system

Abstract

We provide the system. [Solution] A means for capturing the user's actions using an image acquisition device, A means for transmitting captured video data to a processing unit via a network, A means for analyzing the video data in a processing device and generating data related to the user's actions, A means for generating instructions to advise on improving the operation based on the generated data, Means for presenting the aforementioned instructions to the user visually or audibly, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] While the number of people starting golf has increased due to the COVID-19 pandemic, there is a problem that it is difficult to practice sufficiently due to time and location constraints. In addition, hiring a professional coach is costly, and many people feel that the threshold for golf is high. Furthermore, there is a lack of methods to effectively improve golf swings even at home or in limited environments, so there is a problem that it is difficult to improve technology.

Means for Solving the Problems

[0005] This invention provides a system that generates user motion data by capturing user movements using an image acquisition device and analyzing the resulting video data with a processing device. Based on the generated data, this system provides the user with visual or auditory instructions for motion improvement, enabling effective feedback regardless of time or location. Furthermore, by comparing the user's goals with their current state, identifying the optimal motion pattern, and providing real-time feedback, the system reduces coaching costs and enables rapid skill improvement.

[0006] An "image acquisition device" is a device that includes cameras and sensors for capturing the user's actions.

[0007] A "processing device" is a computer or system that analyzes video data transmitted over a network and generates data related to the user's actions.

[0008] "Video data" refers to video and image files that record the user's actions.

[0009] "Analysis" is the process of converting user actions into data based on acquired video data, and identifying patterns and characteristics.

[0010] "Action data" refers to a set of information about the user's actions, including parameters such as speed, position, and angle.

[0011] "Instructions" refer to specific advice or guidelines for improving user behavior.

[0012] "Visual or auditory presentation" refers to a method of providing information to a user through a display, sound, or other means.

[0013] A "user" refers to an individual who uses a system to evaluate their own actions and strive to improve them.

[0014] A "network" is a structure that includes communication lines and protocols used to send and receive data. [Brief explanation of the drawing]

[0015] [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.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a system for analyzing and providing improvement instructions for a golf swing, and is implemented in a configuration that links an image acquisition device, a processing device, and a user terminal. The user uses a terminal such as a smartphone to film their golf swing using the camera function. The terminal temporarily stores this video data and then transmits it to the processing device via a network.

[0037] The server uses the received video data to analyze the swing motion based on an AI model. In this process, it extracts detailed motion data related to the user's swing, such as the club head trajectory, speed, and swing angle. This data is then used to generate optimal swing improvement suggestions in light of the user's goals.

[0038] The generated instructions for improving swing mechanics are transmitted from the server to the terminal via the network and presented to the user visually or audibly on the terminal. This allows the user to receive and implement specific advice for improving their swing, even in specific environments such as their home.

[0039] For example, if a user sets a goal of "reducing slices and hitting the ball straight," this system could identify the timing of body rotation and clubface angle during the swing, and then instruct the user to increase their body rotation speed as a solution. In this way, the server analyzes the user's swing data in detail and provides personalized guidance to help improve their golf skills.

[0040] This invention allows users to continuously improve their swing on their own without the need for a golf coach, making it possible for more people to enjoy golf easily. This system provides a comprehensive solution to support golfers in improving their skills.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user uses a smartphone or other device to record a video of their golf swing. Once recording is complete, the device temporarily saves the video data.

[0044] Step 2:

[0045] The terminal prepares to upload the stored video data to the server via the network. During this process, the video data is compressed as needed to improve transmission efficiency.

[0046] Step 3:

[0047] The server activates an AI model to analyze the video data received via the network. The AI ​​model recognizes the user's swing motion in the video and extracts information such as the club head's trajectory and speed, and the user's body position.

[0048] Step 4:

[0049] The server analyzes the extracted operational data and identifies areas for improvement to achieve the user-defined goal (e.g., reduce slicing).

[0050] Step 5:

[0051] Based on the analysis results, the server generates specific instructions for the user to improve their performance. These instructions include advice on adjusting swing timing and form.

[0052] Step 6:

[0053] The generated instructions are sent from the server to the terminal. The terminal receives the instructions and presents them to the user visually or audibly.

[0054] Step 7:

[0055] Based on the instructions provided, the user puts the advice into practice in their next swing, continuously striving to improve their technique. This cycle is repeated as needed to further refine their swing.

[0056] (Example 1)

[0057] 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."

[0058] When operators seek to improve their performance in specific sports or physical activities, there is a need for efficient means of learning and improvement that do not rely on specialized instruction or expensive equipment. In particular, operators need to be able to effectively improve their skills by receiving individualized and immediate feedback.

[0059] 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.

[0060] In this invention, the server includes means for recording the operator's physical movements using an image acquisition device, means for transferring the recorded video information to a computing device via a communication network, and means for analyzing the video information using a machine learning model on the computing device to extract characteristic information related to the operator's movements. This makes it possible for the operator to obtain appropriate movement improvement suggestions based on their own goals.

[0061] An "image acquisition device" is a video recording device used to record the physical movements of an operator.

[0062] "Operator" refers to the person whose actions are recorded by the image acquisition device.

[0063] "Video information" refers to digital data related to the operator's actions recorded by an image acquisition device.

[0064] A "communication network" is a network infrastructure used to transmit video information to a computing device located in a remote location.

[0065] A "processing unit" is a computer system that receives and analyzes video information transmitted via a communication network.

[0066] A "machine learning model" is an algorithm or program used to analyze video information and extract the operational characteristics of an operator.

[0067] "Characteristic information" refers to quantitative data such as trajectory, speed, and angle related to the operator's movements.

[0068] An "improvement goal" is a specific objective set by the operator with the aim of correcting or improving the operation.

[0069] A "proposal for improving operation" is a suggestion for modifying operation that is tailored to the operator's goals, generated based on the analyzed feature information.

[0070] "Visual or audio notification" refers to a method of delivering generated suggestions for performance improvements to the operator, and is a means of presenting information through a display or speaker.

[0071] This invention provides a system that efficiently analyzes the operator's actions and supports improvement. Specific embodiments thereof are shown below.

[0072] Hardware configuration and data processing

[0073] 1. User (operator) input

[0074] The user records their actions using an image acquisition device built into their smartphone. This image acquisition device should ideally include a camera with a high frame rate to enable detailed recording of the actions.

[0075] 2. Data storage and transfer by the device

[0076] The terminal temporarily stores video information captured by the user. This video information is efficiently processed using data compression technology before being transmitted to the computing device (server) via the communication network.

[0077] 3. Data analysis by the server

[0078] The server analyzes the received video information using machine learning models to extract feature information about the operator's actions. The machine learning models used include generative AI models, which are trained on large datasets. This analysis is performed quickly and efficiently by running on a cloud computing infrastructure.

[0079] 4. Providing the generated instructions

[0080] The server generates suggested improvements to the operation based on the extracted feature information and the improvement goals set by the operator. These suggestions are notified to the user visually or audibly, serving as a reference for the user when modifying the operation.

[0081] Examples of specific cases and prompt statements

[0082] Specific example: For instance, if a user wants to improve their golf swing, the server analyzes the angle and speed of the clubhead during the swing and provides specific improvement suggestions tailored to the user's goals. For example, it might offer advice such as, "Increase your body rotation speed to reduce slices."

[0083] Example of a prompt:

[0084] "Based on the user's goals, please propose specific improvement measures to reduce the slice in their golf swing. The analysis results are as follows: data such as clubhead trajectory, speed, and swing angle."

[0085] This system allows individual users to obtain more specific and practical improvement strategies without needing specialized instruction, thereby effectively promoting skill improvement in sports.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The user uses an image acquisition device such as a smartphone to film their specific actions (e.g., a golf swing). The input is high-quality video data recording the user's actions, and this video information is temporarily stored on the device. The output is the saved video file as local data.

[0089] Step 2:

[0090] The terminal prepares to transmit temporarily stored video data to the processing unit. Specifically, data compression and encryption are performed to ensure secure transfer. The input is the temporarily stored video data, and the output is data processed into a transferable format.

[0091] Step 3:

[0092] The terminal transmits processed video data to the server via the network. The transmitted data is formatted for analysis on the server side. The input is compressed and encrypted data, and the output is a digital data stream received by the server.

[0093] Step 4:

[0094] The server sends the received video data to a machine learning model for motion analysis. As input, the server processes the user's motion data and extracts feature information such as club head trajectory, speed, and swing angle. The output is the feature information generated as a result of this analysis.

[0095] Step 5:

[0096] The server generates improvement suggestions tailored to the user's goals based on the extracted feature information. Here, a generative AI model is used to create prompt sentences, establishing improvement suggestions in natural language. The input is the user's goals and the analyzed feature information, and the output is text data containing specific improvement suggestions.

[0097] Step 6:

[0098] The server sends the generated improvement suggestions to the terminal, which then notifies the user visually or audibly. The input is the improvement suggestion data provided by the server, and the output is the information display or audio guide delivered to the user as feedback. This process allows the user to understand and implement specific improvement measures.

[0099] (Application Example 1)

[0100] 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."

[0101] Traditional golf swing improvement systems had limitations in enabling users to improve their skills independently without professional instruction. Furthermore, there was a lack of tools that provided real-time feedback and allowed for immediate improvement of swing mechanics. This made effective golf swing practice at home difficult.

[0102] 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.

[0103] In this invention, the server includes means for capturing an individual's movements using a camera; means for transmitting the captured video information to a computing device via a communication network; means for analyzing the video information using the computing device and generating information about the individual's movements; and a robotic device for providing real-time feedback on the individual's movements using the generated information and presenting improvement suggestions on the spot. This makes it possible for users to analyze and improve their golf swing even in a home practice environment.

[0104] A "recording device" is a device used to record an individual's actions as video.

[0105] A "communication network" is a network system used to transmit information from one device to another.

[0106] A "processing unit" is a computing device that analyzes input data and generates necessary information.

[0107] "Information about an individual's movements" refers to data such as the trajectory, speed, and angle of movement extracted from recorded video footage.

[0108] "Real-time feedback" refers to immediate responses of improvement and guidance provided in response to user input and actions.

[0109] A "robot device" is a mechanical device that can perform specific tasks autonomously or semi-autonomously.

[0110] "Improvement suggestions" refer to proposals or instructions for modifying a specific action of an individual to make it more appropriate.

[0111] The system that realizes this invention is composed of a combination of multiple hardware and software components. The main components used are an imaging device, a communication network, a computing device, and a robotic device.

[0112] The server records the individual's movements obtained from the camera in real time and transmits them to the computing unit via a communication network. The computing unit analyzes the received video information using an AI analysis module (e.g., TENSORFLOW® or PyTorch) to generate information about the individual's movements. This information includes the movement trajectory, speed, angle, etc.

[0113] When a user sets a desired goal, the server generates suggestions for improving their technique based on that information. These suggestions are created using the generated information and are fed back to the user in real time through the robotic device. This allows users to receive immediate feedback and practice their swing even at home.

[0114] As a concrete example, while a user is practicing their golf swing, a robotic device is placed in front of the user, and the swing motion is filmed with a camera. The filmed footage is sent to a server in real time, and AI analyzes it. As a result, the robotic device provides advice via voice and on-screen display on areas where the swing needs improvement. For example, it might give feedback such as, "Let's try to increase your swing speed a little more."

[0115] An example of a prompt message for a generated AI model is: "Develop an application that analyzes a user's swing and provides real-time suggestions for improvement to find a more efficient swing method."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] When a user performs a golf swing, the device's built-in camera captures the action. During recording, the user's movements are recorded at a high frame rate, capturing each moment of the motion. The input is the user's physical movements, and the output is the recorded video data.

[0119] Step 2:

[0120] The terminal temporarily stores the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the video data acquired by the server.

[0121] Step 3:

[0122] The server analyzes the received video data using an AI analysis engine (e.g., a TensorFlow model). This process extracts information such as motion trajectory, velocity, and swing angle from the video. The input is the video data sent to the server, and the output is the analyzed motion data.

[0123] Step 4:

[0124] The server analyzes the motion data using a generated AI model and generates improvement suggestions based on the user's set goals. For example, it can instruct the user to increase their swing speed by a certain percentage. The input is the analyzed motion data, and the output is the suggested motion improvements.

[0125] Step 5:

[0126] The server sends the generated improvement suggestions to the terminal and the robotic device, and provides this feedback to the user. The improvement suggestions are presented through the terminal's display and audio output, and the robotic device assists with its operation. The input is the improvement suggestions for the operation, and the output is the visual or auditory feedback received by the user.

[0127] Step 6:

[0128] Based on the feedback received, the user works to improve their golf swing. In this final step, the new motion is filmed again, and the system cycle is repeated. The input is the improvement action taken based on the feedback, and the output is the improved swing motion.

[0129] 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.

[0130] This invention combines a system for analyzing and providing improvement instructions for a golf swing with an emotion engine that recognizes the user's emotions. The system consists of an image acquisition device, a processing device, a user terminal, and an emotion engine working in conjunction.

[0131] Users use a smartphone or other device to film their golf swing, simultaneously recording their facial expressions and voice via the camera and microphone. This collects data not only on the user's swing motion but also on their emotional state. The device then transmits this data to a server via the network.

[0132] The server analyzes the received video data based on an AI model and generates motion data related to the swing. Simultaneously, it uses an emotion engine to analyze the user's facial expressions and voice to recognize their emotional state. During this process, the user's reactions to their swing performance and their mental state are also evaluated.

[0133] The server comprehensively analyzes motion and emotional data to generate improvement suggestions to help the user achieve their goals (e.g., improving swing stability while relaxed). These suggestions include not only instructions to correct movements but also feedback that takes the user's mental state into consideration. For example, for a nervous user, the server might first advise them on relaxation techniques before providing technical instructions for their swing.

[0134] The generated instructions are sent from the server to the terminal, which then presents them to the user visually or audibly. This system provides feedback that also takes the user's mental state into account, enabling better support not only for skill improvement but also for the user's overall practice experience.

[0135] For example, if a user is struggling with a stable swing due to nervousness during practice, the emotional engine can detect this state through emotional analysis and provide advice on breathing techniques and mental techniques to alleviate tension, enabling them to practice effectively in a relaxed state. This allows users to improve their swing comprehensively, receiving not only physical but also mental support.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users use smartphones or other devices to film their golf swings, simultaneously recording their facial expressions and voice. This allows for the acquisition of data on both actions and emotions at once.

[0139] Step 2:

[0140] The terminal temporarily stores the collected video and audio data, then compresses or converts the format of the data before sending it to the server via the network.

[0141] Step 3:

[0142] The server feeds the received video data into an AI model to analyze the user's swing motion. This extracts detailed motion data such as the club's trajectory, speed, and the user's posture.

[0143] Step 4:

[0144] The server uses an emotion engine to analyze transmitted voice and facial expression data to identify the user's emotional state. For example, it can identify emotions such as tension, anxiety, and concentration.

[0145] Step 5:

[0146] The server comprehensively evaluates operational and emotional data and generates improvement instructions based on the user's set goals. These instructions take emotional states into consideration and include not only technical improvements but also mental health advice.

[0147] Step 6:

[0148] The server sends the generated instructions to the terminal. The terminal then presents these instructions to the user visually or audibly. For example, it might provide practical feedback such as, "Relax your shoulders and take a deep breath when you swing."

[0149] Step 7:

[0150] The user follows the provided instructions and tries the swing again. The results of the new attempt are also analyzed by the system, and continuous feedback is provided. This loop allows the user to improve both their swing technique and emotional stability simultaneously.

[0151] (Example 2)

[0152] 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".

[0153] Improving user performance requires not only technical guidance but also comprehensive feedback that takes into account their emotional state. However, conventional systems have been unable to consider user emotions in their motion analysis, and therefore have not been able to fully realize the effectiveness of improvements. Furthermore, real-time feedback has been difficult, making it challenging to streamline the entire user practice experience.

[0154] 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.

[0155] In this invention, the server includes means for collecting data using a device for simultaneously recording the user's actions and emotional state; means for analyzing the video data in an information processing device based on an advanced computational model and generating information related to actions; and means for analyzing the user's emotional state using an emotion analysis engine. This makes it possible to provide the user with comprehensive feedback that integrates technical improvements and emotional support in real time.

[0156] A "user" is an individual who uses a system to analyze and improve their own behavior and emotional state.

[0157] "Device" refers to equipment used to acquire and record data on a user's actions and emotions.

[0158] "Data" refers to a systematically organized collection of information about a user's actions and emotional state.

[0159] An "information processing device" is a general-purpose or specialized computer system used to analyze collected data and calculate and process necessary information.

[0160] An "advanced computational model" is an algorithm or model used to analyze user motion data and identify specific movement patterns and areas for improvement.

[0161] "Emotional state" refers to the user's psychological and emotional condition, which is usually judged from facial expressions, voice, and other factors.

[0162] A "sentiment analysis engine" is software or a program that recognizes and analyzes a user's emotions from collected data.

[0163] "Feedback" refers to guidance and advice provided to users for improvement.

[0164] The system of this invention analyzes the user's actions and emotions and provides improvement suggestions. The user collects data by using a device such as a smartphone or dedicated camera to film their actions and simultaneously record their facial expressions and voice. As a result, action data and emotion data are acquired simultaneously and transmitted from the device to a server via the network.

[0165] The server uses advanced computational models (e.g., motion analysis algorithms utilizing deep learning) to analyze the received data and generate detailed information about the user's actions. Simultaneously, it utilizes an emotion analysis engine to analyze the user's emotional state from their facial expressions and voice data. This allows the server to understand the user's mental reactions and state of mind.

[0166] By comprehensively considering the analyzed behavioral and emotional data, the server generates appropriate improvement suggestions for the user. These suggestions include not only guidance on technical behavioral modifications but also emotional support. For example, for a stressed user, it may include advice on breathing techniques and mental techniques to help them relax.

[0167] The generated suggestions are sent from the server to the terminal, which then presents them to the user visually or audibly. Specifically, this includes on-screen guides showing the correct form of actions and audio relaxation instructions. The system aims to support not only the user's skill improvement but also the entire practice experience.

[0168] For example, if a user experiences anxiety during practice and is unable to perform stable movements, the emotional engine can detect this state and provide advice on relaxation techniques and ways to alleviate tension. This allows users to improve their overall performance by receiving mental support in addition to physical skills.

[0169] An example of a prompt message might be: "Analyze the user's actions and emotions, and provide effective feedback and suggestions for improvement, both physically and mentally."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] Users record their movements, facial expressions, and voice using their smartphones or dedicated cameras. This generates movement data and emotion data. The input is the user's recorded video and audio, which is collected by the system as the initial dataset.

[0173] Step 2:

[0174] The terminal transmits the collected video and audio data to the server via the network. This process utilizes compression and transfer protocols to efficiently transmit the data. The input is the raw data on the terminal, while the output is compressed data transmitted over the network.

[0175] Step 3:

[0176] The server inputs the received video data into a generating AI model. This model uses a deep learning algorithm specifically designed for motion analysis to extract detailed information about the user's movements and identify movement patterns and areas for improvement. The input is video data, and the output is the analysis result as motion data.

[0177] Step 4:

[0178] The server inputs facial and voice data into an emotion analysis engine to analyze the user's emotional state. This engine uses machine learning algorithms to classify emotions and evaluate levels of tension, anxiety, relaxation, etc. The input is facial and voice data, and the output is the analysis result as emotion data.

[0179] Step 5:

[0180] The server integrates and analyzes behavioral and emotional data to generate appropriate improvement suggestions for the user. Based on the results of the generative AI model and emotion analysis engine, it incorporates not only technical corrections but also mental advice. The input is behavioral and emotional data, and the output is feedback and improvement instructions for the user.

[0181] Step 6:

[0182] The server sends the generated improvement suggestions to the terminal. In this phase, the output data is formatted in an appropriate format so that the user can easily understand and implement it. The input is the improvement suggestion data on the server, and the output is the visual or auditory presentation sent to the terminal.

[0183] Step 7:

[0184] The device presents improvement suggestions to the user visually or audibly. Through visual and audio guidance, the user receives specific feedback based on their actions and emotions. Input is suggestion data sent from the server, and output is customized feedback information for the user.

[0185] (Application Example 2)

[0186] 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".

[0187] Conventional golf swing analysis systems primarily evaluate only the user's movements, making it difficult to provide feedback that takes into account the user's emotional state. Furthermore, when users practice comfortably at home, there is a lack of support for understanding their mental state and emotions and providing appropriate guidance. Therefore, there was a need for methods that not only improve technique but also enhance the overall quality of the practice experience.

[0188] 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.

[0189] In this invention, the server includes means for capturing the user's actions using an image acquisition device, means for transmitting the captured video data, audio data, and emotion data to a computing device via a network, and means for analyzing the video data and audio data on the computing device to generate information regarding the user's actions and emotions. This not only improves the user's technical performance but also provides appropriate emotional support, enabling an overall improvement in the user's capabilities.

[0190] An "image acquisition device" is a device that captures and acquires data of the user's actions, as well as their voice and facial expressions.

[0191] "Video data" refers to digital information that shows the user's actions, obtained by an image acquisition device.

[0192] "Audio data" refers to sound information that represents the user's voice or ambient sounds.

[0193] "Emotional data" refers to digital information that indicates an emotional state by analyzing the user's facial expressions and voice.

[0194] A "computational device" is a device that analyzes transmitted data and generates information about the user's actions and emotions.

[0195] "Analysis means" refers to techniques for analyzing video and audio data to extract information about actions and emotions.

[0196] "Instruction generation means" refers to a technology that generates specific instructions for improving the user's actions based on information obtained through analysis.

[0197] "Means of visual or auditory presentation" refers to means of presenting generated instructions to the user visually (such as a display) or aurally (such as an audio playback device).

[0198] This invention is a system that assists a user in practicing their golf swing, and includes an image acquisition device for analyzing the user's movements and emotions, a server, and a terminal that presents instructions visually or audibly. The server receives video and audio data captured by the user using a terminal such as a smartphone, and analyzes the data using a computing device.

[0199] Hardware and software configuration

[0200] The server uses a computing device equipped with an AI model to process video and audio data. For video data analysis, a motion analysis program using image processing technology is executed. This analyzes the user's swing form and quantifies body movements. Emotional analysis is performed on audio data and the user's facial expressions to determine their emotional state.

[0201] The device serves to present the generated feedback to the user. Visual feedback is provided through displays or smartphone screens, while auditory feedback is provided through speakers or headsets.

[0202] Specific example

[0203] For example, if a user is swinging while feeling tense, the emotion engine on the server will detect that emotion and generate instructions recommending breathing techniques to help them relax. The system can then provide feedback to the user, either verbally or on-screen, such as, "Take a few deep breaths, then try swinging again."

[0204] Example of a prompt

[0205] The prompt will look like this:

[0206] "Analyze the user's swing and determine their emotions from their facial expressions and voice data. For example, if the user appears frustrated, provide advice on how to relax."

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The user films their golf swing using a smartphone or other device, inputting video and audio data. The device's camera and microphone are used for this process. This input data represents the user's movements, facial expressions, and voice.

[0210] Step 2:

[0211] The terminal transmits the acquired video and audio data to the server via the network. This completes the data transfer to the server, where it is then input as a dataset for analysis.

[0212] Step 3:

[0213] The server runs image processing software to analyze the transmitted video data and outputs the characteristics of the swing motion as numerical data. This process quantifies the user's swing form.

[0214] Step 4:

[0215] The server runs an emotion recognition AI using voice data, generating emotion data by analyzing the tone and pitch of the user's voice. This output represents information indicating the user's emotional state.

[0216] Step 5:

[0217] The server comprehensively analyzes motion and emotion data extracted from video data and uses a generative AI model to generate feedback based on the user's current situation and goals. This output consists of improvement suggestions and specific advice.

[0218] Step 6:

[0219] The device presents feedback received from the server to the user visually or audibly. This includes specific actions such as displaying advice on the screen or providing voice guidance. This allows the user to receive information to improve their own performance.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention relates to a system for analyzing and providing improvement instructions for a golf swing, and is implemented in a configuration that links an image acquisition device, a processing device, and a user terminal. The user uses a terminal such as a smartphone to film their golf swing using the camera function. The terminal temporarily stores this video data and then transmits it to the processing device via a network.

[0237] The server uses the received video data to analyze the swing motion based on an AI model. In this process, it extracts detailed motion data related to the user's swing, such as the club head trajectory, speed, and swing angle. This data is then used to generate optimal swing improvement suggestions in light of the user's goals.

[0238] The generated instructions for improving swing mechanics are transmitted from the server to the terminal via the network and presented to the user visually or audibly on the terminal. This allows the user to receive and implement specific advice for improving their swing, even in specific environments such as their home.

[0239] For example, if a user sets a goal of "reducing slices and hitting the ball straight," this system could identify the timing of body rotation and clubface angle during the swing, and then instruct the user to increase their body rotation speed as a solution. In this way, the server analyzes the user's swing data in detail and provides personalized guidance to help improve their golf skills.

[0240] This invention allows users to continuously improve their swing on their own without the need for a golf coach, making it possible for more people to enjoy golf easily. This system provides a comprehensive solution to support golfers in improving their skills.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user uses a smartphone or other device to record a video of their golf swing. Once recording is complete, the device temporarily saves the video data.

[0244] Step 2:

[0245] The terminal prepares to upload the stored video data to the server via the network. During this process, the video data is compressed as needed to improve transmission efficiency.

[0246] Step 3:

[0247] The server activates an AI model to analyze the video data received via the network. The AI ​​model recognizes the user's swing motion in the video and extracts information such as the club head's trajectory and speed, and the user's body position.

[0248] Step 4:

[0249] The server analyzes the extracted operational data and identifies areas for improvement to achieve the user-defined goal (e.g., reduce slicing).

[0250] Step 5:

[0251] Based on the analysis results, the server generates specific instructions for the user to improve their performance. These instructions include advice on adjusting swing timing and form.

[0252] Step 6:

[0253] The generated instructions are sent from the server to the terminal. The terminal receives the instructions and presents them to the user visually or audibly.

[0254] Step 7:

[0255] Based on the instructions provided, the user puts the advice into practice in their next swing, continuously striving to improve their technique. This cycle is repeated as needed to further refine their swing.

[0256] (Example 1)

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

[0258] When operators seek to improve their performance in specific sports or physical activities, there is a need for efficient means of learning and improvement that do not rely on specialized instruction or expensive equipment. In particular, operators need to be able to effectively improve their skills by receiving individualized and immediate feedback.

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

[0260] In this invention, the server includes means for recording the operator's physical movements using an image acquisition device, means for transferring the recorded video information to a computing device via a communication network, and means for analyzing the video information using a machine learning model on the computing device to extract characteristic information related to the operator's movements. This makes it possible for the operator to obtain appropriate movement improvement suggestions based on their own goals.

[0261] An "image acquisition device" is a video recording device used to record the physical movements of an operator.

[0262] "Operator" refers to the person whose actions are recorded by the image acquisition device.

[0263] "Video information" refers to digital data related to the operator's actions recorded by an image acquisition device.

[0264] A "communication network" is a network infrastructure used to transmit video information to a computing device located in a remote location.

[0265] A "processing unit" is a computer system that receives and analyzes video information transmitted via a communication network.

[0266] A "machine learning model" is an algorithm or program used to analyze video information and extract the operational characteristics of an operator.

[0267] "Characteristic information" refers to quantitative data such as trajectory, speed, and angle related to the operator's movements.

[0268] An "improvement goal" is a specific objective set by the operator with the aim of correcting or improving the operation.

[0269] A "proposal for improving operation" is a suggestion for modifying operation that is tailored to the operator's goals, generated based on the analyzed feature information.

[0270] "Visual or audio notification" refers to a method of delivering generated suggestions for performance improvements to the operator, and is a means of presenting information through a display or speaker.

[0271] This invention provides a system that efficiently analyzes the operator's actions and supports improvement. Specific embodiments thereof are shown below.

[0272] Hardware configuration and data processing

[0273] 1. User (operator) input

[0274] The user records their actions using an image acquisition device built into their smartphone. This image acquisition device should ideally include a camera with a high frame rate to enable detailed recording of the actions.

[0275] 2. Data storage and transfer by the device

[0276] The terminal temporarily stores video information captured by the user. This video information is efficiently processed using data compression technology before being transmitted to the computing device (server) via the communication network.

[0277] 3. Data analysis by the server

[0278] The server analyzes the received video information using a machine learning model and extracts feature information regarding the operator's actions. The machine learning model used includes a generative AI model, which is trained based on a large dataset. This analysis is carried out on a cloud computing infrastructure to be performed quickly and effectively.

[0279] 4. Provision of Generated Instructions

[0280] The server generates an action improvement plan based on the extracted feature information and the improvement goals set by the operator. This improvement plan is notified to the user visually or audibly and serves as a reference when the user modifies their actions.

[0281] Examples of Specific Examples and Prompt Sentences

[0282] Specific example: For example, when the user wants to improve their golf swing, the server analyzes the angle and speed of the club head during the swing and presents specific improvement plans according to the user's goals. For example, specific advice such as "reduce slices by increasing the body rotation speed" can be considered.

[0283] Example of prompt sentence:

[0284] "Please propose specific improvement measures to reduce slices in golf swings based on the user's goals. The analysis results are as follows: data such as club head trajectory, speed, and swing angle"

[0285] With this system, individual users can obtain more specific and practical improvement measures without receiving professional guidance, and as a result, can effectively promote technological improvement in sports.

[0286] The flow of specific processing in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The user uses an image acquisition device such as a smartphone to film their specific actions (e.g., a golf swing). The input is high-quality video data recording the user's actions, and this video information is temporarily stored on the device. The output is the saved video file as local data.

[0289] Step 2:

[0290] The terminal prepares to transmit temporarily stored video data to the processing unit. Specifically, data compression and encryption are performed to ensure secure transfer. The input is the temporarily stored video data, and the output is data processed into a transferable format.

[0291] Step 3:

[0292] The terminal transmits processed video data to the server via the network. The transmitted data is formatted for analysis on the server side. The input is compressed and encrypted data, and the output is a digital data stream received by the server.

[0293] Step 4:

[0294] The server sends the received video data to a machine learning model for motion analysis. As input, the server processes the user's motion data and extracts feature information such as club head trajectory, speed, and swing angle. The output is the feature information generated as a result of this analysis.

[0295] Step 5:

[0296] The server generates improvement suggestions tailored to the user's goals based on the extracted feature information. Here, a generative AI model is used to create prompt sentences, establishing improvement suggestions in natural language. The input is the user's goals and the analyzed feature information, and the output is text data containing specific improvement suggestions.

[0297] Step 6:

[0298] The server sends the generated improvement suggestions to the terminal, which then notifies the user visually or audibly. The input is the improvement suggestion data provided by the server, and the output is the information display or audio guide delivered to the user as feedback. This process allows the user to understand and implement specific improvement measures.

[0299] (Application Example 1)

[0300] 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."

[0301] Traditional golf swing improvement systems had limitations in enabling users to improve their skills independently without professional instruction. Furthermore, there was a lack of tools that provided real-time feedback and allowed for immediate improvement of swing mechanics. This made effective golf swing practice at home difficult.

[0302] 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.

[0303] In this invention, the server includes means for capturing an individual's movements using a camera; means for transmitting the captured video information to a computing device via a communication network; means for analyzing the video information using the computing device and generating information about the individual's movements; and a robotic device for providing real-time feedback on the individual's movements using the generated information and presenting improvement suggestions on the spot. This makes it possible for users to analyze and improve their golf swing even in a home practice environment.

[0304] A "recording device" is a device used to record an individual's actions as video.

[0305] A "communication network" is a network system for transmitting information from one device to another.

[0306] An "arithmetic unit" is a computing device for analyzing input data and generating necessary information.

[0307] "Information on an individual's movement" refers to data such as the trajectory, speed, and angle of movement extracted from the captured video.

[0308] "Real-time feedback" is an improvement or guidance response provided immediately in response to a user's input or movement.

[0309] A "robot device" is a mechanical device that can perform specific tasks autonomously or semi-autonomously.

[0310] An "improvement plan" refers to a proposal or instruction for modifying a specific movement of an individual to be more appropriate.

[0311] The system that realizes this invention is composed of a combination of multiple hardware and software. As the main components, a photographing device, a communication network, an arithmetic unit, and a robot device are used.

[0312] The server records in real time the movement of an individual obtained from the photographing device and transmits it to the arithmetic unit via the communication network. The arithmetic unit analyzes the received video information using an AI analysis module (e.g., TensorFlow or PyTorch) and generates information on the movement of the individual. This information includes the trajectory, speed, angle, etc. of the movement.

[0313] When the user sets a target they desire, the server generates an improvement plan for the movement based on that information. The improvement plan is created based on the generated information and is fed back to the user in real time through the robot device. As a result, even at home, the user can obtain immediate feedback and practice their swing.

[0314] As a concrete example, while a user is practicing their golf swing, a robotic device is placed in front of the user, and the swing motion is filmed with a camera. The filmed footage is sent to a server in real time, and AI analyzes it. As a result, the robotic device provides advice via voice and on-screen display on areas where the swing needs improvement. For example, it might give feedback such as, "Let's try to increase your swing speed a little more."

[0315] An example of a prompt message for a generated AI model is: "Develop an application that analyzes a user's swing and provides real-time suggestions for improvement to find a more efficient swing method."

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] When a user performs a golf swing, the device's built-in camera captures the action. During recording, the user's movements are recorded at a high frame rate, capturing each moment of the motion. The input is the user's physical movements, and the output is the recorded video data.

[0319] Step 2:

[0320] The terminal temporarily stores the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the video data acquired by the server.

[0321] Step 3:

[0322] The server analyzes the received video data using an AI analysis engine (e.g., a TensorFlow model). This process extracts information such as motion trajectory, velocity, and swing angle from the video. The input is the video data sent to the server, and the output is the analyzed motion data.

[0323] Step 4:

[0324] The server analyzes the motion data using a generated AI model and generates improvement suggestions based on the user's set goals. For example, it can instruct the user to increase their swing speed by a certain percentage. The input is the analyzed motion data, and the output is the suggested motion improvements.

[0325] Step 5:

[0326] The server sends the generated improvement suggestions to the terminal and the robotic device, and provides this feedback to the user. The improvement suggestions are presented through the terminal's display and audio output, and the robotic device assists with its operation. The input is the improvement suggestions for the operation, and the output is the visual or auditory feedback received by the user.

[0327] Step 6:

[0328] Based on the feedback received, the user works to improve their golf swing. In this final step, the new motion is filmed again, and the system cycle is repeated. The input is the improvement action taken based on the feedback, and the output is the improved swing motion.

[0329] 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.

[0330] This invention combines a system for analyzing and providing improvement instructions for a golf swing with an emotion engine that recognizes the user's emotions. The system consists of an image acquisition device, a processing device, a user terminal, and an emotion engine working in conjunction.

[0331] Users use a smartphone or other device to film their golf swing, simultaneously recording their facial expressions and voice via the camera and microphone. This collects data not only on the user's swing motion but also on their emotional state. The device then transmits this data to a server via the network.

[0332] The server analyzes the received video data based on an AI model and generates motion data related to the swing. Simultaneously, it uses an emotion engine to analyze the user's facial expressions and voice to recognize their emotional state. During this process, the user's reactions to their swing performance and their mental state are also evaluated.

[0333] The server comprehensively analyzes motion and emotional data to generate improvement suggestions to help the user achieve their goals (e.g., improving swing stability while relaxed). These suggestions include not only instructions to correct movements but also feedback that takes the user's mental state into consideration. For example, for a nervous user, the server might first advise them on relaxation techniques before providing technical instructions for their swing.

[0334] The generated instructions are sent from the server to the terminal, which then presents them to the user visually or audibly. This system provides feedback that also takes the user's mental state into account, enabling better support not only for skill improvement but also for the user's overall practice experience.

[0335] For example, if a user is struggling with a stable swing due to nervousness during practice, the emotional engine can detect this state through emotional analysis and provide advice on breathing techniques and mental techniques to alleviate tension, enabling them to practice effectively in a relaxed state. This allows users to improve their swing comprehensively, receiving not only physical but also mental support.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] Users use smartphones or other devices to film their golf swings, simultaneously recording their facial expressions and voice. This allows for the acquisition of data on both actions and emotions at once.

[0339] Step 2:

[0340] The terminal temporarily stores the collected video and audio data, then compresses or converts the format of the data before sending it to the server via the network.

[0341] Step 3:

[0342] The server feeds the received video data into an AI model to analyze the user's swing motion. This extracts detailed motion data such as the club's trajectory, speed, and the user's posture.

[0343] Step 4:

[0344] The server uses an emotion engine to analyze transmitted voice and facial expression data to identify the user's emotional state. For example, it can identify emotions such as tension, anxiety, and concentration.

[0345] Step 5:

[0346] The server comprehensively evaluates operational and emotional data and generates improvement instructions based on the user's set goals. These instructions take emotional states into consideration and include not only technical improvements but also mental health advice.

[0347] Step 6:

[0348] The server sends the generated instructions to the terminal. The terminal then presents these instructions to the user visually or audibly. For example, it might provide practical feedback such as, "Relax your shoulders and take a deep breath when you swing."

[0349] Step 7:

[0350] The user follows the provided instructions and tries the swing again. The results of the new attempt are also analyzed by the system, and continuous feedback is provided. This loop allows the user to improve both their swing technique and emotional stability simultaneously.

[0351] (Example 2)

[0352] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0353] Improving user performance requires not only technical guidance but also comprehensive feedback that takes into account their emotional state. However, conventional systems have been unable to consider user emotions in their motion analysis, and therefore have not been able to fully realize the effectiveness of improvements. Furthermore, real-time feedback has been difficult, making it challenging to streamline the entire user practice experience.

[0354] 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.

[0355] In this invention, the server includes means for collecting data using a device for simultaneously recording the user's actions and emotional state; means for analyzing the video data in an information processing device based on an advanced computational model and generating information related to actions; and means for analyzing the user's emotional state using an emotion analysis engine. This makes it possible to provide the user with comprehensive feedback that integrates technical improvements and emotional support in real time.

[0356] A "user" is an individual who uses a system to analyze and improve their own behavior and emotional state.

[0357] "Device" refers to equipment used to acquire and record data on a user's actions and emotions.

[0358] "Data" refers to a systematically organized collection of information about a user's actions and emotional state.

[0359] An "information processing device" is a general-purpose or specialized computer system used to analyze collected data and calculate and process necessary information.

[0360] An "advanced computational model" is an algorithm or model used to analyze user motion data and identify specific movement patterns and areas for improvement.

[0361] "Emotional state" refers to the user's psychological and emotional condition, which is usually judged from facial expressions, voice, and other factors.

[0362] A "sentiment analysis engine" is software or a program that recognizes and analyzes a user's emotions from collected data.

[0363] "Feedback" refers to guidance and advice provided to users for improvement.

[0364] The system of this invention analyzes the user's actions and emotions and provides improvement suggestions. The user collects data by using a device such as a smartphone or dedicated camera to film their actions and simultaneously record their facial expressions and voice. As a result, action data and emotion data are acquired simultaneously and transmitted from the device to a server via the network.

[0365] The server uses advanced computational models (e.g., motion analysis algorithms utilizing deep learning) to analyze the received data and generate detailed information about the user's actions. Simultaneously, it utilizes an emotion analysis engine to analyze the user's emotional state from their facial expressions and voice data. This allows the server to understand the user's mental reactions and state of mind.

[0366] By comprehensively considering the analyzed behavioral and emotional data, the server generates appropriate improvement suggestions for the user. These suggestions include not only guidance on technical behavioral modifications but also emotional support. For example, for a stressed user, it may include advice on breathing techniques and mental techniques to help them relax.

[0367] The generated suggestions are sent from the server to the terminal, which then presents them to the user visually or audibly. Specifically, this includes on-screen guides showing the correct form of actions and audio relaxation instructions. The system aims to support not only the user's skill improvement but also the entire practice experience.

[0368] For example, if a user experiences anxiety during practice and is unable to perform stable movements, the emotional engine can detect this state and provide advice on relaxation techniques and ways to alleviate tension. This allows users to improve their overall performance by receiving mental support in addition to physical skills.

[0369] An example of a prompt message might be: "Analyze the user's actions and emotions, and provide effective feedback and suggestions for improvement, both physically and mentally."

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] Users record their movements, facial expressions, and voice using their smartphones or dedicated cameras. This generates movement data and emotion data. The input is the user's recorded video and audio, which is collected by the system as the initial dataset.

[0373] Step 2:

[0374] The terminal transmits the collected video and audio data to the server via the network. This process utilizes compression and transfer protocols to efficiently transmit the data. The input is the raw data on the terminal, while the output is compressed data transmitted over the network.

[0375] Step 3:

[0376] The server inputs the received video data into a generating AI model. This model uses a deep learning algorithm specifically designed for motion analysis to extract detailed information about the user's movements and identify movement patterns and areas for improvement. The input is video data, and the output is the analysis result as motion data.

[0377] Step 4:

[0378] The server inputs facial and voice data into an emotion analysis engine to analyze the user's emotional state. This engine uses machine learning algorithms to classify emotions and evaluate levels of tension, anxiety, relaxation, etc. The input is facial and voice data, and the output is the analysis result as emotion data.

[0379] Step 5:

[0380] The server integrates and analyzes behavioral and emotional data to generate appropriate improvement suggestions for the user. Based on the results of the generative AI model and emotion analysis engine, it incorporates not only technical corrections but also mental advice. The input is behavioral and emotional data, and the output is feedback and improvement instructions for the user.

[0381] Step 6:

[0382] The server sends the generated improvement suggestions to the terminal. In this phase, the output data is formatted in an appropriate format so that the user can easily understand and implement it. The input is the improvement suggestion data on the server, and the output is the visual or auditory presentation sent to the terminal.

[0383] Step 7:

[0384] The device presents improvement suggestions to the user visually or audibly. Through visual and audio guidance, the user receives specific feedback based on their actions and emotions. Input is suggestion data sent from the server, and output is customized feedback information for the user.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] Conventional golf swing analysis systems primarily evaluate only the user's movements, making it difficult to provide feedback that takes into account the user's emotional state. Furthermore, when users practice comfortably at home, there is a lack of support for understanding their mental state and emotions and providing appropriate guidance. Therefore, there was a need for methods that not only improve technique but also enhance the overall quality of the practice experience.

[0388] 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.

[0389] In this invention, the server includes means for capturing the user's actions using an image acquisition device, means for transmitting the captured video data, audio data, and emotion data to a computing device via a network, and means for analyzing the video data and audio data on the computing device to generate information regarding the user's actions and emotions. This not only improves the user's technical performance but also provides appropriate emotional support, enabling an overall improvement in the user's capabilities.

[0390] An "image acquisition device" is a device that captures and acquires data of the user's actions, as well as their voice and facial expressions.

[0391] "Video data" refers to digital information that shows the user's actions, obtained by an image acquisition device.

[0392] "Audio data" refers to sound information that represents the user's voice or ambient sounds.

[0393] "Emotional data" refers to digital information that indicates an emotional state by analyzing the user's facial expressions and voice.

[0394] A "computational device" is a device that analyzes transmitted data and generates information about the user's actions and emotions.

[0395] "Analysis means" refers to techniques for analyzing video and audio data to extract information about actions and emotions.

[0396] "Instruction generation means" refers to a technology that generates specific instructions for improving the user's actions based on information obtained through analysis.

[0397] "Means of visual or auditory presentation" refers to means of presenting generated instructions to the user visually (such as a display) or aurally (such as an audio playback device).

[0398] This invention is a system that assists a user in practicing their golf swing, and includes an image acquisition device for analyzing the user's movements and emotions, a server, and a terminal that presents instructions visually or audibly. The server receives video and audio data captured by the user using a terminal such as a smartphone, and analyzes the data using a computing device.

[0399] Hardware and software configuration

[0400] The server uses a computing device equipped with an AI model to process video and audio data. For video data analysis, a motion analysis program using image processing technology is executed. This analyzes the user's swing form and quantifies body movements. Emotional analysis is performed on audio data and the user's facial expressions to determine their emotional state.

[0401] The device serves to present the generated feedback to the user. Visual feedback is provided through displays or smartphone screens, while auditory feedback is provided through speakers or headsets.

[0402] Specific example

[0403] For example, if a user is swinging while feeling tense, the emotion engine on the server will detect that emotion and generate instructions recommending breathing techniques to help them relax. The system can then provide feedback to the user, either verbally or on-screen, such as, "Take a few deep breaths, then try swinging again."

[0404] Example of a prompt

[0405] The prompt will look like this:

[0406] "Analyze the user's swing and determine their emotions from their facial expressions and voice data. For example, if the user appears frustrated, provide advice on how to relax."

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The user films their golf swing using a smartphone or other device, inputting video and audio data. The device's camera and microphone are used for this process. This input data represents the user's movements, facial expressions, and voice.

[0410] Step 2:

[0411] The terminal transmits the acquired video and audio data to the server via the network. This completes the data transfer to the server, where it is then input as a dataset for analysis.

[0412] Step 3:

[0413] The server runs image processing software to analyze the transmitted video data and outputs the characteristics of the swing motion as numerical data. This process quantifies the user's swing form.

[0414] Step 4:

[0415] The server runs an emotion recognition AI using voice data, generating emotion data by analyzing the tone and pitch of the user's voice. This output represents information indicating the user's emotional state.

[0416] Step 5:

[0417] The server comprehensively analyzes motion and emotion data extracted from video data and uses a generative AI model to generate feedback based on the user's current situation and goals. This output consists of improvement suggestions and specific advice.

[0418] Step 6:

[0419] The device presents feedback received from the server to the user visually or audibly. This includes specific actions such as displaying advice on the screen or providing voice guidance. This allows the user to receive information to improve their own performance.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] This invention relates to a system for analyzing and providing improvement instructions for a golf swing, and is implemented in a configuration that links an image acquisition device, a processing device, and a user terminal. The user uses a terminal such as a smartphone to film their golf swing using the camera function. The terminal temporarily stores this video data and then transmits it to the processing device via a network.

[0437] The server uses the received video data to analyze the swing motion based on an AI model. In this process, it extracts detailed motion data related to the user's swing, such as the club head trajectory, speed, and swing angle. This data is then used to generate optimal swing improvement suggestions in light of the user's goals.

[0438] The generated instructions for improving swing mechanics are transmitted from the server to the terminal via the network and presented to the user visually or audibly on the terminal. This allows the user to receive and implement specific advice for improving their swing, even in specific environments such as their home.

[0439] For example, if a user sets a goal of "reducing slices and hitting the ball straight," this system could identify the timing of body rotation and clubface angle during the swing, and then instruct the user to increase their body rotation speed as a solution. In this way, the server analyzes the user's swing data in detail and provides personalized guidance to help improve their golf skills.

[0440] This invention allows users to continuously improve their swing on their own without the need for a golf coach, making it possible for more people to enjoy golf easily. This system provides a comprehensive solution to support golfers in improving their skills.

[0441] The following describes the processing flow.

[0442] Step 1:

[0443] The user uses a smartphone or other device to record a video of their golf swing. Once recording is complete, the device temporarily saves the video data.

[0444] Step 2:

[0445] The terminal prepares to upload the stored video data to the server via the network. During this process, the video data is compressed as needed to improve transmission efficiency.

[0446] Step 3:

[0447] The server activates an AI model to analyze the video data received via the network. The AI ​​model recognizes the user's swing motion in the video and extracts information such as the club head's trajectory and speed, and the user's body position.

[0448] Step 4:

[0449] The server analyzes the extracted operational data and identifies areas for improvement to achieve the user-defined goal (e.g., reduce slicing).

[0450] Step 5:

[0451] Based on the analysis results, the server generates specific instructions for the user to improve their performance. These instructions include advice on adjusting swing timing and form.

[0452] Step 6:

[0453] The generated instructions are sent from the server to the terminal. The terminal receives the instructions and presents them to the user visually or audibly.

[0454] Step 7:

[0455] Based on the instructions provided, the user puts the advice into practice in their next swing, continuously striving to improve their technique. This cycle is repeated as needed to further refine their swing.

[0456] (Example 1)

[0457] 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."

[0458] When operators seek to improve their performance in specific sports or physical activities, there is a need for efficient means of learning and improvement that do not rely on specialized instruction or expensive equipment. In particular, operators need to be able to effectively improve their skills by receiving individualized and immediate feedback.

[0459] 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.

[0460] In this invention, the server includes means for recording the operator's physical movements using an image acquisition device, means for transferring the recorded video information to a computing device via a communication network, and means for analyzing the video information using a machine learning model on the computing device to extract characteristic information related to the operator's movements. This makes it possible for the operator to obtain appropriate movement improvement suggestions based on their own goals.

[0461] An "image acquisition device" is a video recording device used to record the physical movements of an operator.

[0462] "Operator" refers to the person whose actions are recorded by the image acquisition device.

[0463] "Video information" refers to digital data related to the operator's actions recorded by an image acquisition device.

[0464] A "communication network" is a network infrastructure used to transmit video information to a computing device located in a remote location.

[0465] A "processing unit" is a computer system that receives and analyzes video information transmitted via a communication network.

[0466] A "machine learning model" is an algorithm or program used to analyze video information and extract the operational characteristics of an operator.

[0467] "Characteristic information" refers to quantitative data such as trajectory, speed, and angle related to the operator's movements.

[0468] An "improvement goal" is a specific objective set by the operator with the aim of correcting or improving the operation.

[0469] A "proposal for improving operation" is a suggestion for modifying operation that is tailored to the operator's goals, generated based on the analyzed feature information.

[0470] "Visual or audio notification" refers to a method of delivering generated suggestions for performance improvements to the operator, and is a means of presenting information through a display or speaker.

[0471] This invention provides a system that efficiently analyzes the operator's actions and supports improvement. Specific embodiments thereof are shown below.

[0472] Hardware configuration and data processing

[0473] 1. User (operator) input

[0474] The user records their actions using an image acquisition device built into their smartphone. This image acquisition device should ideally include a camera with a high frame rate to enable detailed recording of the actions.

[0475] 2. Data storage and transfer by the device

[0476] The terminal temporarily stores video information captured by the user. This video information is efficiently processed using data compression technology before being transmitted to the computing device (server) via the communication network.

[0477] 3. Data analysis by the server

[0478] The server analyzes the received video information using machine learning models to extract feature information about the operator's actions. The machine learning models used include generative AI models, which are trained on large datasets. This analysis is performed quickly and efficiently by running on a cloud computing infrastructure.

[0479] 4. Providing the generated instructions

[0480] The server generates suggested improvements to the operation based on the extracted feature information and the improvement goals set by the operator. These suggestions are notified to the user visually or audibly, serving as a reference for the user when modifying the operation.

[0481] Examples of specific cases and prompt statements

[0482] Specific example: For instance, if a user wants to improve their golf swing, the server analyzes the angle and speed of the clubhead during the swing and provides specific improvement suggestions tailored to the user's goals. For example, it might offer advice such as, "Increase your body rotation speed to reduce slices."

[0483] Example of a prompt:

[0484] "Based on the user's goals, please propose specific improvement measures to reduce the slice in their golf swing. The analysis results are as follows: data such as clubhead trajectory, speed, and swing angle."

[0485] This system allows individual users to obtain more specific and practical improvement strategies without needing specialized instruction, thereby effectively promoting skill improvement in sports.

[0486] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0487] Step 1:

[0488] The user uses an image acquisition device such as a smartphone to film their specific actions (e.g., a golf swing). The input is high-quality video data recording the user's actions, and this video information is temporarily stored on the device. The output is the saved video file as local data.

[0489] Step 2:

[0490] The terminal prepares to transmit temporarily stored video data to the processing unit. Specifically, data compression and encryption are performed to ensure secure transfer. The input is the temporarily stored video data, and the output is data processed into a transferable format.

[0491] Step 3:

[0492] The terminal transmits processed video data to the server via the network. The transmitted data is formatted for analysis on the server side. The input is compressed and encrypted data, and the output is a digital data stream received by the server.

[0493] Step 4:

[0494] The server sends the received video data to a machine learning model for motion analysis. As input, the server processes the user's motion data and extracts feature information such as club head trajectory, speed, and swing angle. The output is the feature information generated as a result of this analysis.

[0495] Step 5:

[0496] The server generates improvement suggestions tailored to the user's goals based on the extracted feature information. Here, a generative AI model is used to create prompt sentences, establishing improvement suggestions in natural language. The input is the user's goals and the analyzed feature information, and the output is text data containing specific improvement suggestions.

[0497] Step 6:

[0498] The server sends the generated improvement suggestions to the terminal, which then notifies the user visually or audibly. The input is the improvement suggestion data provided by the server, and the output is the information display or audio guide delivered to the user as feedback. This process allows the user to understand and implement specific improvement measures.

[0499] (Application Example 1)

[0500] 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."

[0501] Traditional golf swing improvement systems had limitations in enabling users to improve their skills independently without professional instruction. Furthermore, there was a lack of tools that provided real-time feedback and allowed for immediate improvement of swing mechanics. This made effective golf swing practice at home difficult.

[0502] 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.

[0503] In this invention, the server includes means for capturing an individual's movements using a camera; means for transmitting the captured video information to a computing device via a communication network; means for analyzing the video information using the computing device and generating information about the individual's movements; and a robotic device for providing real-time feedback on the individual's movements using the generated information and presenting improvement suggestions on the spot. This makes it possible for users to analyze and improve their golf swing even in a home practice environment.

[0504] A "recording device" is a device used to record an individual's actions as video.

[0505] A "communication network" is a network system used to transmit information from one device to another.

[0506] A "processing unit" is a computing device that analyzes input data and generates necessary information.

[0507] "Information about an individual's movements" refers to data such as the trajectory, speed, and angle of movement extracted from recorded video footage.

[0508] "Real-time feedback" refers to immediate responses of improvement and guidance provided in response to user input and actions.

[0509] A "robot device" is a mechanical device that can perform specific tasks autonomously or semi-autonomously.

[0510] "Improvement suggestions" refer to proposals or instructions for modifying a specific action of an individual to make it more appropriate.

[0511] The system that realizes this invention is composed of a combination of multiple hardware and software components. The main components used are an imaging device, a communication network, a computing device, and a robotic device.

[0512] The server records the individual's movements obtained from the camera in real time and transmits them to the computing unit via the communication network. The computing unit analyzes the received video information using an AI analysis module (e.g., TensorFlow or PyTorch) and generates information about the individual's movements. This information includes the movement trajectory, speed, angle, etc.

[0513] When a user sets a desired goal, the server generates suggestions for improving their technique based on that information. These suggestions are created using the generated information and are fed back to the user in real time through the robotic device. This allows users to receive immediate feedback and practice their swing even at home.

[0514] As a concrete example, while a user is practicing their golf swing, a robotic device is placed in front of the user, and the swing motion is filmed with a camera. The filmed footage is sent to a server in real time, and AI analyzes it. As a result, the robotic device provides advice via voice and on-screen display on areas where the swing needs improvement. For example, it might give feedback such as, "Let's try to increase your swing speed a little more."

[0515] An example of a prompt message for a generated AI model is: "Develop an application that analyzes a user's swing and provides real-time suggestions for improvement to find a more efficient swing method."

[0516] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0517] Step 1:

[0518] When a user performs a golf swing, the device's built-in camera captures the action. During recording, the user's movements are recorded at a high frame rate, capturing each moment of the motion. The input is the user's physical movements, and the output is the recorded video data.

[0519] Step 2:

[0520] The terminal temporarily stores the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the video data acquired by the server.

[0521] Step 3:

[0522] The server analyzes the received video data using an AI analysis engine (e.g., a TensorFlow model). This process extracts information such as motion trajectory, velocity, and swing angle from the video. The input is the video data sent to the server, and the output is the analyzed motion data.

[0523] Step 4:

[0524] The server analyzes the motion data using a generated AI model and generates improvement suggestions based on the user's set goals. For example, it can instruct the user to increase their swing speed by a certain percentage. The input is the analyzed motion data, and the output is the suggested motion improvements.

[0525] Step 5:

[0526] The server sends the generated improvement suggestions to the terminal and the robotic device, and provides this feedback to the user. The improvement suggestions are presented through the terminal's display and audio output, and the robotic device assists with its operation. The input is the improvement suggestions for the operation, and the output is the visual or auditory feedback received by the user.

[0527] Step 6:

[0528] Based on the feedback received, the user works to improve their golf swing. In this final step, the new motion is filmed again, and the system cycle is repeated. The input is the improvement action taken based on the feedback, and the output is the improved swing motion.

[0529] 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.

[0530] This invention combines a system for analyzing and providing improvement instructions for a golf swing with an emotion engine that recognizes the user's emotions. The system consists of an image acquisition device, a processing device, a user terminal, and an emotion engine working in conjunction.

[0531] Users use a smartphone or other device to film their golf swing, simultaneously recording their facial expressions and voice via the camera and microphone. This collects data not only on the user's swing motion but also on their emotional state. The device then transmits this data to a server via the network.

[0532] The server analyzes the received video data based on an AI model and generates motion data related to the swing. Simultaneously, it uses an emotion engine to analyze the user's facial expressions and voice to recognize their emotional state. During this process, the user's reactions to their swing performance and their mental state are also evaluated.

[0533] The server comprehensively analyzes motion and emotional data to generate improvement suggestions to help the user achieve their goals (e.g., improving swing stability while relaxed). These suggestions include not only instructions to correct movements but also feedback that takes the user's mental state into consideration. For example, for a nervous user, the server might first advise them on relaxation techniques before providing technical instructions for their swing.

[0534] The generated instructions are sent from the server to the terminal, which then presents them to the user visually or audibly. This system provides feedback that also takes the user's mental state into account, enabling better support not only for skill improvement but also for the user's overall practice experience.

[0535] For example, if a user is struggling with a stable swing due to nervousness during practice, the emotional engine can detect this state through emotional analysis and provide advice on breathing techniques and mental techniques to alleviate tension, enabling them to practice effectively in a relaxed state. This allows users to improve their swing comprehensively, receiving not only physical but also mental support.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] Users use smartphones or other devices to film their golf swings, simultaneously recording their facial expressions and voice. This allows for the acquisition of data on both actions and emotions at once.

[0539] Step 2:

[0540] The terminal temporarily stores the collected video and audio data, then compresses or converts the format of the data before sending it to the server via the network.

[0541] Step 3:

[0542] The server feeds the received video data into an AI model to analyze the user's swing motion. This extracts detailed motion data such as the club's trajectory, speed, and the user's posture.

[0543] Step 4:

[0544] The server uses an emotion engine to analyze transmitted voice and facial expression data to identify the user's emotional state. For example, it can identify emotions such as tension, anxiety, and concentration.

[0545] Step 5:

[0546] The server comprehensively evaluates operational and emotional data and generates improvement instructions based on the user's set goals. These instructions take emotional states into consideration and include not only technical improvements but also mental health advice.

[0547] Step 6:

[0548] The server sends the generated instructions to the terminal. The terminal then presents these instructions to the user visually or audibly. For example, it might provide practical feedback such as, "Relax your shoulders and take a deep breath when you swing."

[0549] Step 7:

[0550] The user follows the provided instructions and tries the swing again. The results of the new attempt are also analyzed by the system, and continuous feedback is provided. This loop allows the user to improve both their swing technique and emotional stability simultaneously.

[0551] (Example 2)

[0552] 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."

[0553] Improving user performance requires not only technical guidance but also comprehensive feedback that takes into account their emotional state. However, conventional systems have been unable to consider user emotions in their motion analysis, and therefore have not been able to fully realize the effectiveness of improvements. Furthermore, real-time feedback has been difficult, making it challenging to streamline the entire user practice experience.

[0554] 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.

[0555] In this invention, the server includes means for collecting data using a device for simultaneously recording the user's actions and emotional state; means for analyzing the video data in an information processing device based on an advanced computational model and generating information related to actions; and means for analyzing the user's emotional state using an emotion analysis engine. This makes it possible to provide the user with comprehensive feedback that integrates technical improvements and emotional support in real time.

[0556] A "user" is an individual who uses a system to analyze and improve their own behavior and emotional state.

[0557] "Device" refers to equipment used to acquire and record data on a user's actions and emotions.

[0558] "Data" refers to a systematically organized collection of information about a user's actions and emotional state.

[0559] An "information processing device" is a general-purpose or specialized computer system used to analyze collected data and calculate and process necessary information.

[0560] An "advanced computational model" is an algorithm or model used to analyze user motion data and identify specific movement patterns and areas for improvement.

[0561] "Emotional state" refers to the user's psychological and emotional condition, which is usually judged from facial expressions, voice, and other factors.

[0562] A "sentiment analysis engine" is software or a program that recognizes and analyzes a user's emotions from collected data.

[0563] "Feedback" refers to guidance and advice provided to users for improvement.

[0564] The system of this invention analyzes the user's actions and emotions and provides improvement suggestions. The user collects data by using a device such as a smartphone or dedicated camera to film their actions and simultaneously record their facial expressions and voice. As a result, action data and emotion data are acquired simultaneously and transmitted from the device to a server via the network.

[0565] The server uses advanced computational models (e.g., motion analysis algorithms utilizing deep learning) to analyze the received data and generate detailed information about the user's actions. Simultaneously, it utilizes an emotion analysis engine to analyze the user's emotional state from their facial expressions and voice data. This allows the server to understand the user's mental reactions and state of mind.

[0566] By comprehensively considering the analyzed behavioral and emotional data, the server generates appropriate improvement suggestions for the user. These suggestions include not only guidance on technical behavioral modifications but also emotional support. For example, for a stressed user, it may include advice on breathing techniques and mental techniques to help them relax.

[0567] The generated suggestions are sent from the server to the terminal, which then presents them to the user visually or audibly. Specifically, this includes on-screen guides showing the correct form of actions and audio relaxation instructions. The system aims to support not only the user's skill improvement but also the entire practice experience.

[0568] For example, if a user experiences anxiety during practice and is unable to perform stable movements, the emotional engine can detect this state and provide advice on relaxation techniques and ways to alleviate tension. This allows users to improve their overall performance by receiving mental support in addition to physical skills.

[0569] An example of a prompt message might be: "Analyze the user's actions and emotions, and provide effective feedback and suggestions for improvement, both physically and mentally."

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] Users record their movements, facial expressions, and voice using their smartphones or dedicated cameras. This generates movement data and emotion data. The input is the user's recorded video and audio, which is collected by the system as the initial dataset.

[0573] Step 2:

[0574] The terminal transmits the collected video and audio data to the server via the network. This process utilizes compression and transfer protocols to efficiently transmit the data. The input is the raw data on the terminal, while the output is compressed data transmitted over the network.

[0575] Step 3:

[0576] The server inputs the received video data into a generating AI model. This model uses a deep learning algorithm specifically designed for motion analysis to extract detailed information about the user's movements and identify movement patterns and areas for improvement. The input is video data, and the output is the analysis result as motion data.

[0577] Step 4:

[0578] The server inputs facial and voice data into an emotion analysis engine to analyze the user's emotional state. This engine uses machine learning algorithms to classify emotions and evaluate levels of tension, anxiety, relaxation, etc. The input is facial and voice data, and the output is the analysis result as emotion data.

[0579] Step 5:

[0580] The server integrates and analyzes behavioral and emotional data to generate appropriate improvement suggestions for the user. Based on the results of the generative AI model and emotion analysis engine, it incorporates not only technical corrections but also mental advice. The input is behavioral and emotional data, and the output is feedback and improvement instructions for the user.

[0581] Step 6:

[0582] The server sends the generated improvement suggestions to the terminal. In this phase, the output data is formatted in an appropriate format so that the user can easily understand and implement it. The input is the improvement suggestion data on the server, and the output is the visual or auditory presentation sent to the terminal.

[0583] Step 7:

[0584] The device presents improvement suggestions to the user visually or audibly. Through visual and audio guidance, the user receives specific feedback based on their actions and emotions. Input is suggestion data sent from the server, and output is customized feedback information for the user.

[0585] (Application Example 2)

[0586] 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."

[0587] Conventional golf swing analysis systems primarily evaluate only the user's movements, making it difficult to provide feedback that takes into account the user's emotional state. Furthermore, when users practice comfortably at home, there is a lack of support for understanding their mental state and emotions and providing appropriate guidance. Therefore, there was a need for methods that not only improve technique but also enhance the overall quality of the practice experience.

[0588] 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.

[0589] In this invention, the server includes means for capturing the user's actions using an image acquisition device, means for transmitting the captured video data, audio data, and emotion data to a computing device via a network, and means for analyzing the video data and audio data on the computing device to generate information regarding the user's actions and emotions. This not only improves the user's technical performance but also provides appropriate emotional support, enabling an overall improvement in the user's capabilities.

[0590] An "image acquisition device" is a device that captures and acquires data of the user's actions, as well as their voice and facial expressions.

[0591] "Video data" refers to digital information that shows the user's actions, obtained by an image acquisition device.

[0592] "Audio data" refers to sound information that represents the user's voice or ambient sounds.

[0593] "Emotional data" refers to digital information that indicates an emotional state by analyzing the user's facial expressions and voice.

[0594] A "computational device" is a device that analyzes transmitted data and generates information about the user's actions and emotions.

[0595] "Analysis means" refers to techniques for analyzing video and audio data to extract information about actions and emotions.

[0596] "Instruction generation means" refers to a technology that generates specific instructions for improving the user's actions based on information obtained through analysis.

[0597] "Means of visual or auditory presentation" refers to means of presenting generated instructions to the user visually (such as a display) or aurally (such as an audio playback device).

[0598] This invention is a system that assists a user in practicing their golf swing, and includes an image acquisition device for analyzing the user's movements and emotions, a server, and a terminal that presents instructions visually or audibly. The server receives video and audio data captured by the user using a terminal such as a smartphone, and analyzes the data using a computing device.

[0599] Hardware and software configuration

[0600] The server uses a computing device equipped with an AI model to process video and audio data. For video data analysis, a motion analysis program using image processing technology is executed. This analyzes the user's swing form and quantifies body movements. Emotional analysis is performed on audio data and the user's facial expressions to determine their emotional state.

[0601] The device serves to present the generated feedback to the user. Visual feedback is provided through displays or smartphone screens, while auditory feedback is provided through speakers or headsets.

[0602] Specific example

[0603] For example, if a user is swinging while feeling tense, the emotion engine on the server will detect that emotion and generate instructions recommending breathing techniques to help them relax. The system can then provide feedback to the user, either verbally or on-screen, such as, "Take a few deep breaths, then try swinging again."

[0604] Example of a prompt

[0605] The prompt will look like this:

[0606] "Analyze the user's swing and determine their emotions from their facial expressions and voice data. For example, if the user appears frustrated, provide advice on how to relax."

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The user films their golf swing using a smartphone or other device, inputting video and audio data. The device's camera and microphone are used for this process. This input data represents the user's movements, facial expressions, and voice.

[0610] Step 2:

[0611] The terminal transmits the acquired video and audio data to the server via the network. This completes the data transfer to the server, where it is then input as a dataset for analysis.

[0612] Step 3:

[0613] The server runs image processing software to analyze the transmitted video data and outputs the characteristics of the swing motion as numerical data. This process quantifies the user's swing form.

[0614] Step 4:

[0615] The server runs an emotion recognition AI using voice data, generating emotion data by analyzing the tone and pitch of the user's voice. This output represents information indicating the user's emotional state.

[0616] Step 5:

[0617] The server comprehensively analyzes motion and emotion data extracted from video data and uses a generative AI model to generate feedback based on the user's current situation and goals. This output consists of improvement suggestions and specific advice.

[0618] Step 6:

[0619] The device presents feedback received from the server to the user visually or audibly. This includes specific actions such as displaying advice on the screen or providing voice guidance. This allows the user to receive information to improve their own performance.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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".

[0637] This invention relates to a system for analyzing and providing improvement instructions for a golf swing, and is implemented in a configuration that links an image acquisition device, a processing device, and a user terminal. The user uses a terminal such as a smartphone to film their golf swing using the camera function. The terminal temporarily stores this video data and then transmits it to the processing device via a network.

[0638] The server uses the received video data to analyze the swing motion based on an AI model. In this process, it extracts detailed motion data related to the user's swing, such as the club head trajectory, speed, and swing angle. This data is then used to generate optimal swing improvement suggestions in light of the user's goals.

[0639] The generated instructions for improving swing mechanics are transmitted from the server to the terminal via the network and presented to the user visually or audibly on the terminal. This allows the user to receive and implement specific advice for improving their swing, even in specific environments such as their home.

[0640] For example, if a user sets a goal of "reducing slices and hitting the ball straight," this system could identify the timing of body rotation and clubface angle during the swing, and then instruct the user to increase their body rotation speed as a solution. In this way, the server analyzes the user's swing data in detail and provides personalized guidance to help improve their golf skills.

[0641] This invention allows users to continuously improve their swing on their own without the need for a golf coach, making it possible for more people to enjoy golf easily. This system provides a comprehensive solution to support golfers in improving their skills.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The user uses a smartphone or other device to record a video of their golf swing. Once recording is complete, the device temporarily saves the video data.

[0645] Step 2:

[0646] The terminal prepares to upload the stored video data to the server via the network. During this process, the video data is compressed as needed to improve transmission efficiency.

[0647] Step 3:

[0648] The server activates an AI model to analyze the video data received via the network. The AI ​​model recognizes the user's swing motion in the video and extracts information such as the club head's trajectory and speed, and the user's body position.

[0649] Step 4:

[0650] The server analyzes the extracted operational data and identifies areas for improvement to achieve the user-defined goal (e.g., reduce slicing).

[0651] Step 5:

[0652] Based on the analysis results, the server generates specific instructions for the user to improve their performance. These instructions include advice on adjusting swing timing and form.

[0653] Step 6:

[0654] The generated instructions are sent from the server to the terminal. The terminal receives the instructions and presents them to the user visually or audibly.

[0655] Step 7:

[0656] Based on the instructions provided, the user puts the advice into practice in their next swing, continuously striving to improve their technique. This cycle is repeated as needed to further refine their swing.

[0657] (Example 1)

[0658] 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".

[0659] When operators seek to improve their performance in specific sports or physical activities, there is a need for efficient means of learning and improvement that do not rely on specialized instruction or expensive equipment. In particular, operators need to be able to effectively improve their skills by receiving individualized and immediate feedback.

[0660] 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.

[0661] In this invention, the server includes means for recording the operator's physical movements using an image acquisition device, means for transferring the recorded video information to a computing device via a communication network, and means for analyzing the video information using a machine learning model on the computing device to extract characteristic information related to the operator's movements. This makes it possible for the operator to obtain appropriate movement improvement suggestions based on their own goals.

[0662] An "image acquisition device" is a video recording device used to record the physical movements of an operator.

[0663] "Operator" refers to the person whose actions are recorded by the image acquisition device.

[0664] "Video information" refers to digital data related to the operator's actions recorded by an image acquisition device.

[0665] A "communication network" is a network infrastructure used to transmit video information to a computing device located in a remote location.

[0666] A "processing unit" is a computer system that receives and analyzes video information transmitted via a communication network.

[0667] A "machine learning model" is an algorithm or program used to analyze video information and extract the operational characteristics of an operator.

[0668] "Characteristic information" refers to quantitative data such as trajectory, speed, and angle related to the operator's movements.

[0669] An "improvement goal" is a specific objective set by the operator with the aim of correcting or improving the operation.

[0670] A "proposal for improving operation" is a suggestion for modifying operation that is tailored to the operator's goals, generated based on the analyzed feature information.

[0671] "Visual or audio notification" refers to a method of delivering generated suggestions for performance improvements to the operator, and is a means of presenting information through a display or speaker.

[0672] This invention provides a system that efficiently analyzes the operator's actions and supports improvement. Specific embodiments thereof are shown below.

[0673] Hardware configuration and data processing

[0674] 1. User (operator) input

[0675] The user records their actions using an image acquisition device built into their smartphone. This image acquisition device should ideally include a camera with a high frame rate to enable detailed recording of the actions.

[0676] 2. Data storage and transfer by the device

[0677] The terminal temporarily stores video information captured by the user. This video information is efficiently processed using data compression technology before being transmitted to the computing device (server) via the communication network.

[0678] 3. Data analysis by the server

[0679] The server analyzes the received video information using machine learning models to extract feature information about the operator's actions. The machine learning models used include generative AI models, which are trained on large datasets. This analysis is performed quickly and efficiently by running on a cloud computing infrastructure.

[0680] 4. Providing the generated instructions

[0681] The server generates suggested improvements to the operation based on the extracted feature information and the improvement goals set by the operator. These suggestions are notified to the user visually or audibly, serving as a reference for the user when modifying the operation.

[0682] Examples of specific cases and prompt statements

[0683] Specific example: For instance, if a user wants to improve their golf swing, the server analyzes the angle and speed of the clubhead during the swing and provides specific improvement suggestions tailored to the user's goals. For example, it might offer advice such as, "Increase your body rotation speed to reduce slices."

[0684] Example of a prompt:

[0685] "Based on the user's goals, please propose specific improvement measures to reduce the slice in their golf swing. The analysis results are as follows: data such as clubhead trajectory, speed, and swing angle."

[0686] This system allows individual users to obtain more specific and practical improvement strategies without needing specialized instruction, thereby effectively promoting skill improvement in sports.

[0687] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0688] Step 1:

[0689] The user uses an image acquisition device such as a smartphone to film their specific actions (e.g., a golf swing). The input is high-quality video data recording the user's actions, and this video information is temporarily stored on the device. The output is the saved video file as local data.

[0690] Step 2:

[0691] The terminal prepares to transmit temporarily stored video data to the processing unit. Specifically, data compression and encryption are performed to ensure secure transfer. The input is the temporarily stored video data, and the output is data processed into a transferable format.

[0692] Step 3:

[0693] The terminal transmits processed video data to the server via the network. The transmitted data is formatted for analysis on the server side. The input is compressed and encrypted data, and the output is a digital data stream received by the server.

[0694] Step 4:

[0695] The server sends the received video data to a machine learning model for motion analysis. As input, the server processes the user's motion data and extracts feature information such as club head trajectory, speed, and swing angle. The output is the feature information generated as a result of this analysis.

[0696] Step 5:

[0697] The server generates improvement suggestions tailored to the user's goals based on the extracted feature information. Here, a generative AI model is used to create prompt sentences, establishing improvement suggestions in natural language. The input is the user's goals and the analyzed feature information, and the output is text data containing specific improvement suggestions.

[0698] Step 6:

[0699] The server sends the generated improvement suggestions to the terminal, which then notifies the user visually or audibly. The input is the improvement suggestion data provided by the server, and the output is the information display or audio guide delivered to the user as feedback. This process allows the user to understand and implement specific improvement measures.

[0700] (Application Example 1)

[0701] 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".

[0702] Traditional golf swing improvement systems had limitations in enabling users to improve their skills independently without professional instruction. Furthermore, there was a lack of tools that provided real-time feedback and allowed for immediate improvement of swing mechanics. This made effective golf swing practice at home difficult.

[0703] 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.

[0704] In this invention, the server includes means for capturing an individual's movements using a camera; means for transmitting the captured video information to a computing device via a communication network; means for analyzing the video information using the computing device and generating information about the individual's movements; and a robotic device for providing real-time feedback on the individual's movements using the generated information and presenting improvement suggestions on the spot. This makes it possible for users to analyze and improve their golf swing even in a home practice environment.

[0705] A "recording device" is a device used to record an individual's actions as video.

[0706] A "communication network" is a network system used to transmit information from one device to another.

[0707] A "processing unit" is a computing device that analyzes input data and generates necessary information.

[0708] "Information about an individual's movements" refers to data such as the trajectory, speed, and angle of movement extracted from recorded video footage.

[0709] "Real-time feedback" refers to immediate responses of improvement and guidance provided in response to user input and actions.

[0710] A "robot device" is a mechanical device that can perform specific tasks autonomously or semi-autonomously.

[0711] "Improvement suggestions" refer to proposals or instructions for modifying a specific action of an individual to make it more appropriate.

[0712] The system that realizes this invention is composed of a combination of multiple hardware and software components. The main components used are an imaging device, a communication network, a computing device, and a robotic device.

[0713] The server records the individual's movements obtained from the camera in real time and transmits them to the computing unit via the communication network. The computing unit analyzes the received video information using an AI analysis module (e.g., TensorFlow or PyTorch) and generates information about the individual's movements. This information includes the movement trajectory, speed, angle, etc.

[0714] When a user sets a desired goal, the server generates suggestions for improving their technique based on that information. These suggestions are created using the generated information and are fed back to the user in real time through the robotic device. This allows users to receive immediate feedback and practice their swing even at home.

[0715] As a concrete example, while a user is practicing their golf swing, a robotic device is placed in front of the user, and the swing motion is filmed with a camera. The filmed footage is sent to a server in real time, and AI analyzes it. As a result, the robotic device provides advice via voice and on-screen display on areas where the swing needs improvement. For example, it might give feedback such as, "Let's try to increase your swing speed a little more."

[0716] An example of a prompt message for a generated AI model is: "Develop an application that analyzes a user's swing and provides real-time suggestions for improvement to find a more efficient swing method."

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] When a user performs a golf swing, the device's built-in camera captures the action. During recording, the user's movements are recorded at a high frame rate, capturing each moment of the motion. The input is the user's physical movements, and the output is the recorded video data.

[0720] Step 2:

[0721] The terminal temporarily stores the captured video data and sends it to the server via the network. The input is the captured video data, and the output is the video data acquired by the server.

[0722] Step 3:

[0723] The server analyzes the received video data using an AI analysis engine (e.g., a TensorFlow model). This process extracts information such as motion trajectory, velocity, and swing angle from the video. The input is the video data sent to the server, and the output is the analyzed motion data.

[0724] Step 4:

[0725] The server analyzes the motion data using a generated AI model and generates improvement suggestions based on the user's set goals. For example, it can instruct the user to increase their swing speed by a certain percentage. The input is the analyzed motion data, and the output is the suggested motion improvements.

[0726] Step 5:

[0727] The server sends the generated improvement suggestions to the terminal and the robotic device, and provides this feedback to the user. The improvement suggestions are presented through the terminal's display and audio output, and the robotic device assists with its operation. The input is the improvement suggestions for the operation, and the output is the visual or auditory feedback received by the user.

[0728] Step 6:

[0729] Based on the feedback received, the user works to improve their golf swing. In this final step, the new motion is filmed again, and the system cycle is repeated. The input is the improvement action taken based on the feedback, and the output is the improved swing motion.

[0730] 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.

[0731] This invention combines a system for analyzing and providing improvement instructions for a golf swing with an emotion engine that recognizes the user's emotions. The system consists of an image acquisition device, a processing device, a user terminal, and an emotion engine working in conjunction.

[0732] Users use a smartphone or other device to film their golf swing, simultaneously recording their facial expressions and voice via the camera and microphone. This collects data not only on the user's swing motion but also on their emotional state. The device then transmits this data to a server via the network.

[0733] The server analyzes the received video data based on an AI model and generates motion data related to the swing. Simultaneously, it uses an emotion engine to analyze the user's facial expressions and voice to recognize their emotional state. During this process, the user's reactions to their swing performance and their mental state are also evaluated.

[0734] The server comprehensively analyzes motion and emotional data to generate improvement suggestions to help the user achieve their goals (e.g., improving swing stability while relaxed). These suggestions include not only instructions to correct movements but also feedback that takes the user's mental state into consideration. For example, for a nervous user, the server might first advise them on relaxation techniques before providing technical instructions for their swing.

[0735] The generated instructions are sent from the server to the terminal, which then presents them to the user visually or audibly. This system provides feedback that also takes the user's mental state into account, enabling better support not only for skill improvement but also for the user's overall practice experience.

[0736] For example, if a user is struggling with a stable swing due to nervousness during practice, the emotional engine can detect this state through emotional analysis and provide advice on breathing techniques and mental techniques to alleviate tension, enabling them to practice effectively in a relaxed state. This allows users to improve their swing comprehensively, receiving not only physical but also mental support.

[0737] The following describes the processing flow.

[0738] Step 1:

[0739] Users use smartphones or other devices to film their golf swings, simultaneously recording their facial expressions and voice. This allows for the acquisition of data on both actions and emotions at once.

[0740] Step 2:

[0741] The terminal temporarily stores the collected video and audio data, then compresses or converts the format of the data before sending it to the server via the network.

[0742] Step 3:

[0743] The server feeds the received video data into an AI model to analyze the user's swing motion. This extracts detailed motion data such as the club's trajectory, speed, and the user's posture.

[0744] Step 4:

[0745] The server uses an emotion engine to analyze transmitted voice and facial expression data to identify the user's emotional state. For example, it can identify emotions such as tension, anxiety, and concentration.

[0746] Step 5:

[0747] The server comprehensively evaluates operational and emotional data and generates improvement instructions based on the user's set goals. These instructions take emotional states into consideration and include not only technical improvements but also mental health advice.

[0748] Step 6:

[0749] The server sends the generated instructions to the terminal. The terminal then presents these instructions to the user visually or audibly. For example, it might provide practical feedback such as, "Relax your shoulders and take a deep breath when you swing."

[0750] Step 7:

[0751] The user follows the provided instructions and tries the swing again. The results of the new attempt are also analyzed by the system, and continuous feedback is provided. This loop allows the user to improve both their swing technique and emotional stability simultaneously.

[0752] (Example 2)

[0753] 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".

[0754] Improving user performance requires not only technical guidance but also comprehensive feedback that takes into account their emotional state. However, conventional systems have been unable to consider user emotions in their motion analysis, and therefore have not been able to fully realize the effectiveness of improvements. Furthermore, real-time feedback has been difficult, making it challenging to streamline the entire user practice experience.

[0755] 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.

[0756] In this invention, the server includes means for collecting data using a device for simultaneously recording the user's actions and emotional state; means for analyzing the video data in an information processing device based on an advanced computational model and generating information related to actions; and means for analyzing the user's emotional state using an emotion analysis engine. This makes it possible to provide the user with comprehensive feedback that integrates technical improvements and emotional support in real time.

[0757] A "user" is an individual who uses a system to analyze and improve their own behavior and emotional state.

[0758] "Device" refers to equipment used to acquire and record data on a user's actions and emotions.

[0759] "Data" refers to a systematically organized collection of information about a user's actions and emotional state.

[0760] An "information processing device" is a general-purpose or specialized computer system used to analyze collected data and calculate and process necessary information.

[0761] An "advanced computational model" is an algorithm or model used to analyze user motion data and identify specific movement patterns and areas for improvement.

[0762] "Emotional state" refers to the user's psychological and emotional condition, which is usually judged from facial expressions, voice, and other factors.

[0763] A "sentiment analysis engine" is software or a program that recognizes and analyzes a user's emotions from collected data.

[0764] "Feedback" refers to guidance and advice provided to users for improvement.

[0765] The system of this invention analyzes the user's actions and emotions and provides improvement suggestions. The user collects data by using a device such as a smartphone or dedicated camera to film their actions and simultaneously record their facial expressions and voice. As a result, action data and emotion data are acquired simultaneously and transmitted from the device to a server via the network.

[0766] The server uses advanced computational models (e.g., motion analysis algorithms utilizing deep learning) to analyze the received data and generate detailed information about the user's actions. Simultaneously, it utilizes an emotion analysis engine to analyze the user's emotional state from their facial expressions and voice data. This allows the server to understand the user's mental reactions and state of mind.

[0767] By comprehensively considering the analyzed behavioral and emotional data, the server generates appropriate improvement suggestions for the user. These suggestions include not only guidance on technical behavioral modifications but also emotional support. For example, for a stressed user, it may include advice on breathing techniques and mental techniques to help them relax.

[0768] The generated suggestions are sent from the server to the terminal, which then presents them to the user visually or audibly. Specifically, this includes on-screen guides showing the correct form of actions and audio relaxation instructions. The system aims to support not only the user's skill improvement but also the entire practice experience.

[0769] For example, if a user experiences anxiety during practice and is unable to perform stable movements, the emotional engine can detect this state and provide advice on relaxation techniques and ways to alleviate tension. This allows users to improve their overall performance by receiving mental support in addition to physical skills.

[0770] An example of a prompt message might be: "Analyze the user's actions and emotions, and provide effective feedback and suggestions for improvement, both physically and mentally."

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] Users record their movements, facial expressions, and voice using their smartphones or dedicated cameras. This generates movement data and emotion data. The input is the user's recorded video and audio, which is collected by the system as the initial dataset.

[0774] Step 2:

[0775] The terminal transmits the collected video and audio data to the server via the network. This process utilizes compression and transfer protocols to efficiently transmit the data. The input is the raw data on the terminal, while the output is compressed data transmitted over the network.

[0776] Step 3:

[0777] The server inputs the received video data into a generating AI model. This model uses a deep learning algorithm specifically designed for motion analysis to extract detailed information about the user's movements and identify movement patterns and areas for improvement. The input is video data, and the output is the analysis result as motion data.

[0778] Step 4:

[0779] The server inputs facial and voice data into an emotion analysis engine to analyze the user's emotional state. This engine uses machine learning algorithms to classify emotions and evaluate levels of tension, anxiety, relaxation, etc. The input is facial and voice data, and the output is the analysis result as emotion data.

[0780] Step 5:

[0781] The server integrates and analyzes behavioral and emotional data to generate appropriate improvement suggestions for the user. Based on the results of the generative AI model and emotion analysis engine, it incorporates not only technical corrections but also mental advice. The input is behavioral and emotional data, and the output is feedback and improvement instructions for the user.

[0782] Step 6:

[0783] The server sends the generated improvement suggestions to the terminal. In this phase, the output data is formatted in an appropriate format so that the user can easily understand and implement it. The input is the improvement suggestion data on the server, and the output is the visual or auditory presentation sent to the terminal.

[0784] Step 7:

[0785] The device presents improvement suggestions to the user visually or audibly. Through visual and audio guidance, the user receives specific feedback based on their actions and emotions. Input is suggestion data sent from the server, and output is customized feedback information for the user.

[0786] (Application Example 2)

[0787] 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".

[0788] Conventional golf swing analysis systems primarily evaluate only the user's movements, making it difficult to provide feedback that takes into account the user's emotional state. Furthermore, when users practice comfortably at home, there is a lack of support for understanding their mental state and emotions and providing appropriate guidance. Therefore, there was a need for methods that not only improve technique but also enhance the overall quality of the practice experience.

[0789] 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.

[0790] In this invention, the server includes means for capturing the user's actions using an image acquisition device, means for transmitting the captured video data, audio data, and emotion data to a computing device via a network, and means for analyzing the video data and audio data on the computing device to generate information regarding the user's actions and emotions. This not only improves the user's technical performance but also provides appropriate emotional support, enabling an overall improvement in the user's capabilities.

[0791] An "image acquisition device" is a device that captures and acquires data of the user's actions, as well as their voice and facial expressions.

[0792] "Video data" refers to digital information that shows the user's actions, obtained by an image acquisition device.

[0793] "Audio data" refers to sound information that represents the user's voice or ambient sounds.

[0794] "Emotional data" refers to digital information that indicates an emotional state by analyzing the user's facial expressions and voice.

[0795] A "computational device" is a device that analyzes transmitted data and generates information about the user's actions and emotions.

[0796] "Analysis means" refers to techniques for analyzing video and audio data to extract information about actions and emotions.

[0797] "Instruction generation means" refers to a technology that generates specific instructions for improving the user's actions based on information obtained through analysis.

[0798] "Means of visual or auditory presentation" refers to means of presenting generated instructions to the user visually (such as a display) or aurally (such as an audio playback device).

[0799] This invention is a system that assists a user in practicing their golf swing, and includes an image acquisition device for analyzing the user's movements and emotions, a server, and a terminal that presents instructions visually or audibly. The server receives video and audio data captured by the user using a terminal such as a smartphone, and analyzes the data using a computing device.

[0800] Hardware and software configuration

[0801] The server uses a computing device equipped with an AI model to process video and audio data. For video data analysis, a motion analysis program using image processing technology is executed. This analyzes the user's swing form and quantifies body movements. Emotional analysis is performed on audio data and the user's facial expressions to determine their emotional state.

[0802] The device serves to present the generated feedback to the user. Visual feedback is provided through displays or smartphone screens, while auditory feedback is provided through speakers or headsets.

[0803] Specific example

[0804] For example, if a user is swinging while feeling tense, the emotion engine on the server will detect that emotion and generate instructions recommending breathing techniques to help them relax. The system can then provide feedback to the user, either verbally or on-screen, such as, "Take a few deep breaths, then try swinging again."

[0805] Example of a prompt

[0806] The prompt will look like this:

[0807] "Analyze the user's swing and determine their emotions from their facial expressions and voice data. For example, if the user appears frustrated, provide advice on how to relax."

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The user films their golf swing using a smartphone or other device, inputting video and audio data. The device's camera and microphone are used for this process. This input data represents the user's movements, facial expressions, and voice.

[0811] Step 2:

[0812] The terminal transmits the acquired video and audio data to the server via the network. This completes the data transfer to the server, where it is then input as a dataset for analysis.

[0813] Step 3:

[0814] The server runs image processing software to analyze the transmitted video data and outputs the characteristics of the swing motion as numerical data. This process quantifies the user's swing form.

[0815] Step 4:

[0816] The server runs an emotion recognition AI using voice data, generating emotion data by analyzing the tone and pitch of the user's voice. This output represents information indicating the user's emotional state.

[0817] Step 5:

[0818] The server comprehensively analyzes motion and emotion data extracted from video data and uses a generative AI model to generate feedback based on the user's current situation and goals. This output consists of improvement suggestions and specific advice.

[0819] Step 6:

[0820] The device presents feedback received from the server to the user visually or audibly. This includes specific actions such as displaying advice on the screen or providing voice guidance. This allows the user to receive information to improve their own performance.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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."

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] The following is further disclosed regarding the embodiments described above.

[0843] (Claim 1)

[0844] A means for capturing the user's actions using an image acquisition device,

[0845] A means for transmitting captured video data to a processing unit via a network,

[0846] A means for analyzing the video data in a processing device and generating data related to the user's actions,

[0847] A means for generating instructions to advise on improving the operation based on the generated data,

[0848] Means for presenting the aforementioned instructions to the user visually or audibly,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, comprising means for comparing the user's goals with the current situation based on the generated data and identifying the optimal operating pattern.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the analysis means and the instruction generation means work together to provide real-time feedback on the user's actions.

[0854] "Example 1"

[0855] (Claim 1)

[0856] A means for recording the operator's physical movements using an image acquisition device,

[0857] A means for transferring recorded video information to a computing device via a communication network,

[0858] A means for analyzing the video information using a machine learning model in a computing device and extracting characteristic information related to the operator's actions,

[0859] A means for generating operational improvement proposals that match the improvement goals set by the operator, based on extracted feature information,

[0860] A means of notifying the operator of the generated improvement suggestions visually or audibly,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising means for comparing the operator's objective with the current state based on generated feature information and determining the optimal operating guideline.

[0864] (Claim 3)

[0865] The system according to claim 1, wherein the analysis means and the instruction generation means work in cooperation to provide immediate guidance to the operator's actions.

[0866] "Application Example 1"

[0867] (Claim 1)

[0868] A means of recording an individual's actions using a recording device,

[0869] A means for transmitting captured video information to a computing device via a communication network,

[0870] A means for analyzing the video information using a computing device and generating information about an individual's actions,

[0871] A means for generating guidance to improve performance based on the generated information,

[0872] Means for presenting the aforementioned information to an individual visually or audibly,

[0873] A means including a robotic device for providing real-time feedback on an individual's actions using generated information and presenting improvement suggestions on the spot,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising means for comparing an individual's goals with their current situation based on generated information and identifying the optimal action pattern.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the analysis means and the instruction generation means work together to provide immediate feedback on the user's actions, and the robot device is configured to suggest improvements to its actions.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] A means of collecting data using a device that simultaneously records the user's actions and emotional state,

[0882] A means for transmitting collected video data and emotional data to an information processing device via a network,

[0883] An information processing device includes means for analyzing the video data based on an advanced computational model and generating information related to the operation,

[0884] Similarly, in an information processing device, a means for analyzing the user's emotional state using an emotion analysis engine,

[0885] A means for integrating and analyzing generated behavioral and emotional data to generate instructions that support the user's mental and technical improvement,

[0886] Means for presenting the aforementioned instructions to the user visually or audibly,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, comprising means for comparing the user's goals with the current situation based on the user's behavioral data and emotional data, and for identifying the optimal behavioral and emotional state.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein the analysis means and the instruction generation means work together to provide real-time feedback on the user's actions and emotions.

[0892] "Application example 2 of combining emotional engines"

[0893] (Claim 1)

[0894] A means for capturing the user's actions using an image acquisition device,

[0895] Means for transmitting captured video data, audio data, and emotional data to a computer via a network,

[0896] A means for analyzing the video data and audio data using a computing device and generating information about the user's actions and emotions,

[0897] A means for generating instructions to advise on improving the operation based on the generated information,

[0898] Means for presenting the aforementioned instructions to the user visually or audibly,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising means for comparing the user's goals with their current situation based on generated data and identifying the optimal behavioral patterns and emotional states.

[0902] (Claim 3)

[0903] The system according to claim 1, wherein the analysis means and the instruction generation means work together to provide real-time feedback on the user's actions and emotions. [Explanation of Symbols]

[0904] 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 capturing the user's actions using an image acquisition device, A means for transmitting captured video data to a processing unit via a network, A means for analyzing the video data in a processing device and generating data related to the user's actions, A means for generating instructions to advise on improving the operation based on the generated data, Means for presenting the aforementioned instructions to the user visually or audibly, A system that includes this.

2. The system according to claim 1, comprising means for comparing the user's goals with the current situation based on the generated data and identifying the optimal operating pattern.

3. The system according to claim 1, wherein the analysis means and the instruction generation means work together to provide real-time feedback on the user's actions.

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