system

Abstract

To provide a system.SOLUTION: A system includes: measurement means for measuring a user's physique; recording means for recording specific movements performed by the user; an emotion engine for recognizing emotion of the user; means for transmitting physique data measured by the measurement means and movement data recorded by the recording means to a server; comparison means for inputting a prompt sentence for comparing the user's movements with movements of a specific professional athlete based on the physique data and the movement data and proposing correction points of the user's movements, into a generated AI model to compare the user's physique with a physique of the professional athlete, and comparing the recorded user's movements with the movements of a specific professional athlete whose physique is similar to that of the user based on the comparison result; means for proposing the correction points of the movements to the user based on a result of the comparison and taking into account information from the emotion engine; and means for displaying the proposed correction points on the user's terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Users who want to learn to move like professional athletes face the challenge of finding it difficult to accurately determine the correct movement corrections due to differences between their own physique and that of professional athletes. [Means for solving the problem]

[0005] As a means for solving this problem, the system of the present invention includes: a measuring means for measuring a user's physique; a recording means for recording specific movements made by the user; an emotion engine for recognizing the emotions of the user; a means for transmitting physique data measured by the measuring means and movement data recorded by the recording means to a server; a comparison means for comparing the user's movements with those of a specific professional athlete based on the physique data and the movement data and inputting a prompt sentence into a generative AI model that suggests modifications to the user's movements, thereby comparing the user's physique with that of the professional athlete, and comparing the recorded user movements with the movements of the specific professional athlete whose physique is similar to that of the user, based on the comparison result; a means for suggesting modifications to the movements to the user, taking information from the emotion engine into consideration, based on the comparison result; and a means for displaying the suggested modifications on the user's terminal. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[0029] "Example 2"

[0030] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[0031] The processing flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[0034] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[0035] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[0036] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[0037] "Example 2"

[0038] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[0039] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[0040] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[0041] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[0042] Example 1

[0043] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] Conventional motion analysis systems have had difficulty accurately measuring and recording a user's physique and movements, and providing appropriate feedback. Furthermore, there was a lack of means for performing highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

[0045] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0046] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with similar physiques, means for suggesting to the user movement corrections based on the comparison results, and means for displaying the suggested corrections on the user's terminal. This allows the user to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

[0047] "User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

[0048] "Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

[0049] "Movement" refers to specific physical movements or performances made by users, which are recorded using cameras and sensors.

[0050] "Server" refers to a computer system that receives data sent by a user, analyzes it using AI means, and provides the results to the user.

[0051] "3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

[0052] "AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

[0053] "Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

[0054] "Fixes" are specific changes or suggestions suggested to improve user behavior.

[0055] "Terminal" refers to the device that a user uses to access the system, including smartphones and tablets.

[0056] "Comparison results" refers to the results of a comparison made by AI means between the user's movements and those of a professional athlete, and includes data that serves as the basis for feedback to the user.

[0057] This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement corrections to the user. Specific embodiments of this system are described below.

[0058] First, the user measures their physique using a device that provides a 3D body scan, such as Kinect or Structure Sensor, which acquires the user's physique data and stores it on the user's device (smartphone or tablet).

[0059] Next, the user performs a specific movement, such as a baseball batting stance or pitching stance. This movement is recorded using a smartphone camera or GoPro. The recorded video data is saved on the user's device.

[0060] The user's device sends the recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

[0061] The server inputs the received movement data and physique data into an AI model. This AI model uses TENSORFLOW (registered trademark) and PyTorch, and uses deep learning to compare the user's movements with those of professional athletes with similar physiques. The server inputs the data into the AI ​​model and begins the comparison process. Once the process is complete, the comparison results are generated.

[0062] The server generates corrections for the user's movements based on the results of the comparison with the AI ​​model. These corrections are summarized as specific advice and methods for improvement. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

[0063] The server sends the generated corrections to the user's device, which then displays them in the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

[0064] As a concrete example, consider a case where a user wants to improve their baseball batting form. First, the user measures their physique using a 3D body scanning device (e.g., Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests corrections to the user's batting form.

[0065] An example of a prompt is, "Please input the user's physique data and a video of their batting form, compare it with the movements of a professional batter, and suggest corrections."

[0066] In this way, users can compare their own movements with those of professionals and get specific corrections.

[0067] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0068] Step 1:

[0069] Users measure their physique using a 3D body scanning device.

[0070] Input: User's physical location

[0071] Specific action: The user stands in front of the Kinect and rotates their body according to the instructions.

[0072] Data processing: The scanning device acquires the user's physique data and generates a 3D model.

[0073] Output: The generated physique data is saved on the user's device.

[0074] Step 2:

[0075] The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

[0076] Input: User actions

[0077] Specific operation: The user sets the smartphone on a tripod and takes a picture of their batting form.

[0078] Data processing: The camera records the user's movements as video data.

[0079] Output: The recorded video data is saved on the user's device.

[0080] Step 3:

[0081] The user's device transmits the recorded movement data and physique data to the server.

[0082] Input: Movement data and physique data

[0083] Specific action: The user presses the "Submit Data" button within the application.

[0084] Data processing: The device compresses the data and uploads it to a server via the Internet.

[0085] Output: Data is sent to the server.

[0086] Step 4:

[0087] The server inputs the received movement data and physique data into the AI ​​model.

[0088] Input: Movement data and physique data

[0089] Specific operation: The server inputs the data into the AI ​​model and begins the comparison process.

[0090] Data calculation: The AI ​​model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[0091] Output: The comparison results are generated.

[0092] Step 5:

[0093] The server generates corrections for the user's behavior based on the comparison results of the AI ​​model.

[0094] Input: Comparison result

[0095] Specific actions: The server generates specific advice such as, "To increase the swing speed of your batting form, you need to rotate your hips faster."

[0096] Data processing: Analyze the comparison results and generate corrections in text format.

[0097] Output: The corrections are generated.

[0098] Step 6:

[0099] The server sends the generated modifications to the user's device.

[0100] Input: Correction

[0101] Specific operation: The server sends the corrections to the user's device.

[0102] Data processing: Convert the corrections into the appropriate format and send them.

[0103] Output: The corrections are sent to the user's device.

[0104] Step 7:

[0105] The user's device will display the received corrections within the application.

[0106] Input: Correction

[0107] What happens: The user opens the application and sees the suggested fixes.

[0108] Data processing: Display the corrections in a user-friendly format.

[0109] Output: User can see the corrections.

[0110] (Application example 1)

[0111] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0112] In conventional factory work, there is a lack of specific feedback to improve the efficiency and safety of workers' movements. As a result, workers continue to perform inappropriate movements, which leads to problems such as reduced work efficiency and increased physical strain. In addition, there is no system to optimize workers' movements, which makes it difficult to provide appropriate guidance to individual workers.

[0113] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with movements of workers with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

[0114] "User" means an individual who uses the System to have their physique and / or movement measured and analyzed.

[0115] "Physique" refers to the physical characteristics and measurements of the user.

[0116] "Movement" refers to a specific physical movement or task performed by a user.

[0117] "Recording means" refers to a device or method for saving a user's actions as video or data.

[0118] "AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

[0119] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[0120] The "means for suggesting corrections" is a method or device for suggesting improvements to the user's behavior based on the analysis results.

[0121] A "factory worker" is a worker who performs specific tasks in a factory.

[0122] The "means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

[0123] "3D body scanning" is a technology for measuring a user's physique in three dimensions.

[0124] A system for implementing this invention includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user corrections to their movements based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers.

[0125] Hardware and software used

[0126] 3D body scanning device: A device for measuring the user's physique in three dimensions.

[0127] Camera: A device used to record user actions. An example is the Logitech C920.

[0128] Smart glasses: A device that visually indicates to the user the corrections to their actions. An example is Google® Glass®.

[0129] Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad.

[0130] Server: A computer system for processing data and running AI models.

[0131] Software: Uses program libraries such as Python (registered trademark), OpenCV (registered trademark), and Keras.

[0132] Data processing and calculation

[0133] 1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

[0134] 2. Recording of actions: The server records the user's actions using a camera, and the recorded actions are saved as image data.

[0135] 3. Movement analysis: The server inputs the recorded image data and physique data into the AI ​​model to analyze the movements. The AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique.

[0136] 4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

[0137] Specific examples

[0138] When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. The analysis results suggest corrections to things like the angle of the waist and the position of the hands when lifting, allowing the worker to work efficiently and safely.

[0139] Prompt Sentence Examples

[0140] "Record factory workers lifting heavy objects and feed this into the AI ​​along with their 3D body scan data to compare with efficient movements and suggest corrections."

[0141] The above is an embodiment of the present invention.

[0142] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0143] Step 1:

[0144] The server acquires the user's physique data using a 3D body scanning device. The input includes the user's physical characteristics and dimensions. The data is then converted into a data format that represents these characteristics and dimensions in three dimensions. The output is the user's physique data.

[0145] Step 2:

[0146] The server records the user's actions using a camera. The input includes specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

[0147] Step 3:

[0148] The server inputs the recorded image data and physique data into the AI ​​model and analyzes the movements. The input includes image data and physique data. For data calculation, the AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

[0149] Step 4:

[0150] The server proposes behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. The data is processed to generate specific suggestions for modifications. The output is the proposed modifications.

[0151] Step 5:

[0152] The server displays the suggested corrections on the smart glasses or tablet. The input includes the suggested corrections. Specific operations include generating data for visually displaying the suggestions and sending it to the smart glasses or tablet. The output is a visually verifiable suggested correction that the user can see.

[0153] The above is the processing flow of this program.

[0154] Example 2

[0155] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0156] Conventional motion analysis systems have difficulty accurately measuring and recording a user's physique and movements, and suggesting appropriate corrections. Furthermore, there is a lack of means to suggest specific corrections to users, which has led to problems with effective improvements to movements.

[0157] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0158] In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for presenting the corrections to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate corrections, and present specific corrections to the user.

[0159] "User" refers to an individual who uses the system to measure and record their physique and movements and receive suggestions for movement modifications.

[0160] "Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

[0161] "Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

[0162] "Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

[0163] "Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

[0164] "Means for Providing Corrections" means a device or application that visually or audibly communicates suggested corrections to the user.

[0165] "3D body scanning" refers to a technology that measures a user's physique in three dimensions.

[0166] "Deep learning" refers to a machine learning technique that uses multiple layers of neural networks used in artificial intelligence tools.

[0167] MODE FOR CARRYING OUT THE INVENTION

[0168] This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests corrections to those movements. Specific embodiments of this system will be described below.

[0169] User's physique measurement

[0170] Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner and a motion capture system. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then sent to a server via wireless communication.

[0171] Recording actions

[0172] Next, the user performs a specific action, such as batting or pitching in baseball. The user's action is recorded using a camera or sensor. The hardware used may be a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

[0173] Behavior analysis

[0174] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the AI ​​model.

[0175] Suggested behavior modifications

[0176] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[0177] Specific examples

[0178] Example 1: Correcting baseball batting form

[0179] The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user via a dedicated application. The user can then work on improving their batting form while viewing the application.

[0180] Example 2: Correcting pitching form

[0181] The user measures their physique using a 3D scanner and sends the data to a server. Then, a motion capture system is used to record their pitching form, and the data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user through a dedicated application. The user works on improving their pitching form while viewing the application.

[0182] Prompt Sentence Examples

[0183] Prompt 1: Baseball batting form

[0184] "We need to generate an AI model that takes a user's 3D body scan data and video data of their batting form as input, compares it with data from professional baseball players, and suggests corrections."

[0185] Prompt 2: Pitching form

[0186] "We need to take the user's 3D body scan data and motion capture data of their pitching form as input, compare it with data from professional baseball players, and generate an AI model that suggests corrections."

[0187] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0188] Program processing flow

[0189] Step 1: Measuring the user's physique

[0190] The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then transmitted wirelessly to a server.

[0191] Input: User's physique scan data

[0192] Data processing: Real-time processing of scan data

[0193] Output: User's physique data

[0194] Step 2: Recording the action

[0195] Using a high-resolution camera and a motion capture system, the user performs a specific action, such as batting or pitching in baseball. The device records the action and generates video or motion capture data, which is then sent to a server.

[0196] Input: User actions

[0197] Data processing: Motion recording and data generation

[0198] Output: Video data or motion capture data

[0199] Step 3: Analyze the behavior

[0200] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the AI ​​model.

[0201] Input: physique data, movement data

[0202] Data processing: deep learning behavior comparison

[0203] Output: Behavior fixes

[0204] Step 4: Propose behavior modifications

[0205] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[0206] Input: Behavior fixes

[0207] Data processing: Preparation for displaying corrections

[0208] Output: User is presented with the fix

[0209] Adding specific actions

[0210] Step 1: Measuring the user's physique

[0211] The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scanned data is processed in real time to generate the user's physique data, which is then transmitted wirelessly to the server.

[0212] Step 2: Recording the action

[0213] The user installs a high-resolution camera and records their batting form. The camera records high-resolution video and saves it on the device. The device then compresses the video data and uploads it to the server.

[0214] Step 3: Analyze the behavior

[0215] The server inputs the received physique data and video data into a deep learning framework. The AI ​​model compares the user's batting form with that of professional baseball players. The comparison uses features such as joint angles and movement speed. The server extracts corrections for the user's movements from the output of the AI ​​model.

[0216] Step 4: Propose behavior modifications

[0217] The server sends the extracted corrections in JSON format to the device. The device then displays the received corrections in a dedicated application. The user can check the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[0218] (Application example 2)

[0219] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0220] Conventional optimization of factory robots' movements relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods may not be effective enough. This creates a risk of a decline in factory productivity and quality.

[0221] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0222] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movement modifications based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movements. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movements of factory robots.

[0223] "User" means an individual or legal entity that uses the System to measure and analyze their physique and movements.

[0224] "Physique" refers to the shape and dimensions of a user's body, as measured by means such as a 3D body scan.

[0225] A "specific action" is a specific movement or task performed by a user, which is recorded using a camera or sensor.

[0226] "Means for recording" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

[0227] "AI means" is a technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

[0228] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[0229] A "professional athlete" is a professional athlete who is active in a particular sport and is the subject to be compared with the user's movements.

[0230] "Behavior Modifications" are specific improvements suggested to optimize a user's behavior.

[0231] "Robots used in factories" are automated machinery used on factory production lines and in workshops.

[0232] "Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

[0233] To implement this invention, the following hardware and software are required. The hardware includes a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). The software includes Python, OpenCV, Keras, etc.

[0234] First, the user measures their body size using a 3D scanner, which captures the user's body shape and dimensions with high precision and stores them as digital data. This data is then sent to a server.

[0235] Next, the user performs a specific action. For example, to mimic the actions of a robot used in a factory, the camera and sensors are used to record the action. The recorded action data is also sent to the server.

[0236] The server inputs the received 3D scan data and movement data into the AI ​​system, which then analyzes the data using deep learning and compares the user's movements with those of professional athletes with a similar physique. Based on the comparison results, the system suggests corrections to the user's movements.

[0237] Furthermore, to optimize the operation of the robots used in the factory, the robot's operational data is also recorded and analyzed using AI methods. Based on the analysis results, corrections to optimize the robot's operation are suggested. These suggestions are displayed on the user's device.

[0238] As a concrete example, consider optimizing the operation of a welding robot used in a factory. A 3D scanner captures the robot's shape data, and cameras and sensors record its operation data. This data is input into an AI tool, which then analyzes it and suggests corrections to the welding angle and speed.

[0239] An example of a prompt sentence to be input into the generative AI model is, "Please input the welding robot's motion data along with the 3D scan data and suggest optimal motion corrections."

[0240] As described above, the present invention enables individual optimization based on the user's physique and movements, and can optimize the movements of factory robots efficiently and with high precision.

[0241] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0242] Step 1:

[0243] Users measure their body size using a 3D scanner.

[0244] Input: User's body

[0245] Data processing: A 3D scanner captures the shape and dimensions of the user's body with high precision and stores them as digital data.

[0246] Output: 3D scan data

[0247] Step 2:

[0248] The user performs a specific action and the action is recorded using cameras and sensors.

[0249] Input: User actions

[0250] Data processing: Cameras and sensors record user actions as video and sensor data.

[0251] Output: Operation data

[0252] Step 3:

[0253] The server receives the 3D scan data and the motion data.

[0254] Input: 3D scan data, motion data

[0255] Data processing: The server receives these data and prepares them for input into the AI ​​means.

[0256] Output: Prepared data

[0257] Step 4:

[0258] The server analyzes the 3D scan data and motion data using AI means.

[0259] Input: Prepared data

[0260] Data processing: Using a deep learning model, the user's movements are compared to those of professional athletes with a similar physique.

[0261] Output: Analysis results

[0262] Step 5:

[0263] The server then suggests modifications to the user's behavior based on the analysis results.

[0264] Input: Analysis results

[0265] Data manipulation: Use the analysis results to generate specific modifications to optimize user behavior.

[0266] Output: suggested fixes

[0267] Step 6:

[0268] Suggested fixes will be displayed on the user's device.

[0269] Input: Suggested fix

[0270] Data processing: Converting the proposal content into a format for display on the user's device.

[0271] Output: The corrections displayed on the user's terminal

[0272] Step 7:

[0273] The movements of robots used in factories are recorded and sent to a server.

[0274] Input: Robot movement

[0275] Data processing: Cameras and sensors record the robot's movements as video and sensor data, which are then sent to a server.

[0276] Output: Robot motion data

[0277] Step 8:

[0278] The server analyzes the robot's operational data using AI means and suggests modifications for optimization.

[0279] Input: Robot motion data

[0280] Data processing: Using deep learning models, the robot's behavior is analyzed and corrections for optimization are generated.

[0281] Output: Suggested modifications to the robot's behavior

[0282] Step 9:

[0283] Suggestions for correcting the robot's behavior are displayed on the user's device.

[0284] Input: Suggested modifications to the robot's behavior

[0285] Data processing: Converting the proposal content into a format for display on the user's device.

[0286] Output: Robot behavior corrections displayed on the user's device

[0287] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0288] "Example 1"

[0289] In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine for recognizing the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI ​​means. The AI ​​means suggests movement modifications taking into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI ​​means takes this information into account and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

[0290] "Example 2"

[0291] In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine for recognizing the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI ​​means. The AI ​​means suggests movement modifications taking into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI ​​means takes this information into account and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

[0292] The processing flow of each embodiment will be described below.

[0293] "Example 1"

[0294] Step 1: Measure the user's physique with a 3D body scan.

[0295] Step 2: A specific action performed by the user (e.g., batting or pitching a baseball) is recorded using a video camera or motion sensor.

[0296] Step 3: The recorded movements are analyzed using AI tools and compared to the movements of professional athletes with similar physiques.

[0297] Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

[0298] Step 5: The AI ​​tool will suggest corrections to the user's movements, taking into account the comparison between the user's movements and those of professional athletes and the user's emotions.

[0299] Step 6: The proposed behavioral modifications are presented to the user through an application on a device such as a smartphone or tablet.

[0300] "Example 2"

[0301] Step 1: Measure the user's physique with a 3D body scan.

[0302] Step 2: A specific action performed by the user (e.g., batting or pitching a baseball) is recorded using a video camera or motion sensor.

[0303] Step 3: The recorded movements are analyzed using AI tools and compared to the movements of professional athletes with similar physiques.

[0304] Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

[0305] Step 5: The AI ​​tool will suggest corrections to the user's movements, taking into account the comparison between the user's movements and those of professional athletes and the user's emotions.

[0306] Step 6: The proposed behavioral modifications are presented to the user through an application on a device such as a smartphone or tablet.

[0307] Example 1

[0308] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0309] Conventional motion improvement systems make suggestions based on the user's physique and motion data, but they do not take the user's emotions into consideration, which can make it difficult for users to accept the suggestions. Also, since the points to correct for the motion are not specific, it is difficult for users to understand how to improve.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0311] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, means for suggesting to the user movement corrections based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement corrections taking into account the emotion data, thereby making it possible to suggest movement improvements taking into account the user's physique and emotions.

[0312] "Means for measuring the user's physique" refers to a device or method for measuring the user's physical characteristics and acquiring them as digital data.

[0313] "Means for recording specific actions performed by a user" refers to a device or method that uses video or sensors to record specific actions performed by a user as digital data.

[0314] "Artificial intelligence means" means software or systems that analyze recorded data and use specific algorithms to compare the user's movements with other data (e.g., the movements of professional athletes).

[0315] The "means for suggesting points for correcting movements" refers to a device or method that suggests points for improving movements or methods for correcting movements to the user based on the analysis results.

[0316] "Means for recognizing user emotions" refers to devices or methods that analyze emotions from the user's facial expressions, tone of voice, etc., and acquire them as digital data.

[0317] The "means for proposing corrections to movements in consideration of emotional data" refers to a device or method that presents corrections to movements in a more acceptable form based on the emotional data of the user.

[0318] "3D body scanning" is a technology that scans the user's body in three dimensions and obtains detailed physique data.

[0319] "Deep learning" is an artificial intelligence technology that uses multi-layered neural networks to analyze data and learn patterns and features.

[0320] This invention is a system that analyzes the user's physique and movements, compares them with the movements of professional athletes, and suggests movement corrections to the user. Furthermore, by recognizing the user's emotions and making suggestions taking into account that emotional data, it is possible to present movement corrections in a way that is easy for the user to accept.

[0321] Hardware and software used

[0322] 1. User's physique measurement

[0323] Users measure their physique using equipment that provides a three-dimensional body scan, such as a 3D scanner or a dedicated body scan application, which captures detailed physique data, including the user's height, weight, and body fat percentage.

[0324] 2. Recording the movement

[0325] Users record specific movements (e.g., batting or pitching a baseball) using a smartphone camera or a dedicated motion capture device, and the recorded data includes detailed movement information such as swing speed and angle.

[0326] 3. Sending operation data

[0327] The device transmits the recorded movement and physique data to a server in real time over the internet, with the data encrypted and using a secure communication protocol.

[0328] 4. Analysis of movement

[0329] The server then inputs the received data into an AI model, which uses deep learning algorithms to compare the user's movements with those of professional athletes. The software used is a deep learning framework such as TensorFlow or PyTorch. For example, it calculates how much the user's swing speed differs from the average speed of professional athletes.

[0330] 5. Emotional Recognition

[0331] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. This emotion engine determines whether the user is frustrated or satisfied and sends that information to a server. The software used is an emotion recognition API (e.g., Microsoft® Azure® Emotion API).

[0332] 6. Suggestions for behavioral modifications

[0333] The server then suggests to the user how to improve their performance based on the comparison of their movements and their emotional data. For example, if the user feels frustrated, the server might suggest, "Maybe your swing will become smoother if you just change the way you grip the bat slightly." The server then notifies the user of this suggestion through an application on their smartphone, tablet, or other device.

[0334] Specific examples

[0335] Example 1: Improving baseball batting form

[0336] The user measures their physique using a 3D scanner and records their batting form with their smartphone camera. The device then sends the recorded data to a server, which then inputs the data into an AI model and compares it with the form of professional baseball players. If the emotion engine detects the user's frustration, the server will make suggestions such as, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions through a smartphone application.

[0337] Example prompts for generative AI models

[0338] "We will take the user's 3D body scan data and a video of their batting form as input, compare it with the form of a professional baseball player, and suggest improvements while taking into account the user's emotions."

[0339] In this way, it becomes possible to suggest movement improvements that take into account the user's physique and emotions.

[0340] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0341] Step 1:

[0342] Users measure their physique using a device that provides a 3D body scan. As input, a 3D scanner is used to scan the user's body. As output, detailed physique data such as the user's height, weight, and body fat percentage is generated. This data is stored on the device.

[0343] Step 2:

[0344] Users record specific movements (e.g., batting or pitching in baseball) using a smartphone camera or a dedicated motion capture device. The input is the user's movements, which are captured by the camera or sensors. The output is detailed movement data, such as the speed and angle of the swing. This data is stored on the device.

[0345] Step 3:

[0346] The device transmits the recorded movement data and physique data to a server. As input, the movement data and physique data stored on the device are used. As output, these data are transmitted to the server via the Internet. The data is encrypted and uses a secure communication protocol.

[0347] Step 4:

[0348] The server inputs the received data into an AI model. The motion and physique data sent to the server are used as input. The output is the result of the AI ​​model comparing the user's motion with that of a professional athlete. This AI model uses deep learning algorithms and utilizes deep learning frameworks such as TensorFlow and PyTorch.

[0349] Step 5:

[0350] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. As input, the user's facial and voice data is captured through a camera and microphone. As output, emotional data is generated, such as whether the user is frustrated or satisfied. This data is then sent to a server.

[0351] Step 6:

[0352] The server then suggests to the user how to modify their movements based on the comparison results and emotional data. The AI ​​model's comparison results and emotional data are used as input. The output is a suggestion for the user to modify their movements. For example, if the user feels frustrated, the server might suggest, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The suggestion is then notified to the user through an application on their device, such as a smartphone or tablet.

[0353] (Application example 1)

[0354] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0355] Conventional motion analysis systems can analyze a user's physique and movements, but they have the problem of not being able to suggest motion modifications that take the user's emotions into account. Furthermore, when optimizing the movements of robots working in factories, there is a lack of real-time monitoring of movement efficiency and error rates and suggestions for improvement. This leads to issues such as reduced movement efficiency for users and robots, preventing optimal performance.

[0356] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0357] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movement modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time. This makes it possible to suggest movement modifications taking the user's emotions into account, and further makes it possible to monitor the movement efficiency and error rate of robots working in factories in real time, thereby achieving optimal movement performance.

[0358] "Means for measuring the user's physique" refers to devices or technologies for accurately measuring the dimensions and shape of the user's body.

[0359] "Means for recording specific actions performed by a user" refers to devices or technologies that use cameras, sensors, etc. to record specific actions performed by a user.

[0360] "AI means for analyzing recorded movements and comparing them with the movements of professional athletes with similar physiques" refers to artificial intelligence technology that analyzes recorded movement data of a user and compares it with the movement data of professional athletes with similar physiques.

[0361] "Means for suggesting to users points for correcting their movements based on the comparison results" refers to devices or technologies that suggest points for improvement or correction to the user's movements based on the results of movement comparison by AI.

[0362] The "emotion engine that recognizes user emotions" is a technology that identifies emotions from the user's facial expressions, tone of voice, etc.

[0363] The "means for proposing modifications to behavior in consideration of information from the emotion engine" refers to a device or technology that suggests modifications to behavior based on the user's emotional information recognized by the emotion engine.

[0364] "Means for optimizing robot operations" refers to devices and technologies that optimize the operations of robots working in factories to ensure that they are efficient and effective.

[0365] "Means for monitoring the operational efficiency and error rate of a robot" refers to devices and technologies that monitor the operational efficiency and error rate of a robot in real time.

[0366] "Means for making improvement suggestions in real time" refers to devices and technologies that analyze robot operation data in real time and immediately suggest areas for improvement.

[0367] A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user how to modify their movements based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting how to modify their movements taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time.

[0368] Hardware and Software Configuration

[0369] Hardware:

[0370] 3D body scanning device

[0371] Camera (e.g. Logitech C920)

[0372] Computer (e.g. Intel Core i7, 16GB RAM)

[0373] software:

[0374] OpenCV: Motion recording and image processing

[0375] Keras: Loading and Predicting Deep Learning Models

[0376] EmotionRecognizer: Emotion recognition library

[0377] Processing flow

[0378] The server first acquires the user's physique data using a 3D body scanning device. Then, it uses a camera to record specific movements performed by the user. The recorded movement data is preprocessed using OpenCV and analyzed using a deep learning model using Keras. The analysis results are compared with the movement data of professional athletes with similar physiques.

[0379] Furthermore, the system uses EmotionRecognizer to recognize the user's emotions and suggests behavioral modifications based on the information from the emotion engine. The suggested modifications are then presented to the user.

[0380] For robots working in factories, measures are used to optimize the robot's operation. The robot's operation efficiency and error rate are monitored in real time, and improvement suggestions are made. This makes it possible to optimize the robot's operation performance.

[0381] Specific examples

[0382] For example, if a robot assembling parts in a factory is moving slowly, the AI ​​will analyze its movements and suggest correcting the angle and speed of its movements. Furthermore, if the robot is making frequent errors, it will determine through emotion recognition that it is feeling frustrated and suggest that it should move more slowly.

[0383] Prompt Sentence Examples

[0384] "To optimize the movements of robots assembling parts in factories, design a system that uses 3D body scanning and cameras to record their movements, analyzes them with AI, and suggests optimal movement patterns. Additionally, add functionality to monitor the robot's movement efficiency and error rate, and make suggestions for improvement in real time."

[0385] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0386] Step 1:

[0387] The server acquires the user's physique data using a 3D body scanning device. As input, it scans the user's body dimensions and shape, and generates 3D physique data as output. This data is used to accurately determine the user's physique.

[0388] Step 2:

[0389] The server uses a camera to record specific actions performed by the user, capturing the actions performed by the user in real time as input and generating video data of the actions as output, which is used for subsequent action analysis.

[0390] Step 3:

[0391] The server preprocesses the recorded motion data using OpenCV. It receives motion video data as input and generates preprocessed image data as output. This preprocessing includes image resizing and noise removal.

[0392] Step 4:

[0393] The server analyzes the preprocessed image data using a deep learning model using Keras. It receives the preprocessed image data as input and extracts movement features as output. These features are used to analyze the user's movements in detail.

[0394] Step 5:

[0395] The server compares the extracted features with the motion data of professional athletes with similar physiques. It receives the user's motion features and the motion data of the professional athletes as input, and generates a comparison result as output. This comparison result is used to identify areas for correction in the user's motion.

[0396] Step 6:

[0397] The server uses EmotionRecognizer to recognize the user's emotions. As input, it captures the user's facial expressions and tone of voice while they are in action, and generates emotional data as output. This emotional data is used to understand the user's emotional state.

[0398] Step 7:

[0399] The server proposes behavioral modifications taking into account information from the emotion engine. It receives the comparison results and emotion data as input and generates behavioral modifications for the user as output. These modifications are proposed taking into account the user's emotional state.

[0400] Step 8:

[0401] The server executes a means for optimizing the operation of a robot working in a factory. The server receives the robot's operation data as input and generates an optimized operation pattern as output. This optimization is performed to improve the robot's operation efficiency.

[0402] Step 9:

[0403] The server monitors the robot's operational efficiency and error rate in real time. It receives the robot's operational data in real time as input and generates the monitoring results of operational efficiency and error rate as output. These monitoring results are used to understand the robot's operational status.

[0404] Step 10:

[0405] The server provides real-time improvement suggestions, taking the monitoring results as input and generating improvement suggestions as output, which are made to optimize the robot's operational performance.

[0406] Example 2

[0407] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0408] Conventional motion improvement systems have difficulty accurately measuring the user's physique and movements and providing appropriate feedback. Furthermore, since motion improvement suggestions do not take the user's emotions into consideration, feedback in a form that is easy for the user to accept is lacking. This limits the effectiveness of motion improvement.

[0409] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0410] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movements, artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking the recognized emotions into consideration. This makes it possible to accurately measure the user's physique and movements and provide appropriate feedback. Furthermore, because suggestions for movement improvement are made taking the user's emotions into consideration, feedback is provided in a form that the user finds easy to accept, improving the effectiveness of movement improvement.

[0411] "Means for measuring the user's physique" refers to devices or technologies that measure the dimensions and shape of the user's body in three dimensions.

[0412] "Means for recording specific actions performed by a user" refers to devices or technologies that use video or sensors to record specific actions performed by a user.

[0413] The "means for transmitting recorded actions" refers to a communication means for transmitting recorded action data to a server or other device.

[0414] "Artificial intelligence means" refers to artificial intelligence technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

[0415] "Means for suggesting corrections to movements" refers to devices or technologies that suggest improvements to movements to users based on the results of analysis by artificial intelligence means.

[0416] "Means for recognizing user emotions" refers to devices or technologies for recognizing emotions from the user's facial expressions, tone of voice, etc.

[0417] "Means for suggesting behavioral modifications taking into account recognized emotions" refers to devices or technologies that suggest behavioral modifications in a way that is easy for the user to accept, based on the user's emotional information.

[0418] The present invention is a system that measures a user's physique, records specific movements, analyzes the movements, and suggests corrections. The system can recognize the user's emotions and take them into consideration when suggesting corrections to movements.

[0419] First, the user measures their physique using a 3D body scanning device, such as the Structure Sensor or Kinect, which then acquires the user's physique data and stores it on the device.

[0420] Next, the user performs a specific movement (e.g., batting or pitching a baseball), which is recorded using a camera or sensor (e.g., a GoPro camera or a Vicon motion capture system), and the recorded movement data is stored on the device.

[0421] The device sends the recorded motion and physique data to a server. The data is transmitted using a secure protocol (e.g., HTTPS).

[0422] The server inputs the received data into an AI model, built using a deep learning framework (e.g., TensorFlow or PyTorch), that compares the user's movements with those of professional athletes of similar build. The server generates a comparison result and identifies corrections to the user's movements.

[0423] The server then sends the identified corrections to the device, which then presents the corrections to the user through an application on the device, such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

[0424] The device also inputs the user's facial expressions and tone of voice into an emotion engine, which uses Affectiva and the Microsoft Azure Emotion API to recognize the user's emotions in real time. The recognition results are stored on the device.

[0425] The device sends the recognized emotional information to the server. The server reevaluates the AI ​​model taking the emotional information into account and suggests modifications that are easy for the user to accept. For example, if the user feels frustrated, the server generates a suggestion such as "First, relax, and then try to be aware of your release point," and sends it to the device. The device then displays this suggestion to the user.

[0426] Specific examples

[0427] Example 1: Improving baseball batting form

[0428] The user uses the "Structure Sensor" to measure their physique and a GoPro camera to record their batting form. The device then sends this data to a server. The server uses TensorFlow to compare the batting form with that of professional baseball players and suggests, via a smartphone app, that the user "increase the angle of the bat's swing a little more." If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to be more conscious of your swing angle."

[0429] Example 2: Improving pitching form

[0430] The user uses Kinect to measure their physique and a Vicon motion capture system to record their pitching form. The device then sends this data to a server. The server uses PyTorch to compare the form with that of professional pitchers and suggests, via a tablet app, that the release point be moved forward a little. If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to focus on the release point."

[0431] Prompt Sentence Examples

[0432] "The user uses a 3D body scanning device to measure their physique and a camera to record their batting form. The AI ​​uses TensorFlow to compare their form with that of professional baseball players and suggests corrections via a smartphone app. If the user is feeling frustrated, the emotion engine recognizes this and the AI ​​suggests corrections in a way that is easy for the user to accept."

[0433] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0434] Step 1:

[0435] Users measure their own physique using a 3D body scanning device. Specifically, users acquire physique data using a "Structure Sensor" or "Kinect." The input is the dimensions and shape of the user's body, and the output is physique data as a 3D model. This data is stored on the device.

[0436] Step 2:

[0437] The user performs a specific movement (e.g., batting or pitching a baseball). The user records the movement using a GoPro camera or a Vicon motion capture system. The input is the user's movement, and the output is high-resolution video data. This data is stored on the device.

[0438] Step 3:

[0439] The device sends the recorded motion data and physique data to the server. The input is the motion data and physique data, and the output is the data sent to the server. The data is sent using a secure protocol (e.g., HTTPS).

[0440] Step 4:

[0441] The server inputs the received data into an AI model. The AI ​​model is built using TensorFlow and PyTorch and compares the user's movements with those of professional athletes with a similar physique. The input is movement data and physique data, and the output is the comparison results, including corrections to the movements.

[0442] Step 5:

[0443] The server sends the identified corrections to the terminal. The input is the comparison result, and the output is the transmission of the corrections to the terminal. The terminal then presents the corrections to the user through an application on a device such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

[0444] Step 6:

[0445] The device inputs the user's facial expressions and tone of voice into the emotion engine. The emotion engine uses "Affectiva" and "Microsoft Azure Emotion API" to recognize the user's emotions in real time. The input is the user's facial expressions and tone of voice, and the output is the recognized emotional information. This information is stored on the device.

[0446] Step 7:

[0447] The device sends the recognized emotional information to the server. The input is emotional information, and the output is the transmission of emotional information to the server. The server reevaluates the AI ​​model taking the emotional information into account and suggests corrections in a way that is easy for the user to accept. For example, if the user feels frustrated, the server generates a suggestion such as "First, relax, and then try to be aware of your release point," and sends it to the device. The device displays this suggestion to the user.

[0448] (Application example 2)

[0449] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0450] Conventional motion analysis systems record the user's physique and movements and compare them with those of professional athletes to suggest corrections, but they were not designed to optimize the movements of robots working in factories. They also lacked the ability to detect abnormalities in the robots and suggest corrections based on those abnormalities. This made it difficult to efficiently manage the movements of robots in factories.

[0451] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0452] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for analyzing the recorded movements and comparing them with those of professional athletes with a similar physique, means for suggesting to the user how to modify their movements based on the comparison results, means for recording the robot's movements and proposing optimal movement patterns, and means for detecting abnormalities in the robot and proposing how to modify its movements based on that information. This not only improves the user's movements, but also enables efficient management and optimization of the robot's movements in factories.

[0453] "User" refers to an individual who uses the System to measure and analyze their physique and movements.

[0454] "Physique" refers to the physical characteristics and measurements of a user.

[0455] "Specific movement" refers to a specific action or movement performed by a user (e.g., batting or pitching form in baseball).

[0456] "Means for recording" refers to devices or methods that use cameras, sensors, etc. to save the actions of users or robots as digital data.

[0457] "AI means" refers to a system that uses artificial intelligence to analyze data and output results according to a specific purpose.

[0458] "Deep learning" refers to a machine learning technique that uses multi-layered neural networks to learn from data and extract complex patterns and features.

[0459] A "professional athlete" is someone who has high skills in a particular sport and is active in that sport as a profession.

[0460] "Behavior Modifications" refers to specific changes or adjustments suggested to improve the user or robot's behavior.

[0461] A "robot" refers to an automated mechanical device that performs work in a factory.

[0462] "Abnormal" refers to conditions such as vibrations or temperature rises that deviate from the robot's normal operation.

[0463] "Means for sensing" refers to methods or devices that use sensors or monitoring devices to detect abnormalities.

[0464] An "optimal motion pattern" refers to a series of procedures or methods of robot motion proposed to perform a task efficiently and effectively.

[0465] An embodiment of the present invention will now be described. First, a 3D body scanning device is used to measure a user's physique. Specifically, a 3D scanner such as Intel RealSense is used to acquire the user's physique data. This data is used to accurately record the user's physical characteristics and dimensions.

[0466] Next, cameras and sensors are used to record specific user actions, such as an Intel RealSense camera, which records user movements with high accuracy. This recorded data is then analyzed using AI tools, which will be described later.

[0467] The recorded movement data is analyzed using deep learning AI tools such as TensorFlow and Keras. The AI ​​tools compare the user's movements with those of professional athletes with a similar physique and suggest corrections to the movement. These suggestions are presented to the user via a smartphone or tablet application.

[0468] Furthermore, the system includes a means for recording the robot's movements and proposing optimal movement patterns in order to optimize the movements of the robots working in the factory. The robot's movements are recorded using a 3D scanner or camera and analyzed by the AI ​​means. The AI ​​means proposes optimal movement patterns and makes the robot's movements more efficient.

[0469] In addition, vibration and temperature sensors are used to detect abnormalities in the robot. OpenCV and PyTorch are used to detect abnormalities and suggest corrections to the robot's behavior based on that information. This optimizes the robot's behavior and enables it to work more efficiently.

[0470] For example, if a robot assembles parts in a factory and its movements slow down or abnormal vibrations occur, the system records the movement and the AI ​​suggests the optimal movement pattern. If the emotion engine detects an abnormality, it will be reflected in the suggestion of a correction to the movement.

[0471] An example of a prompt to input to a generative AI model is as follows:

[0472] "To optimize the movements of robots assembling parts in a factory, AI should suggest optimal movement patterns using data recorded by 3D scans and cameras. It should also detect abnormal vibrations or temperature increases in the robot and suggest movement corrections based on that."

[0473] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0474] Step 1:

[0475] A user acquires physique data using a 3D body scanning device.

[0476] Input: User's body

[0477] Output: 3D body scan data

[0478] How it works: The user stands in front of the 3D scanner, which scans their entire body and generates digital data.

[0479] Step 2:

[0480] The user's specific actions are recorded using cameras and sensors.

[0481] Input: User actions

[0482] Output: Operation record data

[0483] Specific actions: The user performs a specific action (e.g., batting or pitching) in front of the camera, and the camera records the action with high accuracy.

[0484] Step 3:

[0485] The server analyzes the recorded motion data using AI means.

[0486] Input: Motion record data, 3D body scan data

[0487] Output: Motion analysis results

[0488] Specific operation: The server uses TensorFlow and Keras to input motion recording data and 3D body scan data into an AI model to analyze the user's movements.

[0489] Step 4:

[0490] Based on the results of the behavior analysis, the server suggests to the user how to correct their behavior.

[0491] Input: Motion analysis results

[0492] Output: Proposed behavior correction

[0493] Specific actions: Based on the analysis results, the server generates specific corrections to improve the user's behavior and presents them to the user through an application on their smartphone or tablet.

[0494] Step 5:

[0495] The robot's movements are recorded using 3D scanners and cameras.

[0496] Input: Robot movement

[0497] Output: Robot operation record data

[0498] Specific actions: As the robot performs a task, its movements are recorded by 3D scanners and cameras.

[0499] Step 6:

[0500] The server analyzes the robot's operation record data using AI means.

[0501] Input: Robot motion record data

[0502] Output: Robot motion analysis results

[0503] Specific operation: The server uses TensorFlow and Keras to input the robot's motion record data into an AI model and analyze the robot's motion.

[0504] Step 7:

[0505] The server detects an abnormality in the robot.

[0506] Input: Robot vibration data, temperature data

[0507] Output: Anomaly detection results

[0508] Specific operation: The server analyzes data from vibration sensors and temperature sensors using OpenCV and PyTorch to detect abnormalities.

[0509] Step 8:

[0510] The server proposes optimal movement patterns based on the results of the robot's movement analysis and abnormality detection.

[0511] Input: Robot operation analysis results, anomaly detection results

[0512] Output: Optimal motion pattern proposal

[0513] Specific behavior: The server integrates the analysis results and anomaly detection results, generates specific behavior patterns to optimize the robot's behavior, and applies them to the robot.

[0514] 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 a voice indicating a user input regarding the result of the specific processing.

[0515] The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0516] The data generation model 58 is a so-called generative AI (Artificial Intelligence) model. An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0517] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0518] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0519] [Second embodiment]

[0520] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0521] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0522] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0523] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0524] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0525] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0526] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0527] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0528] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0529] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0530] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0531] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0532] "Example 1"

[0533] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[0534] "Example 2"

[0535] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[0536] The processing flow of each embodiment will be described below.

[0537] "Example 1"

[0538] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[0539] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[0540] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[0541] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[0542] "Example 2"

[0543] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[0544] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[0545] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[0546] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[0547] Example 1

[0548] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0549] Conventional motion analysis systems have had difficulty accurately measuring and recording a user's physique and movements, and providing appropriate feedback. Furthermore, there was a lack of means for performing highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

[0550] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0551] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with similar physiques, means for suggesting to the user movement corrections based on the comparison results, and means for displaying the suggested corrections on the user's terminal. This allows the user to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

[0552] "User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

[0553] "Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

[0554] "Movement" refers to specific physical movements or performances made by users, which are recorded using cameras and sensors.

[0555] "Server" refers to a computer system that receives data sent by a user, analyzes it using AI means, and provides the results to the user.

[0556] "3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

[0557] "AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

[0558] "Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

[0559] "Fixes" are specific changes or suggestions suggested to improve user behavior.

[0560] "Terminal" refers to the device that a user uses to access the system, including smartphones and tablets.

[0561] "Comparison results" refers to the results of a comparison made by AI means between the user's movements and those of a professional athlete, and includes data that serves as the basis for feedback to the user.

[0562] This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement corrections to the user. Specific embodiments of this system are described below.

[0563] First, the user measures their physique using a device that provides a 3D body scan, such as Kinect or Structure Sensor, which acquires the user's physique data and stores it on the user's device (smartphone or tablet).

[0564] Next, the user performs a specific movement, such as a baseball batting stance or pitching stance. This movement is recorded using a smartphone camera or GoPro. The recorded video data is saved on the user's device.

[0565] The user's device sends the recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

[0566] The server inputs the received movement data and physique data into an AI model. This AI model uses TensorFlow and PyTorch and uses deep learning to compare the user's movements with those of professional athletes with similar physiques. The server inputs the data into the AI ​​model and begins the comparison process. Once the process is complete, the comparison results are generated.

[0567] The server generates corrections for the user's movements based on the results of the comparison with the AI ​​model. These corrections are summarized as specific advice and methods for improvement. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

[0568] The server sends the generated corrections to the user's device, which then displays them in the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

[0569] As a concrete example, consider a case where a user wants to improve their baseball batting form. First, the user measures their physique using a 3D body scanning device (e.g., Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests corrections to the user's batting form.

[0570] An example of a prompt is, "Please input the user's physique data and a video of their batting form, compare it with the movements of a professional batter, and suggest corrections."

[0571] In this way, users can compare their own movements with those of professionals and get specific corrections.

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1:

[0574] Users measure their physique using a 3D body scanning device.

[0575] Input: User's physical location

[0576] Specific action: The user stands in front of the Kinect and rotates their body according to the instructions.

[0577] Data processing: The scanning device acquires the user's physique data and generates a 3D model.

[0578] Output: The generated physique data is saved on the user's device.

[0579] Step 2:

[0580] The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

[0581] Input: User actions

[0582] Specific operation: The user sets the smartphone on a tripod and takes a picture of their batting form.

[0583] Data processing: The camera records the user's movements as video data.

[0584] Output: The recorded video data is saved on the user's device.

[0585] Step 3:

[0586] The user's device transmits the recorded movement data and physique data to the server.

[0587] Input: Movement data and physique data

[0588] Specific action: The user presses the "Submit Data" button within the application.

[0589] Data processing: The device compresses the data and uploads it to a server via the Internet.

[0590] Output: Data is sent to the server.

[0591] Step 4:

[0592] The server inputs the received movement data and physique data into the AI ​​model.

[0593] Input: Movement data and physique data

[0594] Specific operation: The server inputs the data into the AI ​​model and begins the comparison process.

[0595] Data calculation: The AI ​​model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[0596] Output: The comparison results are generated.

[0597] Step 5:

[0598] The server generates corrections for the user's behavior based on the comparison results of the AI ​​model.

[0599] Input: Comparison result

[0600] Specific actions: The server generates specific advice such as, "To increase the swing speed of your batting form, you need to rotate your hips faster."

[0601] Data processing: Analyze the comparison results and generate corrections in text format.

[0602] Output: The corrections are generated.

[0603] Step 6:

[0604] The server sends the generated modifications to the user's device.

[0605] Input: Correction

[0606] Specific operation: The server sends the corrections to the user's device.

[0607] Data processing: Convert the corrections into the appropriate format and send them.

[0608] Output: The corrections are sent to the user's device.

[0609] Step 7:

[0610] The user's device will display the received corrections within the application.

[0611] Input: Correction

[0612] What happens: The user opens the application and sees the suggested fixes.

[0613] Data processing: Display the corrections in a user-friendly format.

[0614] Output: User can see the corrections.

[0615] (Application example 1)

[0616] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0617] In conventional factory work, there is a lack of specific feedback to improve the efficiency and safety of workers' movements. As a result, workers continue to perform inappropriate movements, which leads to problems such as reduced work efficiency and increased physical strain. In addition, there is no system to optimize workers' movements, which makes it difficult to provide appropriate guidance to individual workers.

[0618] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with movements of workers with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

[0619] "User" means an individual who uses the System to have their physique and / or movement measured and analyzed.

[0620] "Physique" refers to the physical characteristics and measurements of the user.

[0621] "Movement" refers to a specific physical movement or task performed by a user.

[0622] "Recording means" refers to a device or method for saving a user's actions as video or data.

[0623] "AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

[0624] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[0625] The "means for suggesting corrections" is a method or device for suggesting improvements to the user's behavior based on the analysis results.

[0626] A "factory worker" is a worker who performs specific tasks in a factory.

[0627] The "means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

[0628] "3D body scanning" is a technology for measuring a user's physique in three dimensions.

[0629] A system for implementing this invention includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user corrections to their movements based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers.

[0630] Hardware and software used

[0631] 3D body scanning device: A device for measuring the user's physique in three dimensions.

[0632] Camera: A device used to record user actions. An example is the Logitech C920.

[0633] Smart glasses: A device that visually guides the user to correct their movements. An example is Google Glass.

[0634] Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad.

[0635] Server: A computer system for processing data and running AI models.

[0636] Software: Using programming libraries such as Python, OpenCV, and Keras.

[0637] Data processing and calculation

[0638] 1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

[0639] 2. Recording of actions: The server records the user's actions using a camera, and the recorded actions are saved as image data.

[0640] 3. Movement analysis: The server inputs the recorded image data and physique data into the AI ​​model to analyze the movements. The AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique.

[0641] 4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

[0642] Specific examples

[0643] When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. The analysis results suggest corrections to things like the angle of the waist and the position of the hands when lifting, allowing the worker to work efficiently and safely.

[0644] Prompt Sentence Examples

[0645] "Record factory workers lifting heavy objects and feed this into the AI ​​along with their 3D body scan data to compare with efficient movements and suggest corrections."

[0646] The above is an embodiment of the present invention.

[0647] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0648] Step 1:

[0649] The server acquires the user's physique data using a 3D body scanning device. The input includes the user's physical characteristics and dimensions. The data is then converted into a data format that represents these characteristics and dimensions in three dimensions. The output is the user's physique data.

[0650] Step 2:

[0651] The server records the user's actions using a camera. The input includes specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

[0652] Step 3:

[0653] The server inputs the recorded image data and physique data into the AI ​​model and analyzes the movements. The input includes image data and physique data. For data calculation, the AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

[0654] Step 4:

[0655] The server proposes behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. The data is processed to generate specific suggestions for modifications. The output is the proposed modifications.

[0656] Step 5:

[0657] The server displays the suggested corrections on the smart glasses or tablet. The input includes the suggested corrections. Specific operations include generating data for visually displaying the suggestions and sending it to the smart glasses or tablet. The output is a visually verifiable suggested correction that the user can see.

[0658] The above is the processing flow of this program.

[0659] Example 2

[0660] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0661] Conventional motion analysis systems have difficulty accurately measuring and recording a user's physique and movements, and suggesting appropriate corrections. Furthermore, there is a lack of means to suggest specific corrections to users, which has led to problems with effective improvements to movements.

[0662] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0663] In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for presenting the corrections to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate corrections, and present specific corrections to the user.

[0664] "User" refers to an individual who uses the system to measure and record their physique and movements and receive suggestions for movement modifications.

[0665] "Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

[0666] "Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

[0667] "Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

[0668] "Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

[0669] "Means for Providing Corrections" means a device or application that visually or audibly communicates suggested corrections to the user.

[0670] "3D body scanning" refers to a technology that measures a user's physique in three dimensions.

[0671] "Deep learning" refers to a machine learning technique that uses multiple layers of neural networks used in artificial intelligence tools.

[0672] MODE FOR CARRYING OUT THE INVENTION

[0673] This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests corrections to those movements. Specific embodiments of this system will be described below.

[0674] User's physique measurement

[0675] Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner and a motion capture system. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then sent to a server via wireless communication.

[0676] Recording actions

[0677] Next, the user performs a specific action, such as batting or pitching in baseball. The user's action is recorded using a camera or sensor. The hardware used may be a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

[0678] Behavior analysis

[0679] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the AI ​​model.

[0680] Suggested behavior modifications

[0681] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[0682] Specific examples

[0683] Example 1: Correcting baseball batting form

[0684] The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user via a dedicated application. The user can then work on improving their batting form while viewing the application.

[0685] Example 2: Correcting pitching form

[0686] The user measures their physique using a 3D scanner and sends the data to a server. Then, a motion capture system is used to record their pitching form, and the data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user through a dedicated application. The user works on improving their pitching form while viewing the application.

[0687] Prompt Sentence Examples

[0688] Prompt 1: Baseball batting form

[0689] "We need to generate an AI model that takes a user's 3D body scan data and video data of their batting form as input, compares it with data from professional baseball players, and suggests corrections."

[0690] Prompt 2: Pitching form

[0691] "We need to take the user's 3D body scan data and motion capture data of their pitching form as input, compare it with data from professional baseball players, and generate an AI model that suggests corrections."

[0692] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0693] Program processing flow

[0694] Step 1: Measuring the user's physique

[0695] The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then transmitted wirelessly to a server.

[0696] Input: User's physique scan data

[0697] Data processing: Real-time processing of scan data

[0698] Output: User's physique data

[0699] Step 2: Recording the action

[0700] Using a high-resolution camera and a motion capture system, the user performs a specific action, such as batting or pitching in baseball. The device records the action and generates video or motion capture data, which is then sent to a server.

[0701] Input: User actions

[0702] Data processing: Motion recording and data generation

[0703] Output: Video data or motion capture data

[0704] Step 3: Analyze the behavior

[0705] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the AI ​​model.

[0706] Input: physique data, movement data

[0707] Data processing: deep learning behavior comparison

[0708] Output: Behavior fixes

[0709] Step 4: Propose behavior modifications

[0710] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[0711] Input: Behavior fixes

[0712] Data processing: Preparation for displaying corrections

[0713] Output: User is presented with the fix

[0714] Adding specific actions

[0715] Step 1: Measuring the user's physique

[0716] The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scanned data is processed in real time to generate the user's physique data, which is then transmitted wirelessly to the server.

[0717] Step 2: Recording the action

[0718] The user installs a high-resolution camera and records their batting form. The camera records high-resolution video and saves it on the device. The device then compresses the video data and uploads it to the server.

[0719] Step 3: Analyze the behavior

[0720] The server inputs the received physique data and video data into a deep learning framework. The AI ​​model compares the user's batting form with that of professional baseball players. The comparison uses features such as joint angles and movement speed. The server extracts corrections for the user's movements from the output of the AI ​​model.

[0721] Step 4: Propose behavior modifications

[0722] The server sends the extracted corrections in JSON format to the device. The device then displays the received corrections in a dedicated application. The user can check the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[0723] (Application example 2)

[0724] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0725] Conventional optimization of factory robots' movements relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods may not be effective enough. This creates a risk of a decline in factory productivity and quality.

[0726] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0727] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movement modifications based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movements. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movements of factory robots.

[0728] "User" means an individual or legal entity that uses the System to measure and analyze their physique and movements.

[0729] "Physique" refers to the shape and dimensions of a user's body, as measured by means such as a 3D body scan.

[0730] A "specific action" is a specific movement or task performed by a user, which is recorded using a camera or sensor.

[0731] "Means for recording" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

[0732] "AI means" is a technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

[0733] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[0734] A "professional athlete" is a professional athlete who is active in a particular sport and is the subject to be compared with the user's movements.

[0735] "Behavior Modifications" are specific improvements suggested to optimize a user's behavior.

[0736] "Robots used in factories" are automated machinery used on factory production lines and in workshops.

[0737] "Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

[0738] To implement this invention, the following hardware and software are required. The hardware includes a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). The software includes Python, OpenCV, Keras, etc.

[0739] First, the user measures their body size using a 3D scanner, which captures the user's body shape and dimensions with high precision and stores them as digital data. This data is then sent to a server.

[0740] Next, the user performs a specific action. For example, to mimic the actions of a robot used in a factory, the camera and sensors are used to record the action. The recorded action data is also sent to the server.

[0741] The server inputs the received 3D scan data and movement data into the AI ​​system, which then analyzes the data using deep learning and compares the user's movements with those of professional athletes with a similar physique. Based on the comparison results, the system suggests corrections to the user's movements.

[0742] Furthermore, to optimize the operation of the robots used in the factory, the robot's operational data is also recorded and analyzed using AI methods. Based on the analysis results, corrections to optimize the robot's operation are suggested. These suggestions are displayed on the user's device.

[0743] As a concrete example, consider optimizing the operation of a welding robot used in a factory. A 3D scanner captures the robot's shape data, and cameras and sensors record its operation data. This data is input into an AI tool, which then analyzes it and suggests corrections to the welding angle and speed.

[0744] An example of a prompt sentence to be input into the generative AI model is, "Please input the welding robot's motion data along with the 3D scan data and suggest optimal motion corrections."

[0745] As described above, the present invention enables individual optimization based on the user's physique and movements, and can optimize the movements of factory robots efficiently and with high precision.

[0746] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0747] Step 1:

[0748] Users measure their body size using a 3D scanner.

[0749] Input: User's body

[0750] Data processing: A 3D scanner captures the shape and dimensions of the user's body with high precision and stores them as digital data.

[0751] Output: 3D scan data

[0752] Step 2:

[0753] The user performs a specific action and the action is recorded using cameras and sensors.

[0754] Input: User actions

[0755] Data processing: Cameras and sensors record user actions as video and sensor data.

[0756] Output: Operation data

[0757] Step 3:

[0758] The server receives the 3D scan data and the motion data.

[0759] Input: 3D scan data, motion data

[0760] Data processing: The server receives these data and prepares them for input into the AI ​​means.

[0761] Output: Prepared data

[0762] Step 4:

[0763] The server analyzes the 3D scan data and motion data using AI means.

[0764] Input: Prepared data

[0765] Data processing: Using a deep learning model, the user's movements are compared to those of professional athletes with a similar physique.

[0766] Output: Analysis results

[0767] Step 5:

[0768] The server then suggests modifications to the user's behavior based on the analysis results.

[0769] Input: Analysis results

[0770] Data manipulation: Use the analysis results to generate specific modifications to optimize user behavior.

[0771] Output: suggested fixes

[0772] Step 6:

[0773] Suggested fixes will be displayed on the user's device.

[0774] Input: Suggested fix

[0775] Data processing: Converting the proposal content into a format for display on the user's device.

[0776] Output: The corrections displayed on the user's terminal

[0777] Step 7:

[0778] The movements of robots used in factories are recorded and sent to a server.

[0779] Input: Robot movement

[0780] Data processing: Cameras and sensors record the robot's movements as video and sensor data, which are then sent to a server.

[0781] Output: Robot motion data

[0782] Step 8:

[0783] The server analyzes the robot's operational data using AI means and suggests modifications for optimization.

[0784] Input: Robot motion data

[0785] Data processing: Using deep learning models, the robot's behavior is analyzed and corrections for optimization are generated.

[0786] Output: Suggested modifications to the robot's behavior

[0787] Step 9:

[0788] Suggestions for correcting the robot's behavior are displayed on the user's device.

[0789] Input: Suggested modifications to the robot's behavior

[0790] Data processing: Converting the proposal content into a format for display on the user's device.

[0791] Output: Robot behavior corrections displayed on the user's device

[0792] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0793] "Example 1"

[0794] In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine for recognizing the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI ​​means. The AI ​​means suggests movement modifications taking into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI ​​means takes this information into account and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

[0795] "Example 2"

[0796] In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine for recognizing the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI ​​means. The AI ​​means suggests movement modifications taking into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI ​​means takes this information into account and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

[0797] The processing flow of each embodiment will be described below.

[0798] "Example 1"

[0799] Step 1: Measure the user's physique with a 3D body scan.

[0800] Step 2: A specific action performed by the user (e.g., batting or pitching a baseball) is recorded using a video camera or motion sensor.

[0801] Step 3: The recorded movements are analyzed using AI tools and compared to the movements of professional athletes with similar physiques.

[0802] Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

[0803] Step 5: The AI ​​tool will suggest corrections to the user's movements, taking into account the comparison between the user's movements and those of professional athletes and the user's emotions.

[0804] Step 6: The proposed behavioral modifications are presented to the user through an application on a device such as a smartphone or tablet.

[0805] "Example 2"

[0806] Step 1: Measure the user's physique with a 3D body scan.

[0807] Step 2: A specific action performed by the user (e.g., batting or pitching a baseball) is recorded using a video camera or motion sensor.

[0808] Step 3: The recorded movements are analyzed using AI tools and compared to the movements of professional athletes with similar physiques.

[0809] Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

[0810] Step 5: The AI ​​tool will suggest corrections to the user's movements, taking into account the comparison between the user's movements and those of professional athletes and the user's emotions.

[0811] Step 6: The proposed behavioral modifications are presented to the user through an application on a device such as a smartphone or tablet.

[0812] Example 1

[0813] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0814] Conventional motion improvement systems make suggestions based on the user's physique and motion data, but they do not take the user's emotions into consideration, which can make it difficult for users to accept the suggestions. Also, since the points to correct for the motion are not specific, it is difficult for users to understand how to improve.

[0815] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0816] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, means for suggesting to the user movement corrections based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement corrections taking into account the emotion data, thereby making it possible to suggest movement improvements taking into account the user's physique and emotions.

[0817] "Means for measuring the user's physique" refers to a device or method for measuring the user's physical characteristics and acquiring them as digital data.

[0818] "Means for recording specific actions performed by a user" refers to a device or method that uses video or sensors to record specific actions performed by a user as digital data.

[0819] "Artificial intelligence means" means software or systems that analyze recorded data and use specific algorithms to compare the user's movements with other data (e.g., the movements of professional athletes).

[0820] The "means for suggesting points for correcting movements" refers to a device or method that suggests points for improving movements or methods for correcting movements to the user based on the analysis results.

[0821] "Means for recognizing user emotions" refers to devices or methods that analyze emotions from the user's facial expressions, tone of voice, etc., and acquire them as digital data.

[0822] The "means for proposing corrections to movements in consideration of emotional data" refers to a device or method that presents corrections to movements in a more acceptable form based on the emotional data of the user.

[0823] "3D body scanning" is a technology that scans the user's body in three dimensions and obtains detailed physique data.

[0824] "Deep learning" is an artificial intelligence technology that uses multi-layered neural networks to analyze data and learn patterns and features.

[0825] This invention is a system that analyzes the user's physique and movements, compares them with the movements of professional athletes, and suggests movement corrections to the user. Furthermore, by recognizing the user's emotions and making suggestions taking into account that emotional data, it is possible to present movement corrections in a way that is easy for the user to accept.

[0826] Hardware and software used

[0827] 1. User's physique measurement

[0828] Users measure their physique using equipment that provides a three-dimensional body scan, such as a 3D scanner or a dedicated body scan application, which captures detailed physique data, including the user's height, weight, and body fat percentage.

[0829] 2. Recording the movement

[0830] Users record specific movements (e.g., batting or pitching a baseball) using a smartphone camera or a dedicated motion capture device, and the recorded data includes detailed movement information such as swing speed and angle.

[0831] 3. Sending operation data

[0832] The device transmits the recorded movement and physique data to a server in real time over the internet, with the data encrypted and using a secure communication protocol.

[0833] 4. Analysis of movement

[0834] The server then inputs the received data into an AI model, which uses deep learning algorithms to compare the user's movements with those of professional athletes. The software used is a deep learning framework such as TensorFlow or PyTorch. For example, it calculates how much the user's swing speed differs from the average speed of professional athletes.

[0835] 5. Emotional Recognition

[0836] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. This emotion engine determines whether the user is frustrated or satisfied and sends that information to a server. The software used is an emotion recognition API (e.g., Microsoft Azure's Emotion API).

[0837] 6. Suggestions for behavioral modifications

[0838] The server then suggests to the user how to improve their performance based on the comparison of their movements and their emotional data. For example, if the user feels frustrated, the server might suggest, "Maybe your swing will become smoother if you just change the way you grip the bat slightly." The server then notifies the user of this suggestion through an application on their smartphone, tablet, or other device.

[0839] Specific examples

[0840] Example 1: Improving baseball batting form

[0841] The user measures their physique using a 3D scanner and records their batting form with their smartphone camera. The device then sends the recorded data to a server, which then inputs the data into an AI model and compares it with the form of professional baseball players. If the emotion engine detects the user's frustration, the server will make suggestions such as, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions through a smartphone application.

[0842] Example prompts for generative AI models

[0843] "We will take the user's 3D body scan data and a video of their batting form as input, compare it with the form of a professional baseball player, and suggest improvements while taking into account the user's emotions."

[0844] In this way, it becomes possible to suggest movement improvements that take into account the user's physique and emotions.

[0845] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0846] Step 1:

[0847] Users measure their physique using a device that provides a 3D body scan. As input, a 3D scanner is used to scan the user's body. As output, detailed physique data such as the user's height, weight, and body fat percentage is generated. This data is stored on the device.

[0848] Step 2:

[0849] Users record specific movements (e.g., batting or pitching in baseball) using a smartphone camera or a dedicated motion capture device. The input is the user's movements, which are captured by the camera or sensors. The output is detailed movement data, such as the speed and angle of the swing. This data is stored on the device.

[0850] Step 3:

[0851] The device transmits the recorded movement data and physique data to a server. As input, the movement data and physique data stored on the device are used. As output, these data are transmitted to the server via the Internet. The data is encrypted and uses a secure communication protocol.

[0852] Step 4:

[0853] The server inputs the received data into an AI model. The motion and physique data sent to the server are used as input. The output is the result of the AI ​​model comparing the user's motion with that of a professional athlete. This AI model uses deep learning algorithms and utilizes deep learning frameworks such as TensorFlow and PyTorch.

[0854] Step 5:

[0855] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. As input, the user's facial and voice data is captured through a camera and microphone. As output, emotional data is generated, such as whether the user is frustrated or satisfied. This data is then sent to a server.

[0856] Step 6:

[0857] The server then suggests to the user how to modify their movements based on the comparison results and emotional data. The AI ​​model's comparison results and emotional data are used as input. The output is a suggestion for the user to modify their movements. For example, if the user feels frustrated, the server might suggest, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The suggestion is then notified to the user through an application on their device, such as a smartphone or tablet.

[0858] (Application example 1)

[0859] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0860] Conventional motion analysis systems can analyze a user's physique and movements, but they have the problem of not being able to suggest motion modifications that take the user's emotions into account. Furthermore, when optimizing the movements of robots working in factories, there is a lack of real-time monitoring of movement efficiency and error rates and suggestions for improvement. This leads to issues such as reduced movement efficiency for users and robots, preventing optimal performance.

[0861] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0862] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movement modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time. This makes it possible to suggest movement modifications taking the user's emotions into account, and further makes it possible to monitor the movement efficiency and error rate of robots working in factories in real time, thereby achieving optimal movement performance.

[0863] "Means for measuring the user's physique" refers to devices or technologies for accurately measuring the dimensions and shape of the user's body.

[0864] "Means for recording specific actions performed by a user" refers to devices or technologies that use cameras, sensors, etc. to record specific actions performed by a user.

[0865] "AI means for analyzing recorded movements and comparing them with the movements of professional athletes with similar physiques" refers to artificial intelligence technology that analyzes recorded movement data of a user and compares it with the movement data of professional athletes with similar physiques.

[0866] "Means for suggesting to users points for correcting their movements based on the comparison results" refers to devices or technologies that suggest points for improvement or correction to the user's movements based on the results of movement comparison by AI.

[0867] The "emotion engine that recognizes user emotions" is a technology that identifies emotions from the user's facial expressions, tone of voice, etc.

[0868] The "means for proposing modifications to behavior in consideration of information from the emotion engine" refers to a device or technology that suggests modifications to behavior based on the user's emotional information recognized by the emotion engine.

[0869] "Means for optimizing robot operations" refers to devices and technologies that optimize the operations of robots working in factories to ensure that they are efficient and effective.

[0870] "Means for monitoring the operational efficiency and error rate of a robot" refers to devices and technologies that monitor the operational efficiency and error rate of a robot in real time.

[0871] "Means for making improvement suggestions in real time" refers to devices and technologies that analyze robot operation data in real time and immediately suggest areas for improvement.

[0872] A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user how to modify their movements based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting how to modify their movements taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time.

[0873] Hardware and Software Configuration

[0874] Hardware:

[0875] 3D body scanning device

[0876] Camera (e.g. Logitech C920)

[0877] Computer (e.g. Intel Core i7, 16GB RAM)

[0878] software:

[0879] OpenCV: Motion recording and image processing

[0880] Keras: Loading and Predicting Deep Learning Models

[0881] EmotionRecognizer: Emotion recognition library

[0882] Processing flow

[0883] The server first acquires the user's physique data using a 3D body scanning device. Then, it uses a camera to record specific movements performed by the user. The recorded movement data is preprocessed using OpenCV and analyzed using a deep learning model using Keras. The analysis results are compared with the movement data of professional athletes with similar physiques.

[0884] Furthermore, the system uses EmotionRecognizer to recognize the user's emotions and suggests behavioral modifications based on the information from the emotion engine. The suggested modifications are then presented to the user.

[0885] For robots working in factories, measures are used to optimize the robot's operation. The robot's operation efficiency and error rate are monitored in real time, and improvement suggestions are made. This makes it possible to optimize the robot's operation performance.

[0886] Specific examples

[0887] For example, if a robot assembling parts in a factory is moving slowly, the AI ​​will analyze its movements and suggest correcting the angle and speed of its movements. Furthermore, if the robot is making frequent errors, it will determine through emotion recognition that it is feeling frustrated and suggest that it should move more slowly.

[0888] Prompt Sentence Examples

[0889] "To optimize the movements of robots assembling parts in factories, design a system that uses 3D body scanning and cameras to record their movements, analyzes them with AI, and suggests optimal movement patterns. Additionally, add functionality to monitor the robot's movement efficiency and error rate, and make suggestions for improvement in real time."

[0890] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0891] Step 1:

[0892] The server acquires the user's physique data using a 3D body scanning device. As input, it scans the user's body dimensions and shape, and generates 3D physique data as output. This data is used to accurately determine the user's physique.

[0893] Step 2:

[0894] The server uses a camera to record specific actions performed by the user, capturing the actions performed by the user in real time as input and generating video data of the actions as output, which is used for subsequent action analysis.

[0895] Step 3:

[0896] The server preprocesses the recorded motion data using OpenCV. It receives motion video data as input and generates preprocessed image data as output. This preprocessing includes image resizing and noise removal.

[0897] Step 4:

[0898] The server analyzes the preprocessed image data using a deep learning model using Keras. It receives the preprocessed image data as input and extracts movement features as output. These features are used to analyze the user's movements in detail.

[0899] Step 5:

[0900] The server compares the extracted features with the motion data of professional athletes with similar physiques. It receives the user's motion features and the motion data of the professional athletes as input, and generates a comparison result as output. This comparison result is used to identify areas for correction in the user's motion.

[0901] Step 6:

[0902] The server uses EmotionRecognizer to recognize the user's emotions. As input, it captures the user's facial expressions and tone of voice while they are in action, and generates emotional data as output. This emotional data is used to understand the user's emotional state.

[0903] Step 7:

[0904] The server proposes behavioral modifications taking into account information from the emotion engine. It receives the comparison results and emotion data as input and generates behavioral modifications for the user as output. These modifications are proposed taking into account the user's emotional state.

[0905] Step 8:

[0906] The server executes a means for optimizing the operation of a robot working in a factory. The server receives the robot's operation data as input and generates an optimized operation pattern as output. This optimization is performed to improve the robot's operation efficiency.

[0907] Step 9:

[0908] The server monitors the robot's operational efficiency and error rate in real time. It receives the robot's operational data in real time as input and generates the monitoring results of operational efficiency and error rate as output. These monitoring results are used to understand the robot's operational status.

[0909] Step 10:

[0910] The server provides real-time improvement suggestions, taking the monitoring results as input and generating improvement suggestions as output, which are made to optimize the robot's operational performance.

[0911] Example 2

[0912] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0913] Conventional motion improvement systems have difficulty accurately measuring the user's physique and movements and providing appropriate feedback. Furthermore, since motion improvement suggestions do not take the user's emotions into consideration, feedback in a form that is easy for the user to accept is lacking. This limits the effectiveness of motion improvement.

[0914] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0915] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movements, artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking the recognized emotions into consideration. This makes it possible to accurately measure the user's physique and movements and provide appropriate feedback. Furthermore, because suggestions for movement improvement are made taking the user's emotions into consideration, feedback is provided in a form that the user finds easy to accept, improving the effectiveness of movement improvement.

[0916] "Means for measuring the user's physique" refers to devices or technologies that measure the dimensions and shape of the user's body in three dimensions.

[0917] "Means for recording specific actions performed by a user" refers to devices or technologies that use video or sensors to record specific actions performed by a user.

[0918] The "means for transmitting recorded actions" refers to a communication means for transmitting recorded action data to a server or other device.

[0919] "Artificial intelligence means" refers to artificial intelligence technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

[0920] "Means for suggesting corrections to movements" refers to devices or technologies that suggest improvements to movements to users based on the results of analysis by artificial intelligence means.

[0921] "Means for recognizing user emotions" refers to devices or technologies for recognizing emotions from the user's facial expressions, tone of voice, etc.

[0922] "Means for suggesting behavioral modifications taking into account recognized emotions" refers to devices or technologies that suggest behavioral modifications in a way that is easy for the user to accept, based on the user's emotional information.

[0923] The present invention is a system that measures a user's physique, records specific movements, analyzes the movements, and suggests corrections. The system can recognize the user's emotions and take them into consideration when suggesting corrections to movements.

[0924] First, the user measures their physique using a 3D body scanning device, such as the Structure Sensor or Kinect, which then acquires the user's physique data and stores it on the device.

[0925] Next, the user performs a specific movement (e.g., batting or pitching a baseball), which is recorded using a camera or sensor (e.g., a GoPro camera or a Vicon motion capture system), and the recorded movement data is stored on the device.

[0926] The device sends the recorded motion and physique data to a server. The data is transmitted using a secure protocol (e.g., HTTPS).

[0927] The server inputs the received data into an AI model, built using a deep learning framework (e.g., TensorFlow or PyTorch), that compares the user's movements with those of professional athletes of similar build. The server generates a comparison result and identifies corrections to the user's movements.

[0928] The server then sends the identified corrections to the device, which then presents the corrections to the user through an application on the device, such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

[0929] The device also inputs the user's facial expressions and tone of voice into an emotion engine, which uses Affectiva and the Microsoft Azure Emotion API to recognize the user's emotions in real time. The recognition results are stored on the device.

[0930] The device sends the recognized emotional information to the server. The server reevaluates the AI ​​model taking the emotional information into account and suggests modifications that are easy for the user to accept. For example, if the user feels frustrated, the server generates a suggestion such as "First, relax, and then try to be aware of your release point," and sends it to the device. The device then displays this suggestion to the user.

[0931] Specific examples

[0932] Example 1: Improving baseball batting form

[0933] The user uses the "Structure Sensor" to measure their physique and a GoPro camera to record their batting form. The device then sends this data to a server. The server uses TensorFlow to compare the batting form with that of professional baseball players and suggests, via a smartphone app, that the user "increase the angle of the bat's swing a little more." If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to be more conscious of your swing angle."

[0934] Example 2: Improving pitching form

[0935] The user uses Kinect to measure their physique and a Vicon motion capture system to record their pitching form. The device then sends this data to a server. The server uses PyTorch to compare the form with that of professional pitchers and suggests, via a tablet app, that the release point be moved forward a little. If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to focus on the release point."

[0936] Prompt Sentence Examples

[0937] "The user uses a 3D body scanning device to measure their physique and a camera to record their batting form. The AI ​​uses TensorFlow to compare their form with that of professional baseball players and suggests corrections via a smartphone app. If the user is feeling frustrated, the emotion engine recognizes this and the AI ​​suggests corrections in a way that is easy for the user to accept."

[0938] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0939] Step 1:

[0940] Users measure their own physique using a 3D body scanning device. Specifically, users acquire physique data using a "Structure Sensor" or "Kinect." The input is the dimensions and shape of the user's body, and the output is physique data as a 3D model. This data is stored on the device.

[0941] Step 2:

[0942] The user performs a specific movement (e.g., batting or pitching a baseball). The user records the movement using a GoPro camera or a Vicon motion capture system. The input is the user's movement, and the output is high-resolution video data. This data is stored on the device.

[0943] Step 3:

[0944] The device sends the recorded motion data and physique data to the server. The input is the motion data and physique data, and the output is the data sent to the server. The data is sent using a secure protocol (e.g., HTTPS).

[0945] Step 4:

[0946] The server inputs the received data into an AI model. The AI ​​model is built using TensorFlow and PyTorch and compares the user's movements with those of professional athletes with a similar physique. The input is movement data and physique data, and the output is the comparison results, including corrections to the movements.

[0947] Step 5:

[0948] The server sends the identified corrections to the terminal. The input is the comparison result, and the output is the transmission of the corrections to the terminal. The terminal then presents the corrections to the user through an application on a device such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

[0949] Step 6:

[0950] The device inputs the user's facial expressions and tone of voice into the emotion engine. The emotion engine uses "Affectiva" and "Microsoft Azure Emotion API" to recognize the user's emotions in real time. The input is the user's facial expressions and tone of voice, and the output is the recognized emotional information. This information is stored on the device.

[0951] Step 7:

[0952] The device sends the recognized emotional information to the server. The input is emotional information, and the output is the transmission of emotional information to the server. The server reevaluates the AI ​​model taking the emotional information into account and suggests corrections in a way that is easy for the user to accept. For example, if the user feels frustrated, the server generates a suggestion such as "First, relax, and then try to be aware of your release point," and sends it to the device. The device displays this suggestion to the user.

[0953] (Application example 2)

[0954] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0955] Conventional motion analysis systems record the user's physique and movements and compare them with those of professional athletes to suggest corrections, but they were not designed to optimize the movements of robots working in factories. They also lacked the ability to detect abnormalities in the robots and suggest corrections based on those abnormalities. This made it difficult to efficiently manage the movements of robots in factories.

[0956] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0957] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for analyzing the recorded movements and comparing them with those of professional athletes with a similar physique, means for suggesting to the user how to modify their movements based on the comparison results, means for recording the robot's movements and proposing optimal movement patterns, and means for detecting abnormalities in the robot and proposing how to modify its movements based on that information. This not only improves the user's movements, but also enables efficient management and optimization of the robot's movements in factories.

[0958] "User" refers to an individual who uses the System to measure and analyze their physique and movements.

[0959] "Physique" refers to the physical characteristics and measurements of a user.

[0960] "Specific movement" refers to a specific action or movement performed by a user (e.g., batting or pitching form in baseball).

[0961] "Means for recording" refers to devices or methods that use cameras, sensors, etc. to save the actions of users or robots as digital data.

[0962] "AI means" refers to a system that uses artificial intelligence to analyze data and output results according to a specific purpose.

[0963] "Deep learning" refers to a machine learning technique that uses multi-layered neural networks to learn from data and extract complex patterns and features.

[0964] A "professional athlete" is someone who has high skills in a particular sport and is active in that sport as a profession.

[0965] "Behavior Modifications" refers to specific changes or adjustments suggested to improve the user or robot's behavior.

[0966] A "robot" refers to an automated mechanical device that performs work in a factory.

[0967] "Abnormal" refers to conditions such as vibrations or temperature rises that deviate from the robot's normal operation.

[0968] "Means for sensing" refers to methods or devices that use sensors or monitoring devices to detect abnormalities.

[0969] An "optimal motion pattern" refers to a series of procedures or methods of robot motion proposed to perform a task efficiently and effectively.

[0970] An embodiment of the present invention will now be described. First, a 3D body scanning device is used to measure a user's physique. Specifically, a 3D scanner such as Intel RealSense is used to acquire the user's physique data. This data is used to accurately record the user's physical characteristics and dimensions.

[0971] Next, cameras and sensors are used to record specific user actions, such as an Intel RealSense camera, which records user movements with high accuracy. This recorded data is then analyzed using AI tools, which will be described later.

[0972] The recorded movement data is analyzed using deep learning AI tools such as TensorFlow and Keras. The AI ​​tools compare the user's movements with those of professional athletes with a similar physique and suggest corrections to the movement. These suggestions are presented to the user via a smartphone or tablet application.

[0973] Furthermore, the system includes a means for recording the robot's movements and proposing optimal movement patterns in order to optimize the movements of the robots working in the factory. The robot's movements are recorded using a 3D scanner or camera and analyzed by the AI ​​means. The AI ​​means proposes optimal movement patterns and makes the robot's movements more efficient.

[0974] In addition, vibration and temperature sensors are used to detect abnormalities in the robot. OpenCV and PyTorch are used to detect abnormalities and suggest corrections to the robot's behavior based on that information. This optimizes the robot's behavior and enables it to work more efficiently.

[0975] For example, if a robot assembles parts in a factory and its movements slow down or abnormal vibrations occur, the system records the movement and the AI ​​suggests the optimal movement pattern. If the emotion engine detects an abnormality, it will be reflected in the suggestion of a correction to the movement.

[0976] An example of a prompt to input to a generative AI model is as follows:

[0977] "To optimize the movements of robots assembling parts in a factory, AI should suggest optimal movement patterns using data recorded by 3D scans and cameras. It should also detect abnormal vibrations or temperature increases in the robot and suggest movement corrections based on that."

[0978] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0979] Step 1:

[0980] A user acquires physique data using a 3D body scanning device.

[0981] Input: User's body

[0982] Output: 3D body scan data

[0983] How it works: The user stands in front of the 3D scanner, which scans their entire body and generates digital data.

[0984] Step 2:

[0985] The user's specific actions are recorded using cameras and sensors.

[0986] Input: User actions

[0987] Output: Operation record data

[0988] Specific actions: The user performs a specific action (e.g., batting or pitching) in front of the camera, and the camera records the action with high accuracy.

[0989] Step 3:

[0990] The server analyzes the recorded motion data using AI means.

[0991] Input: Motion record data, 3D body scan data

[0992] Output: Motion analysis results

[0993] Specific operation: The server uses TensorFlow and Keras to input motion recording data and 3D body scan data into an AI model to analyze the user's movements.

[0994] Step 4:

[0995] Based on the results of the behavior analysis, the server suggests to the user how to correct their behavior.

[0996] Input: Motion analysis results

[0997] Output: Proposed behavior correction

[0998] Specific actions: Based on the analysis results, the server generates specific corrections to improve the user's behavior and presents them to the user through an application on their smartphone or tablet.

[0999] Step 5:

[1000] The robot's movements are recorded using 3D scanners and cameras.

[1001] Input: Robot movement

[1002] Output: Robot operation record data

[1003] Specific actions: As the robot performs a task, its movements are recorded by 3D scanners and cameras.

[1004] Step 6:

[1005] The server analyzes the robot's operation record data using AI means.

[1006] Input: Robot motion record data

[1007] Output: Robot motion analysis results

[1008] Specific operation: The server uses TensorFlow and Keras to input the robot's motion record data into an AI model and analyze the robot's motion.

[1009] Step 7:

[1010] The server detects an abnormality in the robot.

[1011] Input: Robot vibration data, temperature data

[1012] Output: Anomaly detection results

[1013] Specific operation: The server analyzes data from vibration sensors and temperature sensors using OpenCV and PyTorch to detect abnormalities.

[1014] Step 8:

[1015] The server proposes optimal movement patterns based on the results of the robot's movement analysis and abnormality detection.

[1016] Input: Robot operation analysis results, anomaly detection results

[1017] Output: Optimal motion pattern proposal

[1018] Specific behavior: The server integrates the analysis results and anomaly detection results, generates specific behavior patterns to optimize the robot's behavior, and applies them to the robot.

[1019] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1020] The data generation model 58 is a so-called generative AI (Artificial Intelligence) model. An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1021] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1022] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1023] [Third embodiment]

[1024] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1025] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1027] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1028] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1029] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1031] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1032] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1034] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1035] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1036] "Example 1"

[1037] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[1038] "Example 2"

[1039] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[1040] The processing flow of each embodiment will be described below.

[1041] "Example 1"

[1042] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[1043] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[1044] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[1045] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[1046] "Example 2"

[1047] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[1048] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[1049] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[1050] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[1051] Example 1

[1052] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1053] Conventional motion analysis systems have had difficulty accurately measuring and recording a user's physique and movements, and providing appropriate feedback. Furthermore, there was a lack of means for performing highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1055] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with similar physiques, means for suggesting to the user movement corrections based on the comparison results, and means for displaying the suggested corrections on the user's terminal. This allows the user to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

[1056] "User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

[1057] "Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

[1058] "Movement" refers to specific physical movements or performances made by users, which are recorded using cameras and sensors.

[1059] "Server" refers to a computer system that receives data sent by a user, analyzes it using AI means, and provides the results to the user.

[1060] "3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

[1061] "AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

[1062] "Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

[1063] "Fixes" are specific changes or suggestions suggested to improve user behavior.

[1064] "Terminal" refers to the device that a user uses to access the system, including smartphones and tablets.

[1065] "Comparison results" refers to the results of a comparison made by AI means between the user's movements and those of a professional athlete, and includes data that serves as the basis for feedback to the user.

[1066] This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement corrections to the user. Specific embodiments of this system are described below.

[1067] First, the user measures their physique using a device that provides a 3D body scan, such as Kinect or Structure Sensor, which acquires the user's physique data and stores it on the user's device (smartphone or tablet).

[1068] Next, the user performs a specific movement, such as a baseball batting stance or pitching stance. This movement is recorded using a smartphone camera or GoPro. The recorded video data is saved on the user's device.

[1069] The user's device sends the recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

[1070] The server inputs the received movement data and physique data into an AI model. This AI model uses TensorFlow and PyTorch and uses deep learning to compare the user's movements with those of professional athletes with similar physiques. The server inputs the data into the AI ​​model and begins the comparison process. Once the process is complete, the comparison results are generated.

[1071] The server generates corrections for the user's movements based on the results of the comparison with the AI ​​model. These corrections are summarized as specific advice and methods for improvement. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

[1072] The server sends the generated corrections to the user's device, which then displays them in the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

[1073] As a concrete example, consider a case where a user wants to improve their baseball batting form. First, the user measures their physique using a 3D body scanning device (e.g., Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests corrections to the user's batting form.

[1074] An example of a prompt is, "Please input the user's physique data and a video of their batting form, compare it with the movements of a professional batter, and suggest corrections."

[1075] In this way, users can compare their own movements with those of professionals and get specific corrections.

[1076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1077] Step 1:

[1078] Users measure their physique using a 3D body scanning device.

[1079] Input: User's physical location

[1080] Specific action: The user stands in front of the Kinect and rotates their body according to the instructions.

[1081] Data processing: The scanning device acquires the user's physique data and generates a 3D model.

[1082] Output: The generated physique data is saved on the user's device.

[1083] Step 2:

[1084] The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

[1085] Input: User actions

[1086] Specific operation: The user sets the smartphone on a tripod and takes a picture of their batting form.

[1087] Data processing: The camera records the user's movements as video data.

[1088] Output: The recorded video data is saved on the user's device.

[1089] Step 3:

[1090] The user's device transmits the recorded movement data and physique data to the server.

[1091] Input: Movement data and physique data

[1092] Specific action: The user presses the "Submit Data" button within the application.

[1093] Data processing: The device compresses the data and uploads it to a server via the Internet.

[1094] Output: Data is sent to the server.

[1095] Step 4:

[1096] The server inputs the received movement data and physique data into the AI ​​model.

[1097] Input: Movement data and physique data

[1098] Specific operation: The server inputs the data into the AI ​​model and begins the comparison process.

[1099] Data calculation: The AI ​​model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[1100] Output: The comparison results are generated.

[1101] Step 5:

[1102] The server generates corrections for the user's behavior based on the comparison results of the AI ​​model.

[1103] Input: Comparison result

[1104] Specific actions: The server generates specific advice such as, "To increase the swing speed of your batting form, you need to rotate your hips faster."

[1105] Data processing: Analyze the comparison results and generate corrections in text format.

[1106] Output: The corrections are generated.

[1107] Step 6:

[1108] The server sends the generated modifications to the user's device.

[1109] Input: Correction

[1110] Specific operation: The server sends the corrections to the user's device.

[1111] Data processing: Convert the corrections into the appropriate format and send them.

[1112] Output: The corrections are sent to the user's device.

[1113] Step 7:

[1114] The user's device will display the received corrections within the application.

[1115] Input: Correction

[1116] What happens: The user opens the application and sees the suggested fixes.

[1117] Data processing: Display the corrections in a user-friendly format.

[1118] Output: User can see the corrections.

[1119] (Application example 1)

[1120] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1121] In conventional factory work, there is a lack of specific feedback to improve the efficiency and safety of workers' movements. As a result, workers continue to perform inappropriate movements, which leads to problems such as reduced work efficiency and increased physical strain. In addition, there is no system to optimize workers' movements, which makes it difficult to provide appropriate guidance to individual workers.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with movements of workers with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

[1123] "User" means an individual who uses the System to have their physique and / or movement measured and analyzed.

[1124] "Physique" refers to the physical characteristics and measurements of the user.

[1125] "Movement" refers to a specific physical movement or task performed by a user.

[1126] "Recording means" refers to a device or method for saving a user's actions as video or data.

[1127] "AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

[1128] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[1129] The "means for suggesting corrections" is a method or device for suggesting improvements to the user's behavior based on the analysis results.

[1130] A "factory worker" is a worker who performs specific tasks in a factory.

[1131] The "means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

[1132] "3D body scanning" is a technology for measuring a user's physique in three dimensions.

[1133] A system for implementing this invention includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user corrections to their movements based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers.

[1134] Hardware and software used

[1135] 3D body scanning device: A device for measuring the user's physique in three dimensions.

[1136] Camera: A device used to record user actions. An example is the Logitech C920.

[1137] Smart glasses: A device that visually guides the user to correct their movements. An example is Google Glass.

[1138] Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad.

[1139] Server: A computer system for processing data and running AI models.

[1140] Software: Using programming libraries such as Python, OpenCV, and Keras.

[1141] Data processing and calculation

[1142] 1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

[1143] 2. Recording of actions: The server records the user's actions using a camera, and the recorded actions are saved as image data.

[1144] 3. Movement analysis: The server inputs the recorded image data and physique data into the AI ​​model to analyze the movements. The AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique.

[1145] 4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

[1146] Specific examples

[1147] When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. The analysis results suggest corrections to things like the angle of the waist and the position of the hands when lifting, allowing the worker to work efficiently and safely.

[1148] Prompt Sentence Examples

[1149] "Record factory workers lifting heavy objects and feed this into the AI ​​along with their 3D body scan data to compare with efficient movements and suggest corrections."

[1150] The above is an embodiment of the present invention.

[1151] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1152] Step 1:

[1153] The server acquires the user's physique data using a 3D body scanning device. The input includes the user's physical characteristics and dimensions. The data is then converted into a data format that represents these characteristics and dimensions in three dimensions. The output is the user's physique data.

[1154] Step 2:

[1155] The server records the user's actions using a camera. The input includes specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

[1156] Step 3:

[1157] The server inputs the recorded image data and physique data into the AI ​​model and analyzes the movements. The input includes image data and physique data. For data calculation, the AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

[1158] Step 4:

[1159] The server proposes behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. The data is processed to generate specific suggestions for modifications. The output is the proposed modifications.

[1160] Step 5:

[1161] The server displays the suggested corrections on the smart glasses or tablet. The input includes the suggested corrections. Specific operations include generating data for visually displaying the suggestions and sending it to the smart glasses or tablet. The output is a visually verifiable suggested correction that the user can see.

[1162] The above is the processing flow of this program.

[1163] Example 2

[1164] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1165] Conventional motion analysis systems have difficulty accurately measuring and recording a user's physique and movements, and suggesting appropriate corrections. Furthermore, there is a lack of means to suggest specific corrections to users, which has led to problems with effective improvements to movements.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1167] In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for presenting the corrections to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate corrections, and present specific corrections to the user.

[1168] "User" refers to an individual who uses the system to measure and record their physique and movements and receive suggestions for movement modifications.

[1169] "Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

[1170] "Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

[1171] "Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

[1172] "Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

[1173] "Means for Providing Corrections" means a device or application that visually or audibly communicates suggested corrections to the user.

[1174] "3D body scanning" refers to a technology that measures a user's physique in three dimensions.

[1175] "Deep learning" refers to a machine learning technique that uses multiple layers of neural networks used in artificial intelligence tools.

[1176] MODE FOR CARRYING OUT THE INVENTION

[1177] This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests corrections to those movements. Specific embodiments of this system will be described below.

[1178] User's physique measurement

[1179] Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner and a motion capture system. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then sent to a server via wireless communication.

[1180] Recording actions

[1181] Next, the user performs a specific action, such as batting or pitching in baseball. The user's action is recorded using a camera or sensor. The hardware used may be a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

[1182] Behavior analysis

[1183] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the AI ​​model.

[1184] Suggested behavior modifications

[1185] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[1186] Specific examples

[1187] Example 1: Correcting baseball batting form

[1188] The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user via a dedicated application. The user can then work on improving their batting form while viewing the application.

[1189] Example 2: Correcting pitching form

[1190] The user measures their physique using a 3D scanner and sends the data to a server. Then, a motion capture system is used to record their pitching form, and the data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user through a dedicated application. The user works on improving their pitching form while viewing the application.

[1191] Prompt Sentence Examples

[1192] Prompt 1: Baseball batting form

[1193] "We need to generate an AI model that takes a user's 3D body scan data and video data of their batting form as input, compares it with data from professional baseball players, and suggests corrections."

[1194] Prompt 2: Pitching form

[1195] "We need to take the user's 3D body scan data and motion capture data of their pitching form as input, compare it with data from professional baseball players, and generate an AI model that suggests corrections."

[1196] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1197] Program processing flow

[1198] Step 1: Measuring the user's physique

[1199] The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then transmitted wirelessly to a server.

[1200] Input: User's physique scan data

[1201] Data processing: Real-time processing of scan data

[1202] Output: User's physique data

[1203] Step 2: Recording the action

[1204] Using a high-resolution camera and a motion capture system, the user performs a specific action, such as batting or pitching in baseball. The device records the action and generates video or motion capture data, which is then sent to a server.

[1205] Input: User actions

[1206] Data processing: Motion recording and data generation

[1207] Output: Video data or motion capture data

[1208] Step 3: Analyze the behavior

[1209] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the AI ​​model.

[1210] Input: physique data, movement data

[1211] Data processing: deep learning behavior comparison

[1212] Output: Behavior fixes

[1213] Step 4: Propose behavior modifications

[1214] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[1215] Input: Behavior fixes

[1216] Data processing: Preparation for displaying corrections

[1217] Output: User is presented with the fix

[1218] Adding specific actions

[1219] Step 1: Measuring the user's physique

[1220] The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scanned data is processed in real time to generate the user's physique data, which is then transmitted wirelessly to the server.

[1221] Step 2: Recording the action

[1222] The user installs a high-resolution camera and records their batting form. The camera records high-resolution video and saves it on the device. The device then compresses the video data and uploads it to the server.

[1223] Step 3: Analyze the behavior

[1224] The server inputs the received physique data and video data into a deep learning framework. The AI ​​model compares the user's batting form with that of professional baseball players. The comparison uses features such as joint angles and movement speed. The server extracts corrections for the user's movements from the output of the AI ​​model.

[1225] Step 4: Propose behavior modifications

[1226] The server sends the extracted corrections in JSON format to the device. The device then displays the received corrections in a dedicated application. The user can check the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[1227] (Application example 2)

[1228] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1229] Conventional optimization of factory robots' movements relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods may not be effective enough. This creates a risk of a decline in factory productivity and quality.

[1230] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1231] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movement modifications based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movements. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movements of factory robots.

[1232] "User" means an individual or legal entity that uses the System to measure and analyze their physique and movements.

[1233] "Physique" refers to the shape and dimensions of a user's body, as measured by means such as a 3D body scan.

[1234] A "specific action" is a specific movement or task performed by a user, which is recorded using a camera or sensor.

[1235] "Means for recording" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

[1236] "AI means" is a technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

[1237] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[1238] A "professional athlete" is a professional athlete who is active in a particular sport and is the subject to be compared with the user's movements.

[1239] "Behavior Modifications" are specific improvements suggested to optimize a user's behavior.

[1240] "Robots used in factories" are automated machinery used on factory production lines and in workshops.

[1241] "Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

[1242] To implement this invention, the following hardware and software are required. The hardware includes a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). The software includes Python, OpenCV, Keras, etc.

[1243] First, the user measures their body size using a 3D scanner, which captures the user's body shape and dimensions with high precision and stores them as digital data. This data is then sent to a server.

[1244] Next, the user performs a specific action. For example, to mimic the actions of a robot used in a factory, the camera and sensors are used to record the action. The recorded action data is also sent to the server.

[1245] The server inputs the received 3D scan data and movement data into the AI ​​system, which then analyzes the data using deep learning and compares the user's movements with those of professional athletes with a similar physique. Based on the comparison results, the system suggests corrections to the user's movements.

[1246] Furthermore, to optimize the operation of the robots used in the factory, the robot's operational data is also recorded and analyzed using AI methods. Based on the analysis results, corrections to optimize the robot's operation are suggested. These suggestions are displayed on the user's device.

[1247] As a concrete example, consider optimizing the operation of a welding robot used in a factory. A 3D scanner captures the robot's shape data, and cameras and sensors record its operation data. This data is input into an AI tool, which then analyzes it and suggests corrections to the welding angle and speed.

[1248] An example of a prompt sentence to be input into the generative AI model is, "Please input the welding robot's motion data along with the 3D scan data and suggest optimal motion corrections."

[1249] As described above, the present invention enables individual optimization based on the user's physique and movements, and can optimize the movements of factory robots efficiently and with high precision.

[1250] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1251] Step 1:

[1252] Users measure their body size using a 3D scanner.

[1253] Input: User's body

[1254] Data processing: A 3D scanner captures the shape and dimensions of the user's body with high precision and stores them as digital data.

[1255] Output: 3D scan data

[1256] Step 2:

[1257] The user performs a specific action and the action is recorded using cameras and sensors.

[1258] Input: User actions

[1259] Data processing: Cameras and sensors record user actions as video and sensor data.

[1260] Output: Operation data

[1261] Step 3:

[1262] The server receives the 3D scan data and the motion data.

[1263] Input: 3D scan data, motion data

[1264] Data processing: The server receives these data and prepares them for input into the AI ​​means.

[1265] Output: Prepared data

[1266] Step 4:

[1267] The server analyzes the 3D scan data and motion data using AI means.

[1268] Input: Prepared data

[1269] Data processing: Using a deep learning model, the user's movements are compared to those of professional athletes with a similar physique.

[1270] Output: Analysis results

[1271] Step 5:

[1272] The server then suggests modifications to the user's behavior based on the analysis results.

[1273] Input: Analysis results

[1274] Data manipulation: Use the analysis results to generate specific modifications to optimize user behavior.

[1275] Output: suggested fixes

[1276] Step 6:

[1277] Suggested fixes will be displayed on the user's device.

[1278] Input: Suggested fix

[1279] Data processing: Converting the proposal content into a format for display on the user's device.

[1280] Output: The corrections displayed on the user's terminal

[1281] Step 7:

[1282] The movements of robots used in factories are recorded and sent to a server.

[1283] Input: Robot movement

[1284] Data processing: Cameras and sensors record the robot's movements as video and sensor data, which are then sent to a server.

[1285] Output: Robot motion data

[1286] Step 8:

[1287] The server analyzes the robot's operational data using AI means and suggests modifications for optimization.

[1288] Input: Robot motion data

[1289] Data processing: Using deep learning models, the robot's behavior is analyzed and corrections for optimization are generated.

[1290] Output: Suggested modifications to the robot's behavior

[1291] Step 9:

[1292] Suggestions for correcting the robot's behavior are displayed on the user's device.

[1293] Input: Suggested modifications to the robot's behavior

[1294] Data processing: Converting the proposal content into a format for display on the user's device.

[1295] Output: Robot behavior corrections displayed on the user's device

[1296] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1297] "Example 1"

[1298] In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine that recognizes the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides that information to the AI ​​means. The AI ​​means suggests movement modifications taking into account not only the comparison between the user's movements and those of the professional athletes but also the user's emotions.

[1299] For example, if a user is feeling frustrated, the AI ​​solution can take that information into account and suggest modifications in a way that is acceptable to the user. In this way, it is possible to suggest behavioral improvements that take the user's emotions into account.

[1300] "Example 2"

[1301] In one embodiment of the present invention, a system is provided that includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of professional athletes of a similar physique, a means for suggesting movement modifications to the user based on the comparison results, and an emotion engine for recognizing the user's emotions. The emotion engine recognizes emotions from the user's facial expressions and tone of voice and provides this information to the AI ​​means. The AI ​​means suggests movement modifications taking into account not only the comparison between the user's movements and the movements of the professional athletes, but also the user's emotions. For example, if the user is feeling frustrated, the AI ​​means takes this information into account and suggests modifications in a manner that is easy for the user to accept. In this way, it is possible to suggest movement improvements that take the user's emotions into account.

[1302] The processing flow of each embodiment will be described below.

[1303] "Example 1"

[1304] Step 1: Measure the user's physique with a 3D body scan.

[1305] Step 2: A specific action performed by the user (e.g., batting or pitching a baseball) is recorded using a video camera or motion sensor.

[1306] Step 3: The recorded movements are analyzed using AI tools and compared to the movements of professional athletes with similar physiques.

[1307] Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

[1308] Step 5: The AI ​​tool will suggest corrections to the user's movements, taking into account the comparison between the user's movements and those of professional athletes and the user's emotions.

[1309] Step 6: The proposed behavioral modifications are presented to the user through an application on a device such as a smartphone or tablet.

[1310] "Example 2"

[1311] Step 1: Measure the user's physique with a 3D body scan.

[1312] Step 2: A specific action performed by the user (e.g., batting or pitching a baseball) is recorded using a video camera or motion sensor.

[1313] Step 3: The recorded movements are analyzed using AI tools and compared to the movements of professional athletes with similar physiques.

[1314] Step 4: Understand the user's emotions using an emotion engine that recognizes emotions from the user's facial expressions and tone of voice.

[1315] Step 5: The AI ​​tool will suggest corrections to the user's movements, taking into account the comparison between the user's movements and those of professional athletes and the user's emotions.

[1316] Step 6: The proposed behavioral modifications are presented to the user through an application on a device such as a smartphone or tablet.

[1317] Example 1

[1318] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1319] Conventional motion improvement systems make suggestions based on the user's physique and motion data, but they do not take the user's emotions into consideration, which can make it difficult for users to accept the suggestions. Also, since the points to correct for the motion are not specific, it is difficult for users to understand how to improve.

[1320] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1321] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, means for suggesting to the user movement corrections based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement corrections taking into account the emotion data, thereby making it possible to suggest movement improvements taking into account the user's physique and emotions.

[1322] "Means for measuring the user's physique" refers to a device or method for measuring the user's physical characteristics and acquiring them as digital data.

[1323] "Means for recording specific actions performed by a user" refers to a device or method that uses video or sensors to record specific actions performed by a user as digital data.

[1324] "Artificial intelligence means" means software or systems that analyze recorded data and use specific algorithms to compare the user's movements with other data (e.g., the movements of professional athletes).

[1325] The "means for suggesting points for correcting movements" refers to a device or method that suggests points for improving movements or methods for correcting movements to the user based on the analysis results.

[1326] "Means for recognizing user emotions" refers to devices or methods that analyze emotions from the user's facial expressions, tone of voice, etc., and acquire them as digital data.

[1327] The "means for proposing corrections to movements in consideration of emotional data" refers to a device or method that presents corrections to movements in a more acceptable form based on the emotional data of the user.

[1328] "3D body scanning" is a technology that scans the user's body in three dimensions and obtains detailed physique data.

[1329] "Deep learning" is an artificial intelligence technology that uses multi-layered neural networks to analyze data and learn patterns and features.

[1330] This invention is a system that analyzes the user's physique and movements, compares them with the movements of professional athletes, and suggests movement corrections to the user. Furthermore, by recognizing the user's emotions and making suggestions taking into account that emotional data, it is possible to present movement corrections in a way that is easy for the user to accept.

[1331] Hardware and software used

[1332] 1. User's physique measurement

[1333] Users measure their physique using equipment that provides a three-dimensional body scan, such as a 3D scanner or a dedicated body scan application, which captures detailed physique data, including the user's height, weight, and body fat percentage.

[1334] 2. Recording the movement

[1335] Users record specific movements (e.g., batting or pitching a baseball) using a smartphone camera or a dedicated motion capture device, and the recorded data includes detailed movement information such as swing speed and angle.

[1336] 3. Sending operation data

[1337] The device transmits the recorded movement and physique data to a server in real time over the internet, with the data encrypted and using a secure communication protocol.

[1338] 4. Analysis of movement

[1339] The server then inputs the received data into an AI model, which uses deep learning algorithms to compare the user's movements with those of professional athletes. The software used is a deep learning framework such as TensorFlow or PyTorch. For example, it calculates how much the user's swing speed differs from the average speed of professional athletes.

[1340] 5. Emotional Recognition

[1341] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. This emotion engine determines whether the user is frustrated or satisfied and sends that information to a server. The software used is an emotion recognition API (e.g., Microsoft Azure's Emotion API).

[1342] 6. Suggestions for behavioral modifications

[1343] The server then suggests to the user how to improve their performance based on the comparison of their movements and their emotional data. For example, if the user feels frustrated, the server might suggest, "Maybe your swing will become smoother if you just change the way you grip the bat slightly." The server then notifies the user of this suggestion through an application on their smartphone, tablet, or other device.

[1344] Specific examples

[1345] Example 1: Improving baseball batting form

[1346] The user measures their physique using a 3D scanner and records their batting form with their smartphone camera. The device then sends the recorded data to a server, which then inputs the data into an AI model and compares it with the form of professional baseball players. If the emotion engine detects the user's frustration, the server will make suggestions such as, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The user is notified of these suggestions through a smartphone application.

[1347] Example prompts for generative AI models

[1348] "We will take the user's 3D body scan data and a video of their batting form as input, compare it with the form of a professional baseball player, and suggest improvements while taking into account the user's emotions."

[1349] In this way, it becomes possible to suggest movement improvements that take into account the user's physique and emotions.

[1350] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1351] Step 1:

[1352] Users measure their physique using a device that provides a 3D body scan. As input, a 3D scanner is used to scan the user's body. As output, detailed physique data such as the user's height, weight, and body fat percentage is generated. This data is stored on the device.

[1353] Step 2:

[1354] Users record specific movements (e.g., batting or pitching in baseball) using a smartphone camera or a dedicated motion capture device. The input is the user's movements, which are captured by the camera or sensors. The output is detailed movement data, such as the speed and angle of the swing. This data is stored on the device.

[1355] Step 3:

[1356] The device transmits the recorded movement data and physique data to a server. As input, the movement data and physique data stored on the device are used. As output, these data are transmitted to the server via the Internet. The data is encrypted and uses a secure communication protocol.

[1357] Step 4:

[1358] The server inputs the received data into an AI model. The motion and physique data sent to the server are used as input. The output is the result of the AI ​​model comparing the user's motion with that of a professional athlete. This AI model uses deep learning algorithms and utilizes deep learning frameworks such as TensorFlow and PyTorch.

[1359] Step 5:

[1360] The device uses an emotion engine that analyzes the user's facial expressions and tone of voice in real time. As input, the user's facial and voice data is captured through a camera and microphone. As output, emotional data is generated, such as whether the user is frustrated or satisfied. This data is then sent to a server.

[1361] Step 6:

[1362] The server then suggests to the user how to modify their movements based on the comparison results and emotional data. The AI ​​model's comparison results and emotional data are used as input. The output is a suggestion for the user to modify their movements. For example, if the user feels frustrated, the server might suggest, "Maybe if you just change the way you grip the bat a little, your swing will become smoother." The suggestion is then notified to the user through an application on their device, such as a smartphone or tablet.

[1363] (Application example 1)

[1364] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1365] Conventional motion analysis systems can analyze a user's physique and movements, but they have the problem of not being able to suggest motion modifications that take the user's emotions into account. Furthermore, when optimizing the movements of robots working in factories, there is a lack of real-time monitoring of movement efficiency and error rates and suggestions for improvement. This leads to issues such as reduced movement efficiency for users and robots, preventing optimal performance.

[1366] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1367] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting movement modifications taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time. This makes it possible to suggest movement modifications taking the user's emotions into account, and further makes it possible to monitor the movement efficiency and error rate of robots working in factories in real time, thereby achieving optimal movement performance.

[1368] "Means for measuring the user's physique" refers to devices or technologies for accurately measuring the dimensions and shape of the user's body.

[1369] "Means for recording specific actions performed by a user" refers to devices or technologies that use cameras, sensors, etc. to record specific actions performed by a user.

[1370] "AI means for analyzing recorded movements and comparing them with the movements of professional athletes with similar physiques" refers to artificial intelligence technology that analyzes recorded movement data of a user and compares it with the movement data of professional athletes with similar physiques.

[1371] "Means for suggesting to users points for correcting their movements based on the comparison results" refers to devices or technologies that suggest points for improvement or correction to the user's movements based on the results of movement comparison by AI.

[1372] The "emotion engine that recognizes user emotions" is a technology that identifies emotions from the user's facial expressions, tone of voice, etc.

[1373] The "means for proposing modifications to behavior in consideration of information from the emotion engine" refers to a device or technology that suggests modifications to behavior based on the user's emotional information recognized by the emotion engine.

[1374] "Means for optimizing robot operations" refers to devices and technologies that optimize the operations of robots working in factories to ensure that they are efficient and effective.

[1375] "Means for monitoring the operational efficiency and error rate of a robot" refers to devices and technologies that monitor the operational efficiency and error rate of a robot in real time.

[1376] "Means for making improvement suggestions in real time" refers to devices and technologies that analyze robot operation data in real time and immediately suggest areas for improvement.

[1377] A system for implementing this invention includes means for measuring a user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user how to modify their movements based on the comparison results, an emotion engine for recognizing the user's emotions, means for suggesting how to modify their movements taking into account information from the emotion engine, means for optimizing the robot's movements, means for monitoring the robot's movement efficiency and error rate, and means for making improvement suggestions in real time.

[1378] Hardware and Software Configuration

[1379] Hardware:

[1380] 3D body scanning device

[1381] Camera (e.g. Logitech C920)

[1382] Computer (e.g. Intel Core i7, 16GB RAM)

[1383] software:

[1384] OpenCV: Motion recording and image processing

[1385] Keras: Loading and Predicting Deep Learning Models

[1386] EmotionRecognizer: Emotion recognition library

[1387] Processing flow

[1388] The server first acquires the user's physique data using a 3D body scanning device. Then, it uses a camera to record specific movements performed by the user. The recorded movement data is preprocessed using OpenCV and analyzed using a deep learning model using Keras. The analysis results are compared with the movement data of professional athletes with similar physiques.

[1389] Furthermore, the system uses EmotionRecognizer to recognize the user's emotions and suggests behavioral modifications based on the information from the emotion engine. The suggested modifications are then presented to the user.

[1390] For robots working in factories, measures are used to optimize the robot's operation. The robot's operation efficiency and error rate are monitored in real time, and improvement suggestions are made. This makes it possible to optimize the robot's operation performance.

[1391] Specific examples

[1392] For example, if a robot assembling parts in a factory is moving slowly, the AI ​​will analyze its movements and suggest correcting the angle and speed of its movements. Furthermore, if the robot is making frequent errors, it will determine through emotion recognition that it is feeling frustrated and suggest that it should move more slowly.

[1393] Prompt Sentence Examples

[1394] "To optimize the movements of robots assembling parts in factories, design a system that uses 3D body scanning and cameras to record their movements, analyzes them with AI, and suggests optimal movement patterns. Additionally, add functionality to monitor the robot's movement efficiency and error rate, and make suggestions for improvement in real time."

[1395] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1396] Step 1:

[1397] The server acquires the user's physique data using a 3D body scanning device. As input, it scans the user's body dimensions and shape, and generates 3D physique data as output. This data is used to accurately determine the user's physique.

[1398] Step 2:

[1399] The server uses a camera to record specific actions performed by the user, capturing the actions performed by the user in real time as input and generating video data of the actions as output, which is used for subsequent action analysis.

[1400] Step 3:

[1401] The server preprocesses the recorded motion data using OpenCV. It receives motion video data as input and generates preprocessed image data as output. This preprocessing includes image resizing and noise removal.

[1402] Step 4:

[1403] The server analyzes the preprocessed image data using a deep learning model using Keras. It receives the preprocessed image data as input and extracts movement features as output. These features are used to analyze the user's movements in detail.

[1404] Step 5:

[1405] The server compares the extracted features with the motion data of professional athletes with similar physiques. It receives the user's motion features and the motion data of the professional athletes as input, and generates a comparison result as output. This comparison result is used to identify areas for correction in the user's motion.

[1406] Step 6:

[1407] The server uses EmotionRecognizer to recognize the user's emotions. As input, it captures the user's facial expressions and tone of voice while they are in action, and generates emotional data as output. This emotional data is used to understand the user's emotional state.

[1408] Step 7:

[1409] The server proposes behavioral modifications taking into account information from the emotion engine. It receives the comparison results and emotion data as input and generates behavioral modifications for the user as output. These modifications are proposed taking into account the user's emotional state.

[1410] Step 8:

[1411] The server executes a means for optimizing the operation of a robot working in a factory. The server receives the robot's operation data as input and generates an optimized operation pattern as output. This optimization is performed to improve the robot's operation efficiency.

[1412] Step 9:

[1413] The server monitors the robot's operational efficiency and error rate in real time. It receives the robot's operational data in real time as input and generates the monitoring results of operational efficiency and error rate as output. These monitoring results are used to understand the robot's operational status.

[1414] Step 10:

[1415] The server provides real-time improvement suggestions, taking the monitoring results as input and generating improvement suggestions as output, which are made to optimize the robot's operational performance.

[1416] Example 2

[1417] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1418] Conventional motion improvement systems have difficulty accurately measuring the user's physique and movements and providing appropriate feedback. Furthermore, since motion improvement suggestions do not take the user's emotions into consideration, feedback in a form that is easy for the user to accept is lacking. This limits the effectiveness of motion improvement.

[1419] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1420] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movements, artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, means for suggesting movement modifications to the user based on the comparison results, means for recognizing the user's emotions, and means for suggesting movement modifications taking the recognized emotions into consideration. This makes it possible to accurately measure the user's physique and movements and provide appropriate feedback. Furthermore, because suggestions for movement improvement are made taking the user's emotions into consideration, feedback is provided in a form that the user finds easy to accept, improving the effectiveness of movement improvement.

[1421] "Means for measuring the user's physique" refers to devices or technologies that measure the dimensions and shape of the user's body in three dimensions.

[1422] "Means for recording specific actions performed by a user" refers to devices or technologies that use video or sensors to record specific actions performed by a user.

[1423] The "means for transmitting recorded actions" refers to a communication means for transmitting recorded action data to a server or other device.

[1424] "Artificial intelligence means" refers to artificial intelligence technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

[1425] "Means for suggesting corrections to movements" refers to devices or technologies that suggest improvements to movements to users based on the results of analysis by artificial intelligence means.

[1426] "Means for recognizing user emotions" refers to devices or technologies for recognizing emotions from the user's facial expressions, tone of voice, etc.

[1427] "Means for suggesting behavioral modifications taking into account recognized emotions" refers to devices or technologies that suggest behavioral modifications in a way that is easy for the user to accept, based on the user's emotional information.

[1428] The present invention is a system that measures a user's physique, records specific movements, analyzes the movements, and suggests corrections. The system can recognize the user's emotions and take them into consideration when suggesting corrections to movements.

[1429] First, the user measures their physique using a 3D body scanning device, such as the Structure Sensor or Kinect, which then acquires the user's physique data and stores it on the device.

[1430] Next, the user performs a specific movement (e.g., batting or pitching a baseball), which is recorded using a camera or sensor (e.g., a GoPro camera or a Vicon motion capture system), and the recorded movement data is stored on the device.

[1431] The device sends the recorded motion and physique data to a server. The data is transmitted using a secure protocol (e.g., HTTPS).

[1432] The server inputs the received data into an AI model, built using a deep learning framework (e.g., TensorFlow or PyTorch), that compares the user's movements with those of professional athletes of similar build. The server generates a comparison result and identifies corrections to the user's movements.

[1433] The server then sends the identified corrections to the device, which then presents the corrections to the user through an application on the device, such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

[1434] The device also inputs the user's facial expressions and tone of voice into an emotion engine, which uses Affectiva and the Microsoft Azure Emotion API to recognize the user's emotions in real time. The recognition results are stored on the device.

[1435] The device sends the recognized emotional information to the server. The server reevaluates the AI ​​model taking the emotional information into account and suggests modifications that are easy for the user to accept. For example, if the user feels frustrated, the server generates a suggestion such as "First, relax, and then try to be aware of your release point," and sends it to the device. The device then displays this suggestion to the user.

[1436] Specific examples

[1437] Example 1: Improving baseball batting form

[1438] The user uses the "Structure Sensor" to measure their physique and a GoPro camera to record their batting form. The device then sends this data to a server. The server uses TensorFlow to compare the batting form with that of professional baseball players and suggests, via a smartphone app, that the user "increase the angle of the bat's swing a little more." If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to be more conscious of your swing angle."

[1439] Example 2: Improving pitching form

[1440] The user uses Kinect to measure their physique and a Vicon motion capture system to record their pitching form. The device then sends this data to a server. The server uses PyTorch to compare the form with that of professional pitchers and suggests, via a tablet app, that the release point be moved forward a little. If the user is feeling frustrated, the emotion engine recognizes this and the server suggests, "First, relax, and then try to focus on the release point."

[1441] Prompt Sentence Examples

[1442] "The user uses a 3D body scanning device to measure their physique and a camera to record their batting form. The AI ​​uses TensorFlow to compare their form with that of professional baseball players and suggests corrections via a smartphone app. If the user is feeling frustrated, the emotion engine recognizes this and the AI ​​suggests corrections in a way that is easy for the user to accept."

[1443] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1444] Step 1:

[1445] Users measure their own physique using a 3D body scanning device. Specifically, users acquire physique data using a "Structure Sensor" or "Kinect." The input is the dimensions and shape of the user's body, and the output is physique data as a 3D model. This data is stored on the device.

[1446] Step 2:

[1447] The user performs a specific movement (e.g., batting or pitching a baseball). The user records the movement using a GoPro camera or a Vicon motion capture system. The input is the user's movement, and the output is high-resolution video data. This data is stored on the device.

[1448] Step 3:

[1449] The device sends the recorded motion data and physique data to the server. The input is the motion data and physique data, and the output is the data sent to the server. The data is sent using a secure protocol (e.g., HTTPS).

[1450] Step 4:

[1451] The server inputs the received data into an AI model. The AI ​​model is built using TensorFlow and PyTorch and compares the user's movements with those of professional athletes with a similar physique. The input is movement data and physique data, and the output is the comparison results, including corrections to the movements.

[1452] Step 5:

[1453] The server sends the identified corrections to the terminal. The input is the comparison result, and the output is the transmission of the corrections to the terminal. The terminal then presents the corrections to the user through an application on a device such as a smartphone or tablet. For example, specific advice such as "You should increase the angle of your bat swing a little more" is displayed.

[1454] Step 6:

[1455] The device inputs the user's facial expressions and tone of voice into the emotion engine. The emotion engine uses "Affectiva" and "Microsoft Azure Emotion API" to recognize the user's emotions in real time. The input is the user's facial expressions and tone of voice, and the output is the recognized emotional information. This information is stored on the device.

[1456] Step 7:

[1457] The device sends the recognized emotional information to the server. The input is emotional information, and the output is the transmission of emotional information to the server. The server reevaluates the AI ​​model taking the emotional information into account and suggests corrections in a way that is easy for the user to accept. For example, if the user feels frustrated, the server generates a suggestion such as "First, relax, and then try to be aware of your release point," and sends it to the device. The device displays this suggestion to the user.

[1458] (Application example 2)

[1459] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1460] Conventional motion analysis systems record the user's physique and movements and compare them with those of professional athletes to suggest corrections, but they were not designed to optimize the movements of robots working in factories. They also lacked the ability to detect abnormalities in the robots and suggest corrections based on those abnormalities. This made it difficult to efficiently manage the movements of robots in factories.

[1461] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1462] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for analyzing the recorded movements and comparing them with those of professional athletes with a similar physique, means for suggesting to the user how to modify their movements based on the comparison results, means for recording the robot's movements and proposing optimal movement patterns, and means for detecting abnormalities in the robot and proposing how to modify its movements based on that information. This not only improves the user's movements, but also enables efficient management and optimization of the robot's movements in factories.

[1463] "User" refers to an individual who uses the System to measure and analyze their physique and movements.

[1464] "Physique" refers to the physical characteristics and measurements of a user.

[1465] "Specific movement" refers to a specific action or movement performed by a user (e.g., batting or pitching form in baseball).

[1466] "Means for recording" refers to devices or methods that use cameras, sensors, etc. to save the actions of users or robots as digital data.

[1467] "AI means" refers to a system that uses artificial intelligence to analyze data and output results according to a specific purpose.

[1468] "Deep learning" refers to a machine learning technique that uses multi-layered neural networks to learn from data and extract complex patterns and features.

[1469] A "professional athlete" is someone who has high skills in a particular sport and is active in that sport as a profession.

[1470] "Behavior Modifications" refers to specific changes or adjustments suggested to improve the user or robot's behavior.

[1471] A "robot" refers to an automated mechanical device that performs work in a factory.

[1472] "Abnormal" refers to conditions such as vibrations or temperature rises that deviate from the robot's normal operation.

[1473] "Means for sensing" refers to methods or devices that use sensors or monitoring devices to detect abnormalities.

[1474] An "optimal motion pattern" refers to a series of procedures or methods of robot motion proposed to perform a task efficiently and effectively.

[1475] An embodiment of the present invention will now be described. First, a 3D body scanning device is used to measure a user's physique. Specifically, a 3D scanner such as Intel RealSense is used to acquire the user's physique data. This data is used to accurately record the user's physical characteristics and dimensions.

[1476] Next, cameras and sensors are used to record specific user actions, such as an Intel RealSense camera, which records user movements with high accuracy. This recorded data is then analyzed using AI tools, which will be described later.

[1477] The recorded movement data is analyzed using deep learning AI tools such as TensorFlow and Keras. The AI ​​tools compare the user's movements with those of professional athletes with a similar physique and suggest corrections to the movement. These suggestions are presented to the user via a smartphone or tablet application.

[1478] Furthermore, the system includes a means for recording the robot's movements and proposing optimal movement patterns in order to optimize the movements of the robots working in the factory. The robot's movements are recorded using a 3D scanner or camera and analyzed by the AI ​​means. The AI ​​means proposes optimal movement patterns and makes the robot's movements more efficient.

[1479] In addition, vibration and temperature sensors are used to detect abnormalities in the robot. OpenCV and PyTorch are used to detect abnormalities and suggest corrections to the robot's behavior based on that information. This optimizes the robot's behavior and enables it to work more efficiently.

[1480] For example, if a robot assembles parts in a factory and its movements slow down or abnormal vibrations occur, the system records the movement and the AI ​​suggests the optimal movement pattern. If the emotion engine detects an abnormality, it will be reflected in the suggestion of a correction to the movement.

[1481] An example of a prompt to input to a generative AI model is as follows:

[1482] "To optimize the movements of robots assembling parts in a factory, AI should suggest optimal movement patterns using data recorded by 3D scans and cameras. It should also detect abnormal vibrations or temperature increases in the robot and suggest movement corrections based on that."

[1483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1484] Step 1:

[1485] A user acquires physique data using a 3D body scanning device.

[1486] Input: User's body

[1487] Output: 3D body scan data

[1488] How it works: The user stands in front of the 3D scanner, which scans their entire body and generates digital data.

[1489] Step 2:

[1490] The user's specific actions are recorded using cameras and sensors.

[1491] Input: User actions

[1492] Output: Operation record data

[1493] Specific actions: The user performs a specific action (e.g., batting or pitching) in front of the camera, and the camera records the action with high accuracy.

[1494] Step 3:

[1495] The server analyzes the recorded motion data using AI means.

[1496] Input: Motion record data, 3D body scan data

[1497] Output: Motion analysis results

[1498] Specific operation: The server uses TensorFlow and Keras to input motion recording data and 3D body scan data into an AI model to analyze the user's movements.

[1499] Step 4:

[1500] Based on the results of the behavior analysis, the server suggests to the user how to correct their behavior.

[1501] Input: Motion analysis results

[1502] Output: Proposed behavior correction

[1503] Specific actions: Based on the analysis results, the server generates specific corrections to improve the user's behavior and presents them to the user through an application on their smartphone or tablet.

[1504] Step 5:

[1505] The robot's movements are recorded using 3D scanners and cameras.

[1506] Input: Robot movement

[1507] Output: Robot operation record data

[1508] Specific actions: As the robot performs a task, its movements are recorded by 3D scanners and cameras.

[1509] Step 6:

[1510] The server analyzes the robot's operation record data using AI means.

[1511] Input: Robot motion record data

[1512] Output: Robot motion analysis results

[1513] Specific operation: The server uses TensorFlow and Keras to input the robot's motion record data into an AI model and analyze the robot's motion.

[1514] Step 7:

[1515] The server detects an abnormality in the robot.

[1516] Input: Robot vibration data, temperature data

[1517] Output: Anomaly detection results

[1518] Specific operation: The server analyzes data from vibration sensors and temperature sensors using OpenCV and PyTorch to detect abnormalities.

[1519] Step 8:

[1520] The server proposes optimal movement patterns based on the results of the robot's movement analysis and abnormality detection.

[1521] Input: Robot operation analysis results, anomaly detection results

[1522] Output: Optimal motion pattern proposal

[1523] Specific behavior: The server integrates the analysis results and anomaly detection results, generates specific behavior patterns to optimize the robot's behavior, and applies them to the robot.

[1524] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1525] The data generation model 58 is a so-called generative AI (Artificial Intelligence) model. An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1526] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1527] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1528] [Fourth embodiment]

[1529] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1530] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1531] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34.

[1532] The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1533] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1534] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1535] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1536] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1537] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1538] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1539] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1540] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1541] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1542] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1543] "Example 1"

[1544] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[1545] "Example 2"

[1546] In one embodiment of the present invention, a user first measures their own physique using a 3D body scan. This measurement is performed, for example, using a device that provides 3D body scans. Next, the user performs a specific movement, such as a baseball batting or pitching form. This movement is recorded using a camera or sensor. The recorded movement is analyzed by AI. The AI ​​uses deep learning to compare the user's movement with that of a professional athlete with a similar physique. Based on the comparison results, the AI ​​then suggests movement modifications to the user. These suggestions are presented to the user, for example, through an application on a device such as a smartphone or tablet.

[1547] The processing flow of each embodiment will be described below.

[1548] "Example 1"

[1549] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[1550] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[1551] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[1552] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[1553] "Example 2"

[1554] Step 1: The user measures their physique using a 3D body scan. This measurement is taken, for example, using a device that provides a 3D body scan.

[1555] Step 2: Next, the user performs a specific action, such as batting or pitching a baseball, which is recorded using cameras and sensors.

[1556] Step 3: The recorded movements are analyzed by AI, which uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[1557] Step 4: Based on the comparison results, the system then suggests modifications to the user's behavior. These suggestions are presented to the user via an application on the device, such as a smartphone or tablet.

[1558] Example 1

[1559] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1560] Conventional motion analysis systems have had difficulty accurately measuring and recording a user's physique and movements, and providing appropriate feedback. Furthermore, there was a lack of means for performing highly accurate analysis when comparing a user's movements with those of professional athletes. This made it difficult for users to effectively improve their own movements.

[1561] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1562] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, means for transmitting the recorded movement data and physique data to the server, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with similar physiques, means for suggesting to the user movement corrections based on the comparison results, and means for displaying the suggested corrections on the user's terminal. This allows the user to accurately measure and record their own physique and movements, and compare them with the movements of professional athletes to obtain specific corrections.

[1563] "User" refers to an individual who uses the system to measure and record their physique and movements and receive feedback.

[1564] "Physique" refers to a user's physical characteristics and measurements, including data measured by means such as a 3D body scan.

[1565] "Movement" refers to specific physical movements or performances made by users, which are recorded using cameras and sensors.

[1566] "Server" refers to a computer system that receives data sent by a user, analyzes it using AI means, and provides the results to the user.

[1567] "3D body scanning" refers to technology and equipment that measures a user's physique in three dimensions and is used to obtain detailed data about the user's body.

[1568] "AI means" refers to means that use artificial intelligence technology to analyze a user's movements and compare them with those of professional athletes.

[1569] "Deep learning" is a field of artificial intelligence that uses multi-layer neural networks to learn the characteristics of data and perform advanced analysis and predictions.

[1570] "Fixes" are specific changes or suggestions suggested to improve user behavior.

[1571] "Terminal" refers to the device that a user uses to access the system, including smartphones and tablets.

[1572] "Comparison results" refers to the results of a comparison made by AI means between the user's movements and those of a professional athlete, and includes data that serves as the basis for feedback to the user.

[1573] This invention is a system that measures and records a user's physique and movements, analyzes them using AI, and then suggests specific movement corrections to the user. Specific embodiments of this system are described below.

[1574] First, the user measures their physique using a device that provides a 3D body scan, such as Kinect or Structure Sensor, which acquires the user's physique data and stores it on the user's device (smartphone or tablet).

[1575] Next, the user performs a specific movement, such as a baseball batting stance or pitching stance. This movement is recorded using a smartphone camera or GoPro. The recorded video data is saved on the user's device.

[1576] The user's device sends the recorded movement and physique data to the server. An internet connection is required for transmission. The user presses the "Send Data" button in the application, and the device uploads the data to the server.

[1577] The server inputs the received movement data and physique data into an AI model. This AI model uses TensorFlow and PyTorch and uses deep learning to compare the user's movements with those of professional athletes with similar physiques. The server inputs the data into the AI ​​model and begins the comparison process. Once the process is complete, the comparison results are generated.

[1578] The server generates corrections for the user's movements based on the results of the comparison with the AI ​​model. These corrections are summarized as specific advice and methods for improvement. For example, specific advice such as "To increase the swing speed of your batting form, you need to rotate your hips more quickly" is generated.

[1579] The server sends the generated corrections to the user's device, which then displays them in the application. The user opens the application and checks the suggested corrections. For example, specific advice such as "How to practice to rotate your hips faster" is displayed.

[1580] As a concrete example, consider a case where a user wants to improve their baseball batting form. First, the user measures their physique using a 3D body scanning device (e.g., Kinect). Next, they record their batting form using a smartphone camera. This recorded data is input into a deep learning model using TensorFlow and compared with the movements of professional batters. Based on the comparison results, a dedicated app suggests corrections to the user's batting form.

[1581] An example of a prompt is, "Please input the user's physique data and a video of their batting form, compare it with the movements of a professional batter, and suggest corrections."

[1582] In this way, users can compare their own movements with those of professionals and get specific corrections.

[1583] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1584] Step 1:

[1585] Users measure their physique using a 3D body scanning device.

[1586] Input: User's physical location

[1587] Specific action: The user stands in front of the Kinect and rotates their body according to the instructions.

[1588] Data processing: The scanning device acquires the user's physique data and generates a 3D model.

[1589] Output: The generated physique data is saved on the user's device.

[1590] Step 2:

[1591] The user performs a specific movement (e.g., a baseball batting stance) and records that movement.

[1592] Input: User actions

[1593] Specific operation: The user sets the smartphone on a tripod and takes a picture of their batting form.

[1594] Data processing: The camera records the user's movements as video data.

[1595] Output: The recorded video data is saved on the user's device.

[1596] Step 3:

[1597] The user's device transmits the recorded movement data and physique data to the server.

[1598] Input: Movement data and physique data

[1599] Specific action: The user presses the "Submit Data" button within the application.

[1600] Data processing: The device compresses the data and uploads it to a server via the Internet.

[1601] Output: Data is sent to the server.

[1602] Step 4:

[1603] The server inputs the received movement data and physique data into the AI ​​model.

[1604] Input: Movement data and physique data

[1605] Specific operation: The server inputs the data into the AI ​​model and begins the comparison process.

[1606] Data calculation: The AI ​​model uses deep learning to compare the user's movements with those of professional athletes with a similar physique.

[1607] Output: The comparison results are generated.

[1608] Step 5:

[1609] The server generates corrections for the user's behavior based on the comparison results of the AI ​​model.

[1610] Input: Comparison result

[1611] Specific actions: The server generates specific advice such as, "To increase the swing speed of your batting form, you need to rotate your hips faster."

[1612] Data processing: Analyze the comparison results and generate corrections in text format.

[1613] Output: The corrections are generated.

[1614] Step 6:

[1615] The server sends the generated modifications to the user's device.

[1616] Input: Correction

[1617] Specific operation: The server sends the corrections to the user's device.

[1618] Data processing: Convert the corrections into the appropriate format and send them.

[1619] Output: The corrections are sent to the user's device.

[1620] Step 7:

[1621] The user's device will display the received corrections within the application.

[1622] Input: Correction

[1623] What happens: The user opens the application and sees the suggested fixes.

[1624] Data processing: Display the corrections in a user-friendly format.

[1625] Output: User can see the corrections.

[1626] (Application example 1)

[1627] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1628] In conventional factory work, there is a lack of specific feedback to improve the efficiency and safety of workers' movements. As a result, workers continue to perform inappropriate movements, which leads to problems such as reduced work efficiency and increased physical strain. In addition, there is no system to optimize workers' movements, which makes it difficult to provide appropriate guidance to individual workers.

[1629] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with movements of workers with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers. This makes it possible to provide specific feedback to improve the efficiency and safety of workers' movements.

[1630] "User" means an individual who uses the System to have their physique and / or movement measured and analyzed.

[1631] "Physique" refers to the physical characteristics and measurements of the user.

[1632] "Movement" refers to a specific physical movement or task performed by a user.

[1633] "Recording means" refers to a device or method for saving a user's actions as video or data.

[1634] "AI means" refers to technology that uses artificial intelligence to analyze recorded movements and compare them with other movements.

[1635] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[1636] The "means for suggesting corrections" is a method or device for suggesting improvements to the user's behavior based on the analysis results.

[1637] A "factory worker" is a worker who performs specific tasks in a factory.

[1638] The "means for displaying optimization suggestions" refers to a device or method for visually presenting suggestions to the user for improving operational efficiency and safety.

[1639] "3D body scanning" is a technology for measuring a user's physique in three dimensions.

[1640] A system for implementing this invention includes a means for measuring a user's physique, a means for recording specific movements performed by the user, an AI means for analyzing the recorded movements and comparing them with the movements of workers with a similar physique, a means for suggesting to the user corrections to their movements based on the comparison results, and a means for displaying suggestions for optimizing the movements of factory workers.

[1641] Hardware and software used

[1642] 3D body scanning device: A device for measuring the user's physique in three dimensions.

[1643] Camera: A device used to record user actions. An example is the Logitech C920.

[1644] Smart glasses: A device that visually guides the user to correct their movements. An example is Google Glass.

[1645] Tablet: A device used to display operational corrections and optimization suggestions. An example is the iPad.

[1646] Server: A computer system for processing data and running AI models.

[1647] Software: Using programming libraries such as Python, OpenCV, and Keras.

[1648] Data processing and calculation

[1649] 1. Acquisition of physique data: The server acquires the user's physique data using a 3D body scanning device. This data represents the user's physical characteristics and dimensions in three dimensions.

[1650] 2. Recording of actions: The server records the user's actions using a camera, and the recorded actions are saved as image data.

[1651] 3. Movement analysis: The server inputs the recorded image data and physique data into the AI ​​model to analyze the movements. The AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique.

[1652] 4. Suggested modifications: The server will suggest modifications to the user's behavior based on the analysis results. These suggestions will be displayed on the smart glasses or tablet.

[1653] Specific examples

[1654] When a factory worker lifts a heavy object, their movements are recorded using a 3D body scan and camera, and then analyzed by AI. The analysis results suggest corrections to things like the angle of the waist and the position of the hands when lifting, allowing the worker to work efficiently and safely.

[1655] Prompt Sentence Examples

[1656] "Record factory workers lifting heavy objects and feed this into the AI ​​along with their 3D body scan data to compare with efficient movements and suggest corrections."

[1657] The above is an embodiment of the present invention.

[1658] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1659] Step 1:

[1660] The server acquires the user's physique data using a 3D body scanning device. The input includes the user's physical characteristics and dimensions. The data is then converted into a data format that represents these characteristics and dimensions in three dimensions. The output is the user's physique data.

[1661] Step 2:

[1662] The server records the user's actions using a camera. The input includes specific actions performed by the user. The data is processed by saving the actions as video data. The output is image data of the recorded actions.

[1663] Step 3:

[1664] The server inputs the recorded image data and physique data into the AI ​​model and analyzes the movements. The input includes image data and physique data. For data calculation, the AI ​​model uses deep learning to compare the user's movements with those of workers with a similar physique. The output is the movement analysis results.

[1665] Step 4:

[1666] The server proposes behavioral modifications to the user based on the analysis results. The input includes the behavioral analysis results. The data is processed to generate specific suggestions for modifications. The output is the proposed modifications.

[1667] Step 5:

[1668] The server displays the suggested corrections on the smart glasses or tablet. The input includes the suggested corrections. Specific operations include generating data for visually displaying the suggestions and sending it to the smart glasses or tablet. The output is a visually verifiable suggested correction that the user can see.

[1669] The above is the processing flow of this program.

[1670] Example 2

[1671] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1672] Conventional motion analysis systems have difficulty accurately measuring and recording a user's physique and movements, and suggesting appropriate corrections. Furthermore, there is a lack of means to suggest specific corrections to users, which has led to problems with effective improvements to movements.

[1673] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1674] In this invention, the server includes a means for measuring the user's physique, a means for recording specific movements performed by the user, an artificial intelligence means for analyzing the recorded movements and comparing them with movements of professional athletes with a similar physique, a means for suggesting to the user movement corrections based on the comparison results, and a means for presenting the corrections to the user. This makes it possible to accurately measure and record the user's physique and movements, suggest appropriate corrections, and present specific corrections to the user.

[1675] "User" refers to an individual who uses the system to measure and record their physique and movements and receive suggestions for movement modifications.

[1676] "Means for measuring body size" refers to devices or technologies that measure the dimensions and shape of a user's body in three dimensions.

[1677] "Means for recording specific actions" refers to devices or technologies that use cameras or sensors to record actions performed by users.

[1678] "Artificial intelligence means" refers to AI technology used to analyze recorded movement data and compare the user's movements with those of professional athletes.

[1679] "Means for suggesting corrections" refers to technology that extracts and suggests areas for improvement in the user's behavior based on the analysis results of artificial intelligence means.

[1680] "Means for Providing Corrections" means a device or application that visually or audibly communicates suggested corrections to the user.

[1681] "3D body scanning" refers to a technology that measures a user's physique in three dimensions.

[1682] "Deep learning" refers to a machine learning technique that uses multiple layers of neural networks used in artificial intelligence tools.

[1683] MODE FOR CARRYING OUT THE INVENTION

[1684] This invention relates to a system that measures a user's physique, records and analyzes specific movements, and suggests corrections to those movements. Specific embodiments of this system will be described below.

[1685] User's physique measurement

[1686] Users measure their physique using a 3D body scanning device. Examples of such devices include a 3D scanner and a motion capture system. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then sent to a server via wireless communication.

[1687] Recording actions

[1688] Next, the user performs a specific action, such as batting or pitching in baseball. The user's action is recorded using a camera or sensor. The hardware used may be a high-resolution camera or a motion capture system. The device generates the recorded video data or motion capture data and sends it to a server.

[1689] Behavior analysis

[1690] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The software used is a "deep learning framework." The server extracts corrections for the user's movements from the output of the AI ​​model.

[1691] Suggested behavior modifications

[1692] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[1693] Specific examples

[1694] Example 1: Correcting baseball batting form

[1695] The user measures their physique using a 3D body scanning device and sends the data to a server. Then, a high-resolution camera is used to capture their batting form, and the video data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user via a dedicated application. The user can then work on improving their batting form while viewing the application.

[1696] Example 2: Correcting pitching form

[1697] The user measures their physique using a 3D scanner and sends the data to a server. Then, a motion capture system is used to record their pitching form, and the data is uploaded to the server. The server then analyzes the data using a deep learning framework and extracts corrections. These corrections are presented to the user through a dedicated application. The user works on improving their pitching form while viewing the application.

[1698] Prompt Sentence Examples

[1699] Prompt 1: Baseball batting form

[1700] "We need to generate an AI model that takes a user's 3D body scan data and video data of their batting form as input, compares it with data from professional baseball players, and suggests corrections."

[1701] Prompt 2: Pitching form

[1702] "We need to take the user's 3D body scan data and motion capture data of their pitching form as input, compare it with data from professional baseball players, and generate an AI model that suggests corrections."

[1703] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1704] Program processing flow

[1705] Step 1: Measuring the user's physique

[1706] The user activates the 3D body scanning device. Following the device's instructions, the user rotates 360 degrees to scan their physique. The device processes the scan data in real time and generates the user's physique data. The generated data is then transmitted wirelessly to a server.

[1707] Input: User's physique scan data

[1708] Data processing: Real-time processing of scan data

[1709] Output: User's physique data

[1710] Step 2: Recording the action

[1711] Using a high-resolution camera and a motion capture system, the user performs a specific action, such as batting or pitching in baseball. The device records the action and generates video or motion capture data, which is then sent to a server.

[1712] Input: User actions

[1713] Data processing: Motion recording and data generation

[1714] Output: Video data or motion capture data

[1715] Step 3: Analyze the behavior

[1716] The server inputs the received physique data and movement data into an AI model. This AI model uses deep learning to compare the user's movements with those of professional athletes. The server extracts corrections for the user's movements from the output of the AI ​​model.

[1717] Input: physique data, movement data

[1718] Data processing: deep learning behavior comparison

[1719] Output: Behavior fixes

[1720] Step 4: Propose behavior modifications

[1721] The server sends the extracted corrections to the device, which then displays them in a dedicated application. The user can review the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[1722] Input: Behavior fixes

[1723] Data processing: Preparation for displaying corrections

[1724] Output: User is presented with the fix

[1725] Adding specific actions

[1726] Step 1: Measuring the user's physique

[1727] The user activates the 3D body scanning device and follows the device's instructions to scan their physique. The scanned data is processed in real time to generate the user's physique data, which is then transmitted wirelessly to the server.

[1728] Step 2: Recording the action

[1729] The user installs a high-resolution camera and records their batting form. The camera records high-resolution video and saves it on the device. The device then compresses the video data and uploads it to the server.

[1730] Step 3: Analyze the behavior

[1731] The server inputs the received physique data and video data into a deep learning framework. The AI ​​model compares the user's batting form with that of professional baseball players. The comparison uses features such as joint angles and movement speed. The server extracts corrections for the user's movements from the output of the AI ​​model.

[1732] Step 4: Propose behavior modifications

[1733] The server sends the extracted corrections in JSON format to the device. The device then displays the received corrections in a dedicated application. The user can check the corrections through the application and learn specific ways to improve. For example, specific instructions such as "increase the angle at the start of the bat swing by 10 degrees" are displayed.

[1734] (Application example 2)

[1735] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1736] Conventional optimization of factory robots' movements relies on manual adjustments and experience, making it difficult to achieve efficient and accurate optimization. Furthermore, because individual optimization based on the user's physique and movements is not performed, general-purpose optimization methods may not be effective enough. This creates a risk of a decline in factory productivity and quality.

[1737] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1738] In this invention, the server includes means for measuring the user's physique, means for recording specific movements performed by the user, AI means for analyzing the recorded movements and comparing them with the movements of professional athletes with a similar physique, means for suggesting to the user movement modifications based on the comparison results, means for recording and analyzing the movements of robots used in factories, and means for suggesting modifications to optimize the robot's movements. This enables individual optimization based on the user's physique and movements, making it possible to efficiently and accurately optimize the movements of factory robots.

[1739] "User" means an individual or legal entity that uses the System to measure and analyze their physique and movements.

[1740] "Physique" refers to the shape and dimensions of a user's body, as measured by means such as a 3D body scan.

[1741] A "specific action" is a specific movement or task performed by a user, which is recorded using a camera or sensor.

[1742] "Means for recording" refers to devices or methods that use cameras, sensors, etc. to save user actions as data.

[1743] "AI means" is a technology that uses artificial intelligence to analyze recorded movement data and compare the user's movements with those of professional athletes.

[1744] "Deep learning" is a type of artificial intelligence that uses multi-layered neural networks to analyze and learn from data.

[1745] A "professional athlete" is a professional athlete who is active in a particular sport and is the subject to be compared with the user's movements.

[1746] "Behavior Modifications" are specific improvements suggested to optimize a user's behavior.

[1747] "Robots used in factories" are automated machinery used on factory production lines and in workshops.

[1748] "Optimization" means adjusting something to the most efficient and effective state for a specific purpose.

[1749] To implement this invention, the following hardware and software are required. The hardware includes a 3D scanner, camera, sensor, server, and user terminal (smartphone or tablet). The software includes Python, OpenCV, Keras, etc.

[1750] First, the user measures their body size using a 3D scanner, which captures the user's body shape and dimensions wi...

Claims

[Claim 1] means for acquiring physique data obtained by measuring the physique of a user from a terminal of the user; means for acquiring image data representing a specific action performed by the user from the terminal; A means for inputting the image data into a generative AI model, and the generative AI model outputting the user's motion feature amount; a means for inputting a prompt sentence to the generative AI model, instructing the model to suggest corrections to the user's movements based on the physique data and the movement feature amount, and for the generative AI model to compare the movement feature amount with the movements of a professional athlete having a physique similar to that of the user, and outputting a comparison result including corrections to the user's movements; A means for presenting on the terminal the correction points for the user's actions output by the generative AI model; a means for inputting at least one of the facial expression and tone of voice of the user acquired via the terminal into an emotion engine, and for the emotion engine to recognize the emotion of the user; means for reevaluating points for correcting the user's actions based on the recognized emotions of the user, and presenting the reevaluated points for correcting the actions on the terminal in a form that is easy for the user to accept; A system including:

Images