system

Abstract

We provide the system. [Solution] Means for acquiring personal characteristic data, A means for calculating the characteristics of appropriate sports equipment based on acquired characteristic data, A means of capturing human motion data, A means for analyzing captured motion data and generating advice for motion improvement, Means for displaying the generated advice, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to improve performance in sports, there is a lack of a system that allows individual players to select the optimal equipment for themselves and receive accurate technical guidance. Also, there is a problem in that conventional methods often provide uniform guidance and it is difficult to customize according to individual characteristics and conditions.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a means for acquiring characteristic data of individual people and calculating the characteristics of optimal sports equipment based on this data. It also provides a system that includes means for filming a person's movements, analyzing the movement data, generating and displaying advice on movement improvement. This system enables the provision of individually customized instruction to each player.

[0006] "Personal characteristic data" refers to information that indicates the physical attributes of a specific individual (e.g., height, weight, age, etc.), and is used to select the most suitable tools and services based on this data.

[0007] "Sports equipment characteristics" refer to the specific specifications and standards (e.g., weight, size, material, etc.) of equipment used in a particular sport, and contribute to optimizing performance.

[0008] "Action data" refers to data that describes the details of actions and behaviors performed by a specific person. This data is analyzed and used for technical guidance and identifying areas for improvement.

[0009] "Movement improvement advice" refers to information that provides specific guidance and suggestions for improving a particular person's movements, based on analyzed movement data.

[0010] A "system" is a set of methods or processes constructed by combining the above means, which integrates the collection, analysis, and display of data according to the purpose. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] The system based on this invention is a system that collects user characteristic data, calculates the characteristics of sports equipment, and further analyzes the user's movements. The following process is followed to implement this system.

[0033] First, the user inputs characteristic data such as height, weight, and age using a device such as a smartphone or tablet. The device then transmits this information to a server via the internet. Based on the received characteristic data, the server uses an AI algorithm to calculate the characteristics of sports equipment suitable for the user. For example, in bowling, the optimal ball weight and a comfortable grip size are suggested.

[0034] Next, the user uses the device's camera function to record their movements. The device sends the recorded video file to the server. The server uses advanced image analysis technology to analyze this video in detail and extract the user's pitching form and movement characteristics. Based on the analysis results, the server generates specific advice for the user on how to improve their movements. This advice may include specific details such as "angle your wrist 10 degrees inward."

[0035] The generated advice is sent back to the device and displayed to the user in an easy-to-understand format. The advice can also include visual guides and diagrams to aid user comprehension. Furthermore, users can use this advice to practice repeatedly and improve their own performance.

[0036] As a concrete example, consider a case where a user goes bowling. When the user inputs their height (180cm) and weight (75kg), the server calculates that a 13-pound ball is suitable and displays this on the user's device. Furthermore, when the user films their bowling form and sends it to the server, the server generates advice such as "Try to keep your gaze more directly forward" and displays it on the user's device.

[0037] In this way, the present invention supports the improvement of user performance by providing specific and personalized advice for improving sports skills.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user enters sports-related characteristic data (e.g., height, weight, age) into the device. The device receives this data and temporarily stores it.

[0041] Step 2:

[0042] The device sends characteristic data it has stored to the server using a secure protocol. The data is sent in a format associated with the user ID.

[0043] Step 3:

[0044] The server analyzes the received characteristic data. An AI algorithm is used to calculate the characteristics of sports equipment suitable for the user. These results are then prepared and sent to the terminal.

[0045] Step 4:

[0046] The terminal displays suggested equipment characteristics received from the server to the user. The user reviews the suggestions and uses them as reference during sports activities.

[0047] Step 5:

[0048] The user records their actions using the device's camera. The device saves the recorded video data locally.

[0049] Step 6:

[0050] The device sends the saved video data to the server. The data is sent along with the user's identification information.

[0051] Step 7:

[0052] The server analyzes the received video. Using image analysis technology, it extracts motion characteristics, and the AI ​​generates advice for improving the motion.

[0053] Step 8:

[0054] The server sends advice it generates to the terminal. This advice includes specific corrections and suggestions for improvement.

[0055] Step 9:

[0056] The terminal displays advice received from the server to the user. The user can then work on improving its operation by following the advice.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In recent years, there has been a growing demand for systems that support the selection of sports equipment tailored to individual characteristics and effective exercise improvement. However, conventional systems have struggled to calculate the characteristics of equipment that are individually suitable for each user and to provide specific advice on improving movement. Furthermore, they have faced challenges in adequately addressing the individual needs of each user.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for acquiring a person's characteristic data, means for calculating the characteristics of appropriate equipment using a generation AI model based on the acquired characteristic data, and means for analyzing captured motion data and generating advice on motion improvement using digital analysis technology. This makes it possible to recommend equipment suitable for the individual characteristics of the user and to provide specific improvement advice tailored to each individual's movements.

[0062] "Personal characteristic data" refers to the user's physical information such as height, weight, and age, and is used for calculating the characteristics of sports equipment and analyzing movements.

[0063] A "generative AI model" refers to an artificial intelligence algorithm used to calculate and generate optimal equipment characteristics and advice for improving performance, based on acquired characteristic data.

[0064] "Motion data" refers to data acquired by recording a user's movements and physical actions as video and then digitally analyzing that video.

[0065] "Digital analysis technology" refers to techniques that analyze captured motion data to extract motion characteristics and generate advice for improvement.

[0066] "Advice generation" refers to the process of creating specific advice that helps improve user behavior using digital analysis technology.

[0067] "Calculating characteristics" refers to calculating the optimal specifications for sports equipment and gear for a user based on the acquired characteristic data of that person.

[0068] This invention is a system that collects user characteristic data, calculates the characteristics of appropriate sports equipment based on that data, and further analyzes the user's movements in detail. This system mainly consists of a server, terminals, and users.

[0069] First, the user inputs their personal data (height, weight, age, etc.) using a device such as a smartphone or tablet. This data is transmitted to the server via the internet. Any widely available, general-purpose computer device can be used. The server processes the initially received data and uses a generative AI model to calculate the characteristics of equipment suitable for the user. This model uses machine learning algorithms to perform the necessary data processing quickly and efficiently.

[0070] Next, the user uses the device's camera function to film their sports movements. The recorded video file is sent from the device to the server. The server uses digital analysis technology to analyze the received video in detail and extract the characteristics of the user's movements. In particular, it evaluates form, angles, and rhythm of movement, and generates advice on specific areas for improvement.

[0071] The generated advice is sent back to the device and displayed in an easy-to-understand format for the user. Visual guides and diagrams are added to help users intuitively understand the advice. Based on this advice, users can practice repeatedly and improve their performance.

[0072] For example, if a user is bowling and enters data such as height 180cm and weight 75kg, the server will recommend using a 13-pound ball and, by filming and analyzing the user's movements, provide specific advice such as "angle your wrist 10 degrees inward."

[0073] Examples of prompts include requests such as, "What is the optimal ball weight for a bowler who is 180cm tall and weighs 75kg?" or "Generate the best advice for improving my bowling form." This allows the present invention to achieve personalized improvements to sports performance for each user.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user inputs their personal data using a terminal. Specifically, they fill in items such as height, weight, and age using a dedicated application screen. The terminal verifies that the entered data is in the correct format and confirms its integrity. The input consists of the user's physical numerical data, which is then organized and prepared for transmission to the server.

[0077] Step 2:

[0078] The terminal sends characteristic data obtained from the user to the server. An encryption protocol is applied here to ensure the data is transmitted securely. The output is encrypted characteristic data for transmission to the server. The server receives this data and converts it into a format that can be analyzed within the system.

[0079] Step 3:

[0080] The server inputs the received characteristic data into a generating AI model to calculate the characteristics of the optimal tool for the user. In this process, the AI ​​model refers to past performance data and uses statistical methods to find the optimal solution. The output is the calculated characteristics of the optimal sports equipment, and a recommendation result such as "a 13-pound bowling ball is suitable" is generated.

[0081] Step 4:

[0082] The user uses the device's camera function to record their actions. For video recording, the user sets the frame rate and image quality to ensure that no actions are missed. A video file is generated as input, which is then used for later analysis.

[0083] Step 5:

[0084] The device sends the captured video file to the server. Here too, an encryption protocol is applied for secure data transmission. The output is encrypted video data sent to the server, which then decodes this data into an analyzable format.

[0085] Step 6:

[0086] The server applies digital analysis techniques to the received video data to extract motion features. For example, computer vision algorithms are used to analyze the angle and rhythm of a pitch in detail. Video data serves as input, and the output includes motion patterns and areas for improvement as a result of the analysis.

[0087] Step 7:

[0088] The server generates specific suggestions for improving user behavior based on the analysis results. The generated AI model is reused in this process. The generated suggestions include specific details such as "angle your wrist 10 degrees inward." The final output is sent to the user's device as a suggested improvement.

[0089] Step 8:

[0090] The terminal displays advice sent from the server in an easy-to-understand manner for the user. Visual guides and diagrams are added to make the advice intuitively easy to understand. Users can then practice repeatedly based on the advice they receive.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Traditional methods for selecting sports equipment and improving movement have the drawback of not adequately considering the individual characteristics and movement patterns of each person when providing personalized advice. Furthermore, these improvement suggestions are rarely presented in a visually and audibly easy-to-understand format. Under these circumstances, there is a need for concrete and effective support for each individual to improve their own athletic ability.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for acquiring a person's characteristic data, means for analyzing the captured motion data and generating advice for motion improvement, and means for outputting the generated advice as audio and presenting it as a visual guideline. This makes it possible to select the optimal sports equipment based on each individual's characteristics, and furthermore, to provide advice for individual motion improvement in a visually and audibly easy-to-understand format.

[0096] "Personal characteristic data" refers to information about an individual person's height, weight, age, and other individual physiological or physical attributes.

[0097] "Sports equipment characteristics" refer to the attributes of specific sports equipment, such as weight, size, shape, and material, that are optimal for the user.

[0098] "Action data" refers to digitized information that represents a series of actions performed physically by a person.

[0099] "Movement improvement advice" refers to specific and actionable advice for improving performance, based on the results of an analysis of a person's movement data.

[0100] "Displaying generated advice" means showing the content of the advice to the user visually through the screen or projection function of a digital device.

[0101] "Outputting audio" refers to the act of conveying linguistic information audibly using sound devices such as speakers or headphones.

[0102] "Presenting as a visual guideline" means displaying visual resources such as images, videos, and animations to make the advice easier to understand.

[0103] To implement this invention, the user first inputs their characteristic data into a device such as a smartphone or tablet. For example, they are required to provide information such as height, weight, and age. The device has a function to send this characteristic data to a server, where it is processed. The server uses an AI algorithm to calculate the optimal characteristics of sports equipment based on the received characteristic data. Specifically, it uses an AI framework such as TENSORFLOW® to score the weight and size of equipment suitable for the user and makes suggestions based on that.

[0104] Next, the user uses the device's camera to record their movements. For example, they can record their bowling throwing form on video. The recorded video is sent from the device to the server, where it is analyzed in detail using image analysis software such as OpenCV. This analysis extracts the characteristics of the user's movements and generates specific advice for improving those movements. This advice is presented to the user as audio and visual guidelines. For example, advice such as "angle your wrist 10 degrees inward" or "try to keep your gaze more forward" is sent from the server to the device and displayed.

[0105] For example, after acquiring motion data from a user who is 180cm tall and weighs 75kg while bowling, the server suggests that a 13-pound ball would be suitable. Furthermore, based on video analysis of the bowling form, improvement advice is provided, such as "turn your wrist 10 degrees inward." This advice is displayed on the screen and also provided via audio output, allowing the user to receive it through both sight and sound.

[0106] An example of a prompt using a generative AI model would be: "Please suggest the best bowling ball and provide suggestions for improving the bowling form of a 180cm, 75kg, 35-year-old male."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user inputs their personal data into the device. This input includes personal attribute data such as height, weight, and age. The device converts this data into a digital format and sends it to the server via the internet. This allows the server to receive the user's personal data as input.

[0110] Step 2:

[0111] The server supplies the received characteristic data to an AI algorithm to calculate the optimal characteristics of the sports equipment. In this case, the AI ​​algorithm performs calculations to score the optimal weight and size of the equipment based on the input data. As output, the characteristics of the sports equipment best suited to the user are generated and sent back to the terminal.

[0112] Step 3:

[0113] The user records their actions using the device's camera. For example, they can record their bowling throw as a video. This video data is saved to the device's storage and sent to a server via the internet. This prepares the server to receive the action data as input.

[0114] Step 4:

[0115] The server analyzes the received video data in detail using image analysis software such as OpenCV. The input video data is broken down frame by frame and analyzed to extract motion characteristics. This allows the server to output motion characteristics as digital information and generate specific advice for motion improvement.

[0116] Step 5:

[0117] The advice generated on the server is sent to the terminal and displayed as visual guidelines. Furthermore, it is possible to output the advice as audio using a speech synthesis system. This allows the user to receive specific instructions for improving their performance through both auditory and visual means.

[0118] Step 6:

[0119] Users perform repetitive practice to improve their movements, following visual and audio guidance provided by the server. Based on the feedback received, users can adjust their form and movements to improve their skills.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] The system according to the present invention acquires user characteristic data, motion data, and user emotional data, and comprehensively analyzes them to provide more precise sports instruction. The system according to the present invention is implemented through the following process.

[0122] First, the user inputs characteristic data such as height and weight using a device. The device sends this data to a server, which calculates the characteristics of sports equipment based on the characteristic data. In addition, the user films their movements through a camera. The filmed video is sent to the server by the device, and the server analyzes the movement data in detail to generate appropriate advice for improving movements.

[0123] Furthermore, this system incorporates an emotion engine. The terminal acquires user emotion data using user input feedback and facial recognition technology. This emotion data is sent to a server, where it is integrated with other data and analyzed.

[0124] In addition to the usual analysis of behavioral data, the server performs adjustments that take emotional data into account to generate advice tailored to the user's current mental state. Specifically, if the user is stressed, the advice is adjusted to be more relaxing, and if the user is agitated, the advice is changed to help them exert their energy more effectively.

[0125] The generated advice is sent to the device, where the user can review it and use it to improve their technique. For example, when a user is trying to learn a new bowling technique, the system can provide standard motion advice such as "Focus on making your left hand movement smoother," but it can also add emotion-based advice such as "Take a deep breath and relax before throwing" if the user's face appears tense.

[0126] Thus, the present invention is a system that uses emotional data to provide users with more personalized guidance and support the improvement of their sports performance.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The user uses the device to input characteristic data such as height, weight, and age. The device saves the entered data locally.

[0130] Step 2:

[0131] The device sends the stored characteristic data to the server via the internet. The server receives the characteristic data and calculates the characteristics of the sports equipment based on that data.

[0132] Step 3:

[0133] The server uses an AI algorithm to analyze the received characteristic data and calculate the optimal characteristics of sports equipment for the user. The calculated results are then sent to the terminal.

[0134] Step 4:

[0135] The device displays characteristic information about sports equipment to the user. The user reviews this information and uses it to improve their sports activities.

[0136] Step 5:

[0137] The user uses the device's camera function to record their own actions. The device saves the recorded video.

[0138] Step 6:

[0139] The device sends the saved video data to the server. The server receives the video data and prepares to perform a detailed motion analysis.

[0140] Step 7:

[0141] The server analyzes the video data and extracts the characteristics of the user's actions. It then prepares to generate optimal improvement suggestions using AI.

[0142] Step 8:

[0143] The device captures the user's facial expressions with its camera and uses an emotion engine to recognize the user's emotions. This data is then sent to a server.

[0144] Step 9:

[0145] The server combines emotional data with behavioral analysis results. Based on the user's emotional state, it adjusts behavioral improvement suggestions and generates personalized advice.

[0146] Step 10:

[0147] The server sends generated advice to the terminal. The terminal displays the advice to the user. The user receives the advice and improves their performance by putting it into practice.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] Traditional sports coaching systems are primarily limited to feedback based on physical characteristics and movement data, and do not adequately provide personalized instruction that takes into account the user's emotions and mental state. Therefore, it has been difficult to effectively address the stress and motivational changes experienced by users. Consequently, there is a need to develop a system that integrates user emotional data to provide more precise and effective exercise instruction.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for acquiring characteristic information of a person, means for acquiring emotional information using facial recognition technology and feedback, and means for integrating the emotional information and generating advice based on the person's mental state. This makes it possible to provide precise guidance for improving actions that takes the user's emotional state into account.

[0153] "Personal characteristic information" refers to the basic physical specifications of the person in question, such as height, weight, and age, and is data that represents the individual's characteristics.

[0154] "The characteristics of sports equipment" refer to the detailed attributes and specifications of equipment used in sports and fitness activities, including specifications optimized for the physical characteristics of a particular individual.

[0155] "Motion information" refers to data that represents the physical activities and movement characteristics of a person in question, and is acquired through video analysis or sensors.

[0156] "Facial recognition technology" is a computer vision technology that analyzes emotional and psychological states from a person's facial expressions, and often uses machine learning.

[0157] "Emotional information" refers to data that represents a person's emotions and mental state, and is obtained through facial recognition technology and user-inputted feedback.

[0158] "Movement improvement advice" refers to specific suggestions or recommendations for a person to improve or optimize their movements, based on the analyzed movement information.

[0159] "Mental state-based advice" refers to suggestions that encourage behavioral improvement in a way that is most appropriate to a person's current emotions and mental state, based on data analysis that includes emotional information.

[0160] The system according to this invention aims to provide integrated feedback based on the user's physical characteristics, movements, and emotional state by combining multiple modules.

[0161] terminal

[0162] The user first inputs their personal information using the terminal. The terminal has a standard input interface and can input basic information such as height, weight, and age. The terminal transmits this information to the server via a network such as Wi-Fi or LTE. In addition, the terminal is equipped with a camera that captures the user's movement data. For example, if the user is practicing tennis swings, the movement can be recorded from various angles. The terminal is also equipped with software that uses facial recognition technology to obtain emotional information from the user's facial expressions.

[0163] server

[0164] The server processes the received characteristic information and performs calculations to determine the optimal characteristics of the exercise equipment. Using programming languages ​​such as Python and R, it analyzes motion information and generates specific improvement suggestions for the user. Furthermore, it implements a process that takes emotional information into account to generate advice tailored to the user's mental state. This analysis utilizes machine learning algorithms and generative AI models to improve the accuracy of the feedback.

[0165] Specific example

[0166] For example, video data captured by the device is analyzed on the server, and standard feedback such as "improve the angle of your arm when serving" is generated. At the same time, if tension is detected by the facial recognition function, emotion-based advice such as "take a deep breath and relax your shoulders" is added. This allows users to receive more personalized feedback.

[0167] An example of a prompt for a generative AI model is, "Explain how to provide emotionally appropriate advice to a user who is trying to learn a new basketball shooting technique." By utilizing such prompts, generative AI models can provide more advanced analysis and support.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] Users input their basic personal information (height, weight, age, etc.) through a terminal. This information forms the basis for personalized feedback within the system. This input data is immediately transmitted from the terminal to the server.

[0171] Step 2:

[0172] The device uses its camera to record the user's actions. Users can record, for example, their tennis swing or running form. This video data is used for analysis to improve their performance. The recorded video is first converted to the required format within the device, compressed, and then sent to the server.

[0173] Step 3:

[0174] The device further analyzes the user's facial features using facial recognition technology and acquires emotional information. In this step, variations in emotion are extracted from the user's facial expressions in real time and processed as digital data. The acquired emotional information is integrated with other data and sent to the server.

[0175] Step 4:

[0176] The server receives characteristic information, motion data, and emotion information sent from the terminal and begins integrated data analysis. Specifically, it uses programming tools such as Python and R to analyze each dataset using machine learning algorithms. Here, characteristic information is used to optimize the exercise equipment, motion data is analyzed to derive improvement measures, and emotion information is used to...

[0177] This is taken into consideration to reduce tracing and tension.

[0178] Step 5:

[0179] The server generates specific advice for performance improvement based on the data analysis results. This may include minor form adjustments or advice for specific technical improvements. Furthermore, advice tailored to the user's mental state is added based on emotional information. All advice data is formatted and then sent to the terminal.

[0180] Step 6:

[0181] The device displays advice received from the server to the user. At this stage, the feedback is presented in a visually easy-to-understand format, ensuring that the user can immediately put it into practice. The user can then use this to take action to improve their own behavior and mental state.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] In recent years, interest in exercise and fitness has increased, and there is a demand for exercise guidance optimized for each individual. However, conventional systems do not provide guidance that takes into account the emotional state of the user, resulting in the problem that the effects of exercise are not maximized. Guidance that takes into account the individual differences of users is necessary, but a systematic analytical approach that includes information based on emotions has been insufficient to achieve this.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for acquiring a person's characteristic data, means for recording a person's movement data, and means for acquiring a person's emotional data. This makes it possible to comprehensively analyze the user's characteristic data, movement data, and emotional data, and provide personalized guidance, thereby maximizing the effectiveness of exercise tailored to each user.

[0187] "Personal characteristic data" refers to information that expresses a person's physical characteristics, such as height and weight, as numerical values ​​or attributes.

[0188] "Sports equipment characteristics" refers to information that indicates attributes such as the shape and structure of equipment suitable for a specific sports activity, based on individual characteristic data.

[0189] "Motion data" refers to information that records a person's body movements, and specifically includes the timeline and location information of those movements.

[0190] "Instructions" refer to advice and guidelines based on behavioral and emotional data, used to guide improvements in a person's actions and mental approach.

[0191] "Emotional data" refers to information that quantifies or attributes the emotional state of a person, analyzed from their facial expressions and voice.

[0192] "Individualized instructions" refer to specific guidance tailored to an individual, based on an integrated analysis of their characteristics, behaviors, and emotional data.

[0193] "Presentation" is the act or process of communicating generated instructions to a person through visual or auditory means.

[0194] To implement this invention, the user must first input characteristic data via a terminal. This characteristic data includes basic physical information such as height and weight. This data is transmitted to a server via a network. The server analyzes the received characteristic data and calculates the characteristics of the sports equipment.

[0195] Next, the user records their movements using the camera. The recorded movement data is sent from the device to the server, where the server analyzes the movements. The analysis uses a machine learning model based on TensorFlow to recognize movement patterns and problems. Based on the results, instructions for improving the movements are generated for the user.

[0196] Furthermore, facial recognition technology via the device is used to acquire emotional data. Machine learning frameworks such as TensorFlow and PyTorch are used for facial recognition and voice analysis. The emotional data is used to evaluate the user's current mental state and to correct behavioral improvement instructions into personalized instructions.

[0197] The generated personalized instructions are presented to the user via the device. This allows the user to receive not only improved behavior but also advice optimized according to their emotional state.

[0198] As a concrete example, consider a scenario where a user is trying to learn a new yoga pose. In addition to the usual instruction, "Relax your shoulders and maintain the posture," the server can also generate additional instructions such as "Take a deep breath and relax" if it detects tension in the user's face.

[0199] An example of a prompt when using a generative AI model is: "Analyze the user's facial expressions while they are practicing yoga and generate advice to promote relaxation."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user enters characteristic data into the terminal. The entered data includes physical information such as height and weight. The terminal digitizes this information and sends it to the server.

[0203] Step 2:

[0204] The server analyzes the received characteristic data. Using databases and AI models, it calculates the characteristics of sports equipment best suited to each individual. The results are output and stored internally on the server.

[0205] Step 3:

[0206] The user uses the camera to record their actions. The recorded video data is saved on the device and sent to the server as action data.

[0207] Step 4:

[0208] The server analyzes the received operational data. This analysis uses TensorFlow to extract user behavior patterns and problems. The analysis results are output as basic data for generating instructions to improve the operation.

[0209] Step 5:

[0210] The device uses facial recognition technology to acquire the user's emotional data. The acquired facial and voice data are converted into specific emotional states through emotion analysis, and this is sent to the server as emotional data.

[0211] Step 6:

[0212] The server analyzes emotional data and incorporates it into instructions for improving behavior. Based on the emotional data, the generated instructions are refined into personalized instructions. This results in advice optimized for the user's current mental state.

[0213] Step 7:

[0214] The server sends individually generated instructions to the terminal. The terminal presents these instructions to the user, who then reviews them and uses them to improve their own operation.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] The system based on this invention is a system that collects user characteristic data, calculates the characteristics of sports equipment, and further analyzes the user's movements. The following process is followed to implement this system.

[0232] First, the user inputs characteristic data such as height, weight, and age using a device such as a smartphone or tablet. The device then transmits this information to a server via the internet. Based on the received characteristic data, the server uses an AI algorithm to calculate the characteristics of sports equipment suitable for the user. For example, in bowling, the optimal ball weight and a comfortable grip size are suggested.

[0233] Next, the user uses the device's camera function to record their movements. The device sends the recorded video file to the server. The server uses advanced image analysis technology to analyze this video in detail and extract the user's pitching form and movement characteristics. Based on the analysis results, the server generates specific advice for the user on how to improve their movements. This advice may include specific details such as "angle your wrist 10 degrees inward."

[0234] The generated advice is sent back to the device and displayed to the user in an easy-to-understand format. The advice can also include visual guides and diagrams to aid user comprehension. Furthermore, users can use this advice to practice repeatedly and improve their own performance.

[0235] As a concrete example, consider a case where a user goes bowling. When the user inputs their height (180cm) and weight (75kg), the server calculates that a 13-pound ball is suitable and displays this on the user's device. Furthermore, when the user films their bowling form and sends it to the server, the server generates advice such as "Try to keep your gaze more directly forward" and displays it on the user's device.

[0236] In this way, the present invention supports the improvement of user performance by providing specific and personalized advice for improving sports skills.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user enters sports-related characteristic data (e.g., height, weight, age) into the device. The device receives this data and temporarily stores it.

[0240] Step 2:

[0241] The device sends characteristic data it has stored to the server using a secure protocol. The data is sent in a format associated with the user ID.

[0242] Step 3:

[0243] The server analyzes the received characteristic data. An AI algorithm is used to calculate the characteristics of sports equipment suitable for the user. These results are then prepared and sent to the terminal.

[0244] Step 4:

[0245] The terminal displays suggested equipment characteristics received from the server to the user. The user reviews the suggestions and uses them as reference during sports activities.

[0246] Step 5:

[0247] The user records their actions using the device's camera. The device saves the recorded video data locally.

[0248] Step 6:

[0249] The device sends the saved video data to the server. The data is sent along with the user's identification information.

[0250] Step 7:

[0251] The server analyzes the received video. Using image analysis technology, it extracts motion characteristics, and the AI ​​generates advice for improving the motion.

[0252] Step 8:

[0253] The server sends advice it generates to the terminal. This advice includes specific corrections and suggestions for improvement.

[0254] Step 9:

[0255] The terminal displays advice received from the server to the user. The user can then work on improving its operation by following the advice.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In recent years, there has been a growing demand for systems that support the selection of sports equipment tailored to individual characteristics and effective exercise improvement. However, conventional systems have struggled to calculate the characteristics of equipment that are individually suitable for each user and to provide specific advice on improving movement. Furthermore, they have faced challenges in adequately addressing the individual needs of each user.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for acquiring a person's characteristic data, means for calculating the characteristics of appropriate equipment using a generation AI model based on the acquired characteristic data, and means for analyzing captured motion data and generating advice on motion improvement using digital analysis technology. This makes it possible to recommend equipment suitable for the individual characteristics of the user and to provide specific improvement advice tailored to each individual's movements.

[0261] "Personal characteristic data" refers to the user's physical information such as height, weight, and age, and is used for calculating the characteristics of sports equipment and analyzing movements.

[0262] A "generative AI model" refers to an artificial intelligence algorithm used to calculate and generate optimal equipment characteristics and advice for improving performance, based on acquired characteristic data.

[0263] "Motion data" refers to data acquired by recording a user's movements and physical actions as video and then digitally analyzing that video.

[0264] "Digital analysis technology" refers to techniques that analyze captured motion data to extract motion characteristics and generate advice for improvement.

[0265] "Advice generation" refers to the process of creating specific advice that helps improve user behavior using digital analysis technology.

[0266] "Calculating characteristics" refers to calculating the optimal specifications for sports equipment and gear for a user based on the acquired characteristic data of that person.

[0267] This invention is a system that collects user characteristic data, calculates the characteristics of appropriate sports equipment based on that data, and further analyzes the user's movements in detail. This system mainly consists of a server, terminals, and users.

[0268] First, the user inputs their personal data (height, weight, age, etc.) using a device such as a smartphone or tablet. This data is transmitted to the server via the internet. Any widely available, general-purpose computer device can be used. The server processes the initially received data and uses a generative AI model to calculate the characteristics of equipment suitable for the user. This model uses machine learning algorithms to perform the necessary data processing quickly and efficiently.

[0269] Next, the user uses the device's camera function to film their sports movements. The recorded video file is sent from the device to the server. The server uses digital analysis technology to analyze the received video in detail and extract the characteristics of the user's movements. In particular, it evaluates form, angles, and rhythm of movement, and generates advice on specific areas for improvement.

[0270] The generated advice is sent back to the device and displayed in an easy-to-understand format for the user. Visual guides and diagrams are added to help users intuitively understand the advice. Based on this advice, users can practice repeatedly and improve their performance.

[0271] For example, if a user is bowling and enters data such as height 180cm and weight 75kg, the server will recommend using a 13-pound ball and, by filming and analyzing the user's movements, provide specific advice such as "angle your wrist 10 degrees inward."

[0272] Examples of prompts include requests such as, "What is the optimal ball weight for a bowler who is 180cm tall and weighs 75kg?" or "Generate the best advice for improving my bowling form." This allows the present invention to achieve personalized improvements to sports performance for each user.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user inputs their personal data using a terminal. Specifically, they fill in items such as height, weight, and age using a dedicated application screen. The terminal verifies that the entered data is in the correct format and confirms its integrity. The input consists of the user's physical numerical data, which is then organized and prepared for transmission to the server.

[0276] Step 2:

[0277] The terminal sends characteristic data obtained from the user to the server. An encryption protocol is applied here to ensure the data is transmitted securely. The output is encrypted characteristic data for transmission to the server. The server receives this data and converts it into a format that can be analyzed within the system.

[0278] Step 3:

[0279] The server inputs the received characteristic data into the generative AI model to calculate the optimal tool characteristics for the user. In this process, the AI model refers to past performance data and uses statistical methods to find the optimal solution. The output includes the calculated optimal characteristics of the sports equipment, and for example, a recommendation result such as "A 13-pound bowling ball is suitable" is generated.

[0280] Step 4:

[0281] The user uses the camera function of the terminal to capture their own movements. When shooting a video, set the frame rate and image quality so as not to miss the movements to be used. As input, a video file is generated and this is used for subsequent analysis.

[0282] Step 5:

[0283] The terminal sends the captured video file to the server. Here too, an encryption protocol for secure data transmission is applied. The output is the encrypted video data passed to the server, and the server decodes this data into a form that can be analyzed.

[0284] Step 6:

[0285] The server applies digital analysis techniques to the received video data to extract motion characteristics. For example, use computer vision algorithms to analyze in detail the angle and rhythm of a pitch. The video data serves as the input, and the output includes the motion pattern and areas for improvement as the analysis result.

[0286] Step 7:

[0287] The server generates specific suggestions for improving user behavior based on the analysis results. The generated AI model is reused in this process. The generated suggestions include specific details such as "angle your wrist 10 degrees inward." The final output is sent to the user's device as a suggested improvement.

[0288] Step 8:

[0289] The terminal displays advice sent from the server in an easy-to-understand manner for the user. Visual guides and diagrams are added to make the advice intuitively easy to understand. Users can then practice repeatedly based on the advice they receive.

[0290] (Application Example 1)

[0291] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0292] Traditional methods for selecting sports equipment and improving movement have the drawback of not adequately considering the individual characteristics and movement patterns of each person when providing personalized advice. Furthermore, these improvement suggestions are rarely presented in a visually and audibly easy-to-understand format. Under these circumstances, there is a need for concrete and effective support for each individual to improve their own athletic ability.

[0293] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes means for acquiring a person's characteristic data, means for analyzing the captured motion data and generating advice for motion improvement, and means for outputting the generated advice as audio and presenting it as a visual guideline. This makes it possible to select the optimal sports equipment based on each individual's characteristics, and furthermore, to provide advice for individual motion improvement in a visually and audibly easy-to-understand format.

[0295] "Personal characteristic data" refers to information about an individual person's height, weight, age, and other individual physiological or physical attributes.

[0296] "Sports equipment characteristics" refer to the attributes of specific sports equipment, such as weight, size, shape, and material, that are optimal for the user.

[0297] "Action data" refers to digitized information that represents a series of actions performed physically by a person.

[0298] "Movement improvement advice" refers to specific and actionable advice for improving performance, based on the results of an analysis of a person's movement data.

[0299] "Displaying generated advice" means showing the content of the advice to the user visually through the screen or projection function of a digital device.

[0300] "Outputting audio" refers to the act of conveying linguistic information audibly using sound devices such as speakers or headphones.

[0301] "Presenting as a visual guideline" means displaying visual resources such as images, videos, and animations to make the advice easier to understand.

[0302] To implement this invention, the user first inputs their characteristic data into a device such as a smartphone or tablet. For example, they are required to provide information such as height, weight, and age. The device has a function to send this characteristic data to a server, where it is processed. The server uses an AI algorithm to calculate the characteristics of the optimal sports equipment based on the received characteristic data. Specifically, it uses an AI framework such as TensorFlow to score the weight and size of equipment that is suitable for the user and makes suggestions based on that.

[0303] Next, the user uses the camera of the terminal to shoot their own actions. For example, the bowling throwing form can be recorded as a video. The captured video is sent from the terminal to the server, and the server analyzes the video in detail using image analysis software such as OpenCV. Through this analysis, the characteristics of the user's actions are extracted, and specific advice for improving the actions is generated. This advice is presented to the user as audio and visual guidelines. For example, advice such as "Turn the wrist angle 10 degrees inward" or "Keep the line of sight more straight ahead" is sent from the server to the terminal and displayed.

[0304] Taking a specific example, after obtaining the action data of a user with a height of 180 cm and a weight of 75 kg when bowling, the server proposes that a 13-pound ball is suitable. Also, based on the video analysis of the throwing form, improvement advice such as "It is good to turn the wrist 10 degrees inward" is provided. This advice is not only displayed on the display but also provided as an audio output, and the user can receive it through both vision and hearing.

[0305] As an example of the prompt sentence using the generation AI model, a form such as "Tell me the optimal ball suggestion and the improvement points of the throwing form when a 180 cm, 75 kg, 35-year-old male bowls." can be considered.

[0306] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] The user inputs their characteristic data into the terminal. The input includes personal attribute data such as height, weight, and age. The terminal converts this data into a digital format and sends it to the server via the Internet. Thereby, the server can receive the user's characteristic data as input.

[0309] Step 2:

[0310] The server supplies the received characteristic data to an AI algorithm to calculate the optimal characteristics of the sports equipment. In this case, the AI ​​algorithm performs calculations to score the optimal weight and size of the equipment based on the input data. As output, the characteristics of the sports equipment best suited to the user are generated and sent back to the terminal.

[0311] Step 3:

[0312] The user records their actions using the device's camera. For example, they can record their bowling throw as a video. This video data is saved to the device's storage and sent to a server via the internet. This prepares the server to receive the action data as input.

[0313] Step 4:

[0314] The server analyzes the received video data in detail using image analysis software such as OpenCV. The input video data is broken down frame by frame and analyzed to extract motion characteristics. This allows the server to output motion characteristics as digital information and generate specific advice for motion improvement.

[0315] Step 5:

[0316] The advice generated on the server is sent to the terminal and displayed as visual guidelines. Furthermore, it is possible to output the advice as audio using a speech synthesis system. This allows the user to receive specific instructions for improving their performance through both auditory and visual means.

[0317] Step 6:

[0318] Users perform repetitive practice to improve their movements, following visual and audio guidance provided by the server. Based on the feedback received, users can adjust their form and movements to improve their skills.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] The system according to the present invention acquires user characteristic data, motion data, and user emotional data, and comprehensively analyzes them to provide more precise sports instruction. The system according to the present invention is implemented through the following process.

[0321] First, the user inputs characteristic data such as height and weight using a device. The device sends this data to a server, which calculates the characteristics of sports equipment based on the characteristic data. In addition, the user films their movements through a camera. The filmed video is sent to the server by the device, and the server analyzes the movement data in detail to generate appropriate advice for improving movements.

[0322] Furthermore, this system incorporates an emotion engine. The terminal acquires user emotion data using user input feedback and facial recognition technology. This emotion data is sent to a server, where it is integrated with other data and analyzed.

[0323] In addition to the usual analysis of behavioral data, the server performs adjustments that take emotional data into account to generate advice tailored to the user's current mental state. Specifically, if the user is stressed, the advice is adjusted to be more relaxing, and if the user is agitated, the advice is changed to help them exert their energy more effectively.

[0324] The generated advice is sent to the device, where the user can review it and use it to improve their technique. For example, when a user is trying to learn a new bowling technique, the system can provide standard motion advice such as "Focus on making your left hand movement smoother," but it can also add emotion-based advice such as "Take a deep breath and relax before throwing" if the user's face appears tense.

[0325] Thus, the present invention is a system that uses emotional data to provide users with more personalized guidance and support the improvement of their sports performance.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user uses the device to input characteristic data such as height, weight, and age. The device saves the entered data locally.

[0329] Step 2:

[0330] The device sends the stored characteristic data to the server via the internet. The server receives the characteristic data and calculates the characteristics of the sports equipment based on that data.

[0331] Step 3:

[0332] The server uses an AI algorithm to analyze the received characteristic data and calculate the optimal characteristics of sports equipment for the user. The calculated results are then sent to the terminal.

[0333] Step 4:

[0334] The device displays characteristic information about sports equipment to the user. The user reviews this information and uses it to improve their sports activities.

[0335] Step 5:

[0336] The user uses the device's camera function to record their own actions. The device saves the recorded video.

[0337] Step 6:

[0338] The device sends the saved video data to the server. The server receives the video data and prepares to perform a detailed motion analysis.

[0339] Step 7:

[0340] The server analyzes the video data and extracts the characteristics of the user's actions. It then prepares to generate optimal improvement suggestions using AI.

[0341] Step 8:

[0342] The device captures the user's facial expressions with its camera and uses an emotion engine to recognize the user's emotions. This data is then sent to a server.

[0343] Step 9:

[0344] The server combines emotional data with behavioral analysis results. Based on the user's emotional state, it adjusts behavioral improvement suggestions and generates personalized advice.

[0345] Step 10:

[0346] The server sends generated advice to the terminal. The terminal displays the advice to the user. The user receives the advice and improves their performance by putting it into practice.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] Traditional sports coaching systems are primarily limited to feedback based on physical characteristics and movement data, and do not adequately provide personalized instruction that takes into account the user's emotions and mental state. Therefore, it has been difficult to effectively address the stress and motivational changes experienced by users. Consequently, there is a need to develop a system that integrates user emotional data to provide more precise and effective exercise instruction.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes means for acquiring characteristic information of a person, means for acquiring emotional information using facial recognition technology and feedback, and means for integrating the emotional information and generating advice based on the person's mental state. This makes it possible to provide precise guidance for improving actions that takes the user's emotional state into account.

[0352] "Personal characteristic information" refers to the basic physical specifications of the person in question, such as height, weight, and age, and is data that represents the individual's characteristics.

[0353] "The characteristics of sports equipment" refer to the detailed attributes and specifications of equipment used in sports and fitness activities, including specifications optimized for the physical characteristics of a particular individual.

[0354] "Motion information" refers to data that represents the physical activities and movement characteristics of a person in question, and is acquired through video analysis or sensors.

[0355] "Facial recognition technology" is a computer vision technology that analyzes emotional and psychological states from a person's facial expressions, and often uses machine learning.

[0356] "Emotional information" refers to data that represents a person's emotions and mental state, and is obtained through facial recognition technology and user-inputted feedback.

[0357] "Movement improvement advice" refers to specific suggestions or recommendations for a person to improve or optimize their movements, based on the analyzed movement information.

[0358] "Mental state-based advice" refers to suggestions that encourage behavioral improvement in a way that is most appropriate to a person's current emotions and mental state, based on data analysis that includes emotional information.

[0359] The system according to this invention aims to provide integrated feedback based on the user's physical characteristics, movements, and emotional state by combining multiple modules.

[0360] terminal

[0361] The user first inputs their personal information using the terminal. The terminal has a standard input interface and can input basic information such as height, weight, and age. The terminal transmits this information to the server via a network such as Wi-Fi or LTE. In addition, the terminal is equipped with a camera that captures the user's movement data. For example, if the user is practicing tennis swings, the movement can be recorded from various angles. The terminal is also equipped with software that uses facial recognition technology to obtain emotional information from the user's facial expressions.

[0362] server

[0363] The server processes the received characteristic information and performs calculations to determine the optimal characteristics of the exercise equipment. Using programming languages ​​such as Python and R, it analyzes motion information and generates specific improvement suggestions for the user. Furthermore, it implements a process that takes emotional information into account to generate advice tailored to the user's mental state. This analysis utilizes machine learning algorithms and generative AI models to improve the accuracy of the feedback.

[0364] Specific example

[0365] For example, video data captured by the device is analyzed on the server, and standard feedback such as "improve the angle of your arm when serving" is generated. At the same time, if tension is detected by the facial recognition function, emotion-based advice such as "take a deep breath and relax your shoulders" is added. This allows users to receive more personalized feedback.

[0366] An example of a prompt for a generative AI model is, "Explain how to provide emotionally appropriate advice to a user who is trying to learn a new basketball shooting technique." By utilizing such prompts, generative AI models can provide more advanced analysis and support.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] Users input their basic personal information (height, weight, age, etc.) through a terminal. This information forms the basis for personalized feedback within the system. This input data is immediately transmitted from the terminal to the server.

[0370] Step 2:

[0371] The device uses its camera to record the user's actions. Users can record, for example, their tennis swing or running form. This video data is used for analysis to improve their performance. The recorded video is first converted to the required format within the device, compressed, and then sent to the server.

[0372] Step 3:

[0373] The device further analyzes the user's facial features using facial recognition technology and acquires emotional information. In this step, variations in emotion are extracted from the user's facial expressions in real time and processed as digital data. The acquired emotional information is integrated with other data and sent to the server.

[0374] Step 4:

[0375] The server receives characteristic information, motion data, and emotion information sent from the terminal and begins integrated data analysis. Specifically, it uses programming tools such as Python and R to analyze each dataset using machine learning algorithms. Here, characteristic information is used to optimize the exercise equipment, motion data is analyzed to derive improvement measures, and emotion information is used to...

[0376] This is taken into consideration to reduce tracing and tension.

[0377] Step 5:

[0378] The server generates specific advice for performance improvement based on the data analysis results. This may include minor form adjustments or advice for specific technical improvements. Furthermore, advice tailored to the user's mental state is added based on emotional information. All advice data is formatted and then sent to the terminal.

[0379] Step 6:

[0380] The device displays advice received from the server to the user. At this stage, the feedback is presented in a visually easy-to-understand format, ensuring that the user can immediately put it into practice. The user can then use this to take action to improve their own behavior and mental state.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] In recent years, interest in exercise and fitness has increased, and there is a demand for exercise guidance optimized for each individual. However, conventional systems do not provide guidance that takes into account the emotional state of the user, resulting in the problem that the effects of exercise are not maximized. Guidance that takes into account the individual differences of users is necessary, but a systematic analytical approach that includes information based on emotions has been insufficient to achieve this.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for acquiring a person's characteristic data, means for recording a person's movement data, and means for acquiring a person's emotional data. This makes it possible to comprehensively analyze the user's characteristic data, movement data, and emotional data, and provide personalized guidance, thereby maximizing the effectiveness of exercise tailored to each user.

[0386] "Personal characteristic data" refers to information that expresses a person's physical characteristics, such as height and weight, as numerical values ​​or attributes.

[0387] "Sports equipment characteristics" refers to information that indicates attributes such as the shape and structure of equipment suitable for a specific sports activity, based on individual characteristic data.

[0388] "Motion data" refers to information that records a person's body movements, and specifically includes the timeline and location information of those movements.

[0389] "Instructions" refer to advice and guidelines based on behavioral and emotional data, used to guide improvements in a person's actions and mental approach.

[0390] "Emotional data" refers to information that quantifies or attributes the emotional state of a person, analyzed from their facial expressions and voice.

[0391] "Individualized instructions" refer to specific guidance tailored to an individual, based on an integrated analysis of their characteristics, behaviors, and emotional data.

[0392] "Presentation" is the act or process of communicating generated instructions to a person through visual or auditory means.

[0393] To implement this invention, the user must first input characteristic data via a terminal. This characteristic data includes basic physical information such as height and weight. This data is transmitted to a server via a network. The server analyzes the received characteristic data and calculates the characteristics of the sports equipment.

[0394] Next, the user records their movements using the camera. The recorded movement data is sent from the device to the server, where the server analyzes the movements. The analysis uses a machine learning model based on TensorFlow to recognize movement patterns and problems. Based on the results, instructions for improving the movements are generated for the user.

[0395] Furthermore, facial recognition technology via the device is used to acquire emotional data. Machine learning frameworks such as TensorFlow and PyTorch are used for facial recognition and voice analysis. The emotional data is used to evaluate the user's current mental state and to correct behavioral improvement instructions into personalized instructions.

[0396] The generated personalized instructions are presented to the user via the device. This allows the user to receive not only improved behavior but also advice optimized according to their emotional state.

[0397] As a concrete example, consider a scenario where a user is trying to learn a new yoga pose. In addition to the usual instruction, "Relax your shoulders and maintain the posture," the server can also generate additional instructions such as "Take a deep breath and relax" if it detects tension in the user's face.

[0398] An example of a prompt when using a generative AI model is: "Analyze the user's facial expressions while they are practicing yoga and generate advice to promote relaxation."

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The user enters characteristic data into the terminal. The entered data includes physical information such as height and weight. The terminal digitizes this information and sends it to the server.

[0402] Step 2:

[0403] The server analyzes the received characteristic data. Using databases and AI models, it calculates the characteristics of sports equipment best suited to each individual. The results are output and stored internally on the server.

[0404] Step 3:

[0405] The user uses the camera to record their actions. The recorded video data is saved on the device and sent to the server as action data.

[0406] Step 4:

[0407] The server analyzes the received operational data. This analysis uses TensorFlow to extract user behavior patterns and problems. The analysis results are output as basic data for generating instructions to improve the operation.

[0408] Step 5:

[0409] The device uses facial recognition technology to acquire the user's emotional data. The acquired facial and voice data are converted into specific emotional states through emotion analysis, and this is sent to the server as emotional data.

[0410] Step 6:

[0411] The server analyzes emotional data and incorporates it into instructions for improving behavior. Based on the emotional data, the generated instructions are refined into personalized instructions. This results in advice optimized for the user's current mental state.

[0412] Step 7:

[0413] The server sends individually generated instructions to the terminal. The terminal presents these instructions to the user, who then reviews them and uses them to improve their own operation.

[0414] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0415] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0416] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0420] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0421] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0422] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0423] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0424] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0425] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0426] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0427] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0428] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0429] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0430] The system based on this invention is a system that collects user characteristic data, calculates the characteristics of sports equipment, and further analyzes the user's movements. The following process is followed to implement this system.

[0431] First, the user inputs characteristic data such as height, weight, and age using a device such as a smartphone or tablet. The device then transmits this information to a server via the internet. Based on the received characteristic data, the server uses an AI algorithm to calculate the characteristics of sports equipment suitable for the user. For example, in bowling, the optimal ball weight and a comfortable grip size are suggested.

[0432] Next, the user uses the device's camera function to record their movements. The device sends the recorded video file to the server. The server uses advanced image analysis technology to analyze this video in detail and extract the user's pitching form and movement characteristics. Based on the analysis results, the server generates specific advice for the user on how to improve their movements. This advice may include specific details such as "angle your wrist 10 degrees inward."

[0433] The generated advice is sent back to the device and displayed to the user in an easy-to-understand format. The advice can also include visual guides and diagrams to aid user comprehension. Furthermore, users can use this advice to practice repeatedly and improve their own performance.

[0434] As a concrete example, consider a case where a user goes bowling. When the user inputs their height (180cm) and weight (75kg), the server calculates that a 13-pound ball is suitable and displays this on the user's device. Furthermore, when the user films their bowling form and sends it to the server, the server generates advice such as "Try to keep your gaze more directly forward" and displays it on the user's device.

[0435] In this way, the present invention supports the improvement of user performance by providing specific and personalized advice for improving sports skills.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user enters sports-related characteristic data (e.g., height, weight, age) into the device. The device receives this data and temporarily stores it.

[0439] Step 2:

[0440] The device sends characteristic data it has stored to the server using a secure protocol. The data is sent in a format associated with the user ID.

[0441] Step 3:

[0442] The server analyzes the received characteristic data. An AI algorithm is used to calculate the characteristics of sports equipment suitable for the user. These results are then prepared and sent to the terminal.

[0443] Step 4:

[0444] The terminal displays suggested equipment characteristics received from the server to the user. The user reviews the suggestions and uses them as reference during sports activities.

[0445] Step 5:

[0446] The user records their actions using the device's camera. The device saves the recorded video data locally.

[0447] Step 6:

[0448] The device sends the saved video data to the server. The data is sent along with the user's identification information.

[0449] Step 7:

[0450] The server analyzes the received video. Using image analysis technology, it extracts motion characteristics, and the AI ​​generates advice for improving the motion.

[0451] Step 8:

[0452] The server sends advice it generates to the terminal. This advice includes specific corrections and suggestions for improvement.

[0453] Step 9:

[0454] The terminal displays advice received from the server to the user. The user can then work on improving its operation by following the advice.

[0455] (Example 1)

[0456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] In recent years, there has been a growing demand for systems that support the selection of sports equipment tailored to individual characteristics and effective exercise improvement. However, conventional systems have struggled to calculate the characteristics of equipment that are individually suitable for each user and to provide specific advice on improving movement. Furthermore, they have faced challenges in adequately addressing the individual needs of each user.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0459] In this invention, the server includes means for acquiring a person's characteristic data, means for calculating the characteristics of appropriate equipment using a generation AI model based on the acquired characteristic data, and means for analyzing captured motion data and generating advice on motion improvement using digital analysis technology. This makes it possible to recommend equipment suitable for the individual characteristics of the user and to provide specific improvement advice tailored to each individual's movements.

[0460] "Personal characteristic data" refers to the user's physical information such as height, weight, and age, and is used for calculating the characteristics of sports equipment and analyzing movements.

[0461] A "generative AI model" refers to an artificial intelligence algorithm used to calculate and generate optimal equipment characteristics and advice for improving performance, based on acquired characteristic data.

[0462] "Motion data" refers to data acquired by recording a user's movements and physical actions as video and then digitally analyzing that video.

[0463] "Digital analysis technology" refers to techniques that analyze captured motion data to extract motion characteristics and generate advice for improvement.

[0464] "Advice generation" refers to the process of creating specific advice that helps improve user behavior using digital analysis technology.

[0465] "Calculating characteristics" refers to calculating the optimal specifications for sports equipment and gear for a user based on the acquired characteristic data of that person.

[0466] This invention is a system that collects user characteristic data, calculates the characteristics of appropriate sports equipment based on that data, and further analyzes the user's movements in detail. This system mainly consists of a server, terminals, and users.

[0467] First, the user inputs their personal data (height, weight, age, etc.) using a device such as a smartphone or tablet. This data is transmitted to the server via the internet. Any widely available, general-purpose computer device can be used. The server processes the initially received data and uses a generative AI model to calculate the characteristics of equipment suitable for the user. This model uses machine learning algorithms to perform the necessary data processing quickly and efficiently.

[0468] Next, the user uses the device's camera function to film their sports movements. The recorded video file is sent from the device to the server. The server uses digital analysis technology to analyze the received video in detail and extract the characteristics of the user's movements. In particular, it evaluates form, angles, and rhythm of movement, and generates advice on specific areas for improvement.

[0469] The generated advice is sent back to the device and displayed in an easy-to-understand format for the user. Visual guides and diagrams are added to help users intuitively understand the advice. Based on this advice, users can practice repeatedly and improve their performance.

[0470] For example, if a user is bowling and enters data such as height 180cm and weight 75kg, the server will recommend using a 13-pound ball and, by filming and analyzing the user's movements, provide specific advice such as "angle your wrist 10 degrees inward."

[0471] Examples of prompts include requests such as, "What is the optimal ball weight for a bowler who is 180cm tall and weighs 75kg?" or "Generate the best advice for improving my bowling form." This allows the present invention to achieve personalized improvements to sports performance for each user.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The user inputs their personal data using a terminal. Specifically, they fill in items such as height, weight, and age using a dedicated application screen. The terminal verifies that the entered data is in the correct format and confirms its integrity. The input consists of the user's physical numerical data, which is then organized and prepared for transmission to the server.

[0475] Step 2:

[0476] The terminal sends characteristic data obtained from the user to the server. An encryption protocol is applied here to ensure the data is transmitted securely. The output is encrypted characteristic data for transmission to the server. The server receives this data and converts it into a format that can be analyzed within the system.

[0477] Step 3:

[0478] The server inputs the received characteristic data into a generating AI model to calculate the characteristics of the optimal tool for the user. In this process, the AI ​​model refers to past performance data and uses statistical methods to find the optimal solution. The output is the calculated characteristics of the optimal sports equipment, and a recommendation result such as "a 13-pound bowling ball is suitable" is generated.

[0479] Step 4:

[0480] The user uses the device's camera function to record their actions. For video recording, the user sets the frame rate and image quality to ensure that no actions are missed. A video file is generated as input, which is then used for later analysis.

[0481] Step 5:

[0482] The device sends the captured video file to the server. Here too, an encryption protocol is applied for secure data transmission. The output is encrypted video data sent to the server, which then decodes this data into an analyzable format.

[0483] Step 6:

[0484] The server applies digital analysis techniques to the received video data to extract motion features. For example, computer vision algorithms are used to analyze the angle and rhythm of a pitch in detail. Video data serves as input, and the output includes motion patterns and areas for improvement as a result of the analysis.

[0485] Step 7:

[0486] The server generates specific suggestions for improving user behavior based on the analysis results. The generated AI model is reused in this process. The generated suggestions include specific details such as "angle your wrist 10 degrees inward." The final output is sent to the user's device as a suggested improvement.

[0487] Step 8:

[0488] The terminal displays advice sent from the server in an easy-to-understand manner for the user. Visual guides and diagrams are added to make the advice intuitively easy to understand. Users can then practice repeatedly based on the advice they receive.

[0489] (Application Example 1)

[0490] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] Traditional methods for selecting sports equipment and improving movement have the drawback of not adequately considering the individual characteristics and movement patterns of each person when providing personalized advice. Furthermore, these improvement suggestions are rarely presented in a visually and audibly easy-to-understand format. Under these circumstances, there is a need for concrete and effective support for each individual to improve their own athletic ability.

[0492] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0493] In this invention, the server includes means for acquiring a person's characteristic data, means for analyzing the captured motion data and generating advice for motion improvement, and means for outputting the generated advice as audio and presenting it as a visual guideline. This makes it possible to select the optimal sports equipment based on each individual's characteristics, and furthermore, to provide advice for individual motion improvement in a visually and audibly easy-to-understand format.

[0494] "Personal characteristic data" refers to information about an individual person's height, weight, age, and other individual physiological or physical attributes.

[0495] "Sports equipment characteristics" refer to the attributes of specific sports equipment, such as weight, size, shape, and material, that are optimal for the user.

[0496] "Action data" refers to digitized information that represents a series of actions performed physically by a person.

[0497] "Movement improvement advice" refers to specific and actionable advice for improving performance, based on the results of an analysis of a person's movement data.

[0498] "Displaying generated advice" means showing the content of the advice to the user visually through the screen or projection function of a digital device.

[0499] "Outputting audio" refers to the act of conveying linguistic information audibly using sound devices such as speakers or headphones.

[0500] "Presenting as a visual guideline" means displaying visual resources such as images, videos, and animations to make the advice easier to understand.

[0501] To implement this invention, the user first inputs their characteristic data into a device such as a smartphone or tablet. For example, they are required to provide information such as height, weight, and age. The device has a function to send this characteristic data to a server, where it is processed. The server uses an AI algorithm to calculate the characteristics of the optimal sports equipment based on the received characteristic data. Specifically, it uses an AI framework such as TensorFlow to score the weight and size of equipment that is suitable for the user and makes suggestions based on that.

[0502] Next, the user uses the device's camera to record their movements. For example, they can record their bowling throwing form on video. The recorded video is sent from the device to the server, where it is analyzed in detail using image analysis software such as OpenCV. This analysis extracts the characteristics of the user's movements and generates specific advice for improving those movements. This advice is presented to the user as audio and visual guidelines. For example, advice such as "angle your wrist 10 degrees inward" or "try to keep your gaze more forward" is sent from the server to the device and displayed.

[0503] For example, after acquiring motion data from a user who is 180cm tall and weighs 75kg while bowling, the server suggests that a 13-pound ball would be suitable. Furthermore, based on video analysis of the bowling form, improvement advice is provided, such as "turn your wrist 10 degrees inward." This advice is displayed on the screen and also provided via audio output, allowing the user to receive it through both sight and sound.

[0504] An example of a prompt using a generative AI model would be: "Please suggest the best bowling ball and provide suggestions for improving the bowling form of a 180cm, 75kg, 35-year-old male."

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The user inputs their personal data into the device. This input includes personal attribute data such as height, weight, and age. The device converts this data into a digital format and sends it to the server via the internet. This allows the server to receive the user's personal data as input.

[0508] Step 2:

[0509] The server supplies the received characteristic data to an AI algorithm to calculate the optimal characteristics of the sports equipment. In this case, the AI ​​algorithm performs calculations to score the optimal weight and size of the equipment based on the input data. As output, the characteristics of the sports equipment best suited to the user are generated and sent back to the terminal.

[0510] Step 3:

[0511] The user records their actions using the device's camera. For example, they can record their bowling throw as a video. This video data is saved to the device's storage and sent to a server via the internet. This prepares the server to receive the action data as input.

[0512] Step 4:

[0513] The server analyzes the received video data in detail using image analysis software such as OpenCV. The input video data is broken down frame by frame and analyzed to extract motion characteristics. This allows the server to output motion characteristics as digital information and generate specific advice for motion improvement.

[0514] Step 5:

[0515] The advice generated on the server is sent to the terminal and displayed as visual guidelines. Furthermore, it is possible to output the advice as audio using a speech synthesis system. This allows the user to receive specific instructions for improving their performance through both auditory and visual means.

[0516] Step 6:

[0517] Users perform repetitive practice to improve their movements, following visual and audio guidance provided by the server. Based on the feedback received, users can adjust their form and movements to improve their skills.

[0518] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0519] The system according to the present invention acquires user characteristic data, motion data, and user emotional data, and comprehensively analyzes them to provide more precise sports instruction. The system according to the present invention is implemented through the following process.

[0520] First, the user inputs characteristic data such as height and weight using a device. The device sends this data to a server, which calculates the characteristics of sports equipment based on the characteristic data. In addition, the user films their movements through a camera. The filmed video is sent to the server by the device, and the server analyzes the movement data in detail to generate appropriate advice for improving movements.

[0521] Furthermore, this system incorporates an emotion engine. The terminal acquires user emotion data using user input feedback and facial recognition technology. This emotion data is sent to a server, where it is integrated with other data and analyzed.

[0522] In addition to the usual analysis of behavioral data, the server performs adjustments that take emotional data into account to generate advice tailored to the user's current mental state. Specifically, if the user is stressed, the advice is adjusted to be more relaxing, and if the user is agitated, the advice is changed to help them exert their energy more effectively.

[0523] The generated advice is sent to the device, where the user can review it and use it to improve their technique. For example, when a user is trying to learn a new bowling technique, the system can provide standard motion advice such as "Focus on making your left hand movement smoother," but it can also add emotion-based advice such as "Take a deep breath and relax before throwing" if the user's face appears tense.

[0524] Thus, the present invention is a system that uses emotional data to provide users with more personalized guidance and support the improvement of their sports performance.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The user uses the device to input characteristic data such as height, weight, and age. The device saves the entered data locally.

[0528] Step 2:

[0529] The device sends the stored characteristic data to the server via the internet. The server receives the characteristic data and calculates the characteristics of the sports equipment based on that data.

[0530] Step 3:

[0531] The server uses an AI algorithm to analyze the received characteristic data and calculate the optimal characteristics of sports equipment for the user. The calculated results are then sent to the terminal.

[0532] Step 4:

[0533] The device displays characteristic information about sports equipment to the user. The user reviews this information and uses it to improve their sports activities.

[0534] Step 5:

[0535] The user uses the device's camera function to record their own actions. The device saves the recorded video.

[0536] Step 6:

[0537] The device sends the saved video data to the server. The server receives the video data and prepares to perform a detailed motion analysis.

[0538] Step 7:

[0539] The server analyzes the video data and extracts the characteristics of the user's actions. It then prepares to generate optimal improvement suggestions using AI.

[0540] Step 8:

[0541] The device captures the user's facial expressions with its camera and uses an emotion engine to recognize the user's emotions. This data is then sent to a server.

[0542] Step 9:

[0543] The server combines emotional data with behavioral analysis results. Based on the user's emotional state, it adjusts behavioral improvement suggestions and generates personalized advice.

[0544] Step 10:

[0545] The server sends generated advice to the terminal. The terminal displays the advice to the user. The user receives the advice and improves their performance by putting it into practice.

[0546] (Example 2)

[0547] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0548] Traditional sports coaching systems are primarily limited to feedback based on physical characteristics and movement data, and do not adequately provide personalized instruction that takes into account the user's emotions and mental state. Therefore, it has been difficult to effectively address the stress and motivational changes experienced by users. Consequently, there is a need to develop a system that integrates user emotional data to provide more precise and effective exercise instruction.

[0549] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0550] In this invention, the server includes means for acquiring characteristic information of a person, means for acquiring emotional information using facial recognition technology and feedback, and means for integrating the emotional information and generating advice based on the person's mental state. This makes it possible to provide precise guidance for improving actions that takes the user's emotional state into account.

[0551] "Personal characteristic information" refers to the basic physical specifications of the person in question, such as height, weight, and age, and is data that represents the individual's characteristics.

[0552] "The characteristics of sports equipment" refer to the detailed attributes and specifications of equipment used in sports and fitness activities, including specifications optimized for the physical characteristics of a particular individual.

[0553] "Motion information" refers to data that represents the physical activities and movement characteristics of a person in question, and is acquired through video analysis or sensors.

[0554] "Facial recognition technology" is a computer vision technology that analyzes emotional and psychological states from a person's facial expressions, and often uses machine learning.

[0555] "Emotional information" refers to data that represents a person's emotions and mental state, and is obtained through facial recognition technology and user-inputted feedback.

[0556] "Movement improvement advice" refers to specific suggestions or recommendations for a person to improve or optimize their movements, based on the analyzed movement information.

[0557] "Mental state-based advice" refers to suggestions that encourage behavioral improvement in a way that is most appropriate to a person's current emotions and mental state, based on data analysis that includes emotional information.

[0558] The system according to this invention aims to provide integrated feedback based on the user's physical characteristics, movements, and emotional state by combining multiple modules.

[0559] terminal

[0560] The user first inputs their personal information using the terminal. The terminal has a standard input interface and can input basic information such as height, weight, and age. The terminal transmits this information to the server via a network such as Wi-Fi or LTE. In addition, the terminal is equipped with a camera that captures the user's movement data. For example, if the user is practicing tennis swings, the movement can be recorded from various angles. The terminal is also equipped with software that uses facial recognition technology to obtain emotional information from the user's facial expressions.

[0561] server

[0562] The server processes the received characteristic information and performs calculations to determine the optimal characteristics of the exercise equipment. Using programming languages ​​such as Python and R, it analyzes motion information and generates specific improvement suggestions for the user. Furthermore, it implements a process that takes emotional information into account to generate advice tailored to the user's mental state. This analysis utilizes machine learning algorithms and generative AI models to improve the accuracy of the feedback.

[0563] Specific example

[0564] For example, video data captured by the device is analyzed on the server, and standard feedback such as "improve the angle of your arm when serving" is generated. At the same time, if tension is detected by the facial recognition function, emotion-based advice such as "take a deep breath and relax your shoulders" is added. This allows users to receive more personalized feedback.

[0565] An example of a prompt for a generative AI model is, "Explain how to provide emotionally appropriate advice to a user who is trying to learn a new basketball shooting technique." By utilizing such prompts, generative AI models can provide more advanced analysis and support.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] Users input their basic personal information (height, weight, age, etc.) through a terminal. This information forms the basis for personalized feedback within the system. This input data is immediately transmitted from the terminal to the server.

[0569] Step 2:

[0570] The device uses its camera to record the user's actions. Users can record, for example, their tennis swing or running form. This video data is used for analysis to improve their performance. The recorded video is first converted to the required format within the device, compressed, and then sent to the server.

[0571] Step 3:

[0572] The device further analyzes the user's facial features using facial recognition technology and acquires emotional information. In this step, variations in emotion are extracted from the user's facial expressions in real time and processed as digital data. The acquired emotional information is integrated with other data and sent to the server.

[0573] Step 4:

[0574] The server receives characteristic information, motion data, and emotion information sent from the terminal and begins integrated data analysis. Specifically, it uses programming tools such as Python and R to analyze each dataset using machine learning algorithms. Here, characteristic information is used to optimize the exercise equipment, motion data is analyzed to derive improvement measures, and emotion information is used to...

[0575] This is taken into consideration to reduce tracing and tension.

[0576] Step 5:

[0577] The server generates specific advice for performance improvement based on the data analysis results. This may include minor form adjustments or advice for specific technical improvements. Furthermore, advice tailored to the user's mental state is added based on emotional information. All advice data is formatted and then sent to the terminal.

[0578] Step 6:

[0579] The device displays advice received from the server to the user. At this stage, the feedback is presented in a visually easy-to-understand format, ensuring that the user can immediately put it into practice. The user can then use this to take action to improve their own behavior and mental state.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0582] In recent years, interest in exercise and fitness has increased, and there is a demand for exercise guidance optimized for each individual. However, conventional systems do not provide guidance that takes into account the emotional state of the user, resulting in the problem that the effects of exercise are not maximized. Guidance that takes into account the individual differences of users is necessary, but a systematic analytical approach that includes information based on emotions has been insufficient to achieve this.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes means for acquiring a person's characteristic data, means for recording a person's movement data, and means for acquiring a person's emotional data. This makes it possible to comprehensively analyze the user's characteristic data, movement data, and emotional data, and provide personalized guidance, thereby maximizing the effectiveness of exercise tailored to each user.

[0585] "Personal characteristic data" refers to information that expresses a person's physical characteristics, such as height and weight, as numerical values ​​or attributes.

[0586] "Sports equipment characteristics" refers to information that indicates attributes such as the shape and structure of equipment suitable for a specific sports activity, based on individual characteristic data.

[0587] "Motion data" refers to information that records a person's body movements, and specifically includes the timeline and location information of those movements.

[0588] "Instructions" refer to advice and guidelines based on behavioral and emotional data, used to guide improvements in a person's actions and mental approach.

[0589] "Emotional data" refers to information that quantifies or attributes the emotional state of a person, analyzed from their facial expressions and voice.

[0590] "Individualized instructions" refer to specific guidance tailored to an individual, based on an integrated analysis of their characteristics, behaviors, and emotional data.

[0591] "Presentation" is the act or process of communicating generated instructions to a person through visual or auditory means.

[0592] To implement this invention, the user must first input characteristic data via a terminal. This characteristic data includes basic physical information such as height and weight. This data is transmitted to a server via a network. The server analyzes the received characteristic data and calculates the characteristics of the sports equipment.

[0593] Next, the user records their movements using the camera. The recorded movement data is sent from the device to the server, where the server analyzes the movements. The analysis uses a machine learning model based on TensorFlow to recognize movement patterns and problems. Based on the results, instructions for improving the movements are generated for the user.

[0594] Furthermore, facial recognition technology via the device is used to acquire emotional data. Machine learning frameworks such as TensorFlow and PyTorch are used for facial recognition and voice analysis. The emotional data is used to evaluate the user's current mental state and to correct behavioral improvement instructions into personalized instructions.

[0595] The generated personalized instructions are presented to the user via the device. This allows the user to receive not only improved behavior but also advice optimized according to their emotional state.

[0596] As a concrete example, consider a scenario where a user is trying to learn a new yoga pose. In addition to the usual instruction, "Relax your shoulders and maintain the posture," the server can also generate additional instructions such as "Take a deep breath and relax" if it detects tension in the user's face.

[0597] An example of a prompt when using a generative AI model is: "Analyze the user's facial expressions while they are practicing yoga and generate advice to promote relaxation."

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The user enters characteristic data into the terminal. The entered data includes physical information such as height and weight. The terminal digitizes this information and sends it to the server.

[0601] Step 2:

[0602] The server analyzes the received characteristic data. Using databases and AI models, it calculates the characteristics of sports equipment best suited to each individual. The results are output and stored internally on the server.

[0603] Step 3:

[0604] The user uses the camera to record their actions. The recorded video data is saved on the device and sent to the server as action data.

[0605] Step 4:

[0606] The server analyzes the received operational data. This analysis uses TensorFlow to extract user behavior patterns and problems. The analysis results are output as basic data for generating instructions to improve the operation.

[0607] Step 5:

[0608] The device uses facial recognition technology to acquire the user's emotional data. The acquired facial and voice data are converted into specific emotional states through emotion analysis, and this is sent to the server as emotional data.

[0609] Step 6:

[0610] The server analyzes emotional data and incorporates it into instructions for improving behavior. Based on the emotional data, the generated instructions are refined into personalized instructions. This results in advice optimized for the user's current mental state.

[0611] Step 7:

[0612] The server sends individually generated instructions to the terminal. The terminal presents these instructions to the user, who then reviews them and uses them to improve their own operation.

[0613] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0619] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0620] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0621] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0623] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0624] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0625] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0626] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0627] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0628] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0629] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] The system based on this invention is a system that collects user characteristic data, calculates the characteristics of sports equipment, and further analyzes the user's movements. The following process is followed to implement this system.

[0631] First, the user inputs characteristic data such as height, weight, and age using a device such as a smartphone or tablet. The device then transmits this information to a server via the internet. Based on the received characteristic data, the server uses an AI algorithm to calculate the characteristics of sports equipment suitable for the user. For example, in bowling, the optimal ball weight and a comfortable grip size are suggested.

[0632] Next, the user uses the device's camera function to record their movements. The device sends the recorded video file to the server. The server uses advanced image analysis technology to analyze this video in detail and extract the user's pitching form and movement characteristics. Based on the analysis results, the server generates specific advice for the user on how to improve their movements. This advice may include specific details such as "angle your wrist 10 degrees inward."

[0633] The generated advice is sent back to the device and displayed to the user in an easy-to-understand format. The advice can also include visual guides and diagrams to aid user comprehension. Furthermore, users can use this advice to practice repeatedly and improve their own performance.

[0634] As a concrete example, consider a case where a user goes bowling. When the user inputs their height (180cm) and weight (75kg), the server calculates that a 13-pound ball is suitable and displays this on the user's device. Furthermore, when the user films their bowling form and sends it to the server, the server generates advice such as "Try to keep your gaze more directly forward" and displays it on the user's device.

[0635] In this way, the present invention supports the improvement of user performance by providing specific and personalized advice for improving sports skills.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The user enters sports-related characteristic data (e.g., height, weight, age) into the device. The device receives this data and temporarily stores it.

[0639] Step 2:

[0640] The device sends characteristic data it has stored to the server using a secure protocol. The data is sent in a format associated with the user ID.

[0641] Step 3:

[0642] The server analyzes the received characteristic data. An AI algorithm is used to calculate the characteristics of sports equipment suitable for the user. These results are then prepared and sent to the terminal.

[0643] Step 4:

[0644] The terminal displays suggested equipment characteristics received from the server to the user. The user reviews the suggestions and uses them as reference during sports activities.

[0645] Step 5:

[0646] The user records their actions using the device's camera. The device saves the recorded video data locally.

[0647] Step 6:

[0648] The device sends the saved video data to the server. The data is sent along with the user's identification information.

[0649] Step 7:

[0650] The server analyzes the received video. Using image analysis technology, it extracts motion characteristics, and the AI ​​generates advice for improving the motion.

[0651] Step 8:

[0652] The server sends advice it generates to the terminal. This advice includes specific corrections and suggestions for improvement.

[0653] Step 9:

[0654] The terminal displays advice received from the server to the user. The user can then work on improving its operation by following the advice.

[0655] (Example 1)

[0656] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0657] In recent years, there has been a growing demand for systems that support the selection of sports equipment tailored to individual characteristics and effective exercise improvement. However, conventional systems have struggled to calculate the characteristics of equipment that are individually suitable for each user and to provide specific advice on improving movement. Furthermore, they have faced challenges in adequately addressing the individual needs of each user.

[0658] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0659] In this invention, the server includes means for acquiring a person's characteristic data, means for calculating the characteristics of appropriate equipment using a generation AI model based on the acquired characteristic data, and means for analyzing captured motion data and generating advice on motion improvement using digital analysis technology. This makes it possible to recommend equipment suitable for the individual characteristics of the user and to provide specific improvement advice tailored to each individual's movements.

[0660] "Personal characteristic data" refers to the user's physical information such as height, weight, and age, and is used for calculating the characteristics of sports equipment and analyzing movements.

[0661] A "generative AI model" refers to an artificial intelligence algorithm used to calculate and generate optimal equipment characteristics and advice for improving performance, based on acquired characteristic data.

[0662] "Motion data" refers to data acquired by recording a user's movements and physical actions as video and then digitally analyzing that video.

[0663] "Digital analysis technology" refers to techniques that analyze captured motion data to extract motion characteristics and generate advice for improvement.

[0664] "Advice generation" refers to the process of creating specific advice that helps improve user behavior using digital analysis technology.

[0665] "Calculating characteristics" refers to calculating the optimal specifications for sports equipment and gear for a user based on the acquired characteristic data of that person.

[0666] This invention is a system that collects user characteristic data, calculates the characteristics of appropriate sports equipment based on that data, and further analyzes the user's movements in detail. This system mainly consists of a server, terminals, and users.

[0667] First, the user inputs their personal data (height, weight, age, etc.) using a device such as a smartphone or tablet. This data is transmitted to the server via the internet. Any widely available, general-purpose computer device can be used. The server processes the initially received data and uses a generative AI model to calculate the characteristics of equipment suitable for the user. This model uses machine learning algorithms to perform the necessary data processing quickly and efficiently.

[0668] Next, the user uses the device's camera function to film their sports movements. The recorded video file is sent from the device to the server. The server uses digital analysis technology to analyze the received video in detail and extract the characteristics of the user's movements. In particular, it evaluates form, angles, and rhythm of movement, and generates advice on specific areas for improvement.

[0669] The generated advice is sent back to the device and displayed in an easy-to-understand format for the user. Visual guides and diagrams are added to help users intuitively understand the advice. Based on this advice, users can practice repeatedly and improve their performance.

[0670] For example, if a user is bowling and enters data such as height 180cm and weight 75kg, the server will recommend using a 13-pound ball and, by filming and analyzing the user's movements, provide specific advice such as "angle your wrist 10 degrees inward."

[0671] Examples of prompts include requests such as, "What is the optimal ball weight for a bowler who is 180cm tall and weighs 75kg?" or "Generate the best advice for improving my bowling form." This allows the present invention to achieve personalized improvements to sports performance for each user.

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] The user inputs their personal data using a terminal. Specifically, they fill in items such as height, weight, and age using a dedicated application screen. The terminal verifies that the entered data is in the correct format and confirms its integrity. The input consists of the user's physical numerical data, which is then organized and prepared for transmission to the server.

[0675] Step 2:

[0676] The terminal sends characteristic data obtained from the user to the server. An encryption protocol is applied here to ensure the data is transmitted securely. The output is encrypted characteristic data for transmission to the server. The server receives this data and converts it into a format that can be analyzed within the system.

[0677] Step 3:

[0678] The server inputs the received characteristic data into a generating AI model to calculate the characteristics of the optimal tool for the user. In this process, the AI ​​model refers to past performance data and uses statistical methods to find the optimal solution. The output is the calculated characteristics of the optimal sports equipment, and a recommendation result such as "a 13-pound bowling ball is suitable" is generated.

[0679] Step 4:

[0680] The user uses the device's camera function to record their actions. For video recording, the user sets the frame rate and image quality to ensure that no actions are missed. A video file is generated as input, which is then used for later analysis.

[0681] Step 5:

[0682] The device sends the captured video file to the server. Here too, an encryption protocol is applied for secure data transmission. The output is encrypted video data sent to the server, which then decodes this data into an analyzable format.

[0683] Step 6:

[0684] The server applies digital analysis techniques to the received video data to extract motion features. For example, computer vision algorithms are used to analyze the angle and rhythm of a pitch in detail. Video data serves as input, and the output includes motion patterns and areas for improvement as a result of the analysis.

[0685] Step 7:

[0686] The server generates specific suggestions for improving user behavior based on the analysis results. The generated AI model is reused in this process. The generated suggestions include specific details such as "angle your wrist 10 degrees inward." The final output is sent to the user's device as a suggested improvement.

[0687] Step 8:

[0688] The terminal displays advice sent from the server in an easy-to-understand manner for the user. Visual guides and diagrams are added to make the advice intuitively easy to understand. Users can then practice repeatedly based on the advice they receive.

[0689] (Application Example 1)

[0690] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] Traditional methods for selecting sports equipment and improving movement have the drawback of not adequately considering the individual characteristics and movement patterns of each person when providing personalized advice. Furthermore, these improvement suggestions are rarely presented in a visually and audibly easy-to-understand format. Under these circumstances, there is a need for concrete and effective support for each individual to improve their own athletic ability.

[0692] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0693] In this invention, the server includes means for acquiring a person's characteristic data, means for analyzing the captured motion data and generating advice for motion improvement, and means for outputting the generated advice as audio and presenting it as a visual guideline. This makes it possible to select the optimal sports equipment based on each individual's characteristics, and furthermore, to provide advice for individual motion improvement in a visually and audibly easy-to-understand format.

[0694] "Personal characteristic data" refers to information about an individual person's height, weight, age, and other individual physiological or physical attributes.

[0695] "Sports equipment characteristics" refer to the attributes of specific sports equipment, such as weight, size, shape, and material, that are optimal for the user.

[0696] "Action data" refers to digitized information that represents a series of actions performed physically by a person.

[0697] "Movement improvement advice" refers to specific and actionable advice for improving performance, based on the results of an analysis of a person's movement data.

[0698] "Displaying generated advice" means showing the content of the advice to the user visually through the screen or projection function of a digital device.

[0699] "Outputting audio" refers to the act of conveying linguistic information audibly using sound devices such as speakers or headphones.

[0700] "Presenting as a visual guideline" means displaying visual resources such as images, videos, and animations to make the advice easier to understand.

[0701] To implement this invention, the user first inputs their characteristic data into a device such as a smartphone or tablet. For example, they are required to provide information such as height, weight, and age. The device has a function to send this characteristic data to a server, where it is processed. The server uses an AI algorithm to calculate the characteristics of the optimal sports equipment based on the received characteristic data. Specifically, it uses an AI framework such as TensorFlow to score the weight and size of equipment that is suitable for the user and makes suggestions based on that.

[0702] Next, the user uses the device's camera to record their movements. For example, they can record their bowling throwing form on video. The recorded video is sent from the device to the server, where it is analyzed in detail using image analysis software such as OpenCV. This analysis extracts the characteristics of the user's movements and generates specific advice for improving those movements. This advice is presented to the user as audio and visual guidelines. For example, advice such as "angle your wrist 10 degrees inward" or "try to keep your gaze more forward" is sent from the server to the device and displayed.

[0703] For example, after acquiring motion data from a user who is 180cm tall and weighs 75kg while bowling, the server suggests that a 13-pound ball would be suitable. Furthermore, based on video analysis of the bowling form, improvement advice is provided, such as "turn your wrist 10 degrees inward." This advice is displayed on the screen and also provided via audio output, allowing the user to receive it through both sight and sound.

[0704] An example of a prompt using a generative AI model would be: "Please suggest the best bowling ball and provide suggestions for improving the bowling form of a 180cm, 75kg, 35-year-old male."

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] The user inputs their personal data into the device. This input includes personal attribute data such as height, weight, and age. The device converts this data into a digital format and sends it to the server via the internet. This allows the server to receive the user's personal data as input.

[0708] Step 2:

[0709] The server supplies the received characteristic data to an AI algorithm to calculate the optimal characteristics of the sports equipment. In this case, the AI ​​algorithm performs calculations to score the optimal weight and size of the equipment based on the input data. As output, the characteristics of the sports equipment best suited to the user are generated and sent back to the terminal.

[0710] Step 3:

[0711] The user records their actions using the device's camera. For example, they can record their bowling throw as a video. This video data is saved to the device's storage and sent to a server via the internet. This prepares the server to receive the action data as input.

[0712] Step 4:

[0713] The server analyzes the received video data in detail using image analysis software such as OpenCV. The input video data is broken down frame by frame and analyzed to extract motion characteristics. This allows the server to output motion characteristics as digital information and generate specific advice for motion improvement.

[0714] Step 5:

[0715] The advice generated on the server is sent to the terminal and displayed as visual guidelines. Furthermore, it is possible to output the advice as audio using a speech synthesis system. This allows the user to receive specific instructions for improving their performance through both auditory and visual means.

[0716] Step 6:

[0717] Users perform repetitive practice to improve their movements, following visual and audio guidance provided by the server. Based on the feedback received, users can adjust their form and movements to improve their skills.

[0718] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0719] The system according to the present invention acquires user characteristic data, motion data, and user emotional data, and comprehensively analyzes them to provide more precise sports instruction. The system according to the present invention is implemented through the following process.

[0720] First, the user inputs characteristic data such as height and weight using a device. The device sends this data to a server, which calculates the characteristics of sports equipment based on the characteristic data. In addition, the user films their movements through a camera. The filmed video is sent to the server by the device, and the server analyzes the movement data in detail to generate appropriate advice for improving movements.

[0721] Furthermore, this system incorporates an emotion engine. The terminal acquires user emotion data using user input feedback and facial recognition technology. This emotion data is sent to a server, where it is integrated with other data and analyzed.

[0722] In addition to the usual analysis of behavioral data, the server performs adjustments that take emotional data into account to generate advice tailored to the user's current mental state. Specifically, if the user is stressed, the advice is adjusted to be more relaxing, and if the user is agitated, the advice is changed to help them exert their energy more effectively.

[0723] The generated advice is sent to the device, where the user can review it and use it to improve their technique. For example, when a user is trying to learn a new bowling technique, the system can provide standard motion advice such as "Focus on making your left hand movement smoother," but it can also add emotion-based advice such as "Take a deep breath and relax before throwing" if the user's face appears tense.

[0724] Thus, the present invention is a system that uses emotional data to provide users with more personalized guidance and support the improvement of their sports performance.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The user uses the device to input characteristic data such as height, weight, and age. The device saves the entered data locally.

[0728] Step 2:

[0729] The device sends the stored characteristic data to the server via the internet. The server receives the characteristic data and calculates the characteristics of the sports equipment based on that data.

[0730] Step 3:

[0731] The server uses an AI algorithm to analyze the received characteristic data and calculate the optimal characteristics of sports equipment for the user. The calculated results are then sent to the terminal.

[0732] Step 4:

[0733] The device displays characteristic information about sports equipment to the user. The user reviews this information and uses it to improve their sports activities.

[0734] Step 5:

[0735] The user uses the device's camera function to record their own actions. The device saves the recorded video.

[0736] Step 6:

[0737] The device sends the saved video data to the server. The server receives the video data and prepares to perform a detailed motion analysis.

[0738] Step 7:

[0739] The server analyzes the video data and extracts the characteristics of the user's actions. It then prepares to generate optimal improvement suggestions using AI.

[0740] Step 8:

[0741] The device captures the user's facial expressions with its camera and uses an emotion engine to recognize the user's emotions. This data is then sent to a server.

[0742] Step 9:

[0743] The server combines emotional data with behavioral analysis results. Based on the user's emotional state, it adjusts behavioral improvement suggestions and generates personalized advice.

[0744] Step 10:

[0745] The server sends generated advice to the terminal. The terminal displays the advice to the user. The user receives the advice and improves their performance by putting it into practice.

[0746] (Example 2)

[0747] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] Traditional sports coaching systems are primarily limited to feedback based on physical characteristics and movement data, and do not adequately provide personalized instruction that takes into account the user's emotions and mental state. Therefore, it has been difficult to effectively address the stress and motivational changes experienced by users. Consequently, there is a need to develop a system that integrates user emotional data to provide more precise and effective exercise instruction.

[0749] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0750] In this invention, the server includes means for acquiring characteristic information of a person, means for acquiring emotional information using facial recognition technology and feedback, and means for integrating the emotional information and generating advice based on the person's mental state. This makes it possible to provide precise guidance for improving actions that takes the user's emotional state into account.

[0751] "Personal characteristic information" refers to the basic physical specifications of the person in question, such as height, weight, and age, and is data that represents the individual's characteristics.

[0752] "The characteristics of sports equipment" refer to the detailed attributes and specifications of equipment used in sports and fitness activities, including specifications optimized for the physical characteristics of a particular individual.

[0753] "Motion information" refers to data that represents the physical activities and movement characteristics of a person in question, and is acquired through video analysis or sensors.

[0754] "Facial recognition technology" is a computer vision technology that analyzes emotional and psychological states from a person's facial expressions, and often uses machine learning.

[0755] "Emotional information" refers to data that represents a person's emotions and mental state, and is obtained through facial recognition technology and user-inputted feedback.

[0756] "Movement improvement advice" refers to specific suggestions or recommendations for a person to improve or optimize their movements, based on the analyzed movement information.

[0757] "Mental state-based advice" refers to suggestions that encourage behavioral improvement in a way that is most appropriate to a person's current emotions and mental state, based on data analysis that includes emotional information.

[0758] The system according to this invention aims to provide integrated feedback based on the user's physical characteristics, movements, and emotional state by combining multiple modules.

[0759] terminal

[0760] The user first inputs their personal information using the terminal. The terminal has a standard input interface and can input basic information such as height, weight, and age. The terminal transmits this information to the server via a network such as Wi-Fi or LTE. In addition, the terminal is equipped with a camera that captures the user's movement data. For example, if the user is practicing tennis swings, the movement can be recorded from various angles. The terminal is also equipped with software that uses facial recognition technology to obtain emotional information from the user's facial expressions.

[0761] server

[0762] The server processes the received characteristic information and performs calculations to determine the optimal characteristics of the exercise equipment. Using programming languages ​​such as Python and R, it analyzes motion information and generates specific improvement suggestions for the user. Furthermore, it implements a process that takes emotional information into account to generate advice tailored to the user's mental state. This analysis utilizes machine learning algorithms and generative AI models to improve the accuracy of the feedback.

[0763] Specific example

[0764] For example, video data captured by the device is analyzed on the server, and standard feedback such as "improve the angle of your arm when serving" is generated. At the same time, if tension is detected by the facial recognition function, emotion-based advice such as "take a deep breath and relax your shoulders" is added. This allows users to receive more personalized feedback.

[0765] An example of a prompt for a generative AI model is, "Explain how to provide emotionally appropriate advice to a user who is trying to learn a new basketball shooting technique." By utilizing such prompts, generative AI models can provide more advanced analysis and support.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] Users input their basic personal information (height, weight, age, etc.) through a terminal. This information forms the basis for personalized feedback within the system. This input data is immediately transmitted from the terminal to the server.

[0769] Step 2:

[0770] The device uses its camera to record the user's actions. Users can record, for example, their tennis swing or running form. This video data is used for analysis to improve their performance. The recorded video is first converted to the required format within the device, compressed, and then sent to the server.

[0771] Step 3:

[0772] The device further analyzes the user's facial features using facial recognition technology and acquires emotional information. In this step, variations in emotion are extracted from the user's facial expressions in real time and processed as digital data. The acquired emotional information is integrated with other data and sent to the server.

[0773] Step 4:

[0774] The server receives characteristic information, motion data, and emotion information sent from the terminal and begins integrated data analysis. Specifically, it uses programming tools such as Python and R to analyze each dataset using machine learning algorithms. Here, characteristic information is used to optimize the exercise equipment, motion data is analyzed to derive improvement measures, and emotion information is used to...

[0775] This is taken into consideration to reduce tracing and tension.

[0776] Step 5:

[0777] The server generates specific advice for performance improvement based on the data analysis results. This may include minor form adjustments or advice for specific technical improvements. Furthermore, advice tailored to the user's mental state is added based on emotional information. All advice data is formatted and then sent to the terminal.

[0778] Step 6:

[0779] The device displays advice received from the server to the user. At this stage, the feedback is presented in a visually easy-to-understand format, ensuring that the user can immediately put it into practice. The user can then use this to take action to improve their own behavior and mental state.

[0780] (Application Example 2)

[0781] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] In recent years, interest in exercise and fitness has increased, and there is a demand for exercise guidance optimized for each individual. However, conventional systems do not provide guidance that takes into account the emotional state of the user, resulting in the problem that the effects of exercise are not maximized. Guidance that takes into account the individual differences of users is necessary, but a systematic analytical approach that includes information based on emotions has been insufficient to achieve this.

[0783] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0784] In this invention, the server includes means for acquiring a person's characteristic data, means for recording a person's movement data, and means for acquiring a person's emotional data. This makes it possible to comprehensively analyze the user's characteristic data, movement data, and emotional data, and provide personalized guidance, thereby maximizing the effectiveness of exercise tailored to each user.

[0785] "Personal characteristic data" refers to information that expresses a person's physical characteristics, such as height and weight, as numerical values ​​or attributes.

[0786] "Sports equipment characteristics" refers to information that indicates attributes such as the shape and structure of equipment suitable for a specific sports activity, based on individual characteristic data.

[0787] "Motion data" refers to information that records a person's body movements, and specifically includes the timeline and location information of those movements.

[0788] "Instructions" refer to advice and guidelines based on behavioral and emotional data, used to guide improvements in a person's actions and mental approach.

[0789] "Emotional data" refers to information that quantifies or attributes the emotional state of a person, analyzed from their facial expressions and voice.

[0790] "Individualized instructions" refer to specific guidance tailored to an individual, based on an integrated analysis of their characteristics, behaviors, and emotional data.

[0791] "Presentation" is the act or process of communicating generated instructions to a person through visual or auditory means.

[0792] To implement this invention, the user must first input characteristic data via a terminal. This characteristic data includes basic physical information such as height and weight. This data is transmitted to a server via a network. The server analyzes the received characteristic data and calculates the characteristics of the sports equipment.

[0793] Next, the user records their movements using the camera. The recorded movement data is sent from the device to the server, where the server analyzes the movements. The analysis uses a machine learning model based on TensorFlow to recognize movement patterns and problems. Based on the results, instructions for improving the movements are generated for the user.

[0794] Furthermore, facial recognition technology via the device is used to acquire emotional data. Machine learning frameworks such as TensorFlow and PyTorch are used for facial recognition and voice analysis. The emotional data is used to evaluate the user's current mental state and to correct behavioral improvement instructions into personalized instructions.

[0795] The generated personalized instructions are presented to the user via the device. This allows the user to receive not only improved behavior but also advice optimized according to their emotional state.

[0796] As a concrete example, consider a scenario where a user is trying to learn a new yoga pose. In addition to the usual instruction, "Relax your shoulders and maintain the posture," the server can also generate additional instructions such as "Take a deep breath and relax" if it detects tension in the user's face.

[0797] An example of a prompt when using a generative AI model is: "Analyze the user's facial expressions while they are practicing yoga and generate advice to promote relaxation."

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] The user enters characteristic data into the terminal. The entered data includes physical information such as height and weight. The terminal digitizes this information and sends it to the server.

[0801] Step 2:

[0802] The server analyzes the received characteristic data. Using databases and AI models, it calculates the characteristics of sports equipment best suited to each individual. The results are output and stored internally on the server.

[0803] Step 3:

[0804] The user uses the camera to record their actions. The recorded video data is saved on the device and sent to the server as action data.

[0805] Step 4:

[0806] The server analyzes the received operational data. This analysis uses TensorFlow to extract user behavior patterns and problems. The analysis results are output as basic data for generating instructions to improve the operation.

[0807] Step 5:

[0808] The device uses facial recognition technology to acquire the user's emotional data. The acquired facial and voice data are converted into specific emotional states through emotion analysis, and this is sent to the server as emotional data.

[0809] Step 6:

[0810] The server analyzes emotional data and incorporates it into instructions for improving behavior. Based on the emotional data, the generated instructions are refined into personalized instructions. This results in advice optimized for the user's current mental state.

[0811] Step 7:

[0812] The server sends individually generated instructions to the terminal. The terminal presents these instructions to the user, who then reviews them and uses them to improve their own operation.

[0813] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0814] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0815] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0816] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0817] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0818] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0819] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0820] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0821] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0822] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0823] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0824] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0825] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0826] 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.

[0827] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0828] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0829] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0830] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0831] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0832] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0833] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0834] The following is further disclosed regarding the embodiments described above.

[0835] (Claim 1)

[0836] Means for acquiring personal characteristic data,

[0837] A means for calculating the characteristics of appropriate sports equipment based on acquired characteristic data,

[0838] A means of capturing human motion data,

[0839] A means for analyzing captured motion data and generating advice for motion improvement,

[0840] Means for displaying the generated advice,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, which transmits and receives acquired characteristic data.

[0844] (Claim 3)

[0845] The system according to claim 1, which performs digital analysis to extract specific posture or movement characteristics based on captured motion data.

[0846] "Example 1"

[0847] (Claim 1)

[0848] Means for acquiring personal characteristic data,

[0849] A means of calculating the characteristics of an appropriate tool using a generative AI model based on acquired characteristic data,

[0850] A means of capturing human motion data,

[0851] A means for analyzing captured motion data and generating advice on motion improvement using digital analysis technology,

[0852] Means for displaying the generated advice,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, which transmits and receives acquired characteristic data and operational data.

[0856] (Claim 3)

[0857] The system according to claim 1, which performs digital analysis to extract specific posture and movement characteristics based on captured motion data and provides advice using a generated AI model via prompt messages.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] Means for acquiring personal characteristic data,

[0861] A means for calculating the characteristics of appropriate sports equipment based on acquired characteristic data,

[0862] A means of capturing human motion data,

[0863] A means for analyzing captured motion data and generating advice for motion improvement,

[0864] Means for displaying the generated advice,

[0865] A means of outputting the generated advice as audio and presenting it as a visual guideline,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, which transmits and receives acquired characteristic data.

[0869] (Claim 3)

[0870] The system according to claim 1, which extracts specific posture and movement characteristics based on captured motion data and performs digital analysis to generate advice for improving movement.

[0871] "Example 2 of combining an emotion engine"

[0872] (Claim 1)

[0873] Means for obtaining information about a person's characteristics,

[0874] A means for calculating the characteristics of appropriate sports equipment based on acquired characteristic information,

[0875] A means of capturing information about a person's movements,

[0876] A means for analyzing captured motion information and generating advice for motion improvement,

[0877] Methods for acquiring emotional information using facial recognition technology and feedback,

[0878] A means for integrating emotional information and generating advice based on a person's mental state,

[0879] Means for displaying the generated advice,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, which transmits and receives acquired characteristic information and emotional information.

[0883] (Claim 3)

[0884] The system according to claim 1, which performs digital analysis to extract specific posture and movement characteristics based on captured motion information and generates advice for motion improvement that takes emotional information into consideration.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] Means for acquiring personal characteristic data,

[0888] A means for calculating the characteristics of appropriate sports equipment based on acquired characteristic data,

[0889] A means of recording human motion data,

[0890] A means for analyzing recorded motion data and generating instructions for motion improvement,

[0891] A means of acquiring emotional data of a person,

[0892] A means for correcting instructions regarding behavioral improvement and generating individualized instructions based on acquired emotional data,

[0893] A means of presenting the generated individual response instructions,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, which communicates acquired characteristic data, behavioral data, and emotional data.

[0897] (Claim 3)

[0898] The system according to claim 1, which performs digital analysis to extract attributes of specific postures and movements based on recorded motion data and emotion data. [Explanation of symbols]

[0899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for acquiring personal characteristic data, A means for calculating the characteristics of appropriate sports equipment based on acquired characteristic data, A means of capturing human motion data, A means for analyzing captured motion data and generating advice for motion improvement, A means of displaying the generated advice, A system that includes this.

2. The system according to claim 1, which transmits and receives acquired characteristic data.

3. The system according to claim 1, which performs digital analysis to extract specific posture and movement characteristics based on captured motion data.

Images