the user's body image and exercise habits.

[1328] The "means for editing the generated menu" is a means for the user to edit the generated diet meal menu and exercise program as needed.

[1329] "A means for providing individually optimized training programs and meal menus to fitness facility members by allowing them to take a photo of their own body shape, select the body image they are aiming for, and register their exercise habits" is a means for providing individually optimized training programs and meal menus to fitness facility members by allowing them to take a photo of their own body shape, select the body image they are aiming for, and register their exercise habits.

[1330] The "means of collaborating with in-facility trainers to receive real-time feedback" refers to a means of collaborating with in-facility trainers to receive real-time feedback on training and diet.

[1331] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating diet meal menus, exercises, and durations, means for editing the generated menus, means for providing individually optimized training programs and meal menus by allowing members of a fitness facility to take a photo of their own body shape, select the desired body image, and register their exercise habits, and means for receiving real-time feedback in collaboration with trainers within the facility.

[1332] 1. System Program

[1333] The program of this system performs the following processing.

[1334] 2. Explanation of program processing

[1335] The server receives body images taken by users using their smartphone cameras and processes them. Specifically, it uses Python's PIL library to read the images and preprocess them as needed. It then uses a generative AI model to select the image that most closely resembles the user's desired body shape. This generative AI model uses a pre-trained Keras model.

[1336] The user's exercise habits are registered in text format on a server. This includes information such as how many times a week the user exercises and what type of exercise they do. Based on this information, the generative AI model generates the optimal diet menu, exercise program, and duration for the user.

[1337] The generated menu can be viewed and edited as needed through a smartphone app, and users can also connect with trainers at the fitness facility to receive real-time feedback, including training progress and dietary advice.

[1338] 3. Examples of concrete examples and prompts

[1339] As a concrete example, consider a user who jogs three times a week. Based on this information, the generative AI model generates the optimal meal plan and exercise program for the user. Below is an example of a prompt to input to the generative AI model.

[1340] User body image: user_image.jpg

[1341] Target body shape: target_shape.jpg

[1342] Exercise habits: Jogging three times a week

[1343] Use this information to create the perfect diet and exercise program for you.

[1344] This prompt sentence can be used as input into a generative AI model to generate the optimal menu for the user.

[1345] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1346] Step 1:

[1347] The user takes a photo of their body shape using the smartphone camera and saves the image on the device.

[1348] Input: User's body image

[1349] Output: Body shape image saved on the device

[1350] Specific operation: The user launches the camera app on their smartphone, takes a photo of their body, and then saves the image on their device.

[1351] Step 2:

[1352] The terminal uploads the saved body shape image to the server.

[1353] Input: Body shape image saved on the device

[1354] Output: Body shape image uploaded to the server

[1355] How it works: The device uploads the stored body image to the server via an Internet connection, using an HTTP request.

[1356] Step 3:

[1357] The server receives the uploaded body shape images and processes the images.

[1358] Input: Body image uploaded to the server

[1359] Output: Preprocessed body shape image

[1360] What it does: The server uses Python's PIL library to load the image and perform preprocessing such as resizing and noise removal if necessary.

[1361] Step 4:

[1362] The server uses a generative AI model to select the image that most closely resembles the user's desired body image.

[1363] Input: Preprocessed body shape image

[1364] Output: Image that is closest to the desired body image

[1365] How it works: The server uses a pre-trained Keras model to compare images of the user's body shape with images of the desired body shape and selects the image that is closest.

[1366] Step 5:

[1367] The user inputs their exercise habits into the terminal and sends them to the server.

[1368] Input: User's exercise habits

[1369] Output: Exercise habit information sent to the server

[1370] Specific operation: The user enters their exercise habits (e.g., jogging three times a week) through a smartphone app and sends that information to the server.

[1371] Step 6:

[1372] The server uses a generative AI model to generate optimal diet meal menus and exercise programs based on the user's body image and exercise habit information.

[1373] Input: User's body image, exercise habits information

[1374] Output: Optimal diet and exercise program

[1375] Specific operation: The server inputs prompt sentences into the generative AI model and generates optimal diet meal menus and exercise programs for the user. Examples of prompt sentences are as follows:

[1376] User body image: user_image.jpg

[1377] Target body shape: target_shape.jpg

[1378] Exercise habits: Jogging three times a week

[1379] Use this information to create the perfect diet and exercise program for you.

[1380] Step 7:

[1381] The server sends the generated menu to the user's terminal, where the user edits it as needed.

[1382] Input: Optimal diet and exercise program

[1383] Output: The menu sent to the user's device.

[1384] Specific operation: The server sends the generated menu to the user's device, and the user can check the menu through the smartphone app and edit it as necessary.

[1385] Step 8:

[1386] Users train in fitness facilities and connect with trainers to receive real-time feedback.

[1387] Input: Training progress, dietary advice

[1388] Output: Real-time feedback

[1389] How it works: Users train in fitness facilities, and trainers monitor users' progress through a dedicated application and provide real-time feedback.

[1390] Example 2

[1391] Next, a description will be given of Example 2 of Form 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."

[1392] Conventional exercise support systems have difficulty collecting and analyzing users' exercise data in real time and providing appropriate voice instructions. They also lack the ability to generate specific exercise and meal plans to help users achieve their desired body shape and provide support for exercising based on those plans. This makes it difficult for users to continue exercising effectively.

[1393] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for registering the user's body shape as an image, means for selecting an image that resembles the desired body shape using a generative AI model, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for collecting the user's exercise data, means for analyzing the collected exercise data, means for generating voice instructions based on the analysis results, means for transmitting the generated voice instructions to the user's terminal, and means for playing the voice instructions on the user's terminal. This allows the user to receive appropriate exercise instructions in real time and effectively continue exercising to get closer to their desired body shape.

[1394] "Means for registering the user's body shape as an image" refers to a function for recording the user's current body shape as image data using a device such as a camera or scanner.

[1395] "Means of using a generative AI model to select an image that is close to the desired body shape" is a function that utilizes a generative AI model to present images that are close to the ideal body shape that the user is aiming for, allowing the user to select from among them.

[1396] "Means for registering a user's exercise habits" refers to a function that records information such as the type, frequency, and duration of the exercise a user engages in on a daily basis in a database.

[1397] "Means for generating diet meal menus, exercise plans, and duration" refers to a function that automatically creates an appropriate meal menu, exercise plan, and duration based on the user's goals, current body shape, and exercise habits.

[1398] "Means for editing the generated menu" refers to a function that allows users to manually modify and adjust the automatically generated meal menu and exercise plan.

[1399] "Means for collecting user exercise data" refers to a function that uses a wearable device, smartphone, etc. to obtain exercise data such as the user's heart rate, speed, and distance in real time.

[1400] "Means for analyzing collected exercise data" refers to a function that evaluates and analyzes the user's exercise status and performance based on the collected exercise data.

[1401] The "means for generating voice instructions based on the analysis results" is a function that generates instructions to provide appropriate exercise instructions and advice to the user by voice based on the results of data analysis.

[1402] "Means for transmitting the generated voice instructions to the user's terminal" refers to a function for transmitting the generated voice instructions to the user's smartphone or tablet via a communication means such as the Internet or Bluetooth.

[1403] "Means for playing voice instructions on the user's device" refers to a function that plays voice instructions received by the user's smartphone or tablet through a speaker or earphones.

[1404] This invention is a system that provides audio support to users while exercising. The system registers the user's body shape as an image, uses a generative AI model to select an image that is similar to the desired body shape, and registers the user's exercise habits. Furthermore, the system includes a function to generate diet meal menus, exercises, and durations, and to edit the generated menus.

[1405] The server collects and analyzes the user's exercise data in real time. The hardware used includes wearable devices (e.g., smartwatches and fitness trackers) for collecting the user's exercise data. The software includes algorithms for data analysis and generative AI models (e.g., OpenAI's GPT-4) for voice synthesis.

[1406] The device (user's smartphone or tablet) conveys the voice instructions sent from the server to the user. Specifically, the device plays back the voice data received from the server and provides the user with advice on the next exercise menu and pacing.

[1407] The user receives voice instructions from the device while exercising and continues exercising accordingly. For example, when doing strength training, the user may receive instructions such as "Next, do 20 sit-ups," and then perform sit-ups. When running, if the current pace is not reaching the target time, the user may receive advice such as "Try to increase your pace a little," and adjust the pace accordingly.

[1408] As a concrete example, consider the case where a user is running. The user puts on a smartwatch, picks up their smartphone, and starts running. The smartwatch collects data such as the user's heart rate and speed, and sends it to a server. The server analyzes this data and detects that the user's current pace is not enough to reach their target time. The server uses a generative AI model to generate a voice instruction saying, "Your current pace may not reach your target time. Try picking up the pace a little," and sends it to the device. The device then plays back this voice instruction and relays it to the user.

[1409] Example prompt sentence:

[1410] "When a user is running, if their current pace is not enough to reach their target time, generate voice prompts suggesting they speed up."

[1411] This system allows users to continue to exercise appropriately.

[1412] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1413] Step 1:

[1414] Before starting exercise, the user puts on a wearable device (e.g., a smartwatch) and launches a dedicated smartphone app. The wearable device collects exercise data such as the user's heart rate, speed, and distance in real time. This data is sent to the user's smartphone via Bluetooth or Wi-Fi. The input is the user's exercise data, and the output is the exercise data sent to the smartphone.

[1415] Step 2:

[1416] The server receives the exercise data sent from the user's smartphone. The server uses a data analysis algorithm to evaluate the user's exercise status in real time. For example, it determines whether the user's pace while running is within the target time. The input is the exercise data sent from the smartphone, and the output is the analysis result.

[1417] Step 3:

[1418] The server uses a generative AI model (e.g., generative AI model) to generate appropriate voice instructions for the user. For example, if the user's pace is slow, the server generates a voice instruction such as, "You may not reach your target time at your current pace. Try picking up the pace a little." The input is the analysis result, and the output is the generated voice instruction.

[1419] Step 4:

[1420] The server sends the generated voice instructions to the user's smartphone via an internet connection. The input is the generated voice instructions, and the output is the voice instructions sent to the smartphone.

[1421] Step 5:

[1422] The terminal (user's smartphone) plays the voice instructions received from the server. The user can hear the voice instructions through the smartphone's speaker or earphones. The input is the voice instructions received from the server, and the output is the played voice instructions.

[1423] Step 6:

[1424] The user continues exercising by following the voice instructions from the device. For example, if the user receives the instruction "Next, do 20 sit-ups," the user will do 20 sit-ups. If the user receives the instruction "Try to increase your pace a little," the user will increase the pace of their running. The input is the played voice instructions, and the output is the user's exercise behavior.

[1425] (Application example 2)

[1426] Next, a description will be given of Application Example 2 of Form 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."

[1427] While conventional exercise support systems can provide appropriate exercise menus based on the user's exercise habits and body shape, they have issues with being unable to provide efficient route guidance or time management for delivery work.In addition, there is a lack of means to provide appropriate advice when delivery times are likely to be delayed, so there is a need to improve the efficiency of delivery work.

[1428] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1429] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating diet meal menus, exercises, and periods, means for editing the generated menus, means for registering delivery destination information, means for generating delivery routes, means for managing delivery times, means for providing voice guidance on delivery destinations and routes, and means for encouraging the delivery driver to speed up if the delivery time is likely to be delayed. This enables efficient route guidance and time management in delivery work, and can provide appropriate advice if the delivery time is likely to be delayed.

[1430] "Means for registering the user's body shape as an image" refers to a method for saving the shape and size of the user's body as image data.

[1431] "Method of using generative AI to select an image that is close to the desired body" is a method that uses artificial intelligence to allow users to select an image of the body they are aiming for.

[1432] "Means for registering a user's exercise habits" refers to a method for recording the type and frequency of exercise that a user does on a daily basis.

[1433] The "means for generating diet meal menus, exercise programs, and durations" is a method for creating meal plans, exercise programs, and durations that match the user's goals.

[1434] The "means for editing the generated menu" is a method for modifying the generated meal plan or exercise program according to the user's wishes.

[1435] The "means for registering delivery destination information" is a method for recording information such as the address and contact details of the delivery location.

[1436] The "means for generating a delivery route" is a method for calculating the optimal route for efficiently visiting multiple delivery destinations.

[1437] "Means for managing delivery times" refers to a method for recording and managing the estimated arrival time and actual arrival time at each delivery destination.

[1438] "A means for providing delivery destination and route guidance by voice" is a method for providing voice instructions to delivery personnel regarding the next delivery destination and the optimal route.

[1439] The "means for encouraging delivery staff to speed up when delivery time is likely to be delayed" is a method for instructing delivery staff by voice to speed up when delivery is behind schedule.

[1440] The system for implementing this invention has the functions of registering an image of the user's body shape, using a generation AI to select an image that closely resembles the desired body shape, and registering the user's exercise habits. It also includes functions to generate diet meal menus, exercises, and durations, and to edit the generated menus. It also has functions to register delivery destination information, generate delivery routes, and manage delivery times. It also includes functions to provide voice guidance on delivery destinations and routes, and to encourage the delivery driver to speed up if they are likely to be late.

[1441] System program processing

[1442] The server acquires image data using a smartphone or camera to register the user's body shape as an image and stores it in a database. Using a generative AI model, the server generates and presents prompts to the user to select an image that best represents the body they desire. To register the user's exercise habits, the server collects exercise data from smartphone apps and wearable devices and stores it in a database.

[1443] To generate a diet menu, exercise plan, and duration, the server generates an optimal plan based on the user's goals, current body shape, and exercise habits. To edit the generated menu, the user can modify it using a smartphone app.

[1444] To register delivery destination information, the server stores the delivery destination address and contact information in a database. To generate delivery routes, the server uses map data to calculate the optimal route. To manage delivery times, the server records and manages the estimated and actual arrival times at each delivery destination.

[1445] The server uses voice synthesis technology to provide voice guidance on delivery destinations and routes to the delivery person, and if delivery time is likely to be delayed, the server will give voice instructions to the delivery person to speed up.

[1446] Specific examples

[1447] For example, if a delivery person is delivering to "1-1-1, Shibuya-ku, Tokyo," the server will do the following:

[1448] 1. From the list of delivery addresses, select "1-1-1, Shibuya-ku, Tokyo."

[1449] 2. A voice will say, "Your next delivery destination is 1-1-1, Shibuya-ku, Tokyo. We will guide you to the best route."

[1450] 3. If the delivery time is likely to be delayed, the system will provide a voice message saying, "At the current pace, the delivery time is likely to be delayed. Please speed up the pace a little."

[1451] Example prompts to input to a generative AI model:

[1452] "Your next delivery is to 1-1-1, Shibuya-ku, Tokyo. We'll guide you to the best route. At your current pace, your delivery time will likely be delayed. Please speed up a bit."

[1453] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1454] Step 1:

[1455] The server registers the user's body shape as an image. The user takes a photo of their body using a smartphone or camera and sends the image data to the server. The server stores the received image data in a database. The input is the user's body shape image, and the output is the image data stored in the database.

[1456] Step 2:

[1457] The server uses a generative AI model to generate a prompt to select an image that best suits the desired body shape. When the user inputs the desired body features, the server uses the generative AI model to generate an image based on those features and presents it to the user. The input is the user's desired body features, and the output is the generated image.

[1458] Step 3:

[1459] The server registers the user's exercise habits. The user inputs daily exercise data using a smartphone app or wearable device and sends the data to the server. The server stores the received exercise data in a database. The input is the user's exercise data, and the output is the exercise data stored in the database.

[1460] Step 4:

[1461] The server generates diet meal menus, exercises, and durations. Based on the user's goals, current body shape, and exercise habits, the server generates an optimal diet plan and exercise program and presents it to the user. The input is the user's goals, body shape, and exercise habits, and the output is the generated diet plan and exercise program.

[1462] Step 5:

[1463] The user edits the generated menu. Using a smartphone app, the user modifies the generated meal plan and exercise program and sends the modified data to the server. The server stores the received modified data in a database. The input is the user's modified data, and the output is the modified data stored in the database.

[1464] Step 6:

[1465] The server registers the delivery address information. The user enters the delivery address and contact information and sends that information to the server. The server saves the received delivery address information in a database. The input is the delivery address and contact information, and the output is the delivery address information saved in the database.

[1466] Step 7:

[1467] The server generates a delivery route. Based on the information of the delivery destination, the server uses map data to calculate the optimal route and presents it to the delivery person. The input is the delivery destination information, and the output is the optimal delivery route.

[1468] Step 8:

[1469] The server manages delivery times. It records and manages the estimated and actual arrival times at each delivery destination. The input is delivery destination information and current location information, and the output is the estimated and actual arrival times.

[1470] Step 9:

[1471] The server provides voice guidance on delivery destinations and routes. It uses speech synthesis technology to provide voice instructions to the delivery person on the next delivery destination and the optimal route. The input is delivery route information, and the output is voice guidance.

[1472] Step 10:

[1473] The server prompts the delivery person to speed up if the delivery time is likely to be delayed. If the delivery is behind schedule, the server gives a voice command to the delivery person to speed up. The input is the current location information and the estimated arrival time, and the output is a voice command to speed up.

[1474] 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.

[1475] "Example 1"

[1476] In one embodiment of the present invention, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, and movements during exercise. For example, if the user shows signs of pain while exercising, the emotion engine will capture this information and determine that the user is experiencing excessive stress. This information is fed back to the system, which then adjusts the content and tone of the voice support. Specifically, the system may provide encouraging messages to the user or automatically reduce the intensity of the exercise.

[1477] "Example 2"

[1478] The emotion engine also adjusts diet meal menus and exercise programs based on the user's emotional state. For example, if a user shows a happy expression after exercising, the emotion engine will pick up on that information and determine that the user likes that exercise. This information is fed back to the system, and that exercise will be more frequently incorporated into recommended exercise programs. Also, if a user shows a satisfied expression after eating, that meal menu will be determined to be favorable to the user, and similar meal menus will be recommended.

[1479] The processing flow of each embodiment will be described below.

[1480] "Example 1"

[1481] Step 1: The user begins exercising.

[1482] Step 2: The emotion engine estimates the user's emotions based on their facial expressions, tone of voice, and movements during exercise.

[1483] Step 3: If the emotion engine determines that the user is experiencing excessive stress, it feeds that information back into the system.

[1484] Step 4: The system adjusts the content and tone of the audio support, such as providing encouraging messages or automatically reducing the intensity of the exercise.

[1485] "Example 2"

[1486] Step 1: The user finishes the exercise.

[1487] Step 2: The emotion engine infers emotions from the user's facial expressions.

[1488] Step 3: If the emotion engine determines that the user likes the exercise, it feeds that information back into the system.

[1489] Step 4: Adjust the system to include the exercise frequently in your recommended exercise program.

[1490] Step 5: The user finishes eating.

[1491] Step 6: The emotion engine infers emotions from the user's facial expressions.

[1492] Step 7: If the emotion engine determines that the meal menu is favorable to the user, it feeds that information back into the system.

[1493] Step 8: The system adjusts to recommend similar meals.

[1494] Example 1

[1495] Next, a description will be given of Example 1 of Form 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."

[1496] Conventional diet support systems have had issues with providing effective diet support due to the difficulty of providing individualized menus based on the user's body shape and exercise habits, and a lack of feedback that takes into account the user's emotions and stress levels. Furthermore, it is not possible to detect the stress and fatigue the user feels during exercise in real time and provide appropriate support, making it difficult to maintain the user's motivation.

[1497] 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.

[1498] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means including an emotion engine for recognizing the user's emotions, means for the emotion engine to analyze the user's facial expressions, tone of voice, and movements during exercise to estimate emotions, means for adjusting the content and tone of audio support based on emotions, and means for automatically adjusting exercise intensity. This makes it possible to not only provide an individualized menu based on the user's body shape and exercise habits, but also to detect the user's emotions and stress level in real time and provide support accordingly.

[1499] "Means for registering a user's body shape using an image" refers to a function that allows a user to take a photo of their body shape using a digital camera or a smartphone camera and upload the image to the system.

[1500] "Means of using generative AI to select an image that is closest to the desired body shape" is a function that uses generative AI to select the image that is closest to the body shape the user is aiming for from multiple body shape images prepared within the system.

[1501] "Means for registering user's exercise habits" refers to a function that allows a user to input their own exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system and save that information.

[1502] "Means for generating diet meal menus, exercise programs, and duration" refers to a function that uses a generative AI model to create optimal diet meal menus, exercise programs, and durations based on the user's body image, desired body image, and exercise habits.

[1503] The "means for editing the generated menu" is a function that allows the user to check the generated diet meal menu and exercise program, and make changes or additions as necessary.

[1504] "Means including an emotion engine that recognizes the user's emotions" refers to a function that incorporates an emotion engine into the system to estimate the user's emotions from their facial expressions, tone of voice, movements during exercise, etc.

[1505] "Means for the emotion engine to analyze the user's facial expressions, tone of voice, and movements during exercise to infer emotions" refers to a function in which the emotion engine analyzes data collected through the camera and microphone to infer the user's emotional state.

[1506] "Means for adjusting the content and tone of voice support based on emotions" refers to a function that changes the content and tone of voice support according to the emotional state of the user estimated by the emotion engine, and provides messages of encouragement to the user.

[1507] "Means for automatically adjusting exercise intensity" is a function that allows the emotion engine to appropriately change the intensity of exercise based on the user's emotional state, thereby reducing the user's stress and fatigue.

[1508] MODE FOR CARRYING OUT THE INVENTION

[1509] The present invention begins with the user taking a photo of their own body shape using a digital camera or smartphone camera and registering the image in the system. Next, the user selects the body shape image that most closely resembles their desired body shape from the multiple body shape images available in the system. The user also registers their exercise habits (e.g., jogging three times a week, walking 30 minutes daily, etc.) in the system. Based on this information, the system uses a generative AI model to generate a diet meal menu, exercise program, and duration that are optimal for the user. The generated menu can be edited by the user as needed.

[1510] In one embodiment of the present invention, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, and movements during exercise. For example, if the user shows signs of pain while exercising, the emotion engine will capture this information and determine that the user is experiencing excessive stress. This information is fed back to the system, which then adjusts the content and tone of the voice support. Specifically, the system may provide encouraging messages to the user or automatically reduce the intensity of the exercise.

[1511] Hardware and software used

[1512] This system uses the following hardware and software:

[1513] Digital camera or smartphone camera: Used to take a picture of the user's body shape.

[1514] Terminal (PC, smartphone, tablet, etc.): Used by users to access the system and register images and exercise habits.

[1515] Server: Stores the data sent by the user and generates diet menus and exercise programs using generative AI models.

[1516] Generative AI model: Generates optimal diet menus and exercise programs based on the user's body image, desired body image, and exercise habits.

[1517] Emotion engine: Analyzes the user's facial expressions, tone of voice, and movements during exercise to estimate emotions.

[1518] Specific examples

[1519] Example 1: Registering a body shape image

[1520] Users take a photo of their body with their smartphone camera and upload the image to the system, where the server receives the image and stores it in the user's profile.

[1521] Example 2: Choosing the body image you want to achieve

[1522] The user selects the body shape image they want to achieve from the body shape images in the system and sends that information to the server, which then stores the selected image as the user's goal.

[1523] Example 3: Registering exercise habits

[1524] A user enters their exercise habits, such as "I jog three times a week," into the system and sends them to the server, which adds this information to the user's profile.

[1525] Example 4: Generating a diet menu

[1526] The server uses a generative AI model to generate a one-week diet menu based on the user's body image, desired body shape, and exercise habits. The generated menu is sent to the user's device, where the user can edit it as needed.

[1527] Example 5: Emotional Engine Feedback

[1528] If the user shows any facial expression of pain during exercise, the emotion engine captures that information and sends it back to the server, which then generates an encouraging message and sends it to the device. The intensity of the exercise is also automatically reduced.

[1529] Prompt Sentence Examples

[1530] "Upload a photo of your body and select the body image you want to achieve. Next, enter your exercise habits and we'll generate the optimal diet menu for you. We'll also recognize your emotions during exercise and provide feedback."

[1531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1532] Step 1:

[1533] Users take a photo of their body shape using a digital camera or smartphone camera.

[1534] Input: User's body image

[1535] Output: Photographed body shape image file

[1536] Specific operation: The user launches the camera app and takes a photo from the front so that the entire body is captured. After taking the photo, the image file is saved.

[1537] Step 2:

[1538] The user logs in to the system from their terminal and uploads the images they have taken.

[1539] Input: Body shape image file

[1540] Output: Body shape image saved on the server

[1541] Specific operation: The user accesses the system login screen, enters their user ID and password to log in. After logging in, they move to the image upload screen, select the saved image file, and click the upload button.

[1542] Step 3:

[1543] The server stores the received image and associates it with the user's profile.

[1544] Input: Uploaded body shape image file

[1545] Output: Body image associated with the user's profile

[1546] Specific operation: The server receives the uploaded image file and stores it in the database. The stored image is associated with the user ID.

[1547] Step 4:

[1548] The user browses through multiple body shape images prepared within the system.

[1549] Input: Body shape image data in the system

[1550] Output: A list of body shape images to be viewed by the user

[1551] Specific operation: The user accesses the body shape image selection screen and views the body shape images displayed in slideshow format.

[1552] Step 5:

[1553] Select the image that most closely resembles the body image the user is aiming for.

[1554] Input: User-selected body shape image

[1555] Output: Selected body shape image data

[1556] Specific actions: The user selects the image that most closely resembles the desired body image and clicks the Select button.

[1557] Step 6:

[1558] The server stores the selected image as the user's goal.

[1559] Input: Selected body shape image data

[1560] Output: Target body shape image saved in the user's profile

[1561] Specific operation: The server receives the selected image data and stores it in the user's profile as a target body shape image.

[1562] Step 7:

[1563] Users enter their exercise habits into the system via their device.

[1564] Input: User's exercise habits (e.g., jogging three times a week)

[1565] Output: Input exercise habit data

[1566] Specific operation: The user accesses the exercise habit input screen, enters their exercise habit in the text box, and then clicks the save button.

[1567] Step 8:

[1568] The server receives the entered exercise habits information and adds it to the user's profile.

[1569] Input: Entered exercise habit data

[1570] Output: Exercise habits added to the user's profile

[1571] Specific operation: The server receives the entered exercise habit data and stores it in a database. The stored data is linked to the user ID.

[1572] Step 9:

[1573] The server uses a generative AI model to generate the optimal diet meal menu, exercise program, and duration based on the user's body image, desired body shape, and exercise habits.

[1574] Input: User's body image, desired body image, exercise habits

[1575] Output: Generated diet menu and exercise program

[1576] Specific operation: The server runs the generative AI model and creates a menu based on the input data, taking into account calorie calculations and nutritional balance.

[1577] Step 10:

[1578] The server sends the generated menu to the user's device.

[1579] Input: Generated diet meal menu and exercise program

[1580] Output: The menu sent to the user's device

[1581] Specific operation: The server sends the generated menu to the user's terminal in PDF format or similar.

[1582] Step 11:

[1583] Review the menu the user receives and edit it if necessary.

[1584] Input: Generated diet meal menu and exercise program

[1585] Output: Edited menu

[1586] Specific actions: The user accesses the menu editing screen, changes ingredients and adds exercise, and then clicks the save button after editing.

[1587] Step 12:

[1588] The user uses the system while exercising.

[1589] Input: User's exercise data (e.g. facial expressions, tone of voice, movements during exercise)

[1590] Output: Real-time collected exercise data

[1591] Specific operation: The user exercises while holding the smartphone, and data is collected via the camera and microphone.

[1592] Step 13:

[1593] The emotion engine analyzes the user's facial expressions, tone of voice, and movements during exercise in real time.

[1594] Input: Exercise data collected in real time

[1595] Output: Estimated user emotional state

[1596] Specific operation: The emotion engine analyzes the collected data and estimates the user's emotional state.

[1597] Step 14:

[1598] The emotion engine estimates the user's emotions and, if it determines that the user is experiencing excessive stress, it feeds that information back to the server.

[1599] Input: Estimated user emotional state

[1600] Output: Emotion information fed back to the server

[1601] Specific operation: The emotion engine determines the user's emotional state, and if the user is feeling excessive stress, it sends that information to the server.

[1602] Step 15:

[1603] The server adjusts the content and tone of the voice support based on the feedback and generates encouraging messages for the user.

[1604] Input: Feedback emotional information

[1605] Output: Generated encouraging message

[1606] Specific operation: The server changes the content and tone of the voice support based on the emotional information, generating messages such as "Hang in there! You're almost there!"

[1607] Step 16:

[1608] The server automatically adjusts the intensity of the exercise and sends it to the user's device.

[1609] Input: Feedback emotional information

[1610] Output: Adjusted exercise intensity instructions

[1611] Specific operation: The server appropriately adjusts the exercise intensity based on the emotional information and sends instructions to the user's device, such as instructing them to slow down their exercise pace.

[1612] (Application example 1)

[1613] Next, a description will be given of Application Example 1 of Form 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."

[1614] While conventional diet support systems can provide personalized menus based on a user's body shape and exercise habits, they have the problem of not being able to provide feedback that takes into account the user's emotional state, which can lead to excessive stress and a loss of motivation.

[1615] 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.

[1616] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for recognizing the user's emotions, and means for providing feedback based on the user's emotional state. This makes it possible to not only provide an optimal menu based on the user's individual body shape and exercise habits, but also to provide feedback according to the user's emotional state.

[1617] "Means for registering an image of the user's body shape" refers to a means for a user to take a photo of their body shape using a digital camera or a smartphone camera and register that image in the system.

[1618] "Means for using generative AI to select an image that is closest to the desired body shape" refers to a means for using generative AI to select the image that is closest to the body shape the user is aiming for from multiple body shape images prepared within the system.

[1619] "Means for registering user's exercise habits" refers to the means by which a user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system.

[1620] "Means for generating diet meal menus, exercise programs, and durations" refers to means for using a generative AI to generate optimal diet meal menus, exercise programs, and durations for a user based on the user's body image and exercise habits.

[1621] The "means for editing the generated menu" is a means for the user to edit the generated diet meal menu and exercise program as needed.

[1622] "Means for recognizing the user's emotions" refers to means for estimating emotions from the user's facial expressions, tone of voice, movements during exercise, etc.

[1623] "Means for providing feedback based on the user's emotional state" refers to means for adjusting the content and tone of the audio support according to the user's emotional state, providing encouraging messages to the user, or automatically reducing the intensity of exercise.

[1624] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means for recognizing the user's emotions, and means for providing feedback based on the user's emotional state.

[1625] Hardware and Software Configuration

[1626] Hardware:

[1627] Smartphone (camera, microphone, sensor)

[1628] Server (data processing, execution of generative AI models)

[1629] software:

[1630] Image processing library (OpenCV)

[1631] Emotion Recognition Engine (Microsoft Azure Cognitive Services)

[1632] Generative AI model (GPT-4)

[1633] Database (MySQL)

[1634] Data processing and calculation

[1635] Registering a user's body image:

[1636] Users take a photo of their body shape using their smartphone camera and upload the image to the system. Body shape data is analyzed from the image using an image processing library (OpenCV).

[1637] Select an image that best represents the body you want to achieve:

[1638] Using a generative AI model (GPT-4), the system selects the body shape image that is closest to the user's desired one from multiple body shape images provided within the system.

[1639] Registering your exercise habits:

[1640] Users enter their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system as text data.

[1641] Generate diet menus and exercise programs:

[1642] Enter the following prompt into the generative AI model to generate an optimal diet meal menu and exercise program.

[1643] Based on your body image and exercise habits, please provide the following information:

[1644] 1. The body image that most closely resembles the user's desired body image

[1645] 2. The best diet meal menu (breakfast, lunch, dinner)

[1646] 3. An optimal exercise program (e.g., jogging three times a week, walking 30 minutes daily, etc.)

[1647] 4. Recommended duration

[1648] Edit the generated menu:

[1649] The user can edit the generated diet meal menu and exercise program as needed.

[1650] Emotion recognition and feedback:

[1651] Using an emotion recognition engine (Microsoft Azure Cognitive Services), the system analyzes the user's facial expressions and tone of voice to estimate their emotional state. If the user shows signs of distress during exercise, the system will capture this information and adjust the content and tone of the voice support. Specifically, it will provide an encouraging message to the user or automatically reduce the intensity of the exercise.

[1652] Specific examples

[1653] The user launches the app and takes a photo of their body with their smartphone camera. The app analyzes the image and obtains the user's body data. The user enters their exercise habits (e.g., jogging three times a week). The emotion recognition engine analyzes the user's facial expressions and tone of voice. The generative AI model generates an optimal meal menu and exercise program based on the prompt text. The app displays the generated menu to the user, who can edit it as needed. If the user shows signs of pain while exercising, the emotion engine detects this and provides feedback (e.g., displaying an encouraging message or reducing the intensity of the exercise).

[1654] In this way, users receive an individually tailored diet plan and support according to their emotional state.

[1655] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1656] Step 1:

[1657] The user takes a photo of their own body shape using the smartphone camera.

[1658] Input: User's body image

[1659] Output: Body shape image data

[1660] Specific operation: The user activates the smartphone camera and takes a full-body photo, which is then saved in the application.

[1661] Step 2:

[1662] The device uses an image processing library (OpenCV) to analyze the body shape image.

[1663] Input: Body shape image data

[1664] Output: Body shape analysis data

[1665] Specific operation: The device analyzes the image using OpenCV and extracts the user's body characteristics (e.g., height, weight, body fat percentage, etc.).

[1666] Step 3:

[1667] The user inputs his or her exercise habits as text data.

[1668] Input: Exercise habits data (e.g., jogging three times a week)

[1669] Output: Exercise habits text data

[1670] Specific operation: The user enters their exercise habits into the application's input form and presses the submit button.

[1671] Step 4:

[1672] The server uses a generative AI model (GPT-4) to select an image that is closest to the desired body type.

[1673] Input: Body shape analysis data, exercise habits text data

[1674] Output: Image data that is close to the desired body shape

[1675] Specific operation: The server inputs body shape analysis data and exercise habit text data into a generative AI model based on the prompt text, and generates an image that closely resembles the desired body shape.

[1676] Step 5:

[1677] The server uses the generated AI model to generate diet meal menus and exercise programs.

[1678] Input: Body shape analysis data, exercise habits text data, image data similar to the body you want to achieve

[1679] Output: Diet meal plan, exercise program, recommended duration

[1680] Specific operation: The server inputs the following prompt sentence into the generative AI model to generate an optimal meal menu and exercise program.

[1681] Based on your body image and exercise habits, please provide the following information:

[1682] 1. The body image that most closely resembles the user's desired body image

[1683] 2. The best diet meal menu (breakfast, lunch, dinner)

[1684] 3. An optimal exercise program (e.g., jogging three times a week, walking 30 minutes daily, etc.)

[1685] 4. Recommended duration

[1686] Step 6:

[1687] The user reviews the generated menu and edits it if necessary.

[1688] Input: Diet meal plan, exercise program, recommended duration

[1689] Output: The edited menu

[1690] What happens: The user reviews the menu generated within the application and edits the meal menu and exercise program as needed.

[1691] Step 7:

[1692] The device uses an emotion recognition engine (Microsoft Azure Cognitive Services) to recognize the user's emotions.

[1693] Input: User's facial expression image, voice tone data

[1694] Output: Emotional state data

[1695] What it does: The device uses the camera and microphone to capture the user's facial expressions and tone of voice, which are then analyzed by an emotion recognition engine.

[1696] Step 8:

[1697] The server provides feedback based on the user's emotional state.

[1698] Input: Emotional state data

[1699] Output: Feedback messages, exercise intensity adjustment

[1700] Specific operation: The server generates encouraging messages for the user based on the emotional state data and automatically reduces the intensity of exercise as needed.

[1701] Example 2

[1702] Next, a description will be given of Example 2 of Form 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."

[1703] Conventional exercise support systems have difficulty providing individualized exercise programs that fully consider the user's exercise habits and emotional state. Furthermore, they lack real-time audio support during exercise and functionality that provides feedback based on the user's emotions, making it difficult for users to maintain their motivation to exercise continuously.

[1704] 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.

[1705] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for collecting the user's exercise data, means for analyzing the collected exercise data, means for collecting the user's emotional data, means for analyzing the collected emotional data and adjusting the exercise program, and means for providing voice instructions for the exercise program. This makes it possible to provide an exercise program based on the user's individual exercise habits and emotional state, thereby maintaining the user's motivation to exercise continuously.

[1706] "Means for registering the user's body shape as an image" refers to devices or software for recording the shape and size of the user's body as image data.

[1707] "Generative AI" refers to a system that uses artificial intelligence technology to generate data and information for specific purposes.

[1708] "Means for selecting an image that is close to the desired body shape" refers to an interface or algorithm that allows the user to select an image that is close to the image of the desired body shape.

[1709] "Means for registering a user's exercise habits" refers to devices or software for recording a user's daily exercise patterns and frequency.

[1710] "Means for generating diet meal menus, exercise programs, and duration" refers to a system that automatically creates meal plans and exercise programs according to the user's goals.

[1711] "Means for editing the generated menu" refers to an interface that allows a user to manually modify or adjust the automatically generated meal menu or exercise program.

[1712] "Means for collecting user exercise data" refers to devices or software for recording data such as the type, number, and intensity of exercise performed by the user.

[1713] "Means for analyzing collected athletic data" refers to algorithms or systems used to analyze collected athletic data and evaluate a user's athletic performance and progress.

[1714] "Means for collecting user emotional data" refers to devices or software for recording a user's emotional state from facial expressions and voice.

[1715] "Means for analyzing collected emotional data and adjusting exercise programs" refers to algorithms or systems for analyzing collected emotional data and optimizing exercise programs according to the user's emotional state.

[1716] "Means for providing audio instructions on an exercise menu" refers to a device or software that provides audio guidance to the user on the next exercise menu that should be performed.

[1717] The present invention is a system that provides audio support to a user while exercising and adjusts an exercise program based on the user's emotional state. Specific embodiments of this system are described below.

[1718] First, the user launches an exercise app using a smartphone or wearable device. The smartphone's camera or the wearable device's sensor is used to register an image of the user's body shape. This records the user's body shape and size as image data.

[1719] Next, generative AI is used to select images that are closest to the body the user is aiming for. The generative AI model generates images that are close to the image of the body the user is aiming for, and the user can choose from them.

[1720] To register a user's exercise habits, an application on a smartphone or wearable device is used, which records the user's daily exercise patterns and frequency.

[1721] To generate diet menus, exercises, and durations, the server uses data analysis tools such as Python and TensorFlow, which automatically creates meal plans and exercise programs tailored to the user's goals.

[1722] To edit the generated menu, users use a smartphone application, which allows them to manually modify and adjust the automatically generated meal menu and exercise program.

[1723] To collect user exercise data, sensors in smartphones and wearable devices are used, such as accelerometers and heart rate monitors, to record data such as the type, frequency, and intensity of exercise.

[1724] The server uses Python and TensorFlow to analyze the collected exercise data and evaluate the user's exercise performance and progress.

[1725] To collect user emotional data, the smartphone's camera and microphone are used to record the user's emotional state from their facial expressions and voice.

[1726] The server uses an emotion engine to analyze the collected emotion data and adjust the exercise program, which analyzes the collected emotion data and optimizes the exercise program according to the user's emotional state.

[1727] To provide voice instructions for the exercise menu, the device uses the Google Text-to-Speech API, which provides the user with a voice prompt for the next exercise they should do.

[1728] Examples:

[1729] A user launches a strength training app on their smartphone. The app gives a voice instruction such as "Next, do 20 sit-ups." After the user does sit-ups, the device uses an accelerometer to record the number of exercises. The server analyzes the data and determines the next exercise menu as "Next, do 15 squats," which is sent to the device. The device then reads the instruction aloud. If the user smiles after the exercise, the device uses a camera to record their facial expression and sends it to the server. The server analyzes the data and incorporates sit-ups more frequently into future exercise programs.

[1730] Example prompt sentence:

[1731] "When the user is doing strength training, create a program that gives voice instructions on what to do next. For example, it could say, 'Next, do 20 sit-ups.' Also, analyze the user's emotional state. If they show a happy expression, incorporate that exercise more frequently into future programs."

[1732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1733] Step 1:

[1734] The user begins exercising.

[1735] Input: A user launches an exercise app using a smartphone or wearable device.

[1736] Specific action: The user taps the smartphone app and selects "Strength Training."

[1737] Output: A signal to start exercise is sent to the terminal.

[1738] Step 2:

[1739] The device will provide voice instructions for the exercise menu.

[1740] Input: Signal to start exercise and exercise menu sent from the server.

[1741] What it does: The device uses the Google Text-to-Speech API to read aloud, "Next, do 20 sit-ups."

[1742] Output: Recognize the exercise menu that the user should do next.

[1743] Step 3:

[1744] The user performs an exercise.

[1745] Input: Voice commands from the device.

[1746] Specific action: The user performs 20 sit-ups.

[1747] Output: Performing exercise.

[1748] Step 4:

[1749] The terminal collects the user's exercise data.

[1750] Input: User's exercise performance.

[1751] How it works: The device uses an accelerometer and heart rate monitor to record the number and intensity of your exercise.

[1752] Output: Exercise data is saved on the device.

[1753] Step 5:

[1754] The server analyzes the exercise data.

[1755] Input: Exercise data sent from the device.

[1756] Specific operation: The server uses Python and TensorFlow to analyze exercise data and evaluate the user's exercise performance.

[1757] Output: The analysis results are generated.

[1758] Step 6:

[1759] The server determines the next exercise menu and sends it to the terminal.

[1760] Input: Analysis results of movement data.

[1761] Specific operation: Based on the analysis results, the server determines the next exercise menu to be performed and sends that information to the device. For example, it may decide, "Next, do 15 squats."

[1762] Output: The following exercise menu is sent to the device.

[1763] Step 7:

[1764] The device will then provide voice instructions for the next exercise menu.

[1765] Input: Next exercise menu sent from the server.

[1766] What happens: The device again uses the Google Text-to-Speech API to read aloud, "Now do 15 squats."

[1767] Output: Recognize the exercise menu that the user should do next.

[1768] Step 8:

[1769] The user finishes the exercise.

[1770] Input: User indicates intent to end exercise.

[1771] Specific behavior: The user taps the "Exit" button in the app.

[1772] Output: A signal indicating the end of exercise is sent to the terminal.

[1773] Step 9:

[1774] The device collects the user's emotional data.

[1775] Input: Signal to end exercise.

[1776] Specific operation: The device uses a camera and microphone to collect emotional data from the user's facial expressions and voice.

[1777] Output: Emotion data is saved on the device.

[1778] Step 10:

[1779] The server analyzes the emotional data and adjusts the exercise program.

[1780] Input: Emotion data sent from the device.

[1781] Specific operation: The server uses the emotion engine to analyze the collected emotion data and optimize the exercise program according to the user's emotional state.

[1782] Output: A tailored exercise program is generated.

[1783] (Application example 2)

[1784] Next, a description will be given of Application Example 2 of Form 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."

[1785] Conventional exercise support systems and diet support systems only provide menus based on the user's exercise habits and body shape, but they lack the ability to propose meal menus that take into account the user's emotional state or to link the proposed menus with delivery services. This makes it difficult for users to maintain their motivation to exercise and eat consistently.

[1786] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1787] In this invention, the server includes means for registering an image of the user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and period, means for editing the generated menu, means for analyzing the user's emotional state, means for suggesting a meal menu based on the emotional state, and means for delivering the suggested meal menu in cooperation with a delivery service. This enables the system to suggest an optimal meal menu according to the user's emotional state and to quickly deliver the menu.

[1788] "Means for registering the user's body shape as an image" refers to a device or software for recording the shape and dimensions of the user's body as image data.

[1789] "Means for using generative AI to select an image that is close to the desired body shape" refers to devices or software that use artificial intelligence technology to allow users to select an image of the desired body shape.

[1790] "Means for registering a user's exercise habits" refers to devices or software for recording the type and frequency of a user's daily exercise.

[1791] "Means for generating diet meal menus, exercise programs, and duration" refers to devices or software that automatically create appropriate meal menus, exercise programs, and durations based on the user's goals and current condition.

[1792] "Means for editing the generated menu" refers to devices or software that allow the user to manually modify or adjust the generated meal menu or exercise program.

[1793] "Means for analyzing the user's emotional state" refers to devices or software that analyze the user's facial expressions and feedback to determine their emotional state.

[1794] The "means for proposing a meal menu based on an emotional state" refers to a device or software that takes into account the emotional state of the user and proposes an optimal meal menu.

[1795] The "means for delivering the proposed meal menu in cooperation with a delivery service" refers to a device or software for delivering the proposed meal menu to the user in cooperation with a delivery service.

[1796] A system for implementing this invention includes means for registering an image of a user's body shape, means for using a generation AI to select an image that closely resembles the desired body shape, means for registering the user's exercise habits, means for generating a diet meal menu, exercises, and duration, means for editing the generated menu, means for analyzing the user's emotional state, means for suggesting a meal menu based on the emotional state, and means for delivering the suggested meal menu in cooperation with a delivery service.

[1797] Hardware and software used

[1798] Hardware

[1799] Smartphone

[1800] server

[1801] Delivery service terminal

[1802] software

[1803] Python

[1804] Speech recognition libraries (e.g. Google Speech-to-Text)

[1805] Sentiment analysis library (e.g. IBM Watson)

[1806] Generative AI models (e.g., GPT-3)

[1807] Data processing and calculation

[1808] A means of registering the user's body shape using an image

[1809] Users take a photo of their body using their smartphone camera and send the image data to a server, which stores the image data and uses it for later analysis.

[1810] A method for using generative AI to select an image that is close to the desired body type

[1811] The server uses a generative AI model to generate images of the body the user wants to achieve, and the user selects the desired body shape from the generated images via a smartphone application.

[1812] A means of registering users' exercise habits

[1813] Users use a smartphone application to input the type and frequency of their daily exercise, and this data is sent to a server and stored.

[1814] A means of generating diet menus, exercises, and durations

[1815] The server automatically generates an appropriate meal menu, exercise program, and duration based on the user's body shape data, target body image, and exercise habit data using a generative AI model.

[1816] A way to edit the generated menu

[1817] Users can manually modify and adjust the generated meal menus and exercise programs using a smartphone application, and the edited data is stored on the server.

[1818] A means of analyzing the user's emotional state

[1819] Feedback from users after exercise or meals is collected using the smartphone's camera and microphone, and the server uses an emotion analysis library to analyze this feedback and determine the user's emotional state.

[1820] A method for suggesting meal menus based on emotional state

[1821] Based on the results of the emotion analysis, the server proposes the optimal meal menu according to the user's emotional state. The proposals are automatically generated using a generative AI model.

[1822] A means of delivering the proposed meal menu in collaboration with a delivery service

[1823] The server sends the suggested meal menu to the delivery service's terminal and instructs it to be delivered to the user, who then promptly delivers the suggested menu.

[1824] Examples of concrete examples and prompts

[1825] Specific examples

[1826] When the user is running, they will receive a voice notification if their current pace is not reaching their target time and will be suggested to speed up.

[1827] If the user gives feedback after exercising that it was "fun," the emotion engine will determine this as "happy" and suggest salad and grilled chicken.

[1828] Prompt Sentence Examples

[1829] "When the user is running, analyze their current pace and suggest speeding up if they are not reaching their target time."

[1830] "Analyze user feedback after exercise and suggest appropriate meal plans based on their emotional state."

[1831] The above is an embodiment of the present invention.

[1832] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1833] Step 1:

[1834] The user takes a picture of their body using the smartphone camera and sends the image data to the server. The server stores this image data and uses it for later analysis. The input is the user's body image, and the output is the image data stored on the server.

[1835] Step 2:

[1836] The server uses a generative AI model to generate an image of the body the user desires. The user selects the desired body shape from the generated images through a smartphone application. The input is the user's body shape image and the desired body characteristics, and the output is an image of the desired body shape selected by the user.

[1837] Step 3:

[1838] Users use a smartphone application to input the type and frequency of their daily exercise. This data is sent to and stored on a server. The input is the user's exercise habit data, and the output is the exercise habit data stored on the server.

[1839] Step 4:

[1840] The server generates an appropriate meal menu, exercise program, and duration based on the user's body shape data, image of the target body, and exercise habit data. These menus are automatically created using a generative AI model. The input is the user's body shape data, image of the target body, and exercise habit data, and the output is the generated meal menu and exercise program.

[1841] Step 5:

[1842] Users can manually modify and adjust the generated meal menu and exercise program using a smartphone application. The edited data is saved on the server. The input is the user's edited content, and the output is the edited menu saved on the server.

[1843] Step 6:

[1844] The system collects feedback from users after exercise or meals using the smartphone's camera and microphone. The server uses an emotion analysis library to analyze this feedback and determine the user's emotional state. The input is the user's feedback data, and the output is the analyzed emotional state.

[1845] Step 7:

[1846] The server uses the results of emotion analysis to suggest optimal meal plans based on the user's emotional state. The suggestions are automatically generated using a generative AI model. The input is the analyzed emotional state, and the output is a suggested meal plan.

[1847] Step 8:

[1848] The server sends the suggested meal menu to the delivery service terminal and instructs it to be delivered to the user. The delivery service then promptly delivers the suggested meal. The input is the suggested meal menu, and the output is the meal delivered to the user.

[1849] 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.

[1850] 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> ) 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.

[1851] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1852] 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.

[1853] [Fourth embodiment]

[1854] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1855] 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.

[1856] 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).

[1857] 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.

[1858] 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.

[1859] 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).

[1860] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1861] 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.

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1867] "Example 1"

[1868] In the system of the present invention, the user takes a photo of their own body shape using a digital camera or smartphone camera and registers the image in the system. Next, from multiple body shape images provided in the system, the user selects the one that most closely resembles the body image they are aiming for. In addition, the user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes daily, etc.) in the system. Based on this information, the system uses generative AI to generate a diet meal menu, exercise program, and duration that are optimal for the user. The generated menu can be edited by the user as needed.

[1869] "Example 2"

[1870] The system of the present invention provides audio support when the user is exercising. For example, when doing strength training, the system reads out the next exercise to be done (e.g., "Next, do 20 sit-ups"). When running, the system checks the user's pacing and suggests speeding up or slowing down as necessary (e.g., "You may not be able to reach your target time at your current pace. Try picking up the pace a little"). This allows the user to continue exercising appropriately.

[1871] The processing flow of each embodiment will be described below.

[1872] "Example 1"

[1873] Step 1: The user takes a photo of their body using a digital camera or smartphone camera. The image is then uploaded to the system.

[1874] Step 2: From the body shape images provided in the system, the user selects the one that most closely matches the desired body image. This selection is performed on the system's user interface.

[1875] Step 3: The user registers their exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) in the system. This registration is also done through the system's user interface.

[1876] Step 4: Based on this information, the system uses generative AI to generate the optimal diet menu, exercise program, and duration for the user. The generated menu can be edited by the user as needed.

[1877] "Example 2"

[1878] Step 1: When the user starts exercising, the system provides audio support.

[1879] Step 2: When performing strength training, the system will read out what to do next (e.g., "Next, do 20 sit-ups").

[1880] Step 3: When you run, the system monitors your pacing and suggests speeding up or slowing down as needed (e.g., "Your current pace may not meet your target time. Try picking up the pace a bit.").

[1881] Example 1

[1882] Next, a description will be given of Example 1 of Form 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."

[1883] Conventional diet systems have difficulty generating individual diet plans based on a user's body shape and exercise habits. Furthermore, they lack the ability to edit the generated plan or support functions during exercise, making it difficult for users to continue dieting.

[1884] 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.

[1885] In this invention, the server includes a means for registering a user's body shape as an image, a means for presenting a plurality of body shape images and allowing the user to select a desired body shape, a means for registering the user's exercise habits, a means for generating a diet meal menu, an exercise program, and its duration using a generative AI model based on the user's body shape images, desired body shape, and exercise habits, and a means for editing the generated menu, which enables the generation and editing of a diet plan tailored to the individual needs of the user.

[1886] "Means for registering the user's body shape as an image" refers to a function that allows the user to take a photo of their body shape using a digital camera or a smartphone camera and upload the image to the system.

[1887] The "means for presenting a plurality of body shape images and allowing the user to select the body shape they are aiming for" is a function that presents a plurality of body shape images prepared within the system to the user and allows the user to select the one that is closest to the body shape they are aiming for.

[1888] "Means for registering user's exercise habits" refers to a function that allows users to input their own exercise habits (e.g., jogging three times a week, walking 30 minutes every day, etc.) into the system and register that information.

[1889] "Means for generating diet meal menus, exercise programs, and their duration using a generative AI model" refers to a function that uses a generative AI model to generate optimal diet meal menus, exercise programs, and their duration based on the user's body image, desired body image, and exercise habits.

[1890] The "means for editing the generated menu" is a function that allows the user to check the generated diet plan and edit the meal menu and exercise program as necessary.

[1891] This invention begins when a user takes a photo of their body using a digital camera or smartphone camera and registers the image in the system. The user launches the smartphone's camera app and takes a photo of their body. The captured image is uploaded from the device to the system, and the server receives the uploaded image and stores it in a database. At this time, the quality of the image is checked using an image processing library (e.g., OpenCV).

[1892] Next, the server presents the user with multiple body shape images prepared in the system. The user selects the one that most closely resembles the desired body shape from the presented body shape images. The ID of the selected body shape image is sent from the device to the server, and the server stores the ID in a database.

[1893] Users input their exercise habits on their device. For example, they can enter information such as "I jog three times a week" or "I walk 30 minutes every day." The entered exercise data is sent from the device to a server, which then stores it in a database.

[1894] The server obtains information about the user's body image, desired body shape, and exercise habits, and inputs a prompt into a generative AI model (e.g., GPT-4) to request the generation of a diet plan. An example of a prompt is, "Based on the user's body image, desired body shape, and exercise habits, please generate the optimal diet meal menu, exercise program, and duration." Based on the prompt, the generative AI model generates the optimal meal menu, exercise program, and duration. The generated diet plan is then stored in a database by the server.

[1895] The user can check the generated diet plan on their device and edit the meal menu and exercise program as needed. For example, they can change the breakfast menu from oatmeal to yogurt. The edited content is sent from the device to the server, which updates the database.

[1896] In this way, the system allows users to easily create a diet plan tailored to their goals and edit it as needed.

[1897] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1898] Step 1: Registering a user's body shape image

[1899] Users simply open the camera app on their smartphone and take a photo of their body shape.

[1900] Input: Body image taken by the user

[1901] The device displays the captured image on the system's upload screen, and the user presses the "Upload" button.

[1902] The server receives the uploaded images and checks the quality of the images using an image processing library (e.g., OpenCV).

[1903] Output: Quality-confirmed body shape images

[1904] The server stores the verified images in a database.

[1905] Step 2: Choose your desired body image

[1906] The server retrieves multiple body shape images from the database and displays them on the user's terminal.

[1907] Input: Multiple body shape images stored in a database

[1908] The user taps to select the desired body shape from the displayed body shape images.

[1909] The terminal transmits the ID of the selected body shape image to the server.

[1910] Output: ID of the body shape image selected by the user

[1911] The server stores the ID of the selected body shape image in a database.

[1912] Step 3: Register your exercise habits

[1913] Users enter their exercise habits into an input form on the device (e.g., "I jog three times a week").

[1914] Input: Exercise habit data entered by the user

[1915] The device transmits the entered exercise habit data to the server.

[1916] Output: Exercise habits data

[1917] The server stores exercise habit data in a database.

[1918] Step 4: Generate an optimal diet plan

[1919] The server retrieves information about th...

Claims

1. A means for recording the user's current body shape as image data; A means for presenting to the user an image of a body that the user desires to achieve in a selectable manner; A means for registering information about the user's exercise habits; A means for generating at least one of a diet meal menu and an exercise program, and information regarding a period for which at least one of the diet meal menu and the exercise program is to be continued, based on information regarding the user's current body shape, an image of the desired body selected by the user, and information regarding the user's exercise habits; means for collecting athletic data of said user; means for analyzing the collected athletic data of the user; a prompt generating means for generating voice instructions including exercise instructions for the user in real time based on the analyzed results; A means for generating voice instructions using the generated prompt sentence and the generation AI model; and means for playing the generated voice instructions at the user's terminal.

2. The means for presenting an image of a body that the user desires to achieve in a selectable manner to the user includes: A means for acquiring information on a desired body characteristic of a user, and generating a prompt sentence for generating an image of the desired body of the user based on the information on the desired body characteristic of the user; The system of claim 1 , further comprising: means for generating an image of a body that the user desires to achieve using the generated prompt sentences and a generative AI model.

3. The system according to claim 1 , wherein the means for generating voice instructions includes at least one of: a means for reading out the next menu to be performed in the exercise program; and a means for checking the pacing status during running and suggesting speeding up / slowing down.

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