System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a picture of a child; means for parsing skeletal data from the received picture; means for matching the parsed skeletal data against a database of past successful athletes to determine an optimal sport for the child; and means for notifying a user of the determination.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to reduce the difficulty parents face in choosing appropriate sports for their children by scientifically determining which sports a child is suited to. The objective is to support the healthy growth and development of children, particularly those aged 0 to 14, by providing a means to identify sports that their children excel in and to develop their interests and abilities. [Means for solving the problem]

[0005] The system of the present invention includes a means for receiving a child's photograph, a means for analyzing skeletal data from the received photograph, a means for comparing the analyzed skeletal data with a database of past successful athletes to determine the child's best sport, and a means for notifying the user of the results. This allows the system to scientifically and statistically determine the best sport for each child based on their skeletal structure and provide this information to parents, helping them select the right sport for their child. The system also includes a camera function for taking photographs and a generative AI model for analyzing skeletal data, enabling highly accurate diagnoses.

[0006] A "child" is a human being in a biologically immature stage, usually ranging in age from 0 to 14 years.

[0007] "Photograph" refers to still image data obtained using a camera function, capturing visual information of a subject.

[0008] "Receiving" refers to the process by which information reaches its destination and the data is captured.

[0009] "Skeletal data" refers to data that includes information about the skeletal structure of the subject, including information about the positions, shapes, and sizes of joints and bones.

[0010] "Analysis" refers to the process of examining input data in detail and extracting meaningful information.

[0011] A "database of successful athletes" refers to a database that collects and stores information about athletes who have achieved success in a particular sport in the past.

[0012] "Matching" refers to the process of comparing different data sets to find matches and differences between them.

[0013] "Decision" refers to the process of reaching a conclusion based on certain criteria or algorithms.

[0014] "Notification" refers to the process of conveying specific information to a recipient.

[0015] "User" refers to a person or entity that uses the system.

[0016] A "system" refers to a structure in which multiple elements work together to perform a specific function.

[0017] "Camera Functionality" means the functionality of a device to take and record still images or video.

[0018] A "generative AI model" refers to an algorithm that uses machine learning or deep learning techniques to analyze data and perform specific tasks. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention is a system for analyzing a child's skeleton from a photograph and diagnosing the best sport for that child. Specific embodiments will be described below.

[0041] System Overview

[0042] User Interface

[0043] Using a device operated by a parent, the user launches the application and takes a photo of the child or selects an existing photo, which is then uploaded to the server.

[0044] Upload a photo

[0045] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[0046] Skeletal analysis

[0047] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[0048] Database collation

[0049] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[0050] Generating and notifying results

[0051] The server generates the optimal sports judgment result based on the database comparison results, and the result is notified to the user as a detailed report on the terminal. The report includes the reasons for the judgment and other reference information.

[0052] Specific examples

[0053] Example: If you have an 8-year-old child

[0054] Taking and uploading photos:

[0055] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0056] Photo reception and analysis:

[0057] The server receives the photo data and uses an AI model to analyze the skeleton, extracting data such as the child's shoulder width, leg length, and spine alignment.

[0058] Database collation:

[0059] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[0060] Generate and communicate results:

[0061] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, and sends this report to the parent's device, where they can view the results through the app.

[0062] How to use

[0063] The system is designed to be easily accessible to parents and other users. Simply install the app, take a full-body photo of your child, or upload an existing photo, and the system will automatically analyze the photo and diagnose the best sport for your child. This makes it easier for users to choose a sport that suits their child's characteristics.

[0064] Since the system of the present invention depends on the accuracy of the photographs used and the training data of the generative AI model, it is possible to improve accuracy by collecting and analyzing more data. Furthermore, by regularly updating the model and database, it is possible to always provide the latest analysis technology and information.

[0065] The above is a specific embodiment for carrying out the present invention.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The user takes or selects a photo.

[0069] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[0070] Step 2:

[0071] The device uploads the photo to the server.

[0072] The device will upload the photographed or selected photo data to the server. Specifically, it will use the data sending function within the app to send the selected photo to the cloud server, and after sending, will display a success or failure message.

[0073] Step 3:

[0074] The server receives and stores the photo data.

[0075] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[0076] Step 4:

[0077] The server launches an AI model to analyze the photo.

[0078] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[0079] Step 5:

[0080] The server stores the analyzed skeletal data in a database.

[0081] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[0082] Step 6:

[0083] The server checks the skeletal data against a database.

[0084] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[0085] Step 7:

[0086] The server determines the best sport and generates a report.

[0087] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[0088] Step 8:

[0089] The server sends the report to the device.

[0090] The server sends the judgment result and a report to the terminal. Specifically, it generates the report in JSON or XML format, sends it to the terminal via API, and generates a transmission completion message and records it in the log.

[0091] Step 9:

[0092] The terminal displays the results to the user.

[0093] The terminal displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, and generates visual graphs and explanatory text to provide the results in a format that is easy for the user to understand.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] Conventional technology lacks the means to accurately determine the type of sports that best suit a child's characteristics, making it difficult to make optimal decisions about sports selection for children. Furthermore, it often relies on manual data analysis and expert opinions, which is time-consuming and costly.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes means for receiving an image of a child, means for analyzing skeletal information from the received image, means for comparing the analyzed skeletal information with a database of past successful athletes to determine the most suitable athletic event for the child, and means for notifying the user of the determination result. This makes it possible to quickly and accurately determine the most suitable athletic event for a child without relying on conventional manual data analysis or expert opinions.

[0099] An "image" is data in digital or analog form that captures visual information of an object.

[0100] "Skeletal information" is data such as joint positions, bone lengths, and body shape extracted from an image.

[0101] An "athlete database" is a collection of information that compiles skeletal information and athletic events of successful athletes in the past.

[0102] An "athletic event" is a type of competition or sport that is based on specific rules and techniques.

[0103] A "server" is a computer system that receives, analyzes, and transmits data to clients over a network.

[0104] "User" refers to parents and other parties who use the System.

[0105] A "generative AI model" is an artificial intelligence model that is built using machine learning libraries and runs algorithms to analyze skeletal information from images.

[0106] "Image capture device functionality" refers to a device or software functionality that includes a camera function for capturing images.

[0107] The present invention is a system for analyzing skeletal information from an image of a child and diagnosing the type of exercise that is best suited for that child. Specific embodiments of this system will be described below.

[0108] System Overview

[0109] User Interface

[0110] The user launches the application using a parent-controlled device. The application has a camera function to take a full-body photo of the child, and also provides the option to select an existing photo. After the user takes or selects a photo, the photo is uploaded to the server.

[0111] Upload a photo

[0112] The device uploads the photos selected by the user to the server, which sends the data using a secure communication protocol (e.g., HTTPS). This ensures that the photo data is safely received by the server and saved in the specified directory.

[0113] Skeletal analysis

[0114] The server passes the received photo data to a pre-trained generative AI model, which was built using machine learning libraries TensorFlow and PyTorch. The generative AI model extracts skeletal information from the image, such as joint positions, bone lengths, and body shape, and analyzes the data in numerical form.

[0115] Database Matching

[0116] The server compares the analyzed skeletal information with a database of successful athletes, which includes detailed skeletal information about athletes and data on their success in sports. The server then uses statistical techniques to identify athletes with similar skeletal information and their sports.

[0117] Generation and notification of judgment results

[0118] The server then determines the optimal exercise based on the database comparison results, generates a detailed report of the results, and sends it to the user's device. The user receives a notification on their device and can check the results through the application.

[0119] Specific examples

[0120] Here is an example of the specific process, assuming you are using a full-body photo of an 8-year-old child.

[0121] Taking and uploading photos:

[0122] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0123] Photo reception and analysis:

[0124] The server receives the photo data and uses a generative AI model to analyze skeletal information, extracting data such as the child's shoulder width, leg length, and spine alignment.

[0125] Database collation:

[0126] The extracted skeletal information is then compared against a database of past successful athletes, and the skeleton is determined to resemble a particular athlete, for example, a basketball player.

[0127] Generate and communicate results:

[0128] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, which is then sent to the parent's device, where they can view the results through the app.

[0129] Prompt Sentence Examples

[0130] Here are some example prompts to input to a generative AI model:

[0131] "We analyze a full-body photograph of an eight-year-old child and diagnose the most suitable type of exercise based on their skeletal data. We focus on evaluating shoulder width, leg length, and back muscle alignment to determine the most suitable type of exercise."

[0132] This prompt sentence allows the generative AI model to make appropriate analysis and judgments.

[0133] The present invention makes it possible to easily and accurately determine the type of exercise that is best suited for a child.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] The user launches the application. Once installed on the user's device, the application is launched by tapping, which takes the user to the initial screen, where the user is presented with the option to take a photo of their child or select an existing photo.

[0137] Step 2:

[0138] The user takes or selects a photo. The user presses the "Take Photo" button in the app and uses the device's camera to take a full-body photo of the child. Or, the user presses the "Select Photo" button and selects an existing photo from the device's gallery. The input is a full-body photo of the child, which will be used in the next step.

[0139] Step 3:

[0140] The device uploads the photo to the server. The selected or taken photo is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The input is the photo selected in the previous step, and the output is the photo data stored on the server.

[0141] Step 4:

[0142] The server receives and stores the photo data. The server receives the photo data sent and stores it securely in the specified directory. Once the storage is complete, it is ready to proceed to the next processing step. The input is the photo data sent from the device, and the output is the photo data stored in the specified directory on the server.

[0143] Step 5:

[0144] The server passes the photo data to the generative AI model. The server retrieves the stored photo data and inputs it into a generative AI model for image analysis. This generative AI model is built using TensorFlow and PyTorch. The input is the stored photo data, and the output is skeletal information from the analysis results.

[0145] Step 6:

[0146] The generative AI model analyzes the skeletal information. The generative AI model extracts skeletal information such as joint positions, bone lengths, and body shape from the photo and analyzes it as numerical data. This data is used in the next step. The input is photo data, and the output is numerical data as skeletal information.

[0147] Step 7:

[0148] The server compares the skeletal information with a database. The server compares the skeletal information received from the generative AI model with a database of past successful athletes. The database contains the athletes' skeletal information and data on the areas in which they have been successful. The input is the analyzed skeletal information, and the output is the determination of the optimal athletic event.

[0149] Step 8:

[0150] The server determines the best type of sport for the child based on the results of database matching. The result of the determination also includes scientific reasons based on skeletal information. The input is the result of matching with a database of past successful athletes, and the output is the determined best type of sport.

[0151] Step 9:

[0152] The server generates the results as a detailed report and sends it to the terminal. The server documents the judgment results as a detailed report and sends it to the user's terminal. This report includes the reasons for the judged exercise and related information. The input is the judgment result, and the output is the detailed report sent to the terminal.

[0153] Step 10:

[0154] The user checks the report. The user checks the notification received on the device and views a detailed report through the application. This report can be used as a reference for selecting the most suitable exercise for the child. The input is the report sent from the server, and the output is the assessment result that the user checks.

[0155] (Application example 1)

[0156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] Today's parents spend a lot of time and effort trying to find the right sport for their child. They often seek expert advice and repeatedly try out different sports to determine which is best suited to their child's physical characteristics. Even after identifying a suitable sport, parents can have difficulty selecting the appropriate products and equipment. To address this issue, a system is needed that can efficiently and quickly determine the best sport for a child and recommend related products.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0159] In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination result and recommending related products, and means for displaying the result via an in-store terminal or a visual device, thereby enabling parents to easily find a sport that is suitable for their child and quickly obtain products suitable for that sport.

[0160] "Child photo" is image data of a child's entire body that is input into the system.

[0161] "Skeletal data" refers to numerical data about the skeleton, such as shoulder width, leg length, and joint position, extracted using image processing technology and generative AI models.

[0162] A "generative AI model" is a trained artificial intelligence model for analyzing skeletal features from image data, and is built using machine learning libraries such as TensorFlow and PyTorch.

[0163] A "database" is a collection of information that stores skeletal data and statistical information on successful athletes of the past.

[0164] The "judgment result" is the analysis result that indicates which sport is best for your child, along with the reasons and related data.

[0165] "Relevant Products" are the products and equipment required for the relevant sporting activity that are recommended based on the results of the assessment.

[0166] "In-store terminal" refers to a computer terminal installed in a sporting goods store, which is a device used by users to input and view information.

[0167] A "visual device" is a device that provides visual information when worn by a user, such as smart glasses or a head-mounted display.

[0168] The present invention is a system that analyzes skeletal data from a photograph of a child, determines the child's best sport, and recommends related products. Specific embodiments will be described below.

[0169] System Overview

[0170] User Interface

[0171] Using an in-store terminal or visual device installed in a physical store, the user launches the application and takes a photo of their child or selects an existing photo, which is then uploaded to the server.

[0172] Upload a photo

[0173] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[0174] Skeletal analysis

[0175] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[0176] Database collation

[0177] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[0178] Generating and notifying results

[0179] The server generates the optimal sports judgment result based on the database comparison result. This result, along with related products, is notified to the user via the in-store terminal or visual device. The user can check the result on the display of the in-store terminal or visual device and consider purchasing sports-related products.

[0180] Specific examples

[0181] Example: In a special section of a sporting goods store, a parent puts on smart glasses and takes a full-body photo of their child. The AI ​​analyzes the image on the spot, and the display shows "Basketball is the best choice," along with specific recommendations for basketball-related products (e.g., shoes, jerseys). In this way, parents can easily find the right sport for their child and quickly obtain the necessary equipment related to that sport.

[0182] How to use

[0183] The system is designed to be easily accessible for parents and users. All users need to do is install the app, take a full-body photo of their child, or upload an existing photo. The system will then automatically analyze the photo and diagnose the best sport for their child. This makes it easier for users to choose a sport that suits their child's characteristics.

[0184] Prompt Sentence Examples

[0185] "Upload a full-body photo of your child and have it analyzed to determine the best sport for them. Then generate a list of relevant products based on the results."

[0186] In this way, the present invention is a system that not only allows modern parents to efficiently and quickly find the best sports for their children, but also supports them in selecting related products, thereby significantly reducing time and effort and enabling them to provide a better sports life.

[0187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0188] Step 1:

[0189] The user takes a full-body photo of their child or selects an existing photo and uploads it using a terminal or visual device in the store. The input is the child's full-body photo, and the output is the uploaded image data. The terminal sends this photo to the server.

[0190] Step 2:

[0191] The server reads the received photo data and starts image processing. At this time, the input is the uploaded image data, and the output is the processed data. Specifically, the image is converted to a standard format and pre-processed to remove noise.

[0192] Step 3:

[0193] The server inputs the preprocessed image data into a generative AI model to extract skeletal feature points. The input is preprocessed image data, and the output is analyzed skeletal data. The AI ​​model is built using TensorFlow, PyTorch, etc., and extracts feature points such as joint positions, bone lengths, and body shape as numerical data.

[0194] Step 4:

[0195] The server compares the analyzed skeletal data with a database of successful athletes. The input is the analyzed skeletal data, and the output is the statistically optimal sports judgment result. The server uses a database search algorithm to find athletes with similar skeletal data and analyze which sports they are successful in.

[0196] Step 5:

[0197] The server generates the optimal sports judgment result based on the matching result and recommends related products. The input is the matching result, and the output is the judgment result and a list of recommended products. Specifically, it generates the name of the matching sports and the reason for it, as well as information on related products sold in the store.

[0198] Step 6:

[0199] The server sends the results of the assessment and the list of recommended products to the in-store terminal or visual device. The input is the assessment results and the list of recommended products, and the output is the display information shown to the user. The user can check the optimal sports and related products through the display of the visual device or terminal.

[0200] This process allows the user to quickly find a sport that is suitable for their child and also allows them to immediately obtain the necessary equipment related to that sport.

[0201] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0202] The present invention provides a more user-friendly interface by combining an emotion engine with a system that analyzes a child's skeleton from a photograph and diagnoses the best sport for that child. Specific embodiments will be described below.

[0203] System Overview

[0204] User Interface

[0205] The user launches the application using a device operated by the parent, takes a photo of the child or selects an existing photo, which is then uploaded to the server. If the user expresses an emotion through the photo or voice data, the emotion engine analyzes the emotion.

[0206] Upload a photo

[0207] The device uploads photos taken or selected by the user to the server. The uploaded photo data is received and stored by the server. At this time, the emotion engine also receives and analyzes the user's emotion data.

[0208] Skeleton analysis and emotion analysis

[0209] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores the results as numerical data. At the same time, an emotion engine analyzes the user's emotional state, and the results are stored and analyzed.

[0210] Database collation

[0211] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports athletes with similar skeletal data have been successful in, and determines the best sport for the individual. Emotional data may also be used as relevant information.

[0212] Generating and notifying results

[0213] The server generates the optimal sports judgment result based on the database comparison results. This result is notified to the user on their device as a detailed report. The report includes the reasons for the judged sports and other reference information. The server also adjusts the presentation of the report based on the analysis results of the emotion engine.

[0214] Specific examples

[0215] Example: If you have an 8-year-old child

[0216] Taking and uploading photos:

[0217] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0218] Receiving and analyzing photo and emotion data:

[0219] The server receives the photo data and uses an AI model to analyze the child's skeletal structure. For example, it extracts data such as the child's shoulder width, leg length, and spine alignment. At the same time, an emotion engine analyzes the parent's emotions based on the photos and audio of their reactions.

[0220] Database collation:

[0221] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[0222] Generate and communicate results:

[0223] The server generates a detailed report based on the analysis results, including reasons why basketball is a good fit for the child. Taking into account the analysis results of the emotion engine, if the user shows signs of anxiety, the server adds encouraging messages and positive information about the child's progress. This report is then sent to the parent's device, where the parent can view the results through the app.

[0224] How to use

[0225] Users simply install the application, take a full-body photo of their child, or upload an existing one. The system then automatically analyzes the photo and diagnoses the best sport for them. Furthermore, the system adjusts the way results are presented based on the user's emotional state, creating a more user-friendly interface.

[0226] This system depends on the accuracy of the photos used and the learning data of the generative AI model, so accuracy can be improved by collecting and analyzing more data.In addition, by regularly updating the model, updating the database, and improving the emotion engine, it is possible to always provide the latest analysis technology and information.

[0227] The above is a specific embodiment for carrying out the present invention.

[0228] The processing flow will be explained below.

[0229] Step 1:

[0230] The user takes or selects a photo.

[0231] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[0232] Step 2:

[0233] The device uploads the photo to the server.

[0234] The device uploads the captured or selected photo data to the server. Specifically, it uses the data sending function in the app to send the photo to the cloud server, and displays a success or failure message after sending.

[0235] Step 3:

[0236] The user provides emotion data.

[0237] Users can express their feelings during or after uploading a photo by answering emotional questions or entering a voice message.

[0238] Step 4:

[0239] The device transmits the emotion data to the server.

[0240] The device transmits the emotion data provided by the user to the server, specifically, the answer data to the emotion-related questions and the voice data to the cloud server.

[0241] Step 5:

[0242] The server receives and stores the photo data.

[0243] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[0244] Step 6:

[0245] The server receives and analyzes the emotion data.

[0246] The server receives the emotion data sent from the device and uses an emotion engine to analyze the user's emotional state. Specifically, the server inputs the voice or text data into an emotion recognition algorithm, categorizes the emotional state, and stores it in a database.

[0247] Step 7:

[0248] The server launches an AI model to analyze the photo.

[0249] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[0250] Step 8:

[0251] The server stores the analyzed skeletal data in a database.

[0252] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[0253] Step 9:

[0254] The server checks the skeletal data against a database.

[0255] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[0256] Step 10:

[0257] The server determines the best sport and generates a report.

[0258] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[0259] Step 11:

[0260] The server sends the report and emotion data together to the device.

[0261] The server sends the judgment results, a report, and the analyzed emotion data to the device. Specifically, it generates a report in JSON or XML format, adds a message according to the emotion, sends it to the device via API, and generates a transmission completion message and records it in the log.

[0262] Step 12:

[0263] The terminal displays the results to the user.

[0264] The device displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, generates visual graphs and explanatory text, and adds messages and user guides according to the user's emotional state to provide the results in an easy-to-understand format for the user.

[0265] Example 2

[0266] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0267] Conventional systems for diagnosing children's sports aptitude focus only on analyzing skeletal data, and therefore are unable to provide accurate advice or diagnostic results that take into account the user's emotional state. It is also difficult to create a user-friendly interface for notifying the analysis results.

[0268] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving image data of a child, means for analyzing skeletal data from the received image data, means for comparing the analyzed skeletal data with a database of past successful athletes, means including an emotion engine for receiving and analyzing voice and facial expression data from the user, and means for notifying the user based on the judgment result and the analyzed emotion data. This makes it possible to provide a more familiar and accurate sports diagnosis result that takes into account the user's emotional state.

[0269] "Child" means a minor or young person, usually a human being who is still in the process of developing.

[0270] "Image data" is data that represents visual information in a digital format, such as photographs and video frames.

[0271] A "means for receiving" is a method or mechanism for obtaining or acquiring information from another device or system.

[0272] "Skeletal data" is numerical information about the positions and lengths of bones in the human body, the arrangement of joints, and so on.

[0273] "Analytical means" are methods and techniques for examining, analyzing, and evaluating data.

[0274] A "former successful athlete" is someone who has had a strong track record and achieved success in sports.

[0275] A "database" is a collection of electronic information that allows large amounts of data to be systematically collected, stored, and searched.

[0276] A "matching tool" is a method or technique for comparing two or more data sets and identifying similarities or matches.

[0277] "Audio data" means data that is a digital representation of the human voice or other sounds.

[0278] "Facial expression data" is data that numerically represents various expressions and emotional states of a human face.

[0279] An "emotion engine" is an algorithm or technology that analyzes voice and facial expression data to determine emotional states.

[0280] A "means of notification" is a method or mechanism for conveying information to a user.

[0281] A "generative AI model" is an artificial intelligence model built using machine learning and deep learning techniques, which analyzes data and makes predictions.

[0282] "User" refers to any person or entity that uses the system, and in this context primarily includes parents or guardians of children.

[0283] The present invention is a system that analyzes a child's skeleton from photographic data and diagnoses the best sport for that child. This system provides a user-friendly interface by combining it with an emotion engine. The following describes in detail an embodiment of the present invention.

[0284] System Overview

[0285] User Interface

[0286] The user launches the application on their device (e.g., a smartphone or tablet) and takes a full-body photo of their child or selects an existing photo. The photo is instantly uploaded to the server. At the same time, if the user expresses emotions through voice or facial expressions, that data is also sent to the server.

[0287] Upload a photo

[0288] The device uploads a full-body photo of the child taken or selected by the user to the server via the Internet. The server stores the received photo data and prepares it for the emotion engine to analyze the voice and facial expression data received at the same time.

[0289] Skeleton analysis and emotion analysis

[0290] The server inputs the received photo data into a generative AI model (for example, a model built using machine learning libraries such as TensorFlow or PyTorch) to perform skeletal analysis. This analysis process extracts joint positions, bone lengths, and body features from the photo. At the same time, an emotion engine analyzes voice and facial expression data to identify the user's emotional state. The resulting skeletal and emotional data are quantified and stored on the server.

[0291] Database Matching

[0292] The server uses the analyzed skeletal and emotional data to compare it with a database of successful athletes. The database contains the skeletal data and sport information of many athletes. The server uses a statistical algorithm to identify the athlete who most closely resembles the analyzed skeletal data. At the same time, the server adjusts the presentation of the results based on the emotional data.

[0293] Generating and notifying results

[0294] The server generates the optimal sports judgment result based on the database comparison results. This judgment result is notified to the user as a detailed report. The report includes the reasons for the judged sports, useful information, and appropriate advice based on the analysis results of the emotion engine. For example, if the user shows an anxious expression, an encouraging message is added to the report. The result is sent to the terminal, and the user can check the report through the application.

[0295] Examples of usage

[0296] Example: Parents with an 8-year-old child

[0297] Taking and uploading photos:

[0298] Parents use the camera on their smartphone to take a full-body photo of their child and upload it to the server via the application.

[0299] Receiving and analyzing photo and emotion data:

[0300] The server inputs the photo data into a generative AI model to analyze the skeletal structure. Specifically, it extracts data such as shoulder width, leg length, and spine alignment. At the same time, it analyzes the user's voice and facial expression data using an emotion engine.

[0301] Match against database:

[0302] The extracted skeletal data is compared with a database to determine whether the child resembles a specific basketball player, which diagnoses the child as being suited to basketball.

[0303] Generate and communicate results:

[0304] The server generates a report of the results and adds a message that takes the user's emotional state into account. This report is then sent to a smartphone, where the user can view the results through an application.

[0305] Prompt Sentence Examples

[0306] Example prompts for skeletal and emotional analysis of photos:

[0307] "I've uploaded a full-body photo of an 8-year-old child. Use TensorFlow to analyze the skeletal data from this photo and match it with a database of past athletes to determine the best sport for them. At the same time, use an emotion engine to analyze the parent's emotional state from their voice and facial expressions and adjust how the report is presented."

[0308] The above is a specific embodiment for carrying out the present invention.

[0309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0310] Step 1: Launch the application and select a photo

[0311] The user launches the application using a device controlled by the parent. As input, the user taps the "Take Photo" or "Select Photo" button, which activates the device's camera to take a new photo or select a full-body photo of the child from an existing photo library. As output, the taken or selected photo is displayed on the device, ready for the next step.

[0312] Step 2: Upload a photo

[0313] The device uploads photos selected or taken by the user to the server. The photo data selected by the user is used as input. Data processing involves compressing the photo data and sending it via a transfer protocol. As output, the server checks and saves the received photo data. At this time, the server sends a message to the device saying "Photo upload completed." Voice and facial expression data provided by the user when taking the photo are also sent at the same time.

[0314] Step 3: Skeleton analysis and emotion analysis

[0315] The server inputs the received photo data into a generative AI model and performs skeletal analysis. As input, the server sends the photo data to an AI model built with machine learning libraries such as TensorFlow and PyTorch. To process the data, the AI ​​model extracts joint positions, bone lengths, and body features from the photo. As output, skeletal data is generated as numerical information and stored on the server. At the same time, an emotion engine analyzes voice and facial expression data to determine the user's emotional state. As output, emotional data is stored as numerical data.

[0316] Step 4: Check against the database

[0317] The server compares the analyzed skeletal data with a database of past successful athletes. As input, the server uses the analyzed skeletal data and the athlete data in the database. For data processing, a statistical algorithm is used to identify the athletes most similar to the skeletal data. As output, information about similar athletes and their sports is identified. Furthermore, the way the results are presented is adjusted via emotional data.

[0318] Step 5: Generate and communicate results

[0319] The server generates the optimal sports judgment result based on the results of database matching. Data on similar athletes and emotional data are used as input. The judgment result is combined with an emotional message for data processing. A detailed report is generated as output and notified to the user. For example, the report may include the judged sports, the reasons for the judgement, and an encouraging message based on the emotion. The user can check this report on their device and take appropriate action.

[0320] The above is the specific processing flow of the program of this system.

[0321] (Application example 2)

[0322] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0323] Conventional sports aptitude diagnostic systems based on children's skeletal analysis only provided one-way results to users, making it impossible to consider how users felt about the results. Furthermore, systems optimizing factory work efficiency only provided one-way instructions for improving efficiency without considering skeletal or emotional analyses of workers. This could result in workers' stress and anxiety being ignored, potentially negatively impacting productivity and work satisfaction.

[0324] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination results, emotion engine means for analyzing the user's emotion data, and means for optimizing work efficiency based on the best sport determination results and the emotion data. This makes it possible to provide the best sport while taking the user's emotions into consideration, and further optimize factory work efficiency and reduce worker stress.

[0325] The "means for receiving a child's photo" has a function for transmitting a photo taken or selected by the user to the server, and the server then receives the photo data.

[0326] The "means for analyzing skeletal data" is a system that uses image analysis technology to extract information such as joint positions, bone lengths, and body shape from received photographs and stores it as numerical data.

[0327] The "database of past successful athletes" is a database that accumulates skeletal data and performance data of athletes who have achieved outstanding results.

[0328] The "means for determining the best sport for a child" refers to an algorithm that compares the analyzed skeletal data with a database and statistically determines the best sport for a child.

[0329] The "means for notifying the user of the judgment results" has a function of transmitting the judgment results of the optimal sport to the user's terminal as a detailed report, and allowing the user to view the report.

[0330] The "emotion engine means" refers to algorithms and functions for analyzing photo and audio data to detect the user's emotional state.

[0331] "Means to optimize work efficiency" is a function that evaluates work efficiency based on skeletal data and emotional data, and recommends specific actions to improve efficiency and reduce stress.

[0332] In this invention, the user must first take a photo of their child or select an existing photo using their device and upload the photo data to a server. The server then passes the received photo data to a generative AI model to extract the child's skeletal data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze information such as joint positions, bone lengths, and body shape. The photo and audio data are also passed to an emotion engine to simultaneously analyze the user's emotional state.

[0333] The server compares the analyzed skeletal and emotional data with a database to determine the best sport for the individual. This database contains the skeletal data of successful athletes and includes an algorithm for statistically determining which sport is best suited. The results are then sent to the user's device in the form of a detailed report. The presentation of the report is also adjusted based on the analysis results of the emotion engine, making it possible to provide information that is more user-friendly.

[0334] This system can also be used to optimize work efficiency in factories. Cameras capture the movements and emotions of workers working in the factory, and analyze their skeletal and emotional data. Based on this data, the system can evaluate work efficiency and recommend specific actions to improve it, while also reducing worker stress.

[0335] A specific use case could be implemented in a large automotive parts manufacturing plant. In this factory, robots installed at specific work stations monitor the movements and emotions of workers in real time, helping to improve work efficiency and reduce worker stress. For example, if a worker is slouching while working, skeletal analysis can determine that this posture is not optimal. Furthermore, if emotion recognition determines that the worker is feeling stressed, specific actions (e.g., correcting posture and taking breaks) can be recommended to improve efficiency.

[0336] An example of a prompt to be input into the generative AI model is, "Please create a program that analyzes the worker's skeletal data and recommends optimal movements. Also, please add a function that recommends taking a break if the worker is under high stress." Based on this prompt, the AI ​​will suggest appropriate actions to optimize work efficiency.

[0337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0338] Step 1:

[0339] A user takes a photo of their child or selects an existing photo using a terminal and uploads the photo data to the server. The input is the photo data, and the output is the photo data uploaded to the server.

[0340] Step 2:

[0341] The server passes the received photo data to a generative AI model to extract the child's skeletal data. The input is the received photo data, and the output is analysis data including skeletal feature points. Specifically, the generative AI model uses TensorFlow and PyTorch to extract information such as joint positions, bone lengths, and body shape.

[0342] Step 3:

[0343] The server passes facial expression and voice data from the photo data to the emotion engine, which analyzes the user's emotional state. The input is facial expression and voice data, and the output is emotional state data. Specifically, an emotion analysis algorithm is applied to classify emotions such as stress, anxiety, and joy.

[0344] Step 4:

[0345] The server compares the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child. The input is the skeletal data and the database, and the output is the best sport determination result. Specifically, a matching algorithm is applied to determine the statistically best sport.

[0346] Step 5:

[0347] The server adjusts the notification content to the user based on the judgment result and emotional data. The input is the judgment result and emotional data, and the output is the notification content. Specifically, it takes into account the emotional data and adds positive messages or encouraging words.

[0348] Step 6:

[0349] The server sends the optimal sports judgment result and the adjusted notification content to the user's device. The input is the judgment result and the notification content, and the output is the notification sent to the user's device.

[0350] Step 7:

[0351] In the case of a factory work efficiency optimization system, the server analyzes the movement and emotional data of workers captured by a camera and recommends specific actions to improve efficiency based on skeletal and emotional data. The input is the movement and emotional data of the workers, and the output is recommended actions to improve work efficiency. Specific actions are suggested by applying a recommendation algorithm, such as correcting posture or suggesting breaks.

[0352] Step 8:

[0353] The server notifies the worker of the recommended action in real time to the worker's display terminal. The input is the recommended action, and the output is the notification sent to the worker's display terminal.

[0354] Through the above steps, the system can suggest the most suitable sport while taking into account the user's emotions, thereby improving work efficiency in the factory.

[0355] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0356] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0357] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0358] [Second embodiment]

[0359] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0360] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0361] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0362] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0363] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0364] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0365] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0366] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0367] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0368] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0369] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0370] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0371] The present invention is a system for analyzing a child's skeleton from a photograph and diagnosing the best sport for that child. Specific embodiments will be described below.

[0372] System Overview

[0373] User Interface

[0374] Using a device operated by a parent, the user launches the application and takes a photo of the child or selects an existing photo, which is then uploaded to the server.

[0375] Upload a photo

[0376] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[0377] Skeletal analysis

[0378] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[0379] Database collation

[0380] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[0381] Generating and notifying results

[0382] The server generates the optimal sports judgment result based on the database comparison results, and the result is notified to the user as a detailed report on the terminal. The report includes the reasons for the judgment and other reference information.

[0383] Specific examples

[0384] Example: If you have an 8-year-old child

[0385] Taking and uploading photos:

[0386] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0387] Photo reception and analysis:

[0388] The server receives the photo data and uses an AI model to analyze the skeleton, extracting data such as the child's shoulder width, leg length, and spine alignment.

[0389] Database collation:

[0390] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[0391] Generate and communicate results:

[0392] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, and sends this report to the parent's device, where they can view the results through the app.

[0393] How to use

[0394] The system is designed to be easily accessible to parents and other users. Simply install the app, take a full-body photo of your child, or upload an existing photo, and the system will automatically analyze the photo and diagnose the best sport for your child. This makes it easier for users to choose a sport that suits their child's characteristics.

[0395] Since the system of the present invention depends on the accuracy of the photographs used and the training data of the generative AI model, it is possible to improve accuracy by collecting and analyzing more data. Furthermore, by regularly updating the model and database, it is possible to always provide the latest analysis technology and information.

[0396] The above is a specific embodiment for carrying out the present invention.

[0397] The processing flow will be explained below.

[0398] Step 1:

[0399] The user takes or selects a photo.

[0400] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[0401] Step 2:

[0402] The device uploads the photo to the server.

[0403] The device will upload the photographed or selected photo data to the server. Specifically, it will use the data sending function within the app to send the selected photo to the cloud server, and after sending, will display a success or failure message.

[0404] Step 3:

[0405] The server receives and stores the photo data.

[0406] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[0407] Step 4:

[0408] The server launches an AI model to analyze the photo.

[0409] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[0410] Step 5:

[0411] The server stores the analyzed skeletal data in a database.

[0412] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[0413] Step 6:

[0414] The server checks the skeletal data against a database.

[0415] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[0416] Step 7:

[0417] The server determines the best sport and generates a report.

[0418] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[0419] Step 8:

[0420] The server sends the report to the device.

[0421] The server sends the judgment result and a report to the terminal. Specifically, it generates the report in JSON or XML format, sends it to the terminal via API, and generates a transmission completion message and records it in the log.

[0422] Step 9:

[0423] The terminal displays the results to the user.

[0424] The terminal displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, and generates visual graphs and explanatory text to provide the results in a format that is easy for the user to understand.

[0425] Example 1

[0426] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0427] Conventional technology lacks the means to accurately determine the type of sports that best suit a child's characteristics, making it difficult to make optimal decisions about sports selection for children. Furthermore, it often relies on manual data analysis and expert opinions, which is time-consuming and costly.

[0428] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0429] In this invention, the server includes means for receiving an image of a child, means for analyzing skeletal information from the received image, means for comparing the analyzed skeletal information with a database of past successful athletes to determine the most suitable athletic event for the child, and means for notifying the user of the determination result. This makes it possible to quickly and accurately determine the most suitable athletic event for a child without relying on conventional manual data analysis or expert opinions.

[0430] An "image" is data in digital or analog form that captures visual information of an object.

[0431] "Skeletal information" is data such as joint positions, bone lengths, and body shape extracted from an image.

[0432] An "athlete database" is a collection of information that compiles skeletal information and athletic events of successful athletes in the past.

[0433] An "athletic event" is a type of competition or sport that is based on specific rules and techniques.

[0434] A "server" is a computer system that receives, analyzes, and transmits data to clients over a network.

[0435] "User" refers to parents and other parties who use the System.

[0436] A "generative AI model" is an artificial intelligence model that is built using machine learning libraries and runs algorithms to analyze skeletal information from images.

[0437] "Image capture device functionality" refers to a device or software functionality that includes a camera function for capturing images.

[0438] The present invention is a system for analyzing skeletal information from an image of a child and diagnosing the type of exercise that is best suited for that child. Specific embodiments of this system will be described below.

[0439] System Overview

[0440] User Interface

[0441] The user launches the application using a parent-controlled device. The application has a camera function to take a full-body photo of the child, and also provides the option to select an existing photo. After the user takes or selects a photo, the photo is uploaded to the server.

[0442] Upload a photo

[0443] The device uploads the photos selected by the user to the server, which sends the data using a secure communication protocol (e.g., HTTPS). This ensures that the photo data is safely received by the server and saved in the specified directory.

[0444] Skeletal analysis

[0445] The server passes the received photo data to a pre-trained generative AI model, which was built using machine learning libraries TensorFlow and PyTorch. The generative AI model extracts skeletal information from the image, such as joint positions, bone lengths, and body shape, and analyzes the data in numerical form.

[0446] Database Matching

[0447] The server compares the analyzed skeletal information with a database of successful athletes, which includes detailed skeletal information about athletes and data on their success in sports. The server then uses statistical techniques to identify athletes with similar skeletal information and their sports.

[0448] Generation and notification of judgment results

[0449] The server then determines the optimal exercise based on the database comparison results, generates a detailed report of the results, and sends it to the user's device. The user receives a notification on their device and can check the results through the application.

[0450] Specific examples

[0451] Here is an example of the specific process, assuming you are using a full-body photo of an 8-year-old child.

[0452] Taking and uploading photos:

[0453] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0454] Photo reception and analysis:

[0455] The server receives the photo data and uses a generative AI model to analyze skeletal information, extracting data such as the child's shoulder width, leg length, and spine alignment.

[0456] Database collation:

[0457] The extracted skeletal information is then compared against a database of past successful athletes, and the skeleton is determined to resemble a particular athlete, for example, a basketball player.

[0458] Generate and communicate results:

[0459] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, which is then sent to the parent's device, where they can view the results through the app.

[0460] Prompt Sentence Examples

[0461] Here are some example prompts to input to a generative AI model:

[0462] "We analyze a full-body photograph of an eight-year-old child and diagnose the most suitable type of exercise based on their skeletal data. We focus on evaluating shoulder width, leg length, and back muscle alignment to determine the most suitable type of exercise."

[0463] This prompt sentence allows the generative AI model to make appropriate analysis and judgments.

[0464] The present invention makes it possible to easily and accurately determine the type of exercise that is best suited for a child.

[0465] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0466] Step 1:

[0467] The user launches the application. Once installed on the user's device, the application is launched by tapping, which takes the user to the initial screen, where the user is presented with the option to take a photo of their child or select an existing photo.

[0468] Step 2:

[0469] The user takes or selects a photo. The user presses the "Take Photo" button in the app and uses the device's camera to take a full-body photo of the child. Or, the user presses the "Select Photo" button and selects an existing photo from the device's gallery. The input is a full-body photo of the child, which will be used in the next step.

[0470] Step 3:

[0471] The device uploads the photo to the server. The selected or taken photo is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The input is the photo selected in the previous step, and the output is the photo data stored on the server.

[0472] Step 4:

[0473] The server receives and stores the photo data. The server receives the photo data sent and stores it securely in the specified directory. Once the storage is complete, it is ready to proceed to the next processing step. The input is the photo data sent from the device, and the output is the photo data stored in the specified directory on the server.

[0474] Step 5:

[0475] The server passes the photo data to the generative AI model. The server retrieves the stored photo data and inputs it into a generative AI model for image analysis. This generative AI model is built using TensorFlow and PyTorch. The input is the stored photo data, and the output is skeletal information from the analysis results.

[0476] Step 6:

[0477] The generative AI model analyzes the skeletal information. The generative AI model extracts skeletal information such as joint positions, bone lengths, and body shape from the photo and analyzes it as numerical data. This data is used in the next step. The input is photo data, and the output is numerical data as skeletal information.

[0478] Step 7:

[0479] The server compares the skeletal information with a database. The server compares the skeletal information received from the generative AI model with a database of past successful athletes. The database contains the athletes' skeletal information and data on the areas in which they have been successful. The input is the analyzed skeletal information, and the output is the determination of the optimal athletic event.

[0480] Step 8:

[0481] The server determines the best type of sport for the child based on the results of database matching. The result of the determination also includes scientific reasons based on skeletal information. The input is the result of matching with a database of past successful athletes, and the output is the determined best type of sport.

[0482] Step 9:

[0483] The server generates the results as a detailed report and sends it to the terminal. The server documents the judgment results as a detailed report and sends it to the user's terminal. This report includes the reasons for the judged exercise and related information. The input is the judgment result, and the output is the detailed report sent to the terminal.

[0484] Step 10:

[0485] The user checks the report. The user checks the notification received on the device and views a detailed report through the application. This report can be used as a reference for selecting the most suitable exercise for the child. The input is the report sent from the server, and the output is the assessment result that the user checks.

[0486] (Application example 1)

[0487] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0488] Today's parents spend a lot of time and effort trying to find the right sport for their child. They often seek expert advice and repeatedly try out different sports to determine which is best suited to their child's physical characteristics. Even after identifying a suitable sport, parents can have difficulty selecting the appropriate products and equipment. To address this issue, a system is needed that can efficiently and quickly determine the best sport for a child and recommend related products.

[0489] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0490] In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination result and recommending related products, and means for displaying the result via an in-store terminal or a visual device, thereby enabling parents to easily find a sport that is suitable for their child and quickly obtain products suitable for that sport.

[0491] "Child photo" is image data of a child's entire body that is input into the system.

[0492] "Skeletal data" refers to numerical data about the skeleton, such as shoulder width, leg length, and joint position, extracted using image processing technology and generative AI models.

[0493] A "generative AI model" is a trained artificial intelligence model for analyzing skeletal features from image data, and is built using machine learning libraries such as TensorFlow and PyTorch.

[0494] A "database" is a collection of information that stores skeletal data and statistical information on successful athletes of the past.

[0495] The "judgment result" is the analysis result that indicates which sport is best for your child, along with the reasons and related data.

[0496] "Relevant Products" are the products and equipment required for the relevant sporting activity that are recommended based on the results of the assessment.

[0497] "In-store terminal" refers to a computer terminal installed in a sporting goods store, which is a device used by users to input and view information.

[0498] A "visual device" is a device that provides visual information when worn by a user, such as smart glasses or a head-mounted display.

[0499] The present invention is a system that analyzes skeletal data from a photograph of a child, determines the child's best sport, and recommends related products. Specific embodiments will be described below.

[0500] System Overview

[0501] User Interface

[0502] Using an in-store terminal or visual device installed in a physical store, the user launches the application and takes a photo of their child or selects an existing photo, which is then uploaded to the server.

[0503] Upload a photo

[0504] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[0505] Skeletal analysis

[0506] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[0507] Database collation

[0508] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[0509] Generating and notifying results

[0510] The server generates the optimal sports judgment result based on the database comparison result. This result, along with related products, is notified to the user via the in-store terminal or visual device. The user can check the result on the display of the in-store terminal or visual device and consider purchasing sports-related products.

[0511] Specific examples

[0512] Example: In a special section of a sporting goods store, a parent puts on smart glasses and takes a full-body photo of their child. The AI ​​analyzes the image on the spot, and the display shows "Basketball is the best choice," along with specific recommendations for basketball-related products (e.g., shoes, jerseys). In this way, parents can easily find the right sport for their child and quickly obtain the necessary equipment related to that sport.

[0513] How to use

[0514] The system is designed to be easily accessible for parents and users. All users need to do is install the app, take a full-body photo of their child, or upload an existing photo. The system will then automatically analyze the photo and diagnose the best sport for their child. This makes it easier for users to choose a sport that suits their child's characteristics.

[0515] Prompt Sentence Examples

[0516] "Upload a full-body photo of your child and have it analyzed to determine the best sport for them. Then generate a list of relevant products based on the results."

[0517] In this way, the present invention is a system that not only allows modern parents to efficiently and quickly find the best sports for their children, but also supports them in selecting related products, thereby significantly reducing time and effort and enabling them to provide a better sports life.

[0518] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0519] Step 1:

[0520] The user takes a full-body photo of their child or selects an existing photo and uploads it using a terminal or visual device in the store. The input is the child's full-body photo, and the output is the uploaded image data. The terminal sends this photo to the server.

[0521] Step 2:

[0522] The server reads the received photo data and starts image processing. At this time, the input is the uploaded image data, and the output is the processed data. Specifically, the image is converted to a standard format and pre-processed to remove noise.

[0523] Step 3:

[0524] The server inputs the preprocessed image data into a generative AI model to extract skeletal feature points. The input is preprocessed image data, and the output is analyzed skeletal data. The AI ​​model is built using TensorFlow, PyTorch, etc., and extracts feature points such as joint positions, bone lengths, and body shape as numerical data.

[0525] Step 4:

[0526] The server compares the analyzed skeletal data with a database of successful athletes. The input is the analyzed skeletal data, and the output is the statistically optimal sports judgment result. The server uses a database search algorithm to find athletes with similar skeletal data and analyze which sports they are successful in.

[0527] Step 5:

[0528] The server generates the optimal sports judgment result based on the matching result and recommends related products. The input is the matching result, and the output is the judgment result and a list of recommended products. Specifically, it generates the name of the matching sports and the reason for it, as well as information on related products sold in the store.

[0529] Step 6:

[0530] The server sends the results of the assessment and the list of recommended products to the in-store terminal or visual device. The input is the assessment results and the list of recommended products, and the output is the display information shown to the user. The user can check the optimal sports and related products through the display of the visual device or terminal.

[0531] This process allows the user to quickly find a sport that is suitable for their child and also allows them to immediately obtain the necessary equipment related to that sport.

[0532] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0533] The present invention provides a more user-friendly interface by combining an emotion engine with a system that analyzes a child's skeleton from a photograph and diagnoses the best sport for that child. Specific embodiments will be described below.

[0534] System Overview

[0535] User Interface

[0536] The user launches the application using a device operated by the parent, takes a photo of the child or selects an existing photo, which is then uploaded to the server. If the user expresses an emotion through the photo or voice data, the emotion engine analyzes the emotion.

[0537] Upload a photo

[0538] The device uploads photos taken or selected by the user to the server. The uploaded photo data is received and stored by the server. At this time, the emotion engine also receives and analyzes the user's emotion data.

[0539] Skeleton analysis and emotion analysis

[0540] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores the results as numerical data. At the same time, an emotion engine analyzes the user's emotional state, and the results are stored and analyzed.

[0541] Database collation

[0542] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports athletes with similar skeletal data have been successful in, and determines the best sport for the individual. Emotional data may also be used as relevant information.

[0543] Generating and notifying results

[0544] The server generates the optimal sports judgment result based on the database comparison results. This result is notified to the user on their device as a detailed report. The report includes the reasons for the judged sports and other reference information. The server also adjusts the presentation of the report based on the analysis results of the emotion engine.

[0545] Specific examples

[0546] Example: If you have an 8-year-old child

[0547] Taking and uploading photos:

[0548] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0549] Receiving and analyzing photo and emotion data:

[0550] The server receives the photo data and uses an AI model to analyze the child's skeletal structure. For example, it extracts data such as the child's shoulder width, leg length, and spine alignment. At the same time, an emotion engine analyzes the parent's emotions based on the photos and audio of their reactions.

[0551] Database collation:

[0552] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[0553] Generate and communicate results:

[0554] The server generates a detailed report based on the analysis results, including reasons why basketball is a good fit for the child. Taking into account the analysis results of the emotion engine, if the user shows signs of anxiety, the server adds encouraging messages and positive information about the child's progress. This report is then sent to the parent's device, where the parent can view the results through the app.

[0555] How to use

[0556] Users simply install the application, take a full-body photo of their child, or upload an existing one. The system then automatically analyzes the photo and diagnoses the best sport for them. Furthermore, the system adjusts the way results are presented based on the user's emotional state, creating a more user-friendly interface.

[0557] This system depends on the accuracy of the photos used and the learning data of the generative AI model, so accuracy can be improved by collecting and analyzing more data.In addition, by regularly updating the model, updating the database, and improving the emotion engine, it is possible to always provide the latest analysis technology and information.

[0558] The above is a specific embodiment for carrying out the present invention.

[0559] The processing flow will be explained below.

[0560] Step 1:

[0561] The user takes or selects a photo.

[0562] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[0563] Step 2:

[0564] The device uploads the photo to the server.

[0565] The device uploads the captured or selected photo data to the server. Specifically, it uses the data sending function in the app to send the photo to the cloud server, and displays a success or failure message after sending.

[0566] Step 3:

[0567] The user provides emotion data.

[0568] Users can express their feelings during or after uploading a photo by answering emotional questions or entering a voice message.

[0569] Step 4:

[0570] The device transmits the emotion data to the server.

[0571] The device transmits the emotion data provided by the user to the server, specifically, the answer data to the emotion-related questions and the voice data to the cloud server.

[0572] Step 5:

[0573] The server receives and stores the photo data.

[0574] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[0575] Step 6:

[0576] The server receives and analyzes the emotion data.

[0577] The server receives the emotion data sent from the device and uses an emotion engine to analyze the user's emotional state. Specifically, the server inputs the voice or text data into an emotion recognition algorithm, categorizes the emotional state, and stores it in a database.

[0578] Step 7:

[0579] The server launches an AI model to analyze the photo.

[0580] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[0581] Step 8:

[0582] The server stores the analyzed skeletal data in a database.

[0583] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[0584] Step 9:

[0585] The server checks the skeletal data against a database.

[0586] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[0587] Step 10:

[0588] The server determines the best sport and generates a report.

[0589] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[0590] Step 11:

[0591] The server sends the report and emotion data together to the device.

[0592] The server sends the judgment results, a report, and the analyzed emotion data to the device. Specifically, it generates a report in JSON or XML format, adds a message according to the emotion, sends it to the device via API, and generates a transmission completion message and records it in the log.

[0593] Step 12:

[0594] The terminal displays the results to the user.

[0595] The device displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, generates visual graphs and explanatory text, and adds messages and user guides according to the user's emotional state to provide the results in an easy-to-understand format for the user.

[0596] Example 2

[0597] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] Conventional systems for diagnosing children's sports aptitude focus only on analyzing skeletal data, and therefore are unable to provide accurate advice or diagnostic results that take into account the user's emotional state. It is also difficult to create a user-friendly interface for notifying the analysis results.

[0599] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving image data of a child, means for analyzing skeletal data from the received image data, means for comparing the analyzed skeletal data with a database of past successful athletes, means including an emotion engine for receiving and analyzing voice and facial expression data from the user, and means for notifying the user based on the judgment result and the analyzed emotion data. This makes it possible to provide a more familiar and accurate sports diagnosis result that takes into account the user's emotional state.

[0600] "Child" means a minor or young person, usually a human being who is still in the process of developing.

[0601] "Image data" is data that represents visual information in a digital format, such as photographs and video frames.

[0602] A "means for receiving" is a method or mechanism for obtaining or acquiring information from another device or system.

[0603] "Skeletal data" is numerical information about the positions and lengths of bones in the human body, the arrangement of joints, and so on.

[0604] "Analytical means" are methods and techniques for examining, analyzing, and evaluating data.

[0605] A "former successful athlete" is someone who has had a strong track record and achieved success in sports.

[0606] A "database" is a collection of electronic information that allows large amounts of data to be systematically collected, stored, and searched.

[0607] A "matching tool" is a method or technique for comparing two or more data sets and identifying similarities or matches.

[0608] "Audio data" means data that is a digital representation of the human voice or other sounds.

[0609] "Facial expression data" is data that numerically represents various expressions and emotional states of a human face.

[0610] An "emotion engine" is an algorithm or technology that analyzes voice and facial expression data to determine emotional states.

[0611] A "means of notification" is a method or mechanism for conveying information to a user.

[0612] A "generative AI model" is an artificial intelligence model built using machine learning and deep learning techniques, which analyzes data and makes predictions.

[0613] "User" refers to any person or entity that uses the system, and in this context primarily includes parents or guardians of children.

[0614] The present invention is a system that analyzes a child's skeleton from photographic data and diagnoses the best sport for that child. This system provides a user-friendly interface by combining it with an emotion engine. The following describes in detail an embodiment of the present invention.

[0615] System Overview

[0616] User Interface

[0617] The user launches the application on their device (e.g., a smartphone or tablet) and takes a full-body photo of their child or selects an existing photo. The photo is instantly uploaded to the server. At the same time, if the user expresses emotions through voice or facial expressions, that data is also sent to the server.

[0618] Upload a photo

[0619] The device uploads a full-body photo of the child taken or selected by the user to the server via the Internet. The server stores the received photo data and prepares it for the emotion engine to analyze the voice and facial expression data received at the same time.

[0620] Skeleton analysis and emotion analysis

[0621] The server inputs the received photo data into a generative AI model (for example, a model built using machine learning libraries such as TensorFlow or PyTorch) to perform skeletal analysis. This analysis process extracts joint positions, bone lengths, and body features from the photo. At the same time, an emotion engine analyzes voice and facial expression data to identify the user's emotional state. The resulting skeletal and emotional data are quantified and stored on the server.

[0622] Database Matching

[0623] The server uses the analyzed skeletal and emotional data to compare it with a database of successful athletes. The database contains the skeletal data and sport information of many athletes. The server uses a statistical algorithm to identify the athlete who most closely resembles the analyzed skeletal data. At the same time, the server adjusts the presentation of the results based on the emotional data.

[0624] Generating and notifying results

[0625] The server generates the optimal sports judgment result based on the database comparison results. This judgment result is notified to the user as a detailed report. The report includes the reasons for the judged sports, useful information, and appropriate advice based on the analysis results of the emotion engine. For example, if the user shows an anxious expression, an encouraging message is added to the report. The result is sent to the terminal, and the user can check the report through the application.

[0626] Examples of usage

[0627] Example: Parents with an 8-year-old child

[0628] Taking and uploading photos:

[0629] Parents use the camera on their smartphone to take a full-body photo of their child and upload it to the server via the application.

[0630] Receiving and analyzing photo and emotion data:

[0631] The server inputs the photo data into a generative AI model to analyze the skeletal structure. Specifically, it extracts data such as shoulder width, leg length, and spine alignment. At the same time, it analyzes the user's voice and facial expression data using an emotion engine.

[0632] Match against database:

[0633] The extracted skeletal data is compared with a database to determine whether the child resembles a specific basketball player, which diagnoses the child as being suited to basketball.

[0634] Generate and communicate results:

[0635] The server generates a report of the results and adds a message that takes the user's emotional state into account. This report is then sent to a smartphone, where the user can view the results through an application.

[0636] Prompt Sentence Examples

[0637] Example prompts for skeletal and emotional analysis of photos:

[0638] "I've uploaded a full-body photo of an 8-year-old child. Use TensorFlow to analyze the skeletal data from this photo and match it with a database of past athletes to determine the best sport for them. At the same time, use an emotion engine to analyze the parent's emotional state from their voice and facial expressions and adjust how the report is presented."

[0639] The above is a specific embodiment for carrying out the present invention.

[0640] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0641] Step 1: Launch the application and select a photo

[0642] The user launches the application using a device controlled by the parent. As input, the user taps the "Take Photo" or "Select Photo" button, which activates the device's camera to take a new photo or select a full-body photo of the child from an existing photo library. As output, the taken or selected photo is displayed on the device, ready for the next step.

[0643] Step 2: Upload a photo

[0644] The device uploads photos selected or taken by the user to the server. The photo data selected by the user is used as input. Data processing involves compressing the photo data and sending it via a transfer protocol. As output, the server checks and saves the received photo data. At this time, the server sends a message to the device saying "Photo upload completed." Voice and facial expression data provided by the user when taking the photo are also sent at the same time.

[0645] Step 3: Skeleton analysis and emotion analysis

[0646] The server inputs the received photo data into a generative AI model and performs skeletal analysis. As input, the server sends the photo data to an AI model built with machine learning libraries such as TensorFlow and PyTorch. To process the data, the AI ​​model extracts joint positions, bone lengths, and body features from the photo. As output, skeletal data is generated as numerical information and stored on the server. At the same time, an emotion engine analyzes voice and facial expression data to determine the user's emotional state. As output, emotional data is stored as numerical data.

[0647] Step 4: Check against the database

[0648] The server compares the analyzed skeletal data with a database of past successful athletes. As input, the server uses the analyzed skeletal data and the athlete data in the database. For data processing, a statistical algorithm is used to identify the athletes most similar to the skeletal data. As output, information about similar athletes and their sports is identified. Furthermore, the way the results are presented is adjusted via emotional data.

[0649] Step 5: Generate and communicate results

[0650] The server generates the optimal sports judgment result based on the results of database matching. Data on similar athletes and emotional data are used as input. The judgment result is combined with an emotional message for data processing. A detailed report is generated as output and notified to the user. For example, the report may include the judged sports, the reasons for the judgement, and an encouraging message based on the emotion. The user can check this report on their device and take appropriate action.

[0651] The above is the specific processing flow of the program of this system.

[0652] (Application example 2)

[0653] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0654] Conventional sports aptitude diagnostic systems based on children's skeletal analysis only provided one-way results to users, making it impossible to consider how users felt about the results. Furthermore, systems optimizing factory work efficiency only provided one-way instructions for improving efficiency without considering skeletal or emotional analyses of workers. This could result in workers' stress and anxiety being ignored, potentially negatively impacting productivity and work satisfaction.

[0655] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination results, emotion engine means for analyzing the user's emotion data, and means for optimizing work efficiency based on the best sport determination results and the emotion data. This makes it possible to provide the best sport while taking the user's emotions into consideration, and further optimize factory work efficiency and reduce worker stress.

[0656] The "means for receiving a child's photo" has a function for transmitting a photo taken or selected by the user to the server, and the server then receives the photo data.

[0657] The "means for analyzing skeletal data" is a system that uses image analysis technology to extract information such as joint positions, bone lengths, and body shape from received photographs and stores it as numerical data.

[0658] The "database of past successful athletes" is a database that accumulates skeletal data and performance data of athletes who have achieved outstanding results.

[0659] The "means for determining the best sport for a child" refers to an algorithm that compares the analyzed skeletal data with a database and statistically determines the best sport for a child.

[0660] The "means for notifying the user of the judgment results" has a function of transmitting the judgment results of the optimal sport to the user's terminal as a detailed report, and allowing the user to view the report.

[0661] The "emotion engine means" refers to algorithms and functions for analyzing photo and audio data to detect the user's emotional state.

[0662] "Means to optimize work efficiency" is a function that evaluates work efficiency based on skeletal data and emotional data, and recommends specific actions to improve efficiency and reduce stress.

[0663] In this invention, the user must first take a photo of their child or select an existing photo using their device and upload the photo data to a server. The server then passes the received photo data to a generative AI model to extract the child's skeletal data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze information such as joint positions, bone lengths, and body shape. The photo and audio data are also passed to an emotion engine to simultaneously analyze the user's emotional state.

[0664] The server compares the analyzed skeletal and emotional data with a database to determine the best sport for the individual. This database contains the skeletal data of successful athletes and includes an algorithm for statistically determining which sport is best suited. The results are then sent to the user's device in the form of a detailed report. The presentation of the report is also adjusted based on the analysis results of the emotion engine, making it possible to provide information that is more user-friendly.

[0665] This system can also be used to optimize work efficiency in factories. Cameras capture the movements and emotions of workers working in the factory, and analyze their skeletal and emotional data. Based on this data, the system can evaluate work efficiency and recommend specific actions to improve it, while also reducing worker stress.

[0666] A specific use case could be implemented in a large automotive parts manufacturing plant. In this factory, robots installed at specific work stations monitor the movements and emotions of workers in real time, helping to improve work efficiency and reduce worker stress. For example, if a worker is slouching while working, skeletal analysis can determine that this posture is not optimal. Furthermore, if emotion recognition determines that the worker is feeling stressed, specific actions (e.g., correcting posture and taking breaks) can be recommended to improve efficiency.

[0667] An example of a prompt to be input into the generative AI model is, "Please create a program that analyzes the worker's skeletal data and recommends optimal movements. Also, please add a function that recommends taking a break if the worker is under high stress." Based on this prompt, the AI ​​will suggest appropriate actions to optimize work efficiency.

[0668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0669] Step 1:

[0670] A user takes a photo of their child or selects an existing photo using a terminal and uploads the photo data to the server. The input is the photo data, and the output is the photo data uploaded to the server.

[0671] Step 2:

[0672] The server passes the received photo data to a generative AI model to extract the child's skeletal data. The input is the received photo data, and the output is analysis data including skeletal feature points. Specifically, the generative AI model uses TensorFlow and PyTorch to extract information such as joint positions, bone lengths, and body shape.

[0673] Step 3:

[0674] The server passes facial expression and voice data from the photo data to the emotion engine, which analyzes the user's emotional state. The input is facial expression and voice data, and the output is emotional state data. Specifically, an emotion analysis algorithm is applied to classify emotions such as stress, anxiety, and joy.

[0675] Step 4:

[0676] The server compares the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child. The input is the skeletal data and the database, and the output is the best sport determination result. Specifically, a matching algorithm is applied to determine the statistically best sport.

[0677] Step 5:

[0678] The server adjusts the notification content to the user based on the judgment result and emotional data. The input is the judgment result and emotional data, and the output is the notification content. Specifically, it takes into account the emotional data and adds positive messages or encouraging words.

[0679] Step 6:

[0680] The server sends the optimal sports judgment result and the adjusted notification content to the user's device. The input is the judgment result and the notification content, and the output is the notification sent to the user's device.

[0681] Step 7:

[0682] In the case of a factory work efficiency optimization system, the server analyzes the movement and emotional data of workers captured by a camera and recommends specific actions to improve efficiency based on skeletal and emotional data. The input is the movement and emotional data of the workers, and the output is recommended actions to improve work efficiency. Specific actions are suggested by applying a recommendation algorithm, such as correcting posture or suggesting breaks.

[0683] Step 8:

[0684] The server notifies the worker of the recommended action in real time to the worker's display terminal. The input is the recommended action, and the output is the notification sent to the worker's display terminal.

[0685] Through the above steps, the system can suggest the most suitable sport while taking into account the user's emotions, thereby improving work efficiency in the factory.

[0686] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0687] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0688] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0689] [Third embodiment]

[0690] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0691] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0692] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0693] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0694] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0695] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0696] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0697] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0698] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0699] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0700] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0701] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0702] The present invention is a system for analyzing a child's skeleton from a photograph and diagnosing the best sport for that child. Specific embodiments will be described below.

[0703] System Overview

[0704] User Interface

[0705] Using a device operated by a parent, the user launches the application and takes a photo of the child or selects an existing photo, which is then uploaded to the server.

[0706] Upload a photo

[0707] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[0708] Skeletal analysis

[0709] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[0710] Database collation

[0711] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[0712] Generating and notifying results

[0713] The server generates the optimal sports judgment result based on the database comparison results, and the result is notified to the user as a detailed report on the terminal. The report includes the reasons for the judgment and other reference information.

[0714] Specific examples

[0715] Example: If you have an 8-year-old child

[0716] Taking and uploading photos:

[0717] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0718] Photo reception and analysis:

[0719] The server receives the photo data and uses an AI model to analyze the skeleton, extracting data such as the child's shoulder width, leg length, and spine alignment.

[0720] Database collation:

[0721] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[0722] Generate and communicate results:

[0723] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, and sends this report to the parent's device, where they can view the results through the app.

[0724] How to use

[0725] The system is designed to be easily accessible to parents and other users. Simply install the app, take a full-body photo of your child, or upload an existing photo, and the system will automatically analyze the photo and diagnose the best sport for your child. This makes it easier for users to choose a sport that suits their child's characteristics.

[0726] Since the system of the present invention depends on the accuracy of the photographs used and the training data of the generative AI model, it is possible to improve accuracy by collecting and analyzing more data. Furthermore, by regularly updating the model and database, it is possible to always provide the latest analysis technology and information.

[0727] The above is a specific embodiment for carrying out the present invention.

[0728] The processing flow will be explained below.

[0729] Step 1:

[0730] The user takes or selects a photo.

[0731] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[0732] Step 2:

[0733] The device uploads the photo to the server.

[0734] The device will upload the photographed or selected photo data to the server. Specifically, it will use the data sending function within the app to send the selected photo to the cloud server, and after sending, will display a success or failure message.

[0735] Step 3:

[0736] The server receives and stores the photo data.

[0737] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[0738] Step 4:

[0739] The server launches an AI model to analyze the photo.

[0740] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[0741] Step 5:

[0742] The server stores the analyzed skeletal data in a database.

[0743] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[0744] Step 6:

[0745] The server checks the skeletal data against a database.

[0746] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[0747] Step 7:

[0748] The server determines the best sport and generates a report.

[0749] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[0750] Step 8:

[0751] The server sends the report to the device.

[0752] The server sends the judgment result and a report to the terminal. Specifically, it generates the report in JSON or XML format, sends it to the terminal via API, and generates a transmission completion message and records it in the log.

[0753] Step 9:

[0754] The terminal displays the results to the user.

[0755] The terminal displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, and generates visual graphs and explanatory text to provide the results in a format that is easy for the user to understand.

[0756] Example 1

[0757] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0758] Conventional technology lacks the means to accurately determine the type of sports that best suit a child's characteristics, making it difficult to make optimal decisions about sports selection for children. Furthermore, it often relies on manual data analysis and expert opinions, which is time-consuming and costly.

[0759] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0760] In this invention, the server includes means for receiving an image of a child, means for analyzing skeletal information from the received image, means for comparing the analyzed skeletal information with a database of past successful athletes to determine the most suitable athletic event for the child, and means for notifying the user of the determination result. This makes it possible to quickly and accurately determine the most suitable athletic event for a child without relying on conventional manual data analysis or expert opinions.

[0761] An "image" is data in digital or analog form that captures visual information of an object.

[0762] "Skeletal information" is data such as joint positions, bone lengths, and body shape extracted from an image.

[0763] An "athlete database" is a collection of information that compiles skeletal information and athletic events of successful athletes in the past.

[0764] An "athletic event" is a type of competition or sport that is based on specific rules and techniques.

[0765] A "server" is a computer system that receives, analyzes, and transmits data to clients over a network.

[0766] "User" refers to parents and other parties who use the System.

[0767] A "generative AI model" is an artificial intelligence model that is built using machine learning libraries and runs algorithms to analyze skeletal information from images.

[0768] "Image capture device functionality" refers to a device or software functionality that includes a camera function for capturing images.

[0769] The present invention is a system for analyzing skeletal information from an image of a child and diagnosing the type of exercise that is best suited for that child. Specific embodiments of this system will be described below.

[0770] System Overview

[0771] User Interface

[0772] The user launches the application using a parent-controlled device. The application has a camera function to take a full-body photo of the child, and also provides the option to select an existing photo. After the user takes or selects a photo, the photo is uploaded to the server.

[0773] Upload a photo

[0774] The device uploads the photos selected by the user to the server, which sends the data using a secure communication protocol (e.g., HTTPS). This ensures that the photo data is safely received by the server and saved in the specified directory.

[0775] Skeletal analysis

[0776] The server passes the received photo data to a pre-trained generative AI model, which was built using machine learning libraries TensorFlow and PyTorch. The generative AI model extracts skeletal information from the image, such as joint positions, bone lengths, and body shape, and analyzes the data in numerical form.

[0777] Database Matching

[0778] The server compares the analyzed skeletal information with a database of successful athletes, which includes detailed skeletal information about athletes and data on their success in sports. The server then uses statistical techniques to identify athletes with similar skeletal information and their sports.

[0779] Generation and notification of judgment results

[0780] The server then determines the optimal exercise based on the database comparison results, generates a detailed report of the results, and sends it to the user's device. The user receives a notification on their device and can check the results through the application.

[0781] Specific examples

[0782] Here is an example of the specific process, assuming you are using a full-body photo of an 8-year-old child.

[0783] Taking and uploading photos:

[0784] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0785] Photo reception and analysis:

[0786] The server receives the photo data and uses a generative AI model to analyze skeletal information, extracting data such as the child's shoulder width, leg length, and spine alignment.

[0787] Database collation:

[0788] The extracted skeletal information is then compared against a database of past successful athletes, and the skeleton is determined to resemble a particular athlete, for example, a basketball player.

[0789] Generate and communicate results:

[0790] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, which is then sent to the parent's device, where they can view the results through the app.

[0791] Prompt Sentence Examples

[0792] Here are some example prompts to input to a generative AI model:

[0793] "We analyze a full-body photograph of an eight-year-old child and diagnose the most suitable type of exercise based on their skeletal data. We focus on evaluating shoulder width, leg length, and back muscle alignment to determine the most suitable type of exercise."

[0794] This prompt sentence allows the generative AI model to make appropriate analysis and judgments.

[0795] The present invention makes it possible to easily and accurately determine the type of exercise that is best suited for a child.

[0796] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0797] Step 1:

[0798] The user launches the application. Once installed on the user's device, the application is launched by tapping, which takes the user to the initial screen, where the user is presented with the option to take a photo of their child or select an existing photo.

[0799] Step 2:

[0800] The user takes or selects a photo. The user presses the "Take Photo" button in the app and uses the device's camera to take a full-body photo of the child. Or, the user presses the "Select Photo" button and selects an existing photo from the device's gallery. The input is a full-body photo of the child, which will be used in the next step.

[0801] Step 3:

[0802] The device uploads the photo to the server. The selected or taken photo is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The input is the photo selected in the previous step, and the output is the photo data stored on the server.

[0803] Step 4:

[0804] The server receives and stores the photo data. The server receives the photo data sent and stores it securely in the specified directory. Once the storage is complete, it is ready to proceed to the next processing step. The input is the photo data sent from the device, and the output is the photo data stored in the specified directory on the server.

[0805] Step 5:

[0806] The server passes the photo data to the generative AI model. The server retrieves the stored photo data and inputs it into a generative AI model for image analysis. This generative AI model is built using TensorFlow and PyTorch. The input is the stored photo data, and the output is skeletal information from the analysis results.

[0807] Step 6:

[0808] The generative AI model analyzes the skeletal information. The generative AI model extracts skeletal information such as joint positions, bone lengths, and body shape from the photo and analyzes it as numerical data. This data is used in the next step. The input is photo data, and the output is numerical data as skeletal information.

[0809] Step 7:

[0810] The server compares the skeletal information with a database. The server compares the skeletal information received from the generative AI model with a database of past successful athletes. The database contains the athletes' skeletal information and data on the areas in which they have been successful. The input is the analyzed skeletal information, and the output is the determination of the optimal athletic event.

[0811] Step 8:

[0812] The server determines the best type of sport for the child based on the results of database matching. The result of the determination also includes scientific reasons based on skeletal information. The input is the result of matching with a database of past successful athletes, and the output is the determined best type of sport.

[0813] Step 9:

[0814] The server generates the results as a detailed report and sends it to the terminal. The server documents the judgment results as a detailed report and sends it to the user's terminal. This report includes the reasons for the judged exercise and related information. The input is the judgment result, and the output is the detailed report sent to the terminal.

[0815] Step 10:

[0816] The user checks the report. The user checks the notification received on the device and views a detailed report through the application. This report can be used as a reference for selecting the most suitable exercise for the child. The input is the report sent from the server, and the output is the assessment result that the user checks.

[0817] (Application example 1)

[0818] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0819] Today's parents spend a lot of time and effort trying to find the right sport for their child. They often seek expert advice and repeatedly try out different sports to determine which is best suited to their child's physical characteristics. Even after identifying a suitable sport, parents can have difficulty selecting the appropriate products and equipment. To address this issue, a system is needed that can efficiently and quickly determine the best sport for a child and recommend related products.

[0820] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0821] In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination result and recommending related products, and means for displaying the result via an in-store terminal or a visual device, thereby enabling parents to easily find a sport that is suitable for their child and quickly obtain products suitable for that sport.

[0822] "Child photo" is image data of a child's entire body that is input into the system.

[0823] "Skeletal data" refers to numerical data about the skeleton, such as shoulder width, leg length, and joint position, extracted using image processing technology and generative AI models.

[0824] A "generative AI model" is a trained artificial intelligence model for analyzing skeletal features from image data, and is built using machine learning libraries such as TensorFlow and PyTorch.

[0825] A "database" is a collection of information that stores skeletal data and statistical information on successful athletes of the past.

[0826] The "judgment result" is the analysis result that indicates which sport is best for your child, along with the reasons and related data.

[0827] "Relevant Products" are the products and equipment required for the relevant sporting activity that are recommended based on the results of the assessment.

[0828] "In-store terminal" refers to a computer terminal installed in a sporting goods store, which is a device used by users to input and view information.

[0829] A "visual device" is a device that provides visual information when worn by a user, such as smart glasses or a head-mounted display.

[0830] The present invention is a system that analyzes skeletal data from a photograph of a child, determines the child's best sport, and recommends related products. Specific embodiments will be described below.

[0831] System Overview

[0832] User Interface

[0833] Using an in-store terminal or visual device installed in a physical store, the user launches the application and takes a photo of their child or selects an existing photo, which is then uploaded to the server.

[0834] Upload a photo

[0835] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[0836] Skeletal analysis

[0837] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[0838] Database collation

[0839] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[0840] Generating and notifying results

[0841] The server generates the optimal sports judgment result based on the database comparison result. This result, along with related products, is notified to the user via the in-store terminal or visual device. The user can check the result on the display of the in-store terminal or visual device and consider purchasing sports-related products.

[0842] Specific examples

[0843] Example: In a special section of a sporting goods store, a parent puts on smart glasses and takes a full-body photo of their child. The AI ​​analyzes the image on the spot, and the display shows "Basketball is the best choice," along with specific recommendations for basketball-related products (e.g., shoes, jerseys). In this way, parents can easily find the right sport for their child and quickly obtain the necessary equipment related to that sport.

[0844] How to use

[0845] The system is designed to be easily accessible for parents and users. All users need to do is install the app, take a full-body photo of their child, or upload an existing photo. The system will then automatically analyze the photo and diagnose the best sport for their child. This makes it easier for users to choose a sport that suits their child's characteristics.

[0846] Prompt Sentence Examples

[0847] "Upload a full-body photo of your child and have it analyzed to determine the best sport for them. Then generate a list of relevant products based on the results."

[0848] In this way, the present invention is a system that not only allows modern parents to efficiently and quickly find the best sports for their children, but also supports them in selecting related products, thereby significantly reducing time and effort and enabling them to provide a better sports life.

[0849] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0850] Step 1:

[0851] The user takes a full-body photo of their child or selects an existing photo and uploads it using a terminal or visual device in the store. The input is the child's full-body photo, and the output is the uploaded image data. The terminal sends this photo to the server.

[0852] Step 2:

[0853] The server reads the received photo data and starts image processing. At this time, the input is the uploaded image data, and the output is the processed data. Specifically, the image is converted to a standard format and pre-processed to remove noise.

[0854] Step 3:

[0855] The server inputs the preprocessed image data into a generative AI model to extract skeletal feature points. The input is preprocessed image data, and the output is analyzed skeletal data. The AI ​​model is built using TensorFlow, PyTorch, etc., and extracts feature points such as joint positions, bone lengths, and body shape as numerical data.

[0856] Step 4:

[0857] The server compares the analyzed skeletal data with a database of successful athletes. The input is the analyzed skeletal data, and the output is the statistically optimal sports judgment result. The server uses a database search algorithm to find athletes with similar skeletal data and analyze which sports they are successful in.

[0858] Step 5:

[0859] The server generates the optimal sports judgment result based on the matching result and recommends related products. The input is the matching result, and the output is the judgment result and a list of recommended products. Specifically, it generates the name of the matching sports and the reason for it, as well as information on related products sold in the store.

[0860] Step 6:

[0861] The server sends the results of the assessment and the list of recommended products to the in-store terminal or visual device. The input is the assessment results and the list of recommended products, and the output is the display information shown to the user. The user can check the optimal sports and related products through the display of the visual device or terminal.

[0862] This process allows the user to quickly find a sport that is suitable for their child and also allows them to immediately obtain the necessary equipment related to that sport.

[0863] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0864] The present invention provides a more user-friendly interface by combining an emotion engine with a system that analyzes a child's skeleton from a photograph and diagnoses the best sport for that child. Specific embodiments will be described below.

[0865] System Overview

[0866] User Interface

[0867] The user launches the application using a device operated by the parent, takes a photo of the child or selects an existing photo, which is then uploaded to the server. If the user expresses an emotion through the photo or voice data, the emotion engine analyzes the emotion.

[0868] Upload a photo

[0869] The device uploads photos taken or selected by the user to the server. The uploaded photo data is received and stored by the server. At this time, the emotion engine also receives and analyzes the user's emotion data.

[0870] Skeleton analysis and emotion analysis

[0871] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores the results as numerical data. At the same time, an emotion engine analyzes the user's emotional state, and the results are stored and analyzed.

[0872] Database collation

[0873] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports athletes with similar skeletal data have been successful in, and determines the best sport for the individual. Emotional data may also be used as relevant information.

[0874] Generating and notifying results

[0875] The server generates the optimal sports judgment result based on the database comparison results. This result is notified to the user on their device as a detailed report. The report includes the reasons for the judged sports and other reference information. The server also adjusts the presentation of the report based on the analysis results of the emotion engine.

[0876] Specific examples

[0877] Example: If you have an 8-year-old child

[0878] Taking and uploading photos:

[0879] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[0880] Receiving and analyzing photo and emotion data:

[0881] The server receives the photo data and uses an AI model to analyze the child's skeletal structure. For example, it extracts data such as the child's shoulder width, leg length, and spine alignment. At the same time, an emotion engine analyzes the parent's emotions based on the photos and audio of their reactions.

[0882] Database collation:

[0883] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[0884] Generate and communicate results:

[0885] The server generates a detailed report based on the analysis results, including reasons why basketball is a good fit for the child. Taking into account the analysis results of the emotion engine, if the user shows signs of anxiety, the server adds encouraging messages and positive information about the child's progress. This report is then sent to the parent's device, where the parent can view the results through the app.

[0886] How to use

[0887] Users simply install the application, take a full-body photo of their child, or upload an existing one. The system then automatically analyzes the photo and diagnoses the best sport for them. Furthermore, the system adjusts the way results are presented based on the user's emotional state, creating a more user-friendly interface.

[0888] This system depends on the accuracy of the photos used and the learning data of the generative AI model, so accuracy can be improved by collecting and analyzing more data.In addition, by regularly updating the model, updating the database, and improving the emotion engine, it is possible to always provide the latest analysis technology and information.

[0889] The above is a specific embodiment for carrying out the present invention.

[0890] The processing flow will be explained below.

[0891] Step 1:

[0892] The user takes or selects a photo.

[0893] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[0894] Step 2:

[0895] The device uploads the photo to the server.

[0896] The device uploads the captured or selected photo data to the server. Specifically, it uses the data sending function in the app to send the photo to the cloud server, and displays a success or failure message after sending.

[0897] Step 3:

[0898] The user provides emotion data.

[0899] Users can express their feelings during or after uploading a photo by answering emotional questions or entering a voice message.

[0900] Step 4:

[0901] The device transmits the emotion data to the server.

[0902] The device transmits the emotion data provided by the user to the server, specifically, the answer data to the emotion-related questions and the voice data to the cloud server.

[0903] Step 5:

[0904] The server receives and stores the photo data.

[0905] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[0906] Step 6:

[0907] The server receives and analyzes the emotion data.

[0908] The server receives the emotion data sent from the device and uses an emotion engine to analyze the user's emotional state. Specifically, the server inputs the voice or text data into an emotion recognition algorithm, categorizes the emotional state, and stores it in a database.

[0909] Step 7:

[0910] The server launches an AI model to analyze the photo.

[0911] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[0912] Step 8:

[0913] The server stores the analyzed skeletal data in a database.

[0914] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[0915] Step 9:

[0916] The server checks the skeletal data against a database.

[0917] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[0918] Step 10:

[0919] The server determines the best sport and generates a report.

[0920] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[0921] Step 11:

[0922] The server sends the report and emotion data together to the device.

[0923] The server sends the judgment results, a report, and the analyzed emotion data to the device. Specifically, it generates a report in JSON or XML format, adds a message according to the emotion, sends it to the device via API, and generates a transmission completion message and records it in the log.

[0924] Step 12:

[0925] The terminal displays the results to the user.

[0926] The device displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, generates visual graphs and explanatory text, and adds messages and user guides according to the user's emotional state to provide the results in an easy-to-understand format for the user.

[0927] Example 2

[0928] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0929] Conventional systems for diagnosing children's sports aptitude focus only on analyzing skeletal data, and therefore are unable to provide accurate advice or diagnostic results that take into account the user's emotional state. It is also difficult to create a user-friendly interface for notifying the analysis results.

[0930] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving image data of a child, means for analyzing skeletal data from the received image data, means for comparing the analyzed skeletal data with a database of past successful athletes, means including an emotion engine for receiving and analyzing voice and facial expression data from the user, and means for notifying the user based on the judgment result and the analyzed emotion data. This makes it possible to provide a more familiar and accurate sports diagnosis result that takes into account the user's emotional state.

[0931] "Child" means a minor or young person, usually a human being who is still in the process of developing.

[0932] "Image data" is data that represents visual information in a digital format, such as photographs and video frames.

[0933] A "means for receiving" is a method or mechanism for obtaining or acquiring information from another device or system.

[0934] "Skeletal data" is numerical information about the positions and lengths of bones in the human body, the arrangement of joints, and so on.

[0935] "Analytical means" are methods and techniques for examining, analyzing, and evaluating data.

[0936] A "former successful athlete" is someone who has had a strong track record and achieved success in sports.

[0937] A "database" is a collection of electronic information that allows large amounts of data to be systematically collected, stored, and searched.

[0938] A "matching tool" is a method or technique for comparing two or more data sets and identifying similarities or matches.

[0939] "Audio data" means data that is a digital representation of the human voice or other sounds.

[0940] "Facial expression data" is data that numerically represents various expressions and emotional states of a human face.

[0941] An "emotion engine" is an algorithm or technology that analyzes voice and facial expression data to determine emotional states.

[0942] A "means of notification" is a method or mechanism for conveying information to a user.

[0943] A "generative AI model" is an artificial intelligence model built using machine learning and deep learning techniques, which analyzes data and makes predictions.

[0944] "User" refers to any person or entity that uses the system, and in this context primarily includes parents or guardians of children.

[0945] The present invention is a system that analyzes a child's skeleton from photographic data and diagnoses the best sport for that child. This system provides a user-friendly interface by combining it with an emotion engine. The following describes in detail an embodiment of the present invention.

[0946] System Overview

[0947] User Interface

[0948] The user launches the application on their device (e.g., a smartphone or tablet) and takes a full-body photo of their child or selects an existing photo. The photo is instantly uploaded to the server. At the same time, if the user expresses emotions through voice or facial expressions, that data is also sent to the server.

[0949] Upload a photo

[0950] The device uploads a full-body photo of the child taken or selected by the user to the server via the Internet. The server stores the received photo data and prepares it for the emotion engine to analyze the voice and facial expression data received at the same time.

[0951] Skeleton analysis and emotion analysis

[0952] The server inputs the received photo data into a generative AI model (for example, a model built using machine learning libraries such as TensorFlow or PyTorch) to perform skeletal analysis. This analysis process extracts joint positions, bone lengths, and body features from the photo. At the same time, an emotion engine analyzes voice and facial expression data to identify the user's emotional state. The resulting skeletal and emotional data are quantified and stored on the server.

[0953] Database Matching

[0954] The server uses the analyzed skeletal and emotional data to compare it with a database of successful athletes. The database contains the skeletal data and sport information of many athletes. The server uses a statistical algorithm to identify the athlete who most closely resembles the analyzed skeletal data. At the same time, the server adjusts the presentation of the results based on the emotional data.

[0955] Generating and notifying results

[0956] The server generates the optimal sports judgment result based on the database comparison results. This judgment result is notified to the user as a detailed report. The report includes the reasons for the judged sports, useful information, and appropriate advice based on the analysis results of the emotion engine. For example, if the user shows an anxious expression, an encouraging message is added to the report. The result is sent to the terminal, and the user can check the report through the application.

[0957] Examples of usage

[0958] Example: Parents with an 8-year-old child

[0959] Taking and uploading photos:

[0960] Parents use the camera on their smartphone to take a full-body photo of their child and upload it to the server via the application.

[0961] Receiving and analyzing photo and emotion data:

[0962] The server inputs the photo data into a generative AI model to analyze the skeletal structure. Specifically, it extracts data such as shoulder width, leg length, and spine alignment. At the same time, it analyzes the user's voice and facial expression data using an emotion engine.

[0963] Match against database:

[0964] The extracted skeletal data is compared with a database to determine whether the child resembles a specific basketball player, which diagnoses the child as being suited to basketball.

[0965] Generate and communicate results:

[0966] The server generates a report of the results and adds a message that takes the user's emotional state into account. This report is then sent to a smartphone, where the user can view the results through an application.

[0967] Prompt Sentence Examples

[0968] Example prompts for skeletal and emotional analysis of photos:

[0969] "I've uploaded a full-body photo of an 8-year-old child. Use TensorFlow to analyze the skeletal data from this photo and match it with a database of past athletes to determine the best sport for them. At the same time, use an emotion engine to analyze the parent's emotional state from their voice and facial expressions and adjust how the report is presented."

[0970] The above is a specific embodiment for carrying out the present invention.

[0971] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0972] Step 1: Launch the application and select a photo

[0973] The user launches the application using a device controlled by the parent. As input, the user taps the "Take Photo" or "Select Photo" button, which activates the device's camera to take a new photo or select a full-body photo of the child from an existing photo library. As output, the taken or selected photo is displayed on the device, ready for the next step.

[0974] Step 2: Upload a photo

[0975] The device uploads photos selected or taken by the user to the server. The photo data selected by the user is used as input. Data processing involves compressing the photo data and sending it via a transfer protocol. As output, the server checks and saves the received photo data. At this time, the server sends a message to the device saying "Photo upload completed." Voice and facial expression data provided by the user when taking the photo are also sent at the same time.

[0976] Step 3: Skeleton analysis and emotion analysis

[0977] The server inputs the received photo data into a generative AI model and performs skeletal analysis. As input, the server sends the photo data to an AI model built with machine learning libraries such as TensorFlow and PyTorch. To process the data, the AI ​​model extracts joint positions, bone lengths, and body features from the photo. As output, skeletal data is generated as numerical information and stored on the server. At the same time, an emotion engine analyzes voice and facial expression data to determine the user's emotional state. As output, emotional data is stored as numerical data.

[0978] Step 4: Check against the database

[0979] The server compares the analyzed skeletal data with a database of past successful athletes. As input, the server uses the analyzed skeletal data and the athlete data in the database. For data processing, a statistical algorithm is used to identify the athletes most similar to the skeletal data. As output, information about similar athletes and their sports is identified. Furthermore, the way the results are presented is adjusted via emotional data.

[0980] Step 5: Generate and communicate results

[0981] The server generates the optimal sports judgment result based on the results of database matching. Data on similar athletes and emotional data are used as input. The judgment result is combined with an emotional message for data processing. A detailed report is generated as output and notified to the user. For example, the report may include the judged sports, the reasons for the judgement, and an encouraging message based on the emotion. The user can check this report on their device and take appropriate action.

[0982] The above is the specific processing flow of the program of this system.

[0983] (Application example 2)

[0984] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0985] Conventional sports aptitude diagnostic systems based on children's skeletal analysis only provided one-way results to users, making it impossible to consider how users felt about the results. Furthermore, systems optimizing factory work efficiency only provided one-way instructions for improving efficiency without considering skeletal or emotional analyses of workers. This could result in workers' stress and anxiety being ignored, potentially negatively impacting productivity and work satisfaction.

[0986] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination results, emotion engine means for analyzing the user's emotion data, and means for optimizing work efficiency based on the best sport determination results and the emotion data. This makes it possible to provide the best sport while taking the user's emotions into consideration, and further optimize factory work efficiency and reduce worker stress.

[0987] The "means for receiving a child's photo" has a function for transmitting a photo taken or selected by the user to the server, and the server then receives the photo data.

[0988] The "means for analyzing skeletal data" is a system that uses image analysis technology to extract information such as joint positions, bone lengths, and body shape from received photographs and stores it as numerical data.

[0989] The "database of past successful athletes" is a database that accumulates skeletal data and performance data of athletes who have achieved outstanding results.

[0990] The "means for determining the best sport for a child" refers to an algorithm that compares the analyzed skeletal data with a database and statistically determines the best sport for a child.

[0991] The "means for notifying the user of the judgment results" has a function of transmitting the judgment results of the optimal sport to the user's terminal as a detailed report, and allowing the user to view the report.

[0992] The "emotion engine means" refers to algorithms and functions for analyzing photo and audio data to detect the user's emotional state.

[0993] "Means to optimize work efficiency" is a function that evaluates work efficiency based on skeletal data and emotional data, and recommends specific actions to improve efficiency and reduce stress.

[0994] In this invention, the user must first take a photo of their child or select an existing photo using their device and upload the photo data to a server. The server then passes the received photo data to a generative AI model to extract the child's skeletal data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze information such as joint positions, bone lengths, and body shape. The photo and audio data are also passed to an emotion engine to simultaneously analyze the user's emotional state.

[0995] The server compares the analyzed skeletal and emotional data with a database to determine the best sport for the individual. This database contains the skeletal data of successful athletes and includes an algorithm for statistically determining which sport is best suited. The results are then sent to the user's device in the form of a detailed report. The presentation of the report is also adjusted based on the analysis results of the emotion engine, making it possible to provide information that is more user-friendly.

[0996] This system can also be used to optimize work efficiency in factories. Cameras capture the movements and emotions of workers working in the factory, and analyze their skeletal and emotional data. Based on this data, the system can evaluate work efficiency and recommend specific actions to improve it, while also reducing worker stress.

[0997] A specific use case could be implemented in a large automotive parts manufacturing plant. In this factory, robots installed at specific work stations monitor the movements and emotions of workers in real time, helping to improve work efficiency and reduce worker stress. For example, if a worker is slouching while working, skeletal analysis can determine that this posture is not optimal. Furthermore, if emotion recognition determines that the worker is feeling stressed, specific actions (e.g., correcting posture and taking breaks) can be recommended to improve efficiency.

[0998] An example of a prompt to be input into the generative AI model is, "Please create a program that analyzes the worker's skeletal data and recommends optimal movements. Also, please add a function that recommends taking a break if the worker is under high stress." Based on this prompt, the AI ​​will suggest appropriate actions to optimize work efficiency.

[0999] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1000] Step 1:

[1001] A user takes a photo of their child or selects an existing photo using a terminal and uploads the photo data to the server. The input is the photo data, and the output is the photo data uploaded to the server.

[1002] Step 2:

[1003] The server passes the received photo data to a generative AI model to extract the child's skeletal data. The input is the received photo data, and the output is analysis data including skeletal feature points. Specifically, the generative AI model uses TensorFlow and PyTorch to extract information such as joint positions, bone lengths, and body shape.

[1004] Step 3:

[1005] The server passes facial expression and voice data from the photo data to the emotion engine, which analyzes the user's emotional state. The input is facial expression and voice data, and the output is emotional state data. Specifically, an emotion analysis algorithm is applied to classify emotions such as stress, anxiety, and joy.

[1006] Step 4:

[1007] The server compares the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child. The input is the skeletal data and the database, and the output is the best sport determination result. Specifically, a matching algorithm is applied to determine the statistically best sport.

[1008] Step 5:

[1009] The server adjusts the notification content to the user based on the judgment result and emotional data. The input is the judgment result and emotional data, and the output is the notification content. Specifically, it takes into account the emotional data and adds positive messages or encouraging words.

[1010] Step 6:

[1011] The server sends the optimal sports judgment result and the adjusted notification content to the user's device. The input is the judgment result and the notification content, and the output is the notification sent to the user's device.

[1012] Step 7:

[1013] In the case of a factory work efficiency optimization system, the server analyzes the movement and emotional data of workers captured by a camera and recommends specific actions to improve efficiency based on skeletal and emotional data. The input is the movement and emotional data of the workers, and the output is recommended actions to improve work efficiency. Specific actions are suggested by applying a recommendation algorithm, such as correcting posture or suggesting breaks.

[1014] Step 8:

[1015] The server notifies the worker of the recommended action in real time to the worker's display terminal. The input is the recommended action, and the output is the notification sent to the worker's display terminal.

[1016] Through the above steps, the system can suggest the most suitable sport while taking into account the user's emotions, thereby improving work efficiency in the factory.

[1017] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1018] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1019] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1020] [Fourth embodiment]

[1021] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1022] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1024] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1025] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1026] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1028] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1029] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1030] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1032] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1034] The present invention is a system for analyzing a child's skeleton from a photograph and diagnosing the best sport for that child. Specific embodiments will be described below.

[1035] System Overview

[1036] User Interface

[1037] Using a device operated by a parent, the user launches the application and takes a photo of the child or selects an existing photo, which is then uploaded to the server.

[1038] Upload a photo

[1039] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[1040] Skeletal analysis

[1041] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[1042] Database collation

[1043] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[1044] Generating and notifying results

[1045] The server generates the optimal sports judgment result based on the database comparison results, and the result is notified to the user as a detailed report on the terminal. The report includes the reasons for the judgment and other reference information.

[1046] Specific examples

[1047] Example: If you have an 8-year-old child

[1048] Taking and uploading photos:

[1049] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[1050] Photo reception and analysis:

[1051] The server receives the photo data and uses an AI model to analyze the skeleton, extracting data such as the child's shoulder width, leg length, and spine alignment.

[1052] Database collation:

[1053] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[1054] Generate and communicate results:

[1055] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, and sends this report to the parent's device, where they can view the results through the app.

[1056] How to use

[1057] The system is designed to be easily accessible to parents and other users. Simply install the app, take a full-body photo of your child, or upload an existing photo, and the system will automatically analyze the photo and diagnose the best sport for your child. This makes it easier for users to choose a sport that suits their child's characteristics.

[1058] Since the system of the present invention depends on the accuracy of the photographs used and the training data of the generative AI model, it is possible to improve accuracy by collecting and analyzing more data. Furthermore, by regularly updating the model and database, it is possible to always provide the latest analysis technology and information.

[1059] The above is a specific embodiment for carrying out the present invention.

[1060] The processing flow will be explained below.

[1061] Step 1:

[1062] The user takes or selects a photo.

[1063] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[1064] Step 2:

[1065] The device uploads the photo to the server.

[1066] The device will upload the photographed or selected photo data to the server. Specifically, it will use the data sending function within the app to send the selected photo to the cloud server, and after sending, will display a success or failure message.

[1067] Step 3:

[1068] The server receives and stores the photo data.

[1069] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[1070] Step 4:

[1071] The server launches an AI model to analyze the photo.

[1072] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[1073] Step 5:

[1074] The server stores the analyzed skeletal data in a database.

[1075] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[1076] Step 6:

[1077] The server checks the skeletal data against a database.

[1078] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[1079] Step 7:

[1080] The server determines the best sport and generates a report.

[1081] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[1082] Step 8:

[1083] The server sends the report to the device.

[1084] The server sends the judgment result and a report to the terminal. Specifically, it generates the report in JSON or XML format, sends it to the terminal via API, and generates a transmission completion message and records it in the log.

[1085] Step 9:

[1086] The terminal displays the results to the user.

[1087] The terminal displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, and generates visual graphs and explanatory text to provide the results in a format that is easy for the user to understand.

[1088] Example 1

[1089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1090] Conventional technology lacks the means to accurately determine the type of sports that best suit a child's characteristics, making it difficult to make optimal decisions about sports selection for children. Furthermore, it often relies on manual data analysis and expert opinions, which is time-consuming and costly.

[1091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1092] In this invention, the server includes means for receiving an image of a child, means for analyzing skeletal information from the received image, means for comparing the analyzed skeletal information with a database of past successful athletes to determine the most suitable athletic event for the child, and means for notifying the user of the determination result. This makes it possible to quickly and accurately determine the most suitable athletic event for a child without relying on conventional manual data analysis or expert opinions.

[1093] An "image" is data in digital or analog form that captures visual information of an object.

[1094] "Skeletal information" is data such as joint positions, bone lengths, and body shape extracted from an image.

[1095] An "athlete database" is a collection of information that compiles skeletal information and athletic events of successful athletes in the past.

[1096] An "athletic event" is a type of competition or sport that is based on specific rules and techniques.

[1097] A "server" is a computer system that receives, analyzes, and transmits data to clients over a network.

[1098] "User" refers to parents and other parties who use the System.

[1099] A "generative AI model" is an artificial intelligence model that is built using machine learning libraries and runs algorithms to analyze skeletal information from images.

[1100] "Image capture device functionality" refers to a device or software functionality that includes a camera function for capturing images.

[1101] The present invention is a system for analyzing skeletal information from an image of a child and diagnosing the type of exercise that is best suited for that child. Specific embodiments of this system will be described below.

[1102] System Overview

[1103] User Interface

[1104] The user launches the application using a parent-controlled device. The application has a camera function to take a full-body photo of the child, and also provides the option to select an existing photo. After the user takes or selects a photo, the photo is uploaded to the server.

[1105] Upload a photo

[1106] The device uploads the photos selected by the user to the server, which sends the data using a secure communication protocol (e.g., HTTPS). This ensures that the photo data is safely received by the server and saved in the specified directory.

[1107] Skeletal analysis

[1108] The server passes the received photo data to a pre-trained generative AI model, which was built using machine learning libraries TensorFlow and PyTorch. The generative AI model extracts skeletal information from the image, such as joint positions, bone lengths, and body shape, and analyzes the data in numerical form.

[1109] Database Matching

[1110] The server compares the analyzed skeletal information with a database of successful athletes, which includes detailed skeletal information about athletes and data on their success in sports. The server then uses statistical techniques to identify athletes with similar skeletal information and their sports.

[1111] Generation and notification of judgment results

[1112] The server then determines the optimal exercise based on the database comparison results, generates a detailed report of the results, and sends it to the user's device. The user receives a notification on their device and can check the results through the application.

[1113] Specific examples

[1114] Here is an example of the specific process, assuming you are using a full-body photo of an 8-year-old child.

[1115] Taking and uploading photos:

[1116] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[1117] Photo reception and analysis:

[1118] The server receives the photo data and uses a generative AI model to analyze skeletal information, extracting data such as the child's shoulder width, leg length, and spine alignment.

[1119] Database collation:

[1120] The extracted skeletal information is then compared against a database of past successful athletes, and the skeleton is determined to resemble a particular athlete, for example, a basketball player.

[1121] Generate and communicate results:

[1122] The server then generates a detailed report based on the analysis, including reasons why basketball is a good fit for the child, which is then sent to the parent's device, where they can view the results through the app.

[1123] Prompt Sentence Examples

[1124] Here are some example prompts to input to a generative AI model:

[1125] "We analyze a full-body photograph of an eight-year-old child and diagnose the most suitable type of exercise based on their skeletal data. We focus on evaluating shoulder width, leg length, and back muscle alignment to determine the most suitable type of exercise."

[1126] This prompt sentence allows the generative AI model to make appropriate analysis and judgments.

[1127] The present invention makes it possible to easily and accurately determine the type of exercise that is best suited for a child.

[1128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1129] Step 1:

[1130] The user launches the application. Once installed on the user's device, the application is launched by tapping, which takes the user to the initial screen, where the user is presented with the option to take a photo of their child or select an existing photo.

[1131] Step 2:

[1132] The user takes or selects a photo. The user presses the "Take Photo" button in the app and uses the device's camera to take a full-body photo of the child. Or, the user presses the "Select Photo" button and selects an existing photo from the device's gallery. The input is a full-body photo of the child, which will be used in the next step.

[1133] Step 3:

[1134] The device uploads the photo to the server. The selected or taken photo is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The input is the photo selected in the previous step, and the output is the photo data stored on the server.

[1135] Step 4:

[1136] The server receives and stores the photo data. The server receives the photo data sent and stores it securely in the specified directory. Once the storage is complete, it is ready to proceed to the next processing step. The input is the photo data sent from the device, and the output is the photo data stored in the specified directory on the server.

[1137] Step 5:

[1138] The server passes the photo data to the generative AI model. The server retrieves the stored photo data and inputs it into a generative AI model for image analysis. This generative AI model is built using TensorFlow and PyTorch. The input is the stored photo data, and the output is skeletal information from the analysis results.

[1139] Step 6:

[1140] The generative AI model analyzes the skeletal information. The generative AI model extracts skeletal information such as joint positions, bone lengths, and body shape from the photo and analyzes it as numerical data. This data is used in the next step. The input is photo data, and the output is numerical data as skeletal information.

[1141] Step 7:

[1142] The server compares the skeletal information with a database. The server compares the skeletal information received from the generative AI model with a database of past successful athletes. The database contains the athletes' skeletal information and data on the areas in which they have been successful. The input is the analyzed skeletal information, and the output is the determination of the optimal athletic event.

[1143] Step 8:

[1144] The server determines the best type of sport for the child based on the results of database matching. The result of the determination also includes scientific reasons based on skeletal information. The input is the result of matching with a database of past successful athletes, and the output is the determined best type of sport.

[1145] Step 9:

[1146] The server generates the results as a detailed report and sends it to the terminal. The server documents the judgment results as a detailed report and sends it to the user's terminal. This report includes the reasons for the judged exercise and related information. The input is the judgment result, and the output is the detailed report sent to the terminal.

[1147] Step 10:

[1148] The user checks the report. The user checks the notification received on the device and views a detailed report through the application. This report can be used as a reference for selecting the most suitable exercise for the child. The input is the report sent from the server, and the output is the assessment result that the user checks.

[1149] (Application example 1)

[1150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1151] Today's parents spend a lot of time and effort trying to find the right sport for their child. They often seek expert advice and repeatedly try out different sports to determine which is best suited to their child's physical characteristics. Even after identifying a suitable sport, parents can have difficulty selecting the appropriate products and equipment. To address this issue, a system is needed that can efficiently and quickly determine the best sport for a child and recommend related products.

[1152] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1153] In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination result and recommending related products, and means for displaying the result via an in-store terminal or a visual device, thereby enabling parents to easily find a sport that is suitable for their child and quickly obtain products suitable for that sport.

[1154] "Child photo" is image data of a child's entire body that is input into the system.

[1155] "Skeletal data" refers to numerical data about the skeleton, such as shoulder width, leg length, and joint position, extracted using image processing technology and generative AI models.

[1156] A "generative AI model" is a trained artificial intelligence model for analyzing skeletal features from image data, and is built using machine learning libraries such as TensorFlow and PyTorch.

[1157] A "database" is a collection of information that stores skeletal data and statistical information on successful athletes of the past.

[1158] The "judgment result" is the analysis result that indicates which sport is best for your child, along with the reasons and related data.

[1159] "Relevant Products" are the products and equipment required for the relevant sporting activity that are recommended based on the results of the assessment.

[1160] "In-store terminal" refers to a computer terminal installed in a sporting goods store, which is a device used by users to input and view information.

[1161] A "visual device" is a device that provides visual information when worn by a user, such as smart glasses or a head-mounted display.

[1162] The present invention is a system that analyzes skeletal data from a photograph of a child, determines the child's best sport, and recommends related products. Specific embodiments will be described below.

[1163] System Overview

[1164] User Interface

[1165] Using an in-store terminal or visual device installed in a physical store, the user launches the application and takes a photo of their child or selects an existing photo, which is then uploaded to the server.

[1166] Upload a photo

[1167] The terminal uploads the photos taken or selected by the user to the server, which receives and stores the uploaded photo data.

[1168] Skeletal analysis

[1169] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores it as numerical data.

[1170] Database collation

[1171] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports similar athletes have been successful in, and determines which sports are best suited for the individual.

[1172] Generating and notifying results

[1173] The server generates the optimal sports judgment result based on the database comparison result. This result, along with related products, is notified to the user via the in-store terminal or visual device. The user can check the result on the display of the in-store terminal or visual device and consider purchasing sports-related products.

[1174] Specific examples

[1175] Example: In a special section of a sporting goods store, a parent puts on smart glasses and takes a full-body photo of their child. The AI ​​analyzes the image on the spot, and the display shows "Basketball is the best choice," along with specific recommendations for basketball-related products (e.g., shoes, jerseys). In this way, parents can easily find the right sport for their child and quickly obtain the necessary equipment related to that sport.

[1176] How to use

[1177] The system is designed to be easily accessible for parents and users. All users need to do is install the app, take a full-body photo of their child, or upload an existing photo. The system will then automatically analyze the photo and diagnose the best sport for their child. This makes it easier for users to choose a sport that suits their child's characteristics.

[1178] Prompt Sentence Examples

[1179] "Upload a full-body photo of your child and have it analyzed to determine the best sport for them. Then generate a list of relevant products based on the results."

[1180] In this way, the present invention is a system that not only allows modern parents to efficiently and quickly find the best sports for their children, but also supports them in selecting related products, thereby significantly reducing time and effort and enabling them to provide a better sports life.

[1181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1182] Step 1:

[1183] The user takes a full-body photo of their child or selects an existing photo and uploads it using a terminal or visual device in the store. The input is the child's full-body photo, and the output is the uploaded image data. The terminal sends this photo to the server.

[1184] Step 2:

[1185] The server reads the received photo data and starts image processing. At this time, the input is the uploaded image data, and the output is the processed data. Specifically, the image is converted to a standard format and pre-processed to remove noise.

[1186] Step 3:

[1187] The server inputs the preprocessed image data into a generative AI model to extract skeletal feature points. The input is preprocessed image data, and the output is analyzed skeletal data. The AI ​​model is built using TensorFlow, PyTorch, etc., and extracts feature points such as joint positions, bone lengths, and body shape as numerical data.

[1188] Step 4:

[1189] The server compares the analyzed skeletal data with a database of successful athletes. The input is the analyzed skeletal data, and the output is the statistically optimal sports judgment result. The server uses a database search algorithm to find athletes with similar skeletal data and analyze which sports they are successful in.

[1190] Step 5:

[1191] The server generates the optimal sports judgment result based on the matching result and recommends related products. The input is the matching result, and the output is the judgment result and a list of recommended products. Specifically, it generates the name of the matching sports and the reason for it, as well as information on related products sold in the store.

[1192] Step 6:

[1193] The server sends the results of the assessment and the list of recommended products to the in-store terminal or visual device. The input is the assessment results and the list of recommended products, and the output is the display information shown to the user. The user can check the optimal sports and related products through the display of the visual device or terminal.

[1194] This process allows the user to quickly find a sport that is suitable for their child and also allows them to immediately obtain the necessary equipment related to that sport.

[1195] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1196] The present invention provides a more user-friendly interface by combining an emotion engine with a system that analyzes a child's skeleton from a photograph and diagnoses the best sport for that child. Specific embodiments will be described below.

[1197] System Overview

[1198] User Interface

[1199] The user launches the application using a device operated by the parent, takes a photo of the child or selects an existing photo, which is then uploaded to the server. If the user expresses an emotion through the photo or voice data, the emotion engine analyzes the emotion.

[1200] Upload a photo

[1201] The device uploads photos taken or selected by the user to the server. The uploaded photo data is received and stored by the server. At this time, the emotion engine also receives and analyzes the user's emotion data.

[1202] Skeleton analysis and emotion analysis

[1203] The server passes the received photo data to a generative AI model, which extracts and analyzes skeletal features. The generative AI model is built using machine learning libraries such as TensorFlow and PyTorch. The AI ​​uses image processing technology to extract information such as joint positions, bone lengths, and body shape, and stores the results as numerical data. At the same time, an emotion engine analyzes the user's emotional state, and the results are stored and analyzed.

[1204] Database collation

[1205] The server then compares the analyzed skeletal data with a database of successful athletes. This matching process statistically analyzes which sports athletes with similar skeletal data have been successful in, and determines the best sport for the individual. Emotional data may also be used as relevant information.

[1206] Generating and notifying results

[1207] The server generates the optimal sports judgment result based on the database comparison results. This result is notified to the user on their device as a detailed report. The report includes the reasons for the judged sports and other reference information. The server also adjusts the presentation of the report based on the analysis results of the emotion engine.

[1208] Specific examples

[1209] Example: If you have an 8-year-old child

[1210] Taking and uploading photos:

[1211] Parents use the device to take a full-body photo of their child and upload it to a server through the application.

[1212] Receiving and analyzing photo and emotion data:

[1213] The server receives the photo data and uses an AI model to analyze the child's skeletal structure. For example, it extracts data such as the child's shoulder width, leg length, and spine alignment. At the same time, an emotion engine analyzes the parent's emotions based on the photos and audio of their reactions.

[1214] Database collation:

[1215] The extracted skeletal data is compared with a database and it is determined that the skeleton resembles a particular basketball player. The server then statistically determines that the child is suited to basketball.

[1216] Generate and communicate results:

[1217] The server generates a detailed report based on the analysis results, including reasons why basketball is a good fit for the child. Taking into account the analysis results of the emotion engine, if the user shows signs of anxiety, the server adds encouraging messages and positive information about the child's progress. This report is then sent to the parent's device, where the parent can view the results through the app.

[1218] How to use

[1219] Users simply install the application, take a full-body photo of their child, or upload an existing one. The system then automatically analyzes the photo and diagnoses the best sport for them. Furthermore, the system adjusts the way results are presented based on the user's emotional state, creating a more user-friendly interface.

[1220] This system depends on the accuracy of the photos used and the learning data of the generative AI model, so accuracy can be improved by collecting and analyzing more data.In addition, by regularly updating the model, updating the database, and improving the emotion engine, it is possible to always provide the latest analysis technology and information.

[1221] The above is a specific embodiment for carrying out the present invention.

[1222] The processing flow will be explained below.

[1223] Step 1:

[1224] The user takes or selects a photo.

[1225] Users launch the app and either take a full-body photo of their child or select an existing photo stored on their device by activating the camera function to take a new photo or by selecting an existing photo from the device's gallery.

[1226] Step 2:

[1227] The device uploads the photo to the server.

[1228] The device uploads the captured or selected photo data to the server. Specifically, it uses the data sending function in the app to send the photo to the cloud server, and displays a success or failure message after sending.

[1229] Step 3:

[1230] The user provides emotion data.

[1231] Users can express their feelings during or after uploading a photo by answering emotional questions or entering a voice message.

[1232] Step 4:

[1233] The device transmits the emotion data to the server.

[1234] The device transmits the emotion data provided by the user to the server, specifically, the answer data to the emotion-related questions and the voice data to the cloud server.

[1235] Step 5:

[1236] The server receives and stores the photo data.

[1237] The server receives the photo data sent from the device. Specifically, it receives the photo data via the data reception API, saves it as binary data, creates a new entry in the database, and associates the photo data ID with the user information.

[1238] Step 6:

[1239] The server receives and analyzes the emotion data.

[1240] The server receives the emotion data sent from the device and uses an emotion engine to analyze the user's emotional state. Specifically, the server inputs the voice or text data into an emotion recognition algorithm, categorizes the emotional state, and stores it in a database.

[1241] Step 7:

[1242] The server launches an AI model to analyze the photo.

[1243] The server passes the received photo data to the generative AI model, which extracts and analyzes skeletal features. Specifically, it calls the image analysis engine, inputs the photo data into the AI ​​model, and performs operations to extract feature points and skeletal information.

[1244] Step 8:

[1245] The server stores the analyzed skeletal data in a database.

[1246] The server analyzes specific feature points based on the extracted skeletal information and stores them in a database. Specifically, it organizes the feature points as numerical data, calculates necessary data such as skeletal proportions and muscle balance, and stores the organized data in the database.

[1247] Step 9:

[1248] The server checks the skeletal data against a database.

[1249] The server compares the skeletal data with a database of successful athletes of the past, using a database search algorithm to find athletes with similar skeletal profiles and generate a list of potential sports that are best suited to the athlete.

[1250] Step 10:

[1251] The server determines the best sport and generates a report.

[1252] The server then determines the statistically optimal sport from the matching results and creates a detailed report, specifically evaluating the matching results, selecting the optimal sport and the reasons for it, generating a report including the results, and converting it into a data format for the user.

[1253] Step 11:

[1254] The server sends the report and emotion data together to the device.

[1255] The server sends the judgment results, a report, and the analyzed emotion data to the device. Specifically, it generates a report in JSON or XML format, adds a message according to the emotion, sends it to the device via API, and generates a transmission completion message and records it in the log.

[1256] Step 12:

[1257] The terminal displays the results to the user.

[1258] The device displays the report results received from the server to the user. Specifically, it analyzes the received report data, displays it on the screen, generates visual graphs and explanatory text, and adds messages and user guides according to the user's emotional state to provide the results in an easy-to-understand format for the user.

[1259] Example 2

[1260] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1261] Conventional systems for diagnosing children's sports aptitude focus only on analyzing skeletal data, and therefore are unable to provide accurate advice or diagnostic results that take into account the user's emotional state. It is also difficult to create a user-friendly interface for notifying the analysis results.

[1262] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving image data of a child, means for analyzing skeletal data from the received image data, means for comparing the analyzed skeletal data with a database of past successful athletes, means including an emotion engine for receiving and analyzing voice and facial expression data from the user, and means for notifying the user based on the judgment result and the analyzed emotion data. This makes it possible to provide a more familiar and accurate sports diagnosis result that takes into account the user's emotional state.

[1263] "Child" means a minor or young person, usually a human being who is still in the process of developing.

[1264] "Image data" is data that represents visual information in a digital format, such as photographs and video frames.

[1265] A "means for receiving" is a method or mechanism for obtaining or acquiring information from another device or system.

[1266] "Skeletal data" is numerical information about the positions and lengths of bones in the human body, the arrangement of joints, and so on.

[1267] "Analytical means" are methods and techniques for examining, analyzing, and evaluating data.

[1268] A "former successful athlete" is someone who has had a strong track record and achieved success in sports.

[1269] A "database" is a collection of electronic information that allows large amounts of data to be systematically collected, stored, and searched.

[1270] A "matching tool" is a method or technique for comparing two or more data sets and identifying similarities or matches.

[1271] "Audio data" means data that is a digital representation of the human voice or other sounds.

[1272] "Facial expression data" is data that numerically represents various expressions and emotional states of a human face.

[1273] An "emotion engine" is an algorithm or technology that analyzes voice and facial expression data to determine emotional states.

[1274] A "means of notification" is a method or mechanism for conveying information to a user.

[1275] A "generative AI model" is an artificial intelligence model built using machine learning and deep learning techniques, which analyzes data and makes predictions.

[1276] "User" refers to any person or entity that uses the system, and in this context primarily includes parents or guardians of children.

[1277] The present invention is a system that analyzes a child's skeleton from photographic data and diagnoses the best sport for that child. This system provides a user-friendly interface by combining it with an emotion engine. The following describes in detail an embodiment of the present invention.

[1278] System Overview

[1279] User Interface

[1280] The user launches the application on their device (e.g., a smartphone or tablet) and takes a full-body photo of their child or selects an existing photo. The photo is instantly uploaded to the server. At the same time, if the user expresses emotions through voice or facial expressions, that data is also sent to the server.

[1281] Upload a photo

[1282] The device uploads a full-body photo of the child taken or selected by the user to the server via the Internet. The server stores the received photo data and prepares it for the emotion engine to analyze the voice and facial expression data received at the same time.

[1283] Skeleton analysis and emotion analysis

[1284] The server inputs the received photo data into a generative AI model (for example, a model built using machine learning libraries such as TensorFlow or PyTorch) to perform skeletal analysis. This analysis process extracts joint positions, bone lengths, and body features from the photo. At the same time, an emotion engine analyzes voice and facial expression data to identify the user's emotional state. The resulting skeletal and emotional data are quantified and stored on the server.

[1285] Database Matching

[1286] The server uses the analyzed skeletal and emotional data to compare it with a database of successful athletes. The database contains the skeletal data and sport information of many athletes. The server uses a statistical algorithm to identify the athlete who most closely resembles the analyzed skeletal data. At the same time, the server adjusts the presentation of the results based on the emotional data.

[1287] Generating and notifying results

[1288] The server generates the optimal sports judgment result based on the database comparison results. This judgment result is notified to the user as a detailed report. The report includes the reasons for the judged sports, useful information, and appropriate advice based on the analysis results of the emotion engine. For example, if the user shows an anxious expression, an encouraging message is added to the report. The result is sent to the terminal, and the user can check the report through the application.

[1289] Examples of usage

[1290] Example: Parents with an 8-year-old child

[1291] Taking and uploading photos:

[1292] Parents use the camera on their smartphone to take a full-body photo of their child and upload it to the server via the application.

[1293] Receiving and analyzing photo and emotion data:

[1294] The server inputs the photo data into a generative AI model to analyze the skeletal structure. Specifically, it extracts data such as shoulder width, leg length, and spine alignment. At the same time, it analyzes the user's voice and facial expression data using an emotion engine.

[1295] Match against database:

[1296] The extracted skeletal data is compared with a database to determine whether the child resembles a specific basketball player, which diagnoses the child as being suited to basketball.

[1297] Generate and communicate results:

[1298] The server generates a report of the results and adds a message that takes the user's emotional state into account. This report is then sent to a smartphone, where the user can view the results through an application.

[1299] Prompt Sentence Examples

[1300] Example prompts for skeletal and emotional analysis of photos:

[1301] "I've uploaded a full-body photo of an 8-year-old child. Use TensorFlow to analyze the skeletal data from this photo and match it with a database of past athletes to determine the best sport for them. At the same time, use an emotion engine to analyze the parent's emotional state from their voice and facial expressions and adjust how the report is presented."

[1302] The above is a specific embodiment for carrying out the present invention.

[1303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1304] Step 1: Launch the application and select a photo

[1305] The user launches the application using a device controlled by the parent. As input, the user taps the "Take Photo" or "Select Photo" button, which activates the device's camera to take a new photo or select a full-body photo of the child from an existing photo library. As output, the taken or selected photo is displayed on the device, ready for the next step.

[1306] Step 2: Upload a photo

[1307] The device uploads photos selected or taken by the user to the server. The photo data selected by the user is used as input. Data processing involves compressing the photo data and sending it via a transfer protocol. As output, the server checks and saves the received photo data. At this time, the server sends a message to the device saying "Photo upload completed." Voice and facial expression data provided by the user when taking the photo are also sent at the same time.

[1308] Step 3: Skeleton analysis and emotion analysis

[1309] The server inputs the received photo data into a generative AI model and performs skeletal analysis. As input, the server sends the photo data to an AI model built with machine learning libraries such as TensorFlow and PyTorch. To process the data, the AI ​​model extracts joint positions, bone lengths, and body features from the photo. As output, skeletal data is generated as numerical information and stored on the server. At the same time, an emotion engine analyzes voice and facial expression data to determine the user's emotional state. As output, emotional data is stored as numerical data.

[1310] Step 4: Check against the database

[1311] The server compares the analyzed skeletal data with a database of past successful athletes. As input, the server uses the analyzed skeletal data and the athlete data in the database. For data processing, a statistical algorithm is used to identify the athletes most similar to the skeletal data. As output, information about similar athletes and their sports is identified. Furthermore, the way the results are presented is adjusted via emotional data.

[1312] Step 5: Generate and communicate results

[1313] The server generates the optimal sports judgment result based on the results of database matching. Data on similar athletes and emotional data are used as input. The judgment result is combined with an emotional message for data processing. A detailed report is generated as output and notified to the user. For example, the report may include the judged sports, the reasons for the judgement, and an encouraging message based on the emotion. The user can check this report on their device and take appropriate action.

[1314] The above is the specific processing flow of the program of this system.

[1315] (Application example 2)

[1316] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1317] Conventional sports aptitude diagnostic systems based on children's skeletal analysis only provided one-way results to users, making it impossible to consider how users felt about the results. Furthermore, systems optimizing factory work efficiency only provided one-way instructions for improving efficiency without considering skeletal or emotional analyses of workers. This could result in workers' stress and anxiety being ignored, potentially negatively impacting productivity and work satisfaction.

[1318] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a child's photo, means for analyzing skeletal data from the received photo, means for comparing the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child, means for notifying the user of the determination results, emotion engine means for analyzing the user's emotion data, and means for optimizing work efficiency based on the best sport determination results and the emotion data. This makes it possible to provide the best sport while taking the user's emotions into consideration, and further optimize factory work efficiency and reduce worker stress.

[1319] The "means for receiving a child's photo" has a function for transmitting a photo taken or selected by the user to the server, and the server then receives the photo data.

[1320] The "means for analyzing skeletal data" is a system that uses image analysis technology to extract information such as joint positions, bone lengths, and body shape from received photographs and stores it as numerical data.

[1321] The "database of past successful athletes" is a database that accumulates skeletal data and performance data of athletes who have achieved outstanding results.

[1322] The "means for determining the best sport for a child" refers to an algorithm that compares the analyzed skeletal data with a database and statistically determines the best sport for a child.

[1323] The "means for notifying the user of the judgment results" has a function of transmitting the judgment results of the optimal sport to the user's terminal as a detailed report, and allowing the user to view the report.

[1324] The "emotion engine means" refers to algorithms and functions for analyzing photo and audio data to detect the user's emotional state.

[1325] "Means to optimize work efficiency" is a function that evaluates work efficiency based on skeletal data and emotional data, and recommends specific actions to improve efficiency and reduce stress.

[1326] In this invention, the user must first take a photo of their child or select an existing photo using their device and upload the photo data to a server. The server then passes the received photo data to a generative AI model to extract the child's skeletal data. Specifically, it uses machine learning libraries such as TensorFlow and PyTorch to analyze information such as joint positions, bone lengths, and body shape. The photo and audio data are also passed to an emotion engine to simultaneously analyze the user's emotional state.

[1327] The server compares the analyzed skeletal and emotional data with a database to determine the best sport for the individual. This database contains the skeletal data of successful athletes and includes an algorithm for statistically determining which sport is best suited. The results are then sent to the user's device in the form of a detailed report. The presentation of the report is also adjusted based on the analysis results of the emotion engine, making it possible to provide information that is more user-friendly.

[1328] This system can also be used to optimize work efficiency in factories. Cameras capture the movements and emotions of workers working in the factory, and analyze their skeletal and emotional data. Based on this data, the system can evaluate work efficiency and recommend specific actions to improve it, while also reducing worker stress.

[1329] A specific use case could be implemented in a large automotive parts manufacturing plant. In this factory, robots installed at specific work stations monitor the movements and emotions of workers in real time, helping to improve work efficiency and reduce worker stress. For example, if a worker is slouching while working, skeletal analysis can determine that this posture is not optimal. Furthermore, if emotion recognition determines that the worker is feeling stressed, specific actions (e.g., correcting posture and taking breaks) can be recommended to improve efficiency.

[1330] An example of a prompt to be input into the generative AI model is, "Please create a program that analyzes the worker's skeletal data and recommends optimal movements. Also, please add a function that recommends taking a break if the worker is under high stress." Based on this prompt, the AI ​​will suggest appropriate actions to optimize work efficiency.

[1331] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1332] Step 1:

[1333] A user takes a photo of their child or selects an existing photo using a terminal and uploads the photo data to the server. The input is the photo data, and the output is the photo data uploaded to the server.

[1334] Step 2:

[1335] The server passes the received photo data to a generative AI model to extract the child's skeletal data. The input is the received photo data, and the output is analysis data including skeletal feature points. Specifically, the generative AI model uses TensorFlow and PyTorch to extract information such as joint positions, bone lengths, and body shape.

[1336] Step 3:

[1337] The server passes facial expression and voice data from the photo data to the emotion engine, which analyzes the user's emotional state. The input is facial expression and voice data, and the output is emotional state data. Specifically, an emotion analysis algorithm is applied to classify emotions such as stress, anxiety, and joy.

[1338] Step 4:

[1339] The server compares the analyzed skeletal data with a database of past successful athletes to determine the best sport for the child. The input is the skeletal data and the database, and the output is the best sport determination result. Specifically, a matching algorithm is applied to determine the statistically best sport.

[1340] Step 5:

[1341] The server adjusts the notification content to the user based on the judgment result and emotional data. The input is the judgment result and emotional data, and the output is the notification content. Specifically, it takes into account the emotional data and adds positive messages or encouraging words.

[1342] Step 6:

[1343] The server sends the optimal sports judgment result and the adjusted notification content to the user's device. The input is the judgment result and the notification content, and the output is the notification sent to the user's device.

[1344] Step 7:

[1345] In the case of a factory work efficiency optimization system, the server analyzes the movement and emotional data of workers captured by a camera and recommends specific actions to improve efficiency based on skeletal and emotional data. The input is the movement and emotional data of the workers, and the output is recommended actions to improve work efficiency. Specific actions are suggested by applying a recommendation algorithm, such as correcting posture or suggesting breaks.

[1346] Step 8:

[1347] The server notifies the worker of the recommended action in real time to the worker's display terminal. The input is the recommended action, and the output is the notification sent to the worker's display terminal.

[1348] Through the above steps, the system can suggest the most suitable sport while taking into account the user's emotions, thereby improving work efficiency in the factory.

[1349] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1350] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1351] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1352] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1353] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1354] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1355] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1356] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1357] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1358] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1359] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1360] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1361] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1362] 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.

[1363] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1364] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1365] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1366] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1367] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1368] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1369] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1370] The following is further disclosed regarding the above embodiment.

[1371] (Claim 1)

[1372] means for receiving a photograph of the child;

[1373] means for analyzing skeletal data from the received photograph;

[1374] The analyzed skeletal data is compared with a database of successful athletes from the past,

[1375] A means to determine the best sport for your child;

[1376] means for notifying a user of the determination result;

[1377] A system including:

[1378] (Claim 2)

[1379] 2. The system according to claim 1, further comprising a camera function for taking the photograph.

[1380] (Claim 3)

[1381] The system of claim 1 , further comprising a generative AI model that analyzes the skeletal data.

[1382] "Example 1"

[1383] (Claim 1)

[1384] means for receiving an image of the child;

[1385] means for analyzing skeletal information from the received image;

[1386] The analyzed skeletal information is compared with a database of successful athletes from the past,

[1387] A means for determining the best type of exercise for a child;

[1388] a means for notifying the user of the determination result;

[1389] A system including:

[1390] (Claim 2)

[1391] 2. The system according to claim 1, further comprising a photographing device function for photographing the image.

[1392] (Claim 3)

[1393] The system of claim 1 , further comprising a generative AI model that analyzes the skeletal information.

[1394] "Application Example 1"

[1395] (Claim 1)

[1396] means for receiving a photograph of the child;

[1397] means for analyzing skeletal data from the received photograph;

[1398] The analyzed skeletal data is compared with a database of successful athletes from the past,

[1399] A means to determine the best sport for your child;

[1400] means for notifying the user of the judgment result and recommending related products;

[1401] a means for displaying the results via an in-store terminal or visual device;

[1402] A system including:

[1403] (Claim 2)

[1404] 10. The system of claim 1, further comprising a photographing device for taking photographs.

[1405] (Claim 3)

[1406] 10. The system of claim 1, including a generative AI model that analyzes the skeletal data.

[1407] "Example 2: Combining Emotion Engines"

[1408] (Claim 1)

[1409] means for receiving image data of a child;

[1410] A means for analyzing skeletal data from the received image data;

[1411] The analyzed skeletal data is compared with a database of successful athletes from the past,

[1412] A means to determine the best exercise for a child;

[1413] means including an emotion engine for receiving and analyzing voice and facial expression data from a user;

[1414] a means for notifying a user based on the determination result and the analyzed emotion data;

[1415] A system including:

[1416] (Claim 2)

[1417] 2. The system according to claim 1, further comprising an image capturing function for capturing the image data.

[1418] (Claim 3)

[1419] The system of claim 1 , further comprising a generative AI model that analyzes the skeletal data.

[1420] "Application example 2 when combining emotion engines"

[1421] (Claim 1)

[1422] means for receiving a photograph of the child;

[1423] means for analyzing skeletal data from the received photograph;

[1424] The analyzed skeletal data is compared with a database of successful athletes from the past,

[1425] A means to determine the best sport for your child;

[1426] means for notifying a user of the determination result;

[1427] emotion engine means for analyzing emotion data of a user;

[1428] A method for optimizing work efficiency based on optimal sports judgment results and emotion data,

[1429] A system including:

[1430] (Claim 2)

[1431] 2. The system according to claim 1, further comprising a camera function for taking the photograph.

[1432] (Claim 3)

[1433] The system of claim 1 , further comprising a generative AI model that analyzes the skeletal data. [Explanation of symbols]

[1434] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a photograph of the child; means for analyzing skeletal data from the received photograph; The analyzed skeletal data is compared with a database of successful athletes from the past, A means to determine the best sport for your child; means for notifying a user of the determination result; A system including:

2. The system according to claim 1 , further comprising a camera function for taking the photograph.

3. The system of claim 1 , further comprising a generative AI model that analyzes the skeletal data.

Images