System

Abstract

To provide a system.SOLUTION: A system includes: means for receiving and analyzing performance data of a user; means for generating a training plan using a generative AI model based on a result of analyzing the performance data; and means for displaying the generated training plan to the user.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] In sports, what is generally considered correct form does not necessarily suit everyone, but there is no system that provides optimal training methods tailored to each individual's physical characteristics. [Means for solving the problem]

[0005] The system analyzes the user's physical characteristics and provides a training plan that maximizes their potential based on the results. It also generates videos corresponding to the training plan and provides them to the user. It also provides a system that can converse with the user in real time, providing guidance in easy-to-understand language. [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 Example 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 Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 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] The system of the present invention is equipped with input devices such as sensors and cameras for analyzing the user's physical characteristics. These devices measure the user's physical characteristics, such as height, weight, shoulder width, arm length, etc., and transmit the data to the system. The system analyzes the user's physical characteristics based on the transmitted data.

[0029] "Example 2"

[0030] Based on the analysis results, the system will provide a training plan that maximizes the player's performance. For example, if a baseball player is determined to be better suited to sidearm pitching rather than overhand pitching, the system will provide a training plan for sidearm pitching. This training plan will include specific exercises, their order, and frequency of practice.

[0031] "Example 3"

[0032] To generate videos corresponding to the training plan, the system uses 3D modeling technology. Specifically, it generates a 3D model based on the user's physical characteristics and then uses that model to generate training videos. The generated videos serve as a reference for the user when practicing.

[0033] "Example 4"

[0034] The system has the ability to converse with users in real time. This function is realized using voice recognition technology and natural language processing technology. Users can ask the system questions and request feedback during practice. The system responds to questions and requests from users in easy-to-understand language.

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: Activate input devices such as sensors and cameras to measure the user's physical characteristics.

[0038] Step 2: Measure the user's physical characteristics, such as height, weight, shoulder width, arm length, etc. Step 3: Send the measured data to the system.

[0039] Step 4: The system analyzes the user's physical characteristics based on the transmitted data.

[0040] "Example 2"

[0041] Step 1: The system generates an optimal training plan based on the analysis results.

[0042] Step 2: Provide the generated practice plan to the user.

[0043] "Example 3"

[0044] Step 1: The system generates a 3D model based on the user's physical characteristics.

[0045] Step 2: Generate a practice video using the generated 3D model.

[0046] Step 3: Serving the generated video to the user.

[0047] "Example 4"

[0048] Step 1: The system activates the voice recognition function.

[0049] Step 2: The user can ask the system questions and seek feedback during the exercise.

[0050] Step 3: The system responds to the user's questions and requests in easy-to-understand language.

[0051] Example 1

[0052] 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."

[0053] Conventional systems have difficulty accurately measuring and analyzing a user's physical characteristics. Furthermore, the collection of measurement data and the provision of analysis results are not performed in real time, making them inconvenient for users. Furthermore, interaction with users is limited, making it difficult to respond to individual needs.

[0054] 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.

[0055] In this invention, the server includes means for using an input device to measure a user's physical characteristics, means for collecting measurement data, means for transmitting the collected data to the server, means for the server to analyze the data, means for generating analysis results, means for providing the generated analysis results to the user, and means for interacting with the user in real time. This makes it possible to accurately measure a user's physical characteristics and provide the analysis results in real time. Furthermore, through interaction with the user, it is possible to provide services that meet individual needs.

[0056] "User's physical characteristics" refers to specific data about the user's body, such as height, weight, shoulder width, and arm length.

[0057] An "input device" is a hardware device, such as a sensor or camera, that is used to measure a user's physical characteristics.

[0058] "Measurement Data" is data about a user's physical characteristics collected using an input device.

[0059] A "server" is a computer system for receiving and analyzing measurement data, and generating and providing analysis results.

[0060] "Means for analyzing data" means the process of using algorithms or machine learning models to identify the user's physical characteristics based on the received measurement data.

[0061] "Analysis results" are detailed information about the user's physical characteristics generated by the data analysis means.

[0062] "Real-time interaction means" refers to interfaces and technologies that allow for instant communication with users.

[0063] This invention provides a system for accurately measuring a user's physical characteristics and providing analysis results in real time. The system includes an input device for measuring the user's physical characteristics, a terminal for collecting measurement data, a server for analyzing the collected data, and a means for providing the user with the analysis results.

[0064] The system's program is structured as follows: First, the user logs in to the system and measures their physical characteristics using input devices such as a 3D scanner and a weighing scale. These input devices collect data such as the user's height, weight, shoulder width, and arm length. Specific hardware used includes a 3D scanner, high-resolution camera, and weighing scale.

[0065] The device transmits the data collected from these input devices to a server in real time using a secure communication protocol (e.g., HTTPS). The server uses Python scripts and machine learning libraries such as TENSORFLOW to analyze the received data. The analysis results are generated as a report detailing the user's physical characteristics.

[0066] As a concrete example, consider a scenario in which a user uses the system to measure their own physical characteristics. The user stands in front of a 3D scanner connected to the system and steps on a scale. The camera takes a picture of the user's entire body, and the sensor measures the user's weight. These data are then sent to the server via the device.

[0067] The server analyzes the received data and calculates the user's height, weight, shoulder width, arm length, etc. The analysis results are generated as a report detailing the user's physical characteristics. The generated report is sent to the user's device, where the user can view the report.

[0068] An example of a prompt to input to a generative AI model is as follows:

[0069] Use a 3D scanner and scale to collect data to analyze your users' physical characteristics, calculate their height, weight, shoulder width, and arm length, and generate a detailed report.

[0070] Using this prompt, the generative AI model can accurately analyze the user's physical characteristics and provide the necessary information.

[0071] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0072] Step 1:

[0073] A user logs in to the system.

[0074] Input: Username and Password

[0075] Output: Authentication result (success or failure)

[0076] Specific operation: The user enters the username and password on the system login screen and clicks the "Login" button. The system checks the entered authentication information and authenticates the user. If authentication is successful, the user can proceed to the next step.

[0077] Step 2:

[0078] The user uses an input device to measure a physical characteristic.

[0079] Input: User's physical characteristics (height, weight, shoulder width, arm length, etc.)

[0080] Output: Measurement data

[0081] How it works: The user stands in front of a 3D scanner connected to the system and steps on a scale. The camera captures a full-body image of the user, and the sensor measures the user's weight. The user must remain still until the measurement is complete.

[0082] Step 3:

[0083] The device collects the measurement data.

[0084] Input: Measurement data from a 3D scanner and a weighing scale

[0085] Output: Collected measurement data

[0086] How it works: The device collects data from the 3D scanner and the scale in real time, and stores the collected data in temporary memory on the device.

[0087] Step 4:

[0088] The terminal transmits the collected data to the server.

[0089] Input: Collected measurement data

[0090] Output: Data sent to the server

[0091] Specific operation: The device encrypts the collected data and sends it to the server using the HTTPS protocol. Once the data transmission is complete, the device displays a notification.

[0092] Step 5:

[0093] The server analyzes the data.

[0094] Input: Measurement data sent to the server

[0095] Output: Analysis results

[0096] Specific Operation: The server runs a Python script to analyze the received data. The script applies a machine learning model using the TENSORFLOW library to identify the user's physical characteristics.

[0097] Step 6:

[0098] The server generates the analysis results.

[0099] Input: Parsed data

[0100] Output: Analysis result report

[0101] Specific operation: The server generates a report detailing the user's physical characteristics based on the analysis results. The report is generated in PDF format and saved on the server for the user to download.

[0102] Step 7:

[0103] The server provides the analysis results to the user.

[0104] Input: Generated analysis result report

[0105] Output: Report provided to the user

[0106] Specific operation: The server sends a download link for the generated report to the user's device. The user can click the link on the device to download and view the report.

[0107] (Application example 1)

[0108] 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."

[0109] Traditional try-on services in physical stores require users to actually try on the clothes, which is time-consuming and laborious. Additionally, there is a lack of systems that suggest optimal clothing sizes based on the user's physical characteristics, making it difficult for users to find clothes that fit them. Furthermore, the results of virtual try-on services are not provided in real time, which can lead to lower user satisfaction.

[0110] 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.

[0111] In this invention, the server includes means for analyzing the user's physical characteristics, means for proposing optimally sized clothing based on the analysis results, means for the user to virtually try on the clothing selected by the user, means for providing the user with the results of the virtual try-on, and means for interacting with the user in real time. This allows the user to efficiently find clothing that suits them even in a physical store, and improves user satisfaction because the user can check the results of the try-on in real time through the virtual try-on.

[0112] "Means for analyzing the user's physical characteristics" refers to a device or system that uses input devices such as sensors or cameras to measure the user's physical characteristics such as height, weight, shoulder width, arm length, etc., and performs analysis based on that data.

[0113] The "means for proposing optimally sized clothing based on analysis results" refers to a device or system that selects and proposes optimally sized clothing to a user based on the analysis results of the user's physical characteristics.

[0114] "Means for virtually trying on clothes selected by a user" refers to a device or system that allows a user to try on clothes selected by a user in a virtual space, while fitting the clothes to the user's body.

[0115] The "means for providing the user with the results of the virtual try-on" refers to a device or system that displays and provides the user with the results of the virtual try-on as images or videos.

[0116] A "means for interacting with a user in real time" is a device or system that communicates with a user in real time and responds to the user's questions and requests.

[0117] The system for implementing the present invention includes a series of means for analyzing the user's physical characteristics, proposing clothes of the most suitable size, and performing virtual fitting. Specific embodiments of this system will be described below.

[0118] First, the user captures their own physical characteristics using the smartphone camera. The smartphone is equipped with image processing libraries such as OpenCV (registered trademark), which captures video in real time. The captured video is input into a generative AI model called "Body Measurement Model," which analyzes the user's physical characteristics, such as height, weight, shoulder width, and arm length.

[0119] Next, based on the analysis results, the system recommends the best-fit clothing size for the user. The suggestions are made by a generative AI model called "VirtualTryOnModel," which selects the clothing size that best suits the user's physical characteristics.

[0120] To allow users to virtually try on the selected clothing, the system uses "VirtualTryOnModel" to generate a virtual try-on image that matches the user's body. This virtual try-on image is displayed on the smartphone screen and provided to the user.

[0121] Furthermore, the system has a means of interacting with users in real time: when a user inputs a question or request, the system uses a generative AI model to generate an appropriate response and provide it to the user, allowing the user to receive feedback in real time.

[0122] For example, by inputting the following prompts into a generative AI model, it can suggest the optimal clothing size for the user:

[0123] "The user is 170cm tall, weighs 65kg, has a shoulder width of 45cm, and has an arm length of 60cm. Based on this data, please suggest the best clothing size for them."

[0124] In this way, users can efficiently find clothes that suit them even in physical stores, and can check the fitting results in real time through virtual try-on, thereby increasing user satisfaction.

[0125] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0126] Step 1:

[0127] The user captures their physical characteristics using the smartphone camera.

[0128] Input: Smartphone camera image

[0129] Output: Image data of the captured user's physical characteristics

[0130] How it works: The user turns on the smartphone camera and takes a full-body photo of themselves. The smartphone captures the video in real time and saves it as image data.

[0131] Step 2:

[0132] The device inputs the captured image data into a generated AI model called "Body Measurement Model" to analyze the user's physical characteristics.

[0133] Input: Captured image data of the user's physical characteristics

[0134] Output: Analysis result data such as user's height, weight, shoulder width, arm length, etc.

[0135] Specific operation: The device inputs image data into the "Body Measurement Model," which then processes and analyzes the image. As a result of the analysis, the user's physical characteristics are output as numerical data.

[0136] Step 3:

[0137] Based on the analysis results, the server uses a generative AI model called "VirtualTryOnModel" to suggest the optimal size of clothing.

[0138] Input: Analysis result data of user's physical characteristics

[0139] Output: Data suggesting optimal clothing sizes

[0140] Specific operation: The server inputs the analysis results data into "VirtualTryOnModel," which then selects the clothing size that best suits the user's physical characteristics. As a result, information on the optimal clothing size is output.

[0141] Step 4:

[0142] To allow users to virtually try on the selected clothes, the device uses "VirtualTryOnModel" to generate a virtual try-on image.

[0143] Input: Analysis result data of user's physical characteristics, data of selected clothing

[0144] Output: Virtual try-on image

[0145] Specific operation: The device inputs the user's physical characteristics data and selected clothing data into "VirtualTryOnModel," which then generates a virtual try-on image tailored to the user's body in the virtual space. The generated virtual try-on image is then output.

[0146] Step 5:

[0147] The device provides the user with the results of the virtual try-on.

[0148] Input: Virtual try-on image

[0149] Output: Virtual try-on image displayed to the user

[0150] Specific operation: The device displays the generated virtual try-on image on the smartphone screen and provides it to the user, who can then check the results of the virtual try-on on the screen.

[0151] Step 6:

[0152] The server interacts with the user in real time and generates responses based on questions and requests.

[0153] Input: User question or request

[0154] Output: The generated response data

[0155] How it works: When a user inputs a question or request through their smartphone, the server uses the generative AI model to generate an appropriate response, and the generated response data is provided to the user.

[0156] Example 2

[0157] 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."

[0158] Conventional training plan providing systems were unable to fully utilize the user's physical characteristics and performance data, making it difficult to provide optimal training plans for each individual user. Furthermore, the generated training plans were not specific, making it unclear how the user should actually proceed with their training. Furthermore, the lack of real-time feedback and dialogue made it difficult for users to maintain motivation and practice effectively.

[0159] 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.

[0160] In this invention, the server includes a means for analyzing a user's physical characteristics, a means for providing a training plan that maximizes the user's performance based on the analysis results, a means for generating a video corresponding to the training plan, a means for providing the generated video to the user, a means for interacting with the user in real time, a means for receiving and analyzing the user's performance data, a means for generating an optimal training plan using a generative AI model, and a means for displaying the generated training plan to the user. This makes it possible to provide an optimal training plan based on the user's individual characteristics and clearly show specific training content. Furthermore, real-time feedback and interaction can maintain the user's motivation and enable effective training.

[0161] "User" refers to an individual who utilizes the System to provide their performance data and receive an optimized training plan.

[0162] "Physical characteristics" refers to information about the user's body, such as the user's physique, muscle strength, flexibility, and movement patterns.

[0163] "Performance data" refers to data related to the user's exercise and movements, including, for example, pitching form, speed, accuracy, and past practice history.

[0164] "Means of analysis" refers to the technology and methods used to analyze a user's physical characteristics and performance data and derive the optimal training plan for the user.

[0165] "Practice plan" refers to a plan that includes specific menus, their order, and frequency of practice for the user to practice effectively.

[0166] "Means for generating videos" refers to the technology or method for creating videos corresponding to the user's training plan.

[0167] "Real-time interaction means" refers to technologies and methods for communicating with users in real time and providing feedback and instructions.

[0168] A "generative AI model" refers to a model that uses artificial intelligence to analyze user data and generate an optimal training plan.

[0169] "Display means" refers to the technology or method for visually presenting the generated practice plan or video to the user.

[0170] MODE FOR CARRYING OUT THE INVENTION

[0171] The present invention is a system that provides an optimal training plan based on a user's physical characteristics and performance data. Specific embodiments of this system are described below.

[0172] System configuration

[0173] The system includes a user's terminal, a server, a generative AI model, and a communication protocol for linking these components. The terminal is a device such as a smartphone or tablet that the user uses to input data and check the training plan. The server is a computer system that analyzes the data and generates the training plan. The generative AI model is an artificial intelligence model that generates the optimal training plan based on the user's data.

[0174] Hardware and software used

[0175] The server uses Python® and TENSORFLOW® to analyze data and run generative AI models. The device inputs user data through a dedicated application and communicates with the server. Communication uses a secure protocol (e.g., HTTPS).

[0176] Data entry and analysis

[0177] Users use the device to input their own performance data, including videos of their pitching form, pitching speed, accuracy, and past training history. The device then transmits this data to a server, which analyzes the received data and evaluates the user's physical characteristics and performance.

[0178] Generate a practice plan

[0179] The server then uses the generative AI model to generate an optimal training plan based on the analysis results. For example, if the user is a baseball player and it is determined that sidearm pitching is suitable, the server generates a sidearm pitching training plan. This training plan includes warm-ups, repeated practice of basic movements, practice in a real-game setting, and a cool-down.

[0180] Providing practice plans

[0181] The server sends the generated practice plan to the device, which displays the plan to the user, allowing the user to check the plan on the screen. This allows the user to understand the specific practice content and practice effectively.

[0182] Specific examples

[0183] The user uses a smartphone to film a video of their pitching form and uploads it to a dedicated application. The device then sends this video data, along with pitching speed and accuracy data, to a server. The server then analyzes the video data using Python (registered trademark) and TENSORFLOW (registered trademark) to identify the user's pitching style. Based on the analysis results, a generative AI model is used to generate a sidearm pitching practice plan. The generated practice plan includes a warm-up (10 minutes), repetitive practice of basic sidearm pitching movements (30 minutes), practice in a real-game setting (20 minutes), and a cool-down (10 minutes). The server then sends this practice plan to the device, which then displays it to the user. The user can review and execute the practice plan on their smartphone screen.

[0184] Prompt Sentence Examples

[0185] "Generate an optimal training plan based on the user's pitching form and performance data. The user is a baseball player and is suitable for a sidearm pitch. The training plan should include specific exercises, their order, and frequency."

[0186] In this way, the system provides users with an optimal training plan and supports effective practice.

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

[0188] Step 1:

[0189] The user enters performance data into a terminal.

[0190] Users use a smartphone or tablet to input data such as video of their pitching form, pitching speed, accuracy, and past training history. The input data is then saved on the device via a dedicated application.

[0191] Input: Video of pitching form, pitching speed, accuracy, past practice history

[0192] Output: Performance data stored on the device

[0193] Step 2:

[0194] The terminal sends the input data to the server.

[0195] The terminal transmits the performance data entered by the user to the server using a secure communication protocol (e.g., HTTPS), which ensures the safety of the data.

[0196] Input: Performance data stored on the device

[0197] Output: Performance data sent to the server

[0198] Step 3:

[0199] The server receives the data and begins analyzing it.

[0200] The server receives performance data sent from the device. It uses Python (registered trademark) and TENSORFLOW (registered trademark) to analyze the received data. The server analyzes the video of the pitching form to identify the user's pitching style. It also evaluates the user's performance based on pitching speed and accuracy data.

[0201] Input: Performance data sent to the server

[0202] Output: Analysis results (pitching style, performance evaluation)

[0203] Step 4:

[0204] The server uses the generated AI model to generate an optimal training plan.

[0205] The server then uses the generative AI model to generate an optimal training plan based on the analysis results. For example, if a sidearm pitch is determined to be suitable, it generates a sidearm pitch training plan. This training plan includes warm-ups, repeated practice of basic movements, practice in a real-game setting, and a cool-down.

[0206] Input: Analysis results (pitching style, performance evaluation)

[0207] Output: Generated practice plan

[0208] Step 5:

[0209] The server transmits the generated training plan to the terminal.

[0210] The server transmits the generated training plan to the device using a secure communication protocol, which protects the user's privacy.

[0211] Input: Generated practice plan

[0212] Output: Practice plan sent to the device

[0213] Step 6:

[0214] The terminal displays the training plan to the user.

[0215] The device displays the training plan received from the server to the user. The user can check and execute the training plan on the device screen. For example, the user can check the training menu, its order, and the frequency of training through a smartphone application.

[0216] Input: Practice plan sent to device

[0217] Output: The practice plan displayed to the user

[0218] In this way, the system provides the user with an optimal training plan and supports effective training.

[0219] (Application example 2)

[0220] 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."

[0221] Conventional training plan providing systems can provide optimal training plans based on the user's physical characteristics, but they have the problem of not being able to analyze machine movement data, find optimal movement patterns, and provide training plans based on those movement patterns. Furthermore, they lack the functionality to provide the generated movement procedures to the machine and to communicate with the machine in real time, which makes it difficult to achieve efficient training.

[0222] 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.

[0223] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for analyzing machine motion data and finding optimal motion patterns, means for providing a training plan based on the optimal motion patterns, means for generating motion procedures corresponding to the training plan, means for providing the generated motion procedures to the machine, and means for interacting with the machine in real time. This allows for the provision of optimal training plans for both the user and the machine, enabling efficient training.

[0224] "Means for analyzing the user's physical characteristics" refers to a device or software for collecting and analyzing data on the user's physical characteristics, such as physique, muscle strength, and flexibility.

[0225] "Means for providing a training plan that maximizes the effectiveness of the analysis results" refers to a device or software that creates and provides optimal training menus and training plans to users based on the results of an analysis of their physical characteristics.

[0226] "Means for generating videos corresponding to a training plan" refers to a device or software that creates videos based on a provided training plan to serve as a reference for the user when carrying out the plan.

[0227] The "means for providing the generated video to the user" refers to a device or software for visually providing the generated video to the user.

[0228] A "means for interacting with a user in real time" is a device or software that communicates with a user in real time and provides feedback and instructions.

[0229] "Means for analyzing machine operation data and finding optimal operation patterns" refers to devices or software that collect and analyze machine operation data and identify optimal operation patterns from that data.

[0230] The "means for providing a training plan based on an optimal movement pattern" is a device or software for creating and providing an efficient training plan for a machine based on the analyzed optimal movement pattern.

[0231] The "means for generating an operating procedure corresponding to a training plan" refers to a device or software for generating specific operating procedures for a machine to execute a provided training plan based on the plan.

[0232] The "means for providing the generated operating procedure to a machine" refers to a device or software that transmits the generated operating procedure to a machine and causes the machine to execute the operation.

[0233] A "means for interacting with a machine in real time" is a device or software that communicates with a machine in real time and provides feedback and instructions.

[0234] The following system configuration will be described as an embodiment of the present invention.

[0235] The server uses a data collection device and analysis software to analyze the user's physical characteristics. Specifically, it collects data such as the user's physique, muscle strength, and flexibility, and uses Scikit-learn, a Python (registered trademark) library, to analyze this data. Based on the analysis results, it implements an algorithm to provide the user with an optimal training plan. This algorithm generates a training menu and training plan tailored to the user's characteristics.

[0236] Next, video generation software is used to generate a video corresponding to the training plan. For example, libraries such as OpenCV® or FFmpeg are used to create a video that mimics the user's movements. This video serves as a reference for the user as they execute the training plan.

[0237] The generated videos are provided to users, who can watch them on their smartphones, tablets, or other devices. Furthermore, chatbots and voice assistants are implemented to interact with users in real time, allowing users to receive feedback and instructions during practice.

[0238] Meanwhile, the server collects machine operation data using sensors and cameras to analyze it and find optimal operation patterns. To analyze this data, it again uses machine learning libraries such as Scikit-learn. Based on the analysis results, it implements an algorithm that provides the machine with an optimal training plan.

[0239] To generate the motion procedures corresponding to the training plan, motion simulation software is used. For example, ROS (Robot Operating System) is used to create the specific motion procedures that the machine will follow when executing the training plan. The generated motion procedures are provided to the machine, which then performs the operations according to them.

[0240] Additionally, it implements an interface for real-time interaction with the machine, allowing it to monitor its operating status and provide feedback and instructions as needed.

[0241] As a concrete example, consider a system that analyzes the motion data of a factory robot, finds the optimal motion pattern, and provides a training plan based on that motion pattern. For example, if it is determined that the motion of a robot's arm lifting a particular part is inefficient, the system analyzes the motion data to find the optimal way to lift it, and provides a training plan to help the robot learn that lifting method.

[0242] Examples of prompts for a generative AI model might include:

[0243] "Analyze the motion data of factory robots, find the optimal motion patterns, and generate a training plan based on those motion patterns. The training plan should include specific motion steps, frequency, and sequence."

[0244] In this way, an optimal training plan can be provided for both the user and the machine, enabling efficient training.

[0245] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0246] Step 1:

[0247] The server collects the user's physical characteristics. Specifically, it uses sensors and cameras to obtain data on the user's physique, muscle strength, flexibility, etc. The input is the user's physical characteristics data, and the output is the collected data.

[0248] Step 2:

[0249] The server analyzes the collected physical characteristic data. Specifically, it uses the Python (registered trademark) Scikit-learn library to standardize the data and perform clustering and regression analysis. The input is the collected physical characteristic data, and the output is the analysis results.

[0250] Step 3:

[0251] The server generates an optimal training plan for the user based on the analysis results. Specifically, it uses an algorithm to create a training menu and training plan tailored to the user's characteristics. The input is the analysis results, and the output is the training plan.

[0252] Step 4:

[0253] The server generates a video corresponding to the training plan. Specifically, it uses libraries such as OpenCV (registered trademark) and FFmpeg to create a video that mimics the user's appearance. The input is the training plan, and the output is the generated video.

[0254] Step 5:

[0255] The device provides the generated video to the user, specifically, allowing the user to watch the video through a smartphone or tablet. The input is the generated video, and the output is the video that the user watches.

[0256] Step 6:

[0257] The server interacts with the user in real time, using a chatbot or voice assistant to provide feedback and instructions to the user. The input is the user's question or feedback, and the output is the server's response.

[0258] Step 7:

[0259] The server collects machine operation data. Specifically, it acquires the machine operation data using sensors and cameras. The input is the machine operation data, and the output is the collected data.

[0260] Step 8:

[0261] The server analyzes the collected behavioral data. Specifically, it uses the Scikit-learn library to standardize the data and perform clustering and regression analysis. The input is the collected behavioral data, and the output is the analysis results.

[0262] Step 9:

[0263] The server generates an optimal training plan for the machine based on the analysis results. Specifically, it uses an algorithm to create training menus and movement patterns that match the machine's characteristics. The input is the analysis results, and the output is the training plan.

[0264] Step 10:

[0265] The server generates an operating procedure corresponding to the training plan. Specifically, it uses ROS (Robot Operating System) to create a specific operating procedure for the machine to execute the training plan. The input is the training plan, and the output is the operating procedure.

[0266] Step 11:

[0267] The server provides the generated operation procedure to the machine. Specifically, it transmits the operation procedure to the machine and causes it to execute the operation. The input is the operation procedure, and the output is the operation executed by the machine.

[0268] Step 12:

[0269] The server interacts with the machine in real time. Specifically, it monitors the machine's operating status and provides feedback and instructions as needed. The input is the machine's operating status data, and the output is feedback and instructions from the server.

[0270] Example 3

[0271] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0272] Conventional training planning systems have difficulty providing training plans and videos that fully take into account the user's physical characteristics, and lack real-time interactive functionality. As a result, they lack support for users to practice effectively. Furthermore, it is difficult to provide appropriate responses to users' questions and feedback.

[0273] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0274] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating a 3D model corresponding to the training plan, means for generating a training video using the generated 3D model, means for providing the generated video to the user, means for interacting with the user in real time, means for converting the user's voice into text, means for generating appropriate responses using a generative AI model, and means for communicating the generated responses to the user by voice. This makes it possible to provide training plans and videos based on the user's physical characteristics, and further to provide appropriate responses to the user's questions and feedback through real-time interaction.

[0275] "Means for analyzing the user's physical characteristics" refers to a device or software that analyzes data such as height, weight, and body type provided by the user and identifies the user's physical characteristics.

[0276] The "means for providing an exercise plan" refers to a device or software that creates and provides an optimal exercise plan to a user based on the results of an analysis of the user's physical characteristics.

[0277] A "means for generating a three-dimensional model" is a device or software for creating a three-dimensional model of a user's appearance based on the user's physical characteristics.

[0278] The "means for generating a practice video" is a device or software for using the generated three-dimensional model to create a video corresponding to the user's practice plan.

[0279] The "means for providing the generated video to the user" refers to a device or software for visually providing the created practice video to the user.

[0280] A "means for real-time user interaction" is any device or software that allows a user to communicate with the system in real-time.

[0281] A "means for converting user speech to text" is a device or software for converting user speech into text data.

[0282] A "means for generating an appropriate response using a generative AI model" is a device or software that receives text data as input and uses an artificial intelligence model to generate an appropriate response.

[0283] A "means for audibly communicating a generated response to a user" is a device or software for converting a generated text response into speech and communicating it to a user.

[0284] The present invention provides a system that provides a training plan based on the user's physical characteristics, generates a 3D model to create a training video, and interacts with the user in real time. Specific embodiments of this system are described below.

[0285] Hardware and software used

[0286] server

[0287] The server performs processing using the following software and technology:

[0288] 3D Modeling Software: Using three-dimensional modeling software such as Blender, a three-dimensional model based on the user's physical characteristics is generated.

[0289] Generative AI models: Generative AI models, such as OpenAI's GPT-3, are used to generate appropriate responses to user questions.

[0290] Speech recognition technology: Converts the user's voice into text using Google® Cloud Speech-to-Text API, etc.

[0291] Terminal

[0292] The terminal performs processing using the following software and technologies:

[0293] Speech synthesis technology: Uses technology to convert text data into speech and communicate it to the user.

[0294] Data processing and calculation

[0295] Entering the user's physical characteristics

[0296] Users input their physical characteristics data (height, weight, body type, etc.) through the system interface, which is then sent to the server for analysis.

[0297] 3D model generation

[0298] The server receives the user's physical characteristic data and uses 3D modeling software such as Blender to generate a 3D model of the user, which faithfully reproduces the user's physical characteristics.

[0299] Creating practice videos

[0300] The server uses the generated 3D model to create a training video. For example, if a user wants to practice push-ups, the server simulates the push-up movement on the 3D model and outputs it as a video. This video serves as a reference for the user to practice with the correct form.

[0301] Real-time interaction

[0302] During practice, users can ask the system questions and seek feedback. For example, they can ask questions such as, "Is this form correct?" by voice. The device converts the user's voice into text using the Google® Cloud Speech-to-Text API and sends it to the server. The server inputs the received text data into OpenAI®'s GPT-3® to generate an appropriate response. The generated response is then communicated to the user by voice via the device.

[0303] Examples of concrete examples and prompts

[0304] Specific examples

[0305] If a user asks, "What is the correct form for squats?", the process goes like this:

[0306] 1. The user asks aloud, "What is the correct form for squatting?"

[0307] 2. The device converts the speech into text using the Google® Cloud Speech-to-Text API.

[0308] 3. The server inputs the converted text into OpenAI's GPT-3 to generate an appropriate response.

[0309] 4. The device communicates the generated response to the user using speech synthesis technology.

[0310] Prompt Sentence Examples

[0311] "If a user asks about the correct form for squats, generate an appropriate response."

[0312] By inputting this prompt sentence into the generative AI model, the system generates an appropriate response to the user's question. The flow of the identification process in Example 3 will be described with reference to FIG.

[0313] Step 1:

[0314] The user inputs the physical characteristic data.

[0315] The user inputs their own physical characteristics data (height, weight, body type, etc.) through the system interface. This data is sent to the server. The input data becomes the basis for identifying the user's physical characteristics.

[0316] Step 2:

[0317] The server analyzes the user's physical characteristics.

[0318] The server analyzes the received physical characteristic data to identify the user's physical characteristics. Specifically, it uses data analysis algorithms to analyze information such as the user's height, weight, and body type. The output is a dataset that details the user's physical characteristics.

[0319] Step 3:

[0320] The server generates the three-dimensional model.

[0321] Based on the analysis results, the server uses 3D modeling software such as Blender to generate a 3D model of the user. The input is the user's physical characteristics data, and the output is a 3D model that mimics the user's appearance. Specifically, the modeling software is used to reproduce the user's body shape and posture.

[0322] Step 4:

[0323] The server generates the practice video.

[0324] The server uses the generated 3D model to create a video corresponding to the user's practice plan. The input is the 3D model and the practice plan, and the output is a practice video. Specifically, the server simulates practice movements for the 3D model and outputs the video.

[0325] Step 5:

[0326] The server provides the generated video to the user.

[0327] The server provides the generated practice video to the user. The input is the practice video, and the output is a video file that the user can visually check. Specifically, the server sends the video file to the user's device.

[0328] Step 6:

[0329] Ask questions and ask for feedback as users practice.

[0330] The user can ask the system questions and request feedback during practice. The input is the user's voice, and the output is voice data. Specifically, the user inputs questions by voice through a microphone.

[0331] Step 7:

[0332] The device converts the user's speech into text.

[0333] The device converts the user's voice into text using the Google® Cloud Speech-to-Text API. The input is the user's voice data, and the output is text data. Specifically, the device converts the voice data into text in real time using voice recognition technology.

[0334] Step 8:

[0335] The server generates an appropriate response.

[0336] The server inputs the received text data into OpenAI's GPT-3 (registered trademark) to generate an appropriate response. The input is text data, and the output is response text. Specifically, the server uses a generative AI model to generate an appropriate response to the user's question.

[0337] Step 9:

[0338] The terminal then vocalizes the generated response to the user.

[0339] The terminal communicates the generated response to the user using speech synthesis technology. The input is the response text, and the output is voice data. Specifically, the text data is converted into voice and communicated to the user through a speaker.

[0340] (Application example 3)

[0341] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0342] Conventional training planning systems have difficulty providing training plans that fully take into account the user's physical characteristics, and lack real-time interactive functionality. This has led to issues such as users being unable to practice efficiently, reducing the effectiveness of their training. Furthermore, there has been a lack of systems that can reproduce the movements of factory workers and efficiently learn work procedures.

[0343] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0344] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for generating a 3D model based on the user's physical characteristics, means for generating a training video for work procedures using the generated 3D model, means for responding to the user's questions and feedback using speech recognition technology and natural language processing technology, and means for responding to the user by voice using speech synthesis technology. This enables the provision of a training plan that takes the user's physical characteristics into consideration and efficient training through real-time interaction.

[0345] "Means for analyzing the user's physical characteristics" refers to devices or software for collecting and analyzing physical data such as the user's height, weight, age, and body type.

[0346] "Means for providing a training plan that maximizes the user's performance" refers to devices and software that design and provide a training program that is optimized for the user's physical characteristics and abilities based on the analysis results.

[0347] "Means for generating videos corresponding to a practice plan" refers to a device or software that creates videos that can be used as a reference when a user actually practices based on the provided practice plan.

[0348] The "means for providing the generated video to the user" refers to a device or software for visually providing the generated practice video to the user.

[0349] "Means for interacting with users in real time" refers to devices or software that allow users to respond in real time when they ask questions or give feedback to the system.

[0350] "Means for generating a 3D model based on the user's physical characteristics" refers to devices or software for creating a 3D model of a user based on the user's physical characteristics.

[0351] "Means for generating a practice video of work procedures using a generated 3D model" refers to a device or software that uses a generated 3D model to create a practice video that shows the work procedures that a user should perform.

[0352] "Means for responding to user questions and feedback using speech recognition technology and natural language processing technology" refers to devices and software that recognize a user's speech, analyze the content using natural language processing technology, and generate appropriate responses.

[0353] "Means for responding to a user by voice using speech synthesis technology" refers to a device or software for conveying the generated response to the user as voice.

[0354] The system for implementing this invention analyzes the user's physical characteristics, provides an optimal exercise plan based on the analysis, and generates videos corresponding to the exercise plan. The system also has the ability to interact with the user in real time and respond to the user's questions and feedback.

[0355] Hardware and software used

[0356] Hardware:

[0357] Camera: Used to scan the user's physical characteristics.

[0358] Microphone: Used to recognize the user's voice.

[0359] Speaker: Used to communicate system audio responses to the user.

[0360] software:

[0361] 3D Modeling Library: Generates 3D models based on the user's physical characteristics.

[0362] Video generation library: Generate practice videos using generated 3D models.

[0363] Speech Recognition Library (speech_recognition): Recognizes the user's voice.

[0364] Natural language processing library: Analyzes user questions and generates appropriate responses.

[0365] Speech synthesis library (pyttsx3): Outputs the generated response as voice.

[0366] Specific operation of the system

[0367] 1. Scanning your physical characteristics:

[0368] It uses a camera to scan the user's physical features and collects data, which is then analyzed using a 3D modeling library to generate a 3D model of the user.

[0369] 2. Providing a practice plan:

[0370] Based on the generated 3D model and the user's physical characteristics, an optimal training plan is designed, which is customized to the user's abilities and goals.

[0371] 3. Generate practice videos:

[0372] Using a video generation library, videos corresponding to the practice plan are generated, which serve as a reference for users when actually practicing.

[0373] 4. Real-time interaction:

[0374] The system recognizes the user's voice using a microphone and analyzes it using a speech recognition library. The analyzed voice data is interpreted as questions or feedback using a natural language processing library. The system generates an appropriate response and responds aloud through the speaker using a speech synthesis library.

[0375] Specific examples

[0376] New workers use the system to learn factory procedures: they ask the system, "What should I do next?", and the system responds, "Next, place the item on the conveyor belt."

[0377] Prompt Sentence Examples

[0378] User: What should I do next?

[0379] System: Next, place the item on the conveyor belt.

[0380] In this way, the system provides a training plan that takes into account the user's physical characteristics and enables efficient training through real-time dialogue.

[0381] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0382] Step 1:

[0383] Scanning the user's physical characteristics

[0384] The server uses a camera to scan the user's physical characteristics. As input, it takes the user's physical data (height, weight, body type, etc.) and analyzes this data using a 3D modeling library. As output, it generates a 3D model of the user.

[0385] Step 2:

[0386] Providing practice plans

[0387] The server designs an optimal training plan based on the generated 3D model and the user's physical characteristics. As input, it uses the user's 3D model and physical characteristics data and applies a training plan generation algorithm. As output, it provides the user with an optimized training plan.

[0388] Step 3:

[0389] Creating practice videos

[0390] The server uses a video generation library to generate videos corresponding to the practice plan. As input, it uses the practice plan and a 3D model of the user and applies a video generation algorithm. As output, a practice video is generated that can be used as a reference for the user when actually practicing.

[0391] Step 4:

[0392] Providing practice videos

[0393] The server provides the generated practice video to the user. As input, it uses the generated practice video and sends the video to the user's device. As output, the user can visually check the practice video.

[0394] Step 5:

[0395] Initiating real-time dialogue

[0396] When a user asks a question or gives feedback during practice, the server recognizes the user's voice using a microphone. As input, the server acquires the user's voice data and analyzes it using a speech recognition library. As output, the user's question or feedback is acquired as text data.

[0397] Step 6:

[0398] Parsing questions and feedback

[0399] The server analyzes the acquired text data using a natural language processing library. It uses the text data of the user's question and feedback as input and applies natural language processing algorithms. As output, an appropriate response is generated.

[0400] Step 7:

[0401] Generate a voice response

[0402] The server converts the generated response into speech data using a speech synthesis library. As input, it applies a speech synthesis algorithm using the text data of the generated response. As output, a speech response is generated for the user.

[0403] Step 8:

[0404] Providing voice responses

[0405] The server provides the generated voice response to the user through the speaker. The server uses the generated voice data as input and outputs the voice from the speaker. As output, the user can receive the voice response.

[0406] 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.

[0407] "Example 1"

[0408] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions from the user's tone of voice and facial expressions. Specifically, it analyzes the tone, pitch, volume, and other aspects of the user's voice while practicing, and uses this information to infer the user's emotional state. It also uses a camera or other visual sensors to capture the user's facial expressions and interpret emotions from those expressions.

[0409] "Example 2"

[0410] Furthermore, in one embodiment of the present invention, an emotion engine is utilized during interaction with the user. Specifically, the emotion engine recognizes the user's emotions in real time during interaction with the user and adjusts the style and content of the system's interaction according to those emotions. For example, if the system senses that the user is struggling with practice, it will provide words of encouragement. Conversely, if the system senses that the user is enjoying practice, it will engage in interaction that shares that enjoyment.

[0411] "Example 3"

[0412] In one embodiment of the present invention, the emotion engine is also used to create practice plans. Specifically, the system provides an optimal practice plan taking into account the user's emotional state. For example, if the user feels unmotivated about practicing, the system can provide a practice plan that motivates the user. Conversely, if the user feels excited about practicing, the system can provide a practice plan that maintains that excitement.

[0413] The processing flow of each embodiment will be described below.

[0414] "Example 1"

[0415] Step 1: When the user starts practicing, the system activates sensors and cameras to capture the user's tone of voice and facial expressions.

[0416] Step 2: The emotion engine analyzes the captured tone and facial expression data to estimate the user's current emotional state.

[0417] Step 3: The emotion engine feeds the estimated emotional state back to the system, which uses that information to interact with the user and adjust the training plan.

[0418] "Example 2"

[0419] Step 1: When a user starts interacting with the system, the emotion engine recognizes the user's emotions in real time.

[0420] Step 2: The emotion engine instructs the system to adjust the style and content of the interaction based on the recognized emotion.

[0421] Step 3: The system adjusts the style and content of the dialogue according to the instructions from the emotion engine and continues the dialogue with the user.

[0422] "Example 3"

[0423] Step 1: When the user starts practicing, the emotion engine monitors the user's emotional state in real time.

[0424] Step 2: The emotion engine generates an optimal exercise plan based on the user's emotional state.

[0425] Step 3: The system presents the user with the practice plan provided by the emotion engine and proceeds with the practice.

[0426] Example 1

[0427] 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."

[0428] Conventional systems have difficulty not only analyzing a user's physical characteristics but also grasping their emotional state in real time and providing appropriate training plans and feedback based on that. Furthermore, there has been a lack of a means to comprehensively analyze a user's physical characteristics and emotional state and provide individually optimized training plans.

[0429] 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.

[0430] In this invention, the server includes means for measuring the user's physical characteristics, means for analyzing the measurement data, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating a video corresponding to the training plan, means for providing the generated video to the user, means for capturing the user's tone of voice and facial expression, means for analyzing the captured data to estimate the user's emotional state, and means for interacting with the user in real time. This makes it possible to comprehensively analyze the user's physical characteristics and emotional state and provide an individually optimized training plan.

[0431] "Means for measuring the user's physical characteristics" refers to a device that uses an input device such as a sensor or camera to measure the user's physical characteristics such as height, weight, shoulder width, and arm length.

[0432] "Means for analyzing measurement data" refers to software or systems that use statistical methods or algorithms based on the measured data to analyze the user's physical characteristics.

[0433] The "means for providing a training plan that maximizes the user's performance" is a system that creates and provides a training plan that is optimized for the user's physical characteristics based on the analysis results.

[0434] The "means for generating a video corresponding to a practice plan" refers to software or a system for generating a video showing specific practice methods and procedures based on the user's practice plan.

[0435] The "means for providing the generated video to the user" refers to an output device such as a display or mobile device for visually providing the generated video to the user.

[0436] The "means for capturing the user's tone of voice and facial expression" is a device that uses an input device such as a microphone or camera to collect the user's tone of voice and facial expression in real time.

[0437] "Means for analyzing captured data to infer a user's emotional state" refers to software or algorithms for analyzing collected audio and video data to infer a user's emotional state.

[0438] "Means for real-time user interaction" refers to an interface or system that communicates with users in real time and provides feedback and instructions.

[0439] MODE FOR CARRYING OUT THE INVENTION

[0440] The present invention is a system that comprehensively analyzes a user's physical characteristics and emotional state and provides an individually optimized training plan. Specific embodiments of this system are described below.

[0441] Hardware and software used

[0442] This system uses the following hardware and software:

[0443] Hardware:

[0444] 3D scanner (e.g., a general 3D scanner)

[0445] Weight scale (e.g., a regular weight scale)

[0446] Camera (e.g. a regular webcam)

[0447] Microphone (e.g., a regular microphone)

[0448] software:

[0449] Python (registered trademark) and its libraries (NumPy, Pandas, OpenCV (registered trademark), Librosa, TENSORFLOW (registered trademark), etc.)

[0450] Data collection and transmission

[0451] When a user stands in front of the system, a 3D scanner and a scale connected to the device measure the user's physical characteristics. For example, the 3D scanner measures the user's height, shoulder width, and arm length, and the scale measures their weight. This data is sent to a server in real time. The data is often sent in JSON format.

[0452] Data analysis

[0453] The server analyzes the received data using Python (registered trademark) NumPy and Pandas. For example, it calculates the user's BMI based on their height and weight. Based on the analysis results, it creates a training plan optimized for the user's physical characteristics.

[0454] Providing practice plans

[0455] The server generates a training plan based on the analysis results and generates a video corresponding to that plan. The generated video is provided to the user via their device. The video mimics the user's appearance and shows specific training methods and procedures.

[0456] Emotional state capture and analysis

[0457] The microphone and camera connected to the device capture the user's voice tone and facial expressions. The audio data is sent to the server in WAV format, and the video data in MP4 format. The server analyzes the audio data using Librosa to extract the tone, pitch, and volume of the voice. The video data is analyzed using OpenCV (registered trademark) and TENSORFLOW (registered trademark), and emotions are recognized from facial expressions.

[0458] Emotional state estimation and feedback

[0459] The server integrates the results of the analysis of the audio and video data to estimate the user's emotional state, and provides appropriate feedback and instructions to the user in real time based on the estimated emotional state.

[0460] Examples of concrete examples and prompts

[0461] Specific examples

[0462] The user stands in front of the system and measures their physical characteristics using a 3D scanner and a scale. The measurement data is sent to the server and analyzed using Python (registered trademark)'s NumPy and Pandas. The analysis results calculate the user's BMI. At the same time, the user's speech is collected by a microphone and analyzed by Librosa. Facial expression data captured by the camera is used to recognize emotions using OpenCV (registered trademark) and TENSORFLOW (registered trademark).

[0463] Prompt Sentence Examples

[0464] "Measure the user's height and weight and calculate their BMI. Also, recognize the user's emotions from their tone of voice and facial expressions."

[0465] In this way, the system can analyze the user's physical characteristics and emotional state in real time and provide an individually optimized exercise plan.

[0466] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0467] Step 1:

[0468] The user stands in front of the system

[0469] Input: User's location

[0470] Operation: The user stands in front of the system and presses the measurement start button.

[0471] Output: Trigger signal to start measurement

[0472] Step 2:

[0473] The device measures the user's physical characteristics

[0474] Input: Trigger signal to start measurement

[0475] How it works: A 3D scanner connected to the device measures the user's height, shoulder width, and arm length, and a scale measures their weight.

[0476] Output: Physical characteristics data (height, shoulder width, arm length, weight)

[0477] Step 3:

[0478] The device sends the measurement data to the server

[0479] Input: Physical characteristics data

[0480] Operation: The device sends the measured data to the server in real time. The data is sent in JSON format.

[0481] Output: Physical characteristics data sent to the server

[0482] Step 4:

[0483] The server analyzes the data

[0484] Input: Physical characteristics data sent to the server

[0485] Operation: The server analyzes the received data using Python (registered trademark) NumPy and Pandas. For example, it calculates BMI from height and weight.

[0486] Output: Analysis results (BMI, etc.)

[0487] Step 5:

[0488] The server generates the analysis results

[0489] Input: Analysis results (BMI, etc.)

[0490] How it works: The server uses the analysis results to create a report about the user's physical characteristics.

[0491] Output: Physical Description Report

[0492] Step 6:

[0493] The device captures the user's tone of voice and facial expressions

[0494] Input: Trigger signal to start measurement

[0495] How it works: A microphone attached to the device collects the user's voice, and a camera captures facial expressions.

[0496] Output: Audio data (WAV format), video data (MP4 format)

[0497] Step 7:

[0498] The device sends audio and video data to the server.

[0499] Input: Audio data, video data

[0500] Operation: The device sends the collected audio and video data to the server.

[0501] Output: Audio and video data sent to the server

[0502] Step 8:

[0503] The server analyzes the data using an emotion engine

[0504] Input: Audio and video data sent to the server

[0505] How it works: The server uses Librosa to analyze audio data and extract voice tone, pitch, and volume. It also uses OpenCV (registered trademark) and TENSORFLOW (registered trademark) to analyze video data and recognize emotions from facial expressions.

[0506] Output: Emotion analysis results

[0507] Step 9:

[0508] The server estimates the emotional state

[0509] Input: Sentiment analysis results

[0510] How it works: The server integrates the results of analyzing audio and video data to estimate the user's emotional state.

[0511] Output: Emotional state report

[0512] Step 10:

[0513] The server generates the final result and sends it to the device.

[0514] Input: Physical characteristics report, Emotional state report

[0515] How it works: The server generates the final results of the physical characteristics and emotional state and sends them to the device, which displays the results to the user.

[0516] Output: Final result (integrated report of physical characteristics and emotional state)

[0517] (Application example 1)

[0518] 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."

[0519] Traditional shopping experiences lack personalized product suggestions that take into account the user's physical characteristics and emotional state, making it difficult for users to find the best products. Furthermore, the lack of appropriate responses based on the user's emotional state can degrade the quality of the shopping experience. This can lead to lower user satisfaction and potentially impact store sales.

[0520] 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.

[0521] In this invention, the server includes means for analyzing the user's physical characteristics, means for proposing optimal products based on the analysis results, means for recognizing the user's emotional state, means for adjusting the product proposals based on the recognized emotional state, means for providing the proposals to the user, and means for interacting with the user in real time. This enables personalized product proposals based on the user's physical characteristics and emotional state, thereby improving user satisfaction.

[0522] "Means for analyzing the user's physical characteristics" refers to devices or algorithms that use sensors or cameras to measure the user's physical characteristics, such as height, weight, shoulder width, and arm length, and analyze the data.

[0523] "Means for suggesting optimal products" refers to systems or algorithms that select and suggest the most suitable products for users based on their physical characteristics and emotional state.

[0524] "Means for recognizing the user's emotional state" refers to a device or algorithm that analyzes the user's tone of voice and facial expressions and infers the user's emotional state from that information.

[0525] "Means for tailoring product recommendations based on a perceived emotional state" refers to a system or algorithm that takes into account the user's emotional state and tailors the content and method of product recommendations.

[0526] The "means for providing the user with the proposed content" refers to a device or system for visually or audibly providing the user with the analysis results and proposed product information.

[0527] "Means for interacting with users in real time" refers to systems or algorithms that communicate with users in real time and provide appropriate responses to their questions or requests.

[0528] The system for carrying out the present invention includes a series of means for analyzing the physical characteristics of a user and proposing optimal products. Specific embodiments are described below.

[0529] Hardware Configuration

[0530] Smartphone: A device equipped with a camera, microphone, and sensors that collects the user's physical characteristics and emotional state.

[0531] Server: A computer system for performing data processing and analysis.

[0532] Network: Infrastructure for data communication between smartphones and servers.

[0533] Software Configuration

[0534] Mobile Application: Runs on the iOS or ANDROID platform and provides the user interface.

[0535] Emotion recognition engine: Using Microsoft® Azure® Emotion API, etc., estimates the user's emotional state from their tone of voice and facial expressions.

[0536] Physical feature analysis algorithm: Analyzes the user's physical features using TENSORFLOW (registered trademark) and OpenCV (registered trademark).

[0537] Database: We use MySQL (registered trademark) and other software to manage user data and product information.

[0538] Processing flow

[0539] 1. Data Collection: Using the smartphone's camera and sensors, the system measures the user's physical characteristics (height, weight, shoulder width, arm length, etc.), and uses the microphone to collect the user's voice tone, pitch, and volume.

[0540] 2. Data transmission: The collected data is transmitted to the server.

[0541] 3. Data Analysis: On the server, a physical feature analysis algorithm is used to analyze the user's physical features, and an emotion recognition engine is used to infer the user's emotional state from their tone of voice and facial expressions.

[0542] 4. Product Recommendations: Based on the analysis results, the system recommends the most suitable products for the user. The recommendations are personalized based on the user's physical characteristics and emotional state.

[0543] 5. Providing suggested content: Proposed product information is displayed on the smartphone application.

[0544] 6. Real-time interaction: Interact with users in real time and provide appropriate responses to their questions and requests.

[0545] Specific examples

[0546] When a user enters a store, the app automatically launches and collects data using a camera and microphone. It analyzes the user's physical characteristics and emotional state in real time and suggests products that the user might be interested in. For example, if the emotion engine determines that the user is a little tired, it will suggest items that will help them relax (e.g., aroma candles or massage machines).

[0547] Prompt Sentence Examples

[0548] Develop an application that analyzes a user's physical characteristics (height, weight, shoulder width, arm length, etc.) and emotional state (tone of voice, facial expression) in real time to suggest the most suitable products. Specifically, the application will collect data using the smartphone's camera and microphone, analyze it on the server, and display the results in the app.

[0549] In this way, personalized product recommendations based on the user's physical characteristics and emotional state are possible, improving user satisfaction.

[0550] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0551] Step 1:

[0552] Data collection

[0553] The device (smartphone) uses a camera and sensors to measure the user's physical characteristics (height, weight, shoulder width, arm length, etc.), and a microphone to collect the user's voice tone, pitch, and volume.

[0554] Input: User's physical characteristics and voice data

[0555] Output: Measured physical characteristics data and voice data

[0556] Step 2:

[0557] Data transmission

[0558] The terminal transmits the collected data to the server.

[0559] Input: Measured physical characteristics data and voice data

[0560] Output: Data sent to the server

[0561] Step 3:

[0562] Data analysis

[0563] The server analyzes the user's physical characteristics using a physical feature analysis algorithm (TENSORFLOW® or OpenCV®), and estimates the user's emotional state from their tone of voice and facial expressions using an emotion recognition engine (Microsoft® Azure® Emotion API).

[0564] Input: Transmitted physical characteristic data and voice data

[0565] Output: Analyzed physical feature data and emotional state data

[0566] Step 4:

[0567] Product suggestion

[0568] The server then uses the analysis results to suggest the best products for the user, personalized based on the user's physical characteristics and emotional state.

[0569] Input: Analyzed physical feature data and emotional state data

[0570] Output: Personalized product recommendation data

[0571] Step 5:

[0572] Proposal content provided

[0573] The server transmits the suggested product information to the terminal, which then provides the information to the user visually or audibly.

[0574] Input: Personalized product suggestion data

[0575] Output: Product information provided to the user

[0576] Step 6:

[0577] Real-time dialogue

[0578] The device interacts with the user in real time and provides appropriate responses to questions and requests. The server performs additional data analysis as needed and sends response data to the device.

[0579] Input: User questions and requests

[0580] Output: The response data that is provided to the user

[0581] In this way, personalized product recommendations based on the user's physical characteristics and emotional state are possible, improving user satisfaction.

[0582] Example 2

[0583] 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."

[0584] Conventional training plan providing systems were unable to fully utilize the user's physical characteristics and performance data, making it difficult to provide optimal training plans for individual users. Furthermore, because dialogue did not take the user's emotions into consideration, it was difficult to maintain the user's motivation. Furthermore, because the training plan was not adjusted to reflect the user's feedback, there was also the problem of not maximizing the effectiveness of training.

[0585] 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.

[0586] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for recognizing the user's emotions in real time and adjusting the style and content of the interaction in response to the emotions, means for collecting the user's practice data and performance data, means for analyzing the user's performance using the collected data, means for generating a training plan based on the analysis results, means for providing the generated training plan to the user, and means for collecting user feedback and adjusting the training plan. This makes it possible to provide an optimal training plan based on the user's individual characteristics and to interact with the user in response to their emotions, thereby maximizing the effectiveness of training while maintaining the user's motivation.

[0587] "Means for analyzing the user's physical characteristics" refers to devices or software for collecting the user's physical data and analyzing that data.

[0588] "Means for providing a training plan that maximizes the effectiveness of the analysis results" refers to devices and software that create and provide optimal training plans to users based on the analysis results of their physical characteristics.

[0589] "Means for generating videos corresponding to a practice plan" refers to a device or software that creates videos that can be used as a reference when a user actually practices based on the provided practice plan.

[0590] "Means for providing the generated video to the user" refers to devices or software that allow the user to view the generated video.

[0591] "Means for interacting with users in real time" refers to devices and software for communicating with users in real time.

[0592] "Means for recognizing a user's emotions in real time and adjusting the style and content of a conversation in response to those emotions" refers to devices or software that analyze a user's emotions in real time and change the method and content of a conversation based on the results.

[0593] "Means for collecting user practice data and performance data" refers to devices and software for collecting data on user practice and performance results.

[0594] "Means for analyzing user performance using collected data" refers to devices or software for analyzing collected practice data and performance data and evaluating user performance.

[0595] "Means for generating a training plan based on the analysis results" refers to devices or software for creating an optimal training plan for a user based on the results of an analysis of the user's performance.

[0596] "Means for providing the generated practice plan to the user" refers to a device or software for presenting the created practice plan to the user.

[0597] "Means for collecting user feedback and adjusting practice plans" refers to devices or software that collect user feedback and modify or adjust practice plans based on that feedback.

[0598] This invention is a system that collects and analyzes a user's physical characteristics and performance data to provide an optimal training plan. It also has the ability to recognize the user's emotions in real time and adjust the style and content of the dialogue accordingly.

[0599] The server uses devices such as smartphones and tablets to collect users' practice data and performance data. Users input their practice data through these devices and send it to the server. For example, users can input their pitching speed and control accuracy after practice.

[0600] The server uses the collected data to analyze the user's performance. This analysis uses machine learning libraries such as Python's Scikit-learn and TENSORFLOW. The server analyzes the user's pitching form and performance trends to identify the optimal training method. For example, it analyzes video data of the user's pitching form to determine whether a sidearm pitch is suitable.

[0601] Based on the analysis results, the server generates an optimal training plan for the user. For example, the following menu is created for a sidearm pitching training plan.

[0602] Check basic operation (10 minutes)

[0603] Catch ball (20 minutes)

[0604] Pitching practice (30 minutes)

[0605] The server transmits the training plan to the terminal for provision to the user, who can then check and execute the training plan through the terminal.

[0606] Furthermore, the server uses an emotion engine to converse with the user. The emotion engine uses natural language processing tools such as NVIDIA's Jarvis and Google's Dialogflow. The server recognizes the user's emotions in real time and adjusts the style and content of the dialogue accordingly. For example, if the server senses that the user is struggling with practice, it will offer words of encouragement. Conversely, if the server senses that the user is enjoying practicing, it will engage in a dialogue that shares that enjoyment.

[0607] The user can also input their emotions using the device while practicing. For example, they can input "I'm not doing well in practice today." The server uses an emotion engine to analyze the user's emotions and generates an encouraging message. The server then sends this message to the device and displays it to the user.

[0608] Examples of prompts to be input to a generative AI model include:

[0609] "Generate an optimal practice plan based on the user's practice data. The user is a baseball player who wants to practice sidearm throwing. Include specific practice items, order, and frequency. Also, generate dialogue to offer words of encouragement if the user is struggling with practice."

[0610] In this way, it is possible to provide an optimal practice plan based on the individual characteristics of the user and to have a dialogue that responds to the user's emotions, thereby maximizing the effectiveness of practice while maintaining the user's motivation.

[0611] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0612] Step 1: Collect user data

[0613] Users use devices such as smartphones and tablets to input practice data and performance data. For example, after practice, they input pitching speed and control accuracy. The device then sends this data to a server. The input data includes pitching speed, control accuracy, practice time, etc. The server receives this data and stores it in a database.

[0614] Step 2: Analyze the data

[0615] The server uses the collected data to analyze the user's performance. This analysis uses machine learning libraries such as Python's Scikit-learn and TENSORFLOW. The server analyzes the user's pitching form and performance trends to identify the optimal practice method. The input data includes the practice data collected in step 1. The server analyzes this data and determines whether the user's pitching form is suitable for sidearm pitching. The output data includes an evaluation of the pitching form's suitability as a result of the analysis.

[0616] Step 3: Generate a practice plan

[0617] The server generates an optimal practice plan for the user based on the analysis results. For example, the following menu is created as a sidearm practice plan. The input data includes the analysis results obtained in step 2. The server determines the specific practice menu, practice order, and practice frequency based on this data. The output data includes the generated practice plan. Specifically, the server creates the following practice menu.

[0618] Check basic operation (10 minutes)

[0619] Catch ball (20 minutes)

[0620] Pitching practice (30 minutes)

[0621] Step 4: Provide a practice plan

[0622] The server provides the generated practice plan to the user. The user can check and execute the practice plan through the device. The input data includes the practice plan generated in step 3. The server sends this practice plan to the device. The output data includes the practice plan in a format that can be checked by the user. Specifically, the server sends the practice plan to the device, and the user opens the app and checks the practice menu.

[0623] Step 5: Emotional Engine Dialogue

[0624] The user inputs their emotions using the terminal while practicing. For example, they input "Practice is not going well today." The server uses an emotion engine to analyze the user's emotions and generate an encouraging message. The input data includes the emotion information entered by the user. The server uses the emotion engine to analyze this data and generate an appropriate message. The output data includes the generated encouraging message. Specifically, the server generates a message such as the one below and sends it to the terminal.

[0625] "That's tough, but I'm improving little by little. Let's keep going!"

[0626] Step 6: Gather feedback and adjust your practice plan

[0627] After practice, the user inputs feedback and sends it to the server. For example, they might input, "Today's practice was effective." The server collects this feedback and adjusts the practice plan. The input data includes the feedback information entered by the user. The server corrects and adjusts the practice plan based on this data. The output data includes the adjusted practice plan. Specifically, the server analyzes the feedback and changes the practice menu and frequency as necessary.

[0628] (Application example 2)

[0629] 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."

[0630] Conventional training plan providing systems have difficulty providing optimal training plans that fully consider the user's physical characteristics and emotional state. Furthermore, they are unable to provide real-time feedback based on the user's emotions, making it difficult to maintain the user's motivation. Furthermore, there has been a lack of systems that provide optimal work procedures and training plans for factory workers operating and maintaining robots.

[0631] 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.

[0632] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for recognizing the user's emotions in real time, means for adjusting the content of the interaction in accordance with the recognized emotions, means for analyzing the user's past performance data and providing optimal work procedures and training plans, and means for providing feedback in accordance with the emotions. This allows the server to provide optimal training plans and work procedures that take into account the user's physical characteristics and emotional state, enabling efficient training and work while maintaining the user's motivation.

[0633] "Means for analyzing the user's physical characteristics" refers to devices or software for collecting the user's physical data and analyzing that data.

[0634] "Means for providing a training plan that maximizes the benefits" refers to devices and software that suggest optimal training methods and schedules to users based on the analysis results.

[0635] "Means for generating videos corresponding to a practice plan" refers to a device or software that creates videos that can be used as a reference when a user actually practices based on the provided practice plan.

[0636] The "means for providing the generated video to the user" refers to a device or software for visually providing the created video to the user.

[0637] "Means for interacting with users in real time" refers to devices or software that allow for instant communication with users.

[0638] "Means for recognizing user emotions in real time" refers to devices or software that instantly analyze emotions from the user's facial expressions, voice, etc.

[0639] "Means for adjusting dialogue content in response to recognized emotions" refers to devices or software for changing the content or tone of dialogue based on the user's emotional state.

[0640] "Means for analyzing a user's past performance data and providing optimal work procedures and training plans" refers to devices or software that analyze a user's past performance data and propose optimal work procedures and training plans based on the results.

[0641] A "means for providing emotional feedback" is a device or software that provides appropriate encouragement or advice based on the user's emotional state.

[0642] The system for implementing this invention has the function of analyzing the user's physical characteristics, providing an optimal exercise plan, and recognizing the user's emotions in real time to adjust the dialogue content. Specific embodiments of this system are described below.

[0643] System configuration

[0644] The system consists of the following main components:

[0645] 1. Means for analyzing the user's physical characteristics: Devices and software for collecting and analyzing the user's physical data. Specifically, cameras and sensors are used to capture and analyze the user's movements and postures.

[0646] 2. Means of providing optimal training plans: Devices and software that suggest optimal training methods and schedules based on the analysis results. Generative AI models are used to generate optimal training plans from user data.

[0647] 3. Means for generating videos corresponding to the practice plan: Devices and software that create videos to serve as reference for the user when actually practicing based on the provided practice plan. A video generation algorithm is used to generate videos that mimic the user's appearance.

[0648] 4. Means for providing the generated video to the user: Devices and software for visually presenting the created video to the user. The video is displayed to the user using smart glasses or a head-mounted display.

[0649] 5. Means of real-time interaction with users: Devices and software for communicating with users instantly. Dialogue with users is realized using voice recognition and voice synthesis technology.

[0650] 6. Means for recognizing user emotions in real time: Devices and software for instantly analyzing emotions from a user's facial expressions, voice, etc. Recognizing user emotions in real time using an emotion recognition model.

[0651] 7. Means for adjusting dialogue content according to perceived emotions: Devices or software that change the content or tone of dialogue based on the user's emotional state, providing emotionally appropriate feedback.

[0652] 8. Means for analyzing a user's past performance data and providing optimal work procedures and training plans: Devices and software for analyzing a user's past performance data and proposing optimal work procedures and training plans based on the results.

[0653] 9. Emotional feedback: Devices or software that provide appropriate encouragement or advice based on the user's emotional state.

[0654] Program processing explanation

[0655] The server uses cameras and sensors to collect and analyze the user's physical characteristics. Based on the analysis results, it uses a generative AI model to generate an optimal training plan. Based on the generated training plan, it uses a video generation algorithm to create training videos, which are then provided to the user via smart glasses or a head-mounted display.

[0656] As the user practices, the server uses an emotion recognition model to analyze the user's emotions in real time and adjusts the dialogue accordingly, for example, offering words of encouragement if the user is feeling stressed, or sharing their joy if the user is having fun.

[0657] In addition, the server analyzes the user's past performance data and provides optimal work procedures and training plans, allowing the user to train efficiently.

[0658] Specific examples

[0659] For example, consider a scenario in which a factory worker is performing maintenance on a robot. The worker wears smart glasses, and the system captures and analyzes the worker's movements. Based on the analysis results, the optimal work procedure is displayed on the smart glasses. If the worker is feeling stressed, the system displays a message saying, "Relax and try again." If the worker is enjoying themselves, the system displays a message saying, "Good job! Keep going."

[0660] Example prompts to input to a generative AI model:

[0661] Generate optimal training plans based on your workers' past performance data. Provide feedback to encourage them when they're stressed or share their joy when they're having fun.

[0662] The above is a specific embodiment for carrying out the present invention.

[0663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0664] Step 1:

[0665] The server uses cameras and sensors to collect the user's physical characteristics. Specifically, it captures the user's movements and postures and stores them as digital data. The input is video data from the cameras and sensors, and the output is digital data that indicates the user's physical characteristics.

[0666] Step 2:

[0667] The server analyzes the collected physical characteristic data. Image processing algorithms and machine learning models are used for the analysis. The input is the digital data obtained in step 1, and the output is the analysis results of the user's physical characteristics. Specifically, the server extracts the user's posture and movement characteristics and saves them as numerical data.

[0668] Step 3:

[0669] The server generates an optimal training plan using a generative AI model based on the analysis results. The input is the analysis results obtained in step 2, and the output is the optimal training plan for the user. Specifically, it generates a training menu and schedule based on the user's physical characteristics.

[0670] Step 4:

[0671] The server uses a video generation algorithm to create a practice video based on the generated practice plan. The input is the practice plan obtained in step 3, and the output is a practice video. Specifically, it generates an animated video that mimics the user's appearance and visually shows the practice steps.

[0672] Step 5:

[0673] The server provides the generated practice video to the user. Specifically, the video is displayed through smart glasses or a head-mounted display. The input is the practice video obtained in step 4, and the output is a video that the user can visually confirm.

[0674] Step 6:

[0675] The server uses an emotion recognition model to analyze the user's emotions in real time as they practice. The input is video and audio data from a camera and microphone, and the output is the user's emotional state. Specifically, the server analyzes the user's facial expressions and tone of voice to determine their emotions.

[0676] Step 7:

[0677] The server adjusts the dialogue content according to the recognized emotion. The input is the emotional state obtained in step 6, and the output is the adjusted dialogue content. Specifically, if the user is feeling stressed, the server will provide words of encouragement, and if the user is having fun, the server will engage in dialogue to share that enjoyment.

[0678] Step 8:

[0679] The server analyzes the user's past performance data and provides optimal work procedures and training plans. The input is the user's past performance data, and the output is the optimal work procedures and training plans. Specifically, it analyzes past data and proposes optimal procedures and plans to the user.

[0680] Step 9:

[0681] The server provides feedback according to the emotion. The input is the emotional state obtained in step 6, and the output is feedback according to the emotion. Specifically, if the user is feeling stressed, the server provides a message such as "Relax and try again." If the user is having fun, the server provides a message such as "Good luck! Keep going."

[0682] Example 3

[0683] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0684] Conventional practice planning systems have difficulty providing practice plans that fully take into account the user's physical characteristics and emotional state, and their real-time interactive functions are limited. As a result, it is difficult to maintain the user's motivation and practice effectively. Furthermore, they lack the ability to generate videos that mimic the user's appearance or provide appropriate responses, which leads to problems with reduced user satisfaction.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0686] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for analyzing the user's emotional state and adjusting the training plan based on the analyzed videos, and means for recognizing the user's voice input and generating appropriate responses. This makes it possible to provide a training plan that takes the user's physical characteristics and emotional state into consideration, enhances real-time interaction functions, and realizes the generation of videos that mimic the user's appearance and the provision of appropriate responses.

[0687] "Means for analyzing the user's physical characteristics" refers to means for analyzing physical data provided by the user, such as height, weight, and body type, and for identifying the user's physical characteristics based on that data.

[0688] "Means for providing a training plan that maximizes the user's performance" refers to means for creating and providing a training plan that is optimal for the user, taking into consideration the user's physical characteristics and emotional state.

[0689] The "means for generating a video corresponding to a training plan" is a means for creating a video that reproduces training movements using a 3D model generated based on the user's physical characteristics.

[0690] The "means for providing the generated video to the user" refers to a means for visually providing the generated practice video to the user.

[0691] "Means for interacting with the user in real time" refers to a means by which the user can ask questions or give feedback to the system during practice, and the system can respond to those questions in real time.

[0692] The "means for analyzing the user's emotional state and adjusting the exercise plan based thereon" refers to a means for analyzing the user's emotional data and dynamically adjusting the exercise plan based on the results.

[0693] The "means for recognizing a user's voice input and generating an appropriate response" is a means for converting a user's voice input into text and analyzing the text to generate an appropriate response.

[0694] The present invention provides a system that provides a training plan that takes into account the user's physical characteristics and emotional state, and enhances real-time interactive functions. Specific embodiments of this system are described below.

[0695] 1. Creation of practice videos using 3D modeling technology

[0696] The server generates a 3D model based on the user's physical characteristics using 3D modeling software such as Blender. Specifically, the server receives the user's physical data (height, weight, body type, etc.) as input and creates a 3D model based on that data.

[0697] The server then uses the generated 3D model to generate a practice video. In this process, a game engine such as Unity is used to simulate the 3D model and reproduce the practice movements. The generated video serves as a reference for the user when practicing.

[0698] Example: If a user provides data of height 170 cm and weight 65 kg, the server generates a 3D model based on that data and creates a running practice video.

[0699] Example prompt: "Generate a running practice video for a user who is 170cm tall and weighs 65kg."

[0700] 2. Real-time interaction

[0701] The device is equipped with the ability to interact with users in real time. This is achieved using voice recognition technologies such as Google® Cloud Speech-to-Text and natural language processing technologies such as OpenAI®'s GPT-3®. Users can ask the device questions and seek feedback during practice.

[0702] The server generates appropriate responses to user questions and requests. For example, if a user asks "Is this behavior correct?", the server analyzes the question and provides appropriate feedback.

[0703] Example: If a user asks, "What's the next exercise?", the device uses voice recognition technology to convert the question into text, and the server analyzes the text and responds, "Next is stretching."

[0704] Example prompt: "When the user asks what the next exercise is, generate an appropriate response."

[0705] 3. Creating a practice plan using the emotion engine

[0706] The server provides an optimal training plan that takes into account the user's emotional state. In this process, it uses an emotion engine to analyze the user's emotional data. For example, if the user feels unmotivated to practice, the server will provide a motivating training plan.

[0707] Example: If a user types, "I'm not feeling motivated today," the server analyzes the emotional data and provides a workout plan that includes light exercise and relaxation to boost motivation.

[0708] Example prompt: "If the user feels unmotivated, generate a motivational exercise plan."

[0709] This system makes it possible to provide a training plan that takes into account the user's physical characteristics and emotional state, enhances real-time dialogue functions, and realizes the generation of videos that mimic the user's appearance and the provision of appropriate responses. The flow of the identification process in Example 3 will be explained using Figure 21.

[0710] Step 1:

[0711] The user inputs their physical characteristics such as height, weight, and body type into the system. This data is provided through an input form on the terminal. The input data is then sent to the server.

[0712] Step 2:

[0713] The server receives the user's physical data and generates a 3D model using 3D modeling software such as Blender. Specifically, the server analyzes the input data and sets each parameter of the 3D model based on that data. The output is a 3D model that reflects the user's physical characteristics.

[0714] Step 3:

[0715] The server imports the generated 3D model into a game engine such as Unity and performs a motion simulation. The server then executes the training motion script and reproduces the motion on the 3D model. As an output, a video reproducing the training motion is generated.

[0716] Step 4:

[0717] The server provides the generated practice video to the user. Specifically, it transmits the video file to the terminal so that the user can visually check it. As an output, the practice video that the user can watch is provided.

[0718] Step 5:

[0719] During practice, the user can input voice data to the device to ask questions or request feedback. For example, the user can ask, "What is the next practice menu?" The input voice data is sent to the device.

[0720] Step 6:

[0721] The device converts the user's speech into text using speech recognition technology such as Google® Cloud Speech-to-Text. The converted text is sent to the server. As an output, the speech data is converted into text data.

[0722] Step 7:

[0723] The server uses natural language processing technology, such as OpenAI's GPT-3, to analyze the user's question or request. Based on the analysis results, it generates an appropriate response. As an output, an appropriate response to the user's question is generated.

[0724] Step 8:

[0725] The terminal provides the response received from the server to the user by voice, for example, "Next is stretching." As an output, the voice response is provided to the user.

[0726] Step 9:

[0727] The user inputs their emotional state into the system. For example, they input "I'm not motivated today." The input emotional data is sent to the server.

[0728] Step 10:

[0729] The server analyzes the user's emotional data using the emotion engine, evaluates the user's emotional state based on the analysis results, and obtains an evaluation result regarding the user's emotional state as an output.

[0730] Step 11:

[0731] The server generates an optimal training plan based on the analysis results. For example, if the user feels unmotivated, the server will provide a training plan that includes light exercise and relaxation to boost motivation. The output is a training plan that corresponds to the user's emotional state.

[0732] Step 12:

[0733] The terminal provides the practice plan received from the server to the user, who then practices according to the practice plan provided to him / her, and the practice plan is provided to the user as an output.

[0734] (Application example 3)

[0735] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0736] Conventional factory robot operation and maintenance techniques lack appropriate training methods for workers to learn efficiently. It is also difficult to provide training plans that take into account the worker's emotional state, creating a need for improved work efficiency and safety. Furthermore, a lack of real-time dialogue and feedback makes it difficult to quickly respond to problems faced by workers. To address these issues, a training system that takes into account the user's physical characteristics and emotional state is needed.

[0737] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0738] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for detecting the user's emotional state, means for adjusting the training plan based on the user's emotional state, means for explaining factory robot operation methods and maintenance procedures through videos using 3D models, means for responding to worker questions using voice recognition technology and natural language processing technology, and means for detecting the worker's emotional state and providing an optimal training plan. This enables workers to efficiently learn how to operate and maintain factory robots and provides an optimal training plan based on their emotional state.

[0739] "Means for analyzing the user's physical characteristics" refers to a device or software for acquiring and analyzing the user's physical characteristics such as height, weight, and body type as data.

[0740] A "means for providing an exercise plan" is a device or software that suggests optimal exercise content and schedule based on the user's physical characteristics and emotional state.

[0741] "Means for generating videos" refers to a device or software that creates training videos using 3D models based on the user's physical characteristics and training plan.

[0742] "Means for providing to the user" means a device or software for visually or audibly presenting the generated video or training plan to the user.

[0743] A "real-time interaction means" is a device or software that allows for real-time communication with a user through voice or text.

[0744] "Means for detecting emotional state" refers to a device or software that analyzes and detects the emotional state of a user from their voice, facial expression, behavior, etc.

[0745] A "means for adjusting an exercise plan" is a device or software for optimizing or modifying an existing exercise plan based on the sensed emotional state of the user.

[0746] "Means for explaining the operation methods and maintenance procedures of factory robots through video using 3D models" refers to a device or software for visually explaining the operation methods and maintenance procedures of factory robots using 3D models.

[0747] "Voice recognition technology" is a technology that converts a user's voice into text data.

[0748] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning.

[0749] A "means for providing an optimal training plan" is a device or software that suggests the most effective training content and schedule based on the user's emotional state and physical characteristics.

[0750] A system for implementing this invention analyzes a user's physical characteristics, provides an optimal training plan, generates videos corresponding to the training plan, and provides them to the user. It also has the ability to interact with the user in real time, detect the user's emotional state, and adjust the training plan accordingly. Furthermore, the system uses 3D models to explain factory robot operation methods and maintenance procedures through videos, and responds to worker questions using voice recognition and natural language processing.

[0751] Hardware and software used

[0752] Hardware:

[0753] Microphone: To capture audio input.

[0754] Computer: The environment in which a program runs.

[0755] software:

[0756] Python (registered trademark): Program implementation language.

[0757] OpenAI® API: For natural language processing.

[0758] pyttsx3: For speech synthesis.

[0759] speech_recognition: For speech recognition.

[0760] emotion_recognition: For emotion recognition.

[0761] video_generator: For 3D model and video generation.

[0762] training_plan: To adjust your training plan.

[0763] Data processing and calculation

[0764] The server acquires the user's physical characteristics and generates a 3D model based on them. Using the generated 3D model, it creates a practice video that is optimal for the user and provides it to the user. If the user asks a question during practice, the question is converted into text using speech recognition technology, and an appropriate response is generated using natural language processing technology. The response is returned to the user using speech synthesis technology.

[0765] The server also detects the user's emotional state and adjusts the training plan based on that emotional state. For example, if the emotion engine determines that the user is tired, the server will provide a training plan that encourages rest.

[0766] Specific examples

[0767] When a worker asks, "Please tell me how to install this part," the server will explain it with a video using a 3D model and provide details via audio.

[0768] If the emotion engine determines that the worker is tired, the server will provide a training plan that encourages rest.

[0769] Prompt Sentence Examples

[0770] User: "How do I install this part?"

[0771] System: "Here's how to install the part. Play the video."

[0772] In this way, a system is provided that allows efficient learning of the operation and maintenance of factory robots.

[0773] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0774] Step 1:

[0775] The server acquires the user's physical characteristics, receives data such as height, weight, and body type entered by the user, and generates a 3D model based on this. The input data is the user's physical characteristics, and the output is a 3D model.

[0776] Step 2:

[0777] The server uses the generated 3D model to create a training video that is optimal for the user. Based on the 3D model, a video corresponding to the training plan is generated. The input data are the 3D model and the training plan, and the output is the training video.

[0778] Step 3:

[0779] The server provides the generated practice video to the user. The video is sent to the user's device and visually presented. The input data is the practice video, and the output is the user's visual feedback.

[0780] Step 4:

[0781] When the user asks a question during practice, the device receives voice input. The device uses a microphone to capture the user's voice and transmits it to the server. The input data is the user's voice, and the output is voice data.

[0782] Step 5:

[0783] The server uses voice recognition technology to convert the user's voice into text, analyzes the voice data, and outputs it as text data. The input data is voice data, and the output is text data.

[0784] Step 6:

[0785] The server uses natural language processing technology to generate appropriate responses to user questions. It analyzes text data and generates appropriate responses. The input data is text data, and the output is response text.

[0786] Step 7:

[0787] The server uses speech synthesis technology to convert the generated response text into speech and respond to the user. The server outputs the response text as voice data and sends it to the user's device. The input data is the response text, and the output is voice data.

[0788] Step 8:

[0789] The server detects the user's emotional state by analyzing and detecting the user's emotional state from their voice, facial expression, behavior, etc. The input data is the user's voice and facial expression data, and the output is the user's emotional state.

[0790] Step 9:

[0791] The server adjusts the practice plan based on the detected emotional state, or optimizes or modifies the existing practice plan taking the emotional state into account. The input data are the emotional state and the existing practice plan, and the output is the adjusted practice plan.

[0792] Step 10:

[0793] The server provides the adjusted practice plan to the user, and sends the new practice plan to the user's device and presents it visually or audibly. The input data is the adjusted practice plan, and the output is the user's feedback.

[0794] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0795] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / OpenAI.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[0796] , 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 the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0798] 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.

[0799] [Second embodiment]

[0800] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0801] 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.

[0802] 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).

[0803] 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.

[0804] 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.

[0805] 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).

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0812] "Example 1"

[0813] The system of the present invention is equipped with input devices such as sensors and cameras for analyzing the user's physical characteristics. These devices measure the user's physical characteristics, such as height, weight, shoulder width, arm length, etc., and transmit the data to the system. The system analyzes the user's physical characteristics based on the transmitted data.

[0814] "Example 2"

[0815] Based on the analysis results, the system will provide a training plan that maximizes the player's performance. For example, if a baseball player is determined to be better suited to sidearm pitching rather than overhand pitching, the system will provide a training plan for sidearm pitching. This training plan will include specific exercises, their order, and frequency of practice.

[0816] "Example 3"

[0817] To generate videos corresponding to the training plan, the system uses 3D modeling technology. Specifically, it generates a 3D model based on the user's physical characteristics and then uses that model to generate training videos. The generated videos serve as a reference for the user when practicing.

[0818] "Example 4"

[0819] The system has the ability to converse with users in real time. This function is realized using voice recognition technology and natural language processing technology. Users can ask the system questions and request feedback during practice. The system responds to questions and requests from users in easy-to-understand language.

[0820] The processing flow of each embodiment will be described below.

[0821] "Example 1"

[0822] Step 1: Activate input devices such as sensors and cameras to measure the user's physical characteristics.

[0823] Step 2: Measure the user's physical characteristics, such as height, weight, shoulder width, arm length, etc. Step 3: Send the measured data to the system.

[0824] Step 4: The system analyzes the user's physical characteristics based on the transmitted data.

[0825] "Example 2"

[0826] Step 1: The system generates an optimal training plan based on the analysis results.

[0827] Step 2: Provide the generated practice plan to the user.

[0828] "Example 3"

[0829] Step 1: The system generates a 3D model based on the user's physical characteristics.

[0830] Step 2: Generate a practice video using the generated 3D model.

[0831] Step 3: Serving the generated video to the user.

[0832] "Example 4"

[0833] Step 1: The system activates the voice recognition function.

[0834] Step 2: The user can ask the system questions and seek feedback during the exercise.

[0835] Step 3: The system responds to the user's questions and requests in easy-to-understand language.

[0836] Example 1

[0837] 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."

[0838] Conventional systems have difficulty accurately measuring and analyzing a user's physical characteristics. Furthermore, the collection of measurement data and the provision of analysis results are not performed in real time, making them inconvenient for users. Furthermore, interaction with users is limited, making it difficult to respond to individual needs.

[0839] 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.

[0840] In this invention, the server includes means for using an input device to measure a user's physical characteristics, means for collecting measurement data, means for transmitting the collected data to the server, means for the server to analyze the data, means for generating analysis results, means for providing the generated analysis results to the user, and means for interacting with the user in real time. This makes it possible to accurately measure a user's physical characteristics and provide the analysis results in real time. Furthermore, through interaction with the user, it is possible to provide services that meet individual needs.

[0841] "User's physical characteristics" refers to specific data about the user's body, such as height, weight, shoulder width, and arm length.

[0842] An "input device" is a hardware device, such as a sensor or camera, that is used to measure a user's physical characteristics.

[0843] "Measurement Data" is data about a user's physical characteristics collected using an input device.

[0844] A "server" is a computer system for receiving and analyzing measurement data, and generating and providing analysis results.

[0845] "Means for analyzing data" means the process of using algorithms or machine learning models to identify the user's physical characteristics based on the received measurement data.

[0846] "Analysis results" are detailed information about the user's physical characteristics generated by the data analysis means.

[0847] "Real-time interaction means" refers to interfaces and technologies that allow for instant communication with users.

[0848] This invention provides a system for accurately measuring a user's physical characteristics and providing analysis results in real time. The system includes an input device for measuring the user's physical characteristics, a terminal for collecting measurement data, a server for analyzing the collected data, and a means for providing the user with the analysis results.

[0849] The system's program is structured as follows: First, the user logs in to the system and measures their physical characteristics using input devices such as a 3D scanner and a weighing scale. These input devices collect data such as the user's height, weight, shoulder width, and arm length. Specific hardware used includes a 3D scanner, high-resolution camera, and weighing scale.

[0850] The device transmits the data collected from these input devices to a server in real time using a secure communication protocol (e.g., HTTPS). The server uses Python scripts and machine learning libraries such as TENSORFLOW to analyze the received data. The analysis results are generated as a report detailing the user's physical characteristics.

[0851] As a concrete example, consider a scenario in which a user uses the system to measure their own physical characteristics. The user stands in front of a 3D scanner connected to the system and steps on a scale. The camera takes a picture of the user's entire body, and the sensor measures the user's weight. These data are then sent to the server via the device.

[0852] The server analyzes the received data and calculates the user's height, weight, shoulder width, arm length, etc. The analysis results are generated as a report detailing the user's physical characteristics. The generated report is sent to the user's device, where the user can view the report.

[0853] An example of a prompt to input to a generative AI model is as follows:

[0854] Use a 3D scanner and scale to collect data to analyze your users' physical characteristics, calculate their height, weight, shoulder width, and arm length, and generate a detailed report.

[0855] Using this prompt, the generative AI model can accurately analyze the user's physical characteristics and provide the necessary information.

[0856] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0857] Step 1:

[0858] A user logs in to the system.

[0859] Input: Username and Password

[0860] Output: Authentication result (success or failure)

[0861] Specific operation: The user enters the username and password on the system login screen and clicks the "Login" button. The system checks the entered authentication information and authenticates the user. If authentication is successful, the user can proceed to the next step.

[0862] Step 2:

[0863] The user uses an input device to measure a physical characteristic.

[0864] Input: User's physical characteristics (height, weight, shoulder width, arm length, etc.)

[0865] Output: Measurement data

[0866] How it works: The user stands in front of a 3D scanner connected to the system and steps on a scale. The camera captures a full-body image of the user, and the sensor measures the user's weight. The user must remain still until the measurement is complete.

[0867] Step 3:

[0868] The device collects the measurement data.

[0869] Input: Measurement data from a 3D scanner and a weighing scale

[0870] Output: Collected measurement data

[0871] How it works: The device collects data from the 3D scanner and the scale in real time, and stores the collected data in temporary memory on the device.

[0872] Step 4:

[0873] The terminal transmits the collected data to the server.

[0874] Input: Collected measurement data

[0875] Output: Data sent to the server

[0876] Specific operation: The device encrypts the collected data and sends it to the server using the HTTPS protocol. Once the data transmission is complete, the device displays a notification.

[0877] Step 5:

[0878] The server analyzes the data.

[0879] Input: Measurement data sent to the server

[0880] Output: Analysis results

[0881] Specific Operation: The server runs a Python script to analyze the received data. The script applies a machine learning model using the TENSORFLOW library to identify the user's physical characteristics.

[0882] Step 6:

[0883] The server generates the analysis results.

[0884] Input: Parsed data

[0885] Output: Analysis result report

[0886] Specific operation: The server generates a report detailing the user's physical characteristics based on the analysis results. The report is generated in PDF format and saved on the server for the user to download.

[0887] Step 7:

[0888] The server provides the analysis results to the user.

[0889] Input: Generated analysis result report

[0890] Output: Report provided to the user

[0891] Specific operation: The server sends a download link for the generated report to the user's device. The user can click the link on the device to download and view the report.

[0892] (Application example 1)

[0893] 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."

[0894] Traditional try-on services in physical stores require users to actually try on the clothes, which is time-consuming and laborious. Additionally, there is a lack of systems that suggest optimal clothing sizes based on the user's physical characteristics, making it difficult for users to find clothes that fit them. Furthermore, the results of virtual try-on services are not provided in real time, which can lead to lower user satisfaction.

[0895] 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.

[0896] In this invention, the server includes means for analyzing the user's physical characteristics, means for proposing optimally sized clothing based on the analysis results, means for the user to virtually try on the clothing selected by the user, means for providing the user with the results of the virtual try-on, and means for interacting with the user in real time. This allows the user to efficiently find clothing that suits them even in a physical store, and improves user satisfaction because the user can check the results of the try-on in real time through the virtual try-on.

[0897] "Means for analyzing the user's physical characteristics" refers to a device or system that uses input devices such as sensors or cameras to measure the user's physical characteristics such as height, weight, shoulder width, arm length, etc., and performs analysis based on that data.

[0898] The "means for proposing optimally sized clothing based on analysis results" refers to a device or system that selects and proposes optimally sized clothing to a user based on the analysis results of the user's physical characteristics.

[0899] "Means for virtually trying on clothes selected by a user" refers to a device or system that allows a user to try on clothes selected by a user in a virtual space, while fitting the clothes to the user's body.

[0900] The "means for providing the user with the results of the virtual try-on" refers to a device or system that displays and provides the user with the results of the virtual try-on as images or videos.

[0901] A "means for interacting with a user in real time" is a device or system that communicates with a user in real time and responds to the user's questions and requests.

[0902] The system for implementing the present invention includes a series of means for analyzing the user's physical characteristics, proposing clothes of the most suitable size, and performing virtual fitting. Specific embodiments of this system will be described below.

[0903] First, the user captures their own physical characteristics using the smartphone camera. The smartphone is equipped with image processing libraries such as OpenCV (registered trademark), which captures video in real time. The captured video is input into a generative AI model called "Body Measurement Model," which analyzes the user's physical characteristics, such as height, weight, shoulder width, and arm length.

[0904] Next, based on the analysis results, the system recommends the best-fit clothing size for the user. The suggestions are made by a generative AI model called "VirtualTryOnModel," which selects the clothing size that best suits the user's physical characteristics.

[0905] To allow users to virtually try on the selected clothing, the system uses "VirtualTryOnModel" to generate a virtual try-on image that matches the user's body. This virtual try-on image is displayed on the smartphone screen and provided to the user.

[0906] Furthermore, the system has a means of interacting with users in real time: when a user inputs a question or request, the system uses a generative AI model to generate an appropriate response and provide it to the user, allowing the user to receive feedback in real time.

[0907] For example, by inputting the following prompts into a generative AI model, it can suggest the optimal clothing size for the user:

[0908] "The user is 170cm tall, weighs 65kg, has a shoulder width of 45cm, and has an arm length of 60cm. Based on this data, please suggest the best clothing size for them."

[0909] In this way, users can efficiently find clothes that suit them even in physical stores, and can check the fitting results in real time through virtual try-on, thereby increasing user satisfaction.

[0910] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0911] Step 1:

[0912] The user captures their physical characteristics using the smartphone camera.

[0913] Input: Smartphone camera image

[0914] Output: Image data of the captured user's physical characteristics

[0915] How it works: The user turns on the smartphone camera and takes a full-body photo of themselves. The smartphone captures the video in real time and saves it as image data.

[0916] Step 2:

[0917] The device inputs the captured image data into a generated AI model called "Body Measurement Model" to analyze the user's physical characteristics.

[0918] Input: Captured image data of the user's physical characteristics

[0919] Output: Analysis result data such as user's height, weight, shoulder width, arm length, etc.

[0920] Specific operation: The device inputs image data into the "Body Measurement Model," which then processes and analyzes the image. As a result of the analysis, the user's physical characteristics are output as numerical data.

[0921] Step 3:

[0922] Based on the analysis results, the server uses a generative AI model called "VirtualTryOnModel" to suggest the optimal size of clothing.

[0923] Input: Analysis result data of user's physical characteristics

[0924] Output: Data suggesting optimal clothing sizes

[0925] Specific operation: The server inputs the analysis results data into "VirtualTryOnModel," which then selects the clothing size that best suits the user's physical characteristics. As a result, information on the optimal clothing size is output.

[0926] Step 4:

[0927] To allow users to virtually try on the selected clothes, the device uses "VirtualTryOnModel" to generate a virtual try-on image.

[0928] Input: Analysis result data of user's physical characteristics, data of selected clothing

[0929] Output: Virtual try-on image

[0930] Specific operation: The device inputs the user's physical characteristics data and selected clothing data into "VirtualTryOnModel," which then generates a virtual try-on image tailored to the user's body in the virtual space. The generated virtual try-on image is then output.

[0931] Step 5:

[0932] The device provides the user with the results of the virtual try-on.

[0933] Input: Virtual try-on image

[0934] Output: Virtual try-on image displayed to the user

[0935] Specific operation: The device displays the generated virtual try-on image on the smartphone screen and provides it to the user, who can then check the results of the virtual try-on on the screen.

[0936] Step 6:

[0937] The server interacts with the user in real time and generates responses based on questions and requests.

[0938] Input: User question or request

[0939] Output: Generated response data

[0940] How it works: When a user inputs a question or request through their smartphone, the server uses the generative AI model to generate an appropriate response, and the generated response data is provided to the user.

[0941] Example 2

[0942] 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."

[0943] Conventional training plan providing systems were unable to fully utilize the user's physical characteristics and performance data, making it difficult to provide optimal training plans for each individual user. Furthermore, the generated training plans were not specific, making it unclear how the user should actually proceed with their training. Furthermore, the lack of real-time feedback and dialogue made it difficult for users to maintain motivation and practice effectively.

[0944] 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.

[0945] In this invention, the server includes a means for analyzing a user's physical characteristics, a means for providing a training plan that maximizes the user's performance based on the analysis results, a means for generating a video corresponding to the training plan, a means for providing the generated video to the user, a means for interacting with the user in real time, a means for receiving and analyzing the user's performance data, a means for generating an optimal training plan using a generative AI model, and a means for displaying the generated training plan to the user. This makes it possible to provide an optimal training plan based on the user's individual characteristics and clearly show specific training content. Furthermore, real-time feedback and interaction can maintain the user's motivation and enable effective training.

[0946] "User" refers to an individual who utilizes the System to provide their performance data and receive an optimized training plan.

[0947] "Physical characteristics" refers to information about the user's body, such as the user's physique, muscle strength, flexibility, and movement patterns.

[0948] "Performance data" refers to data related to the user's exercise and movements, including, for example, pitching form, speed, accuracy, and past practice history.

[0949] "Means of analysis" refers to the technology and methods used to analyze a user's physical characteristics and performance data and derive the optimal training plan for the user.

[0950] "Practice plan" refers to a plan that includes specific menus, their order, and frequency of practice for the user to practice effectively.

[0951] "Means for generating videos" refers to the technology or method for creating videos corresponding to the user's training plan.

[0952] "Real-time interaction means" refers to technologies and methods for communicating with users in real time and providing feedback and instructions.

[0953] A "generative AI model" refers to a model that uses artificial intelligence to analyze user data and generate an optimal training plan.

[0954] "Display means" refers to the technology or method for visually presenting the generated practice plan or video to the user.

[0955] MODE FOR CARRYING OUT THE INVENTION

[0956] The present invention is a system that provides an optimal training plan based on a user's physical characteristics and performance data. Specific embodiments of this system are described below.

[0957] System configuration

[0958] The system includes a user's terminal, a server, a generative AI model, and a communication protocol for linking these components. The terminal is a device such as a smartphone or tablet that the user uses to input data and check the training plan. The server is a computer system that analyzes the data and generates the training plan. The generative AI model is an artificial intelligence model that generates the optimal training plan based on the user's data.

[0959] Hardware and software used

[0960] The server uses Python® and TENSORFLOW® to analyze data and run generative AI models. The device inputs user data through a dedicated application and communicates with the server. Communication uses a secure protocol (e.g., HTTPS).

[0961] Data entry and analysis

[0962] Users use the device to input their own performance data, including videos of their pitching form, pitching speed, accuracy, and past training history. The device then transmits this data to a server, which analyzes the received data and evaluates the user's physical characteristics and performance.

[0963] Generate a practice plan

[0964] The server then uses the generative AI model to generate an optimal training plan based on the analysis results. For example, if the user is a baseball player and it is determined that sidearm pitching is suitable, the server generates a sidearm pitching training plan. This training plan includes warm-ups, repeated practice of basic movements, practice in a real-game setting, and a cool-down.

[0965] Providing practice plans

[0966] The server sends the generated practice plan to the device, which displays the plan to the user, allowing the user to check the plan on the screen. This allows the user to understand the specific practice content and practice effectively.

[0967] Specific examples

[0968] The user uses a smartphone to film a video of their pitching form and uploads it to a dedicated application. The device then sends this video data, along with pitching speed and accuracy data, to a server. The server then analyzes the video data using Python (registered trademark) and TENSORFLOW (registered trademark) to identify the user's pitching style. Based on the analysis results, a generative AI model is used to generate a sidearm pitching practice plan. The generated practice plan includes a warm-up (10 minutes), repetitive practice of basic sidearm pitching movements (30 minutes), practice in a real-game setting (20 minutes), and a cool-down (10 minutes). The server then sends this practice plan to the device, which then displays it to the user. The user can review and execute the practice plan on their smartphone screen.

[0969] Prompt Sentence Examples

[0970] "Generate an optimal training plan based on the user's pitching form and performance data. The user is a baseball player and is suitable for a sidearm pitch. The training plan should include specific exercises, their order, and frequency."

[0971] In this way, the system provides users with an optimal training plan and supports effective practice.

[0972] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0973] Step 1:

[0974] The user enters performance data into a terminal.

[0975] Users use a smartphone or tablet to input data such as video of their pitching form, pitching speed, accuracy, and past training history. The input data is then saved on the device via a dedicated application.

[0976] Input: Video of pitching form, pitching speed, accuracy, past practice history

[0977] Output: Performance data stored on the device

[0978] Step 2:

[0979] The terminal sends the input data to the server.

[0980] The terminal transmits the performance data entered by the user to the server using a secure communication protocol (e.g., HTTPS), which ensures the safety of the data.

[0981] Input: Performance data stored on the device

[0982] Output: Performance data sent to the server

[0983] Step 3:

[0984] The server receives the data and begins analyzing it.

[0985] The server receives performance data sent from the device. It uses Python (registered trademark) and TENSORFLOW (registered trademark) to analyze the received data. The server analyzes the video of the pitching form to identify the user's pitching style. It also evaluates the user's performance based on pitching speed and accuracy data.

[0986] Input: Performance data sent to the server

[0987] Output: Analysis results (pitching style, performance evaluation)

[0988] Step 4:

[0989] The server uses the generated AI model to generate an optimal training plan.

[0990] The server then uses the generative AI model to generate an optimal training plan based on the analysis results. For example, if a sidearm pitch is determined to be suitable, it generates a sidearm pitch training plan. This training plan includes warm-ups, repeated practice of basic movements, practice in a real-game setting, and a cool-down.

[0991] Input: Analysis results (pitching style, performance evaluation)

[0992] Output: Generated practice plan

[0993] Step 5:

[0994] The server transmits the generated training plan to the terminal.

[0995] The server transmits the generated training plan to the device using a secure communication protocol, which protects the user's privacy.

[0996] Input: Generated practice plan

[0997] Output: Practice plan sent to the device

[0998] Step 6:

[0999] The terminal displays the training plan to the user.

[1000] The device displays the training plan received from the server to the user. The user can check and execute the training plan on the device screen. For example, the user can check the training menu, its order, and the frequency of training through a smartphone application.

[1001] Input: Practice plan sent to device

[1002] Output: The practice plan displayed to the user

[1003] In this way, the system provides the user with an optimal training plan and supports effective training.

[1004] (Application example 2)

[1005] 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."

[1006] Conventional training plan providing systems can provide optimal training plans based on the user's physical characteristics, but they have the problem of not being able to analyze machine movement data, find optimal movement patterns, and provide training plans based on those movement patterns. Furthermore, they lack the functionality to provide the generated movement procedures to the machine and to communicate with the machine in real time, which makes it difficult to achieve efficient training.

[1007] 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.

[1008] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for analyzing machine motion data and finding optimal motion patterns, means for providing a training plan based on the optimal motion patterns, means for generating motion procedures corresponding to the training plan, means for providing the generated motion procedures to the machine, and means for interacting with the machine in real time. This allows for the provision of optimal training plans for both the user and the machine, enabling efficient training.

[1009] "Means for analyzing the user's physical characteristics" refers to a device or software for collecting and analyzing data on the user's physical characteristics, such as physique, muscle strength, and flexibility.

[1010] "Means for providing a training plan that maximizes the effectiveness of the analysis results" refers to a device or software that creates and provides optimal training menus and training plans to users based on the results of an analysis of their physical characteristics.

[1011] "Means for generating videos corresponding to a training plan" refers to a device or software that creates videos based on a provided training plan to serve as a reference for the user when carrying out the plan.

[1012] The "means for providing the generated video to the user" refers to a device or software for visually providing the generated video to the user.

[1013] A "means for interacting with a user in real time" is a device or software that communicates with a user in real time and provides feedback and instructions.

[1014] "Means for analyzing machine operation data and finding optimal operation patterns" refers to devices or software that collect and analyze machine operation data and identify optimal operation patterns from that data.

[1015] The "means for providing a training plan based on an optimal movement pattern" is a device or software for creating and providing an efficient training plan for a machine based on the analyzed optimal movement pattern.

[1016] The "means for generating an operating procedure corresponding to a training plan" refers to a device or software for generating specific operating procedures for a machine to execute a provided training plan based on the plan.

[1017] The "means for providing the generated operating procedure to a machine" refers to a device or software that transmits the generated operating procedure to a machine and causes the machine to execute the operation.

[1018] A "means for interacting with a machine in real time" is a device or software that communicates with a machine in real time and provides feedback and instructions.

[1019] The following system configuration will be described as an embodiment of the present invention.

[1020] The server uses a data collection device and analysis software to analyze the user's physical characteristics. Specifically, it collects data such as the user's physique, muscle strength, and flexibility, and uses Scikit-learn, a Python (registered trademark) library, to analyze this data. Based on the analysis results, it implements an algorithm to provide the user with an optimal training plan. This algorithm generates a training menu and training plan tailored to the user's characteristics.

[1021] Next, video generation software is used to generate a video corresponding to the training plan. For example, libraries such as OpenCV® or FFmpeg are used to create a video that mimics the user's movements. This video serves as a reference for the user as they execute the training plan.

[1022] The generated videos are provided to users, who can watch them on their smartphones, tablets, or other devices. Furthermore, chatbots and voice assistants are implemented to interact with users in real time, allowing users to receive feedback and instructions during practice.

[1023] Meanwhile, the server collects machine operation data using sensors and cameras to analyze it and find optimal operation patterns. To analyze this data, it again uses machine learning libraries such as Scikit-learn. Based on the analysis results, it implements an algorithm that provides the machine with an optimal training plan.

[1024] To generate the motion procedures corresponding to the training plan, motion simulation software is used. For example, ROS (Robot Operating System) is used to create the specific motion procedures that the machine will follow when executing the training plan. The generated motion procedures are provided to the machine, which then performs the operations according to them.

[1025] Additionally, it implements an interface for real-time interaction with the machine, allowing it to monitor its operating status and provide feedback and instructions as needed.

[1026] As a concrete example, consider a system that analyzes the motion data of a factory robot, finds the optimal motion pattern, and provides a training plan based on that motion pattern. For example, if it is determined that the motion of a robot's arm lifting a particular part is inefficient, the system analyzes the motion data to find the optimal way to lift it, and provides a training plan to help the robot learn that lifting method.

[1027] Examples of prompts for a generative AI model might include:

[1028] "Analyze the motion data of factory robots, find the optimal motion patterns, and generate a training plan based on those motion patterns. The training plan should include specific motion steps, frequency, and sequence."

[1029] In this way, an optimal training plan can be provided for both the user and the machine, enabling efficient training.

[1030] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1031] Step 1:

[1032] The server collects the user's physical characteristics. Specifically, it uses sensors and cameras to obtain data on the user's physique, muscle strength, flexibility, etc. The input is the user's physical characteristics data, and the output is the collected data.

[1033] Step 2:

[1034] The server analyzes the collected physical characteristic data. Specifically, it uses the Python (registered trademark) Scikit-learn library to standardize the data and perform clustering and regression analysis. The input is the collected physical characteristic data, and the output is the analysis results.

[1035] Step 3:

[1036] The server generates an optimal training plan for the user based on the analysis results. Specifically, it uses an algorithm to create a training menu and training plan tailored to the user's characteristics. The input is the analysis results, and the output is the training plan.

[1037] Step 4:

[1038] The server generates a video corresponding to the training plan. Specifically, it uses libraries such as OpenCV (registered trademark) and FFmpeg to create a video that mimics the user's appearance. The input is the training plan, and the output is the generated video.

[1039] Step 5:

[1040] The device provides the generated video to the user, specifically, allowing the user to watch the video through a smartphone or tablet. The input is the generated video, and the output is the video that the user watches.

[1041] Step 6:

[1042] The server interacts with the user in real time, using a chatbot or voice assistant to provide feedback and instructions to the user. The input is the user's question or feedback, and the output is the server's response.

[1043] Step 7:

[1044] The server collects machine operation data. Specifically, it acquires the machine operation data using sensors and cameras. The input is the machine operation data, and the output is the collected data.

[1045] Step 8:

[1046] The server analyzes the collected behavioral data. Specifically, it uses the Scikit-learn library to standardize the data and perform clustering and regression analysis. The input is the collected behavioral data, and the output is the analysis results.

[1047] Step 9:

[1048] The server generates an optimal training plan for the machine based on the analysis results. Specifically, it uses an algorithm to create training menus and movement patterns that match the machine's characteristics. The input is the analysis results, and the output is the training plan.

[1049] Step 10:

[1050] The server generates an operating procedure corresponding to the training plan. Specifically, it uses ROS (Robot Operating System) to create a specific operating procedure for the machine to execute the training plan. The input is the training plan, and the output is the operating procedure.

[1051] Step 11:

[1052] The server provides the generated operation procedure to the machine. Specifically, it transmits the operation procedure to the machine and causes it to execute the operation. The input is the operation procedure, and the output is the operation executed by the machine.

[1053] Step 12:

[1054] The server interacts with the machine in real time. Specifically, it monitors the machine's operating status and provides feedback and instructions as needed. The input is the machine's operating status data, and the output is feedback and instructions from the server.

[1055] Example 3

[1056] Next, a description will be given of Example 3 of Form Example 3. 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."

[1057] Conventional training planning systems have difficulty providing training plans and videos that fully take into account the user's physical characteristics, and lack real-time interactive functionality. As a result, they lack support for users to practice effectively. Furthermore, it is difficult to provide appropriate responses to users' questions and feedback.

[1058] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1059] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating a 3D model corresponding to the training plan, means for generating a training video using the generated 3D model, means for providing the generated video to the user, means for interacting with the user in real time, means for converting the user's voice into text, means for generating appropriate responses using a generative AI model, and means for communicating the generated responses to the user by voice. This makes it possible to provide training plans and videos based on the user's physical characteristics, and further to provide appropriate responses to the user's questions and feedback through real-time interaction.

[1060] "Means for analyzing the user's physical characteristics" refers to a device or software that analyzes data such as height, weight, and body type provided by the user and identifies the user's physical characteristics.

[1061] The "means for providing an exercise plan" refers to a device or software that creates and provides an optimal exercise plan to a user based on the results of an analysis of the user's physical characteristics.

[1062] A "means for generating a three-dimensional model" is a device or software for creating a three-dimensional model of a user's appearance based on the user's physical characteristics.

[1063] The "means for generating a practice video" is a device or software for using the generated three-dimensional model to create a video corresponding to the user's practice plan.

[1064] The "means for providing the generated video to the user" refers to a device or software for visually providing the created practice video to the user.

[1065] A "means for real-time user interaction" is any device or software that allows a user to communicate with the system in real-time.

[1066] A "means for converting user speech to text" is a device or software for converting user speech into text data.

[1067] A "means for generating an appropriate response using a generative AI model" is a device or software that receives text data as input and uses an artificial intelligence model to generate an appropriate response.

[1068] A "means for audibly communicating a generated response to a user" is a device or software for converting a generated text response into speech and communicating it to a user.

[1069] The present invention provides a system that provides a training plan based on the user's physical characteristics, generates a 3D model to create a training video, and interacts with the user in real time. Specific embodiments of this system are described below.

[1070] Hardware and software used

[1071] server

[1072] The server performs processing using the following software and technology:

[1073] 3D Modeling Software: Using three-dimensional modeling software such as Blender, a three-dimensional model based on the user's physical characteristics is generated.

[1074] Generative AI models: Generative AI models, such as OpenAI's GPT-3, are used to generate appropriate responses to user questions.

[1075] Speech recognition technology: Converts the user's voice into text using Google® Cloud Speech-to-Text API, etc.

[1076] Terminal

[1077] The terminal performs processing using the following software and technologies:

[1078] Speech synthesis technology: Uses technology to convert text data into speech and communicate it to the user.

[1079] Data processing and calculation

[1080] Entering the user's physical characteristics

[1081] Users input their physical characteristics data (height, weight, body type, etc.) through the system interface, which is then sent to the server for analysis.

[1082] 3D model generation

[1083] The server receives the user's physical characteristic data and uses 3D modeling software such as Blender to generate a 3D model of the user, which faithfully reproduces the user's physical characteristics.

[1084] Creating practice videos

[1085] The server uses the generated 3D model to create a training video. For example, if a user wants to practice push-ups, the server simulates the push-up movement on the 3D model and outputs it as a video. This video serves as a reference for the user to practice with the correct form.

[1086] Real-time interaction

[1087] During practice, users can ask the system questions and seek feedback. For example, they can ask questions such as, "Is this form correct?" by voice. The device converts the user's voice into text using the Google® Cloud Speech-to-Text API and sends it to the server. The server inputs the received text data into OpenAI®'s GPT-3® to generate an appropriate response. The generated response is then communicated to the user by voice via the device.

[1088] Examples of concrete examples and prompts

[1089] Specific examples

[1090] If a user asks, "What is the correct form for squats?", the process goes like this:

[1091] 1. The user asks aloud, "What is the correct form for squatting?"

[1092] 2. The device converts the speech into text using the Google® Cloud Speech-to-Text API.

[1093] 3. The server inputs the converted text into OpenAI's GPT-3 to generate an appropriate response.

[1094] 4. The device communicates the generated response to the user using speech synthesis technology.

[1095] Prompt Sentence Examples

[1096] "If a user asks about the correct form for squats, generate an appropriate response."

[1097] By inputting this prompt sentence into the generative AI model, the system generates an appropriate response to the user's question. The flow of the identification process in Example 3 will be described with reference to FIG.

[1098] Step 1:

[1099] The user inputs the physical characteristic data.

[1100] The user inputs their own physical characteristics data (height, weight, body type, etc.) through the system interface. This data is sent to the server. The input data becomes the basis for identifying the user's physical characteristics.

[1101] Step 2:

[1102] The server analyzes the user's physical characteristics.

[1103] The server analyzes the received physical characteristic data to identify the user's physical characteristics. Specifically, it uses data analysis algorithms to analyze information such as the user's height, weight, and body type. The output is a dataset that details the user's physical characteristics.

[1104] Step 3:

[1105] The server generates the three-dimensional model.

[1106] Based on the analysis results, the server uses 3D modeling software such as Blender to generate a 3D model of the user. The input is the user's physical characteristics data, and the output is a 3D model that mimics the user's appearance. Specifically, the modeling software is used to reproduce the user's body shape and posture.

[1107] Step 4:

[1108] The server generates the practice video.

[1109] The server uses the generated 3D model to create a video corresponding to the user's practice plan. The input is the 3D model and the practice plan, and the output is a practice video. Specifically, the server simulates practice movements for the 3D model and outputs the video.

[1110] Step 5:

[1111] The server provides the generated video to the user.

[1112] The server provides the generated practice video to the user. The input is the practice video, and the output is a video file that the user can visually check. Specifically, the server sends the video file to the user's device.

[1113] Step 6:

[1114] Ask questions and ask for feedback as users practice.

[1115] The user can ask the system questions and request feedback during practice. The input is the user's voice, and the output is voice data. Specifically, the user inputs questions by voice through a microphone.

[1116] Step 7:

[1117] The device converts the user's speech into text.

[1118] The device converts the user's voice into text using the Google® Cloud Speech-to-Text API. The input is the user's voice data, and the output is text data. Specifically, the device converts the voice data into text in real time using voice recognition technology.

[1119] Step 8:

[1120] The server generates an appropriate response.

[1121] The server inputs the received text data into OpenAI's GPT-3 (registered trademark) to generate an appropriate response. The input is text data, and the output is response text. Specifically, the server uses a generative AI model to generate an appropriate response to the user's question.

[1122] Step 9:

[1123] The terminal then vocalizes the generated response to the user.

[1124] The terminal communicates the generated response to the user using speech synthesis technology. The input is the response text, and the output is voice data. Specifically, the text data is converted into voice and communicated to the user through a speaker.

[1125] (Application example 3)

[1126] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1127] Conventional training planning systems have difficulty providing training plans that fully take into account the user's physical characteristics, and lack real-time interactive functionality. This has led to issues such as users being unable to practice efficiently, reducing the effectiveness of their training. Furthermore, there has been a lack of systems that can reproduce the movements of factory workers and efficiently learn work procedures.

[1128] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1129] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for generating a 3D model based on the user's physical characteristics, means for generating a training video for work procedures using the generated 3D model, means for responding to the user's questions and feedback using speech recognition technology and natural language processing technology, and means for responding to the user by voice using speech synthesis technology. This enables the provision of a training plan that takes the user's physical characteristics into consideration and efficient training through real-time interaction.

[1130] "Means for analyzing the user's physical characteristics" refers to devices or software for collecting and analyzing physical data such as the user's height, weight, age, and body type.

[1131] "Means for providing a training plan that maximizes the user's performance" refers to devices and software that design and provide a training program that is optimized for the user's physical characteristics and abilities based on the analysis results.

[1132] "Means for generating videos corresponding to a practice plan" refers to a device or software that creates videos that can be used as a reference when a user actually practices based on the provided practice plan.

[1133] The "means for providing the generated video to the user" refers to a device or software for visually providing the generated practice video to the user.

[1134] "Means for interacting with users in real time" refers to devices or software that allow users to respond in real time when they ask questions or give feedback to the system.

[1135] "Means for generating a 3D model based on the user's physical characteristics" refers to devices or software for creating a 3D model of a user based on the user's physical characteristics.

[1136] "Means for generating a practice video of work procedures using a generated 3D model" refers to a device or software that uses a generated 3D model to create a practice video that shows the work procedures that a user should perform.

[1137] "Means for responding to user questions and feedback using speech recognition technology and natural language processing technology" refers to devices and software that recognize a user's speech, analyze the content using natural language processing technology, and generate appropriate responses.

[1138] "Means for responding to a user by voice using speech synthesis technology" refers to a device or software for conveying the generated response to the user as voice.

[1139] The system for implementing this invention analyzes the user's physical characteristics, provides an optimal exercise plan based on the analysis, and generates videos corresponding to the exercise plan. The system also has the ability to interact with the user in real time and respond to the user's questions and feedback.

[1140] Hardware and software used

[1141] Hardware:

[1142] Camera: Used to scan the user's physical characteristics.

[1143] Microphone: Used to recognize the user's voice.

[1144] Speaker: Used to communicate system audio responses to the user.

[1145] software:

[1146] 3D Modeling Library: Generates 3D models based on the user's physical characteristics.

[1147] Video generation library: Generate practice videos using generated 3D models.

[1148] Speech Recognition Library (speech_recognition): Recognizes the user's voice.

[1149] Natural language processing library: Analyzes user questions and generates appropriate responses.

[1150] Speech synthesis library (pyttsx3): Outputs the generated response as voice.

[1151] Specific operation of the system

[1152] 1. Scanning your physical characteristics:

[1153] It uses a camera to scan the user's physical features and collects data, which is then analyzed using a 3D modeling library to generate a 3D model of the user.

[1154] 2. Providing a practice plan:

[1155] Based on the generated 3D model and the user's physical characteristics, an optimal training plan is designed, which is customized to the user's abilities and goals.

[1156] 3. Generate practice videos:

[1157] Using a video generation library, videos corresponding to the practice plan are generated, which serve as a reference for users when actually practicing.

[1158] 4. Real-time interaction:

[1159] The system recognizes the user's voice using a microphone and analyzes it using a speech recognition library. The analyzed voice data is interpreted as questions or feedback using a natural language processing library. The system generates an appropriate response and responds aloud through the speaker using a speech synthesis library.

[1160] Specific examples

[1161] New workers use the system to learn factory procedures: they ask the system, "What should I do next?", and the system responds, "Next, place the item on the conveyor belt."

[1162] Prompt Sentence Examples

[1163] User: What should I do next?

[1164] System: Next, place the item on the conveyor belt.

[1165] In this way, the system provides a training plan that takes into account the user's physical characteristics and enables efficient training through real-time dialogue.

[1166] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1167] Step 1:

[1168] Scanning the user's physical characteristics

[1169] The server uses a camera to scan the user's physical characteristics. As input, it takes the user's physical data (height, weight, body type, etc.) and analyzes this data using a 3D modeling library. As output, it generates a 3D model of the user.

[1170] Step 2:

[1171] Providing practice plans

[1172] The server designs an optimal training plan based on the generated 3D model and the user's physical characteristics. As input, it uses the user's 3D model and physical characteristics data and applies a training plan generation algorithm. As output, it provides the user with an optimized training plan.

[1173] Step 3:

[1174] Creating practice videos

[1175] The server uses a video generation library to generate videos corresponding to the practice plan. As input, it uses the practice plan and a 3D model of the user and applies a video generation algorithm. As output, a practice video is generated that can be used as a reference for the user when actually practicing.

[1176] Step 4:

[1177] Providing practice videos

[1178] The server provides the generated practice video to the user. As input, it uses the generated practice video and sends the video to the user's device. As output, the user can visually check the practice video.

[1179] Step 5:

[1180] Initiating real-time dialogue

[1181] When a user asks a question or gives feedback during practice, the server recognizes the user's voice using a microphone. As input, the server acquires the user's voice data and analyzes it using a speech recognition library. As output, the user's question or feedback is acquired as text data.

[1182] Step 6:

[1183] Parsing questions and feedback

[1184] The server analyzes the acquired text data using a natural language processing library. It uses the text data of the user's question and feedback as input and applies natural language processing algorithms. As output, an appropriate response is generated.

[1185] Step 7:

[1186] Generate a voice response

[1187] The server converts the generated response into speech data using a speech synthesis library. As input, it applies a speech synthesis algorithm using the text data of the generated response. As output, a speech response is generated for the user.

[1188] Step 8:

[1189] Providing voice responses

[1190] The server provides the generated voice response to the user through the speaker. The server uses the generated voice data as input and outputs the voice from the speaker. As output, the user can receive the voice response.

[1191] 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.

[1192] "Example 1"

[1193] In one embodiment of the present invention, an emotion engine is incorporated. This emotion engine recognizes emotions from the user's tone of voice and facial expressions. Specifically, it analyzes the tone, pitch, volume, and other aspects of the user's voice while practicing, and uses this information to infer the user's emotional state. It also uses a camera or other visual sensors to capture the user's facial expressions and interpret emotions from those expressions.

[1194] "Example 2"

[1195] Furthermore, in one embodiment of the present invention, an emotion engine is utilized during interaction with the user. Specifically, the emotion engine recognizes the user's emotions in real time during interaction with the user and adjusts the style and content of the system's interaction according to those emotions. For example, if the system senses that the user is struggling with practice, it will provide words of encouragement. Conversely, if the system senses that the user is enjoying practice, it will engage in interaction that shares that enjoyment.

[1196] "Example 3"

[1197] In one embodiment of the present invention, the emotion engine is also used to create practice plans. Specifically, the system provides an optimal practice plan taking into account the user's emotional state. For example, if the user feels unmotivated about practicing, the system can provide a practice plan that motivates the user. Conversely, if the user feels excited about practicing, the system can provide a practice plan that maintains that excitement.

[1198] The processing flow of each embodiment will be described below.

[1199] "Example 1"

[1200] Step 1: When the user starts practicing, the system activates sensors and cameras to capture the user's tone of voice and facial expressions.

[1201] Step 2: The emotion engine analyzes the captured tone and facial expression data to estimate the user's current emotional state.

[1202] Step 3: The emotion engine feeds the estimated emotional state back to the system, which uses that information to interact with the user and adjust the training plan.

[1203] "Example 2"

[1204] Step 1: When a user starts interacting with the system, the emotion engine recognizes the user's emotions in real time.

[1205] Step 2: The emotion engine instructs the system to adjust the style and content of the interaction based on the recognized emotion.

[1206] Step 3: The system adjusts the style and content of the dialogue according to the instructions from the emotion engine and continues the dialogue with the user.

[1207] "Example 3"

[1208] Step 1: When the user starts practicing, the emotion engine monitors the user's emotional state in real time.

[1209] Step 2: The emotion engine generates an optimal exercise plan based on the user's emotional state.

[1210] Step 3: The system presents the user with the practice plan provided by the emotion engine and proceeds with the practice.

[1211] Example 1

[1212] 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."

[1213] Conventional systems have difficulty not only analyzing a user's physical characteristics but also grasping their emotional state in real time and providing appropriate training plans and feedback based on that. Furthermore, there has been a lack of a means to comprehensively analyze a user's physical characteristics and emotional state and provide individually optimized training plans.

[1214] 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.

[1215] In this invention, the server includes means for measuring the user's physical characteristics, means for analyzing the measurement data, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating a video corresponding to the training plan, means for providing the generated video to the user, means for capturing the user's tone of voice and facial expression, means for analyzing the captured data to estimate the user's emotional state, and means for interacting with the user in real time. This makes it possible to comprehensively analyze the user's physical characteristics and emotional state and provide an individually optimized training plan.

[1216] "Means for measuring the user's physical characteristics" refers to a device that uses an input device such as a sensor or camera to measure the user's physical characteristics such as height, weight, shoulder width, and arm length.

[1217] "Means for analyzing measurement data" refers to software or systems that use statistical methods or algorithms based on the measured data to analyze the user's physical characteristics.

[1218] The "means for providing a training plan that maximizes the user's performance" is a system that creates and provides a training plan that is optimized for the user's physical characteristics based on the analysis results.

[1219] The "means for generating a video corresponding to a practice plan" refers to software or a system for generating a video showing specific practice methods and procedures based on the user's practice plan.

[1220] The "means for providing the generated video to the user" refers to an output device such as a display or mobile device for visually providing the generated video to the user.

[1221] The "means for capturing the user's tone of voice and facial expression" is a device that uses an input device such as a microphone or camera to collect the user's tone of voice and facial expression in real time.

[1222] "Means for analyzing captured data to infer a user's emotional state" refers to software or algorithms for analyzing collected audio and video data to infer a user's emotional state.

[1223] "Means for real-time user interaction" refers to an interface or system that communicates with users in real time and provides feedback and instructions.

[1224] MODE FOR CARRYING OUT THE INVENTION

[1225] The present invention is a system that comprehensively analyzes a user's physical characteristics and emotional state and provides an individually optimized training plan. Specific embodiments of this system are described below.

[1226] Hardware and software used

[1227] This system uses the following hardware and software:

[1228] Hardware:

[1229] 3D scanner (e.g., a general 3D scanner)

[1230] Weight scale (e.g., a regular weight scale)

[1231] Camera (e.g. a regular webcam)

[1232] Microphone (e.g., a regular microphone)

[1233] software:

[1234] Python (registered trademark) and its libraries (NumPy, Pandas, OpenCV (registered trademark), Librosa, TENSORFLOW (registered trademark), etc.)

[1235] Data collection and transmission

[1236] When a user stands in front of the system, a 3D scanner and a scale connected to the device measure the user's physical characteristics. For example, the 3D scanner measures the user's height, shoulder width, and arm length, and the scale measures their weight. This data is sent to a server in real time. The data is often sent in JSON format.

[1237] Data analysis

[1238] The server analyzes the received data using Python (registered trademark) NumPy and Pandas. For example, it calculates the user's BMI based on their height and weight. Based on the analysis results, it creates a training plan optimized for the user's physical characteristics.

[1239] Providing practice plans

[1240] The server generates a training plan based on the analysis results and generates a video corresponding to that plan. The generated video is provided to the user via their device. The video mimics the user's appearance and shows specific training methods and procedures.

[1241] Emotional state capture and analysis

[1242] The microphone and camera connected to the device capture the user's voice tone and facial expressions. The audio data is sent to the server in WAV format, and the video data in MP4 format. The server analyzes the audio data using Librosa to extract the tone, pitch, and volume of the voice. The video data is analyzed using OpenCV (registered trademark) and TENSORFLOW (registered trademark), and emotions are recognized from facial expressions.

[1243] Emotional state estimation and feedback

[1244] The server integrates the results of the analysis of the audio and video data to estimate the user's emotional state, and provides appropriate feedback and instructions to the user in real time based on the estimated emotional state.

[1245] Examples of concrete examples and prompts

[1246] Specific examples

[1247] The user stands in front of the system and measures their physical characteristics using a 3D scanner and a scale. The measurement data is sent to the server and analyzed using Python (registered trademark)'s NumPy and Pandas. The analysis results calculate the user's BMI. At the same time, the user's speech is collected by a microphone and analyzed by Librosa. Facial expression data captured by the camera is used to recognize emotions using OpenCV (registered trademark) and TENSORFLOW (registered trademark).

[1248] Prompt Sentence Examples

[1249] "Measure the user's height and weight and calculate their BMI. Also, recognize the user's emotions from their tone of voice and facial expressions."

[1250] In this way, the system can analyze the user's physical characteristics and emotional state in real time and provide an individually optimized exercise plan.

[1251] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1252] Step 1:

[1253] The user stands in front of the system

[1254] Input: User's location

[1255] Operation: The user stands in front of the system and presses the measurement start button.

[1256] Output: Trigger signal to start measurement

[1257] Step 2:

[1258] The device measures the user's physical characteristics

[1259] Input: Trigger signal to start measurement

[1260] How it works: A 3D scanner connected to the device measures the user's height, shoulder width, and arm length, and a scale measures their weight.

[1261] Output: Physical characteristics data (height, shoulder width, arm length, weight)

[1262] Step 3:

[1263] The device sends the measurement data to the server

[1264] Input: Physical characteristics data

[1265] Operation: The device sends the measured data to the server in real time. The data is sent in JSON format.

[1266] Output: Physical characteristics data sent to the server

[1267] Step 4:

[1268] The server analyzes the data

[1269] Input: Physical characteristics data sent to the server

[1270] Operation: The server analyzes the received data using Python (registered trademark) NumPy and Pandas. For example, it calculates BMI from height and weight.

[1271] Output: Analysis results (BMI, etc.)

[1272] Step 5:

[1273] The server generates the analysis results

[1274] Input: Analysis results (BMI, etc.)

[1275] How it works: The server uses the analysis results to create a report about the user's physical characteristics.

[1276] Output: Physical Description Report

[1277] Step 6:

[1278] The device captures the user's tone of voice and facial expressions

[1279] Input: Trigger signal to start measurement

[1280] How it works: A microphone attached to the device collects the user's voice, and a camera captures facial expressions.

[1281] Output: Audio data (WAV format), video data (MP4 format)

[1282] Step 7:

[1283] The device sends audio and video data to the server.

[1284] Input: Audio data, video data

[1285] Operation: The device sends the collected audio and video data to the server.

[1286] Output: Audio and video data sent to the server

[1287] Step 8:

[1288] The server analyzes the data using an emotion engine

[1289] Input: Audio and video data sent to the server

[1290] How it works: The server uses Librosa to analyze audio data and extract voice tone, pitch, and volume. It also uses OpenCV (registered trademark) and TENSORFLOW (registered trademark) to analyze video data and recognize emotions from facial expressions.

[1291] Output: Emotion analysis results

[1292] Step 9:

[1293] The server estimates the emotional state

[1294] Input: Sentiment analysis results

[1295] How it works: The server integrates the results of analyzing audio and video data to estimate the user's emotional state.

[1296] Output: Emotional state report

[1297] Step 10:

[1298] The server generates the final result and sends it to the device.

[1299] Input: Physical characteristics report, Emotional state report

[1300] How it works: The server generates the final results of the physical characteristics and emotional state and sends them to the device, which displays the results to the user.

[1301] Output: Final result (integrated report of physical characteristics and emotional state)

[1302] (Application example 1)

[1303] 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."

[1304] Traditional shopping experiences lack personalized product suggestions that take into account the user's physical characteristics and emotional state, making it difficult for users to find the best products. Furthermore, the lack of appropriate responses based on the user's emotional state can degrade the quality of the shopping experience. This can lead to lower user satisfaction and potentially impact store sales.

[1305] 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.

[1306] In this invention, the server includes means for analyzing the user's physical characteristics, means for proposing optimal products based on the analysis results, means for recognizing the user's emotional state, means for adjusting the product proposals based on the recognized emotional state, means for providing the proposals to the user, and means for interacting with the user in real time. This enables personalized product proposals based on the user's physical characteristics and emotional state, thereby improving user satisfaction.

[1307] "Means for analyzing the user's physical characteristics" refers to devices or algorithms that use sensors or cameras to measure the user's physical characteristics, such as height, weight, shoulder width, and arm length, and analyze the data.

[1308] "Means for suggesting optimal products" refers to systems or algorithms that select and suggest the most suitable products for users based on their physical characteristics and emotional state.

[1309] "Means for recognizing the user's emotional state" refers to a device or algorithm that analyzes the user's tone of voice and facial expressions and infers the user's emotional state from that information.

[1310] "Means for tailoring product recommendations based on a perceived emotional state" refers to a system or algorithm that takes into account the user's emotional state and tailors the content and method of product recommendations.

[1311] The "means for providing the user with the proposed content" refers to a device or system for visually or audibly providing the user with the analysis results and proposed product information.

[1312] "Means for interacting with users in real time" refers to systems or algorithms that communicate with users in real time and provide appropriate responses to their questions or requests.

[1313] The system for carrying out the present invention includes a series of means for analyzing the physical characteristics of a user and proposing optimal products. Specific embodiments are described below.

[1314] Hardware Configuration

[1315] Smartphone: A device equipped with a camera, microphone, and sensors that collects the user's physical characteristics and emotional state.

[1316] Server: A computer system for performing data processing and analysis.

[1317] Network: Infrastructure for data communication between smartphones and servers.

[1318] Software Configuration

[1319] Mobile application: Runs on the iOS or Android platform and provides the user interface.

[1320] Emotion recognition engine: Using Microsoft® Azure® Emotion API, etc., estimates the user's emotional state from their tone of voice and facial expressions.

[1321] Physical feature analysis algorithm: Analyzes the user's physical features using TENSORFLOW (registered trademark) and OpenCV (registered trademark).

[1322] Database: Uses MySQL or similar to manage user data and product information.

[1323] Processing flow

[1324] 1. Data Collection: Using the smartphone's camera and sensors, the system measures the user's physical characteristics (height, weight, shoulder width, arm length, etc.), and uses the microphone to collect the user's voice tone, pitch, and volume.

[1325] 2. Data transmission: The collected data is transmitted to the server.

[1326] 3. Data Analysis: On the server, a physical feature analysis algorithm is used to analyze the user's physical features, and an emotion recognition engine is used to infer the user's emotional state from their tone of voice and facial expressions.

[1327] 4. Product Recommendations: Based on the analysis results, the system recommends the most suitable products for the user. The recommendations are personalized based on the user's physical characteristics and emotional state.

[1328] 5. Providing suggested content: Proposed product information is displayed on the smartphone application.

[1329] 6. Real-time interaction: Interact with users in real time and provide appropriate responses to their questions and requests.

[1330] Specific examples

[1331] When a user enters a store, the app automatically launches and collects data using a camera and microphone. It analyzes the user's physical characteristics and emotional state in real time and suggests products that the user might be interested in. For example, if the emotion engine determines that the user is a little tired, it will suggest items that will help them relax (e.g., aroma candles or massage machines).

[1332] Prompt Sentence Examples

[1333] Develop an application that analyzes a user's physical characteristics (height, weight, shoulder width, arm length, etc.) and emotional state (tone of voice, facial expression) in real time to suggest the most suitable products. Specifically, the application will collect data using the smartphone's camera and microphone, analyze it on the server, and display the results in the app.

[1334] In this way, personalized product recommendations based on the user's physical characteristics and emotional state are possible, improving user satisfaction.

[1335] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1336] Step 1:

[1337] Data collection

[1338] The device (smartphone) uses a camera and sensors to measure the user's physical characteristics (height, weight, shoulder width, arm length, etc.), and a microphone to collect the user's voice tone, pitch, and volume.

[1339] Input: User's physical characteristics and voice data

[1340] Output: Measured physical characteristics data and voice data

[1341] Step 2:

[1342] Data transmission

[1343] The terminal transmits the collected data to the server.

[1344] Input: Measured physical characteristics data and voice data

[1345] Output: Data sent to the server

[1346] Step 3:

[1347] Data analysis

[1348] The server analyzes the user's physical characteristics using a physical feature analysis algorithm (TENSORFLOW® or OpenCV®), and estimates the user's emotional state from their tone of voice and facial expressions using an emotion recognition engine (Microsoft® Azure® Emotion API).

[1349] Input: Transmitted physical characteristic data and voice data

[1350] Output: Analyzed physical feature data and emotional state data

[1351] Step 4:

[1352] Product suggestion

[1353] The server then uses the analysis results to suggest the best products for the user, personalized based on the user's physical characteristics and emotional state.

[1354] Input: Analyzed physical feature data and emotional state data

[1355] Output: Personalized product recommendation data

[1356] Step 5:

[1357] Proposal content provided

[1358] The server transmits the suggested product information to the terminal, which then provides the information to the user visually or audibly.

[1359] Input: Personalized product suggestion data

[1360] Output: Product information provided to the user

[1361] Step 6:

[1362] Real-time dialogue

[1363] The device interacts with the user in real time and provides appropriate responses to questions and requests. The server performs additional data analysis as needed and sends response data to the device.

[1364] Input: User questions and requests

[1365] Output: The response data that is provided to the user

[1366] In this way, personalized product recommendations based on the user's physical characteristics and emotional state are possible, improving user satisfaction.

[1367] Example 2

[1368] 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."

[1369] Conventional training plan providing systems were unable to fully utilize the user's physical characteristics and performance data, making it difficult to provide optimal training plans for individual users. Furthermore, because dialogue did not take the user's emotions into consideration, it was difficult to maintain the user's motivation. Furthermore, because the training plan was not adjusted to reflect the user's feedback, there was also the problem of not maximizing the effectiveness of training.

[1370] 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.

[1371] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for recognizing the user's emotions in real time and adjusting the style and content of the interaction in response to the emotions, means for collecting the user's practice data and performance data, means for analyzing the user's performance using the collected data, means for generating a training plan based on the analysis results, means for providing the generated training plan to the user, and means for collecting user feedback and adjusting the training plan. This makes it possible to provide an optimal training plan based on the user's individual characteristics and to interact with the user in response to their emotions, thereby maximizing the effectiveness of training while maintaining the user's motivation.

[1372] "Means for analyzing the user's physical characteristics" refers to devices or software for collecting the user's physical data and analyzing that data.

[1373] "Means for providing a training plan that maximizes the effectiveness of the analysis results" refers to devices and software that create and provide optimal training plans to users based on the analysis results of their physical characteristics.

[1374] "Means for generating videos corresponding to a practice plan" refers to a device or software that creates videos that can be used as a reference when a user actually practices based on the provided practice plan.

[1375] "Means for providing the generated video to the user" refers to devices or software that allow the user to view the generated video.

[1376] "Means for interacting with users in real time" refers to devices and software for communicating with users in real time.

[1377] "Means for recognizing a user's emotions in real time and adjusting the style and content of a conversation in response to those emotions" refers to devices or software that analyze a user's emotions in real time and change the method and content of a conversation based on the results.

[1378] "Means for collecting user practice data and performance data" refers to devices and software for collecting data on user practice and performance results.

[1379] "Means for analyzing user performance using collected data" refers to devices or software for analyzing collected practice data and performance data and evaluating user performance.

[1380] "Means for generating a training plan based on the analysis results" refers to devices or software for creating an optimal training plan for a user based on the results of an analysis of the user's performance.

[1381] "Means for providing the generated practice plan to the user" refers to a device or software for presenting the created practice plan to the user.

[1382] "Means for collecting user feedback and adjusting practice plans" refers to devices or software that collect user feedback and modify or adjust practice plans based on that feedback.

[1383] This invention is a system that collects and analyzes a user's physical characteristics and performance data to provide an optimal training plan. It also has the ability to recognize the user's emotions in real time and adjust the style and content of the dialogue accordingly.

[1384] The server uses devices such as smartphones and tablets to collect users' practice data and performance data. Users input their practice data through these devices and send it to the server. For example, users can input their pitching speed and control accuracy after practice.

[1385] The server uses the collected data to analyze the user's performance. This analysis uses machine learning libraries such as Python's Scikit-learn and TENSORFLOW. The server analyzes the user's pitching form and performance trends to identify the optimal training method. For example, it analyzes video data of the user's pitching form to determine whether a sidearm pitch is suitable.

[1386] Based on the analysis results, the server generates an optimal training plan for the user. For example, the following menu is created for a sidearm pitching training plan.

[1387] Check basic operation (10 minutes)

[1388] Catch ball (20 minutes)

[1389] Pitching practice (30 minutes)

[1390] The server transmits the training plan to the terminal for provision to the user, who can then check and execute the training plan through the terminal.

[1391] Furthermore, the server uses an emotion engine to converse with the user. The emotion engine uses natural language processing tools such as NVIDIA's Jarvis and Google's Dialogflow. The server recognizes the user's emotions in real time and adjusts the style and content of the dialogue accordingly. For example, if the server senses that the user is struggling with practice, it will offer words of encouragement. Conversely, if the server senses that the user is enjoying practicing, it will engage in a dialogue that shares that enjoyment.

[1392] The user can also input their emotions using the device while practicing. For example, they can input "I'm not doing well in practice today." The server analyzes the user's emotions using an emotion engine and generates an encouraging message. The server then sends this message to the device and displays it to the user.

[1393] Examples of prompts to be input to a generative AI model include:

[1394] "Generate an optimal practice plan based on the user's practice data. The user is a baseball player who wants to practice sidearm throwing. Include specific practice items, order, and frequency. Also, generate dialogue to offer words of encouragement if the user is struggling with practice."

[1395] In this way, it is possible to provide an optimal practice plan based on the individual characteristics of the user and to have a dialogue that responds to the user's emotions, thereby maximizing the effectiveness of practice while maintaining the user's motivation.

[1396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1397] Step 1: Collect user data

[1398] Users use devices such as smartphones and tablets to input practice data and performance data. For example, after practice, they input pitching speed and control accuracy. The device then sends this data to a server. The input data includes pitching speed, control accuracy, practice time, etc. The server receives this data and stores it in a database.

[1399] Step 2: Analyze the data

[1400] The server uses the collected data to analyze the user's performance. This analysis uses machine learning libraries such as Python's Scikit-learn and TENSORFLOW. The server analyzes the user's pitching form and performance trends to identify the optimal practice method. The input data includes the practice data collected in step 1. The server analyzes this data and determines whether the user's pitching form is suitable for sidearm pitching. The output data includes an evaluation of the pitching form's suitability as a result of the analysis.

[1401] Step 3: Generate a practice plan

[1402] The server generates an optimal practice plan for the user based on the analysis results. For example, the following menu is created as a sidearm practice plan. The input data includes the analysis results obtained in step 2. The server determines the specific practice menu, practice order, and practice frequency based on this data. The output data includes the generated practice plan. Specifically, the server creates the following practice menu.

[1403] Check basic operation (10 minutes)

[1404] Catch ball (20 minutes)

[1405] Pitching practice (30 minutes)

[1406] Step 4: Provide a practice plan

[1407] The server provides the generated practice plan to the user. The user can check and execute the practice plan through the device. The input data includes the practice plan generated in step 3. The server sends this practice plan to the device. The output data includes the practice plan in a format that can be checked by the user. Specifically, the server sends the practice plan to the device, and the user opens the app and checks the practice menu.

[1408] Step 5: Emotional Engine Dialogue

[1409] The user inputs their emotions using the terminal while practicing. For example, they input "Practice is not going well today." The server uses an emotion engine to analyze the user's emotions and generate an encouraging message. The input data includes the emotion information entered by the user. The server uses the emotion engine to analyze this data and generate an appropriate message. The output data includes the generated encouraging message. Specifically, the server generates a message such as the one below and sends it to the terminal.

[1410] "That's tough, but I'm improving little by little. Let's keep going!"

[1411] Step 6: Gather feedback and adjust your practice plan

[1412] After practice, the user inputs feedback and sends it to the server. For example, they might input, "Today's practice was effective." The server collects this feedback and adjusts the practice plan. The input data includes the feedback information entered by the user. The server corrects and adjusts the practice plan based on this data. The output data includes the adjusted practice plan. Specifically, the server analyzes the feedback and changes the practice menu and frequency as necessary.

[1413] (Application example 2)

[1414] 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."

[1415] Conventional training plan providing systems have difficulty providing optimal training plans that fully consider the user's physical characteristics and emotional state. Furthermore, they are unable to provide real-time feedback based on the user's emotions, making it difficult to maintain the user's motivation. Furthermore, there has been a lack of systems that provide optimal work procedures and training plans for factory workers operating and maintaining robots.

[1416] 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.

[1417] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for recognizing the user's emotions in real time, means for adjusting the content of the interaction in accordance with the recognized emotions, means for analyzing the user's past performance data and providing optimal work procedures and training plans, and means for providing feedback in accordance with the emotions. This allows the server to provide optimal training plans and work procedures that take into account the user's physical characteristics and emotional state, enabling efficient training and work while maintaining the user's motivation.

[1418] "Means for analyzing the user's physical characteristics" refers to devices or software for collecting the user's physical data and analyzing that data.

[1419] "Means for providing a training plan that maximizes the benefits" refers to devices and software that suggest optimal training methods and schedules to users based on the analysis results.

[1420] "Means for generating videos corresponding to a practice plan" refers to a device or software that creates videos that can be used as a reference when a user actually practices based on the provided practice plan.

[1421] The "means for providing the generated video to the user" refers to a device or software for visually providing the created video to the user.

[1422] "Means for interacting with users in real time" refers to devices or software that allow for instant communication with users.

[1423] "Means for recognizing user emotions in real time" refers to devices or software that instantly analyze emotions from the user's facial expressions, voice, etc.

[1424] "Means for adjusting dialogue content in response to recognized emotions" refers to devices or software for changing the content or tone of dialogue based on the user's emotional state.

[1425] "Means for analyzing a user's past performance data and providing optimal work procedures and training plans" refers to devices or software that analyze a user's past performance data and propose optimal work procedures and training plans based on the results.

[1426] A "means for providing emotional feedback" is a device or software that provides appropriate encouragement or advice based on the user's emotional state.

[1427] The system for implementing this invention has the function of analyzing the user's physical characteristics, providing an optimal exercise plan, and recognizing the user's emotions in real time to adjust the dialogue content. Specific embodiments of this system are described below.

[1428] System configuration

[1429] The system consists of the following main components:

[1430] 1. Means for analyzing the user's physical characteristics: Devices and software for collecting and analyzing the user's physical data. Specifically, cameras and sensors are used to capture and analyze the user's movements and postures.

[1431] 2. Means of providing optimal training plans: Devices and software that suggest optimal training methods and schedules based on the analysis results. Generative AI models are used to generate optimal training plans from user data.

[1432] 3. Means for generating videos corresponding to the practice plan: Devices and software that create videos to serve as reference for the user when actually practicing based on the provided practice plan. A video generation algorithm is used to generate videos that mimic the user's appearance.

[1433] 4. Means for providing the generated video to the user: Devices and software for visually presenting the created video to the user. The video is displayed to the user using smart glasses or a head-mounted display.

[1434] 5. Means of real-time interaction with users: Devices and software for communicating with users instantly. Dialogue with users is realized using voice recognition and voice synthesis technology.

[1435] 6. Means for recognizing user emotions in real time: Devices and software for instantly analyzing emotions from a user's facial expressions, voice, etc. Recognizing user emotions in real time using an emotion recognition model.

[1436] 7. Means for adjusting dialogue content according to perceived emotions: Devices or software that change the content or tone of dialogue based on the user's emotional state, providing emotionally appropriate feedback.

[1437] 8. Means for analyzing a user's past performance data and providing optimal work procedures and training plans: Devices and software for analyzing a user's past performance data and proposing optimal work procedures and training plans based on the results.

[1438] 9. Emotional feedback: Devices or software that provide appropriate encouragement or advice based on the user's emotional state.

[1439] Program processing explanation

[1440] The server uses cameras and sensors to collect and analyze the user's physical characteristics. Based on the analysis results, it uses a generative AI model to generate an optimal training plan. Based on the generated training plan, it uses a video generation algorithm to create training videos, which are then provided to the user via smart glasses or a head-mounted display.

[1441] As the user practices, the server uses an emotion recognition model to analyze the user's emotions in real time and adjusts the dialogue accordingly, for example, offering words of encouragement if the user is feeling stressed, or sharing their joy if the user is having fun.

[1442] In addition, the server analyzes the user's past performance data and provides optimal work procedures and training plans, allowing the user to train efficiently.

[1443] Specific examples

[1444] For example, consider a scenario in which a factory worker is performing maintenance on a robot. The worker wears smart glasses, and the system captures and analyzes the worker's movements. Based on the analysis results, the optimal work procedure is displayed on the smart glasses. If the worker is feeling stressed, the system displays a message saying, "Relax and try again." If the worker is enjoying themselves, the system displays a message saying, "Good job! Keep going."

[1445] Example prompts to input to a generative AI model:

[1446] Generate optimal training plans based on your workers' past performance data. Provide feedback to encourage them when they're stressed or share their joy when they're having fun.

[1447] The above is a specific embodiment for carrying out the present invention.

[1448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1449] Step 1:

[1450] The server uses cameras and sensors to collect the user's physical characteristics. Specifically, it captures the user's movements and postures and stores them as digital data. The input is video data from the cameras and sensors, and the output is digital data that indicates the user's physical characteristics.

[1451] Step 2:

[1452] The server analyzes the collected physical characteristic data. Image processing algorithms and machine learning models are used for the analysis. The input is the digital data obtained in step 1, and the output is the analysis results of the user's physical characteristics. Specifically, the server extracts the user's posture and movement characteristics and saves them as numerical data.

[1453] Step 3:

[1454] The server generates an optimal training plan using a generative AI model based on the analysis results. The input is the analysis results obtained in step 2, and the output is the optimal training plan for the user. Specifically, it generates a training menu and schedule based on the user's physical characteristics.

[1455] Step 4:

[1456] The server uses a video generation algorithm to create a practice video based on the generated practice plan. The input is the practice plan obtained in step 3, and the output is a practice video. Specifically, it generates an animated video that mimics the user's appearance and visually shows the practice steps.

[1457] Step 5:

[1458] The server provides the generated practice video to the user. Specifically, the video is displayed through smart glasses or a head-mounted display. The input is the practice video obtained in step 4, and the output is a video that the user can visually confirm.

[1459] Step 6:

[1460] The server uses an emotion recognition model to analyze the user's emotions in real time as they practice. The input is video and audio data from a camera and microphone, and the output is the user's emotional state. Specifically, the server analyzes the user's facial expressions and tone of voice to determine their emotions.

[1461] Step 7:

[1462] The server adjusts the dialogue content according to the recognized emotion. The input is the emotional state obtained in step 6, and the output is the adjusted dialogue content. Specifically, if the user is feeling stressed, the server will provide words of encouragement, and if the user is having fun, the server will engage in dialogue to share that enjoyment.

[1463] Step 8:

[1464] The server analyzes the user's past performance data and provides optimal work procedures and training plans. The input is the user's past performance data, and the output is the optimal work procedures and training plans. Specifically, it analyzes past data and proposes optimal procedures and plans to the user.

[1465] Step 9:

[1466] The server provides feedback according to the emotion. The input is the emotional state obtained in step 6, and the output is feedback according to the emotion. Specifically, if the user is feeling stressed, the server provides a message such as "Relax and try again." If the user is having fun, the server provides a message such as "Good luck! Keep going."

[1467] Example 3

[1468] Next, a description will be given of Example 3 of Form Example 3. 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."

[1469] Conventional practice planning systems have difficulty providing practice plans that fully take into account the user's physical characteristics and emotional state, and their real-time interactive functions are limited. As a result, it is difficult to maintain the user's motivation and practice effectively. Furthermore, they lack the ability to generate videos that mimic the user's appearance or provide appropriate responses, which leads to problems with reduced user satisfaction.

[1470] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1471] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for analyzing the user's emotional state and adjusting the training plan based thereon, and means for recognizing the user's voice input and generating appropriate responses. This makes it possible to provide a training plan that takes the user's physical characteristics and emotional state into consideration, enhances real-time interaction functions, and realizes the generation of videos that mimic the user's appearance and the provision of appropriate responses.

[1472] "Means for analyzing the user's physical characteristics" refers to means for analyzing physical data provided by the user, such as height, weight, and body type, and for identifying the user's physical characteristics based on that data.

[1473] "Means for providing a training plan that maximizes the user's performance" refers to means for creating and providing a training plan that is optimal for the user, taking into consideration the user's physical characteristics and emotional state.

[1474] The "means for generating a video corresponding to a training plan" is a means for creating a video that reproduces training movements using a 3D model generated based on the user's physical characteristics.

[1475] The "means for providing the generated video to the user" refers to a means for visually providing the generated practice video to the user.

[1476] "Means for interacting with the user in real time" refers to a means by which the user can ask questions or give feedback to the system during practice, and the system can respond to those questions in real time.

[1477] The "means for analyzing the user's emotional state and adjusting the exercise plan based thereon" refers to a means for analyzing the user's emotional data and dynamically adjusting the exercise plan based on the results.

[1478] The "means for recognizing a user's voice input and generating an appropriate response" is a means for converting a user's voice input into text and analyzing the text to generate an appropriate response.

[1479] The present invention provides a system that provides a training plan that takes into account the user's physical characteristics and emotional state, and enhances real-time interactive functions. Specific embodiments of this system are described below.

[1480] 1. Creation of practice videos using 3D modeling technology

[1481] The server generates a 3D model based on the user's physical characteristics using 3D modeling software such as Blender. Specifically, the server receives the user's physical data (height, weight, body type, etc.) as input and creates a 3D model based on that data.

[1482] The server then uses the generated 3D model to generate a practice video. In this process, a game engine such as Unity is used to simulate the 3D model and reproduce the practice movements. The generated video serves as a reference for the user when practicing.

[1483] Example: If a user provides data of height 170 cm and weight 65 kg, the server generates a 3D model based on that data and creates a running practice video.

[1484] Example prompt: "Generate a running practice video for a user who is 170cm tall and weighs 65kg."

[1485] 2. Real-time interaction

[1486] The device is equipped with the ability to interact with users in real time. This is achieved using voice recognition technologies such as Google® Cloud Speech-to-Text and natural language processing technologies such as OpenAI®'s GPT-3®. Users can ask the device questions and seek feedback during practice.

[1487] The server generates appropriate responses to user questions and requests. For example, if a user asks "Is this behavior correct?", the server analyzes the question and provides appropriate feedback.

[1488] Example: If a user asks, "What's the next exercise?", the device uses voice recognition technology to convert the question into text, and the server analyzes the text and responds, "Next is stretching."

[1489] Example prompt: "When the user asks what the next exercise is, generate an appropriate response."

[1490] 3. Creating a practice plan using the emotion engine

[1491] The server provides an optimal training plan that takes into account the user's emotional state. In this process, it uses an emotion engine to analyze the user's emotional data. For example, if the user feels unmotivated to practice, the server will provide a motivating training plan.

[1492] Example: If a user types, "I'm not feeling motivated today," the server analyzes the emotional data and provides a workout plan that includes light exercise and relaxation to boost motivation.

[1493] Example prompt: "If the user feels unmotivated, generate a motivational exercise plan."

[1494] This system makes it possible to provide a training plan that takes into account the user's physical characteristics and emotional state, enhances real-time dialogue functions, and realizes the generation of videos that mimic the user's appearance and the provision of appropriate responses. The flow of the identification process in Example 3 will be explained using Figure 21.

[1495] Step 1:

[1496] The user inputs their physical characteristics such as height, weight, and body type into the system. This data is provided through an input form on the terminal. The input data is then sent to the server.

[1497] Step 2:

[1498] The server receives the user's physical data and generates a 3D model using 3D modeling software such as Blender. Specifically, the server analyzes the input data and sets each parameter of the 3D model based on that data. The output is a 3D model that reflects the user's physical characteristics.

[1499] Step 3:

[1500] The server imports the generated 3D model into a game engine such as Unity and performs a motion simulation. The server then executes the training motion script and reproduces the motion on the 3D model. As an output, a video reproducing the training motion is generated.

[1501] Step 4:

[1502] The server provides the generated practice video to the user. Specifically, it transmits the video file to the terminal so that the user can visually check it. As an output, the practice video that the user can watch is provided.

[1503] Step 5:

[1504] During practice, the user can input voice data to the device to ask questions or request feedback. For example, the user can ask, "What is the next practice menu?" The input voice data is sent to the device.

[1505] Step 6:

[1506] The device converts the user's speech into text using speech recognition technology such as Google® Cloud Speech-to-Text. The converted text is sent to the server. As an output, the speech data is converted into text data.

[1507] Step 7:

[1508] The server uses natural language processing technology, such as OpenAI's GPT-3, to analyze the user's question or request. Based on the analysis results, it generates an appropriate response. As an output, an appropriate response to the user's question is generated.

[1509] Step 8:

[1510] The terminal provides the response received from the server to the user by voice, for example, "Next is stretching." As an output, the voice response is provided to the user.

[1511] Step 9:

[1512] The user inputs their emotional state into the system. For example, they input "I'm not motivated today." The input emotional data is sent to the server.

[1513] Step 10:

[1514] The server analyzes the user's emotional data using the emotion engine, evaluates the user's emotional state based on the analysis results, and obtains an evaluation result regarding the user's emotional state as an output.

[1515] Step 11:

[1516] The server generates an optimal training plan based on the analysis results. For example, if the user feels unmotivated, the server will provide a training plan that includes light exercise and relaxation to boost motivation. The output is a training plan that corresponds to the user's emotional state.

[1517] Step 12:

[1518] The terminal provides the practice plan received from the server to the user, who then practices according to the practice plan provided to him / her, and the practice plan is provided to the user as an output.

[1519] (Application example 3)

[1520] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1521] Conventional factory robot operation and maintenance techniques lack appropriate training methods for workers to learn efficiently. It is also difficult to provide training plans that take into account the worker's emotional state, creating a need for improved work efficiency and safety. Furthermore, a lack of real-time dialogue and feedback makes it difficult to quickly respond to problems faced by workers. To address these issues, a training system that takes into account the user's physical characteristics and emotional state is needed.

[1522] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1523] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for detecting the user's emotional state, means for adjusting the training plan based on the user's emotional state, means for explaining factory robot operation methods and maintenance procedures through videos using 3D models, means for responding to worker questions using voice recognition technology and natural language processing technology, and means for detecting the worker's emotional state and providing an optimal training plan. This enables workers to efficiently learn how to operate and maintain factory robots and provides an optimal training plan based on their emotional state.

[1524] "Means for analyzing the user's physical characteristics" refers to a device or software for acquiring and analyzing the user's physical characteristics such as height, weight, and body type as data.

[1525] A "means for providing an exercise plan" is a device or software that suggests optimal exercise content and schedule based on the user's physical characteristics and emotional state.

[1526] "Means for generating videos" refers to a device or software that creates training videos using 3D models based on the user's physical characteristics and training plan.

[1527] "Means for providing to the user" means a device or software for visually or audibly presenting the generated video or training plan to the user.

[1528] A "real-time interaction means" is a device or software that allows for real-time communication with a user through voice or text.

[1529] "Means for detecting emotional state" refers to a device or software that analyzes and detects the emotional state of a user from their voice, facial expression, behavior, etc.

[1530] A "means for adjusting an exercise plan" is a device or software for optimizing or modifying an existing exercise plan based on the sensed emotional state of the user.

[1531] "Means for explaining the operation methods and maintenance procedures of factory robots through video using 3D models" refers to a device or software for visually explaining the operation methods and maintenance procedures of factory robots using 3D models.

[1532] "Voice recognition technology" is a technology that converts a user's voice into text data.

[1533] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning.

[1534] A "means for providing an optimal training plan" is a device or software that suggests the most effective training content and schedule based on the user's emotional state and physical characteristics.

[1535] A system for implementing this invention analyzes a user's physical characteristics, provides an optimal training plan, generates videos corresponding to the training plan, and provides them to the user. It also has the ability to interact with the user in real time, detect the user's emotional state, and adjust the training plan accordingly. Furthermore, the system uses 3D models to explain factory robot operation methods and maintenance procedures through videos, and responds to worker questions using voice recognition and natural language processing.

[1536] Hardware and software used

[1537] Hardware:

[1538] Microphone: To capture audio input.

[1539] Computer: The environment in which a program runs.

[1540] software:

[1541] Python (registered trademark): Program implementation language.

[1542] OpenAI® API: For natural language processing.

[1543] pyttsx3: For speech synthesis.

[1544] speech_recognition: For speech recognition.

[1545] emotion_recognition: For emotion recognition.

[1546] video_generator: For 3D model and video generation.

[1547] training_plan: To adjust your training plan.

[1548] Data processing and calculation

[1549] The server acquires the user's physical characteristics and generates a 3D model based on them. Using the generated 3D model, it creates a practice video that is optimal for the user and provides it to the user. If the user asks a question during practice, the question is converted into text using speech recognition technology, and an appropriate response is generated using natural language processing technology. The response is returned to the user using speech synthesis technology.

[1550] The server also detects the user's emotional state and adjusts the training plan based on that emotional state. For example, if the emotion engine determines that the user is tired, the server will provide a training plan that encourages rest.

[1551] Specific examples

[1552] When a worker asks, "Please tell me how to install this part," the server will explain it with a video using a 3D model and provide details via audio.

[1553] If the emotion engine determines that the worker is tired, the server will provide a training plan that encourages rest.

[1554] Prompt Sentence Examples

[1555] User: "How do I install this part?"

[1556] System: "Here's how to install the part. Play the video."

[1557] In this way, a system is provided that allows efficient learning of the operation and maintenance of factory robots.

[1558] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1559] Step 1:

[1560] The server acquires the user's physical characteristics, receives data such as height, weight, and body type entered by the user, and generates a 3D model based on this. The input data is the user's physical characteristics, and the output is a 3D model.

[1561] Step 2:

[1562] The server uses the generated 3D model to create a training video that is optimal for the user. Based on the 3D model, a video corresponding to the training plan is generated. The input data are the 3D model and the training plan, and the output is the training video.

[1563] Step 3:

[1564] The server provides the generated practice video to the user. The video is sent to the user's device and visually presented. The input data is the practice video, and the output is the user's visual feedback.

[1565] Step 4:

[1566] When the user asks a question during practice, the device receives voice input. The device uses a microphone to capture the user's voice and transmits it to the server. The input data is the user's voice, and the output is voice data.

[1567] Step 5:

[1568] The server uses voice recognition technology to convert the user's voice into text, analyzes the voice data, and outputs it as text data. The input data is voice data, and the output is text data.

[1569] Step 6:

[1570] The server uses natural language processing technology to generate appropriate responses to user questions. It analyzes text data and generates appropriate responses. The input data is text data, and the output is response text.

[1571] Step 7:

[1572] The server uses speech synthesis technology to convert the generated response text into speech and respond to the user. The server outputs the response text as voice data and sends it to the user's device. The input data is the response text, and the output is voice data.

[1573] Step 8:

[1574] The server detects the user's emotional state by analyzing and detecting the user's emotional state from their voice, facial expression, behavior, etc. The input data is the user's voice and facial expression data, and the output is the user's emotional state.

[1575] Step 9:

[1576] The server adjusts the practice plan based on the detected emotional state, or optimizes or modifies the existing practice plan taking the emotional state into account. The input data are the emotional state and the existing practice plan, and the output is the adjusted practice plan.

[1577] Step 10:

[1578] The server provides the adjusted practice plan to the user, and sends the new practice plan to the user's device and presents it visually or audibly. The input data is the adjusted practice plan, and the output is the user's feedback.

[1579] 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.

[1580] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / OpenAI.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[1581] , 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 the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1582] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1583] 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.

[1584] [Third embodiment]

[1585] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1586] 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.

[1587] 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).

[1588] 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.

[1589] 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.

[1590] 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).

[1591] 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.

[1592] 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.

[1593] 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.

[1594] 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.

[1595] 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.

[1596] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1597] "Example 1"

[1598] The system of the present invention is equipped with input devices such as sensors and cameras for analyzing the user's physical characteristics. These devices measure the user's physical characteristics, such as height, weight, shoulder width, arm length, etc., and transmit the data to the system. The system analyzes the user's physical characteristics based on the transmitted data.

[1599] "Example 2"

[1600] Based on the analysis results, the system will provide a training plan that maximizes the player's performance. For example, if a baseball player is determined to be better suited to sidearm pitching rather than overhand pitching, the system will provide a training plan for sidearm pitching. This training plan will include specific exercises, their order, and frequency of practice.

[1601] "Example 3"

[1602] To generate videos corresponding to the training plan, the system uses 3D modeling technology. Specifically, it generates a 3D model based on the user's physical characteristics and then uses that model to generate training videos. The generated videos serve as a reference for the user when practicing.

[1603] "Example 4"

[1604] The system has the ability to converse with users in real time. This function is realized using voice recognition technology and natural language processing technology. Users can ask the system questions and request feedback during practice. The system responds to questions and requests from users in easy-to-understand language.

[1605] The processing flow of each embodiment will be described below.

[1606] "Example 1"

[1607] Step 1: Activate input devices such as sensors and cameras to measure the user's physical characteristics.

[1608] Step 2: Measure the user's physical characteristics, such as height, weight, shoulder width, arm length, etc. Step 3: Send the measured data to the system.

[1609] Step 4: The system analyzes the user's physical characteristics based on the transmitted data.

[1610] "Example 2"

[1611] Step 1: The system generates an optimal training plan based on the analysis results.

[1612] Step 2: Provide the generated practice plan to the user.

[1613] "Example 3"

[1614] Step 1: The system generates a 3D model based on the user's physical characteristics.

[1615] Step 2: Generate a practice video using the generated 3D model.

[1616] Step 3: Serving the generated video to the user.

[1617] "Example 4"

[1618] Step 1: The system activates the voice recognition function.

[1619] Step 2: The user can ask the system questions and seek feedback during the exercise.

[1620] Step 3: The system responds to the user's questions and requests in easy-to-understand language.

[1621] Example 1

[1622] 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."

[1623] Conventional systems have difficulty accurately measuring and analyzing a user's physical characteristics. Furthermore, the collection of measurement data and the provision of analysis results are not performed in real time, making them inconvenient for users. Furthermore, interaction with users is limited, making it difficult to respond to individual needs.

[1624] 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.

[1625] In this invention, the server includes means for using an input device to measure a user's physical characteristics, means for collecting measurement data, means for transmitting the collected data to the server, means for the server to analyze the data, means for generating analysis results, means for providing the generated analysis results to the user, and means for interacting with the user in real time. This makes it possible to accurately measure a user's physical characteristics and provide the analysis results in real time. Furthermore, through interaction with the user, it is possible to provide services that meet individual needs.

[1626] "User's physical characteristics" refers to specific data about the user's body, such as height, weight, shoulder width, and arm length.

[1627] An "input device" is a hardware device, such as a sensor or camera, that is used to measure a user's physical characteristics.

[1628] "Measurement Data" is data about a user's physical characteristics collected using an input device.

[1629] A "server" is a computer system for receiving and analyzing measurement data, and generating and providing analysis results.

[1630] "Means for analyzing data" means the process of using algorithms or machine learning models to identify the user's physical characteristics based on the received measurement data.

[1631] "Analysis results" are detailed information about the user's physical characteristics generated by the data analysis means.

[1632] "Real-time interaction means" refers to interfaces and technologies that allow for instant communication with users.

[1633] This invention provides a system for accurately measuring a user's physical characteristics and providing analysis results in real time. The system includes an input device for measuring the user's physical characteristics, a terminal for collecting measurement data, a server for analyzing the collected data, and a means for providing the user with the analysis results.

[1634] The system's program is structured as follows: First, the user logs in to the system and measures their physical characteristics using input devices such as a 3D scanner and a weighing scale. These input devices collect data such as the user's height, weight, shoulder width, and arm length. Specific hardware used includes a 3D scanner, high-resolution camera, and weighing scale.

[1635] The device transmits the data collected from these input devices to a server in real time using a secure communication protocol (e.g., HTTPS). The server uses Python scripts and machine learning libraries such as TENSORFLOW to analyze the received data. The analysis results are generated as a report detailing the user's physical characteristics.

[1636] As a concrete example, consider a scenario in which a user uses the system to measure their own physical characteristics. The user stands in front of a 3D scanner connected to the system and steps on a scale. The camera takes a picture of the user's entire body, and the sensor measures the user's weight. These data are then sent to the server via the device.

[1637] The server analyzes the received data and calculates the user's height, weight, shoulder width, arm length, etc. The analysis results are generated as a report detailing the user's physical characteristics. The generated report is sent to the user's device, where the user can view the report.

[1638] An example of a prompt to input to a generative AI model is as follows:

[1639] Use a 3D scanner and scale to collect data to analyze your users' physical characteristics, calculate their height, weight, shoulder width, and arm length, and generate a detailed report.

[1640] Using this prompt, the generative AI model can accurately analyze the user's physical characteristics and provide the necessary information.

[1641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1642] Step 1:

[1643] A user logs in to the system.

[1644] Input: Username and Password

[1645] Output: Authentication result (success or failure)

[1646] Specific operation: The user enters the username and password on the system login screen and clicks the "Login" button. The system checks the entered authentication information and authenticates the user. If authentication is successful, the user can proceed to the next step.

[1647] Step 2:

[1648] The user uses an input device to measure a physical characteristic.

[1649] Input: User's physical characteristics (height, weight, shoulder width, arm length, etc.)

[1650] Output: Measurement data

[1651] How it works: The user stands in front of a 3D scanner connected to the system and steps on a scale. The camera captures a full-body image of the user, and the sensor measures the user's weight. The user must remain still until the measurement is complete.

[1652] Step 3:

[1653] The device collects the measurement data.

[1654] Input: Measurement data from a 3D scanner and a weighing scale

[1655] Output: Collected measurement data

[1656] How it works: The device collects data from the 3D scanner and the scale in real time, and stores the collected data in temporary memory on the device.

[1657] Step 4:

[1658] The terminal transmits the collected data to the server.

[1659] Input: Collected measurement data

[1660] Output: Data sent to the server

[1661] Specific operation: The device encrypts the collected data and sends it to the server using the HTTPS protocol. Once the data transmission is complete, the device displays a notification.

[1662] Step 5:

[1663] The server analyzes the data.

[1664] Input: Measurement data sent to the server

[1665] Output: Analysis results

[1666] Specific Operation: The server runs a Python script to analyze the received data. The script applies a machine learning model using the TENSORFLOW library to identify the user's physical characteristics.

[1667] Step 6:

[1668] The server generates the analysis results.

[1669] Input: Parsed data

[1670] Output: Analysis result report

[1671] Specific operation: The server generates a report detailing the user's physical characteristics based on the analysis results. The report is generated in PDF format and saved on the server for the user to download.

[1672] Step 7:

[1673] The server provides the analysis results to the user.

[1674] Input: Generated analysis result report

[1675] Output: Report provided to the user

[1676] Specific operation: The server sends a download link for the generated report to the user's device. The user can click the link on the device to download and view the report.

[1677] (Application example 1)

[1678] 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."

[1679] Traditional try-on services in physical stores require users to actually try on the clothes, which is time-consuming and laborious. Additionally, there is a lack of systems that suggest optimal clothing sizes based on the user's physical characteristics, making it difficult for users to find clothes that fit them. Furthermore, the results of virtual try-on services are not provided in real time, which can lead to lower user satisfaction.

[1680] 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.

[1681] In this invention, the server includes means for analyzing the user's physical characteristics, means for proposing optimally sized clothing based on the analysis results, means for the user to virtually try on the clothing selected by the user, means for providing the user with the results of the virtual try-on, and means for interacting with the user in real time. This allows the user to efficiently find clothing that suits them even in a physical store, and improves user satisfaction because the user can check the results of the try-on in real time through the virtual try-on.

[1682] "Means for analyzing the user's physical characteristics" refers to a device or system that uses input devices such as sensors or cameras to measure the user's physical characteristics such as height, weight, shoulder width, arm length, etc., and performs analysis based on that data.

[1683] The "means for proposing optimally sized clothing based on analysis results" refers to a device or system that selects and proposes optimally sized clothing to a user based on the analysis results of the user's physical characteristics.

[1684] "Means for virtually trying on clothes selected by a user" refers to a device or system that allows a user to try on clothes selected by a user in a virtual space, while fitting the clothes to the user's body.

[1685] The "means for providing the user with the results of the virtual try-on" refers to a device or system that displays and provides the user with the results of the virtual try-on as images or videos.

[1686] A "means for interacting with a user in real time" is a device or system that communicates with a user in real time and responds to the user's questions and requests.

[1687] The system for implementing the present invention includes a series of means for analyzing the user's physical characteristics, proposing clothes of the most suitable size, and performing virtual fitting. Specific embodiments of this system will be described below.

[1688] First, the user captures their own physical characteristics using the smartphone camera. The smartphone is equipped with image processing libraries such as OpenCV (registered trademark), which captures video in real time. The captured video is input into a generative AI model called "Body Measurement Model," which analyzes the user's physical characteristics, such as height, weight, shoulder width, and arm length.

[1689] Next, based on the analysis results, the system recommends the best-fit clothing size for the user. The suggestions are made by a generative AI model called "VirtualTryOnModel," which selects the clothing size that best suits the user's physical characteristics.

[1690] To allow users to virtually try on the selected clothing, the system uses "VirtualTryOnModel" to generate a virtual try-on image that matches the user's body. This virtual try-on image is displayed on the smartphone screen and provided to the user.

[1691] Furthermore, the system has a means of interacting with users in real time: when a user inputs a question or request, the system uses a generative AI model to generate an appropriate response and provide it to the user, allowing the user to receive feedback in real time.

[1692] For example, by inputting the following prompts into a generative AI model, it can suggest the optimal clothing size for the user:

[1693] "The user is 170cm tall, weighs 65kg, has a shoulder width of 45cm, and has an arm length of 60cm. Based on this data, please suggest the best clothing size for them."

[1694] In this way, users can efficiently find clothes that suit them even in physical stores, and can check the fitting results in real time through virtual try-on, thereby increasing user satisfaction.

[1695] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1696] Step 1:

[1697] The user captures their physical characteristics using the smartphone camera.

[1698] Input: Smartphone camera image

[1699] Output: Image data of the captured user's physical characteristics

[1700] How it works: The user turns on the smartphone camera and takes a full-body photo of themselves. The smartphone captures the video in real time and saves it as image data.

[1701] Step 2:

[1702] The device inputs the captured image data into a generated AI model called "Body Measurement Model" to analyze the user's physical characteristics.

[1703] Input: Captured image data of the user's physical characteristics

[1704] Output: Analysis result data such as user's height, weight, shoulder width, arm length, etc.

[1705] Specific operation: The device inputs image data into the "Body Measurement Model," which then processes and analyzes the image. As a result of the analysis, the user's physical characteristics are output as numerical data.

[1706] Step 3:

[1707] Based on the analysis results, the server uses a generative AI model called "VirtualTryOnModel" to suggest the optimal size of clothing.

[1708] Input: Analysis result data of user's physical characteristics

[1709] Output: Data suggesting optimal clothing sizes

[1710] Specific operation: The server inputs the analysis results data into "VirtualTryOnModel," which then selects the clothing size that best suits the user's physical characteristics. As a result, information on the optimal clothing size is output.

[1711] Step 4:

[1712] To allow users to virtually try on the selected clothes, the device uses "VirtualTryOnModel" to generate a virtual try-on image.

[1713] Input: Analysis result data of user's physical characteristics, data of selected clothing

[1714] Output: Virtual try-on image

[1715] Specific operation: The device inputs the user's physical characteristics data and selected clothing data into "VirtualTryOnModel," which then generates a virtual try-on image tailored to the user's body in the virtual space. The generated virtual try-on image is then output.

[1716] Step 5:

[1717] The device provides the user with the results of the virtual try-on.

[1718] Input: Virtual try-on image

[1719] Output: Virtual try-on image displayed to the user

[1720] Specific operation: The device displays the generated virtual try-on image on the smartphone screen and provides it to the user, who can then check the results of the virtual try-on on the screen.

[1721] Step 6:

[1722] The server interacts with the user in real time and generates responses based on questions and requests.

[1723] Input: User question or request

[1724] Output: Generated response data

[1725] How it works: When a user inputs a question or request through their smartphone, the server uses the generative AI model to generate an appropriate response, and the generated response data is provided to the user.

[1726] Example 2

[1727] 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."

[1728] Conventional training plan providing systems were unable to fully utilize the user's physical characteristics and performance data, making it difficult to provide optimal training plans for each individual user. Furthermore, the generated training plans were not specific, making it unclear how the user should actually proceed with their training. Furthermore, the lack of real-time feedback and dialogue made it difficult for users to maintain motivation and practice effectively.

[1729] 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.

[1730] In this invention, the server includes a means for analyzing a user's physical characteristics, a means for providing a training plan that maximizes the user's performance based on the analysis results, a means for generating a video corresponding to the training plan, a means for providing the generated video to the user, a means for interacting with the user in real time, a means for receiving and analyzing the user's performance data, a means for generating an optimal training plan using a generative AI model, and a means for displaying the generated training plan to the user. This makes it possible to provide an optimal training plan based on the user's individual characteristics and clearly show specific training content. Furthermore, real-time feedback and interaction can maintain the user's motivation and enable effective training.

[1731] "User" refers to an individual who utilizes the System to provide their performance data and receive an optimized training plan.

[1732] "Physical characteristics" refers to information about the user's body, such as the user's physique, muscle strength, flexibility, and movement patterns.

[1733] "Performance data" refers to data related to the user's exercise and movements, including, for example, pitching form, speed, accuracy, and past practice history.

[1734] "Means of analysis" refers to the technology and methods used to analyze a user's physical characteristics and performance data and derive the optimal training plan for the user.

[1735] "Practice plan" refers to a plan that includes specific menus, their order, and frequency of practice for the user to practice effectively.

[1736] "Means for generating videos" refers to the technology or method for creating videos corresponding to the user's training plan.

[1737] "Real-time interaction means" refers to technologies and methods for communicating with users in real time and providing feedback and instructions.

[1738] A "generative AI model" refers to a model that uses artificial intelligence to analyze user data and generate an optimal training plan.

[1739] "Display means" refers to the technology or method for visually presenting the generated practice plan or video to the user.

[1740] MODE FOR CARRYING OUT THE INVENTION

[1741] This invention is a system that provides an optimal training plan based on a user's physical characteristics and performance data. Specific embodiments of this system are described below.

[1742] System configuration

[1743] The system includes a user's terminal, a server, a generative AI model, and a communication protocol for linking these components. The terminal is a device such as a smartphone or tablet that the user uses to input data and check the training plan. The server is a computer system that analyzes the data and generates the training plan. The generative AI model is an artificial intelligence model that generates the optimal training plan based on the user's data.

[1744] Hardware and software used

[1745] The server uses Python® and TENSORFLOW® to analyze data and run generative AI models. The device inputs user data through a dedicated application and communicates with the server. Communication uses a secure protocol (e.g., HTTPS).

[1746] Data entry and analysis

[1747] Users use the device to input their own performance data, including videos of their pitching form, pitching speed, accuracy, and past training history. The device then transmits this data to a server, which analyzes the received data and evaluates the user's physical characteristics and performance.

[1748] Generate a practice plan

[1749] The server then uses the generative AI model to generate an optimal training plan based on the analysis results. For example, if the user is a baseball player and it is determined that sidearm pitching is suitable, the server generates a sidearm pitching training plan. This training plan includes warm-ups, repeated practice of basic movements, practice in a real-game setting, and a cool-down.

[1750] Providing practice plans

[1751] The server sends the generated practice plan to the device, which displays the plan to the user, allowing the user to check the plan on the screen. This allows the user to understand the specific practice content and practice effectively.

[1752] Specific examples

[1753] The user uses a smartphone to film a video of their pitching form and uploads it to a dedicated application. The device then sends this video data, along with pitching speed and accuracy data, to a server. The server then analyzes the video data using Python (registered trademark) and TENSORFLOW (registered trademark) to identify the user's pitching style. Based on the analysis results, a generative AI model is used to generate a sidearm pitching practice plan. The generated practice plan includes a warm-up (10 minutes), repetitive practice of basic sidearm pitching movements (30 minutes), practice in a real-game setting (20 minutes), and a cool-down (10 minutes). The server then sends this practice plan to the device, which then displays it to the user. The user can review and execute the practice plan on their smartphone screen.

[1754] Prompt Sentence Examples

[1755] "Generate an optimal training plan based on the user's pitching form and performance data. The user is a baseball player and is suitable for a sidearm pitch. The training plan should include specific exercises, their order, and frequency."

[1756] In this way, the system provides users with an optimal training plan and supports effective practice.

[1757] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1758] Step 1:

[1759] The user enters performance data into a terminal.

[1760] Users use a smartphone or tablet to input data such as video of their pitching form, pitching speed, accuracy, and past training history. The input data is then saved on the device via a dedicated application.

[1761] Input: Video of pitching form, pitching speed, accuracy, past practice history

[1762] Output: Performance data stored on the device

[1763] Step 2:

[1764] The terminal sends the input data to the server.

[1765] The terminal transmits the performance data entered by the user to the server using a secure communication protocol (e.g., HTTPS), which ensures the safety of the data.

[1766] Input: Performance data stored on the device

[1767] Output: Performance data sent to the server

[1768] Step 3:

[1769] The server receives the data and begins analyzing it.

[1770] The server receives performance data sent from the device. It uses Python (registered trademark) and TENSORFLOW (registered trademark) to analyze the received data. The server analyzes the video of the pitching form to identify the user's pitching style. It also evaluates the user's performance based on pitching speed and accuracy data.

[1771] Input: Performance data sent to the server

[1772] Output: Analysis results (pitching style, performance evaluation)

[1773] Step 4:

[1774] The server uses the generated AI model to generate an optimal training plan.

[1775] The server then uses the generative AI model to generate an optimal training plan based on the analysis results. For example, if a sidearm pitch is determined to be suitable, it generates a sidearm pitch training plan. This training plan includes warm-ups, repeated practice of basic movements, practice in a real-game setting, and a cool-down.

[1776] Input: Analysis results (pitching style, performance evaluation)

[1777] Output: Generated practice plan

[1778] Step 5:

[1779] The server transmits the generated training plan to the terminal.

[1780] The server transmits the generated training plan to the device using a secure communication protocol, which protects the user's privacy.

[1781] Input: Generated practice plan

[1782] Output: Practice plan sent to the device

[1783] Step 6:

[1784] The terminal displays the training plan to the user.

[1785] The device displays the training plan received from the server to the user. The user can check and execute the training plan on the device screen. For example, the user can check the training menu, its order, and the frequency of training through a smartphone application.

[1786] Input: Practice plan sent to device

[1787] Output: The practice plan displayed to the user

[1788] In this way, the system provides the user with an optimal training plan and supports effective training.

[1789] (Application example 2)

[1790] 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."

[1791] Conventional training plan providing systems can provide optimal training plans based on the user's physical characteristics, but they have the problem of not being able to analyze machine movement data, find optimal movement patterns, and provide training plans based on those movement patterns. Furthermore, they lack the functionality to provide the generated movement procedures to the machine and to communicate with the machine in real time, which makes it difficult to achieve efficient training.

[1792] 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.

[1793] In this invention, the server includes means for analyzing the user's physical characteristics, means for providing a training plan that maximizes the user's performance based on the analysis results, means for generating videos corresponding to the training plan, means for providing the generated videos to the user, means for interacting with the user in real time, means for analyzing machine motion data and finding optimal motion patterns, means for providing a training plan based on the optimal motion patterns, means for generating motion procedures corresponding to the training plan, means for providing the generated motion procedures to the machine, and means for interacting with the machine in real time. This allows for the provision of optimal training plans for both the user and the machine, enabling efficient training.

[1794] "Means for analyzing the user's physical characteristics" refers to a device or software for collecting and analyzing data on the user's physical characteristics, such as physique, muscle strength, and flexibility.

[1795] "Means for providing a training plan that maximizes the effectiveness of the analysis results" refers to a device or software that creates and provides optimal training menus and training plans to users based on the results of an analysis of their physical characteristics.

[1796] "Means for generating videos corresponding to a training plan" refers to a device or software that creates videos based on a provided training plan to serve as a reference for the user when carrying out the plan.

[1797] The "means for providing the generated video to the user" refers to a device or software for visually providing the generated video to the user.

[1798] A "means for interacting with a user in real time" is a device or software that communicates with a user in real time and provides feedback and instructions.

[1799] "Means for analyzing machine operation data and finding optimal operation patterns" refers to devices or software that collect and analyze machine operation data and id...

Claims

1. means for receiving and analyzing user performance data; A means for generating a training plan using a generation AI model based on the analysis result of the performance data; A means for displaying the generated practice plan to a user; A system including:

2. A means for analyzing a physical characteristic of a user; a means for providing a training plan based on the analysis results; means for generating a three-dimensional model corresponding to the training plan; A means for generating a practice video using the generated three-dimensional model; A means for providing the generated video to a user; A means of interacting with users in real time; A means for converting a user's speech into text; and a means for generating an appropriate response using a generative AI model; a means for audibly communicating the generated response to a user; and Including system.

3. 3. The system according to claim 2, wherein the means for generating the practice video generates the video using a three-dimensional model that resembles the user.

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