system

Abstract

We provide the system. [Solution] A means of acquiring a human body image, A conversion means for converting a human body image acquired by the acquisition means into three-dimensional data, An analysis means that analyzes the three-dimensional data obtained by the conversion means and generates body shape information, A creation means for creating an individually optimized training plan based on the body shape information generated by the aforementioned analysis means, A means for providing the user with the training plan created by the creation means, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is not easy for an individual to appropriately set their own training and diet guidelines and efficiently achieve an ideal body shape. In particular, there is a problem that motivation cannot be maintained because the appropriate methods and plans are unknown. Furthermore, in the conventional methods, continuous intervention by an expert is required to obtain individualized feedback, which is costly and time-consuming.

Means for Solving the Problems

[0005] This invention provides a system that uses an acquisition means to capture human body images to capture individual body shapes in three dimensions and obtains body shape information by analyzing that data. Based on this body shape information, the system provides means for creating and providing personalized training plans and dietary guidelines to users, thereby providing feedback tailored to individual needs in a low-cost and efficient manner, and helping to maintain motivation.

[0006] "Means of acquisition" refers to the functions and devices necessary to collect images of the human body.

[0007] "Conversion means" refers to the function or process of converting acquired human body images into three-dimensional data.

[0008] "Analysis means" refers to functions and algorithms for generating body shape information based on converted three-dimensional data.

[0009] "Creation method" refers to the functions and processes for creating individually optimized training plans based on analysis.

[0010] "Means of delivery" refers to functions or devices used to communicate the created training plan and other feedback to the user.

[0011] "Three-dimensional data" refers to digital information that represents the human body in three dimensions and serves as the basis for analysis.

[0012] "Body shape information" refers to data on the dimensions and shape of the human body generated by analytical methods. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the language used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system for analyzing individual users' body shape data in detail and providing training and dietary guidelines based on this data. The system operates via the user's terminal, a server, and a communication network between them.

[0035] System Overview

[0036] Users take images of their body shape using a dedicated application and input the data into the device. The device has a function to check the image quality and convert it into a format that can be transmitted as data.

[0037] The device uploads the captured image data to the server. The image data is securely delivered via a communication protocol.

[0038] Server-based data analysis

[0039] The server feeds the received image data into a 3D body scanning algorithm to generate three-dimensional body shape data of the user. This data provides a detailed representation of the dimensions, shape, and posture of each part of the user's body.

[0040] From the analyzed 3D data, the server quantifies the user's current body shape information and stores it in a database. Furthermore, the server has the function to compare it with past data and identify changes.

[0041] Creating and providing feedback

[0042] Based on the analysis results, the server generates the most effective training plan for the user. This plan includes detailed information on specific strength training and aerobic exercise.

[0043] Furthermore, the server generates dietary guidelines based on body shape information and recommends nutritional intake tailored to the user's goals.

[0044] The device displays this feedback to the user in real time, enabling continuous daily guidance through the application. It also includes a reminder function for when the user should take the next action.

[0045] Specific example

[0046] For example, if a user scans their body shape with the app once a week and sends the data to the server, the server immediately analyzes this data and creates a new training plan based on the results. If a user is aiming to slim their waist, the server will create a plan that includes planks and core strengthening exercises and provide a system to monitor their progress weekly.

[0047] Thus, the system of the present invention provides customized feedback to individual users, supporting the maintenance of motivation and promoting efficient body shape improvement.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The user launches a dedicated application and takes photos of their body from various angles. The image is set to capture the entire body accurately, and the application provides instructions for taking photos as needed.

[0051] Step 2:

[0052] The device optimizes the image data received from the user and converts it into a data format suitable for 3D body scanning. After conversion, it verifies the quality and integrity of the data.

[0053] Step 3:

[0054] The terminal securely transmits the converted image data to the server using a communication protocol. The data is sent encrypted via the internet or a dedicated network.

[0055] Step 4:

[0056] The server sends the received image data to a 3D body scanning algorithm to generate a three-dimensional body model. The model contains data such as body dimensions and shape.

[0057] Step 5:

[0058] The server quantifies the user's body shape information from the generated 3D model and compares it with past data. This comparison measures specific changes in the body and analyzes trends in those changes.

[0059] Step 6:

[0060] The server creates an individually optimized training plan and dietary guidelines based on the analysis results. The training plan includes specific types of exercise and recommended repetitions, while the dietary guidelines include recommendations regarding nutrients and calories.

[0061] Step 7:

[0062] The device provides users with training plans and dietary guidelines from the server. Users can receive feedback through the application and use it to improve their future training and eating habits.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] The present invention aims to provide a system that can efficiently improve body shape by providing effective and individually optimized training plans and dietary guidelines based on the acquisition and analysis of individual user body shape data. Current fitness and health management systems have problems maintaining motivation and continuous body shape improvement because it is difficult to accurately grasp individual body shape changes and provide immediate feedback.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes a device for acquiring human body data, a conversion device for converting the human body data acquired by the device into three-dimensional information, and a processing device for processing the three-dimensional information obtained by the conversion device and generating physical information. This makes it possible to provide detailed and accurate body shape information to individual users. Furthermore, based on this information, individually optimized training plans and nutritional guidance can be quickly created, and continuous feedback can be provided to help maintain motivation.

[0068] "Human body data" refers to information that describes the physical characteristics of the human body, such as its shape and dimensions.

[0069] A "device" refers to a combination of hardware and software designed to perform a specific function.

[0070] "Three-dimensional information" refers to data used to represent the shape and structure of an object in three-dimensional space.

[0071] A "conversion device" is a device that has the function of converting data in one format to another format.

[0072] A "processing device" is a device that processes input data based on specific algorithms or rules and generates results.

[0073] "Physical information" refers to detailed data about the user's body, including body shape and posture.

[0074] A "training plan" is a program of exercises or movements designed to achieve a specific objective.

[0075] A "planning device" is a device that has the function of creating a plan.

[0076] A "providing device" is a device that has the function of supplying data and information to users.

[0077] "Nutritional guidance" refers to instruction that provides suggestions and recommendations for meals tailored to the user's health and fitness goals.

[0078] An "evaluation device" is a device that has the function of evaluating, comparing, and analyzing data.

[0079] The embodiments for carrying out the present invention will now be described. This system is operated through the collaboration of a user, a terminal, and a server.

[0080] Users use a device with a dedicated application installed to capture and input their body shape data. The device converts the captured image data into an appropriate format and securely transmits it to the server. Image processing software is used for the image data conversion. Secure protocols are used for communication to maintain data confidentiality.

[0081] The server executes a three-dimensional body scanning algorithm to convert the received image data into three-dimensional information. This algorithm is built on machine learning and utilizes generative AI models. Specifically, technologies such as Python and TENSORFLOW® can be used. This process generates three-dimensional data that accurately represents the dimensions and posture of each part of the user's body. The server analyzes the generated data, extracts and stores the user's body information, and evaluates changes in body shape by comparing it with past data.

[0082] After analysis, the server creates a training plan optimized for the user based on the generated physical information. This plan includes specific menus for strength training and aerobic exercise. Furthermore, a meal plan is also created that takes into account the user's goals and nutritional guidance. The server sends this data to the terminal, which displays feedback and the plan to the user in real time. The terminal also has a function to remind the user of the timing of the next training and meals, thus supporting the user.

[0083] As a concrete example, when a user enters a prompt such as "Please suggest a training plan aimed at slimming my waist" into the application, the server immediately generates and provides a suitable plan. This system enables users to effectively and continuously improve their physique.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user takes an image of their body shape using a dedicated application. The device receives this image data as input and checks the image resolution and clarity. Specifically, the device performs image filtering, and crops and compresses the image as needed. As a result, it outputs image data converted into a format suitable for data analysis.

[0087] Step 2:

[0088] The terminal sends the converted image data to the server. The server receives this image data as input and verifies the data's security via a secure communication protocol. Specifically, the data is protected using an encrypted channel. As output, the data is successfully stored on the server.

[0089] Step 3:

[0090] The server inputs the received image data into a three-dimensional body scanning algorithm to generate three-dimensional information. Here, a machine learning algorithm using a generative AI model analyzes the image and constructs the user's three-dimensional body data. Based on the input image data, it outputs three-dimensional data that quantifies the user's body dimensions and shape.

[0091] Step 4:

[0092] The server analyzes body information using the generated three-dimensional data and stores it in a database. It also evaluates current body shape changes by comparing them with past data. Specifically, the server queries the dataset and performs analytical calculations to find patterns of change. As output, it generates information on the user's body shape changes over time.

[0093] Step 5:

[0094] The server creates an individually optimized training plan and nutritional guidance based on physical information. Here, a generative AI model processes prompts that take the user's goals into consideration and plans specific exercises and nutritional plans. It takes physical information and the user's objectives as input and generates a customized training and meal plan as output.

[0095] Step 6:

[0096] The device receives training plans and dietary guidance sent from the server and displays them to the user. The input is a generated plan, which the device visualizes and presents in a user-friendly interface. Specifically, it supports the user's continued actions through features such as reminders and progress tracking. The output provides clear guidance information to help the user decide on their next actions.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Traditional health management systems have struggled to accurately understand each user's body type and nutritional status, and to provide personalized exercise plans and nutritional guidelines based on that information. Furthermore, they lacked effective means of tracking user progress and maintaining motivation. As a result, users were unable to obtain a health management plan that was optimal for them, making efficient health improvement difficult.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes acquisition means for acquiring human body data, conversion means for converting it into three-dimensional data, analysis means for analyzing and generating body shape data, and tracking means. This enables the provision of personalized exercise plans and nutritional guidelines, as well as effective tracking of progress.

[0102] "Human body data" refers to data that includes information about the user's body shape and size.

[0103] "3D data" refers to data that is represented three-dimensionally based on acquired human body data.

[0104] "Body shape data" refers to information about body dimensions and balance generated by analyzing three-dimensional data.

[0105] An "exercise plan" is an exercise program created individually based on the user's body shape data.

[0106] "Nutritional guidelines" are advice on appropriate nutritional intake provided based on the user's body shape data.

[0107] A "tracking method" is a means of observing a user's fitness progress and recording their achievements and areas for improvement.

[0108] A "server" is a computer system that analyzes data obtained from users and generates plans related to exercise and nutrition.

[0109] This system provides users with exercise plans and nutritional guidelines to improve their health. Users acquire their own body data using a terminal and convert it into 3D data. This process utilizes devices equipped with cameras and sensors, processing the image data in 3D. Next, a server analyzes this 3D data to generate body shape data. Advanced algorithms are applied to the analysis, deriving individualized body shape information for each user.

[0110] The server creates an exercise plan based on the generated body shape data. This plan includes detailed exercises necessary for improving physical fitness and maintaining health. The server also provides nutritional guidance to improve the user's nutritional status. This guidance includes specific advice to promote a balanced diet.

[0111] Furthermore, the server monitors the user's progress through tracking mechanisms, recording achievements and areas for improvement. This feature allows users to adjust their exercise and diet to align with their health goals. The feedback is also provided interactively, designed to help users maintain motivation.

[0112] As a concrete example, consider a scenario where a user scans their body shape with a device every morning upon waking up and sends the results to a server. Based on this data, the server proposes an ideal exercise and diet plan for the user. An example of a prompt to the generative AI model would be, "I have sent my current body shape data. Based on this data, please suggest an exercise and diet plan for today to achieve my health goals." This prompt allows the system to quickly provide appropriate advice to the user.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user takes a photo of their own body shape using their device.

[0116] The input is an image of a human body taken with the device's camera. After acquiring this image data, the device checks the image resolution and quality, converts it to an appropriate format, and outputs it.

[0117] Step 2:

[0118] The terminal sends the converted image data to the server.

[0119] The input is formatted image data. The terminal transfers the image data to the server using a secure communication protocol. The output is the image data that successfully reached the server.

[0120] Step 3:

[0121] The server inputs the received image data into a 3D conversion algorithm to generate 3D data.

[0122] The input is image data that has arrived at the server. The server uses advanced image processing technology to convert this into 3D data. The output is data that represents the user's body shape in three dimensions.

[0123] Step 4:

[0124] The server analyzes the 3D data and creates body shape data.

[0125] The input is 3D data generated by the server. Using this data, the server quantifies the dimensions and posture of each part of the user's body. The output is data summarizing the user's body shape information.

[0126] Step 5:

[0127] The server creates personalized exercise plans and nutritional guidelines based on body shape data.

[0128] The input is data reflecting the user's body shape information. The server uses a machine learning model to generate an appropriate exercise and nutrition plan based on the analysis results. The output is a plan optimized for each individual user.

[0129] Step 6:

[0130] The server provides the user with a generated exercise plan and nutritional guidelines.

[0131] The input consists of an exercise plan and nutritional guidelines. The server sends the plan to the terminal, allowing the user to view and execute it. The output is the plan information displayed on the user's terminal.

[0132] Step 7:

[0133] Users perform their activities according to the plan provided by the server.

[0134] The input consists of an exercise plan and nutritional guidelines. The user acts according to these in their daily life and strives towards their health goals. The output is the results of their daily health activities.

[0135] Step 8:

[0136] The server tracks the user's progress and provides feedback.

[0137] The input consists of progress information and activity data reported by the user. The server uses this data to analyze achievement levels and areas for improvement, and uses this information to inform future instruction. The output is feedback information for the user.

[0138] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0139] This invention combines a system that analyzes changes in a user's body shape in detail and provides an individually optimized training plan and dietary guidance with an emotion engine that recognizes the user's emotions. This system, which includes the user's terminal, server, and emotion engine, provides detailed feedback tailored to the user's state.

[0140] System-wide configuration

[0141] The user takes a photo of their body shape using a dedicated application and inputs this data into their device. The image is then converted into a format suitable for three-dimensional analysis.

[0142] The device sends the converted image data, along with the user's voice and facial expression information, to the emotion engine. This information is then transferred to the server via a secure connection.

[0143] Server Processing

[0144] The server analyzes the image data using 3D body scanning technology to generate three-dimensional body shape data. This data contains detailed information about the user's dimensions and shape and is stored in a database.

[0145] The analyzed body shape data is compared with past data to evaluate the degree of improvement in body shape.

[0146] How the emotion engine works

[0147] The emotion engine recognizes the user's emotions from voice and facial expression data and sends the user's emotional state to the server.

[0148] This emotional data is reflected in training plans and feedback messages, generating personalized content tailored to the user's emotions.

[0149] Generating and providing feedback

[0150] The server creates an optimal training plan based on analyzed body shape information and emotional data. For example, if a user is feeling anxious, it can prioritize providing simple exercises.

[0151] The device receives feedback data from the server and displays it to the user. This feedback includes training guidelines, as well as emotionally-based encouraging messages and advice.

[0152] Specific example

[0153] For example, when a user is feeling down, the system can use its emotion engine to identify that emotion and provide a message to boost motivation, such as, "Why not try some stress-relieving exercises today to lift your spirits?" This allows users to receive support optimized for their situation and achieve their health goals more effectively.

[0154] Thus, the present invention allows users to receive highly personalized training and feedback based on their own physical and emotional state. This makes it possible to maximize the effectiveness of training and bring about continuous improvement.

[0155] The following describes the processing flow.

[0156] Step 1:

[0157] The user launches a dedicated application and takes photos of their body from multiple angles. Once the shooting is complete, the images are viewed within the application and saved in a format suitable for the next processing step.

[0158] Step 2:

[0159] The device performs a process to convert the stored image data into three-dimensional data. This conversion makes it possible to capture the user's body shape in three dimensions.

[0160] Step 3:

[0161] The device sends the converted 3D data to the server, while simultaneously acquiring emotional data based on the user's voice and facial expressions, and sending this data to the server as well. This data is transmitted securely via a secure communication protocol.

[0162] Step 4:

[0163] The server analyzes the received 3D data and generates detailed body shape information for the user. This information includes data on the dimensions and posture of each body part.

[0164] Step 5:

[0165] The server compares the generated body shape information with past data to evaluate changes in current body shape. This evaluation is used to adjust training and meal plans.

[0166] Step 6:

[0167] The emotion engine analyzes the received emotion data to identify the user's current emotional state. The identified emotion is then processed as an element to be reflected in the feedback.

[0168] Step 7:

[0169] The server generates individually optimized training plans and feedback messages based on body type information and emotional data. It adjusts the intensity of the content and the coaching style according to the emotional state indicated by the emotional data.

[0170] Step 8:

[0171] The device displays real-time feedback from the server to the user. The user then uses this feedback to conduct daily training and receive advice and motivation as needed.

[0172] This allows users to pursue their physical goals while also receiving emotional support, enabling them to continue training consistently.

[0173] (Example 2)

[0174] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0175] In modern health management, providing personalized exercise plans and nutritional guidelines is crucial. However, conventional systems typically provide plans based solely on the user's body type information, lacking feedback that considers the user's emotional state. As a result, the provided plans are not always optimal for the user's current situation, posing challenges in maintaining motivation and achieving effective improvement.

[0176] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0177] In this invention, the server includes means for acquiring human body data, means for converting the human body data acquired by the acquisition means into three-dimensional information, means for analyzing the three-dimensional information obtained by the conversion means and generating body shape data, and emotion analysis means for acquiring and analyzing emotion data. This makes it possible to provide a user-optimized exercise plan and feedback based on the user's body shape data and emotion data.

[0178] "Human body data" refers to information about body shape obtained from users, specifically including external body shape and dimensions.

[0179] "Three-dimensional information" refers to data obtained by converting two-dimensional shape data into three-dimensional data, providing the user's three-dimensional body shape information.

[0180] "Body shape data" refers to detailed information about the user's current physical characteristics, generated based on analyzed three-dimensional information.

[0181] An "exercise plan" is a personalized exercise plan optimized based on the user's body type and emotional data.

[0182] "Emotional data" refers to information about a user's psychological state, analyzed from their voice, facial expressions, and other data.

[0183] "Emotional analysis means" refers to technologies and devices for collecting and analyzing user emotional data, and for identifying the user's psychological state.

[0184] "Feedback" refers to the information and advice provided to users based on analysis results and plans.

[0185] The system of this invention relies on the collaboration of a user, a terminal, and a server. First, the user uses a terminal with a dedicated application installed. The user acquires image data of their body shape through this application. The image data is converted into three-dimensional information on the terminal. This is done using AI-based image analysis software.

[0186] The terminal transmits the converted three-dimensional information and emotional data collected from the user's voice and facial expressions to the server via a secure communication protocol. The server analyzes the received data, generates the user's body shape data, and further recognizes the user's emotional state using emotion analysis tools.

[0187] Based on this information, the server generates a personalized exercise plan. This plan reflects the user's body type data and emotional state. For example, if the server detects that the user is stressed, relaxation-focused exercises may be recommended.

[0188] Finally, the server provides the generated feedback and advice to the device, which then displays it to the user. This allows the user to access an appropriate health management plan tailored to their physical and emotional state.

[0189] For example, a prompt might instruct you to "Assume the user is feeling down and generate a feedback message using the emotion engine." In response to this, the system might generate feedback such as, "How about trying some stress-relieving exercises together today to cheer you up?" This is generated using a generative AI model and optimizes the user experience.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The user takes images of their body shape using a dedicated application. The application requests images from multiple angles to facilitate the collection of three-dimensional data. The input is the multiple image data points captured. The output is the storage of these image data points on the device.

[0193] Step 2:

[0194] The terminal converts image data input from the user into three-dimensional information. This conversion uses AI-based image analysis software. The input is the image data obtained in step 1, and the output is three-dimensional information. In this process, the information of each pixel in the image is analyzed to construct a 3D model.

[0195] Step 3:

[0196] The device captures voice and facial expression data from the user and acquires it as data for emotion analysis. The input is the user's voice and facial expression information, and the output is stored on the device as emotion data. This process uses voice recognition technology and facial recognition technology.

[0197] Step 4:

[0198] The terminal transmits acquired 3D information and emotional data to the server using a secure communication protocol. The input is 3D information and emotional data, and the output is the data transferred to the server. The data is protected by encryption technologies such as SSL / TLS.

[0199] Step 5:

[0200] The server analyzes the received 3D information and generates the user's current body shape data. This analysis utilizes 3D body scanning technology. The input is the 3D information received in step 4, and the output is the user's body shape data. Specifically, it calculates the size of each body part and the overall proportions.

[0201] Step 6:

[0202] The server analyzes the received voice and facial expression data using emotion analysis tools to evaluate the user's emotional state. The input is the emotion data received in step 4, and the output is information about the user's emotional state. This allows specific emotions (e.g., stress, anxiety, joy) to be quantified.

[0203] Step 7:

[0204] The server generates an individually optimized exercise plan based on the generated body shape data and emotional state information. The input is the data obtained in steps 5 and 6, and the output is the individual plan. Specifically, it organizes exercises corresponding to the user's body shape goals and makes adjustments according to their emotional state.

[0205] Step 8:

[0206] The server generates an exercise plan and feedback based on emotional data, and sends it to the device. The input is the raw material for the individual plan and feedback, while the output is the feedback information sent to the device. This feedback includes individual exercise instructions and encouraging messages.

[0207] Step 9:

[0208] The terminal displays feedback information received from the server to the user. The input is the feedback information obtained in step 8, and the output is the information presented to the user. Throughout this process, exercise plans and advice are displayed through a user-friendly interface.

[0209] (Application Example 2)

[0210] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0211] Traditional health management systems have struggled to provide not only exercise plans based on users' body shape data, but also appropriate feedback tailored to their individual emotional states. As a result, users may struggle to maintain motivation, hindering the achievement of their health goals. Furthermore, previous systems evaluated body shape data and emotional states separately, resulting in insufficient overall personalization.

[0212] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0213] In this invention, the server includes an acquisition means for acquiring human body information, an emotion recognition means having an emotion recognition function to identify emotional states, and a generation means for generating suggestions that reflect the emotional state. This makes it possible to provide highly personalized exercise plans and nutritional guidelines that take into account both body shape data and emotional states.

[0214] "Human body information" refers to data related to the external shape and dimensions of the human body, and this information is used to understand and analyze changes in the body shape of individual users.

[0215] "Three-dimensional information" refers to information that represents the external shape of the human body as three-dimensional spatial data, which enables three-dimensional body shape analysis.

[0216] "Body shape data" refers to a collection of detailed information about the user's body shape, including dimensions and form, generated based on three-dimensional information.

[0217] An "exercise plan" refers to a series of suggestions that outline the content and schedule of exercises that a user should perform, individually optimized based on their body type data.

[0218] "Delivery means" refers to the function of delivering, displaying, or notifying the user of the generated exercise plan and feedback message.

[0219] "Emotion recognition function" refers to technology that analyzes the user's voice, facial expressions, etc., to determine their emotional state at that time.

[0220] "Emotion identification means" refers to a function that specifically identifies the user's emotional state based on information acquired by the emotion recognition function.

[0221] "Generative means" refers to technology that comprehensively considers the user's body shape data and emotional state to generate exercise plans and feedback for improvement.

[0222] The system for carrying out the present invention includes a user, a terminal, a server, and an emotion engine. The user interacts with a dedicated consumer robot and receives daily health management. The terminal acquires the user's body shape information and emotion data using a camera and microphone mounted on the robot.

[0223] The server first analyzes body shape information transmitted from the terminal using 3D body scanning technology to generate detailed body shape data. OpenCV is used as the image processing library for this analysis to construct the 3D data. Furthermore, TensorFlow is used for speech and facial recognition in the analysis of emotion data. This enables real-time emotion identification.

[0224] The analyzed body shape and emotional data are stored on the server and compared with past records before a process to generate an optimal exercise plan for the user. The generated exercise plan and nutritional guidelines are provided via the device along with feedback messages that take into account the user's emotional state. For example, if the server determines that the user is feeling down, it will create an encouraging message such as, "Let's do some light exercise today to refresh yourself."

[0225] For example, if a user asks the robot, "What should I eat today?", the robot might respond, "Considering your current physical condition and emotional state, I recommend a balanced meal that includes a salad." This allows the user to receive physically and emotionally optimized support, enabling them to effectively work towards their health goals.

[0226] An example of a prompt for the generating AI model would be the text, "Consider the user's current body shape data and emotional state, and generate an appropriate training and meal plan."

[0227] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0228] Step 1:

[0229] The user acquires body shape information and audio data through the device's camera and microphone. Body shape photos are input as high-resolution images, and audio is input as audio data for sentiment analysis. This data is stored on the device as initial data.

[0230] Step 2:

[0231] The device converts the acquired body shape photograph into three-dimensional information using the OpenCV library. The input is a high-resolution image, and the output is three-dimensional mesh data. This data conversion generates detailed dimensional information about the body shape.

[0232] Step 3:

[0233] The device analyzes audio data using TensorFlow to identify emotional states. The input is audio data, and the output is the identified emotional tag. This process allows the user's current emotional state to be understood.

[0234] Step 4:

[0235] The device transmits three-dimensional body shape data and emotional state data to the server via a secure connection. Here, the device creates a data package and transfers the data to the server.

[0236] Step 5:

[0237] The server analyzes the received 3D data and evaluates the user's body shape data. 3D body shape mesh data is used as input, and the output generates statistics on the user's dimensions and changes in body shape. This includes comparisons with historical data.

[0238] Step 6:

[0239] The server uses emotional state data to generate an optimal exercise plan for the user. Inputs are emotional tags and body type data, and output is a personalized exercise plan and nutritional guidelines. This ensures that appropriate content is prepared based on the user's emotional state.

[0240] Step 7:

[0241] The server sends the generated exercise plan and nutritional guidelines to the terminal, which then provides them to the user. The terminal communicates the information to the user via the robot's display or voice. The outputted information serves as a guide for maintaining health in the user's daily life.

[0242] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0243] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0244] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0245] [Second Embodiment]

[0246] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0247] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0248] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0249] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0250] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0251] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0252] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0253] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0254] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0255] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0256] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0257] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0258] This invention relates to a system for analyzing individual users' body shape data in detail and providing training and dietary guidelines based on this data. The system operates via the user's terminal, a server, and a communication network between them.

[0259] System Overview

[0260] Users take images of their body shape using a dedicated application and input the data into the device. The device has a function to check the image quality and convert it into a format that can be transmitted as data.

[0261] The device uploads the captured image data to the server. The image data is securely delivered via a communication protocol.

[0262] Server-based data analysis

[0263] The server feeds the received image data into a 3D body scanning algorithm to generate three-dimensional body shape data of the user. This data provides a detailed representation of the dimensions, shape, and posture of each part of the user's body.

[0264] From the analyzed 3D data, the server quantifies the user's current body shape information and stores it in a database. Furthermore, the server has the function to compare it with past data and identify changes.

[0265] Creating and providing feedback

[0266] Based on the analysis results, the server generates the most effective training plan for the user. This plan includes detailed information on specific strength training and aerobic exercise.

[0267] Furthermore, the server generates dietary guidelines based on body shape information and recommends nutritional intake tailored to the user's goals.

[0268] The device displays this feedback to the user in real time, enabling continuous daily guidance through the application. It also includes a reminder function for when the user should take the next action.

[0269] Specific example

[0270] For example, if a user scans their body shape with the app once a week and sends the data to the server, the server immediately analyzes this data and creates a new training plan based on the results. If a user is aiming to slim their waist, the server will create a plan that includes planks and core strengthening exercises and provide a system to monitor their progress weekly.

[0271] Thus, the system of the present invention provides customized feedback to individual users, supporting the maintenance of motivation and promoting efficient body shape improvement.

[0272] The following describes the processing flow.

[0273] Step 1:

[0274] The user launches a dedicated application and takes photos of their body from various angles. The image is set to capture the entire body accurately, and the application provides instructions for taking photos as needed.

[0275] Step 2:

[0276] The device optimizes the image data received from the user and converts it into a data format suitable for 3D body scanning. After conversion, it verifies the quality and integrity of the data.

[0277] Step 3:

[0278] The terminal securely transmits the converted image data to the server using a communication protocol. The data is sent encrypted via the internet or a dedicated network.

[0279] Step 4:

[0280] The server sends the received image data to a 3D body scanning algorithm to generate a three-dimensional body model. The model contains data such as body dimensions and shape.

[0281] Step 5:

[0282] The server quantifies the user's body shape information from the generated 3D model and compares it with past data. This comparison measures specific changes in the body and analyzes trends in those changes.

[0283] Step 6:

[0284] The server creates a training plan and diet guidelines optimized individually based on the analysis results. The training plan includes specific types of exercises and recommended numbers of repetitions, and the diet guidelines include recommendations regarding nutrients and calories.

[0285] Step 7:

[0286] The terminal provides the user with the training plan and diet guidelines provided by the server. The user can receive feedback through the application and utilize it for future training and improvement of their diet.

[0287] (Example 1)

[0288] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] An object of the present invention is to provide a system that can efficiently improve body shape by providing an effective and individually optimized training plan and diet guidelines based on the acquisition and analysis of body shape data of individual users. In current fitness and health management systems, it is difficult to accurately grasp individual body shape changes and provide immediate feedback, resulting in problems such as difficulty in maintaining motivation and continuously improving body shape.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0291] In this invention, the server includes a device for acquiring human body data, a conversion device for converting the human body data acquired by the device into three-dimensional information, and a processing device for processing the three-dimensional information obtained by the conversion device and generating physical information. This makes it possible to provide detailed and accurate body shape information to individual users. Furthermore, based on this information, individually optimized training plans and nutritional guidance can be quickly created, and continuous feedback can be provided to help maintain motivation.

[0292] "Human body data" refers to information that describes the physical characteristics of the human body, such as its shape and dimensions.

[0293] A "device" refers to a combination of hardware and software designed to perform a specific function.

[0294] "Three-dimensional information" refers to data used to represent the shape and structure of an object in three-dimensional space.

[0295] A "conversion device" is a device that has the function of converting data in one format to another format.

[0296] A "processing device" is a device that processes input data based on specific algorithms or rules and generates results.

[0297] "Physical information" refers to detailed data about the user's body, including body shape and posture.

[0298] A "training plan" is a program of exercises or movements designed to achieve a specific objective.

[0299] A "planning device" is a device that has the function of creating a plan.

[0300] A "providing device" is a device that has the function of supplying data and information to users.

[0301] "Nutritional guidance" refers to instruction that provides suggestions and recommendations for meals tailored to the user's health and fitness goals.

[0302] An "evaluation device" is a device that has the function of evaluating, comparing, and analyzing data.

[0303] The embodiments for carrying out the present invention will now be described. This system is operated through the collaboration of a user, a terminal, and a server.

[0304] Users use a device with a dedicated application installed to capture and input their body shape data. The device converts the captured image data into an appropriate format and securely transmits it to the server. Image processing software is used for the image data conversion. Secure protocols are used for communication to maintain data confidentiality.

[0305] The server executes a 3D body scanning algorithm to convert the received image data into three-dimensional information. This algorithm is built on machine learning and utilizes generative AI models. Specifically, technologies such as Python and TensorFlow can be used. This process generates 3D data that accurately represents the dimensions and posture of each part of the user's body. The server analyzes the generated data, extracts and stores the user's body information, and evaluates changes in body shape by comparing it with past data.

[0306] After analysis, the server creates a training plan optimized for the user based on the generated physical information. This plan includes specific menus for strength training and aerobic exercise. Furthermore, a meal plan is also created that takes into account the user's goals and nutritional guidance. The server sends this data to the terminal, which displays feedback and the plan to the user in real time. The terminal also has a function to remind the user of the timing of the next training and meals, thus supporting the user.

[0307] As a specific example, when a user inputs a prompt sentence such as "Please propose a training plan aiming at tightening the waist" into the application, the server immediately generates and provides an appropriate plan. With this system, the user can effectively and continuously improve their body shape.

[0308] The flow of the specific process in Example 1 will be described using FIG. 11.

[0309] Step 1:

[0310] The user uses a dedicated application to take a picture of their body shape. The terminal receives this image data as input and checks the resolution and sharpness of the image. As a specific operation, the terminal performs image filtering processing and implements trimming and compression as necessary. As a result, the image data converted into a format suitable for data analysis is output.

[0311] Step 2:

[0312] The terminal transmits the converted image data to the server. The server receives this image data as input and checks the security of the data via a secure communication protocol. Specifically, the data is protected using an encrypted channel. As output, the data is successfully saved on the server.

[0313] Step 3:

[0314] The server inputs the received image data into a three-dimensional body scan algorithm to generate three-dimensional information. Here, a machine learning algorithm analyzes the image using a generated AI model to construct the user's three-dimensional body data. Based on the input image data, three-dimensional data quantifying the user's body dimensions and shape is output.

[0315] Step 4:

[0316] The server analyzes body information using the generated three-dimensional data and stores it in a database. It also evaluates current body shape changes by comparing them with past data. Specifically, the server queries the dataset and performs analytical calculations to find patterns of change. As output, it generates information on the user's body shape changes over time.

[0317] Step 5:

[0318] The server creates an individually optimized training plan and nutritional guidance based on physical information. Here, a generative AI model processes prompts that take the user's goals into consideration and plans specific exercises and nutritional plans. It takes physical information and the user's objectives as input and generates a customized training and meal plan as output.

[0319] Step 6:

[0320] The device receives training plans and dietary guidance sent from the server and displays them to the user. The input is a generated plan, which the device visualizes and presents in a user-friendly interface. Specifically, it supports the user's continued actions through features such as reminders and progress tracking. The output provides clear guidance information to help the user decide on their next actions.

[0321] (Application Example 1)

[0322] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0323] Traditional health management systems have struggled to accurately understand each user's body type and nutritional status, and to provide personalized exercise plans and nutritional guidelines based on that information. Furthermore, they lacked effective means of tracking user progress and maintaining motivation. As a result, users were unable to obtain a health management plan that was optimal for them, making efficient health improvement difficult.

[0324] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0325] In this invention, the server includes acquisition means for acquiring human body data, conversion means for converting it into three-dimensional data, analysis means for analyzing and generating body shape data, and tracking means. This enables the provision of personalized exercise plans and nutritional guidelines, as well as effective tracking of progress.

[0326] "Human body data" refers to data that includes information about the user's body shape and size.

[0327] "3D data" refers to data that is represented three-dimensionally based on acquired human body data.

[0328] "Body shape data" refers to information about body dimensions and balance generated by analyzing three-dimensional data.

[0329] An "exercise plan" is an exercise program created individually based on the user's body shape data.

[0330] "Nutritional guidelines" are advice on appropriate nutritional intake provided based on the user's body shape data.

[0331] A "tracking method" is a means of observing a user's fitness progress and recording their achievements and areas for improvement.

[0332] A "server" is a computer system that analyzes data obtained from users and generates plans related to exercise and nutrition.

[0333] This system provides users with exercise plans and nutritional guidelines to improve their health. Users acquire their own body data using a terminal and convert it into 3D data. This process utilizes devices equipped with cameras and sensors, processing the image data in 3D. Next, a server analyzes this 3D data to generate body shape data. Advanced algorithms are applied to the analysis, deriving individualized body shape information for each user.

[0334] The server creates an exercise plan based on the generated body shape data. This plan includes detailed exercises necessary for improving physical fitness and maintaining health. The server also provides nutritional guidance to improve the user's nutritional status. This guidance includes specific advice to promote a balanced diet.

[0335] Furthermore, the server monitors the user's progress through tracking mechanisms, recording achievements and areas for improvement. This feature allows users to adjust their exercise and diet to align with their health goals. The feedback is also provided interactively, designed to help users maintain motivation.

[0336] As a concrete example, consider a scenario where a user scans their body shape with a device every morning upon waking up and sends the results to a server. Based on this data, the server proposes an ideal exercise and diet plan for the user. An example of a prompt to the generative AI model would be, "I have sent my current body shape data. Based on this data, please suggest an exercise and diet plan for today to achieve my health goals." This prompt allows the system to quickly provide appropriate advice to the user.

[0337] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0338] Step 1:

[0339] The user takes a photo of their own body shape using their device.

[0340] The input is an image of a human body taken with the device's camera. After acquiring this image data, the device checks the image resolution and quality, converts it to an appropriate format, and outputs it.

[0341] Step 2:

[0342] The terminal sends the converted image data to the server.

[0343] The input is formatted image data. The terminal transfers the image data to the server using a secure communication protocol. The output is the image data that successfully reached the server.

[0344] Step 3:

[0345] The server inputs the received image data into a 3D conversion algorithm to generate 3D data.

[0346] The input is image data that has arrived at the server. The server uses advanced image processing technology to convert this into 3D data. The output is data that represents the user's body shape in three dimensions.

[0347] Step 4:

[0348] The server analyzes the 3D data and creates body shape data.

[0349] The input is 3D data generated by the server. Using this data, the server quantifies the dimensions and posture of each part of the user's body. The output is data summarizing the user's body shape information.

[0350] Step 5:

[0351] The server creates personalized exercise plans and nutritional guidelines based on body shape data.

[0352] The input is data reflecting the user's body shape information. The server uses a machine learning model to generate an appropriate exercise and nutrition plan based on the analysis results. The output is a plan optimized for each individual user.

[0353] Step 6:

[0354] The server provides the user with a generated exercise plan and nutritional guidelines.

[0355] The input consists of an exercise plan and nutritional guidelines. The server sends the plan to the terminal, allowing the user to view and execute it. The output is the plan information displayed on the user's terminal.

[0356] Step 7:

[0357] Users perform their activities according to the plan provided by the server.

[0358] The input consists of an exercise plan and nutritional guidelines. The user acts according to these in their daily life and strives towards their health goals. The output is the results of their daily health activities.

[0359] Step 8:

[0360] The server tracks the user's progress and provides feedback.

[0361] The input consists of progress information and activity data reported by the user. The server uses this data to analyze achievement levels and areas for improvement, and uses this information to inform future instruction. The output is feedback information for the user.

[0362] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0363] This invention combines a system that analyzes changes in a user's body shape in detail and provides an individually optimized training plan and dietary guidance with an emotion engine that recognizes the user's emotions. This system, which includes the user's terminal, server, and emotion engine, provides detailed feedback tailored to the user's state.

[0364] System-wide configuration

[0365] The user takes a photo of their body shape using a dedicated application and inputs this data into their device. The image is then converted into a format suitable for three-dimensional analysis.

[0366] The device sends the converted image data, along with the user's voice and facial expression information, to the emotion engine. This information is then transferred to the server via a secure connection.

[0367] Server Processing

[0368] The server analyzes the image data using 3D body scanning technology to generate three-dimensional body shape data. This data contains detailed information about the user's dimensions and shape and is stored in a database.

[0369] The analyzed body shape data is compared with past data to evaluate the degree of improvement in body shape.

[0370] How the emotion engine works

[0371] The emotion engine recognizes the user's emotions from voice and facial expression data and sends the user's emotional state to the server.

[0372] This emotional data is reflected in training plans and feedback messages, generating personalized content tailored to the user's emotions.

[0373] Generating and providing feedback

[0374] The server creates an optimal training plan based on analyzed body shape information and emotional data. For example, if a user is feeling anxious, it can prioritize providing simple exercises.

[0375] The device receives feedback data from the server and displays it to the user. This feedback includes training guidelines, as well as emotionally-based encouraging messages and advice.

[0376] Specific example

[0377] For example, when a user is feeling down, the system can use its emotion engine to identify that emotion and provide a message to boost motivation, such as, "Why not try some stress-relieving exercises today to lift your spirits?" This allows users to receive support optimized for their situation and achieve their health goals more effectively.

[0378] Thus, the present invention allows users to receive highly personalized training and feedback based on their own physical and emotional state. This makes it possible to maximize the effectiveness of training and bring about continuous improvement.

[0379] The following describes the processing flow.

[0380] Step 1:

[0381] The user launches a dedicated application and takes photos of their body from multiple angles. Once the shooting is complete, the images are viewed within the application and saved in a format suitable for the next processing step.

[0382] Step 2:

[0383] The device performs a process to convert the stored image data into three-dimensional data. This conversion makes it possible to capture the user's body shape in three dimensions.

[0384] Step 3:

[0385] The device sends the converted 3D data to the server, while simultaneously acquiring emotional data based on the user's voice and facial expressions, and sending this data to the server as well. This data is transmitted securely via a secure communication protocol.

[0386] Step 4:

[0387] The server analyzes the received 3D data and generates detailed body shape information for the user. This information includes data on the dimensions and posture of each body part.

[0388] Step 5:

[0389] The server compares the generated body shape information with past data to evaluate changes in current body shape. This evaluation is used to adjust training and meal plans.

[0390] Step 6:

[0391] The emotion engine analyzes the received emotion data to identify the user's current emotional state. The identified emotion is then processed as an element to be reflected in the feedback.

[0392] Step 7:

[0393] The server generates individually optimized training plans and feedback messages based on body type information and emotional data. It adjusts the intensity of the content and the coaching style according to the emotional state indicated by the emotional data.

[0394] Step 8:

[0395] The device displays real-time feedback from the server to the user. The user then uses this feedback to conduct daily training and receive advice and motivation as needed.

[0396] This allows users to pursue their physical goals while also receiving emotional support, enabling them to continue training consistently.

[0397] (Example 2)

[0398] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0399] In modern health management, providing personalized exercise plans and nutritional guidelines is crucial. However, conventional systems typically provide plans based solely on the user's body type information, lacking feedback that considers the user's emotional state. As a result, the provided plans are not always optimal for the user's current situation, posing challenges in maintaining motivation and achieving effective improvement.

[0400] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0401] In this invention, the server includes means for acquiring human body data, means for converting the human body data acquired by the acquisition means into three-dimensional information, means for analyzing the three-dimensional information obtained by the conversion means and generating body shape data, and emotion analysis means for acquiring and analyzing emotion data. This makes it possible to provide a user-optimized exercise plan and feedback based on the user's body shape data and emotion data.

[0402] "Human body data" refers to information about body shape obtained from users, specifically including external body shape and dimensions.

[0403] "Three-dimensional information" refers to data obtained by converting two-dimensional shape data into three-dimensional data, providing the user's three-dimensional body shape information.

[0404] "Body shape data" refers to detailed information about the user's current physical characteristics, generated based on analyzed three-dimensional information.

[0405] An "exercise plan" is a personalized exercise plan optimized based on the user's body type and emotional data.

[0406] "Emotional data" refers to information about a user's psychological state, analyzed from their voice, facial expressions, and other data.

[0407] "Emotional analysis means" refers to technologies and devices for collecting and analyzing user emotional data, and for identifying the user's psychological state.

[0408] "Feedback" refers to the information and advice provided to users based on analysis results and plans.

[0409] The system of this invention relies on the collaboration of a user, a terminal, and a server. First, the user uses a terminal with a dedicated application installed. The user acquires image data of their body shape through this application. The image data is converted into three-dimensional information on the terminal. This is done using AI-based image analysis software.

[0410] The terminal transmits the converted three-dimensional information and emotional data collected from the user's voice and facial expressions to the server via a secure communication protocol. The server analyzes the received data, generates the user's body shape data, and further recognizes the user's emotional state using emotion analysis tools.

[0411] Based on this information, the server generates a personalized exercise plan. This plan reflects the user's body type data and emotional state. For example, if the server detects that the user is stressed, relaxation-focused exercises may be recommended.

[0412] Finally, the server provides the generated feedback and advice to the device, which then displays it to the user. This allows the user to access an appropriate health management plan tailored to their physical and emotional state.

[0413] For example, a prompt might instruct you to "Assume the user is feeling down and generate a feedback message using the emotion engine." In response to this, the system might generate feedback such as, "How about trying some stress-relieving exercises together today to cheer you up?" This is generated using a generative AI model and optimizes the user experience.

[0414] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0415] Step 1:

[0416] The user takes images of their body shape using a dedicated application. The application requests images from multiple angles to facilitate the collection of three-dimensional data. The input is the multiple image data points captured. The output is the storage of these image data points on the device.

[0417] Step 2:

[0418] The terminal converts image data input from the user into three-dimensional information. This conversion uses AI-based image analysis software. The input is the image data obtained in step 1, and the output is three-dimensional information. In this process, the information of each pixel in the image is analyzed to construct a 3D model.

[0419] Step 3:

[0420] The device captures voice and facial expression data from the user and acquires it as data for emotion analysis. The input is the user's voice and facial expression information, and the output is stored on the device as emotion data. This process uses voice recognition technology and facial recognition technology.

[0421] Step 4:

[0422] The terminal transmits acquired 3D information and emotional data to the server using a secure communication protocol. The input is 3D information and emotional data, and the output is the data transferred to the server. The data is protected by encryption technologies such as SSL / TLS.

[0423] Step 5:

[0424] The server analyzes the received 3D information and generates the user's current body shape data. This analysis utilizes 3D body scanning technology. The input is the 3D information received in step 4, and the output is the user's body shape data. Specifically, it calculates the size of each body part and the overall proportions.

[0425] Step 6:

[0426] The server analyzes the received voice and facial expression data using emotion analysis tools to evaluate the user's emotional state. The input is the emotion data received in step 4, and the output is information about the user's emotional state. This allows specific emotions (e.g., stress, anxiety, joy) to be quantified.

[0427] Step 7:

[0428] The server generates an individually optimized exercise plan based on the generated body shape data and emotional state information. The input is the data obtained in steps 5 and 6, and the output is the individual plan. Specifically, it organizes exercises corresponding to the user's body shape goals and makes adjustments according to their emotional state.

[0429] Step 8:

[0430] The server generates an exercise plan and feedback based on emotional data, and sends it to the device. The input is the raw material for the individual plan and feedback, while the output is the feedback information sent to the device. This feedback includes individual exercise instructions and encouraging messages.

[0431] Step 9:

[0432] The terminal displays feedback information received from the server to the user. The input is the feedback information obtained in step 8, and the output is the information presented to the user. Throughout this process, exercise plans and advice are displayed through a user-friendly interface.

[0433] (Application Example 2)

[0434] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0435] Traditional health management systems have struggled to provide not only exercise plans based on users' body shape data, but also appropriate feedback tailored to their individual emotional states. As a result, users may struggle to maintain motivation, hindering the achievement of their health goals. Furthermore, previous systems evaluated body shape data and emotional states separately, resulting in insufficient overall personalization.

[0436] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0437] In this invention, the server includes an acquisition means for acquiring human body information, an emotion recognition means having an emotion recognition function to identify emotional states, and a generation means for generating suggestions that reflect the emotional state. This makes it possible to provide highly personalized exercise plans and nutritional guidelines that take into account both body shape data and emotional states.

[0438] "Human body information" refers to data related to the external shape and dimensions of the human body, and this information is used to understand and analyze changes in the body shape of individual users.

[0439] "Three-dimensional information" refers to information that represents the external shape of the human body as three-dimensional spatial data, which enables three-dimensional body shape analysis.

[0440] "Body shape data" refers to a collection of detailed information about the user's body shape, including dimensions and form, generated based on three-dimensional information.

[0441] An "exercise plan" refers to a series of suggestions that outline the content and schedule of exercises that a user should perform, individually optimized based on their body type data.

[0442] "Delivery means" refers to the function of delivering, displaying, or notifying the user of the generated exercise plan and feedback message.

[0443] "Emotion recognition function" refers to technology that analyzes the user's voice, facial expressions, etc., to determine their emotional state at that time.

[0444] "Emotion identification means" refers to a function that specifically identifies the user's emotional state based on information acquired by the emotion recognition function.

[0445] "Generative means" refers to technology that comprehensively considers the user's body shape data and emotional state to generate exercise plans and feedback for improvement.

[0446] The system for carrying out the present invention includes a user, a terminal, a server, and an emotion engine. The user interacts with a dedicated consumer robot and receives daily health management. The terminal acquires the user's body shape information and emotion data using a camera and microphone mounted on the robot.

[0447] The server first analyzes body shape information transmitted from the terminal using 3D body scanning technology to generate detailed body shape data. OpenCV is used as the image processing library for this analysis to construct the 3D data. Furthermore, TensorFlow is used for speech and facial recognition in the analysis of emotion data. This enables real-time emotion identification.

[0448] The analyzed body shape and emotional data are stored on the server and compared with past records before a process to generate an optimal exercise plan for the user. The generated exercise plan and nutritional guidelines are provided via the device along with feedback messages that take into account the user's emotional state. For example, if the server determines that the user is feeling down, it will create an encouraging message such as, "Let's do some light exercise today to refresh yourself."

[0449] For example, if a user asks the robot, "What should I eat today?", the robot might respond, "Considering your current physical condition and emotional state, I recommend a balanced meal that includes a salad." This allows the user to receive physically and emotionally optimized support, enabling them to effectively work towards their health goals.

[0450] An example of a prompt for the generating AI model would be the text, "Consider the user's current body shape data and emotional state, and generate an appropriate training and meal plan."

[0451] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0452] Step 1:

[0453] The user acquires body shape information and audio data through the device's camera and microphone. Body shape photos are input as high-resolution images, and audio is input as audio data for sentiment analysis. This data is stored on the device as initial data.

[0454] Step 2:

[0455] The device converts the acquired body shape photograph into three-dimensional information using the OpenCV library. The input is a high-resolution image, and the output is three-dimensional mesh data. This data conversion generates detailed dimensional information about the body shape.

[0456] Step 3:

[0457] The device analyzes audio data using TensorFlow to identify emotional states. The input is audio data, and the output is the identified emotional tag. This process allows the user's current emotional state to be understood.

[0458] Step 4:

[0459] The device transmits three-dimensional body shape data and emotional state data to the server via a secure connection. Here, the device creates a data package and transfers the data to the server.

[0460] Step 5:

[0461] The server analyzes the received 3D data and evaluates the user's body shape data. 3D body shape mesh data is used as input, and the output generates statistics on the user's dimensions and changes in body shape. This includes comparisons with historical data.

[0462] Step 6:

[0463] The server uses emotional state data to generate an optimal exercise plan for the user. Inputs are emotional tags and body type data, and output is a personalized exercise plan and nutritional guidelines. This ensures that appropriate content is prepared based on the user's emotional state.

[0464] Step 7:

[0465] The server sends the generated exercise plan and nutritional guidelines to the terminal, which then provides them to the user. The terminal communicates the information to the user via the robot's display or voice. The outputted information serves as a guide for maintaining health in the user's daily life.

[0466] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0467] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0468] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0469] [Third Embodiment]

[0470] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0471] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0472] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0473] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0474] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0475] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0476] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0477] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0478] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0479] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0480] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0481] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0482] This invention relates to a system for analyzing individual users' body shape data in detail and providing training and dietary guidelines based on this data. The system operates via the user's terminal, a server, and a communication network between them.

[0483] System Overview

[0484] Users take images of their body shape using a dedicated application and input the data into the device. The device has a function to check the image quality and convert it into a format that can be transmitted as data.

[0485] The device uploads the captured image data to the server. The image data is securely delivered via a communication protocol.

[0486] Server-based data analysis

[0487] The server feeds the received image data into a 3D body scanning algorithm to generate three-dimensional body shape data of the user. This data provides a detailed representation of the dimensions, shape, and posture of each part of the user's body.

[0488] From the analyzed 3D data, the server quantifies the user's current body shape information and stores it in a database. Furthermore, the server has the function to compare it with past data and identify changes.

[0489] Creating and providing feedback

[0490] Based on the analysis results, the server generates the most effective training plan for the user. This plan includes detailed information on specific strength training and aerobic exercise.

[0491] Furthermore, the server generates dietary guidelines based on body shape information and recommends nutritional intake tailored to the user's goals.

[0492] The device displays this feedback to the user in real time, enabling continuous daily guidance through the application. It also includes a reminder function for when the user should take the next action.

[0493] Specific example

[0494] For example, if a user scans their body shape with the app once a week and sends the data to the server, the server immediately analyzes this data and creates a new training plan based on the results. If a user is aiming to slim their waist, the server will create a plan that includes planks and core strengthening exercises and provide a system to monitor their progress weekly.

[0495] Thus, the system of the present invention provides customized feedback to individual users, supporting the maintenance of motivation and promoting efficient body shape improvement.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] The user launches a dedicated application and takes photos of their body from various angles. The image is set to capture the entire body accurately, and the application provides instructions for taking photos as needed.

[0499] Step 2:

[0500] The device optimizes the image data received from the user and converts it into a data format suitable for 3D body scanning. After conversion, it verifies the quality and integrity of the data.

[0501] Step 3:

[0502] The terminal securely transmits the converted image data to the server using a communication protocol. The data is sent encrypted via the internet or a dedicated network.

[0503] Step 4:

[0504] The server sends the received image data to a 3D body scanning algorithm to generate a three-dimensional body model. The model contains data such as body dimensions and shape.

[0505] Step 5:

[0506] The server quantifies the user's body shape information from the generated 3D model and compares it with past data. This comparison measures specific changes in the body and analyzes trends in those changes.

[0507] Step 6:

[0508] The server creates an individually optimized training plan and dietary guidelines based on the analysis results. The training plan includes specific types of exercise and recommended repetitions, while the dietary guidelines include recommendations regarding nutrients and calories.

[0509] Step 7:

[0510] The device provides users with training plans and dietary guidelines delivered from the server. Users can receive feedback through the application and use it to improve their future training and eating habits.

[0511] (Example 1)

[0512] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0513] The present invention aims to provide a system that can efficiently improve body shape by providing effective and individually optimized training plans and dietary guidelines based on the acquisition and analysis of individual user body shape data. Current fitness and health management systems have problems maintaining motivation and continuous body shape improvement because it is difficult to accurately grasp individual body shape changes and provide immediate feedback.

[0514] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0515] In this invention, the server includes a device for acquiring human body data, a conversion device for converting the human body data acquired by the device into three-dimensional information, and a processing device for processing the three-dimensional information obtained by the conversion device and generating physical information. This makes it possible to provide detailed and accurate body shape information to individual users. Furthermore, based on this information, individually optimized training plans and nutritional guidance can be quickly created, and continuous feedback can be provided to help maintain motivation.

[0516] "Human body data" refers to information that describes the physical characteristics of the human body, such as its shape and dimensions.

[0517] A "device" refers to a combination of hardware and software designed to perform a specific function.

[0518] "Three-dimensional information" refers to data used to represent the shape and structure of an object in three-dimensional space.

[0519] A "conversion device" is a device that has the function of converting data in one format to another format.

[0520] A "processing device" is a device that processes input data based on specific algorithms or rules and generates results.

[0521] "Physical information" refers to detailed data about the user's body, including body shape and posture.

[0522] A "training plan" is a program of exercises or movements designed to achieve a specific objective.

[0523] A "planning device" is a device that has the function of creating a plan.

[0524] A "providing device" is a device that has the function of supplying data and information to users.

[0525] "Nutritional guidance" refers to instruction that provides suggestions and recommendations for meals tailored to the user's health and fitness goals.

[0526] An "evaluation device" is a device that has the function of evaluating, comparing, and analyzing data.

[0527] The embodiments for carrying out the present invention will now be described. This system is operated through the collaboration of a user, a terminal, and a server.

[0528] Users use a device with a dedicated application installed to capture and input their body shape data. The device converts the captured image data into an appropriate format and securely transmits it to the server. Image processing software is used for the image data conversion. Secure protocols are used for communication to maintain data confidentiality.

[0529] The server executes a 3D body scanning algorithm to convert the received image data into three-dimensional information. This algorithm is built on machine learning and utilizes generative AI models. Specifically, technologies such as Python and TensorFlow can be used. This process generates 3D data that accurately represents the dimensions and posture of each part of the user's body. The server analyzes the generated data, extracts and stores the user's body information, and evaluates changes in body shape by comparing it with past data.

[0530] After analysis, the server creates a training plan optimized for the user based on the generated physical information. This plan includes specific menus for strength training and aerobic exercise. Furthermore, a meal plan is also created that takes into account the user's goals and nutritional guidance. The server sends this data to the terminal, which displays feedback and the plan to the user in real time. The terminal also has a function to remind the user of the timing of the next training and meals, thus supporting the user.

[0531] As a concrete example, when a user enters a prompt such as "Please suggest a training plan aimed at slimming my waist" into the application, the server immediately generates and provides a suitable plan. This system enables users to effectively and continuously improve their physique.

[0532] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0533] Step 1:

[0534] The user takes an image of their body shape using a dedicated application. The device receives this image data as input and checks the image resolution and clarity. Specifically, the device performs image filtering, and crops and compresses the image as needed. As a result, it outputs image data converted into a format suitable for data analysis.

[0535] Step 2:

[0536] The terminal sends the converted image data to the server. The server receives this image data as input and verifies the data's security via a secure communication protocol. Specifically, the data is protected using an encrypted channel. As output, the data is successfully stored on the server.

[0537] Step 3:

[0538] The server inputs the received image data into a three-dimensional body scanning algorithm to generate three-dimensional information. Here, a machine learning algorithm using a generative AI model analyzes the image and constructs the user's three-dimensional body data. Based on the input image data, it outputs three-dimensional data that quantifies the user's body dimensions and shape.

[0539] Step 4:

[0540] The server analyzes body information using the generated three-dimensional data and stores it in a database. It also evaluates current body shape changes by comparing them with past data. Specifically, the server queries the dataset and performs analytical calculations to find patterns of change. As output, it generates information on the user's body shape changes over time.

[0541] Step 5:

[0542] The server creates an individually optimized training plan and nutritional guidance based on physical information. Here, a generative AI model processes prompts that take the user's goals into consideration and plans specific exercises and nutritional plans. It takes physical information and the user's objectives as input and generates a customized training and meal plan as output.

[0543] Step 6:

[0544] The device receives training plans and dietary guidance sent from the server and displays them to the user. The input is a generated plan, which the device visualizes and presents in a user-friendly interface. Specifically, it supports the user's continued actions through features such as reminders and progress tracking. The output provides clear guidance information to help the user decide on their next actions.

[0545] (Application Example 1)

[0546] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] Traditional health management systems have struggled to accurately understand each user's body type and nutritional status, and to provide personalized exercise plans and nutritional guidelines based on that information. Furthermore, they lacked effective means of tracking user progress and maintaining motivation. As a result, users were unable to obtain a health management plan that was optimal for them, making efficient health improvement difficult.

[0548] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0549] In this invention, the server includes acquisition means for acquiring human body data, conversion means for converting it into three-dimensional data, analysis means for analyzing and generating body shape data, and tracking means. This enables the provision of personalized exercise plans and nutritional guidelines, as well as effective tracking of progress.

[0550] "Human body data" refers to data that includes information about the user's body shape and size.

[0551] "3D data" refers to data that is represented three-dimensionally based on acquired human body data.

[0552] "Body shape data" refers to information about body dimensions and balance generated by analyzing three-dimensional data.

[0553] An "exercise plan" is an exercise program created individually based on the user's body shape data.

[0554] "Nutritional guidelines" are advice on appropriate nutritional intake provided based on the user's body shape data.

[0555] A "tracking method" is a means of observing a user's fitness progress and recording their achievements and areas for improvement.

[0556] A "server" is a computer system that analyzes data obtained from users and generates plans related to exercise and nutrition.

[0557] This system provides users with exercise plans and nutritional guidelines to improve their health. Users acquire their own body data using a terminal and convert it into 3D data. This process utilizes devices equipped with cameras and sensors, processing the image data in 3D. Next, a server analyzes this 3D data to generate body shape data. Advanced algorithms are applied to the analysis, deriving individualized body shape information for each user.

[0558] The server creates an exercise plan based on the generated body shape data. This plan includes detailed exercises necessary for improving physical fitness and maintaining health. The server also provides nutritional guidance to improve the user's nutritional status. This guidance includes specific advice to promote a balanced diet.

[0559] Furthermore, the server monitors the user's progress through tracking mechanisms, recording achievements and areas for improvement. This feature allows users to adjust their exercise and diet to align with their health goals. The feedback is also provided interactively, designed to help users maintain motivation.

[0560] As a concrete example, consider a scenario where a user scans their body shape with a device every morning upon waking up and sends the results to a server. Based on this data, the server proposes an ideal exercise and diet plan for the user. An example of a prompt to the generative AI model would be, "I have sent my current body shape data. Based on this data, please suggest an exercise and diet plan for today to achieve my health goals." This prompt allows the system to quickly provide appropriate advice to the user.

[0561] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0562] Step 1:

[0563] The user takes a photo of their own body shape using their device.

[0564] The input is an image of a human body captured by the device's camera. After acquiring this image data, the device checks the image resolution and quality, converts it to an appropriate format, and outputs it.

[0565] Step 2:

[0566] The terminal sends the converted image data to the server.

[0567] The input is formatted image data. The terminal transfers the image data to the server using a secure communication protocol. The output is the image data that successfully reached the server.

[0568] Step 3:

[0569] The server inputs the received image data into a 3D conversion algorithm to generate 3D data.

[0570] The input is image data that has arrived at the server. The server uses advanced image processing technology to convert this into 3D data. The output is data that represents the user's body shape in three dimensions.

[0571] Step 4:

[0572] The server analyzes the 3D data and creates body shape data.

[0573] The input is 3D data generated by the server. Using this data, the server quantifies the dimensions and posture of each part of the user's body. The output is data summarizing the user's body shape information.

[0574] Step 5:

[0575] The server creates personalized exercise plans and nutritional guidelines based on body shape data.

[0576] The input is data reflecting the user's body shape information. The server uses a machine learning model to generate an appropriate exercise and nutrition plan based on the analysis results. The output is a plan optimized for each individual user.

[0577] Step 6:

[0578] The server provides the user with a generated exercise plan and nutritional guidelines.

[0579] The input consists of an exercise plan and nutritional guidelines. The server sends the plan to the terminal, allowing the user to view and execute it. The output is the plan information displayed on the user's terminal.

[0580] Step 7:

[0581] Users perform their activities according to the plan provided by the server.

[0582] The input consists of an exercise plan and nutritional guidelines. The user acts according to these in their daily life and strives towards their health goals. The output is the results of their daily health activities.

[0583] Step 8:

[0584] The server tracks the user's progress and provides feedback.

[0585] The input consists of progress information and activity data reported by the user. The server uses this data to analyze achievement levels and areas for improvement, and uses this information to inform future instruction. The output is feedback information for the user.

[0586] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0587] This invention combines a system that analyzes changes in a user's body shape in detail and provides an individually optimized training plan and dietary guidance with an emotion engine that recognizes the user's emotions. This system, which includes the user's terminal, server, and emotion engine, provides detailed feedback tailored to the user's state.

[0588] System-wide configuration

[0589] The user takes a photo of their body shape using a dedicated application and inputs this data into their device. The image is then converted into a format suitable for three-dimensional analysis.

[0590] The device sends the converted image data, along with the user's voice and facial expression information, to the emotion engine. This information is then transferred to the server via a secure connection.

[0591] Server Processing

[0592] The server analyzes the image data using 3D body scanning technology to generate three-dimensional body shape data. This data contains detailed information about the user's dimensions and shape and is stored in a database.

[0593] The analyzed body shape data is compared with past data to evaluate the degree of improvement in body shape.

[0594] How the emotion engine works

[0595] The emotion engine recognizes the user's emotions from voice and facial expression data and sends the user's emotional state to the server.

[0596] This emotional data is reflected in training plans and feedback messages, generating personalized content tailored to the user's emotions.

[0597] Generating and providing feedback

[0598] The server creates an optimal training plan based on analyzed body shape information and emotional data. For example, if a user is feeling anxious, it can prioritize providing simple exercises.

[0599] The device receives feedback data from the server and displays it to the user. This feedback includes training guidelines, as well as emotionally-based encouraging messages and advice.

[0600] Specific example

[0601] For example, when a user is feeling down, the system can use its emotion engine to identify that emotion and provide a message to boost motivation, such as, "Why not try some stress-relieving exercises today to lift your spirits?" This allows users to receive support optimized for their situation and achieve their health goals more effectively.

[0602] Thus, the present invention allows users to receive highly personalized training and feedback based on their own physical and emotional state. This makes it possible to maximize the effectiveness of training and bring about continuous improvement.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user launches a dedicated application and takes photos of their body from multiple angles. Once the shooting is complete, the images are viewed within the application and saved in a format suitable for the next processing step.

[0606] Step 2:

[0607] The device performs a process to convert the stored image data into three-dimensional data. This conversion makes it possible to capture the user's body shape in three dimensions.

[0608] Step 3:

[0609] The device sends the converted 3D data to the server, while simultaneously acquiring emotional data based on the user's voice and facial expressions, and sending this data to the server as well. This data is transmitted securely via a secure communication protocol.

[0610] Step 4:

[0611] The server analyzes the received 3D data and generates detailed body shape information for the user. This information includes data on the dimensions and posture of each body part.

[0612] Step 5:

[0613] The server compares the generated body shape information with past data to evaluate changes in current body shape. This evaluation is used to adjust training and meal plans.

[0614] Step 6:

[0615] The emotion engine analyzes the received emotion data to identify the user's current emotional state. The identified emotion is then processed as an element to be reflected in the feedback.

[0616] Step 7:

[0617] The server generates individually optimized training plans and feedback messages based on body type information and emotional data. It adjusts the intensity of the content and the coaching style according to the emotional state indicated by the emotional data.

[0618] Step 8:

[0619] The device displays real-time feedback from the server to the user. The user then uses this feedback to conduct daily training and receive advice and motivation as needed.

[0620] This allows users to pursue their physical goals while also receiving emotional support, enabling them to continue training consistently.

[0621] (Example 2)

[0622] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0623] In modern health management, providing personalized exercise plans and nutritional guidelines is crucial. However, conventional systems typically provide plans based solely on the user's body type information, lacking feedback that considers the user's emotional state. As a result, the provided plans are not always optimal for the user's current situation, posing challenges in maintaining motivation and achieving effective improvement.

[0624] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0625] In this invention, the server includes means for acquiring human body data, means for converting the human body data acquired by the acquisition means into three-dimensional information, means for analyzing the three-dimensional information obtained by the conversion means and generating body shape data, and emotion analysis means for acquiring and analyzing emotion data. This makes it possible to provide a user-optimized exercise plan and feedback based on the user's body shape data and emotion data.

[0626] "Human body data" refers to information about body shape obtained from users, specifically including external body shape and dimensions.

[0627] "Three-dimensional information" refers to data obtained by converting two-dimensional shape data into three-dimensional data, providing the user's three-dimensional body shape information.

[0628] "Body shape data" refers to detailed information about the user's current physical characteristics, generated based on analyzed three-dimensional information.

[0629] An "exercise plan" is a personalized exercise plan optimized based on the user's body type and emotional data.

[0630] "Emotional data" refers to information about a user's psychological state, analyzed from their voice, facial expressions, and other data.

[0631] "Emotional analysis means" refers to technologies and devices for collecting and analyzing user emotional data, and for identifying the user's psychological state.

[0632] "Feedback" refers to the information and advice provided to users based on analysis results and plans.

[0633] The system of this invention relies on the collaboration of a user, a terminal, and a server. First, the user uses a terminal with a dedicated application installed. The user acquires image data of their body shape through this application. The image data is converted into three-dimensional information on the terminal. This is done using AI-based image analysis software.

[0634] The terminal transmits the converted three-dimensional information and emotional data collected from the user's voice and facial expressions to the server via a secure communication protocol. The server analyzes the received data, generates the user's body shape data, and further recognizes the user's emotional state using emotion analysis tools.

[0635] Based on this information, the server generates a personalized exercise plan. This plan reflects the user's body type data and emotional state. For example, if the server detects that the user is stressed, relaxation-focused exercises may be recommended.

[0636] Finally, the server provides the generated feedback and advice to the device, which then displays it to the user. This allows the user to access an appropriate health management plan tailored to their physical and emotional state.

[0637] For example, a prompt might instruct you to "Assume the user is feeling down and generate a feedback message using the emotion engine." In response to this, the system might generate feedback such as, "How about trying some stress-relieving exercises together today to cheer you up?" This is generated using a generative AI model and optimizes the user experience.

[0638] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0639] Step 1:

[0640] The user takes images of their body shape using a dedicated application. The application requests images from multiple angles to facilitate the collection of three-dimensional data. The input is the multiple image data points captured. The output is the storage of these image data points on the device.

[0641] Step 2:

[0642] The terminal converts image data input from the user into three-dimensional information. This conversion uses AI-based image analysis software. The input is the image data obtained in step 1, and the output is three-dimensional information. In this process, the information of each pixel in the image is analyzed to construct a 3D model.

[0643] Step 3:

[0644] The device captures voice and facial expression data from the user and acquires it as data for emotion analysis. The input is the user's voice and facial expression information, and the output is stored on the device as emotion data. This process uses voice recognition technology and facial recognition technology.

[0645] Step 4:

[0646] The terminal transmits acquired 3D information and emotional data to the server using a secure communication protocol. The input is 3D information and emotional data, and the output is the data transferred to the server. The data is protected by encryption technologies such as SSL / TLS.

[0647] Step 5:

[0648] The server analyzes the received 3D information and generates the user's current body shape data. This analysis utilizes 3D body scanning technology. The input is the 3D information received in step 4, and the output is the user's body shape data. Specifically, it calculates the size of each body part and the overall proportions.

[0649] Step 6:

[0650] The server analyzes the received voice and facial expression data using emotion analysis tools to evaluate the user's emotional state. The input is the emotion data received in step 4, and the output is information about the user's emotional state. This allows specific emotions (e.g., stress, anxiety, joy) to be quantified.

[0651] Step 7:

[0652] The server generates an individually optimized exercise plan based on the generated body shape data and emotional state information. The input is the data obtained in steps 5 and 6, and the output is the individual plan. Specifically, it organizes exercises corresponding to the user's body shape goals and makes adjustments according to their emotional state.

[0653] Step 8:

[0654] The server generates an exercise plan and feedback based on emotional data, and sends it to the device. The input is the raw material for the individual plan and feedback, while the output is the feedback information sent to the device. This feedback includes individual exercise instructions and encouraging messages.

[0655] Step 9:

[0656] The terminal displays feedback information received from the server to the user. The input is the feedback information obtained in step 8, and the output is the information presented to the user. Throughout this process, exercise plans and advice are displayed through a user-friendly interface.

[0657] (Application Example 2)

[0658] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0659] Traditional health management systems have struggled to provide not only exercise plans based on users' body shape data, but also appropriate feedback tailored to their individual emotional states. As a result, users may struggle to maintain motivation, hindering the achievement of their health goals. Furthermore, previous systems evaluated body shape data and emotional states separately, resulting in insufficient overall personalization.

[0660] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0661] In this invention, the server includes an acquisition means for acquiring human body information, an emotion recognition means having an emotion recognition function to identify emotional states, and a generation means for generating suggestions that reflect the emotional state. This makes it possible to provide highly personalized exercise plans and nutritional guidelines that take into account both body shape data and emotional states.

[0662] "Human body information" refers to data related to the external shape and dimensions of the human body, and this information is used to understand and analyze changes in the body shape of individual users.

[0663] "Three-dimensional information" refers to information that represents the external shape of the human body as three-dimensional spatial data, which enables three-dimensional body shape analysis.

[0664] "Body shape data" refers to a collection of detailed information about the user's body shape, including dimensions and form, generated based on three-dimensional information.

[0665] An "exercise plan" refers to a series of suggestions that outline the content and schedule of exercises that a user should perform, individually optimized based on their body type data.

[0666] "Delivery means" refers to the function of delivering, displaying, or notifying the user of the generated exercise plan and feedback message.

[0667] "Emotion recognition function" refers to technology that analyzes the user's voice, facial expressions, etc., to determine their emotional state at that time.

[0668] "Emotion identification means" refers to a function that specifically identifies the user's emotional state based on information acquired by the emotion recognition function.

[0669] "Generative means" refers to technology that comprehensively considers the user's body shape data and emotional state to generate exercise plans and feedback for improvement.

[0670] The system for carrying out the present invention includes a user, a terminal, a server, and an emotion engine. The user interacts with a dedicated consumer robot and receives daily health management. The terminal acquires the user's body shape information and emotion data using a camera and microphone mounted on the robot.

[0671] The server first analyzes body shape information transmitted from the terminal using 3D body scanning technology to generate detailed body shape data. OpenCV is used as the image processing library for this analysis to construct the 3D data. Furthermore, TensorFlow is used for speech and facial recognition in the analysis of emotion data. This enables real-time emotion identification.

[0672] The analyzed body shape and emotional data are stored on the server and compared with past records before a process to generate an optimal exercise plan for the user. The generated exercise plan and nutritional guidelines are provided via the device along with feedback messages that take into account the user's emotional state. For example, if the server determines that the user is feeling down, it will create an encouraging message such as, "Let's do some light exercise today to refresh yourself."

[0673] For example, if a user asks the robot, "What should I eat today?", the robot might respond, "Considering your current physical condition and emotional state, I recommend a balanced meal that includes a salad." This allows the user to receive physically and emotionally optimized support, enabling them to effectively work towards their health goals.

[0674] An example of a prompt for the generating AI model would be the text, "Consider the user's current body shape data and emotional state, and generate an appropriate training and meal plan."

[0675] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0676] Step 1:

[0677] The user acquires body shape information and audio data through the device's camera and microphone. Body shape photos are input as high-resolution images, and audio is input as audio data for sentiment analysis. This data is stored on the device as initial data.

[0678] Step 2:

[0679] The device converts the acquired body shape photograph into three-dimensional information using the OpenCV library. The input is a high-resolution image, and the output is three-dimensional mesh data. This data conversion generates detailed dimensional information about the body shape.

[0680] Step 3:

[0681] The device analyzes audio data using TensorFlow to identify emotional states. The input is audio data, and the output is the identified emotional tag. This process allows the user's current emotional state to be understood.

[0682] Step 4:

[0683] The device transmits three-dimensional body shape data and emotional state data to the server via a secure connection. Here, the device creates a data package and transfers the data to the server.

[0684] Step 5:

[0685] The server analyzes the received 3D data and evaluates the user's body shape data. 3D body shape mesh data is used as input, and the output generates statistics on the user's dimensions and changes in body shape. This includes comparisons with historical data.

[0686] Step 6:

[0687] The server uses emotional state data to generate an optimal exercise plan for the user. Inputs are emotional tags and body type data, and output is a personalized exercise plan and nutritional guidelines. This ensures that appropriate content is prepared based on the user's emotional state.

[0688] Step 7:

[0689] The server sends the generated exercise plan and nutritional guidelines to the terminal, which then provides them to the user. The terminal communicates the information to the user via the robot's display or voice. The outputted information serves as a guide for maintaining health in the user's daily life.

[0690] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0691] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0692] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0693] [Fourth Embodiment]

[0694] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0695] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0696] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0697] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0698] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0699] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0700] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0701] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0702] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0703] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0704] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0705] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0706] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0707] This invention relates to a system for analyzing individual users' body shape data in detail and providing training and dietary guidelines based on this data. The system operates via the user's terminal, a server, and a communication network between them.

[0708] System Overview

[0709] Users take images of their body shape using a dedicated application and input the data into the device. The device has a function to check the image quality and convert it into a format that can be transmitted as data.

[0710] The device uploads the captured image data to the server. The image data is securely delivered via a communication protocol.

[0711] Server-based data analysis

[0712] The server feeds the received image data into a 3D body scanning algorithm to generate three-dimensional body shape data of the user. This data provides a detailed representation of the dimensions, shape, and posture of each part of the user's body.

[0713] From the analyzed 3D data, the server quantifies the user's current body shape information and stores it in a database. Furthermore, the server has the function to compare it with past data and identify changes.

[0714] Creating and providing feedback

[0715] Based on the analysis results, the server generates the most effective training plan for the user. This plan includes detailed information on specific strength training and aerobic exercise.

[0716] Furthermore, the server generates dietary guidelines based on body shape information and recommends nutritional intake tailored to the user's goals.

[0717] The device displays this feedback to the user in real time, enabling continuous daily guidance through the application. It also includes a reminder function for when the user should take the next action.

[0718] Specific example

[0719] For example, if a user scans their body shape with the app once a week and sends the data to the server, the server immediately analyzes this data and creates a new training plan based on the results. If a user is aiming to slim their waist, the server will create a plan that includes planks and core strengthening exercises and provide a system to monitor their progress weekly.

[0720] Thus, the system of the present invention provides customized feedback to individual users, supporting the maintenance of motivation and promoting efficient body shape improvement.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] The user launches a dedicated application and takes photos of their body from various angles. The image is set to capture the entire body accurately, and the application provides instructions for taking photos as needed.

[0724] Step 2:

[0725] The device optimizes the image data received from the user and converts it into a data format suitable for 3D body scanning. After conversion, it verifies the quality and integrity of the data.

[0726] Step 3:

[0727] The terminal securely transmits the converted image data to the server using a communication protocol. The data is sent encrypted via the internet or a dedicated network.

[0728] Step 4:

[0729] The server sends the received image data to a 3D body scanning algorithm to generate a three-dimensional body model. The model contains data such as body dimensions and shape.

[0730] Step 5:

[0731] The server quantifies the user's body shape information from the generated 3D model and compares it with past data. This comparison measures specific changes in the body and analyzes trends in those changes.

[0732] Step 6:

[0733] The server creates an individually optimized training plan and dietary guidelines based on the analysis results. The training plan includes specific types of exercise and recommended repetitions, while the dietary guidelines include recommendations regarding nutrients and calories.

[0734] Step 7:

[0735] The device provides users with training plans and dietary guidelines delivered from the server. Users can receive feedback through the application and use it to improve their future training and eating habits.

[0736] (Example 1)

[0737] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0738] The present invention aims to provide a system that can efficiently improve body shape by providing effective and individually optimized training plans and dietary guidelines based on the acquisition and analysis of individual user body shape data. Current fitness and health management systems have problems maintaining motivation and continuous body shape improvement because it is difficult to accurately grasp individual body shape changes and provide immediate feedback.

[0739] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0740] In this invention, the server includes a device for acquiring human body data, a conversion device for converting the human body data acquired by the device into three-dimensional information, and a processing device for processing the three-dimensional information obtained by the conversion device and generating physical information. This makes it possible to provide detailed and accurate body shape information to individual users. Furthermore, based on this information, individually optimized training plans and nutritional guidance can be quickly created, and continuous feedback can be provided to help maintain motivation.

[0741] "Human body data" refers to information that describes the physical characteristics of the human body, such as its shape and dimensions.

[0742] A "device" refers to a combination of hardware and software designed to perform a specific function.

[0743] "Three-dimensional information" refers to data used to represent the shape and structure of an object in three-dimensional space.

[0744] A "conversion device" is a device that has the function of converting data in one format to another format.

[0745] A "processing device" is a device that processes input data based on specific algorithms or rules and generates results.

[0746] "Physical information" refers to detailed data about the user's body, including body shape and posture.

[0747] A "training plan" is a program of exercises or movements designed to achieve a specific objective.

[0748] A "planning device" is a device that has the function of creating a plan.

[0749] A "providing device" is a device that has the function of supplying data and information to users.

[0750] "Nutritional guidance" refers to instruction that provides suggestions and recommendations for meals tailored to the user's health and fitness goals.

[0751] An "evaluation device" is a device that has the function of evaluating, comparing, and analyzing data.

[0752] The embodiments for carrying out the present invention will now be described. This system is operated through the collaboration of a user, a terminal, and a server.

[0753] Users use a device with a dedicated application installed to capture and input their body shape data. The device converts the captured image data into an appropriate format and securely transmits it to the server. Image processing software is used for the image data conversion. Secure protocols are used for communication to maintain data confidentiality.

[0754] The server executes a 3D body scanning algorithm to convert the received image data into three-dimensional information. This algorithm is built on machine learning and utilizes generative AI models. Specifically, technologies such as Python and TensorFlow can be used. This process generates 3D data that accurately represents the dimensions and posture of each part of the user's body. The server analyzes the generated data, extracts and stores the user's body information, and evaluates changes in body shape by comparing it with past data.

[0755] After analysis, the server creates a training plan optimized for the user based on the generated physical information. This plan includes specific menus for strength training and aerobic exercise. Furthermore, a meal plan is also created that takes into account the user's goals and nutritional guidance. The server sends this data to the terminal, which displays feedback and the plan to the user in real time. The terminal also has a function to remind the user of the timing of the next training and meals, thus supporting the user.

[0756] As a concrete example, when a user enters a prompt such as "Please suggest a training plan aimed at slimming my waist" into the application, the server immediately generates and provides a suitable plan. This system enables users to effectively and continuously improve their physique.

[0757] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0758] Step 1:

[0759] The user takes an image of their body shape using a dedicated application. The device receives this image data as input and checks the image resolution and clarity. Specifically, the device performs image filtering, and crops and compresses the image as needed. As a result, it outputs image data converted into a format suitable for data analysis.

[0760] Step 2:

[0761] The terminal sends the converted image data to the server. The server receives this image data as input and verifies the data's security via a secure communication protocol. Specifically, the data is protected using an encrypted channel. As output, the data is successfully stored on the server.

[0762] Step 3:

[0763] The server inputs the received image data into a three-dimensional body scanning algorithm to generate three-dimensional information. Here, a machine learning algorithm using a generative AI model analyzes the image and constructs the user's three-dimensional body data. Based on the input image data, it outputs three-dimensional data that quantifies the user's body dimensions and shape.

[0764] Step 4:

[0765] The server analyzes body information using the generated three-dimensional data and stores it in a database. It also evaluates current body shape changes by comparing them with past data. Specifically, the server queries the dataset and performs analytical calculations to find patterns of change. As output, it generates information on the user's body shape changes over time.

[0766] Step 5:

[0767] The server creates an individually optimized training plan and nutritional guidance based on physical information. Here, a generative AI model processes prompts that take the user's goals into consideration and plans specific exercises and nutritional plans. It takes physical information and the user's objectives as input and generates a customized training and meal plan as output.

[0768] Step 6:

[0769] The device receives training plans and dietary guidance sent from the server and displays them to the user. The input is a generated plan, which the device visualizes and presents in a user-friendly interface. Specifically, it supports the user's continued actions through features such as reminders and progress tracking. The output provides clear guidance information to help the user decide on their next actions.

[0770] (Application Example 1)

[0771] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0772] Traditional health management systems have struggled to accurately understand each user's body type and nutritional status, and to provide personalized exercise plans and nutritional guidelines based on that information. Furthermore, they lacked effective means of tracking user progress and maintaining motivation. As a result, users were unable to obtain a health management plan that was optimal for them, making efficient health improvement difficult.

[0773] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0774] In this invention, the server includes acquisition means for acquiring human body data, conversion means for converting it into three-dimensional data, analysis means for analyzing and generating body shape data, and tracking means. This enables the provision of personalized exercise plans and nutritional guidelines, as well as effective tracking of progress.

[0775] "Human body data" refers to data that includes information about the user's body shape and size.

[0776] "3D data" refers to data that is represented three-dimensionally based on acquired human body data.

[0777] "Body shape data" refers to information about body dimensions and balance generated by analyzing three-dimensional data.

[0778] An "exercise plan" is an exercise program created individually based on the user's body shape data.

[0779] "Nutritional guidelines" are advice on appropriate nutritional intake provided based on the user's body shape data.

[0780] A "tracking method" is a means of observing a user's fitness progress and recording their achievements and areas for improvement.

[0781] A "server" is a computer system that analyzes data obtained from users and generates plans related to exercise and nutrition.

[0782] This system provides users with exercise plans and nutritional guidelines to improve their health. Users acquire their own body data using a terminal and convert it into 3D data. This process utilizes devices equipped with cameras and sensors, processing the image data in 3D. Next, a server analyzes this 3D data to generate body shape data. Advanced algorithms are applied to the analysis, deriving individualized body shape information for each user.

[0783] The server creates an exercise plan based on the generated body shape data. This plan includes detailed exercises necessary for improving physical fitness and maintaining health. The server also provides nutritional guidance to improve the user's nutritional status. This guidance includes specific advice to promote a balanced diet.

[0784] Furthermore, the server monitors the user's progress through tracking mechanisms, recording achievements and areas for improvement. This feature allows users to adjust their exercise and diet to align with their health goals. The feedback is also provided interactively, designed to help users maintain motivation.

[0785] As a concrete example, consider a scenario where a user scans their body shape with a device every morning upon waking up and sends the results to a server. Based on this data, the server proposes an ideal exercise and diet plan for the user. An example of a prompt to the generative AI model would be, "I have sent my current body shape data. Based on this data, please suggest an exercise and diet plan for today to achieve my health goals." This prompt allows the system to quickly provide appropriate advice to the user.

[0786] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0787] Step 1:

[0788] The user takes a photo of their own body shape using their device.

[0789] The input is an image of a human body captured by the device's camera. After acquiring this image data, the device checks the image resolution and quality, converts it to an appropriate format, and outputs it.

[0790] Step 2:

[0791] The terminal sends the converted image data to the server.

[0792] The input is formatted image data. The terminal transfers the image data to the server using a secure communication protocol. The output is the image data that successfully reached the server.

[0793] Step 3:

[0794] The server inputs the received image data into a 3D conversion algorithm to generate 3D data.

[0795] The input is image data that has arrived at the server. The server uses advanced image processing technology to convert this into 3D data. The output is data that represents the user's body shape in three dimensions.

[0796] Step 4:

[0797] The server analyzes the 3D data and creates body shape data.

[0798] The input is 3D data generated by the server. Using this data, the server quantifies the dimensions and posture of each part of the user's body. The output is data summarizing the user's body shape information.

[0799] Step 5:

[0800] The server creates personalized exercise plans and nutritional guidelines based on body shape data.

[0801] The input is data reflecting the user's body shape information. The server uses a machine learning model to generate an appropriate exercise and nutrition plan based on the analysis results. The output is a plan optimized for each individual user.

[0802] Step 6:

[0803] The server provides the user with a generated exercise plan and nutritional guidelines.

[0804] The input consists of an exercise plan and nutritional guidelines. The server sends the plan to the terminal, allowing the user to view and execute it. The output is the plan information displayed on the user's terminal.

[0805] Step 7:

[0806] Users perform their activities according to the plan provided by the server.

[0807] The input consists of an exercise plan and nutritional guidelines. The user acts according to these in their daily life and strives towards their health goals. The output is the results of their daily health activities.

[0808] Step 8:

[0809] The server tracks the user's progress and provides feedback.

[0810] The input consists of progress information and activity data reported by the user. The server uses this data to analyze achievement levels and areas for improvement, and uses this information to inform future instruction. The output is feedback information for the user.

[0811] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0812] This invention combines a system that analyzes changes in a user's body shape in detail and provides an individually optimized training plan and dietary guidance with an emotion engine that recognizes the user's emotions. This system, which includes the user's terminal, server, and emotion engine, provides detailed feedback tailored to the user's state.

[0813] System-wide configuration

[0814] The user takes a photo of their body shape using a dedicated application and inputs this data into their device. The image is then converted into a format suitable for three-dimensional analysis.

[0815] The device sends the converted image data, along with the user's voice and facial expression information, to the emotion engine. This information is then transferred to the server via a secure connection.

[0816] Server Processing

[0817] The server analyzes the image data using 3D body scanning technology to generate three-dimensional body shape data. This data contains detailed information about the user's dimensions and shape and is stored in a database.

[0818] The analyzed body shape data is compared with past data to evaluate the degree of improvement in body shape.

[0819] How the emotion engine works

[0820] The emotion engine recognizes the user's emotions from voice and facial expression data and sends the user's emotional state to the server.

[0821] This emotional data is reflected in training plans and feedback messages, generating personalized content tailored to the user's emotions.

[0822] Generating and providing feedback

[0823] The server creates an optimal training plan based on analyzed body shape information and emotional data. For example, if a user is feeling anxious, it can prioritize providing simple exercises.

[0824] The device receives feedback data from the server and displays it to the user. This feedback includes training guidelines, as well as emotionally-based encouraging messages and advice.

[0825] Specific example

[0826] For example, when a user is feeling down, the system can use its emotion engine to identify that emotion and provide a message to boost motivation, such as, "Why not try some stress-relieving exercises today to lift your spirits?" This allows users to receive support optimized for their situation and achieve their health goals more effectively.

[0827] Thus, the present invention allows users to receive highly personalized training and feedback based on their own physical and emotional state. This makes it possible to maximize the effectiveness of the training and bring about continuous improvement.

[0828] The following describes the processing flow.

[0829] Step 1:

[0830] The user launches a dedicated application and takes photos of their body from multiple angles. Once the shooting is complete, the images are viewed within the application and saved in a format suitable for the next processing step.

[0831] Step 2:

[0832] The device performs a process to convert the stored image data into three-dimensional data. This conversion makes it possible to capture the user's body shape in three dimensions.

[0833] Step 3:

[0834] The device sends the converted 3D data to the server, while simultaneously acquiring emotional data based on the user's voice and facial expressions, and sending this data to the server as well. This data is transmitted securely via a secure communication protocol.

[0835] Step 4:

[0836] The server analyzes the received 3D data and generates detailed body shape information for the user. This information includes data on the dimensions and posture of each body part.

[0837] Step 5:

[0838] The server compares the generated body shape information with past data to evaluate changes in current body shape. This evaluation is used to adjust training and meal plans.

[0839] Step 6:

[0840] The emotion engine analyzes the received emotion data to identify the user's current emotional state. The identified emotion is then processed as an element to be reflected in the feedback.

[0841] Step 7:

[0842] The server generates individually optimized training plans and feedback messages based on body type information and emotional data. It adjusts the intensity of the content and the coaching style according to the emotional data's state.

[0843] Step 8:

[0844] The device displays real-time feedback from the server to the user. The user then uses this feedback to conduct daily training and receive advice and motivation as needed.

[0845] This allows users to pursue their physical goals while also receiving emotional support, enabling them to continue training consistently.

[0846] (Example 2)

[0847] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0848] In modern health management, providing personalized exercise plans and nutritional guidelines is crucial. However, conventional systems typically provide plans based solely on the user's body type information, lacking feedback that considers the user's emotional state. As a result, the provided plans are not always optimal for the user's current situation, posing challenges in maintaining motivation and achieving effective improvement.

[0849] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0850] In this invention, the server includes means for acquiring human body data, means for converting the human body data acquired by the acquisition means into three-dimensional information, means for analyzing the three-dimensional information obtained by the conversion means and generating body shape data, and emotion analysis means for acquiring and analyzing emotion data. This makes it possible to provide a user-optimized exercise plan and feedback based on the user's body shape data and emotion data.

[0851] "Human body data" refers to information about body shape obtained from users, specifically including external body shape and dimensions.

[0852] "Three-dimensional information" refers to data obtained by converting two-dimensional shape data into three-dimensional data, providing the user's three-dimensional body shape information.

[0853] "Body shape data" refers to detailed information about the user's current physical characteristics, generated based on analyzed three-dimensional information.

[0854] An "exercise plan" is a personalized exercise plan optimized based on the user's body type and emotional data.

[0855] "Emotional data" refers to information about a user's psychological state, analyzed from their voice, facial expressions, and other data.

[0856] "Emotional analysis means" refers to technologies and devices for collecting and analyzing user emotional data, and for identifying the user's psychological state.

[0857] "Feedback" refers to the information and advice provided to users based on analysis results and plans.

[0858] The system of this invention relies on the collaboration of a user, a terminal, and a server. First, the user uses a terminal with a dedicated application installed. The user acquires image data of their body shape through this application. The image data is converted into three-dimensional information on the terminal. This is done using AI-based image analysis software.

[0859] The terminal transmits the converted three-dimensional information and emotional data collected from the user's voice and facial expressions to the server via a secure communication protocol. The server analyzes the received data, generates the user's body shape data, and further recognizes the user's emotional state using emotion analysis tools.

[0860] Based on this information, the server generates a personalized exercise plan. This plan reflects the user's body type data and emotional state. For example, if the server detects that the user is stressed, relaxation-focused exercises may be recommended.

[0861] Finally, the server provides the generated feedback and advice to the device, which then displays it to the user. This allows the user to access an appropriate health management plan tailored to their physical and emotional state.

[0862] For example, a prompt might instruct you to "Assume the user is feeling down and generate a feedback message using the emotion engine." In response to this, the system might generate feedback such as, "How about trying some stress-relieving exercises together today to cheer you up?" This is generated using a generative AI model and optimizes the user experience.

[0863] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0864] Step 1:

[0865] The user takes images of their body shape using a dedicated application. The application requests images from multiple angles to facilitate the collection of three-dimensional data. The input is the multiple image data points captured. The output is the storage of these image data points on the device.

[0866] Step 2:

[0867] The terminal converts image data input from the user into three-dimensional information. This conversion uses AI-based image analysis software. The input is the image data obtained in step 1, and the output is three-dimensional information. In this process, the information of each pixel in the image is analyzed to construct a 3D model.

[0868] Step 3:

[0869] The device captures voice and facial expression data from the user and acquires it as data for emotion analysis. The input is the user's voice and facial expression information, and the output is stored on the device as emotion data. This process uses voice recognition technology and facial recognition technology.

[0870] Step 4:

[0871] The terminal transmits acquired 3D information and emotional data to the server using a secure communication protocol. The input is 3D information and emotional data, and the output is the data transferred to the server. The data is protected by encryption technologies such as SSL / TLS.

[0872] Step 5:

[0873] The server analyzes the received 3D information and generates the user's current body shape data. This analysis utilizes 3D body scanning technology. The input is the 3D information received in step 4, and the output is the user's body shape data. Specifically, it calculates the size of each body part and the overall proportions.

[0874] Step 6:

[0875] The server analyzes the received voice and facial expression data using emotion analysis tools to evaluate the user's emotional state. The input is the emotion data received in step 4, and the output is information about the user's emotional state. This allows specific emotions (e.g., stress, anxiety, joy) to be quantified.

[0876] Step 7:

[0877] The server generates an individually optimized exercise plan based on the generated body shape data and emotional state information. The input is the data obtained in steps 5 and 6, and the output is the individual plan. Specifically, it organizes exercises corresponding to the user's body shape goals and makes adjustments according to their emotional state.

[0878] Step 8:

[0879] The server generates an exercise plan and feedback based on emotional data, and sends it to the device. The input is the raw material for the individual plan and feedback, while the output is the feedback information sent to the device. This feedback includes individual exercise instructions and encouraging messages.

[0880] Step 9:

[0881] The terminal displays feedback information received from the server to the user. The input is the feedback information obtained in step 8, and the output is the information presented to the user. Throughout this process, exercise plans and advice are displayed through a user-friendly interface.

[0882] (Application Example 2)

[0883] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0884] Traditional health management systems have struggled to provide not only exercise plans based on users' body shape data, but also appropriate feedback tailored to their individual emotional states. As a result, users may struggle to maintain motivation, hindering the achievement of their health goals. Furthermore, previous systems evaluated body shape data and emotional states separately, resulting in insufficient overall personalization.

[0885] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0886] In this invention, the server includes an acquisition means for acquiring human body information, an emotion recognition means having an emotion recognition function to identify emotional states, and a generation means for generating suggestions that reflect the emotional state. This makes it possible to provide highly personalized exercise plans and nutritional guidelines that take into account both body shape data and emotional states.

[0887] "Human body information" refers to data related to the external shape and dimensions of the human body, and this information is used to understand and analyze changes in the body shape of individual users.

[0888] "Three-dimensional information" refers to information that represents the external shape of the human body as three-dimensional spatial data, which enables three-dimensional body shape analysis.

[0889] "Body shape data" refers to a collection of detailed information about the user's body shape, including dimensions and form, generated based on three-dimensional information.

[0890] An "exercise plan" refers to a series of suggestions that outline the content and schedule of exercises that a user should perform, individually optimized based on their body type data.

[0891] "Delivery means" refers to the function of delivering, displaying, or notifying the user of the generated exercise plan and feedback message.

[0892] "Emotion recognition function" refers to technology that analyzes the user's voice, facial expressions, etc., to determine their emotional state at that time.

[0893] "Emotion identification means" refers to a function that specifically identifies the user's emotional state based on information acquired by the emotion recognition function.

[0894] "Generative means" refers to technology that comprehensively considers the user's body shape data and emotional state to generate exercise plans and feedback for improvement.

[0895] The system for carrying out the present invention includes a user, a terminal, a server, and an emotion engine. The user interacts with a dedicated consumer robot and receives daily health management. The terminal acquires the user's body shape information and emotion data using a camera and microphone mounted on the robot.

[0896] The server first analyzes body shape information transmitted from the terminal using 3D body scanning technology to generate detailed body shape data. OpenCV is used as the image processing library for this analysis to construct the 3D data. Furthermore, TensorFlow is used for speech and facial recognition in the analysis of emotion data. This enables real-time emotion identification.

[0897] The analyzed body shape and emotional data are stored on the server and compared with past records before a process to generate an optimal exercise plan for the user. The generated exercise plan and nutritional guidelines are provided via the device along with feedback messages that take into account the user's emotional state. For example, if the server determines that the user is feeling down, it will create an encouraging message such as, "Let's do some light exercise today to refresh yourself."

[0898] For example, if a user asks the robot, "What should I eat today?", the robot might respond, "Considering your current physical condition and emotional state, I recommend a balanced meal that includes a salad." This allows the user to receive physically and emotionally optimized support, enabling them to effectively work towards their health goals.

[0899] An example of a prompt for the generating AI model would be the text, "Consider the user's current body shape data and emotional state, and generate an appropriate training and meal plan."

[0900] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0901] Step 1:

[0902] The user acquires body shape information and audio data through the device's camera and microphone. Body shape photos are input as high-resolution images, and audio is input as audio data for sentiment analysis. This data is stored on the device as initial data.

[0903] Step 2:

[0904] The device converts the acquired body shape photograph into three-dimensional information using the OpenCV library. The input is a high-resolution image, and the output is three-dimensional mesh data. This data conversion generates detailed dimensional information about the body shape.

[0905] Step 3:

[0906] The device analyzes audio data using TensorFlow to identify emotional states. The input is audio data, and the output is the identified emotional tag. This process allows the user's current emotional state to be understood.

[0907] Step 4:

[0908] The device transmits three-dimensional body shape data and emotional state data to the server via a secure connection. Here, the device creates a data package and transfers the data to the server.

[0909] Step 5:

[0910] The server analyzes the received 3D data and evaluates the user's body shape data. 3D body shape mesh data is used as input, and the output generates statistics on the user's dimensions and changes in body shape. This includes comparisons with historical data.

[0911] Step 6:

[0912] The server uses emotional state data to generate an optimal exercise plan for the user. Inputs are emotional tags and body type data, and output is a personalized exercise plan and nutritional guidelines. This ensures that appropriate content is prepared based on the user's emotional state.

[0913] Step 7:

[0914] The server sends the generated exercise plan and nutritional guidelines to the terminal, which then provides them to the user. The terminal communicates the information to the user via the robot's display or voice. The outputted information serves as a guide for maintaining health in the user's daily life.

[0915] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0916] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0917] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0918] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0919] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0920] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0921] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0922] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0923] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0924] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0925] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0926] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0927] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0928] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0929] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0930] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0931] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0932] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0933] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0934] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0935] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0936] The following is further disclosed regarding the embodiments described above.

[0937] (Claim 1)

[0938] A means of acquiring a human body image,

[0939] A conversion means for converting a human body image acquired by the acquisition means into three-dimensional data,

[0940] An analysis means that analyzes the three-dimensional data obtained by the conversion means and generates body shape information,

[0941] A creation means for creating an individually optimized training plan based on the body shape information generated by the aforementioned analysis means,

[0942] A means for providing the user with the training plan created by the creation means,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, characterized in that the providing means provides dietary guidelines based on the body shape information along with the provision of the training plan.

[0946] (Claim 3)

[0947] The system according to claim 1, characterized in that the analysis means further comprises an evaluation means for evaluating current changes by comparing them with past body shape information.

[0948] "Example 1"

[0949] (Claim 1)

[0950] A device for acquiring human body data,

[0951] A conversion device that converts human body data acquired by the aforementioned device into three-dimensional information,

[0952] A processing device that processes the three-dimensional information obtained by the aforementioned conversion device and generates body information,

[0953] A planning device that formulates an individually optimized training plan based on the physical information generated by the processing device,

[0954] A providing device that provides the training plan formulated by the aforementioned planning device to the user,

[0955] In addition to providing the aforementioned training plan, the instruction device provides nutritional guidance based on the aforementioned physical information,

[0956] An evaluation device that compares current physical information with past physical information,

[0957] A system that includes this.

[0958] (Claim 2)

[0959] The system according to claim 1, characterized in that the instruction device includes a reminder function to prompt the user to perform their next activity.

[0960] (Claim 3)

[0961] The system according to claim 1, characterized in that the conversion device generates three-dimensional body information using a machine learning model.

[0962] "Application Example 1"

[0963] (Claim 1)

[0964] A means of acquiring human body data,

[0965] A conversion means for converting human body data acquired by the acquisition means into three-dimensional data,

[0966] An analysis means analyzes the three-dimensional data obtained by the conversion means and generates body shape data,

[0967] A creation means for creating an individually optimized exercise plan based on the body shape data generated by the aforementioned analysis means,

[0968] A means for providing a human being with an exercise plan created by the creation means,

[0969] Along with providing the aforementioned exercise plan, the system provides nutritional guidelines based on the aforementioned body shape data and includes tracking means for tracking progress.

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, characterized in that the means of providing information interactively provides feedback to a human and maintains motivation.

[0973] (Claim 3)

[0974] The system according to claim 1, further comprising the function of the analysis means to evaluate current changes by comparing them with past body shape data and to send a reminder.

[0975] "Example 2 of combining an emotion engine"

[0976] (Claim 1)

[0977] Means for acquiring human body data,

[0978] A means for converting human body data acquired by the acquisition means into three-dimensional information,

[0979] A means for analyzing the three-dimensional information obtained by the aforementioned conversion means and generating body shape data,

[0980] A means for creating an individually optimized exercise plan based on body shape data generated by the aforementioned analysis means,

[0981] A means of sentiment analysis that acquires and analyzes sentiment data,

[0982] A means for providing the user with feedback based on the exercise plan and emotional data created by the creation means,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, characterized in that the providing means provides nutritional guidance based on the body shape data and emotional data, along with the provision of the exercise plan.

[0986] (Claim 3)

[0987] The system according to claim 1, wherein the analysis means further comprises an evaluation means for evaluating current changes by comparing them with past body shape data, and takes emotional data into consideration.

[0988] "Application example 2 when combining with an emotional engine"

[0989] (Claim 1)

[0990] Means for acquiring human body information,

[0991] A conversion means for converting human body information acquired by the acquisition means into three-dimensional information,

[0992] An analysis means that analyzes the three-dimensional information obtained by the conversion means and generates body shape data,

[0993] A creation means for creating an individually optimized exercise plan based on the body shape data generated by the analysis means,

[0994] A means for providing the user with an exercise plan created by the creation means,

[0995] An emotion recognition means that has an emotion recognition function and identifies an emotional state,

[0996] A generation means that reflects the emotional state identified by the emotion identification means in the exercise plan and feedback message, and generates suggestions based on emotion,

[0997] A system that includes this.

[0998] (Claim 2)

[0999] The system according to claim 1, characterized in that the providing means provides nutritional guidelines based on body shape data and emotional state, along with the provision of the exercise plan.

[1000] (Claim 3)

[1001] The system according to claim 1, wherein the analysis means further comprises an evaluation means for evaluating current changes by comparing them with past body shape data and emotional history. [Explanation of Symbols]

[1002] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring a human body image, A conversion means for converting a human body image acquired by the acquisition means into three-dimensional data, An analysis means that analyzes the three-dimensional data obtained by the conversion means and generates body shape information, A creation means for creating an individually optimized training plan based on the body shape information generated by the aforementioned analysis means, A means for providing the user with the training plan created by the creation means, A system that includes this.

2. The system according to claim 1, characterized in that the providing means provides dietary guidelines based on the body shape information along with the provision of the training plan.

3. The system according to claim 1, characterized in that the analysis means further comprises an evaluation means for evaluating current changes by comparing them with past body shape information.

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