system

Abstract

We provide the system. [Solution] A means for recording user actions, A comparison means for comparing acquired user behavior data with exemplary behavior data, A means for presenting areas for improvement based on the aforementioned comparison results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is very important for a user to efficiently improve their form and enhance their skills in sports. However, with conventional methods, it is difficult to objectively evaluate one's own form. As a result, there is a problem that improvement is hindered because practice with an incorrect form continues for a long time. For this reason, there is a need for a technology that allows users to accurately and effectively analyze their form and identify areas for improvement.

Means for Solving the Problems

[0005] This invention enables users to perform a detailed analysis of their own form by using motion acquisition means to record user movements and comparison means to compare the acquired motion data with exemplary motion data. It also includes presentation means to visually present specific areas for improvement to the user based on the comparison results. This system allows users to scientifically and accurately analyze their own form and practice more efficiently.

[0006] "Action acquisition means" refers to a device or method for recording and acquiring user actions as data.

[0007] A "comparison means" is a device or method that compares recorded user behavior data with exemplary behavior data and analyzes the differences and degree of similarity.

[0008] A "presentation means" is a device or method that visually shows users areas for improvement or more efficient exercise methods based on data obtained through comparison.

[0009] "Exemplary movement data" refers to data on ideal or standard movements in sports, and is comparative data built on the movements of top-level athletes.

[0010] "User action data" refers to recorded data of actions actually performed by the user in a field such as a sports field. [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, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 following describes a method for implementing the present invention in which a user uses this system for the purpose of improving their sports performance.

[0033] The user records their sports movements using a motion capture device. The terminal acquires data from this motion capture device in real time and generates the user's movement data. This data includes coordinate and angular information consisting of numerous frames.

[0034] Next, the terminal uploads the recorded user movement data to the server. The server retrieves exemplary movement data of top athletes from its database and compares it with the user's movement data. Here, the comparison mechanism built into the server uses an algorithm to quantify the degree of similarity in movement and identify differences. Based on the identified differences in movement, the server also generates information about specific areas where the user should improve.

[0035] This generated comparison information is sent to the device and presented to the user. The device overlays the user's video of their movements with exemplary movements of top athletes, visually showing the differences in their movements. This comparison allows the user to intuitively understand which parts need correction and what kind of training is required.

[0036] For example, if a user wants to improve their baseball pitching form, the server will identify specific points such as the angle of their elbow when throwing and the length of their stride. The user can then check this information through their device and incorporate the suggested improvements into their next practice session.

[0037] This allows users to receive scientifically-based feedback and efficiently improve their skills. The present invention is a system that enhances the quality of self-practice and helps users reach their desired goals in the shortest possible time.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user sets up the motion capture device and prepares to record their movements. The terminal connects to the device to acquire motion capture data and begins recording movements. The user's movement data is captured frame by frame along the timeline and saved to local storage.

[0041] Step 2:

[0042] The terminal compresses the saved motion data and uploads it to the server. The server receives the uploaded data and stores it in a database. The server searches for appropriate model motion data according to the sport and target motion, and prepares for the comparison process.

[0043] Step 3:

[0044] The server uses a motion analysis algorithm to compare the user's motion data with exemplary motion data. It extracts characteristic points of the motion (elbow angle, shoulder position, etc.) and quantifies the differences. As a result of the analysis, it calculates the degree of agreement between the user's motion and the exemplary motion and identifies areas that need improvement.

[0045] Step 4:

[0046] The server sends the analysis results to the terminal. Based on these results, the terminal generates a video of the user's actions and an overlay video of exemplary actions. The terminal also visually shows the user the differences in their actions and displays specific areas for improvement and advice on the screen.

[0047] Step 5:

[0048] Based on the suggested areas for improvement, the user practices to improve their form. Motion capture is performed again as needed to confirm the effectiveness of the improvements. The user can repeat this process to obtain further feedback.

[0049] (Example 1)

[0050] 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."

[0051] There is a need to provide users with a quick and accurate means to objectively evaluate their own actions and identify areas for improvement. Furthermore, existing technologies have the problem that analyzing actions and providing feedback takes time, making it difficult for users to implement improvements in real time.

[0052] 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.

[0053] In this invention, the server includes an information acquisition means, a comparison means, and an information generation means. This allows the user to quickly analyze their own operation data and obtain specific and immediate areas for improvement, enabling them to appropriately modify their actions.

[0054] An "information acquisition means" is a technological device for recording user activity and acquiring information related to that activity as digital data.

[0055] The "comparison means" is a function that evaluates the similarities and differences between actions by comparing acquired user information with reference activity information.

[0056] A "presentation method" is a configuration that has the ability to provide information to the user in a visual or other format and to present specific areas for improvement or advice.

[0057] "Communication means" refers to a method for efficiently and securely sending and receiving information between a terminal and a server, and is a function that enables data transmission.

[0058] "Information generation means" refers to a function that analyzes data received by the server and uses the output of a generated AI model to produce information such as feedback.

[0059] To implement this invention, the user uses a motion capture device as a means of acquiring information to record movements. For example, common capture techniques include optical and inertial motion capture devices. This device measures the user's movements with high precision and generates detailed digital data, including the position and angle of each joint.

[0060] The terminal is responsible for transmitting the acquired user activity data to the server in the appropriate format. The data is transmitted to the server in real time via communication methods, utilizing wireless communication technologies such as Wi-Fi and Bluetooth.

[0061] The server analyzes the received user behavior data and compares it to the activity information of a benchmark elite athlete using a comparison mechanism. This analysis utilizes a generative AI model to identify similarities and differences between the user's behavior and the exemplary behavior. Then, the information generation mechanism creates feedback with specific areas for improvement along with numerical data.

[0062] This feedback information is transmitted to the user's device through a presentation device. The device uses the feedback data to overlay exemplary movements onto the user's video footage, visually showing which parts are different. This allows the user to intuitively understand specific areas for improvement and use this information to modify their training methods.

[0063] As a concrete example, suppose a user wants to improve their baseball pitching form. They record their pitching motion using a motion capture device, send the data to a server, and the server compares it to the pitching form data of a top-level player. As a result, feedback is generated showing differences in elbow angle and stride length. The user can review these suggestions for improvement through their device and incorporate them into their next practice session.

[0064] Examples of prompts to input into a generative AI model are as follows:

[0065] "Use the user's pitching form data and compare it to exemplary data from top-level players to identify differences in elbow angle and stride length, and provide specific suggestions for improvement."

[0066] In this way, the invention aims to improve user actions efficiently and scientifically.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The user wears a motion capture device to record sports movements. The input includes real-time acquisition of the user's body position and angle information. This data is output as motion information, including numerical data with joint coordinates and angles in each frame.

[0070] Step 2:

[0071] The terminal receives motion data acquired from the motion capture device. The input is raw data from the motion capture device, which is then formatted appropriately. The formatted data is then encrypted and sent to the server as output.

[0072] Step 3:

[0073] The server analyzes the user's movement data received. The data received as input is compared with exemplary movement data of top athletes obtained from a database. A generative AI model is used to identify differences in movement and output them numerically.

[0074] Step 4:

[0075] The server identifies areas for improvement and generates feedback. It uses information about behavioral differences identified by the generating AI model as input. The feedback is output as text and image data, including specific points for improvement and advice.

[0076] Step 5:

[0077] The terminal presents the user with feedback information from the server. The input is improvement suggestions from the server. The system displays exemplary actions overlaid on the user's video footage, visually showing areas for improvement, allowing the user to modify their actions based on this information.

[0078] (Application Example 1)

[0079] 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."

[0080] The present invention aims to provide a system that allows users to receive appropriate support in a home environment in order to efficiently improve their sports and athletic skills. In particular, it aims to enable users to intuitively understand the areas for improvement pointed out through motion recording and real-time feedback, and to reflect these improvements in their practice.

[0081] 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.

[0082] In this invention, the server includes an observation device for recording user movements, a comparison device for comparing the recorded user movement data with reference movement data, a presentation device for suggesting corrections based on the comparison results, and an auxiliary device mounted on a home automation device that provides real-time support for user movements. This enables users to scientifically and efficiently improve their motor skills while at home.

[0083] A "user" is an individual who uses this system to record their actions and receive feedback.

[0084] An "observation device" is a device used to record a user's actions, and mainly includes cameras and sensors.

[0085] "Action data" refers to data that records the user's actions and consists of multiple frames that include location information and angle information.

[0086] "Reference movement data" refers to data from top-level athletes or target movements, and is used as a standard for comparison.

[0087] A "comparison device" is a device used to compare recorded operation data with reference operation data.

[0088] "Comparison results" refer to data obtained by comparing operational data with reference operational data, and indicate differences in operation.

[0089] A "presentation device" is a device that presents users with points for correction or improvement.

[0090] "Home automation devices" refer to automated devices used within the home that provide real-time support to the user's actions.

[0091] An "assistive device" is a device designed to provide real-time support to users as they improve their motor skills.

[0092] The system that realizes this application provides comprehensive support for users to improve their motor skills at home. The system consists of an automated home device that integrates an observation device, a matching device, and a presentation device.

[0093] The user records their movements through an observation device. This device includes high-resolution cameras and motion sensors, enabling detailed capture of the user's movement data. This movement data is transmitted to a server via the network.

[0094] The server compares the received behavioral data with reference behavioral data. This comparison is performed using a generative AI model, which quantifies the degree of agreement for each behavior. This process utilizes a machine learning framework such as TENSORFLOW®. The AI ​​model identifies differences in behavior and generates data to recommend which parts should be corrected.

[0095] The generated data is presented to the user through a presentation device. The presentation uses both visual and auditory elements, allowing the user to intuitively understand the necessary corrections. The home automation device is equipped with a Snapdragon processor, enabling it to provide rapid feedback.

[0096] For example, when a user practices their tennis swing at home, the observation device records the swing motion and sends the data to a server. The server identifies how the user's swing differs from a baseline and notifies the user if there is room for improvement in the range of arm movement.

[0097] An example of a prompt for the generating AI model would be: "Analyze the user's latest swing data and compare it to data from top players to identify areas for improvement in your swing."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] When the user begins to move, the device's monitoring system records the movement using a high-resolution camera and motion sensors. The input is the user's physical movement, and the output is motion data (including position and angle information) across multiple frames. This data is captured in real time and stored in the device's memory.

[0101] Step 2:

[0102] Recorded motion data is uploaded from the terminal to the server via the network. The server compares this motion data with reference motion data. The input is the motion data, and the output is an analysis value for comparison. On the server, a generating AI model analyzes the motion data and quantifies the degree of agreement for each motion.

[0103] Step 3:

[0104] The server uses the analyzed data to identify the differences between user behavior and baseline behavior. The input is the analyzed value from the previous step, and the output is specific behavior points where correction is recommended. The server uses a generative AI model to calculate the differences for each behavior parameter and highlight particularly significant differences.

[0105] Step 4:

[0106] The server sends the identified correction point information to the terminal. Immediately, the terminal presents this information to the user through a display device. The input is the correction point information, and the output is the feedback provided to the user visually and audibly. The terminal overlays the collected data on the screen and provides voice guidance.

[0107] Step 5:

[0108] Based on the feedback provided, users adjust their movements. By continuing to train according to the feedback, users can improve the precision of their movements. Specifically, users may swing again, widening the range of motion of their arms. Through this process, users repeatedly train and effectively improve their movements.

[0109] 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.

[0110] This invention is an emotion-recognition sports training system that scientifically analyzes and improves user actions and provides appropriate feedback based on the user's emotional state. The system includes action acquisition means, comparison means, presentation means, and an emotion engine.

[0111] The user first records their movements using a motion capture device. The terminal acquires this motion capture data and saves it to storage as user movement data. Simultaneously, an emotion engine analyzes the user's facial expressions and voice to detect their emotional state during movement.

[0112] The recorded motion data is uploaded to a server and compared to exemplary motion data from top athletes. The server analyzes the motion data and identifies characteristic differences in movement. It then evaluates how the movement can be improved and sends the results to the terminal.

[0113] The device comprehensively analyzes the results from the server and the emotional data detected by the emotion engine. It understands the user's emotions—whether they are satisfied, impatient, or dissatisfied—and suggests improvements accordingly. For example, if the device determines that the user is impatient, it may offer simple introductory training to help improve the operation or suggest a message to boost their motivation.

[0114] For example, when evaluating a baseball pitching form, if the user shows signs of dissatisfaction during the motion, in addition to suggesting improvements, it might be helpful to offer relaxation techniques to reduce psychological pressure.

[0115] Thus, by combining motion analysis and emotion recognition, this invention provides users with personalized training effects and enables efficient skill improvement. This allows users to receive support in their sports practice from both psychological and technical perspectives.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The user prepares to record their sports movements using a motion capture device. The terminal connects to the device and generates frame data of the movements. In parallel, the terminal records the user's facial expressions and voice through its camera and microphone, collecting emotional data.

[0119] Step 2:

[0120] The device uploads motion data to the server. Simultaneously, it also sends collected emotion data to the server. The server receives this data and stores the motion data in its database.

[0121] Step 3:

[0122] The server compares the stored motion data with exemplary movements of the target sport. Using comparison tools, it performs numerical analysis on the details of the movements (e.g., joint angles and body balance) to identify differences.

[0123] Step 4:

[0124] The server analyzes the emotional data it receives simultaneously using an emotion engine. Based on facial expression analysis and voice data, it evaluates the user's emotional state and determines whether psychological consideration is necessary.

[0125] Step 5:

[0126] By combining the analysis results and emotion assessment, the server generates suggestions for improvement for the user. For example, if the user is feeling tense, it might include instructions on stretching or breathing exercises to help them relax.

[0127] Step 6:

[0128] The server sends the generated improvement suggestions to the terminal. The terminal visually displays specific points for improving performance to the user and provides emotionally responsive advice.

[0129] Step 7:

[0130] Based on the suggested improvements, users will modify their action forms and implement mental health measures. If necessary, they will record their actions again and re-evaluate to confirm the improvements.

[0131] (Example 2)

[0132] 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".

[0133] Traditional sports training systems provided improvement suggestions based solely on user movement data, making it difficult to provide feedback that considered the user's emotional state and thus hindering efficient movement improvement. Furthermore, it was challenging to offer appropriate countermeasures when users were emotionally anxious or dissatisfied.

[0134] 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.

[0135] In this invention, the server includes an action acquisition means for recording user actions, a comparison means for comparing the acquired user action data with exemplary action data, an emotion analysis means for analyzing the user's emotional state, a presentation means for suggesting areas for improvement based on the comparison results and emotional state, and a feedback adjustment means for adjusting the feedback. This enables personalized feedback based on both the user's actions and emotional state.

[0136] "Motion acquisition means" refers to a technological device for measuring the user's body movements and acquiring them as digital data.

[0137] "Comparison means" refers to an algorithm or function for comparing acquired user behavior data with reference behavior data and identifying any differences between them.

[0138] "Presentation method" refers to an interface used to notify users of areas for improvement or suggestions based on analyzed data.

[0139] "Emotional analysis methods" refer to technologies that recognize and analyze a user's emotional state using the user's facial expressions, voice, and other physiological data.

[0140] "Feedback adjustment means" refers to a function that dynamically adjusts the content and format of the feedback presented according to the user's actions and emotional state.

[0141] This invention relates to a system that scientifically analyzes a user's actions and emotional state and provides appropriate feedback to the user. This system includes means for acquiring actions, means for comparison, means for presentation, means for emotion analysis, and means for adjusting feedback.

[0142] The user records their movements using a motion capture device. Here, general motion tracking technology is used to acquire the user's movements as 3D coordinate data. The device then stores this data as motion data in its storage. Furthermore, it incorporates technology to analyze the user's facial expressions and voice using emotion analysis methods to determine the user's emotional state in real time. This utilizes general emotion recognition technology and analysis software.

[0143] The acquired motion data is uploaded to a server in the cloud, where it is compared with exemplary motion data from top athletes. The server analyzes the acquired motion data and identifies characteristic differences in movement. Machine learning algorithms are often used for this analysis.

[0144] The server transmits the analysis results to the terminal, which then uses this data to provide comprehensive feedback to the user. Presentation tools are used to visually and audibly provide suggestions for improvement and operational advice. Furthermore, feedback adjustment tools are used to appropriately adjust the feedback according to the user's emotional state.

[0145] For example, a user might want to improve their baseball pitching form. In this case, the system would assess the user's emotional state and, if the user is feeling anxious, provide feedback to encourage relaxation. This feedback may be provided along with a message to boost motivation.

[0146] Examples of input prompts for a generative AI model:

[0147] "Analyze the user's behavioral data and propose improvements to their sports training based on their emotional state. If the user is feeling anxious, add a message to boost their motivation."

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The user records their movements using a motion capture device. The input is the user's movements, and the output is 3D coordinate data. This allows the user's movements to be collected as digital information. Based on this motion acquisition, the system quantifies the user's specific movements and proceeds to the next processing step.

[0151] Step 2:

[0152] The terminal receives 3D coordinate data acquired by the motion acquisition means and saves it to storage. The input is the output data from step 1, and the output is the saved motion data. By saving it to storage, it becomes possible to retain the user's motion history for later analysis.

[0153] Step 3:

[0154] The device uses emotion analysis techniques to analyze the user's emotional state from their facial expressions and voice. The input is the user's live video and audio signals, and the output is data indicating the user's emotional state. Specifically, it categorizes emotions using video and audio signal processing and outputs the state as numerical values ​​or text.

[0155] Step 4:

[0156] The device uploads the saved motion data and analyzed emotion data to the server. The input is the output data from steps 2 and 3, and the output is the integrated data sent to the server. With this transmission, the server obtains comprehensive data on the user and prepares for the next analysis.

[0157] Step 5:

[0158] The server analyzes the received motion data to compare it with exemplary data from top athletes. The input is the integrated data and exemplary data from step 4, and the output is the analysis results showing the differences in motion characteristics. Machine learning algorithms are used to specifically identify which elements of the motion can be improved.

[0159] Step 6:

[0160] The server sends the analysis results to the terminal. The input is the output data from step 5, and the output is the analysis results received by the terminal. Based on these results, feedback is provided to the user in the next step.

[0161] Step 7:

[0162] The device adjusts the feedback it provides based on the analysis results and emotional data. The input is the analysis results from step 6 and the emotional data from step 3, and the output is the adjusted feedback information. Specifically, it considers the user's emotional state to determine the most appropriate advice and message.

[0163] Step 8:

[0164] The device presents the user with adjusted feedback. The input is the feedback information from step 7, and the output is the specific feedback the user receives. Using the presentation method, suggestions for improvement and advice on actions are provided in text or audio, allowing the user to train efficiently based on this.

[0165] (Application Example 2)

[0166] 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".

[0167] In sports and fitness training, there is a need to analyze users' physical movements with high accuracy and provide personalized feedback tailored to the user's emotional state. However, conventional technologies focus solely on motion analysis and do not consider the user's emotional state during training, resulting in difficulties in efficiently improving movement.

[0168] 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.

[0169] In this invention, the server includes data acquisition means for recording the user's physical movements, analysis means for comparing the acquired movement information with reference movement information, information presentation means for suggesting improvements based on the comparison results, an emotional analysis function, and a function for providing personalized movement improvement suggestions. This enables comprehensive and personalized feedback to the user from both movement data and emotional state perspectives.

[0170] A "user" refers to a person who uses the system to have their physical movements recorded and analyzed.

[0171] "Physical movements" refer to the physical actions performed by the user and are the subject of data acquired using image processing technology.

[0172] "Data acquisition means" encompasses all devices and technologies for recording a user's physical movements, and refers to methods for collecting motion data using image processing technology.

[0173] "Reference motion information" refers to data of exemplary motions used for comparison, and is motion information that is compared to the user's physical movements.

[0174] An "analysis tool" is a tool that has the function of comparing acquired operational information with reference operational information and identifying the differences and areas for improvement.

[0175] "Information presentation means" refers to methods for communicating analysis results and improvement points to users visually and audibly.

[0176] The "emotional analysis function" is a feature that understands the user's emotional state and evaluates their emotions during operation.

[0177] "Personalized performance improvement suggestions" refer to improvement suggestions tailored to specific users based on the user's emotional state and performance analysis.

[0178] In this invention, the user first uses a device to record physical movements. Specifically, when the user performs an action, the movement is captured by a device equipped with a camera and sensors as data acquisition means. This device uses image processing technology to collect the user's movement data and transmit it to a terminal. Furthermore, an emotional analysis function is realized by using a microphone and voice analysis software to analyze the user's voice and facial expressions.

[0179] The server compares the motion data transmitted from the terminal with reference motion information. Here, OpenCV, which provides computer vision technology, and analysis algorithms for evaluating differences in motion are used as analysis tools. Through this process, the differences between the user's actions and the reference actions are clarified, and areas for improvement are identified.

[0180] As a means of presenting information, the terminal visually and audibly presents areas for improvement to the user based on the analysis results. For example, if a user has a problem with a particular form while running, the system provides specific advice on that point visually and audibly. If the emotional analysis function detects the user's emotional state, it provides suggestions for improving their movements in accordance with the user's emotions. For this purpose, the robot can also provide motivational messages audibly.

[0181] For example, when a user who is jogging shows signs of fatigue through voice or facial expressions, the system will voice a message such as, "You're almost at the finish line! Keep going!" This helps the user renew their motivation and continue training efficiently.

[0182] When using a generative AI model to generate specific behavioral improvement suggestions, the following prompt can be used: "Analyze user behavioral and emotional data to generate optimal feedback and specific improvement suggestions."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The user performs actions using cameras and sensors to record their body movements. The input is the user's body movements, and the output is captured motion data. This data is transferred to a terminal, where image processing technology is used to extract details of the position and movement.

[0186] Step 2:

[0187] The device uses a microphone and camera to record the user's voice and facial expressions. The input is the user's voice and facial expressions, and the output is analyzed emotional data. The emotional analysis function analyzes this data with voice analysis software to identify the user's emotional state.

[0188] Step 3:

[0189] The terminal sends the data acquired in Step 1 and Step 2 to the server. The input is motion data and emotional data, and the output is a dataset for analysis. The server compares the received data with baseline behavior information and uses analysis algorithms such as OpenCV to identify areas where the user's behavior needs improvement.

[0190] Step 4:

[0191] The server generates suggestions for improving user behavior based on the analysis results. The input is the comparison result, and the output is a specific improvement suggestion. When generating a specific behavior improvement suggestion using the generation AI model, prompts are used.

[0192] Step 5:

[0193] The terminal receives suggestions from the server and presents improvement proposals to the user visually and audibly. The input is the improvement suggestions from the server, and the output is the feedback presented to the user. Specific operational improvements and motivational messages are provided to make it easier for the user to implement the feedback.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] The following describes a method for implementing the present invention in which a user uses this system for the purpose of improving their sports performance.

[0211] The user records their sports movements using a motion capture device. The terminal acquires data from this motion capture device in real time and generates the user's movement data. This data includes coordinate and angular information consisting of numerous frames.

[0212] Next, the terminal uploads the recorded user movement data to the server. The server retrieves exemplary movement data of top athletes from its database and compares it with the user's movement data. Here, the comparison mechanism built into the server uses an algorithm to quantify the degree of similarity in movement and identify differences. Based on the identified differences in movement, the server also generates information about specific areas where the user should improve.

[0213] This generated comparison information is sent to the device and presented to the user. The device overlays the user's video of their movements with exemplary movements of top athletes, visually showing the differences in their movements. This comparison allows the user to intuitively understand which parts need correction and what kind of training is required.

[0214] For example, if a user wants to improve their baseball pitching form, the server will identify specific points such as the angle of their elbow when throwing and the length of their stride. The user can then check this information through their device and incorporate the suggested improvements into their next practice session.

[0215] This allows users to receive scientifically-based feedback and efficiently improve their skills. The present invention is a system that enhances the quality of self-practice and helps users reach their desired goals in the shortest possible time.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The user sets up the motion capture device and prepares to record their movements. The terminal connects to the device to acquire motion capture data and begins recording movements. The user's movement data is captured frame by frame along the timeline and saved to local storage.

[0219] Step 2:

[0220] The terminal compresses the saved motion data and uploads it to the server. The server receives the uploaded data and stores it in a database. The server searches for appropriate model motion data according to the sport and target motion, and prepares for the comparison process.

[0221] Step 3:

[0222] The server uses a motion analysis algorithm to compare the user's motion data with exemplary motion data. It extracts characteristic points of the motion (elbow angle, shoulder position, etc.) and quantifies the differences. As a result of the analysis, it calculates the degree of agreement between the user's motion and the exemplary motion and identifies areas that need improvement.

[0223] Step 4:

[0224] The server sends the analysis results to the terminal. Based on these results, the terminal generates a video of the user's actions and an overlay video of exemplary actions. The terminal also visually shows the user the differences in their actions and displays specific areas for improvement and advice on the screen.

[0225] Step 5:

[0226] Based on the suggested areas for improvement, the user practices to improve their form. Motion capture is performed again as needed to confirm the effectiveness of the improvements. The user can repeat this process to obtain further feedback.

[0227] (Example 1)

[0228] 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."

[0229] There is a need to provide users with a quick and accurate means to objectively evaluate their own actions and identify areas for improvement. Furthermore, existing technologies have the problem that analyzing actions and providing feedback takes time, making it difficult for users to implement improvements in real time.

[0230] 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.

[0231] In this invention, the server includes an information acquisition means, a comparison means, and an information generation means. This allows the user to quickly analyze their own operation data and obtain specific and immediate areas for improvement, enabling them to appropriately modify their actions.

[0232] An "information acquisition means" is a technological device for recording user activity and acquiring information related to that activity as digital data.

[0233] The "comparison means" is a function that evaluates the similarities and differences between actions by comparing acquired user information with reference activity information.

[0234] A "presentation method" is a configuration that has the ability to provide information to the user in a visual or other format and to present specific areas for improvement or advice.

[0235] "Communication means" refers to a method for efficiently and securely sending and receiving information between a terminal and a server, and is a function that enables data transmission.

[0236] "Information generation means" refers to a function that analyzes data received by the server and uses the output of a generated AI model to produce information such as feedback.

[0237] To implement this invention, the user uses a motion capture device as a means of acquiring information to record movements. For example, common capture techniques include optical and inertial motion capture devices. This device measures the user's movements with high precision and generates detailed digital data, including the position and angle of each joint.

[0238] The terminal is responsible for transmitting the acquired user activity data to the server in the appropriate format. The data is transmitted to the server in real time via communication methods, utilizing wireless communication technologies such as Wi-Fi and Bluetooth.

[0239] The server analyzes the received user behavior data and compares it to the activity information of a benchmark elite athlete using a comparison mechanism. This analysis utilizes a generative AI model to identify similarities and differences between the user's behavior and the exemplary behavior. Then, the information generation mechanism creates feedback with specific areas for improvement along with numerical data.

[0240] This feedback information is transmitted to the user's device through a presentation device. The device uses the feedback data to overlay exemplary movements onto the user's video footage, visually showing which parts are different. This allows the user to intuitively understand specific areas for improvement and use this information to modify their training methods.

[0241] As a concrete example, suppose a user wants to improve their baseball pitching form. They record their pitching motion using a motion capture device, send the data to a server, and the server compares it to the pitching form data of a top-level player. As a result, feedback is generated showing differences in elbow angle and stride length. The user can review these suggestions for improvement through their device and incorporate them into their next practice session.

[0242] Examples of prompts to input into a generative AI model are as follows:

[0243] "Use the user's pitching form data and compare it to exemplary data from top-level players to identify differences in elbow angle and stride length, and provide specific suggestions for improvement."

[0244] In this way, the invention aims to improve user actions efficiently and scientifically.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The user wears a motion capture device to record sports movements. The input includes real-time acquisition of the user's body position and angle information. This data is output as motion information, including numerical data with joint coordinates and angles in each frame.

[0248] Step 2:

[0249] The terminal receives motion data acquired from the motion capture device. The input is raw data from the motion capture device, which is then formatted appropriately. The formatted data is then encrypted and sent to the server as output.

[0250] Step 3:

[0251] The server analyzes the user's movement data received. The data received as input is compared with exemplary movement data of top athletes obtained from a database. A generative AI model is used to identify differences in movement and output them numerically.

[0252] Step 4:

[0253] The server identifies areas for improvement and generates feedback. It uses information about behavioral differences identified by the generating AI model as input. The feedback is output as text and image data, including specific points for improvement and advice.

[0254] Step 5:

[0255] The terminal presents the user with feedback information from the server. The input is improvement suggestions from the server. The system displays exemplary actions overlaid on the user's video footage, visually showing areas for improvement, allowing the user to modify their actions based on this information.

[0256] (Application Example 1)

[0257] 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."

[0258] The present invention aims to provide a system that allows users to receive appropriate support in a home environment in order to efficiently improve their sports and athletic skills. In particular, it aims to enable users to intuitively understand the areas for improvement pointed out through motion recording and real-time feedback, and to reflect these improvements in their practice.

[0259] 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.

[0260] In this invention, the server includes an observation device for recording user movements, a comparison device for comparing the recorded user movement data with reference movement data, a presentation device for suggesting corrections based on the comparison results, and an auxiliary device mounted on a home automation device that provides real-time support for user movements. This enables users to scientifically and efficiently improve their motor skills while at home.

[0261] A "user" is an individual who uses this system to record their actions and receive feedback.

[0262] An "observation device" is a device used to record a user's actions, and mainly includes cameras and sensors.

[0263] "Action data" refers to data that records the user's actions and consists of multiple frames that include location information and angle information.

[0264] "Reference movement data" refers to data from top-level athletes or target movements, and is used as a standard for comparison.

[0265] A "comparison device" is a device used to compare recorded operation data with reference operation data.

[0266] "Comparison results" refer to data obtained by comparing operational data with reference operational data, and indicate differences in operation.

[0267] A "presentation device" is a device that presents users with points for correction or improvement.

[0268] "Home automation devices" refer to automated devices used within the home that provide real-time support to the user's actions.

[0269] An "assistive device" is a device designed to provide real-time support to users as they improve their motor skills.

[0270] The system that realizes this application provides comprehensive support for users to improve their motor skills at home. The system consists of an automated home device that integrates an observation device, a matching device, and a presentation device.

[0271] The user records their movements through an observation device. This device includes high-resolution cameras and motion sensors, enabling detailed capture of the user's movement data. This movement data is transmitted to a server via the network.

[0272] The server compares the received behavioral data with reference behavioral data. This comparison is performed using a generative AI model, which quantifies the degree of agreement for each behavior. A machine learning framework such as TensorFlow is used in this process. The AI ​​model identifies differences in behavior and generates data to recommend which parts should be corrected.

[0273] The generated data is presented to the user through a presentation device. The presentation uses both visual and auditory elements, allowing the user to intuitively understand the necessary corrections. The home automation device is equipped with a Snapdragon processor, enabling it to provide rapid feedback.

[0274] For example, when a user practices their tennis swing at home, the observation device records the swing motion and sends the data to a server. The server identifies how the user's swing differs from a baseline and notifies the user if there is room for improvement in the range of arm movement.

[0275] An example of a prompt for the generating AI model would be: "Analyze the user's latest swing data and compare it to data from top players to identify areas for improvement in your swing."

[0276] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0277] Step 1:

[0278] When the user starts an operation, the observation device of the terminal records the operation with a high-resolution camera and a motion sensor. The input is the physical operation of the user, and the output is operation data (including position information and angle information) over a number of frames. This data is captured in real time and stored in the memory of the terminal.

[0279] Step 2:

[0280] The recorded operation data is uploaded from the terminal to the server through the network. The server collates this operation data with the reference operation data. The input is the operation data, and the output is the analysis value for comparison. On the server, the generated AI model analyzes the operation data and quantifies the degree of match of each operation.

[0281] Step 3:

[0282] The server uses the analyzed data to identify the differences between the user's operation and the reference operation. The input is the analysis value in the previous step, and the output is the specific operation points where corrections are recommended. The server uses the generated AI model to calculate the differences in each operation parameter and emphasizes particularly prominent differences.

[0283] Step 4:

[0284] The server sends the correction point information identified to the terminal. Immediately, the terminal presents this information to the user through the presentation device. The input is the correction point information, and the output is the feedback provided to the user visually and audibly. The terminal overlays and displays the collected data on the screen and provides voice guidance.

[0285] Step 5:

[0286] Based on the presented feedback, the user adjusts their actions. By continuing the training according to the feedback, the user can improve the accuracy of their actions. As a specific action, the user swings again to widen the swing amplitude of the arm. Through this process, the user conducts repeated training and effectively improves their actions.

[0287] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0288] The present invention is an emotion recognition sports training system that scientifically analyzes and improves the user's actions and appropriately provides feedback on the results according to the user's emotional state. This system includes an action acquisition means, a comparison means, a presentation means, and an emotion engine.

[0289] First, the user records their actions using a motion capture device. The terminal acquires this motion capture data and stores it in storage as the user's action data. At the same time, the emotion engine analyzes the user's facial expressions and voice to detect the emotional state during the action.

[0290] The recorded action data is uploaded to the server and compared with the exemplary action data of top athletes. The server analyzes the motion data and identifies the differences in characteristic actions. Here, it evaluates how the actions can be improved and transmits the results to the terminal.

[0291] The terminal comprehensively analyzes the analysis results from the server and the emotion data detected by the emotion engine. It grasps the emotions of whether the user is satisfied, anxious, or feeling dissatisfied with the action, and presents improvement plans accordingly. For example, if the user is determined to be anxious, it provides simple introductory training useful for improving the action or proposes a message to boost certain motivation.

[0292] For example, when evaluating a baseball pitching form, if the user shows signs of dissatisfaction during the motion, in addition to suggesting improvements, it might be helpful to offer relaxation techniques to reduce psychological pressure.

[0293] Thus, by combining motion analysis and emotion recognition, this invention provides users with personalized training effects and enables efficient skill improvement. This allows users to receive support in their sports practice from both psychological and technical perspectives.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The user prepares to record their sports movements using a motion capture device. The terminal connects to the device and generates frame data of the movements. In parallel, the terminal records the user's facial expressions and voice through its camera and microphone, collecting emotional data.

[0297] Step 2:

[0298] The device uploads motion data to the server. Simultaneously, it also sends collected emotion data to the server. The server receives this data and stores the motion data in its database.

[0299] Step 3:

[0300] The server compares the stored motion data with exemplary movements of the target sport. Using comparison tools, it performs numerical analysis on the details of the movements (e.g., joint angles and body balance) to identify differences.

[0301] Step 4:

[0302] The server analyzes the simultaneously received emotion data by means of an emotion engine. It evaluates the user's emotional state from facial expression analysis and voice, and determines whether psychological consideration based on this is necessary.

[0303] Step 5:

[0304] Combining the analysis results and the emotion evaluation, the server generates improvement plans for the user. For example, when the user is tense, it includes guidance on stretching and breathing methods to relieve tension.

[0305] Step 6:

[0306] The server transmits the generated improvement plans to the terminal. The terminal visually shows the specific points for improving the operation to the user and displays advice according to the emotion.

[0307] Step 7:

[0308] Based on the presented improvement plans, the user implements corrections to the operation form and mental health measures. If necessary, the operation is recorded again and re-evaluation is performed to confirm the improved operation.

[0309] (Example 2)

[0310] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In the conventional sports training system, improvement plans were provided only based on the user's motion data, so feedback considering the user's emotional state could not be provided, and it was difficult to improve the operation efficiently. Also, when the user was emotionally anxious or dissatisfied, it was difficult to present appropriate countermeasures according to that state.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0313] In this invention, the server includes an action acquisition means for recording user actions, a comparison means for comparing the acquired user action data with exemplary action data, an emotion analysis means for analyzing the user's emotional state, a presentation means for suggesting areas for improvement based on the comparison results and emotional state, and a feedback adjustment means for adjusting the feedback. This enables personalized feedback based on both the user's actions and emotional state.

[0314] "Motion acquisition means" refers to a technological device for measuring the user's body movements and acquiring them as digital data.

[0315] "Comparison means" refers to an algorithm or function for comparing acquired user behavior data with reference behavior data and identifying any differences between them.

[0316] "Presentation method" refers to an interface used to notify users of areas for improvement or suggestions based on analyzed data.

[0317] "Emotional analysis methods" refer to technologies that recognize and analyze a user's emotional state using the user's facial expressions, voice, and other physiological data.

[0318] "Feedback adjustment means" refers to a function that dynamically adjusts the content and format of the feedback presented according to the user's actions and emotional state.

[0319] This invention relates to a system that scientifically analyzes a user's actions and emotional state and provides appropriate feedback to the user. This system includes means for acquiring actions, means for comparison, means for presentation, means for emotion analysis, and means for adjusting feedback.

[0320] The user records their movements using a motion capture device. Here, general motion tracking technology is used to acquire the user's movements as 3D coordinate data. The device then stores this data as motion data in its storage. Furthermore, it incorporates technology to analyze the user's facial expressions and voice using emotion analysis methods to determine the user's emotional state in real time. This utilizes general emotion recognition technology and analysis software.

[0321] The acquired motion data is uploaded to a server in the cloud, where it is compared with exemplary motion data from top athletes. The server analyzes the acquired motion data and identifies characteristic differences in movement. Machine learning algorithms are often used for this analysis.

[0322] The server transmits the analysis results to the terminal, which then uses this data to provide comprehensive feedback to the user. Presentation tools are used to visually and audibly provide suggestions for improvement and operational advice. Furthermore, feedback adjustment tools are used to appropriately adjust the feedback according to the user's emotional state.

[0323] For example, a user might want to improve their baseball pitching form. In this case, the system would assess the user's emotional state and, if the user is feeling anxious, provide feedback to encourage relaxation. This feedback may be provided along with a message to boost motivation.

[0324] Examples of input prompts for a generative AI model:

[0325] "Analyze the user's behavioral data and propose improvements to their sports training based on their emotional state. If the user is feeling anxious, add a message to boost their motivation."

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The user records their movements using a motion capture device. The input is the user's movements, and the output is 3D coordinate data. This allows the user's movements to be collected as digital information. Based on this motion acquisition, the system quantifies the user's specific movements and proceeds to the next processing step.

[0329] Step 2:

[0330] The terminal receives 3D coordinate data acquired by the motion acquisition means and saves it to storage. The input is the output data from step 1, and the output is the saved motion data. By saving it to storage, it becomes possible to retain the user's motion history for later analysis.

[0331] Step 3:

[0332] The device uses emotion analysis techniques to analyze the user's emotional state from their facial expressions and voice. The input is the user's live video and audio signals, and the output is data indicating the user's emotional state. Specifically, it categorizes emotions using video and audio signal processing and outputs the state as numerical values ​​or text.

[0333] Step 4:

[0334] The device uploads the saved motion data and analyzed emotion data to the server. The input is the output data from steps 2 and 3, and the output is the integrated data sent to the server. With this transmission, the server obtains comprehensive data on the user and prepares for the next analysis.

[0335] Step 5:

[0336] The server analyzes the received motion data to compare it with exemplary data from top athletes. The input is the integrated data and exemplary data from step 4, and the output is the analysis results showing the differences in motion characteristics. Machine learning algorithms are used to specifically identify which elements of the motion can be improved.

[0337] Step 6:

[0338] The server sends the analysis results to the terminal. The input is the output data from step 5, and the output is the analysis results received by the terminal. Based on these results, feedback is provided to the user in the next step.

[0339] Step 7:

[0340] The device adjusts the feedback it provides based on the analysis results and emotional data. The input is the analysis results from step 6 and the emotional data from step 3, and the output is the adjusted feedback information. Specifically, it considers the user's emotional state to determine the most appropriate advice and message.

[0341] Step 8:

[0342] The device presents the user with adjusted feedback. The input is the feedback information from step 7, and the output is the specific feedback the user receives. Using the presentation method, suggestions for improvement and advice on actions are provided in text or audio, allowing the user to train efficiently based on this.

[0343] (Application Example 2)

[0344] 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 as the "terminal".

[0345] In sports and fitness training, there is a need to analyze users' physical movements with high accuracy and provide personalized feedback tailored to the user's emotional state. However, conventional technologies focus solely on motion analysis and do not consider the user's emotional state during training, resulting in difficulties in efficiently improving movement.

[0346] 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.

[0347] In this invention, the server includes data acquisition means for recording the user's physical movements, analysis means for comparing the acquired movement information with reference movement information, information presentation means for suggesting improvements based on the comparison results, an emotional analysis function, and a function for providing personalized movement improvement suggestions. This enables comprehensive and personalized feedback to the user from both movement data and emotional state perspectives.

[0348] A "user" refers to a person who uses the system to have their physical movements recorded and analyzed.

[0349] "Physical movements" refer to the physical actions performed by the user and are the subject of data acquired using image processing technology.

[0350] "Data acquisition means" encompasses all devices and technologies for recording a user's physical movements, and refers to methods for collecting motion data using image processing technology.

[0351] "Reference motion information" refers to data of exemplary motions used for comparison, and is motion information that is compared to the user's physical movements.

[0352] An "analysis tool" is a tool that has the function of comparing acquired operational information with reference operational information and identifying the differences and areas for improvement.

[0353] "Information presentation means" refers to methods for communicating analysis results and improvement points to users visually and audibly.

[0354] The "emotional analysis function" is a feature that understands the user's emotional state and evaluates their emotions during operation.

[0355] "Personalized performance improvement suggestions" refer to improvement suggestions tailored to specific users based on the user's emotional state and performance analysis.

[0356] In this invention, the user first uses a device to record physical movements. Specifically, when the user performs an action, the movement is captured by a device equipped with a camera and sensors as data acquisition means. This device uses image processing technology to collect the user's movement data and transmit it to a terminal. Furthermore, an emotional analysis function is realized by using a microphone and voice analysis software to analyze the user's voice and facial expressions.

[0357] The server compares the motion data transmitted from the terminal with reference motion information. Here, OpenCV, which provides computer vision technology, and analysis algorithms for evaluating differences in motion are used as analysis tools. Through this process, the differences between the user's actions and the reference actions are clarified, and areas for improvement are identified.

[0358] As a means of presenting information, the terminal visually and audibly presents areas for improvement to the user based on the analysis results. For example, if a user has a problem with a particular form while running, the system provides specific advice on that point visually and audibly. If the emotional analysis function detects the user's emotional state, it provides suggestions for improving their movements in accordance with the user's emotions. For this purpose, the robot can also provide motivational messages audibly.

[0359] For example, when a user who is jogging shows signs of fatigue through voice or facial expressions, the system will voice a message such as, "You're almost at the finish line! Keep going!" This helps the user renew their motivation and continue training efficiently.

[0360] When using a generative AI model to generate specific behavioral improvement suggestions, the following prompt can be used: "Analyze user behavioral and emotional data to generate optimal feedback and specific improvement suggestions."

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The user performs actions using cameras and sensors to record their body movements. The input is the user's body movements, and the output is captured motion data. This data is transferred to a terminal, where image processing technology is used to extract details of the position and movement.

[0364] Step 2:

[0365] The device uses a microphone and camera to record the user's voice and facial expressions. The input is the user's voice and facial expressions, and the output is analyzed emotional data. The emotional analysis function analyzes this data with voice analysis software to identify the user's emotional state.

[0366] Step 3:

[0367] The terminal sends the data acquired in Step 1 and Step 2 to the server. The input is motion data and emotional data, and the output is a dataset for analysis. The server compares the received data with baseline behavior information and uses analysis algorithms such as OpenCV to identify areas where the user's behavior needs improvement.

[0368] Step 4:

[0369] The server generates suggestions for improving user behavior based on the analysis results. The input is the comparison result, and the output is a specific improvement suggestion. When generating a specific behavior improvement suggestion using the generation AI model, prompts are used.

[0370] Step 5:

[0371] The terminal receives suggestions from the server and presents improvement proposals to the user visually and audibly. The input is the improvement suggestions from the server, and the output is the feedback presented to the user. Specific operational improvements and motivational messages are provided to make it easier for the user to implement the feedback.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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".

[0388] The following describes a method for implementing the present invention in which a user uses this system for the purpose of improving their sports performance.

[0389] The user records their sports movements using a motion capture device. The terminal acquires data from this motion capture device in real time and generates the user's movement data. This data includes coordinate and angular information consisting of numerous frames.

[0390] Next, the terminal uploads the recorded user movement data to the server. The server retrieves exemplary movement data of top athletes from its database and compares it with the user's movement data. Here, the comparison mechanism built into the server uses an algorithm to quantify the degree of similarity in movement and identify differences. Based on the identified differences in movement, the server also generates information about specific areas where the user should improve.

[0391] This generated comparison information is sent to the device and presented to the user. The device overlays the user's video of their movements with exemplary movements of top athletes, visually showing the differences in their movements. This comparison allows the user to intuitively understand which parts need correction and what kind of training is required.

[0392] For example, if a user wants to improve their baseball pitching form, the server will identify specific points such as the angle of their elbow when throwing and the length of their stride. The user can then check this information through their device and incorporate the suggested improvements into their next practice session.

[0393] This allows users to receive scientifically-based feedback and efficiently improve their skills. The present invention is a system that enhances the quality of self-practice and helps users reach their desired goals in the shortest possible time.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The user sets up the motion capture device and prepares to record their movements. The terminal connects to the device to acquire motion capture data and begins recording movements. The user's movement data is captured frame by frame along the timeline and saved to local storage.

[0397] Step 2:

[0398] The terminal compresses the saved motion data and uploads it to the server. The server receives the uploaded data and stores it in a database. The server searches for appropriate model motion data according to the sport and target motion, and prepares for the comparison process.

[0399] Step 3:

[0400] The server uses a motion analysis algorithm to compare the user's motion data with exemplary motion data. It extracts characteristic points of the motion (elbow angle, shoulder position, etc.) and quantifies the differences. As a result of the analysis, it calculates the degree of agreement between the user's motion and the exemplary motion and identifies areas that need improvement.

[0401] Step 4:

[0402] The server sends the analysis results to the terminal. Based on these results, the terminal generates a video of the user's actions and an overlay video of exemplary actions. The terminal also visually shows the user the differences in their actions and displays specific areas for improvement and advice on the screen.

[0403] Step 5:

[0404] Based on the suggested areas for improvement, the user practices to improve their form. Motion capture is performed again as needed to confirm the effectiveness of the improvements. The user can repeat this process to obtain further feedback.

[0405] (Example 1)

[0406] 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."

[0407] There is a need to provide users with a quick and accurate means to objectively evaluate their own actions and identify areas for improvement. Furthermore, existing technologies have the problem that analyzing actions and providing feedback takes time, making it difficult for users to implement improvements in real time.

[0408] 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.

[0409] In this invention, the server includes an information acquisition means, a comparison means, and an information generation means. This allows the user to quickly analyze their own operation data and obtain specific and immediate areas for improvement, enabling them to appropriately modify their actions.

[0410] An "information acquisition means" is a technological device for recording user activity and acquiring information related to that activity as digital data.

[0411] The "comparison means" is a function that evaluates the similarities and differences between actions by comparing acquired user information with reference activity information.

[0412] A "presentation method" is a configuration that has the ability to provide information to the user in a visual or other format and to present specific areas for improvement or advice.

[0413] "Communication means" refers to a method for efficiently and securely sending and receiving information between a terminal and a server, and is a function that enables data transmission.

[0414] "Information generation means" refers to a function that analyzes data received by the server and uses the output of a generated AI model to produce information such as feedback.

[0415] To implement this invention, the user uses a motion capture device as a means of acquiring information to record movements. For example, common capture techniques include optical and inertial motion capture devices. This device measures the user's movements with high precision and generates detailed digital data, including the position and angle of each joint.

[0416] The terminal is responsible for transmitting the acquired user activity data to the server in the appropriate format. The data is transmitted to the server in real time via communication methods, utilizing wireless communication technologies such as Wi-Fi and Bluetooth.

[0417] The server analyzes the received user behavior data and compares it to the activity information of a benchmark elite athlete using a comparison mechanism. This analysis utilizes a generative AI model to identify similarities and differences between the user's behavior and the exemplary behavior. Then, the information generation mechanism creates feedback with specific areas for improvement along with numerical data.

[0418] This feedback information is transmitted to the user's device through a presentation device. The device uses the feedback data to overlay exemplary movements onto the user's video footage, visually showing which parts are different. This allows the user to intuitively understand specific areas for improvement and use this information to modify their training methods.

[0419] As a concrete example, suppose a user wants to improve their baseball pitching form. They record their pitching motion using a motion capture device, send the data to a server, and the server compares it to the pitching form data of a top-level player. As a result, feedback is generated showing differences in elbow angle and stride length. The user can review these suggestions for improvement through their device and incorporate them into their next practice session.

[0420] Examples of prompts to input into a generative AI model are as follows:

[0421] "Use the user's pitching form data and compare it to exemplary data from top-level players to identify differences in elbow angle and stride length, and provide specific suggestions for improvement."

[0422] In this way, the invention aims to improve user actions efficiently and scientifically.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] The user wears a motion capture device to record sports movements. The input includes real-time acquisition of the user's body position and angle information. This data is output as motion information, including numerical data with joint coordinates and angles in each frame.

[0426] Step 2:

[0427] The terminal receives motion data acquired from the motion capture device. The input is raw data from the motion capture device, which is then formatted appropriately. The formatted data is then encrypted and sent to the server as output.

[0428] Step 3:

[0429] The server analyzes the user's movement data received. The data received as input is compared with exemplary movement data of top athletes obtained from a database. A generative AI model is used to identify differences in movement and output them numerically.

[0430] Step 4:

[0431] The server identifies areas for improvement and generates feedback. It uses information about behavioral differences identified by the generating AI model as input. The feedback is output as text and image data, including specific points for improvement and advice.

[0432] Step 5:

[0433] The terminal presents the user with feedback information from the server. The input is improvement suggestions from the server. The system displays exemplary actions overlaid on the user's video footage, visually showing areas for improvement, allowing the user to modify their actions based on this information.

[0434] (Application Example 1)

[0435] 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."

[0436] The present invention aims to provide a system that allows users to receive appropriate support in a home environment in order to efficiently improve their sports and athletic skills. In particular, it aims to enable users to intuitively understand the areas for improvement pointed out through motion recording and real-time feedback, and to reflect these improvements in their practice.

[0437] 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.

[0438] In this invention, the server includes an observation device for recording user movements, a comparison device for comparing the recorded user movement data with reference movement data, a presentation device for suggesting corrections based on the comparison results, and an auxiliary device mounted on a home automation device that provides real-time support for user movements. This enables users to scientifically and efficiently improve their motor skills while at home.

[0439] A "user" is an individual who uses this system to record their actions and receive feedback.

[0440] An "observation device" is a device used to record a user's actions, and mainly includes cameras and sensors.

[0441] "Action data" refers to data that records the user's actions and consists of multiple frames that include location information and angle information.

[0442] "Reference movement data" refers to data from top-level athletes or target movements, and is used as a standard for comparison.

[0443] A "comparison device" is a device used to compare recorded operation data with reference operation data.

[0444] "Comparison results" refer to data obtained by comparing operational data with reference operational data, and indicate differences in operation.

[0445] A "presentation device" is a device that presents users with points for correction or improvement.

[0446] "Home automation devices" refer to automated devices used within the home that provide real-time support to the user's actions.

[0447] An "assistive device" is a device designed to provide real-time support to users as they improve their motor skills.

[0448] The system that realizes this application provides comprehensive support for users to improve their motor skills at home. The system consists of an automated home device that integrates an observation device, a matching device, and a presentation device.

[0449] The user records their movements through an observation device. This device includes high-resolution cameras and motion sensors, enabling detailed capture of the user's movement data. This movement data is transmitted to a server via the network.

[0450] The server compares the received behavioral data with reference behavioral data. This comparison is performed using a generative AI model, which quantifies the degree of agreement for each behavior. A machine learning framework such as TensorFlow is used in this process. The AI ​​model identifies differences in behavior and generates data to recommend which parts should be corrected.

[0451] The generated data is presented to the user through a presentation device. The presentation uses both visual and auditory elements, allowing the user to intuitively understand the necessary corrections. The home automation device is equipped with a Snapdragon processor, enabling it to provide rapid feedback.

[0452] For example, when a user practices their tennis swing at home, the observation device records the swing motion and sends the data to a server. The server identifies how the user's swing differs from a baseline and notifies the user if there is room for improvement in the range of arm movement.

[0453] An example of a prompt for the generating AI model would be: "Analyze the user's latest swing data and compare it to data from top players to identify areas for improvement in your swing."

[0454] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0455] Step 1:

[0456] When the user begins to move, the device's monitoring system records the movement using a high-resolution camera and motion sensors. The input is the user's physical movement, and the output is motion data (including position and angle information) across multiple frames. This data is captured in real time and stored in the device's memory.

[0457] Step 2:

[0458] Recorded motion data is uploaded from the terminal to the server via the network. The server compares this motion data with reference motion data. The input is the motion data, and the output is an analysis value for comparison. On the server, a generating AI model analyzes the motion data and quantifies the degree of agreement for each motion.

[0459] Step 3:

[0460] The server uses the analyzed data to identify the differences between user behavior and baseline behavior. The input is the analyzed value from the previous step, and the output is specific behavior points where correction is recommended. The server uses a generative AI model to calculate the differences for each behavior parameter and highlight particularly significant differences.

[0461] Step 4:

[0462] The server sends the identified correction point information to the terminal. Immediately, the terminal presents this information to the user through a display device. The input is the correction point information, and the output is the feedback provided to the user visually and audibly. The terminal overlays the collected data on the screen and provides voice guidance.

[0463] Step 5:

[0464] Based on the feedback provided, users adjust their movements. By continuing to train according to the feedback, users can improve the precision of their movements. Specifically, users may swing again, widening the range of motion of their arms. Through this process, users repeatedly train and effectively improve their movements.

[0465] 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.

[0466] This invention is an emotion-recognition sports training system that scientifically analyzes and improves user actions and provides appropriate feedback based on the user's emotional state. The system includes action acquisition means, comparison means, presentation means, and an emotion engine.

[0467] The user first records their movements using a motion capture device. The terminal acquires this motion capture data and saves it to storage as user movement data. Simultaneously, an emotion engine analyzes the user's facial expressions and voice to detect their emotional state during movement.

[0468] The recorded motion data is uploaded to a server and compared to exemplary motion data from top athletes. The server analyzes the motion data and identifies characteristic differences in movement. It then evaluates how the movement can be improved and sends the results to the terminal.

[0469] The device comprehensively analyzes the results from the server and the emotional data detected by the emotion engine. It understands the user's emotions—whether they are satisfied, impatient, or dissatisfied—and suggests improvements accordingly. For example, if the device determines that the user is impatient, it may offer simple introductory training to help improve the operation or suggest a message to boost their motivation.

[0470] For example, when evaluating a baseball pitching form, if the user shows signs of dissatisfaction during the motion, in addition to suggesting improvements, it might be helpful to offer relaxation techniques to reduce psychological pressure.

[0471] Thus, by combining motion analysis and emotion recognition, this invention provides users with personalized training effects and enables efficient skill improvement. This allows users to receive support in their sports practice from both psychological and technical perspectives.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The user prepares to record their sports movements using a motion capture device. The terminal connects to the device and generates frame data of the movements. In parallel, the terminal records the user's facial expressions and voice through its camera and microphone, collecting emotional data.

[0475] Step 2:

[0476] The device uploads motion data to the server. Simultaneously, it also sends collected emotion data to the server. The server receives this data and stores the motion data in its database.

[0477] Step 3:

[0478] The server compares the stored motion data with exemplary movements of the target sport. Using comparison tools, it performs numerical analysis on the details of the movements (e.g., joint angles and body balance) to identify differences.

[0479] Step 4:

[0480] The server analyzes the emotional data it receives simultaneously using an emotion engine. Based on facial expression analysis and voice data, it evaluates the user's emotional state and determines whether psychological consideration is necessary.

[0481] Step 5:

[0482] By combining the analysis results and emotion assessment, the server generates suggestions for improvement for the user. For example, if the user is feeling tense, it might include instructions on stretching or breathing exercises to help them relax.

[0483] Step 6:

[0484] The server sends the generated improvement suggestions to the terminal. The terminal visually displays specific points for improving performance to the user and provides emotionally responsive advice.

[0485] Step 7:

[0486] Based on the suggested improvements, users will modify their action forms and implement mental health measures. If necessary, they will record their actions again and re-evaluate to confirm the improvements.

[0487] (Example 2)

[0488] 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."

[0489] Traditional sports training systems provided improvement suggestions based solely on user movement data, making it difficult to provide feedback that considered the user's emotional state and thus hindering efficient movement improvement. Furthermore, it was challenging to offer appropriate countermeasures when users were emotionally anxious or dissatisfied.

[0490] 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.

[0491] In this invention, the server includes an action acquisition means for recording user actions, a comparison means for comparing the acquired user action data with exemplary action data, an emotion analysis means for analyzing the user's emotional state, a presentation means for suggesting areas for improvement based on the comparison results and emotional state, and a feedback adjustment means for adjusting the feedback. This enables personalized feedback based on both the user's actions and emotional state.

[0492] "Motion acquisition means" refers to a technological device for measuring the user's body movements and acquiring them as digital data.

[0493] "Comparison means" refers to an algorithm or function for comparing acquired user behavior data with reference behavior data and identifying any differences between them.

[0494] "Presentation method" refers to an interface used to notify users of areas for improvement or suggestions based on analyzed data.

[0495] "Emotional analysis methods" refer to technologies that recognize and analyze a user's emotional state using the user's facial expressions, voice, and other physiological data.

[0496] "Feedback adjustment means" refers to a function that dynamically adjusts the content and format of the feedback presented according to the user's actions and emotional state.

[0497] This invention relates to a system that scientifically analyzes a user's actions and emotional state and provides appropriate feedback to the user. This system includes means for acquiring actions, means for comparison, means for presentation, means for emotion analysis, and means for adjusting feedback.

[0498] The user records their movements using a motion capture device. Here, general motion tracking technology is used to acquire the user's movements as 3D coordinate data. The device then stores this data as motion data in its storage. Furthermore, it incorporates technology to analyze the user's facial expressions and voice using emotion analysis methods to determine the user's emotional state in real time. This utilizes general emotion recognition technology and analysis software.

[0499] The acquired motion data is uploaded to a server in the cloud, where it is compared with exemplary motion data from top athletes. The server analyzes the acquired motion data and identifies characteristic differences in movement. Machine learning algorithms are often used for this analysis.

[0500] The server transmits the analysis results to the terminal, which then uses this data to provide comprehensive feedback to the user. Presentation tools are used to visually and audibly provide suggestions for improvement and operational advice. Furthermore, feedback adjustment tools are used to appropriately adjust the feedback according to the user's emotional state.

[0501] For example, a user might want to improve their baseball pitching form. In this case, the system would assess the user's emotional state and, if the user is feeling anxious, provide feedback to encourage relaxation. This feedback may be provided along with a message to boost motivation.

[0502] Examples of input prompts for a generative AI model:

[0503] "Analyze the user's behavioral data and propose improvements to their sports training based on their emotional state. If the user is feeling anxious, add a message to boost their motivation."

[0504] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0505] Step 1:

[0506] The user records their movements using a motion capture device. The input is the user's movements, and the output is 3D coordinate data. This allows the user's movements to be collected as digital information. Based on this motion acquisition, the system quantifies the user's specific movements and proceeds to the next processing step.

[0507] Step 2:

[0508] The terminal receives 3D coordinate data acquired by the motion acquisition means and saves it to storage. The input is the output data from step 1, and the output is the saved motion data. By saving it to storage, it becomes possible to retain the user's motion history for later analysis.

[0509] Step 3:

[0510] The device uses emotion analysis techniques to analyze the user's emotional state from their facial expressions and voice. The input is the user's live video and audio signals, and the output is data indicating the user's emotional state. Specifically, it categorizes emotions using video and audio signal processing and outputs the state as numerical values ​​or text.

[0511] Step 4:

[0512] The device uploads the saved motion data and analyzed emotion data to the server. The input is the output data from steps 2 and 3, and the output is the integrated data sent to the server. With this transmission, the server obtains comprehensive data on the user and prepares for the next analysis.

[0513] Step 5:

[0514] The server analyzes the received motion data to compare it with exemplary data from top athletes. The input is the integrated data and exemplary data from step 4, and the output is the analysis results showing the differences in motion characteristics. Machine learning algorithms are used to specifically identify which elements of the motion can be improved.

[0515] Step 6:

[0516] The server sends the analysis results to the terminal. The input is the output data from step 5, and the output is the analysis results received by the terminal. Based on these results, feedback is provided to the user in the next step.

[0517] Step 7:

[0518] The device adjusts the feedback it provides based on the analysis results and emotional data. The input is the analysis results from step 6 and the emotional data from step 3, and the output is the adjusted feedback information. Specifically, it considers the user's emotional state to determine the most appropriate advice and message.

[0519] Step 8:

[0520] The device presents the user with adjusted feedback. The input is the feedback information from step 7, and the output is the specific feedback the user receives. Using the presentation method, suggestions for improvement and advice on actions are provided in text or audio, allowing the user to train efficiently based on this.

[0521] (Application Example 2)

[0522] 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."

[0523] In sports and fitness training, there is a need to analyze users' physical movements with high accuracy and provide personalized feedback tailored to the user's emotional state. However, conventional technologies focus solely on motion analysis and do not consider the user's emotional state during training, resulting in difficulties in efficiently improving movement.

[0524] 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.

[0525] In this invention, the server includes data acquisition means for recording the user's physical movements, analysis means for comparing the acquired movement information with reference movement information, information presentation means for suggesting improvements based on the comparison results, an emotional analysis function, and a function for providing personalized movement improvement suggestions. This enables comprehensive and personalized feedback to the user from both movement data and emotional state perspectives.

[0526] A "user" refers to a person who uses the system to have their physical movements recorded and analyzed.

[0527] "Physical movements" refer to the physical actions performed by the user and are the subject of data acquired using image processing technology.

[0528] "Data acquisition means" encompasses all devices and technologies for recording a user's physical movements, and refers to methods for collecting motion data using image processing technology.

[0529] "Reference motion information" refers to data of exemplary motions used for comparison, and is motion information that is compared to the user's physical movements.

[0530] An "analysis tool" is a tool that has the function of comparing acquired operational information with reference operational information and identifying the differences and areas for improvement.

[0531] "Information presentation means" refers to methods for communicating analysis results and improvement points to users visually and audibly.

[0532] The "emotional analysis function" is a feature that understands the user's emotional state and evaluates their emotions during operation.

[0533] "Personalized performance improvement suggestions" refer to improvement suggestions tailored to specific users based on the user's emotional state and performance analysis.

[0534] In this invention, the user first uses a device to record physical movements. Specifically, when the user performs an action, the movement is captured by a device equipped with a camera and sensors as data acquisition means. This device uses image processing technology to collect the user's movement data and transmit it to a terminal. Furthermore, an emotional analysis function is realized by using a microphone and voice analysis software to analyze the user's voice and facial expressions.

[0535] The server compares the motion data transmitted from the terminal with reference motion information. Here, OpenCV, which provides computer vision technology, and analysis algorithms for evaluating differences in motion are used as analysis tools. Through this process, the differences between the user's actions and the reference actions are clarified, and areas for improvement are identified.

[0536] As a means of presenting information, the terminal visually and audibly presents areas for improvement to the user based on the analysis results. For example, if a user has a problem with a particular form while running, the system provides specific advice on that point visually and audibly. If the emotional analysis function detects the user's emotional state, it provides suggestions for improving their movements in accordance with the user's emotions. For this purpose, the robot can also provide motivational messages audibly.

[0537] For example, when a user who is jogging shows signs of fatigue through voice or facial expressions, the system will voice a message such as, "You're almost at the finish line! Keep going!" This helps the user renew their motivation and continue training efficiently.

[0538] When using a generative AI model to generate specific behavioral improvement suggestions, the following prompt can be used: "Analyze user behavioral and emotional data to generate optimal feedback and specific improvement suggestions."

[0539] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0540] Step 1:

[0541] The user performs actions using cameras and sensors to record their body movements. The input is the user's body movements, and the output is captured motion data. This data is transferred to a terminal, where image processing technology is used to extract details of the position and movement.

[0542] Step 2:

[0543] The device uses a microphone and camera to record the user's voice and facial expressions. The input is the user's voice and facial expressions, and the output is analyzed emotional data. The emotional analysis function analyzes this data with voice analysis software to identify the user's emotional state.

[0544] Step 3:

[0545] The terminal sends the data acquired in Step 1 and Step 2 to the server. The input is motion data and emotional data, and the output is a dataset for analysis. The server compares the received data with baseline behavior information and uses analysis algorithms such as OpenCV to identify areas where the user's behavior needs improvement.

[0546] Step 4:

[0547] The server generates suggestions for improving user behavior based on the analysis results. The input is the comparison result, and the output is a specific improvement suggestion. When generating a specific behavior improvement suggestion using the generation AI model, prompts are used.

[0548] Step 5:

[0549] The terminal receives suggestions from the server and presents improvement proposals to the user visually and audibly. The input is the improvement suggestions from the server, and the output is the feedback presented to the user. Specific operational improvements and motivational messages are provided to make it easier for the user to implement the feedback.

[0550] 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.

[0551] 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.

[0552] 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.

[0553] [Fourth Embodiment]

[0554] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0555] 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.

[0556] 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).

[0557] 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.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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".

[0567] The following describes a method for implementing the present invention in which a user uses this system for the purpose of improving their sports performance.

[0568] The user records their sports movements using a motion capture device. The terminal acquires data from this motion capture device in real time and generates the user's movement data. This data includes coordinate and angular information consisting of numerous frames.

[0569] Next, the terminal uploads the recorded user movement data to the server. The server retrieves exemplary movement data of top athletes from its database and compares it with the user's movement data. Here, the comparison mechanism built into the server uses an algorithm to quantify the degree of similarity in movement and identify differences. Based on the identified differences in movement, the server also generates information about specific areas where the user should improve.

[0570] This generated comparison information is sent to the device and presented to the user. The device overlays the user's video of their movements with exemplary movements of top athletes, visually showing the differences in their movements. This comparison allows the user to intuitively understand which parts need correction and what kind of training is required.

[0571] For example, if a user wants to improve their baseball pitching form, the server will identify specific points such as the angle of their elbow when throwing and the length of their stride. The user can then check this information through their device and incorporate the suggested improvements into their next practice session.

[0572] This allows users to receive scientifically-based feedback and efficiently improve their skills. The present invention is a system that enhances the quality of self-practice and helps users reach their desired goals in the shortest possible time.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] The user sets up the motion capture device and prepares to record their movements. The terminal connects to the device to acquire motion capture data and begins recording movements. The user's movement data is captured frame by frame along the timeline and saved to local storage.

[0576] Step 2:

[0577] The terminal compresses the saved motion data and uploads it to the server. The server receives the uploaded data and stores it in a database. The server searches for appropriate model motion data according to the sport and target motion, and prepares for the comparison process.

[0578] Step 3:

[0579] The server uses a motion analysis algorithm to compare the user's motion data with exemplary motion data. It extracts characteristic points of the motion (elbow angle, shoulder position, etc.) and quantifies the differences. As a result of the analysis, it calculates the degree of agreement between the user's motion and the exemplary motion and identifies areas that need improvement.

[0580] Step 4:

[0581] The server sends the analysis results to the terminal. Based on these results, the terminal generates a video of the user's actions and an overlay video of exemplary actions. The terminal also visually shows the user the differences in their actions and displays specific areas for improvement and advice on the screen.

[0582] Step 5:

[0583] Based on the suggested areas for improvement, the user practices to improve their form. Motion capture is performed again as needed to confirm the effectiveness of the improvements. The user can repeat this process to obtain further feedback.

[0584] (Example 1)

[0585] 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".

[0586] There is a need to provide users with a quick and accurate means to objectively evaluate their own actions and identify areas for improvement. Furthermore, existing technologies have the problem that analyzing actions and providing feedback takes time, making it difficult for users to implement improvements in real time.

[0587] 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.

[0588] In this invention, the server includes an information acquisition means, a comparison means, and an information generation means. This allows the user to quickly analyze their own operation data and obtain specific and immediate areas for improvement, enabling them to appropriately modify their actions.

[0589] An "information acquisition means" is a technological device for recording user activity and acquiring information related to that activity as digital data.

[0590] The "comparison means" is a function that evaluates the similarities and differences between actions by comparing acquired user information with reference activity information.

[0591] A "presentation method" is a configuration that has the ability to provide information to the user in a visual or other format and to present specific areas for improvement or advice.

[0592] "Communication means" refers to a method for efficiently and securely sending and receiving information between a terminal and a server, and is a function that enables data transmission.

[0593] "Information generation means" refers to a function that analyzes data received by the server and uses the output of a generated AI model to produce information such as feedback.

[0594] To implement this invention, the user uses a motion capture device as a means of acquiring information to record movements. For example, common capture techniques include optical and inertial motion capture devices. This device measures the user's movements with high precision and generates detailed digital data, including the position and angle of each joint.

[0595] The terminal is responsible for transmitting the acquired user activity data to the server in the appropriate format. The data is transmitted to the server in real time via communication methods, utilizing wireless communication technologies such as Wi-Fi and Bluetooth.

[0596] The server analyzes the received user behavior data and compares it to the activity information of a benchmark elite athlete using a comparison mechanism. This analysis utilizes a generative AI model to identify similarities and differences between the user's behavior and the exemplary behavior. Then, the information generation mechanism creates feedback with specific areas for improvement along with numerical data.

[0597] This feedback information is transmitted to the user's device through a presentation device. The device uses the feedback data to overlay exemplary movements onto the user's video footage, visually showing which parts are different. This allows the user to intuitively understand specific areas for improvement and use this information to modify their training methods.

[0598] As a concrete example, suppose a user wants to improve their baseball pitching form. They record their pitching motion using a motion capture device, send the data to a server, and the server compares it to the pitching form data of a top-level player. As a result, feedback is generated showing differences in elbow angle and stride length. The user can review these suggestions for improvement through their device and incorporate them into their next practice session.

[0599] Examples of prompts to input into a generative AI model are as follows:

[0600] "Use the user's pitching form data and compare it to exemplary data from top-level players to identify differences in elbow angle and stride length, and provide specific suggestions for improvement."

[0601] In this way, the invention aims to improve user actions efficiently and scientifically.

[0602] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0603] Step 1:

[0604] The user wears a motion capture device to record sports movements. The input includes real-time acquisition of the user's body position and angle information. This data is output as motion information, including numerical data with joint coordinates and angles in each frame.

[0605] Step 2:

[0606] The terminal receives motion data acquired from the motion capture device. The input is raw data from the motion capture device, which is then formatted appropriately. The formatted data is then encrypted and sent to the server as output.

[0607] Step 3:

[0608] The server analyzes the user's movement data received. The data received as input is compared with exemplary movement data of top athletes obtained from a database. A generative AI model is used to identify differences in movement and output them numerically.

[0609] Step 4:

[0610] The server identifies areas for improvement and generates feedback. It uses information about behavioral differences identified by the generating AI model as input. The feedback is output as text and image data, including specific points for improvement and advice.

[0611] Step 5:

[0612] The terminal presents the user with feedback information from the server. The input is improvement suggestions from the server. The system displays exemplary actions overlaid on the user's video footage, visually showing areas for improvement, allowing the user to modify their actions based on this information.

[0613] (Application Example 1)

[0614] 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".

[0615] The present invention aims to provide a system that allows users to receive appropriate support in a home environment in order to efficiently improve their sports and athletic skills. In particular, it aims to enable users to intuitively understand the areas for improvement pointed out through motion recording and real-time feedback, and to reflect these improvements in their practice.

[0616] 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.

[0617] In this invention, the server includes an observation device for recording user movements, a comparison device for comparing the recorded user movement data with reference movement data, a presentation device for suggesting corrections based on the comparison results, and an auxiliary device mounted on a home automation device that provides real-time support for user movements. This enables users to scientifically and efficiently improve their motor skills while at home.

[0618] A "user" is an individual who uses this system to record their actions and receive feedback.

[0619] An "observation device" is a device used to record a user's actions, and mainly includes cameras and sensors.

[0620] "Action data" refers to data that records the user's actions and consists of multiple frames that include location information and angle information.

[0621] "Reference movement data" refers to data from top-level athletes or target movements, and is used as a standard for comparison.

[0622] A "comparison device" is a device used to compare recorded operation data with reference operation data.

[0623] "Comparison results" refer to data obtained by comparing operational data with reference operational data, and indicate differences in operation.

[0624] A "presentation device" is a device that presents users with points for correction or improvement.

[0625] "Home automation devices" refer to automated devices used within the home that provide real-time support to the user's actions.

[0626] An "assistive device" is a device designed to provide real-time support to users as they improve their motor skills.

[0627] The system that realizes this application provides comprehensive support for users to improve their motor skills at home. The system consists of an automated home device that integrates an observation device, a matching device, and a presentation device.

[0628] The user records their movements through an observation device. This device includes high-resolution cameras and motion sensors, enabling detailed capture of the user's movement data. This movement data is transmitted to a server via the network.

[0629] The server compares the received behavioral data with reference behavioral data. This comparison is performed using a generative AI model, which quantifies the degree of agreement for each behavior. A machine learning framework such as TensorFlow is used in this process. The AI ​​model identifies differences in behavior and generates data to recommend which parts should be corrected.

[0630] The generated data is presented to the user through a presentation device. The presentation uses both visual and auditory elements, allowing the user to intuitively understand the necessary corrections. The home automation device is equipped with a Snapdragon processor, enabling it to provide rapid feedback.

[0631] For example, when a user practices their tennis swing at home, the observation device records the swing motion and sends the data to a server. The server identifies how the user's swing differs from a baseline and notifies the user if there is room for improvement in the range of arm movement.

[0632] An example of a prompt for the generating AI model would be: "Analyze the user's latest swing data and compare it to data from top players to identify areas for improvement in your swing."

[0633] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0634] Step 1:

[0635] When the user begins to move, the device's monitoring system records the movement using a high-resolution camera and motion sensors. The input is the user's physical movement, and the output is motion data (including position and angle information) across multiple frames. This data is captured in real time and stored in the device's memory.

[0636] Step 2:

[0637] Recorded motion data is uploaded from the terminal to the server via the network. The server compares this motion data with reference motion data. The input is the motion data, and the output is an analysis value for comparison. On the server, a generating AI model analyzes the motion data and quantifies the degree of agreement for each motion.

[0638] Step 3:

[0639] The server uses the analyzed data to identify the differences between user behavior and baseline behavior. The input is the analyzed value from the previous step, and the output is specific behavior points where correction is recommended. The server uses a generative AI model to calculate the differences for each behavior parameter and highlight particularly significant differences.

[0640] Step 4:

[0641] The server sends the identified correction point information to the terminal. Immediately, the terminal presents this information to the user through a display device. The input is the correction point information, and the output is the feedback provided to the user visually and audibly. The terminal overlays the collected data on the screen and provides voice guidance.

[0642] Step 5:

[0643] Based on the feedback provided, users adjust their movements. By continuing to train according to the feedback, users can improve the precision of their movements. Specifically, users may swing again, widening the range of motion of their arms. Through this process, users repeatedly train and effectively improve their movements.

[0644] 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.

[0645] This invention is an emotion-recognition sports training system that scientifically analyzes and improves user actions and provides appropriate feedback based on the user's emotional state. The system includes action acquisition means, comparison means, presentation means, and an emotion engine.

[0646] The user first records their movements using a motion capture device. The terminal acquires this motion capture data and saves it to storage as user movement data. Simultaneously, an emotion engine analyzes the user's facial expressions and voice to detect their emotional state during movement.

[0647] The recorded motion data is uploaded to a server and compared to exemplary motion data from top athletes. The server analyzes the motion data and identifies characteristic differences in movement. It then evaluates how the movement can be improved and sends the results to the terminal.

[0648] The device comprehensively analyzes the results from the server and the emotional data detected by the emotion engine. It understands the user's emotions—whether they are satisfied, impatient, or dissatisfied—and suggests improvements accordingly. For example, if the device determines that the user is impatient, it may offer simple introductory training to help improve the operation or suggest a message to boost their motivation.

[0649] For example, when evaluating a baseball pitching form, if the user shows signs of dissatisfaction during the motion, in addition to suggesting improvements, it might be helpful to offer relaxation techniques to reduce psychological pressure.

[0650] Thus, by combining motion analysis and emotion recognition, this invention provides users with personalized training effects and enables efficient skill improvement. This allows users to receive support in their sports practice from both psychological and technical perspectives.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The user prepares to record their sports movements using a motion capture device. The terminal connects to the device and generates frame data of the movements. In parallel, the terminal records the user's facial expressions and voice through its camera and microphone, collecting emotional data.

[0654] Step 2:

[0655] The device uploads motion data to the server. Simultaneously, it also sends collected emotion data to the server. The server receives this data and stores the motion data in its database.

[0656] Step 3:

[0657] The server compares the stored motion data with exemplary movements of the target sport. Using comparison tools, it performs numerical analysis on the details of the movements (e.g., joint angles and body balance) to identify differences.

[0658] Step 4:

[0659] The server analyzes the emotional data it receives simultaneously using an emotion engine. Based on facial expression analysis and voice data, it evaluates the user's emotional state and determines whether psychological consideration is necessary.

[0660] Step 5:

[0661] By combining the analysis results and emotion assessment, the server generates suggestions for improvement for the user. For example, if the user is feeling tense, it might include instructions on stretching or breathing exercises to help them relax.

[0662] Step 6:

[0663] The server sends the generated improvement suggestions to the terminal. The terminal visually displays specific points for improving performance to the user and provides emotionally responsive advice.

[0664] Step 7:

[0665] Based on the suggested improvements, users will modify their action forms and implement mental health measures. If necessary, they will record their actions again and re-evaluate to confirm the improvements.

[0666] (Example 2)

[0667] 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".

[0668] Traditional sports training systems provided improvement suggestions based solely on user movement data, making it difficult to provide feedback that considered the user's emotional state and thus hindering efficient movement improvement. Furthermore, it was challenging to offer appropriate countermeasures when users were emotionally anxious or dissatisfied.

[0669] 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.

[0670] In this invention, the server includes an action acquisition means for recording user actions, a comparison means for comparing the acquired user action data with exemplary action data, an emotion analysis means for analyzing the user's emotional state, a presentation means for suggesting areas for improvement based on the comparison results and emotional state, and a feedback adjustment means for adjusting the feedback. This enables personalized feedback based on both the user's actions and emotional state.

[0671] "Motion acquisition means" refers to a technological device for measuring the user's body movements and acquiring them as digital data.

[0672] "Comparison means" refers to an algorithm or function for comparing acquired user behavior data with reference behavior data and identifying any differences between them.

[0673] "Presentation method" refers to an interface used to notify users of areas for improvement or suggestions based on analyzed data.

[0674] "Emotional analysis methods" refer to technologies that recognize and analyze a user's emotional state using the user's facial expressions, voice, and other physiological data.

[0675] "Feedback adjustment means" refers to a function that dynamically adjusts the content and format of the feedback presented according to the user's actions and emotional state.

[0676] This invention relates to a system that scientifically analyzes a user's actions and emotional state and provides appropriate feedback to the user. This system includes means for acquiring actions, means for comparison, means for presentation, means for emotion analysis, and means for adjusting feedback.

[0677] The user records their movements using a motion capture device. Here, general motion tracking technology is used to acquire the user's movements as 3D coordinate data. The device then stores this data as motion data in its storage. Furthermore, it incorporates technology to analyze the user's facial expressions and voice using emotion analysis methods to determine the user's emotional state in real time. This utilizes general emotion recognition technology and analysis software.

[0678] The acquired motion data is uploaded to a server in the cloud, where it is compared with exemplary motion data from top athletes. The server analyzes the acquired motion data and identifies characteristic differences in movement. Machine learning algorithms are often used for this analysis.

[0679] The server transmits the analysis results to the terminal, which then uses this data to provide comprehensive feedback to the user. Presentation tools are used to visually and audibly provide suggestions for improvement and operational advice. Furthermore, feedback adjustment tools are used to appropriately adjust the feedback according to the user's emotional state.

[0680] For example, a user might want to improve their baseball pitching form. In this case, the system would assess the user's emotional state and, if the user is feeling anxious, provide feedback to encourage relaxation. This feedback may be provided along with a message to boost motivation.

[0681] Examples of input prompts for a generative AI model:

[0682] "Analyze the user's behavioral data and propose improvements to their sports training based on their emotional state. If the user is feeling anxious, add a message to boost their motivation."

[0683] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0684] Step 1:

[0685] The user records their movements using a motion capture device. The input is the user's movements, and the output is 3D coordinate data. This allows the user's movements to be collected as digital information. Based on this motion acquisition, the system quantifies the user's specific movements and proceeds to the next processing step.

[0686] Step 2:

[0687] The terminal receives 3D coordinate data acquired by the motion acquisition means and saves it to storage. The input is the output data from step 1, and the output is the saved motion data. By saving it to storage, it becomes possible to retain the user's motion history for later analysis.

[0688] Step 3:

[0689] The device uses emotion analysis techniques to analyze the user's emotional state from their facial expressions and voice. The input is the user's live video and audio signals, and the output is data indicating the user's emotional state. Specifically, it categorizes emotions using video and audio signal processing and outputs the state as numerical values ​​or text.

[0690] Step 4:

[0691] The device uploads the saved motion data and analyzed emotion data to the server. The input is the output data from steps 2 and 3, and the output is the integrated data sent to the server. With this transmission, the server obtains comprehensive data on the user and prepares for the next analysis.

[0692] Step 5:

[0693] The server analyzes the received motion data to compare it with exemplary data from top athletes. The input is the integrated data and exemplary data from step 4, and the output is the analysis results showing the differences in motion characteristics. Machine learning algorithms are used to specifically identify which elements of the motion can be improved.

[0694] Step 6:

[0695] The server sends the analysis results to the terminal. The input is the output data from step 5, and the output is the analysis results received by the terminal. Based on these results, feedback is provided to the user in the next step.

[0696] Step 7:

[0697] The device adjusts the feedback it provides based on the analysis results and emotional data. The input is the analysis results from step 6 and the emotional data from step 3, and the output is the adjusted feedback information. Specifically, it considers the user's emotional state to determine the most appropriate advice and message.

[0698] Step 8:

[0699] The device presents the user with adjusted feedback. The input is the feedback information from step 7, and the output is the specific feedback the user receives. Using the presentation method, suggestions for improvement and advice on actions are provided in text or audio, allowing the user to train efficiently based on this.

[0700] (Application Example 2)

[0701] 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".

[0702] In sports and fitness training, there is a need to analyze users' physical movements with high accuracy and provide personalized feedback tailored to the user's emotional state. However, conventional technologies focus solely on motion analysis and do not consider the user's emotional state during training, resulting in difficulties in efficiently improving movement.

[0703] 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.

[0704] In this invention, the server includes data acquisition means for recording the user's physical movements, analysis means for comparing the acquired movement information with reference movement information, information presentation means for suggesting improvements based on the comparison results, an emotional analysis function, and a function for providing personalized movement improvement suggestions. This enables comprehensive and personalized feedback to the user from both movement data and emotional state perspectives.

[0705] A "user" refers to a person who uses the system to have their physical movements recorded and analyzed.

[0706] "Physical movements" refer to the physical actions performed by the user and are the subject of data acquired using image processing technology.

[0707] "Data acquisition means" encompasses all devices and technologies for recording a user's physical movements, and refers to methods for collecting motion data using image processing technology.

[0708] "Reference motion information" refers to data of exemplary motions used for comparison, and is motion information that is compared to the user's physical movements.

[0709] An "analysis tool" is a tool that has the function of comparing acquired operational information with reference operational information and identifying the differences and areas for improvement.

[0710] "Information presentation means" refers to methods for communicating analysis results and improvement points to users visually and audibly.

[0711] The "emotional analysis function" is a feature that understands the user's emotional state and evaluates their emotions during operation.

[0712] "Personalized performance improvement suggestions" refer to improvement suggestions tailored to specific users based on the user's emotional state and performance analysis.

[0713] In this invention, the user first uses a device to record physical movements. Specifically, when the user performs an action, the movement is captured by a device equipped with a camera and sensors as data acquisition means. This device uses image processing technology to collect the user's movement data and transmit it to a terminal. Furthermore, an emotional analysis function is realized by using a microphone and voice analysis software to analyze the user's voice and facial expressions.

[0714] The server compares the motion data transmitted from the terminal with reference motion information. Here, OpenCV, which provides computer vision technology, and analysis algorithms for evaluating differences in motion are used as analysis tools. Through this process, the differences between the user's actions and the reference actions are clarified, and areas for improvement are identified.

[0715] As a means of presenting information, the terminal visually and audibly presents areas for improvement to the user based on the analysis results. For example, if a user has a problem with a particular form while running, the system provides specific advice on that point visually and audibly. If the emotional analysis function detects the user's emotional state, it provides suggestions for improving their movements in accordance with the user's emotions. For this purpose, the robot can also provide motivational messages audibly.

[0716] For example, when a user who is jogging shows signs of fatigue through voice or facial expressions, the system will voice a message such as, "You're almost at the finish line! Keep going!" This helps the user renew their motivation and continue training efficiently.

[0717] When using a generative AI model to generate specific behavioral improvement suggestions, the following prompt can be used: "Analyze user behavioral and emotional data to generate optimal feedback and specific improvement suggestions."

[0718] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0719] Step 1:

[0720] The user performs actions using cameras and sensors to record their body movements. The input is the user's body movements, and the output is captured motion data. This data is transferred to a terminal, where image processing technology is used to extract details of the position and movement.

[0721] Step 2:

[0722] The device uses a microphone and camera to record the user's voice and facial expressions. The input is the user's voice and facial expressions, and the output is analyzed emotional data. The emotional analysis function analyzes this data with voice analysis software to identify the user's emotional state.

[0723] Step 3:

[0724] The terminal sends the data acquired in Step 1 and Step 2 to the server. The input is motion data and emotional data, and the output is a dataset for analysis. The server compares the received data with baseline behavior information and uses analysis algorithms such as OpenCV to identify areas where the user's behavior needs improvement.

[0725] Step 4:

[0726] The server generates suggestions for improving user behavior based on the analysis results. The input is the comparison result, and the output is a specific improvement suggestion. When generating a specific behavior improvement suggestion using the generation AI model, prompts are used.

[0727] Step 5:

[0728] The terminal receives suggestions from the server and presents improvement proposals to the user visually and audibly. The input is the improvement suggestions from the server, and the output is the feedback presented to the user. Specific operational improvements and motivational messages are provided to make it easier for the user to implement the feedback.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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."

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] The following is further disclosed regarding the embodiments described above.

[0751] (Claim 1)

[0752] A means for recording user actions,

[0753] A comparison means for comparing acquired user behavior data with exemplary behavior data,

[0754] A means for presenting areas for improvement based on the aforementioned comparison results,

[0755] A system that includes this.

[0756] (Claim 2)

[0757] The system according to claim 1, wherein the motion acquisition means acquires the user's movements by motion capture.

[0758] (Claim 3)

[0759] The system according to claim 1, wherein the presentation means visually presents the improvements to the user.

[0760] "Example 1"

[0761] (Claim 1)

[0762] A means of acquiring information to record user activity,

[0763] A comparison method for comparing acquired user information with baseline activity information,

[0764] A means for presenting areas for improvement based on the aforementioned comparison results,

[0765] A means of communication by which a terminal sends information to a server,

[0766] A server identifies areas for improvement and generates feedback using a generated AI model along with numerical data, providing an information generation method.

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, wherein the information acquisition means acquires user activity by a tracking technology.

[0770] (Claim 3)

[0771] The system according to claim 1, wherein the presentation means visually presents the improvements to the user.

[0772] "Application Example 1"

[0773] (Claim 1)

[0774] An observation device for recording user actions,

[0775] A comparison device that compares recorded user operation data with reference operation data,

[0776] A presentation device that presents correction points based on the aforementioned verification results,

[0777] An auxiliary device installed in a home automation system that provides real-time support for the user's actions,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, wherein the observation device records the user's actions using an industrial device.

[0781] (Claim 3)

[0782] The system according to claim 1, wherein the presentation device presents the corrections to the user visually and audibly.

[0783] "Example 2 of combining an emotion engine"

[0784] (Claim 1)

[0785] A means for recording user actions,

[0786] A comparison means for comparing acquired user behavior data with exemplary behavior data,

[0787] A means for presenting areas for improvement based on the aforementioned comparison results,

[0788] A sentiment analysis tool for analyzing the emotional state of users,

[0789] A feedback adjustment means that adjusts the feedback based on the aforementioned emotional state,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, wherein the motion acquisition means acquires the user's actions using motion tracking technology.

[0793] (Claim 3)

[0794] The system according to claim 1, wherein the presentation means presents improvements to the user visually and audibly.

[0795] "Application example 2 when combining with an emotional engine"

[0796] (Claim 1)

[0797] A means for acquiring data to record the user's physical movements,

[0798] An analysis means for comparing acquired operational information with reference operational information,

[0799] Information presentation means for suggesting areas for improvement based on the aforementioned comparison results,

[0800] An emotional analysis function that recognizes the user's emotions and evaluates their emotional state during operation,

[0801] A function that provides personalized suggestions for behavioral improvements based on the user's emotional state,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, wherein the data acquisition means acquires the user's body movements using image processing technology.

[0805] (Claim 3)

[0806] The system according to claim 1, wherein the information presentation means presents improvements to the user visually and audibly. [Explanation of symbols]

[0807] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for recording user actions, A comparison means for comparing acquired user behavior data with exemplary behavior data, A means for presenting areas for improvement based on the aforementioned comparison results, A system that includes this.

2. The system according to claim 1, wherein the motion acquisition means acquires the user's movements by motion capture.

3. The system according to claim 1, wherein the presentation means visually presents the points of improvement to the user.

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