system

Abstract

We provide the system. [Solution] A tablet device with a camera, A means of inputting the trainee's physical data, age data, and skill data, A server equipped with an AI model for analyzing captured image data, Based on data analyzed by an AI model, an automatic ball release device controls the type, speed, and direction of the ball hit to suit the player. A tablet device with a camera that captures the release of the batted ball and the process of the player handling the ball in real time, A method for quantifying and evaluating the ball-handling skills of trainees, A system that includes means of providing feedback to help trainees improve their skills.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Knock practice mainly relies on manual work by skilled instructors, but the aging of instructors and the shortage of manpower are progressing. In addition, the automatic release machine simply releases the ball, and there is a problem that it is difficult to practice according to the individual characteristics of the trainee. Therefore, there is a need for a system that can release balls according to the physique and processing ability of the trainee. Also, there is a lack of means for objectively evaluating the practice effect and providing feedback for improvement.

Means for Solving the Problems

[0005] This invention aims to solve the above-mentioned problems by combining a tablet terminal with a camera, a server equipped with an AI model, and an automatic ball release device. The system takes the user's physical data, age data, and skill data as input, and the AI ​​analyzes this data to control the type, speed, and direction of the ball hit. Furthermore, the tablet terminal with a camera captures the user's movements in real time, and the analysis results are sent to the server to quantify and evaluate the user's skills. Based on the evaluation results, feedback is provided, and a plan for the next practice is automatically generated. This allows the user to objectively evaluate their own skills and improve them efficiently.

[0006] A "tablet device with a camera" is a tablet device that has the function of capturing the movements of a practitioner in real time and processing and transmitting that video data.

[0007] "Means for inputting trainee's physical data, age data, and skill data" refers to an interface for inputting trainee's physical characteristics (height, weight, etc.), age, and current defensive skill level.

[0008] A "server equipped with an AI model" is a server that incorporates an algorithm to analyze the user's data and determine the appropriate type, speed, and direction of the ball.

[0009] An "automatic release device" is a machine controlled by an AI model that releases balls of a specified type, speed, and direction.

[0010] "A means of filming the ball handling process in real time" refers to a camera system that records the process of a player handling a ball in real time and uses that footage for analysis.

[0011] "Methods for quantifying and evaluating skills" refers to algorithms that analyze a trainee's ball-handling skills and quantify their evaluation in terms of success / failure, processing time, etc.

[0012] "Means of providing feedback for skill improvement" refers to an interface that provides users with information to instruct them on areas for improvement and specific practice methods based on analysis results.

[0013] "A means of automatically generating a plan to improve the technical challenges of the trainees during the next practice session" refers to an algorithm that automatically generates the challenges to be addressed in the next practice session and a specific practice plan based on past practice data and evaluation results. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that provides personalized practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, and an automatic release device. Specific embodiments of this system are described below.

[0036] Introduction

[0037] This system is designed to individually optimize baseball fielding practice. It takes user data such as physical characteristics, age, and skill level, and based on this, the AI ​​releases appropriate batted balls. Furthermore, it supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[0038] Overview of program processing

[0039] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[0040] User data entry and settings

[0041] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0042] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[0043] Setting the practice mode and releasing the batted ball

[0044] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[0045] Terminal: Sends the selected practice mode and difficulty information to the server.

[0046] Server: The server analyzes the received data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[0047] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[0048] Filming and analysis of the ball handling process

[0049] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. For example, it captures the moment the user catches a designated ball.

[0050] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[0051] Feedback and next practice plan

[0052] Terminal: Displays analysis results on the screen. Users can check their skill level and areas for improvement. For example, it might display something like, "This ball handling was successful, and the processing time was 1.4 seconds."

[0053] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to strengthen areas for improvement. This allows users to efficiently improve their skills.

[0054] Specific example

[0055] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[0056] The device uses its camera to capture the user's ball-handling process and sends the video data to the server. The server analyzes the received video and evaluates it as "Success" with a "Processing Time: 1.4 seconds." The analysis results are displayed on the device's screen, allowing the user to check their skill level and areas for improvement. Based on the analysis results, the server automatically generates a practice plan for the next session and suggests it to the device.

[0057] This system allows users to objectively evaluate and improve their own skills, enabling them to practice more effectively on their own.

[0058] The following describes the processing flow.

[0059] Step 1:

[0060] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0061] Step 2:

[0062] The terminal sends the user's entered physical data, age data, and skill data to the server. The server receives this data and stores it in its database.

[0063] Step 3:

[0064] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[0065] Step 4:

[0066] The device sends the user's selected practice mode and difficulty level information to the server. Based on the data received by the server, the AI ​​calculates the optimal type, speed, and direction of the shot.

[0067] Step 5:

[0068] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[0069] Step 6:

[0070] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[0071] Step 7:

[0072] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[0073] Step 8:

[0074] The server analyzes the received video data and quantifies the user's ball-handling skills. Specifically, it evaluates whether the ball was caught, the processing time, and other factors.

[0075] Step 9:

[0076] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[0077] Step 10:

[0078] Based on the analysis results, the server automatically generates a plan for the next practice session. In particular, it creates a plan that includes specific practice content to improve the user's technical challenges.

[0079] Step 11:

[0080] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[0081] In this way, the system continuously measures the user's technical skills and provides feedback for efficient improvement.

[0082] (Example 1)

[0083] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] Traditional baseball training systems struggle to maximize training effectiveness by releasing batted balls tailored to the individual characteristics of each trainee, and they lack visualization and feedback on training results. Therefore, it is difficult for trainees to accurately understand their own skill level and train effectively. Furthermore, because the training content is uniform, it is impossible to pinpoint and improve each trainee's technical weaknesses.

[0085] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0086] In this invention, the server includes means for determining the optimal batted ball based on user input data, means for capturing and analyzing the user's batted ball processing process in real time, and means for automatically generating the next practice plan based on the analysis results. This enables users to efficiently improve their skills by releasing batted balls tailored to their individual characteristics and evaluating and analyzing their practice process in real time.

[0087] A "camera-equipped information terminal" is a portable information terminal device that has a built-in digital camera and is used for user input and data capture.

[0088] "Means for inputting user physical data, age data, and skill data" refers to systems or interfaces for users to input information such as their physical characteristics, age, and skill level.

[0089] A "computer equipped with an artificial intelligence model" is an electronic computer that incorporates algorithms capable of performing machine learning and data analysis.

[0090] An "automatic ball launcher" is a mechanical device that automatically launches balls based on set conditions.

[0091] A "camera-equipped information terminal that films the user's ball-handling process in real time" is a portable information terminal device that has the function of filming the user's ball-handling process in real time and recording the video.

[0092] "Means for quantifying and evaluating a user's ball-catching skills" refers to systems and methods for analyzing a user's ball-catching skills and evaluating them using numerical values ​​or indicators.

[0093] "Means of providing feedback to improve users' skills" refers to systems and methods that present users with the information and areas for improvement necessary to enhance their skills.

[0094] "Means for determining the optimal batted ball based on user input data" refers to a method or system that analyzes user input data and calculates the most suitable type, speed, and direction of the batted ball.

[0095] "Means for real-time filming and analysis of a user's ball-handling process" refers to a system or method that films the process of a user handling a ball in real time and analyzes the resulting video data.

[0096] "Methods for automatically generating the next practice plan based on analysis results" refers to systems or methods that automatically generate new practice content and plans based on the analysis results of individual users.

[0097] This invention is a system that provides personalized fielding practice by combining a camera-equipped information terminal, a computer with an artificial intelligence model, and an automatic release device. This system is designed to individually optimize baseball fielding practice. Detailed embodiments of this system are described below.

[0098] Introduction

[0099] This system takes user data such as physical characteristics, age, and skill level as input, and based on that, artificial intelligence (AI) releases appropriate batted balls. It also supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[0100] Hardware and software to be used

[0101] This system consists of the following main hardware and software components.

[0102] Information terminals with cameras (e.g., tablet devices)

[0103] Computers equipped with artificial intelligence models (e.g., servers)

[0104] Automatic release device (e.g., automatic ball launcher)

[0105] System Processing Overview

[0106] This system's program operates through the collaboration of users, terminals, and servers. The specific operation of each core module is described below.

[0107] User data entry

[0108] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate". This data will serve as the basis for individually customizing the training content.

[0109] Sending user data

[0110] Terminal: Sends the entered data to the server. The server uses this data to determine the optimal shot for the user.

[0111] Selecting a practice mode

[0112] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range) and set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[0113] Sending practice mode data

[0114] Terminal: Sends selected practice mode and difficulty information to the server. The server analyzes this information to determine the type, speed, and direction of the hit.

[0115] The decision on the batted ball

[0116] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and traveling at 40 km / h.

[0117] Release of batted ball

[0118] Terminal: Based on instructions from the server, it controls the automatic release device and releases the specified ball.

[0119] Filming the process of handling a batted ball.

[0120] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[0121] Sending batted ball processing data

[0122] Terminal: Sends the captured video data directly to the server. The server analyzes the received video data and evaluates the user's skills.

[0123] Analysis of video data

[0124] Server: The analysis results are quantified as "success" or "failure" and the processing time is recorded. For example, it might be evaluated as "success" with "processing time: 1.4 seconds".

[0125] Display of analysis results

[0126] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement.

[0127] Generating the next practice plan

[0128] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested on the terminal, allowing users to efficiently improve their skills.

[0129] Specific example

[0130] The user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and sets the next batted ball to be released to the left at a 30° angle, at a speed of 40 km / h. The terminal controls the automatic release device and releases the batted ball. Simultaneously, the terminal's camera films the ball handling process and sends the video data to the server. The server analyzes the video and evaluates it as "success" with a "processing time: 1.4 seconds," displaying the result on the terminal's screen. Furthermore, the server automatically generates a plan for the next practice session based on the analysis results and suggests it to the terminal. This system allows users to objectively evaluate and improve their skills and effectively engage in independent practice.

[0131] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0132] Step 1:

[0133] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate".

[0134] Input: User's physical size, age, and skill data

[0135] Output: Input data (physique, age, skill level)

[0136] Step 2:

[0137] Terminal: Sends the entered data to the server. The user's entered physical characteristics, age, and skill data are sent directly to the server.

[0138] Input: Data entered in Step 1

[0139] Output: Sending data to the server

[0140] Step 3:

[0141] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range), and then set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[0142] Input: Objective and difficulty setting for the knocking practice.

[0143] Output: Selected practice objective and difficulty level

[0144] Step 4:

[0145] Terminal: Sends the selected practice mode and difficulty information to the server. This data is used to determine the type, speed, and direction of the next batted ball.

[0146] Input: Data selected in Step 3

[0147] Output: Sending data to the server

[0148] Step 5:

[0149] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. Based on the user's input data, practice objectives, and difficulty level, the server might, for example, set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[0150] Input: Data submitted in Steps 2 and 4

[0151] Output: Determination of batted ball type, speed, and direction.

[0152] Step 6:

[0153] Terminal: Based on instructions from the server, it controls the automatic release device and releases the set batted ball. The automatic release device has the function of releasing the specified batted ball. For example, the automatic release device releases a ground ball at a speed of 40 km / h at a 30-degree angle to the left.

[0154] Input: Instructions from the server (type of hit, speed, direction)

[0155] Output: Release of the specified batted ball.

[0156] Step 7:

[0157] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[0158] Input: Real-time video of the ball handling process.

[0159] Output: Captured video data

[0160] Step 8:

[0161] Terminal: Sends the captured video data directly to the server.

[0162] Input: Video data captured in Step 7

[0163] Output: Sending video data to the server

[0164] Step 9:

[0165] Server: Analyzes received video data and evaluates the user's ball-handling skills. The server analyzes the actions in the video and quantifies "success" or "failure" and processing time. For example, it might evaluate as "success" with "processing time: 1.4 seconds".

[0166] Input: Video data sent in Step 8

[0167] Output: Analysis results (success / failure, processing time, etc.)

[0168] Step 10:

[0169] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement. For example, it might display results such as, "This ball handling was successful, and the processing time was 1.4 seconds."

[0170] Input: Analysis results from the server

[0171] Output: Display of analysis results

[0172] Step 11:

[0173] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to the user. For example, a plan such as "In the next practice, let's work on reducing the reaction time to a ball hit at a 30° angle to the left by 0.2 seconds" is generated.

[0174] Input: Analysis results (success / failure, processing time, etc.)

[0175] Output: Automatic generation of the next practice plan

[0176] (Application Example 1)

[0177] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0178] Conventional factory robot training for parts picking lacks sufficient individual optimization to improve efficiency and accuracy. In particular, there is a lack of means to provide real-time motion analysis and feedback, resulting in decreased work efficiency and accuracy. Furthermore, the automatic generation of training plans tailored to the characteristics of each robot is difficult, limiting the effectiveness of the training.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0180] In this invention, the server includes means for inputting physical attribute data and work capability data of the training target, means equipped with a generative AI model for analyzing captured image data, and an automatic execution device that controls the type, speed, and direction of work suitable for the training target based on the data analyzed by the generative AI model. This enables real-time motion analysis and feedback, as well as the automatic generation of individually optimized training plans.

[0181] A "camera-equipped information processing device" is an information processing device equipped with a camera, which is a terminal capable of capturing, analyzing, and displaying images and videos.

[0182] "Training target" refers to an object or device that is the subject of training or work, and in this invention, this mainly refers to factory robots.

[0183] "Physical attribute data" refers to data that shows the specific physical characteristics of an object or piece of equipment, such as arm length and weight.

[0184] "Work capability data" refers to data about the work capabilities of an object or piece of equipment, including accuracy, speed, and capability level.

[0185] A "generative AI model" is an algorithmic model generated using machine learning or deep learning techniques, which analyzes input data and provides appropriate instructions or predictions.

[0186] A "computing device" is a piece of equipment used for processing and analyzing data, and generally includes computers and servers.

[0187] An "automatic execution device" is a device that automatically performs actions instructed by a program or AI model.

[0188] A "work processing process" is a series of actions performed by the object of the work (for example, a robot) in which it actually carries out the task.

[0189] "Feedback" refers to evaluation and improvement information provided by the system, intended to help improve the performance and technology of the subject.

[0190] A "plan" is a plan for the next training session or work, and is automatically generated based on areas for improvement and goals.

[0191] This invention is a system for individually optimizing the training of factory robots for parts picking. This system functions by combining an information processing device with a camera, a computing device equipped with a generative AI model, and an automated execution device.

[0192] System Configuration

[0193] Camera-equipped information processing device

[0194] A camera-equipped information processing device is a device equipped with a camera that can capture, analyze, and display images and videos. This system uses a device such as an iPad Pro.

[0195] computing device

[0196] A computing device is a computer or server that processes and analyzes data. In this invention, an Amazon Web Services (AWS) EC2 instance is used to host the generative AI model.

[0197] Automatic execution device

[0198] An automated execution device is a device that automatically performs actions instructed by a program or AI model. For example, a robotic arm that places parts in a specific location is one such device.

[0199] Program processing

[0200] The server first acquires user data, which is then used by the generated AI model for analysis. Specifically, it uses robot physical attribute data (e.g., arm length, velocity characteristics) and work capability data (e.g., picking ability, accuracy) as input data. Based on this, the AI ​​model determines the optimal type, speed, and direction of movement.

[0201] The generative AI model is developed using a deep learning framework such as TensorFlow. Captured video data is sent from a camera-equipped information processing device to a server and analyzed in real time. The analysis results provide evaluations such as "success," "failure," and "work time."

[0202] The feedback function displays the analysis results to the user, and the next training plan is automatically generated. This allows for efficient improvement of the robot's work capabilities.

[0203] Specific example

[0204] As an example, consider the following scenario.

[0205] 1. The user enters the physical attribute data of the factory robot (arm length 1.5m, speed performance "medium speed") and work capacity data (picking capacity "high") on the initial setup screen.

[0206] 2. Send this data from the tablet device to the server.

[0207] 3. The AI ​​model on the server analyzes the data and determines the optimal picking action (e.g., position "X: 50cm, Y: 30cm", speed "0.5 seconds").

[0208] 4. The camera captures the robot's picking movements in real time and sends the video data to the server.

[0209] 5. The server evaluates the success or failure of the operation and the time required based on the analysis results, and displays the results on the user screen.

[0210] 6. Next, based on the analysis results, the system automatically generates a training plan for the next session and proposes it to the user.

[0211] Examples of prompts to input into a generative AI model

[0212] "Develop an AI model that determines the optimal parts picking motion based on the robot's shape characteristics and work capabilities. The model will use arm length and speed performance as input data for the robot's shape characteristics, and picking capability as input data, to determine the optimal type, speed, and direction of movement. It will also capture actual picking movements with a camera and analyze whether the movement was successful or unsuccessful, as well as the work time. Additionally, build a system that automatically generates a training plan for the next session."

[0213] In this way, inputting specific requirements as prompts helps in developing high-performance AI models.

[0214] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0215] Step 1:

[0216] On the initial setup screen, the user enters the physical attribute data and work capacity data of the factory robot. For example, the user might enter an arm length of 1.5m, a speed performance of "medium speed," and a picking capacity of "high." This input data is sent from the terminal to the server.

[0217] Step 2:

[0218] The server provides the received user data as input data to the generative AI model. The generative AI model uses a deep learning framework such as TensorFlow and predicts the optimal action based on the received data. Specifically, it analyzes numerical values ​​such as arm length, speed performance, and picking ability to determine the optimal type, speed, and direction of action.

[0219] Step 3:

[0220] The server generates and sends operation instructions to the terminal. The terminal then issues instructions to the automated execution device, controlling, for example, a robotic arm to place a part in a specific location. The executed actions are captured in real time by a camera-equipped information processing device, and the video data is sent to the server.

[0221] Step 4:

[0222] The server analyzes the received video data in real time. The server uses a generated AI model to analyze the video data and quantifies evaluation items such as the success or failure of an action and the time taken. For example, evaluation results such as "Success," "Failure," and "Time taken: 0.45 seconds" can be obtained.

[0223] Step 5:

[0224] The server generates feedback based on the analysis results and sends it to the terminal. The terminal displays the analysis results on the screen and provides specific feedback to the user. For example, it might display, "This picking operation was successful, and the work time was 0.45 seconds."

[0225] Step 6:

[0226] Based on the analysis results, the server automatically generates the next training plan. Using the generated AI model, the server creates a new plan to address technical challenges and sends it to the user's device. The user can then review the next training plan through their device.

[0227] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0228] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[0229] Introduction

[0230] This system is designed to individually optimize baseball fielding practice. By incorporating data on the trainee's physique, age, and skill level, as well as emotional data during practice, it aims to provide the trainee with the most suitable batted balls, promoting skill improvement while maintaining psychological motivation.

[0231] Overview of program processing

[0232] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[0233] User data entry and settings

[0234] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, enter height 170cm, weight 65kg, age 20, and skill level "Intermediate".

[0235] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[0236] Setting the practice mode and releasing the batted ball

[0237] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[0238] Terminal: Sends the selected practice mode and difficulty information to the server.

[0239] Server: The server analyzes the received data, and the AI ​​model calculates the optimal type, speed, and direction of the batted ball. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h.

[0240] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[0241] Filming and analysis of the ball handling process

[0242] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. The user catches the designated ball.

[0243] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[0244] Acquisition and analysis of emotional data

[0245] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it analyzes whether the user is feeling stressed or enjoying the practice.

[0246] Server: Based on analyzed emotional data, the server adjusts the difficulty and type of the ball hits. For example, if the user is feeling stressed, the difficulty level is slightly reduced.

[0247] Feedback and next practice plan

[0248] Terminal: Displays analysis results and emotional data on the screen. Users can check their skill level and emotional state. For example, it might display, "This ball was handled successfully, and the handling time was 1.4 seconds. Your current psychological state is slightly stressed."

[0249] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. In particular, it creates a plan that includes specific practice content that takes into account the user's technical challenges and emotional state.

[0250] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[0251] Specific example

[0252] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[0253] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a processing time of 1.4 seconds. Furthermore, the camera captures the user's emotional data during practice, and if the emotional engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[0254] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[0255] This system allows users to continuously measure their technical skills and improve them efficiently, as well as engage in effective self-practice that takes psychological motivation into account.

[0256] The following describes the processing flow.

[0257] Step 1:

[0258] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0259] Step 2:

[0260] The terminal sends the user's entered physical measurements, age, and skill data to the server. The server receives this data and stores it in its database.

[0261] Step 3:

[0262] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[0263] Step 4:

[0264] The device sends the user's selected practice mode and difficulty level information to the server. The server analyzes the received data, and the AI ​​calculates the optimal type, speed, and direction of the shot.

[0265] Step 5:

[0266] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[0267] Step 6:

[0268] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[0269] Step 7:

[0270] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[0271] Step 8:

[0272] The device uses its camera to capture the user's facial expressions and voice tone, and sends this data to the emotion engine. The emotion engine then analyzes the user's emotional state. For example, it analyzes whether the user is feeling stressed or enjoying themselves during practice.

[0273] Step 9:

[0274] The server analyzes the received video data and emotional data to evaluate the user's ball-handling skills. Simultaneously, the emotional engine analyzes the user's emotional state and evaluates their stress level and motivation.

[0275] Step 10:

[0276] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds," "Stress level: Medium") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[0277] Step 11:

[0278] The server automatically generates the next practice plan based on the analysis results and emotional data. For example, it creates a plan that includes specific practice content that takes into account the user's technical challenges and stress levels.

[0279] Step 12:

[0280] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[0281] In this way, the system can not only continuously measure and efficiently improve the user's technical skills, but also enable effective self-practice that takes psychological motivation into consideration.

[0282] (Example 2)

[0283] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart device 14 is referred to as a "terminal".

[0284] In a conventional baseball defensive practice system, there is a lack of personalized practice based on the physique and skill level of the practitioner. In addition, since there is no function to monitor the mental state of the practitioner in real time and adjust the difficulty and content of the practice, it is difficult to effectively maintain the motivation of the practitioner. As a result, there is a problem that it takes time for the practitioner to improve their skills and efficient practice cannot be carried out.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. [[ID=II]]

[0286] In this invention, the server includes means for inputting the physique data, age data, and skill data of the practitioner, means having an artificial intelligence model for analyzing the captured image data, and means for controlling an automatic release device that controls the type, speed, and direction of hitting suitable for the practitioner based on the data analyzed by the artificial intelligence model. Thereby, an optimal practice plan can be generated based on the individual data of the practitioner, and by further analyzing the mental state by the emotion engine and providing feedback, it becomes possible to improve the skills of the practitioner and maintain the mental motivation.

[0287] The "tablet terminal with a camera" is a portable information terminal that incorporates a camera and is capable of recording video and displaying data.

[0288] The "means for inputting the physique data, age data, and skill data of the practitioner" is an interface device for inputting the height, weight, age, proficiency in defense, etc. of the practitioner into the system.

[0289] The "artificial intelligence model" is a machine learning algorithm trained using a large amount of data, and is a computer program for performing a specific task (in this case, the calculation and control of the type, speed, and direction of hitting).

[0290] A "server" is a high-performance computer system used for data analysis and overall system control.

[0291] An "automatic release device" is a device that releases balls according to the system's instructions.

[0292] "Real-time recording" means recording and transmitting the trainee's movements and environment via camera almost simultaneously (without delay).

[0293] "Methods for quantifying and evaluating batted ball handling skills" refers to a system device that analyzes acquired video data and evaluates the batted ball handling ability of the trainee as numerical data.

[0294] The "emotion engine" is software that analyzes the facial expressions and voice tone of the trainees captured by the camera to infer their psychological state at that time.

[0295] "Means of providing feedback" refers to an interface device that displays technical evaluations and psychological advice to the user based on analyzed data and inferred psychological state.

[0296] "Plans automatically generated based on technical challenges" refers to the algorithm and its results used by the system to automatically determine the content and difficulty level of practice in order to compensate for the technical shortcomings of the practitioner.

[0297] The "next practice plan" is a specific practice plan suggested by the system, based on current practice results and emotional data, outlining what and how to practice in the next session.

[0298] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an artificial intelligence model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[0299] This system is designed to optimize baseball defensive practice individually. By incorporating the emotional data during practice in addition to the physical data, age data, and skill data of the practitioner, it aims to provide the most suitable hits for the practitioner, promote skill improvement, and maintain psychological motivation.

[0300] Input and setting of user data

[0301] User: On the initial setting screen, input your physical build (height, weight), age, skill level, and defensive position. For example, the user inputs a height of 170 cm, a weight of 65 kg, an age of 20 years old, and a skill level of "intermediate".

[0302] Terminal: Transmit the input data to the server. This data becomes the basic data on which the server's artificial intelligence model determines the appropriate hits.

[0303] Setting of practice mode and release of hits

[0304] User: Select the purpose (e.g., catching practice, expanding defensive range, etc.) and difficulty level of the hitting practice. Example: Select the difficulty level of "intermediate" in the "catching practice mode".

[0305] Terminal: Transmit the selected practice mode and difficulty level information to the server.

[0306] Server: Analyze the received data, and the artificial intelligence model calculates the type (ground ball, liner, fly ball, etc.), speed, and direction of the optimal hit. For example, set a ground ball at 30° to the left direction and a speed of 40 km / h.

[0307] Terminal: Based on the instructions from the server, the automatic release device releases the specified hit.

[0308] Shooting and analysis of the hit processing process

[0309] Terminal: Shoot the user's hit processing process in real time with a camera and transmit the data to the server. The user catches the specified hit.

[0310] Server: Analyzes the received video data and evaluates the user's ball-handling skills. For example, "success," "failure," and "processing time: 1.4 seconds" are quantified.

[0311] Acquisition and analysis of emotional data

[0312] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it can determine whether the user is satisfied or stressed.

[0313] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, it will slightly lower the difficulty level.

[0314] Feedback and generation of the next practice plan

[0315] Terminal: Displays analysis results and emotional data on the screen. For example, it provides feedback to the user such as, "This ball was handled successfully, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[0316] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content that takes into account the user's technical challenges and emotional state.

[0317] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[0318] Specific example

[0319] For example, a user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to release. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[0320] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a processing time of 1.4 seconds. Furthermore, the camera captures the user's emotional data during practice, and if the emotional engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[0321] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[0322] Example of a prompt

[0323] "If a user who is 170cm tall, weighs 65kg, is 20 years old, and has an intermediate skill level selects the intermediate difficulty level in catching practice mode, how should the type, speed, and direction of the next batted ball be set?"

[0324] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0325] Step 1:

[0326] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. The entered data will be "Height: 170cm, Weight: 65kg, Age: 20, Skill Level: Intermediate". This is the initial data input for the system.

[0327] Terminal: Sends the entered data to the server. Based on the input data, the server prepares to generate an individually optimized practice plan.

[0328] Step 2:

[0329] User: Select the purpose of the practice drill (e.g., catching practice, expanding fielding range, etc.) and the difficulty level (e.g., "Catching Practice Mode", difficulty level "Intermediate").

[0330] Terminal: Sends selected practice mode and difficulty information to the server. Based on this input, the server analyzes the practice mode and difficulty data and prepares the AI ​​model to calculate the optimal batting type, speed, and direction.

[0331] Step 3:

[0332] Server: The server analyzes the received user's physique, age, skill data, practice mode, and difficulty information, and uses a generating AI model to calculate the optimal type of hit (ground ball, line drive, fly ball, etc.), speed, and direction. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h. The result of this calculation is output from the server.

[0333] Server: Sends the generated batting settings data to the terminal.

[0334] Step 4:

[0335] Terminal: Based on instructions from the server, it controls the automatic release device and releases a specified ball (e.g., a ground ball at 40 km / h, 30° to the left). It records the release log so that it can be provided to the user later.

[0336] Step 5:

[0337] Terminal: The camera captures the user's ball-hitting process in real time. The captured video data is used as input data.

[0338] Terminal: Sends captured video data to the server. This data is then passed to the server for later analysis.

[0339] Step 6:

[0340] Server: Analyzes the received video data and evaluates the user's ball-handling skills. Specifically, it generates evaluation data such as "Success," "Failure," and "Processing Time: 1.4 seconds." This is the output data after analysis.

[0341] Step 7:

[0342] The device uses a camera to capture the user's facial expressions and voice tone in real time, and analyzes them using an emotion engine. For example, it analyzes whether the user is satisfied or stressed. The acquired data is then sent to the server.

[0343] Step 8:

[0344] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, the difficulty is slightly reduced. This is output as setting data that will be reflected in the next practice session.

[0345] Step 9:

[0346] Terminal: Displays analysis results and feedback sent from the server on the screen. Specifically, it provides feedback such as, "This ball handling was successful, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[0347] Step 10:

[0348] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content considering the user's technical challenges and emotional state. This plan will be executed in the next practice session.

[0349] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0352] Traditional baseball fielding drills make it difficult to provide an optimal program tailored to the individual characteristics of each player, such as their current skill level, physique, and age. Furthermore, it is virtually impossible to assess players' psychological stress and motivation in real time during practice and provide corresponding feedback or adjust the practice plan accordingly. Moreover, it is challenging to control the optimal type, speed, and direction of batted balls while taking all these factors into consideration.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0354] In this invention, the server includes means for inputting the user's physical data, age data, and skill data; means equipped with an artificial intelligence model for analyzing captured image data; and means for adjusting the difficulty and type of balls based on the analyzed emotional data. This provides the user with optimal balls, enabling improvement in technical skills and maintenance of psychological motivation.

[0355] A "camera-equipped information terminal" is a computer terminal equipped with a camera for capturing video, and its role is to record and transmit the actions of the person practicing.

[0356] A "user" is an individual who uses this system for practice and is the one who inputs physical data, age data, and skill data.

[0357] "Physical data" refers to information that shows the physical characteristics of the trainee, such as height and weight.

[0358] "Age data" refers to information indicating the age of the trainee.

[0359] "Skill data" refers to information that indicates a player's skill level and proficiency in baseball.

[0360] An "artificial intelligence model" is a system that includes algorithms and neural networks that analyze input data and generate appropriate output results.

[0361] A "computer" refers to a computer or server used for processing data, and is a device that runs artificial intelligence models.

[0362] An "automatic ball dispenser" is a device that automatically dispenses practice balls based on analyzed data.

[0363] The "ball handling process" refers to a series of actions taken by the user to catch or handle an incoming ball.

[0364] "Methods for quantifying and evaluating" refers to a system that evaluates a player's ball-handling skills as quantitative numerical data.

[0365] "Means of providing feedback for skill improvement" refers to a system that provides feedback to practitioners on areas for improvement in their technique and the results of their work.

[0366] "Emotional data" refers to data such as the trainee's psychological state, facial expressions, and voice tone during practice.

[0367] "A means of adjusting the difficulty and type of balls based on analyzed emotional data" refers to a system that analyzes the psychological state of the trainee and optimizes the training content based on the results.

[0368] This invention is designed to individually optimize baseball fielding practice and is a system that combines a camera-equipped information terminal, a computer with an artificial intelligence model, an automatic release device, and an emotional data analysis means. Based on the user's physical data, age data, and skill data, it provides the optimal type, speed, and direction of batted balls, and further monitors and analyzes the user's emotional state during practice to adjust the practice content in real time, thereby improving skills and maintaining motivation.

[0369] System Configuration

[0370] 1. Initial setup and user data entry

[0371] Users input their physical data (height, weight, etc.), age data, and skill data (proficiency level, etc.) using a camera-equipped information terminal. This data is transmitted to a server via wireless communication such as Wi-Fi or Bluetooth.

[0372] 2. Release of batted balls and real-time filming

[0373] The server uses an artificial intelligence model based on the received data to determine the appropriate type, speed, and direction of the batted ball. The computer uses Python and TensorFlow for data analysis. The automatic batted ball release device releases the specified batted ball based on the computer's instructions. A camera-equipped information terminal captures the batted ball release and the user's batting process in real time, and transmits the video data to the server.

[0374] 3. Analysis of video data and technical evaluation

[0375] The server analyzes the received video data using libraries such as OpenCV to quantify the ball-handling technique. Evaluation items include "success" or "failure," and processing time. The analysis results are displayed on a camera-equipped information terminal as feedback to help the user improve their skills.

[0376] 4. Acquisition and Analysis of Emotional Data

[0377] Emotional analysis is performed based on data such as facial expressions and voice tone acquired from a camera-equipped information terminal. An existing emotion recognition engine is utilized for this purpose. The analyzed emotional data is sent to a server and used to adjust the training content. For example, if the user is experiencing stress, the server will automatically lower the difficulty level of the ball-hitting exercises.

[0378] 5. Automatic generation of the next practice plan.

[0379] The server automatically generates the next practice plan based on the analysis results and emotional data. The generated practice plan is displayed to the user via a camera-equipped information terminal. This allows the user to obtain an effective practice plan that takes into account their technical challenges and psychological state.

[0380] Specific example

[0381] As a concrete example, consider the following situation.

[0382] User settings: The user enters their height (175cm), weight (70kg), age (25), and skill level ("Advanced") on a camera-equipped information terminal. They select shortstop as their defensive position, "Expanded Defensive Range Mode" as their practice mode, and "Advanced" as their difficulty level.

[0383] Smartphone app feedback display: "This ball was handled successfully. Handling time: 1.2 seconds. Current mental state: Relaxed."

[0384] Examples of prompts for generative AI models

[0385] I want to implement an application like this:

[0386] 1. Enter data such as the user's physique, age, and skill level into the smartphone app.

[0387] 2. The user's practice process is filmed using the camera on a camera-equipped information terminal.

[0388] 3. Analyze video data on the server and generate a technical evaluation.

[0389] 4. Analyze psychological state using the Emotion Engine and reflect it in the training plan.

[0390] 5. The next practice plan will be displayed on the smartphone app.

[0391] This allows users to efficiently improve their skills and maintain their motivation.

[0392] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0393] Step 1:

[0394] The user performs the initial setup using a camera-equipped information terminal. Specifically, they input physical data such as height, weight, age, and skill level, as well as age and skill data, and then transmit the setting data. Based on this input data, the server performs an initial analysis and calculates the optimal batting parameters for the user. The output provides initial settings for batting type, speed, and direction.

[0395] Step 2:

[0396] The terminal controls the automatic release device to release the appropriate ball based on initial setup data received from the server. The type, speed, and direction of the ball released by the automatic release device are controlled by the data used. The ball is then physically released to the user as output.

[0397] Step 3:

[0398] The terminal uses its camera to capture the user's ball-handling process in real time. The captured video data is sent to the server via the terminal. Using this video data as input, the server performs motion analysis using OpenCV or similar tools to quantify and evaluate the ball-handling technique. The output is technical evaluation data (success / failure, processing time, etc.).

[0399] Step 4:

[0400] The server collects technical evaluation data and provides feedback to the terminal. Specific feedback includes success / failure results, processing time, and areas for improvement. The feedback data is displayed to the user via the terminal. Here, technical evaluation data is taken as input, and feedback data is output.

[0401] Step 5:

[0402] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the server in real time. Based on this input data, the server uses an emotion analysis engine to analyze the user's psychological state. TensorFlow is used as an example. Emotion evaluation data is output as a result of the analysis.

[0403] Step 6:

[0404] The server adjusts the difficulty and type of batting practice based on emotional assessment data. For example, if the user is stressed, the difficulty is lowered, and if they are relaxed, the difficulty is increased. Emotional assessment data is then input, and an adjusted practice plan is output.

[0405] Step 7:

[0406] The server automatically generates the next practice plan based on technical and emotional evaluation data. This practice plan includes specific practice content that reflects the user's technical challenges and psychological state. The generated practice plan is presented to the user via the terminal. Here, the technical and emotional evaluation data are used as input, and a new practice plan is output.

[0407] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0408] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0409] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0410] [Second Embodiment]

[0411] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0412] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0413] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0414] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0415] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0416] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0417] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0418] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0419] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0420] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0421] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0422] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0423] This invention is a system that provides personalized practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, and an automatic release device. Specific embodiments of this system are described below.

[0424] Introduction

[0425] This system is designed to individually optimize baseball fielding practice. It takes user data such as physical characteristics, age, and skill level, and based on this, the AI ​​releases appropriate batted balls. Furthermore, it supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[0426] Overview of program processing

[0427] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[0428] User data entry and settings

[0429] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0430] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[0431] Setting the practice mode and releasing the batted ball

[0432] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[0433] Terminal: Sends the selected practice mode and difficulty information to the server.

[0434] Server: The server analyzes the received data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[0435] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[0436] Filming and analysis of the ball handling process

[0437] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. For example, it captures the moment the user catches a designated ball.

[0438] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[0439] Feedback and next practice plan

[0440] Terminal: Displays analysis results on the screen. Users can check their skill level and areas for improvement. For example, it might display something like, "This ball handling was successful, and the processing time was 1.4 seconds."

[0441] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to strengthen areas for improvement. This allows users to efficiently improve their skills.

[0442] Specific example

[0443] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[0444] The device uses its camera to capture the user's ball-handling process and sends the video data to the server. The server analyzes the received video and evaluates it as "Success" with a "Processing Time: 1.4 seconds." The analysis results are displayed on the device's screen, allowing the user to check their skill level and areas for improvement. Based on the analysis results, the server automatically generates a practice plan for the next session and suggests it to the device.

[0445] This system allows users to objectively evaluate and improve their own skills, enabling them to practice more effectively on their own.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0449] Step 2:

[0450] The terminal sends the user's entered physical data, age data, and skill data to the server. The server receives this data and stores it in its database.

[0451] Step 3:

[0452] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[0453] Step 4:

[0454] The device sends the user's selected practice mode and difficulty level information to the server. Based on the data received by the server, the AI ​​calculates the optimal type, speed, and direction of the shot.

[0455] Step 5:

[0456] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[0457] Step 6:

[0458] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[0459] Step 7:

[0460] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[0461] Step 8:

[0462] The server analyzes the received video data and quantifies the user's ball-handling skills. Specifically, it evaluates whether the ball was caught, the processing time, and other factors.

[0463] Step 9:

[0464] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[0465] Step 10:

[0466] Based on the analysis results, the server automatically generates a plan for the next practice session. In particular, it creates a plan that includes specific practice content to improve the user's technical challenges.

[0467] Step 11:

[0468] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[0469] In this way, the system continuously measures the user's technical skills and provides feedback for efficient improvement.

[0470] (Example 1)

[0471] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0472] Traditional baseball training systems struggle to maximize training effectiveness by releasing batted balls tailored to the individual characteristics of each trainee, and they lack visualization and feedback on training results. Therefore, it is difficult for trainees to accurately understand their own skill level and train effectively. Furthermore, because the training content is uniform, it is impossible to pinpoint and improve each trainee's technical weaknesses.

[0473] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0474] In this invention, the server includes means for determining the optimal batted ball based on user input data, means for capturing and analyzing the user's batted ball processing process in real time, and means for automatically generating the next practice plan based on the analysis results. This enables users to efficiently improve their skills by releasing batted balls tailored to their individual characteristics and evaluating and analyzing their practice process in real time.

[0475] A "camera-equipped information terminal" is a portable information terminal device that has a built-in digital camera and is used for user input and data capture.

[0476] "Means for inputting user physical data, age data, and skill data" refers to systems or interfaces for users to input information such as their physical characteristics, age, and skill level.

[0477] A "computer equipped with an artificial intelligence model" is an electronic computer that incorporates algorithms capable of performing machine learning and data analysis.

[0478] An "automatic ball launcher" is a mechanical device that automatically launches balls based on set conditions.

[0479] A "camera-equipped information terminal that films the user's ball-handling process in real time" is a portable information terminal device that has the function of filming the user's ball-handling process in real time and recording the video.

[0480] "Means for quantifying and evaluating a user's ball-catching skills" refers to systems and methods for analyzing a user's ball-catching skills and evaluating them using numerical values ​​or indicators.

[0481] "Means of providing feedback to improve users' skills" refers to systems and methods that present users with the information and areas for improvement necessary to enhance their skills.

[0482] "Means for determining the optimal batted ball based on user input data" refers to a method or system that analyzes user input data and calculates the most suitable type, speed, and direction of the batted ball.

[0483] "Means for real-time filming and analysis of a user's ball-handling process" refers to a system or method that films the process of a user handling a ball in real time and analyzes the resulting video data.

[0484] "Methods for automatically generating the next practice plan based on analysis results" refers to systems or methods that automatically generate new practice content and plans based on the analysis results of individual users.

[0485] This invention is a system that provides personalized fielding practice by combining a camera-equipped information terminal, a computer with an artificial intelligence model, and an automatic release device. This system is designed to individually optimize baseball fielding practice. Detailed embodiments of this system are described below.

[0486] Introduction

[0487] This system takes user data such as physical characteristics, age, and skill level as input, and based on that, artificial intelligence (AI) releases appropriate batted balls. It also supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[0488] Hardware and software to be used

[0489] This system consists of the following main hardware and software components.

[0490] Information terminals with cameras (e.g., tablet devices)

[0491] Computers equipped with artificial intelligence models (e.g., servers)

[0492] Automatic release device (e.g., automatic ball launcher)

[0493] System Processing Overview

[0494] This system's program operates through the collaboration of users, terminals, and servers. The specific operation of each core module is described below.

[0495] User data entry

[0496] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate". This data will serve as the basis for individually customizing the training content.

[0497] Sending user data

[0498] Terminal: Sends the entered data to the server. The server uses this data to determine the optimal shot for the user.

[0499] Selecting a practice mode

[0500] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range) and set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[0501] Sending practice mode data

[0502] Terminal: Sends selected practice mode and difficulty information to the server. The server analyzes this information to determine the type, speed, and direction of the hit.

[0503] The decision on the batted ball

[0504] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and traveling at 40 km / h.

[0505] Release of batted ball

[0506] Terminal: Based on instructions from the server, it controls the automatic release device and releases the specified ball.

[0507] Filming the process of handling a batted ball.

[0508] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[0509] Sending batted ball processing data

[0510] Terminal: Sends the captured video data directly to the server. The server analyzes the received video data and evaluates the user's skills.

[0511] Analysis of video data

[0512] Server: The analysis results are quantified as "success" or "failure" and the processing time is recorded. For example, it might be evaluated as "success" with "processing time: 1.4 seconds".

[0513] Display of analysis results

[0514] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement.

[0515] Generating the next practice plan

[0516] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested on the terminal, allowing users to efficiently improve their skills.

[0517] Specific example

[0518] The user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and sets the next batted ball to be released to the left at a 30° angle, at a speed of 40 km / h. The terminal controls the automatic release device and releases the batted ball. Simultaneously, the terminal's camera films the ball handling process and sends the video data to the server. The server analyzes the video and evaluates it as "success" with a "processing time: 1.4 seconds," displaying the result on the terminal's screen. Furthermore, the server automatically generates a plan for the next practice session based on the analysis results and suggests it to the terminal. This system allows users to objectively evaluate and improve their skills and effectively engage in independent practice.

[0519] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0520] Step 1:

[0521] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate".

[0522] Input: User's physical size, age, and skill data

[0523] Output: Input data (physique, age, skill level)

[0524] Step 2:

[0525] Terminal: Sends the entered data to the server. The user's entered physical characteristics, age, and skill data are sent directly to the server.

[0526] Input: Data entered in Step 1

[0527] Output: Sending data to the server

[0528] Step 3:

[0529] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range), and then set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[0530] Input: Objective and difficulty setting for the knocking practice.

[0531] Output: Selected practice objective and difficulty level

[0532] Step 4:

[0533] Terminal: Sends the selected practice mode and difficulty information to the server. This data is used to determine the type, speed, and direction of the next batted ball.

[0534] Input: Data selected in Step 3

[0535] Output: Sending data to the server

[0536] Step 5:

[0537] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. Based on the user's input data, practice objectives, and difficulty level, the server might, for example, set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[0538] Input: Data submitted in Steps 2 and 4

[0539] Output: Determination of batted ball type, speed, and direction.

[0540] Step 6:

[0541] Terminal: Based on instructions from the server, it controls the automatic release device and releases the set batted ball. The automatic release device has the function of releasing the specified batted ball. For example, the automatic release device releases a ground ball at a speed of 40 km / h at a 30-degree angle to the left.

[0542] Input: Instructions from the server (type of hit, speed, direction)

[0543] Output: Release of the specified batted ball.

[0544] Step 7:

[0545] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[0546] Input: Real-time video of the ball handling process.

[0547] Output: Captured video data

[0548] Step 8:

[0549] Terminal: Sends the captured video data directly to the server.

[0550] Input: Video data captured in Step 7

[0551] Output: Sending video data to the server

[0552] Step 9:

[0553] Server: Analyzes received video data and evaluates the user's ball-handling skills. The server analyzes the actions in the video and quantifies "success" or "failure" and processing time. For example, it might evaluate as "success" with "processing time: 1.4 seconds".

[0554] Input: Video data sent in Step 8

[0555] Output: Analysis results (success / failure, processing time, etc.)

[0556] Step 10:

[0557] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement. For example, it might display results such as, "This ball handling was successful, and the processing time was 1.4 seconds."

[0558] Input: Analysis results from the server

[0559] Output: Display of analysis results

[0560] Step 11:

[0561] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to the user. For example, a plan such as "In the next practice, let's work on reducing the reaction time to a ball hit at a 30° angle to the left by 0.2 seconds" is generated.

[0562] Input: Analysis results (success / failure, processing time, etc.)

[0563] Output: Automatic generation of the next practice plan

[0564] (Application Example 1)

[0565] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0566] Conventional factory robot training for parts picking lacks sufficient individual optimization to improve efficiency and accuracy. In particular, there is a lack of means to provide real-time motion analysis and feedback, resulting in decreased work efficiency and accuracy. Furthermore, the automatic generation of training plans tailored to the characteristics of each robot is difficult, limiting the effectiveness of the training.

[0567] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0568] In this invention, the server includes means for inputting physical attribute data and work capability data of the training target, means equipped with a generative AI model for analyzing captured image data, and an automatic execution device that controls the type, speed, and direction of work suitable for the training target based on the data analyzed by the generative AI model. This enables real-time motion analysis and feedback, as well as the automatic generation of individually optimized training plans.

[0569] A "camera-equipped information processing device" is an information processing device equipped with a camera, which is a terminal capable of capturing, analyzing, and displaying images and videos.

[0570] "Training target" refers to an object or device that is the subject of training or work, and in this invention, this mainly refers to factory robots.

[0571] "Physical attribute data" refers to data that shows the specific physical characteristics of an object or piece of equipment, such as arm length and weight.

[0572] "Work capability data" refers to data about the work capabilities of an object or piece of equipment, including accuracy, speed, and capability level.

[0573] A "generative AI model" is an algorithmic model generated using machine learning or deep learning techniques, which analyzes input data and provides appropriate instructions or predictions.

[0574] A "computing device" is a piece of equipment used for processing and analyzing data, and generally includes computers and servers.

[0575] An "automatic execution device" is a device that automatically performs actions instructed by a program or AI model.

[0576] A "work processing process" is a series of actions performed by the object of the work (for example, a robot) in which it actually carries out the task.

[0577] "Feedback" refers to evaluation and improvement information provided by the system, intended to help improve the performance and technology of the subject.

[0578] A "plan" is a plan for the next training session or work, and is automatically generated based on areas for improvement and goals.

[0579] This invention is a system for individually optimizing the training of factory robots for parts picking. This system functions by combining an information processing device with a camera, a computing device equipped with a generative AI model, and an automated execution device.

[0580] System Configuration

[0581] Camera-equipped information processing device

[0582] A camera-equipped information processing device is a device equipped with a camera that can capture, analyze, and display images and videos. This system uses a device such as an iPad Pro.

[0583] computing device

[0584] A computing device is a computer or server that processes and analyzes data. In this invention, an Amazon Web Services (AWS) EC2 instance is used to host the generative AI model.

[0585] Automatic execution device

[0586] An automated execution device is a device that automatically performs actions instructed by a program or AI model. For example, a robotic arm that places parts in a specific location is one such device.

[0587] Program processing

[0588] The server first acquires user data, which is then used by the generated AI model for analysis. Specifically, it uses robot physical attribute data (e.g., arm length, velocity characteristics) and work capability data (e.g., picking ability, accuracy) as input data. Based on this, the AI ​​model determines the optimal type, speed, and direction of movement.

[0589] The generative AI model is developed using a deep learning framework such as TensorFlow. Captured video data is sent from a camera-equipped information processing device to a server and analyzed in real time. The analysis results provide evaluations such as "success," "failure," and "work time."

[0590] The feedback function displays the analysis results to the user, and the next training plan is automatically generated. This allows for efficient improvement of the robot's work capabilities.

[0591] Specific example

[0592] As an example, consider the following scenario.

[0593] 1. The user enters the physical attribute data of the factory robot (arm length 1.5m, speed performance "medium speed") and work capacity data (picking capacity "high") on the initial setup screen.

[0594] 2. Send this data from the tablet device to the server.

[0595] 3. The AI ​​model on the server analyzes the data and determines the optimal picking action (e.g., position "X: 50cm, Y: 30cm", speed "0.5 seconds").

[0596] 4. The camera captures the robot's picking movements in real time and sends the video data to the server.

[0597] 5. The server evaluates the success or failure of the operation and the time required based on the analysis results, and displays the results on the user screen.

[0598] 6. Next, based on the analysis results, the system automatically generates a training plan for the next session and proposes it to the user.

[0599] Examples of prompts to input into a generative AI model

[0600] "Develop an AI model that determines the optimal parts picking motion based on the robot's shape characteristics and work capabilities. The model will use arm length and speed performance as input data for the robot's shape characteristics, and picking capability as input data, to determine the optimal type, speed, and direction of movement. It will also capture actual picking movements with a camera and analyze whether the movement was successful or unsuccessful, as well as the work time. Additionally, build a system that automatically generates a training plan for the next session."

[0601] In this way, inputting specific requirements as prompts helps in developing high-performance AI models.

[0602] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0603] Step 1:

[0604] On the initial setup screen, the user enters the physical attribute data and work capacity data of the factory robot. For example, the user might enter an arm length of 1.5m, a speed performance of "medium speed," and a picking capacity of "high." This input data is sent from the terminal to the server.

[0605] Step 2:

[0606] The server provides the received user data as input data to the generative AI model. The generative AI model uses a deep learning framework such as TensorFlow and predicts the optimal action based on the received data. Specifically, it analyzes numerical values ​​such as arm length, speed performance, and picking ability to determine the optimal type, speed, and direction of action.

[0607] Step 3:

[0608] The server generates and sends operation instructions to the terminal. The terminal then issues instructions to the automated execution device, controlling, for example, a robotic arm to place a part in a specific location. The executed actions are captured in real time by a camera-equipped information processing device, and the video data is sent to the server.

[0609] Step 4:

[0610] The server analyzes the received video data in real time. The server uses a generated AI model to analyze the video data and quantifies evaluation items such as the success or failure of an action and the time taken. For example, evaluation results such as "Success," "Failure," and "Time taken: 0.45 seconds" can be obtained.

[0611] Step 5:

[0612] The server generates feedback based on the analysis results and sends it to the terminal. The terminal displays the analysis results on the screen and provides specific feedback to the user. For example, it might display, "This picking operation was successful, and the work time was 0.45 seconds."

[0613] Step 6:

[0614] Based on the analysis results, the server automatically generates the next training plan. Using the generated AI model, the server creates a new plan to address technical challenges and sends it to the user's device. The user can then review the next training plan through their device.

[0615] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0616] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[0617] Introduction

[0618] This system is designed to individually optimize baseball fielding practice. By incorporating data on the trainee's physique, age, and skill level, as well as emotional data during practice, it aims to provide the trainee with the most suitable batted balls, promoting skill improvement while maintaining psychological motivation.

[0619] Overview of program processing

[0620] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[0621] User data entry and settings

[0622] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, enter height 170cm, weight 65kg, age 20, and skill level "Intermediate".

[0623] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[0624] Setting the practice mode and releasing the batted ball

[0625] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[0626] Terminal: Sends the selected practice mode and difficulty information to the server.

[0627] Server: The server analyzes the received data, and the AI ​​model calculates the optimal type, speed, and direction of the batted ball. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h.

[0628] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[0629] Filming and analysis of the ball handling process

[0630] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. The user catches the designated ball.

[0631] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[0632] Acquisition and analysis of emotional data

[0633] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it analyzes whether the user is feeling stressed or enjoying the practice.

[0634] Server: Based on analyzed emotional data, the server adjusts the difficulty and type of the ball hits. For example, if the user is feeling stressed, the difficulty level is slightly reduced.

[0635] Feedback and next practice plan

[0636] Terminal: Displays analysis results and emotional data on the screen. Users can check their skill level and emotional state. For example, it might display, "This ball was handled successfully, and the handling time was 1.4 seconds. Your current psychological state is slightly stressed."

[0637] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. In particular, it creates a plan that includes specific practice content that takes into account the user's technical challenges and emotional state.

[0638] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[0639] Specific example

[0640] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[0641] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a processing time of 1.4 seconds. Furthermore, the camera captures the user's emotional data during practice, and if the emotional engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[0642] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[0643] This system allows users to continuously measure their technical skills and improve them efficiently, as well as engage in effective self-practice that takes psychological motivation into account.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0647] Step 2:

[0648] The terminal sends the user's entered physical measurements, age, and skill data to the server. The server receives this data and stores it in its database.

[0649] Step 3:

[0650] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[0651] Step 4:

[0652] The device sends the user's selected practice mode and difficulty level information to the server. The server analyzes the received data, and the AI ​​calculates the optimal type, speed, and direction of the shot.

[0653] Step 5:

[0654] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[0655] Step 6:

[0656] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[0657] Step 7:

[0658] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[0659] Step 8:

[0660] The device uses its camera to capture the user's facial expressions and voice tone, and sends this data to the emotion engine. The emotion engine then analyzes the user's emotional state. For example, it analyzes whether the user is feeling stressed or enjoying themselves during practice.

[0661] Step 9:

[0662] The server analyzes the received video data and emotional data to evaluate the user's ball-handling skills. Simultaneously, the emotional engine analyzes the user's emotional state and evaluates their stress level and motivation.

[0663] Step 10:

[0664] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds," "Stress level: Medium") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[0665] Step 11:

[0666] The server automatically generates the next practice plan based on the analysis results and emotional data. For example, it creates a plan that includes specific practice content that takes into account the user's technical challenges and stress levels.

[0667] Step 12:

[0668] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[0669] In this way, the system can not only continuously measure and efficiently improve the user's technical skills, but also enable effective self-practice that takes psychological motivation into consideration.

[0670] (Example 2)

[0671] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0672] Traditional baseball fielding practice systems lack personalized training based on the player's physique and skill level. Furthermore, they lack the ability to monitor the player's mental state in real time and adjust the difficulty and content of the training accordingly, making it difficult to effectively maintain player motivation. This results in slower skill improvement and inefficient practice.

[0673] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0674] In this invention, the server includes means for inputting the trainee's physical data, age data, and skill data; means equipped with an artificial intelligence model for analyzing captured image data; and means for controlling an automatic release device that controls the type, speed, and direction of the batted ball appropriate for the trainee based on the data analyzed by the artificial intelligence model. This enables the generation of an optimal training plan based on the trainee's individual data, and further analyzes their psychological state using an emotion engine to provide feedback, thereby improving the trainee's skills and maintaining their psychological motivation.

[0675] A "tablet device with a camera" is a portable information terminal that has a built-in camera and is capable of recording video and displaying data.

[0676] "Means for inputting trainee's physical data, age data, and skill data" refers to an interface device for inputting trainee's height, weight, age, defensive proficiency level, etc., into the system.

[0677] An "artificial intelligence model" is a machine learning algorithm trained using a large amount of data, and is a computer program designed to perform a specific task (in this case, calculating and controlling the type, speed, and direction of a ball hit).

[0678] A "server" is a high-performance computer system used for data analysis and overall system control.

[0679] An "automatic release device" is a device that releases balls according to the system's instructions.

[0680] "Real-time recording" means recording and transmitting the trainee's movements and environment via camera almost simultaneously (without delay).

[0681] "Methods for quantifying and evaluating batted ball handling skills" refers to a system device that analyzes acquired video data and evaluates the batted ball handling ability of the trainee as numerical data.

[0682] The "emotion engine" is software that analyzes the facial expressions and voice tone of the trainees captured by the camera to infer their psychological state at that time.

[0683] "Means of providing feedback" refers to an interface device that displays technical evaluations and psychological advice to the user based on analyzed data and inferred psychological state.

[0684] "Plans automatically generated based on technical challenges" refers to the algorithm and its results used by the system to automatically determine the content and difficulty level of practice in order to compensate for the technical shortcomings of the practitioner.

[0685] The "next practice plan" is a specific practice plan suggested by the system, based on current practice results and emotional data, outlining what and how to practice in the next session.

[0686] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an artificial intelligence model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[0687] This system is designed to individually optimize baseball fielding practice. By incorporating the player's physical data, age data, skill data, and emotional data during practice, it aims to provide the player with the optimal batted ball, promote skill improvement, and maintain psychological motivation.

[0688] User data entry and settings

[0689] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate".

[0690] Terminal: Sends the entered data to the server. This data serves as the basis for the server's artificial intelligence model to determine the appropriate batted ball.

[0691] Setting the practice mode and releasing the batted ball

[0692] User: Select the purpose of the practice drill (e.g., catching practice, expanding fielding range, etc.) and the difficulty level. Example: Select "Intermediate" difficulty level in "Catching Practice Mode".

[0693] Terminal: Sends the selected practice mode and difficulty information to the server.

[0694] Server: The server analyzes the received data, and the artificial intelligence model calculates the optimal type of batted ball (ground ball, line drive, fly ball, etc.), speed, and direction. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h.

[0695] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[0696] Filming and analysis of the ball handling process

[0697] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. The user catches the designated ball.

[0698] Server: Analyzes the received video data and evaluates the user's ball-handling skills. For example, "success," "failure," and "processing time: 1.4 seconds" are quantified.

[0699] Acquisition and analysis of emotional data

[0700] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it can determine whether the user is satisfied or stressed.

[0701] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, it will slightly lower the difficulty level.

[0702] Feedback and generation of the next practice plan

[0703] Terminal: Displays analysis results and emotional data on the screen. For example, it provides feedback to the user such as, "This ball was handled successfully, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[0704] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content that takes into account the user's technical challenges and emotional state.

[0705] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[0706] Specific example

[0707] For example, a user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to release. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[0708] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a processing time of 1.4 seconds. Furthermore, the camera captures the user's emotional data during practice, and if the emotional engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[0709] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[0710] Example of a prompt

[0711] "If a user who is 170cm tall, weighs 65kg, is 20 years old, and has an intermediate skill level selects the intermediate difficulty level in catching practice mode, how should the type, speed, and direction of the next batted ball be set?"

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. The entered data will be "Height: 170cm, Weight: 65kg, Age: 20, Skill Level: Intermediate". This is the initial data input for the system.

[0715] Terminal: Sends the entered data to the server. Based on the input data, the server prepares to generate an individually optimized practice plan.

[0716] Step 2:

[0717] User: Select the purpose of the practice drill (e.g., catching practice, expanding fielding range, etc.) and the difficulty level (e.g., "Catching Practice Mode", difficulty level "Intermediate").

[0718] Terminal: Sends selected practice mode and difficulty information to the server. Based on this input, the server analyzes the practice mode and difficulty data and prepares the AI ​​model to calculate the optimal batting type, speed, and direction.

[0719] Step 3:

[0720] Server: The server analyzes the received user's physique, age, skill data, practice mode, and difficulty information, and uses a generating AI model to calculate the optimal type of hit (ground ball, line drive, fly ball, etc.), speed, and direction. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h. The result of this calculation is output from the server.

[0721] Server: Sends the generated batting settings data to the terminal.

[0722] Step 4:

[0723] Terminal: Based on instructions from the server, it controls the automatic release device and releases a specified ball (e.g., a ground ball at 40 km / h, 30° to the left). It records the release log so that it can be provided to the user later.

[0724] Step 5:

[0725] Terminal: The camera captures the user's ball-hitting process in real time. The captured video data is used as input data.

[0726] Terminal: Sends captured video data to the server. This data is then passed to the server for later analysis.

[0727] Step 6:

[0728] Server: Analyzes the received video data and evaluates the user's ball-handling skills. Specifically, it generates evaluation data such as "Success," "Failure," and "Processing Time: 1.4 seconds." This is the output data after analysis.

[0729] Step 7:

[0730] The device uses a camera to capture the user's facial expressions and voice tone in real time, and analyzes them using an emotion engine. For example, it analyzes whether the user is satisfied or stressed. The acquired data is then sent to the server.

[0731] Step 8:

[0732] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, the difficulty is slightly reduced. This is output as setting data that will be reflected in the next practice session.

[0733] Step 9:

[0734] Terminal: Displays analysis results and feedback sent from the server on the screen. Specifically, it provides feedback such as, "This ball handling was successful, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[0735] Step 10:

[0736] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content considering the user's technical challenges and emotional state. This plan will be executed in the next practice session.

[0737] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[0738] (Application Example 2)

[0739] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0740] Traditional baseball fielding drills make it difficult to provide an optimal program tailored to the individual characteristics of each player, such as their current skill level, physique, and age. Furthermore, it is virtually impossible to assess players' psychological stress and motivation in real time during practice and provide corresponding feedback or adjust the practice plan accordingly. Moreover, it is challenging to control the optimal type, speed, and direction of batted balls while taking all these factors into consideration.

[0741] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0742] In this invention, the server includes means for inputting the user's physical data, age data, and skill data; means equipped with an artificial intelligence model for analyzing captured image data; and means for adjusting the difficulty and type of balls based on the analyzed emotional data. This provides the user with optimal balls, enabling improvement in technical skills and maintenance of psychological motivation.

[0743] A "camera-equipped information terminal" is a computer terminal equipped with a camera for capturing video, and its role is to record and transmit the actions of the person practicing.

[0744] A "user" is an individual who uses this system for practice and is the one who inputs physical data, age data, and skill data.

[0745] "Physical data" refers to information that shows the physical characteristics of the trainee, such as height and weight.

[0746] "Age data" refers to information indicating the age of the trainee.

[0747] "Skill data" refers to information that indicates a player's skill level and proficiency in baseball.

[0748] An "artificial intelligence model" is a system that includes algorithms and neural networks that analyze input data and generate appropriate output results.

[0749] A "computer" refers to a computer or server used for processing data, and is a device that runs artificial intelligence models.

[0750] An "automatic ball dispenser" is a device that automatically dispenses practice balls based on analyzed data.

[0751] The "ball handling process" refers to a series of actions taken by the user to catch or handle an incoming ball.

[0752] "Methods for quantifying and evaluating" refers to a system that evaluates a player's ball-handling skills as quantitative numerical data.

[0753] "Means of providing feedback for skill improvement" refers to a system that provides feedback to practitioners on areas for improvement in their technique and the results of their work.

[0754] "Emotional data" refers to data such as the trainee's psychological state, facial expressions, and voice tone during practice.

[0755] "A means of adjusting the difficulty and type of balls based on analyzed emotional data" refers to a system that analyzes the psychological state of the trainee and optimizes the training content based on the results.

[0756] This invention is designed to individually optimize baseball fielding practice and is a system that combines a camera-equipped information terminal, a computer with an artificial intelligence model, an automatic release device, and an emotional data analysis means. Based on the user's physical data, age data, and skill data, it provides the optimal type, speed, and direction of batted balls, and further monitors and analyzes the user's emotional state during practice to adjust the practice content in real time, thereby improving skills and maintaining motivation.

[0757] System Configuration

[0758] 1. Initial setup and user data entry

[0759] Users input their physical data (height, weight, etc.), age data, and skill data (proficiency level, etc.) using a camera-equipped information terminal. This data is transmitted to a server via wireless communication such as Wi-Fi or Bluetooth.

[0760] 2. Release of batted balls and real-time filming

[0761] The server uses an artificial intelligence model based on the received data to determine the appropriate type, speed, and direction of the batted ball. The computer uses Python and TensorFlow for data analysis. The automatic batted ball release device releases the specified batted ball based on the computer's instructions. A camera-equipped information terminal captures the batted ball release and the user's batting process in real time, and transmits the video data to the server.

[0762] 3. Analysis of video data and technical evaluation

[0763] The server analyzes the received video data using libraries such as OpenCV to quantify the ball-handling technique. Evaluation items include "success" or "failure," and processing time. The analysis results are displayed on a camera-equipped information terminal as feedback to help the user improve their skills.

[0764] 4. Acquisition and Analysis of Emotional Data

[0765] Emotional analysis is performed based on data such as facial expressions and voice tone acquired from a camera-equipped information terminal. An existing emotion recognition engine is utilized for this purpose. The analyzed emotional data is sent to a server and used to adjust the training content. For example, if the user is experiencing stress, the server will automatically lower the difficulty level of the ball-hitting exercises.

[0766] 5. Automatic generation of the next practice plan.

[0767] The server automatically generates the next practice plan based on the analysis results and emotional data. The generated practice plan is displayed to the user via a camera-equipped information terminal. This allows the user to obtain an effective practice plan that takes into account their technical challenges and psychological state.

[0768] Specific example

[0769] As a concrete example, consider the following situation.

[0770] User settings: The user enters their height (175cm), weight (70kg), age (25), and skill level ("Advanced") on a camera-equipped information terminal. They select shortstop as their defensive position, "Expanded Defensive Range Mode" as their practice mode, and "Advanced" as their difficulty level.

[0771] Smartphone app feedback display: "This ball was handled successfully. Handling time: 1.2 seconds. Current mental state: Relaxed."

[0772] Examples of prompts for generative AI models

[0773] I want to implement an application like this:

[0774] 1. Enter data such as the user's physique, age, and skill level into the smartphone app.

[0775] 2. The user's practice process is filmed using the camera on a camera-equipped information terminal.

[0776] 3. Analyze video data on the server and generate a technical evaluation.

[0777] 4. Analyze psychological state using the Emotion Engine and reflect it in the training plan.

[0778] 5. The next practice plan will be displayed on the smartphone app.

[0779] This allows users to efficiently improve their skills and maintain their motivation.

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] The user performs the initial setup using a camera-equipped information terminal. Specifically, they input physical data such as height, weight, age, and skill level, as well as age and skill data, and then transmit the setting data. Based on this input data, the server performs an initial analysis and calculates the optimal batting parameters for the user. The output provides initial settings for batting type, speed, and direction.

[0783] Step 2:

[0784] The terminal controls the automatic release device to release the appropriate ball based on initial setup data received from the server. The type, speed, and direction of the ball released by the automatic release device are controlled by the data used. The ball is then physically released to the user as output.

[0785] Step 3:

[0786] The terminal uses its camera to capture the user's ball-handling process in real time. The captured video data is sent to the server via the terminal. Using this video data as input, the server performs motion analysis using OpenCV or similar tools to quantify and evaluate the ball-handling technique. The output is technical evaluation data (success / failure, processing time, etc.).

[0787] Step 4:

[0788] The server collects technical evaluation data and provides feedback to the terminal. Specific feedback includes success / failure results, processing time, and areas for improvement. The feedback data is displayed to the user via the terminal. Here, technical evaluation data is taken as input, and feedback data is output.

[0789] Step 5:

[0790] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the server in real time. Based on this input data, the server uses an emotion analysis engine to analyze the user's psychological state. TensorFlow is used as an example. Emotion evaluation data is output as a result of the analysis.

[0791] Step 6:

[0792] The server adjusts the difficulty and type of batting practice based on emotional assessment data. For example, if the user is stressed, the difficulty is lowered, and if they are relaxed, the difficulty is increased. Emotional assessment data is then input, and an adjusted practice plan is output.

[0793] Step 7:

[0794] The server automatically generates the next practice plan based on technical and emotional evaluation data. This practice plan includes specific practice content that reflects the user's technical challenges and psychological state. The generated practice plan is presented to the user via the terminal. Here, the technical and emotional evaluation data are used as input, and a new practice plan is output.

[0795] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0796] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0797] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0798] [Third Embodiment]

[0799] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0800] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0801] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0802] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0803] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0804] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0805] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0806] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0807] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0808] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0809] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0810] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0811] This invention is a system that provides personalized practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, and an automatic release device. Specific embodiments of this system are described below.

[0812] Introduction

[0813] This system is designed to individually optimize baseball fielding practice. It takes user data such as physical characteristics, age, and skill level, and based on this, the AI ​​releases appropriate batted balls. Furthermore, it supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[0814] Overview of program processing

[0815] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[0816] User data entry and settings

[0817] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0818] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[0819] Setting the practice mode and releasing the batted ball

[0820] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[0821] Terminal: Sends the selected practice mode and difficulty information to the server.

[0822] Server: The server analyzes the received data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[0823] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[0824] Filming and analysis of the ball handling process

[0825] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. For example, it captures the moment the user catches a designated ball.

[0826] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[0827] Feedback and next practice plan

[0828] Terminal: Displays analysis results on the screen. Users can check their skill level and areas for improvement. For example, it might display something like, "This ball handling was successful, and the processing time was 1.4 seconds."

[0829] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to strengthen areas for improvement. This allows users to efficiently improve their skills.

[0830] Specific example

[0831] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[0832] The device uses its camera to capture the user's ball-handling process and sends the video data to the server. The server analyzes the received video and evaluates it as "Success" with a "Processing Time: 1.4 seconds." The analysis results are displayed on the device's screen, allowing the user to check their skill level and areas for improvement. Based on the analysis results, the server automatically generates a practice plan for the next session and suggests it to the device.

[0833] This system allows users to objectively evaluate and improve their own skills, enabling them to practice more effectively on their own.

[0834] The following describes the processing flow.

[0835] Step 1:

[0836] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[0837] Step 2:

[0838] The terminal sends the user's entered physical data, age data, and skill data to the server. The server receives this data and stores it in its database.

[0839] Step 3:

[0840] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[0841] Step 4:

[0842] The device sends the user's selected practice mode and difficulty level information to the server. Based on the data received by the server, the AI ​​calculates the optimal type, speed, and direction of the shot.

[0843] Step 5:

[0844] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[0845] Step 6:

[0846] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[0847] Step 7:

[0848] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[0849] Step 8:

[0850] The server analyzes the received video data and quantifies the user's ball-handling skills. Specifically, it evaluates whether the ball was caught, the processing time, and other factors.

[0851] Step 9:

[0852] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[0853] Step 10:

[0854] Based on the analysis results, the server automatically generates a plan for the next practice session. In particular, it creates a plan that includes specific practice content to improve the user's technical challenges.

[0855] Step 11:

[0856] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[0857] In this way, the system continuously measures the user's technical skills and provides feedback for efficient improvement.

[0858] (Example 1)

[0859] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0860] Traditional baseball training systems struggle to maximize training effectiveness by releasing batted balls tailored to the individual characteristics of each trainee, and they lack visualization and feedback on training results. Therefore, it is difficult for trainees to accurately understand their own skill level and train effectively. Furthermore, because the training content is uniform, it is impossible to pinpoint and improve each trainee's technical weaknesses.

[0861] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0862] In this invention, the server includes means for determining the optimal batted ball based on user input data, means for capturing and analyzing the user's batted ball processing process in real time, and means for automatically generating the next practice plan based on the analysis results. This enables users to efficiently improve their skills by releasing batted balls tailored to their individual characteristics and evaluating and analyzing their practice process in real time.

[0863] A "camera-equipped information terminal" is a portable information terminal device that has a built-in digital camera and is used for user input and data capture.

[0864] "Means for inputting user physical data, age data, and skill data" refers to systems or interfaces for users to input information such as their physical characteristics, age, and skill level.

[0865] A "computer equipped with an artificial intelligence model" is an electronic computer that incorporates algorithms capable of performing machine learning and data analysis.

[0866] An "automatic ball launcher" is a mechanical device that automatically launches balls based on set conditions.

[0867] A "camera-equipped information terminal that films the user's ball-handling process in real time" is a portable information terminal device that has the function of filming the user's ball-handling process in real time and recording the video.

[0868] "Means for quantifying and evaluating a user's ball-catching skills" refers to systems and methods for analyzing a user's ball-catching skills and evaluating them using numerical values ​​or indicators.

[0869] "Means of providing feedback to improve users' skills" refers to systems and methods that present users with the information and areas for improvement necessary to enhance their skills.

[0870] "Means for determining the optimal batted ball based on user input data" refers to a method or system that analyzes user input data and calculates the most suitable type, speed, and direction of the batted ball.

[0871] "Means for real-time filming and analysis of a user's ball-handling process" refers to a system or method that films the process of a user handling a ball in real time and analyzes the resulting video data.

[0872] "Methods for automatically generating the next practice plan based on analysis results" refers to systems or methods that automatically generate new practice content and plans based on the analysis results of individual users.

[0873] This invention is a system that provides personalized fielding practice by combining a camera-equipped information terminal, a computer with an artificial intelligence model, and an automatic release device. This system is designed to individually optimize baseball fielding practice. Detailed embodiments of this system are described below.

[0874] Introduction

[0875] This system takes user data such as physical characteristics, age, and skill level as input, and based on that, artificial intelligence (AI) releases appropriate batted balls. It also supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[0876] Hardware and software to be used

[0877] This system consists of the following main hardware and software components.

[0878] Information terminals with cameras (e.g., tablet devices)

[0879] Computers equipped with artificial intelligence models (e.g., servers)

[0880] Automatic release device (e.g., automatic ball launcher)

[0881] System Processing Overview

[0882] This system's program operates through the collaboration of users, terminals, and servers. The specific operation of each core module is described below.

[0883] User data entry

[0884] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate". This data will serve as the basis for individually customizing the training content.

[0885] Sending user data

[0886] Terminal: Sends the entered data to the server. The server uses this data to determine the optimal shot for the user.

[0887] Selecting a practice mode

[0888] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range) and set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[0889] Sending practice mode data

[0890] Terminal: Sends selected practice mode and difficulty information to the server. The server analyzes this information to determine the type, speed, and direction of the hit.

[0891] The decision on the batted ball

[0892] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and traveling at 40 km / h.

[0893] Release of batted ball

[0894] Terminal: Based on instructions from the server, it controls the automatic release device and releases the specified ball.

[0895] Filming the process of handling a batted ball.

[0896] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[0897] Sending batted ball processing data

[0898] Terminal: Sends the captured video data directly to the server. The server analyzes the received video data and evaluates the user's skills.

[0899] Analysis of video data

[0900] Server: The analysis results are quantified as "success" or "failure" and the processing time is recorded. For example, it might be evaluated as "success" with "processing time: 1.4 seconds".

[0901] Display of analysis results

[0902] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement.

[0903] Generating the next practice plan

[0904] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested on the terminal, allowing users to efficiently improve their skills.

[0905] Specific example

[0906] The user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and sets the next batted ball to be released to the left at a 30° angle, at a speed of 40 km / h. The terminal controls the automatic release device and releases the batted ball. Simultaneously, the terminal's camera films the ball handling process and sends the video data to the server. The server analyzes the video and evaluates it as "success" with a "processing time: 1.4 seconds," displaying the result on the terminal's screen. Furthermore, the server automatically generates a plan for the next practice session based on the analysis results and suggests it to the terminal. This system allows users to objectively evaluate and improve their skills and effectively engage in independent practice.

[0907] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0908] Step 1:

[0909] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate".

[0910] Input: User's physical size, age, and skill data

[0911] Output: Input data (physique, age, skill level)

[0912] Step 2:

[0913] Terminal: Sends the entered data to the server. The user's entered physical characteristics, age, and skill data are sent directly to the server.

[0914] Input: Data entered in Step 1

[0915] Output: Sending data to the server

[0916] Step 3:

[0917] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range), and then set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[0918] Input: Objective and difficulty setting for the knocking practice.

[0919] Output: Selected practice objective and difficulty level

[0920] Step 4:

[0921] Terminal: Sends the selected practice mode and difficulty information to the server. This data is used to determine the type, speed, and direction of the next batted ball.

[0922] Input: Data selected in Step 3

[0923] Output: Sending data to the server

[0924] Step 5:

[0925] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. Based on the user's input data, practice objectives, and difficulty level, the server might, for example, set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[0926] Input: Data submitted in Steps 2 and 4

[0927] Output: Determination of batted ball type, speed, and direction.

[0928] Step 6:

[0929] Terminal: Based on instructions from the server, it controls the automatic release device and releases the set batted ball. The automatic release device has the function of releasing the specified batted ball. For example, the automatic release device releases a ground ball at a speed of 40 km / h at a 30-degree angle to the left.

[0930] Input: Instructions from the server (type of hit, speed, direction)

[0931] Output: Release of the specified batted ball.

[0932] Step 7:

[0933] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[0934] Input: Real-time video of the ball handling process.

[0935] Output: Captured video data

[0936] Step 8:

[0937] Terminal: Sends the captured video data directly to the server.

[0938] Input: Video data captured in Step 7

[0939] Output: Sending video data to the server

[0940] Step 9:

[0941] Server: Analyzes received video data and evaluates the user's ball-handling skills. The server analyzes the actions in the video and quantifies "success" or "failure" and processing time. For example, it might evaluate as "success" with "processing time: 1.4 seconds".

[0942] Input: Video data sent in Step 8

[0943] Output: Analysis results (success / failure, processing time, etc.)

[0944] Step 10:

[0945] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement. For example, it might display results such as, "This ball handling was successful, and the processing time was 1.4 seconds."

[0946] Input: Analysis results from the server

[0947] Output: Display of analysis results

[0948] Step 11:

[0949] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to the user. For example, a plan such as "In the next practice, let's work on reducing the reaction time to a ball hit at a 30° angle to the left by 0.2 seconds" is generated.

[0950] Input: Analysis results (success / failure, processing time, etc.)

[0951] Output: Automatic generation of the next practice plan

[0952] (Application Example 1)

[0953] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0954] Conventional factory robot training for parts picking lacks sufficient individual optimization to improve efficiency and accuracy. In particular, there is a lack of means to provide real-time motion analysis and feedback, resulting in decreased work efficiency and accuracy. Furthermore, the automatic generation of training plans tailored to the characteristics of each robot is difficult, limiting the effectiveness of the training.

[0955] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0956] In this invention, the server includes means for inputting physical attribute data and work capability data of the training target, means equipped with a generative AI model for analyzing captured image data, and an automatic execution device that controls the type, speed, and direction of work suitable for the training target based on the data analyzed by the generative AI model. This enables real-time motion analysis and feedback, as well as the automatic generation of individually optimized training plans.

[0957] A "camera-equipped information processing device" is an information processing device equipped with a camera, which is a terminal capable of capturing, analyzing, and displaying images and videos.

[0958] "Training target" refers to an object or device that is the subject of training or work, and in this invention, this mainly refers to factory robots.

[0959] "Physical attribute data" refers to data that shows the specific physical characteristics of an object or piece of equipment, such as arm length and weight.

[0960] "Work capability data" refers to data about the work capabilities of an object or piece of equipment, including accuracy, speed, and capability level.

[0961] A "generative AI model" is an algorithmic model generated using machine learning or deep learning techniques, which analyzes input data and provides appropriate instructions or predictions.

[0962] A "computing device" is a piece of equipment used for processing and analyzing data, and generally includes computers and servers.

[0963] An "automatic execution device" is a device that automatically performs actions instructed by a program or AI model.

[0964] A "work processing process" is a series of actions performed by the object of the work (for example, a robot) in which it actually carries out the task.

[0965] "Feedback" refers to evaluation and improvement information provided by the system, intended to help improve the performance and technology of the subject.

[0966] A "plan" is a plan for the next training session or work, and is automatically generated based on areas for improvement and goals.

[0967] This invention is a system for individually optimizing the training of factory robots for parts picking. This system functions by combining an information processing device with a camera, a computing device equipped with a generative AI model, and an automated execution device.

[0968] System Configuration

[0969] Camera-equipped information processing device

[0970] A camera-equipped information processing device is a device equipped with a camera that can capture, analyze, and display images and videos. This system uses a device such as an iPad Pro.

[0971] computing device

[0972] A computing device is a computer or server that processes and analyzes data. In this invention, an Amazon Web Services (AWS) EC2 instance is used to host the generative AI model.

[0973] Automatic execution device

[0974] An automated execution device is a device that automatically performs actions instructed by a program or AI model. For example, a robotic arm that places parts in a specific location is one such device.

[0975] Program processing

[0976] The server first acquires user data, which is then used by the generated AI model for analysis. Specifically, it uses robot physical attribute data (e.g., arm length, velocity characteristics) and work capability data (e.g., picking ability, accuracy) as input data. Based on this, the AI ​​model determines the optimal type, speed, and direction of movement.

[0977] The generative AI model is developed using a deep learning framework such as TensorFlow. Captured video data is sent from a camera-equipped information processing device to a server and analyzed in real time. The analysis results provide evaluations such as "success," "failure," and "work time."

[0978] The feedback function displays the analysis results to the user, and the next training plan is automatically generated. This allows for efficient improvement of the robot's work capabilities.

[0979] Specific example

[0980] As an example, consider the following scenario.

[0981] 1. The user enters the physical attribute data of the factory robot (arm length 1.5m, speed performance "medium speed") and work capacity data (picking capacity "high") on the initial setup screen.

[0982] 2. Send this data from the tablet device to the server.

[0983] 3. The AI ​​model on the server analyzes the data and determines the optimal picking action (e.g., position "X: 50cm, Y: 30cm", speed "0.5 seconds").

[0984] 4. The camera captures the robot's picking movements in real time and sends the video data to the server.

[0985] 5. The server evaluates the success or failure of the operation and the time required based on the analysis results, and displays the results on the user screen.

[0986] 6. Next, based on the analysis results, the system automatically generates a training plan for the next session and proposes it to the user.

[0987] Examples of prompts to input into a generative AI model

[0988] "Develop an AI model that determines the optimal parts picking motion based on the robot's shape characteristics and work capabilities. The model will use arm length and speed performance as input data for the robot's shape characteristics, and picking capability as input data, to determine the optimal type, speed, and direction of movement. It will also capture actual picking movements with a camera and analyze whether the movement was successful or unsuccessful, as well as the work time. Additionally, build a system that automatically generates a training plan for the next session."

[0989] In this way, inputting specific requirements as prompts helps in developing high-performance AI models.

[0990] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0991] Step 1:

[0992] On the initial setup screen, the user enters the physical attribute data and work capacity data of the factory robot. For example, the user might enter an arm length of 1.5m, a speed performance of "medium speed," and a picking capacity of "high." This input data is sent from the terminal to the server.

[0993] Step 2:

[0994] The server provides the received user data as input data to the generative AI model. The generative AI model uses a deep learning framework such as TensorFlow and predicts the optimal action based on the received data. Specifically, it analyzes numerical values ​​such as arm length, speed performance, and picking ability to determine the optimal type, speed, and direction of action.

[0995] Step 3:

[0996] The server generates and sends operation instructions to the terminal. The terminal then issues instructions to the automated execution device, controlling, for example, a robotic arm to place a part in a specific location. The executed actions are captured in real time by a camera-equipped information processing device, and the video data is sent to the server.

[0997] Step 4:

[0998] The server analyzes the received video data in real time. The server uses a generated AI model to analyze the video data and quantifies evaluation items such as the success or failure of an action and the time taken. For example, evaluation results such as "Success," "Failure," and "Time taken: 0.45 seconds" can be obtained.

[0999] Step 5:

[1000] The server generates feedback based on the analysis results and sends it to the terminal. The terminal displays the analysis results on the screen and provides specific feedback to the user. For example, it might display, "This picking operation was successful, and the work time was 0.45 seconds."

[1001] Step 6:

[1002] Based on the analysis results, the server automatically generates the next training plan. Using the generated AI model, the server creates a new plan to address technical challenges and sends it to the user's device. The user can then review the next training plan through their device.

[1003] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1004] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[1005] Introduction

[1006] This system is designed to individually optimize baseball fielding practice. By incorporating data on the trainee's physique, age, and skill level, as well as emotional data during practice, it aims to provide the trainee with the most suitable batted balls, promoting skill improvement while maintaining psychological motivation.

[1007] Overview of program processing

[1008] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[1009] User data entry and settings

[1010] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, enter height 170cm, weight 65kg, age 20, and skill level "Intermediate".

[1011] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[1012] Setting the practice mode and releasing the batted ball

[1013] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[1014] Terminal: Sends the selected practice mode and difficulty information to the server.

[1015] Server: The server analyzes the received data, and the AI ​​model calculates the optimal type, speed, and direction of the batted ball. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h.

[1016] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[1017] Filming and analysis of the ball handling process

[1018] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. The user catches the designated ball.

[1019] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[1020] Acquisition and analysis of emotional data

[1021] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it analyzes whether the user is feeling stressed or enjoying the practice.

[1022] Server: Based on analyzed emotional data, the server adjusts the difficulty and type of the ball hits. For example, if the user is feeling stressed, the difficulty level is slightly reduced.

[1023] Feedback and next practice plan

[1024] Terminal: Displays analysis results and emotional data on the screen. Users can check their skill level and emotional state. For example, it might display, "This ball was handled successfully, and the handling time was 1.4 seconds. Your current psychological state is slightly stressed."

[1025] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. In particular, it creates a plan that includes specific practice content that takes into account the user's technical challenges and emotional state.

[1026] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[1027] Specific example

[1028] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[1029] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a processing time of 1.4 seconds. Furthermore, the camera captures the user's emotional data during practice, and if the emotional engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[1030] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[1031] This system allows users to continuously measure their technical skills and improve them efficiently, as well as engage in effective self-practice that takes psychological motivation into account.

[1032] The following describes the processing flow.

[1033] Step 1:

[1034] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[1035] Step 2:

[1036] The terminal sends the user's entered physical measurements, age, and skill data to the server. The server receives this data and stores it in its database.

[1037] Step 3:

[1038] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[1039] Step 4:

[1040] The device sends the user's selected practice mode and difficulty level information to the server. The server analyzes the received data, and the AI ​​calculates the optimal type, speed, and direction of the shot.

[1041] Step 5:

[1042] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[1043] Step 6:

[1044] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[1045] Step 7:

[1046] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[1047] Step 8:

[1048] The device uses its camera to capture the user's facial expressions and voice tone, and sends this data to the emotion engine. The emotion engine then analyzes the user's emotional state. For example, it analyzes whether the user is feeling stressed or enjoying themselves during practice.

[1049] Step 9:

[1050] The server analyzes the received video data and emotional data to evaluate the user's ball-handling skills. Simultaneously, the emotional engine analyzes the user's emotional state and evaluates their stress level and motivation.

[1051] Step 10:

[1052] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds," "Stress level: Medium") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[1053] Step 11:

[1054] The server automatically generates the next practice plan based on the analysis results and emotional data. For example, it creates a plan that includes specific practice content that takes into account the user's technical challenges and stress levels.

[1055] Step 12:

[1056] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[1057] In this way, the system can not only continuously measure and efficiently improve the user's technical skills, but also enable effective self-practice that takes psychological motivation into consideration.

[1058] (Example 2)

[1059] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1060] Traditional baseball fielding practice systems lack personalized training based on the player's physique and skill level. Furthermore, they lack the ability to monitor the player's mental state in real time and adjust the difficulty and content of the training accordingly, making it difficult to effectively maintain player motivation. This results in slower skill improvement and inefficient practice.

[1061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1062] In this invention, the server includes means for inputting the trainee's physical data, age data, and skill data; means equipped with an artificial intelligence model for analyzing captured image data; and means for controlling an automatic release device that controls the type, speed, and direction of the batted ball appropriate for the trainee based on the data analyzed by the artificial intelligence model. This enables the generation of an optimal training plan based on the trainee's individual data, and further analyzes their psychological state using an emotion engine to provide feedback, thereby improving the trainee's skills and maintaining their psychological motivation.

[1063] A "tablet device with a camera" is a portable information terminal that has a built-in camera and is capable of recording video and displaying data.

[1064] "Means for inputting trainee's physical data, age data, and skill data" refers to an interface device for inputting trainee's height, weight, age, defensive proficiency level, etc., into the system.

[1065] An "artificial intelligence model" is a machine learning algorithm trained using a large amount of data, and is a computer program designed to perform a specific task (in this case, calculating and controlling the type, speed, and direction of a ball hit).

[1066] A "server" is a high-performance computer system used for data analysis and overall system control.

[1067] An "automatic release device" is a device that releases balls according to the system's instructions.

[1068] "Real-time recording" means recording and transmitting the trainee's movements and environment via camera almost simultaneously (without delay).

[1069] "Methods for quantifying and evaluating batted ball handling skills" refers to a system device that analyzes acquired video data and evaluates the batted ball handling ability of the trainee as numerical data.

[1070] The "emotion engine" is software that analyzes the facial expressions and voice tone of the trainees captured by the camera to infer their psychological state at that time.

[1071] "Means of providing feedback" refers to an interface device that displays technical evaluations and psychological advice to the user based on analyzed data and inferred psychological state.

[1072] "Plans automatically generated based on technical challenges" refers to the algorithm and its results used by the system to automatically determine the content and difficulty level of practice in order to compensate for the technical shortcomings of the practitioner.

[1073] The "next practice plan" is a specific practice plan suggested by the system, based on current practice results and emotional data, outlining what and how to practice in the next session.

[1074] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an artificial intelligence model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[1075] This system is designed to individually optimize baseball fielding practice. By incorporating the player's physical data, age data, skill data, and emotional data during practice, it aims to provide the player with the optimal batted ball, promote skill improvement, and maintain psychological motivation.

[1076] User data entry and settings

[1077] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate".

[1078] Terminal: Sends the entered data to the server. This data serves as the basis for the server's artificial intelligence model to determine the appropriate batted ball.

[1079] Setting the practice mode and releasing the batted ball

[1080] User: Select the purpose of the practice drill (e.g., catching practice, expanding fielding range, etc.) and the difficulty level. Example: Select "Intermediate" difficulty level in "Catching Practice Mode".

[1081] Terminal: Sends the selected practice mode and difficulty information to the server.

[1082] Server: The server analyzes the received data, and the artificial intelligence model calculates the optimal type of batted ball (ground ball, line drive, fly ball, etc.), speed, and direction. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h.

[1083] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[1084] Filming and analysis of the ball handling process

[1085] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. The user catches the designated ball.

[1086] Server: Analyzes the received video data and evaluates the user's ball-handling skills. For example, "success," "failure," and "processing time: 1.4 seconds" are quantified.

[1087] Acquisition and analysis of emotional data

[1088] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it can determine whether the user is satisfied or stressed.

[1089] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, it will slightly lower the difficulty level.

[1090] Feedback and generation of the next practice plan

[1091] Terminal: Displays analysis results and emotional data on the screen. For example, it provides feedback to the user such as, "This ball was handled successfully, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[1092] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content that takes into account the user's technical challenges and emotional state.

[1093] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[1094] Specific example

[1095] For example, a user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to release. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[1096] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a processing time of 1.4 seconds. Furthermore, the camera captures the user's emotional data during practice, and if the emotional engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[1097] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[1098] Example of a prompt

[1099] "If a user who is 170cm tall, weighs 65kg, is 20 years old, and has an intermediate skill level selects the intermediate difficulty level in catching practice mode, how should the type, speed, and direction of the next batted ball be set?"

[1100] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1101] Step 1:

[1102] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. The entered data will be "Height: 170cm, Weight: 65kg, Age: 20, Skill Level: Intermediate". This is the initial data input for the system.

[1103] Terminal: Sends the entered data to the server. Based on the input data, the server prepares to generate an individually optimized practice plan.

[1104] Step 2:

[1105] User: Select the purpose of the practice drill (e.g., catching practice, expanding fielding range, etc.) and the difficulty level (e.g., "Catching Practice Mode", difficulty level "Intermediate").

[1106] Terminal: Sends selected practice mode and difficulty information to the server. Based on this input, the server analyzes the practice mode and difficulty data and prepares the AI ​​model to calculate the optimal batting type, speed, and direction.

[1107] Step 3:

[1108] Server: The server analyzes the received user's physique, age, skill data, practice mode, and difficulty information, and uses a generating AI model to calculate the optimal type of hit (ground ball, line drive, fly ball, etc.), speed, and direction. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h. The result of this calculation is output from the server.

[1109] Server: Sends the generated batting settings data to the terminal.

[1110] Step 4:

[1111] Terminal: Based on instructions from the server, it controls the automatic release device and releases a specified ball (e.g., a ground ball at 40 km / h, 30° to the left). It records the release log so that it can be provided to the user later.

[1112] Step 5:

[1113] Terminal: The camera captures the user's ball-hitting process in real time. The captured video data is used as input data.

[1114] Terminal: Sends captured video data to the server. This data is then passed to the server for later analysis.

[1115] Step 6:

[1116] Server: Analyzes the received video data and evaluates the user's ball-handling skills. Specifically, it generates evaluation data such as "Success," "Failure," and "Processing Time: 1.4 seconds." This is the output data after analysis.

[1117] Step 7:

[1118] The device uses a camera to capture the user's facial expressions and voice tone in real time, and analyzes them using an emotion engine. For example, it analyzes whether the user is satisfied or stressed. The acquired data is then sent to the server.

[1119] Step 8:

[1120] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, the difficulty is slightly reduced. This is output as setting data that will be reflected in the next practice session.

[1121] Step 9:

[1122] Terminal: Displays analysis results and feedback sent from the server on the screen. Specifically, it provides feedback such as, "This ball handling was successful, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[1123] Step 10:

[1124] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content considering the user's technical challenges and emotional state. This plan will be executed in the next practice session.

[1125] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[1126] (Application Example 2)

[1127] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1128] Traditional baseball fielding drills make it difficult to provide an optimal program tailored to the individual characteristics of each player, such as their current skill level, physique, and age. Furthermore, it is virtually impossible to assess players' psychological stress and motivation in real time during practice and provide corresponding feedback or adjust the practice plan accordingly. Moreover, it is challenging to control the optimal type, speed, and direction of batted balls while taking all these factors into consideration.

[1129] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1130] In this invention, the server includes means for inputting the user's physical data, age data, and skill data; means equipped with an artificial intelligence model for analyzing captured image data; and means for adjusting the difficulty and type of balls based on the analyzed emotional data. This provides the user with optimal balls, enabling improvement in technical skills and maintenance of psychological motivation.

[1131] A "camera-equipped information terminal" is a computer terminal equipped with a camera for capturing video, and its role is to record and transmit the actions of the person practicing.

[1132] A "user" is an individual who uses this system for practice and is the one who inputs physical data, age data, and skill data.

[1133] "Physical data" refers to information that shows the physical characteristics of the trainee, such as height and weight.

[1134] "Age data" refers to information indicating the age of the trainee.

[1135] "Skill data" refers to information that indicates a player's skill level and proficiency in baseball.

[1136] An "artificial intelligence model" is a system that includes algorithms and neural networks that analyze input data and generate appropriate output results.

[1137] A "computer" refers to a computer or server used for processing data, and is a device that runs artificial intelligence models.

[1138] An "automatic ball dispenser" is a device that automatically dispenses practice balls based on analyzed data.

[1139] The "ball handling process" refers to a series of actions taken by the user to catch or handle an incoming ball.

[1140] "Methods for quantifying and evaluating" refers to a system that evaluates a player's ball-handling skills as quantitative numerical data.

[1141] "Means of providing feedback for skill improvement" refers to a system that provides feedback to practitioners on areas for improvement in their technique and the results of their work.

[1142] "Emotional data" refers to data such as the trainee's psychological state, facial expressions, and voice tone during practice.

[1143] "A means of adjusting the difficulty and type of balls based on analyzed emotional data" refers to a system that analyzes the psychological state of the trainee and optimizes the training content based on the results.

[1144] This invention is designed to individually optimize baseball fielding practice and is a system that combines a camera-equipped information terminal, a computer with an artificial intelligence model, an automatic release device, and an emotional data analysis means. Based on the user's physical data, age data, and skill data, it provides the optimal type, speed, and direction of batted balls, and further monitors and analyzes the user's emotional state during practice to adjust the practice content in real time, thereby improving skills and maintaining motivation.

[1145] System Configuration

[1146] 1. Initial setup and user data entry

[1147] Users input their physical data (height, weight, etc.), age data, and skill data (proficiency level, etc.) using a camera-equipped information terminal. This data is transmitted to a server via wireless communication such as Wi-Fi or Bluetooth.

[1148] 2. Release of batted balls and real-time filming

[1149] The server uses an artificial intelligence model based on the received data to determine the appropriate type, speed, and direction of the batted ball. The computer uses Python and TensorFlow for data analysis. The automatic batted ball release device releases the specified batted ball based on the computer's instructions. A camera-equipped information terminal captures the batted ball release and the user's batting process in real time, and transmits the video data to the server.

[1150] 3. Analysis of video data and technical evaluation

[1151] The server analyzes the received video data using libraries such as OpenCV to quantify the ball-handling technique. Evaluation items include "success" or "failure," and processing time. The analysis results are displayed on a camera-equipped information terminal as feedback to help the user improve their skills.

[1152] 4. Acquisition and Analysis of Emotional Data

[1153] Emotional analysis is performed based on data such as facial expressions and voice tone acquired from a camera-equipped information terminal. An existing emotion recognition engine is utilized for this purpose. The analyzed emotional data is sent to a server and used to adjust the training content. For example, if the user is experiencing stress, the server will automatically lower the difficulty level of the ball-hitting exercises.

[1154] 5. Automatic generation of the next practice plan.

[1155] The server automatically generates the next practice plan based on the analysis results and emotional data. The generated practice plan is displayed to the user via a camera-equipped information terminal. This allows the user to obtain an effective practice plan that takes into account their technical challenges and psychological state.

[1156] Specific example

[1157] As a concrete example, consider the following situation.

[1158] User settings: The user enters their height (175cm), weight (70kg), age (25), and skill level ("Advanced") on a camera-equipped information terminal. They select shortstop as their defensive position, "Expanded Defensive Range Mode" as their practice mode, and "Advanced" as their difficulty level.

[1159] Smartphone app feedback display: "This ball was handled successfully. Handling time: 1.2 seconds. Current mental state: Relaxed."

[1160] Examples of prompts for generative AI models

[1161] I want to implement an application like this:

[1162] 1. Enter data such as the user's physique, age, and skill level into the smartphone app.

[1163] 2. The user's practice process is filmed using the camera on a camera-equipped information terminal.

[1164] 3. Analyze video data on the server and generate a technical evaluation.

[1165] 4. Analyze psychological state using the Emotion Engine and reflect it in the training plan.

[1166] 5. The next practice plan will be displayed on the smartphone app.

[1167] This allows users to efficiently improve their skills and maintain their motivation.

[1168] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1169] Step 1:

[1170] The user performs the initial setup using a camera-equipped information terminal. Specifically, they input physical data such as height, weight, age, and skill level, as well as age and skill data, and then transmit the setting data. Based on this input data, the server performs an initial analysis and calculates the optimal batting parameters for the user. The output provides initial settings for batting type, speed, and direction.

[1171] Step 2:

[1172] The terminal controls the automatic release device to release the appropriate ball based on initial setup data received from the server. The type, speed, and direction of the ball released by the automatic release device are controlled by the data used. The ball is then physically released to the user as output.

[1173] Step 3:

[1174] The terminal uses its camera to capture the user's ball-handling process in real time. The captured video data is sent to the server via the terminal. Using this video data as input, the server performs motion analysis using OpenCV or similar tools to quantify and evaluate the ball-handling technique. The output is technical evaluation data (success / failure, processing time, etc.).

[1175] Step 4:

[1176] The server collects technical evaluation data and provides feedback to the terminal. Specific feedback includes success / failure results, processing time, and areas for improvement. The feedback data is displayed to the user via the terminal. Here, technical evaluation data is taken as input, and feedback data is output.

[1177] Step 5:

[1178] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the server in real time. Based on this input data, the server uses an emotion analysis engine to analyze the user's psychological state. TensorFlow is used as an example. Emotion evaluation data is output as a result of the analysis.

[1179] Step 6:

[1180] The server adjusts the difficulty and type of batting practice based on emotional assessment data. For example, if the user is stressed, the difficulty is lowered, and if they are relaxed, the difficulty is increased. Emotional assessment data is then input, and an adjusted practice plan is output.

[1181] Step 7:

[1182] The server automatically generates the next practice plan based on technical and emotional evaluation data. This practice plan includes specific practice content that reflects the user's technical challenges and psychological state. The generated practice plan is presented to the user via the terminal. Here, the technical and emotional evaluation data are used as input, and a new practice plan is output.

[1183] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1184] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1185] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1186] [Fourth Embodiment]

[1187] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1188] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1189] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1190] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1191] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1192] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1193] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1194] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1195] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1196] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1197] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1198] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1199] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1200] This invention is a system that provides personalized practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, and an automatic release device. Specific embodiments of this system are described below.

[1201] Introduction

[1202] This system is designed to individually optimize baseball fielding practice. It takes user data such as physical characteristics, age, and skill level, and based on this, the AI ​​releases appropriate batted balls. Furthermore, it supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[1203] Overview of program processing

[1204] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[1205] User data entry and settings

[1206] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[1207] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[1208] Setting the practice mode and releasing the batted ball

[1209] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[1210] Terminal: Sends the selected practice mode and difficulty information to the server.

[1211] Server: The server analyzes the received data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[1212] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[1213] Filming and analysis of the ball handling process

[1214] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. For example, it captures the moment the user catches a designated ball.

[1215] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[1216] Feedback and next practice plan

[1217] Terminal: Displays analysis results on the screen. Users can check their skill level and areas for improvement. For example, it might display something like, "This ball handling was successful, and the processing time was 1.4 seconds."

[1218] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to strengthen areas for improvement. This allows users to efficiently improve their skills.

[1219] Specific example

[1220] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[1221] The device uses its camera to capture the user's ball-handling process and sends the video data to the server. The server analyzes the received video and evaluates it as "Success" with a "Processing Time: 1.4 seconds." The analysis results are displayed on the device's screen, allowing the user to check their skill level and areas for improvement. Based on the analysis results, the server automatically generates a practice plan for the next session and suggests it to the device.

[1222] This system allows users to objectively evaluate and improve their own skills, enabling them to practice more effectively on their own.

[1223] The following describes the processing flow.

[1224] Step 1:

[1225] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[1226] Step 2:

[1227] The terminal sends the user's entered physical data, age data, and skill data to the server. The server receives this data and stores it in its database.

[1228] Step 3:

[1229] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[1230] Step 4:

[1231] The device sends the user's selected practice mode and difficulty level information to the server. Based on the data received by the server, the AI ​​calculates the optimal type, speed, and direction of the shot.

[1232] Step 5:

[1233] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[1234] Step 6:

[1235] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[1236] Step 7:

[1237] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[1238] Step 8:

[1239] The server analyzes the received video data and quantifies the user's ball-handling skills. Specifically, it evaluates whether the ball was caught, the processing time, and other factors.

[1240] Step 9:

[1241] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[1242] Step 10:

[1243] Based on the analysis results, the server automatically generates a plan for the next practice session. In particular, it creates a plan that includes specific practice content to improve the user's technical challenges.

[1244] Step 11:

[1245] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[1246] In this way, the system continuously measures the user's technical skills and provides feedback for efficient improvement.

[1247] (Example 1)

[1248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1249] Traditional baseball training systems struggle to maximize training effectiveness by releasing batted balls tailored to the individual characteristics of each trainee, and they lack visualization and feedback on training results. Therefore, it is difficult for trainees to accurately understand their own skill level and train effectively. Furthermore, because the training content is uniform, it is impossible to pinpoint and improve each trainee's technical weaknesses.

[1250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1251] In this invention, the server includes means for determining the optimal batted ball based on user input data, means for capturing and analyzing the user's batted ball processing process in real time, and means for automatically generating the next practice plan based on the analysis results. This enables users to efficiently improve their skills by releasing batted balls tailored to their individual characteristics and evaluating and analyzing their practice process in real time.

[1252] A "camera-equipped information terminal" is a portable information terminal device that has a built-in digital camera and is used for user input and data capture.

[1253] "Means for inputting user physical data, age data, and skill data" refers to systems or interfaces for users to input information such as their physical characteristics, age, and skill level.

[1254] A "computer equipped with an artificial intelligence model" is an electronic computer that incorporates algorithms capable of performing machine learning and data analysis.

[1255] An "automatic ball launcher" is a mechanical device that automatically launches balls based on set conditions.

[1256] A "camera-equipped information terminal that captures the user's ball-handling process in real time" is a portable information terminal device that has the function of capturing the user's ball-handling process in real time and recording the video footage.

[1257] "Means for quantifying and evaluating a user's ball-catching skills" refers to systems and methods for analyzing a user's ball-catching skills and evaluating them using numerical values ​​or indicators.

[1258] "Means of providing feedback to improve users' skills" refers to systems and methods that present users with the information and areas for improvement necessary to enhance their skills.

[1259] "Means for determining the optimal batted ball based on user input data" refers to a method or system that analyzes user input data and calculates the most suitable type, speed, and direction of the batted ball.

[1260] "Means for real-time filming and analysis of a user's ball handling process" refers to a system or method that films the process of a user handling a ball in real time and analyzes the resulting video data.

[1261] "Methods for automatically generating the next practice plan based on analysis results" refers to systems or methods that automatically generate new practice content and plans based on the analysis results of individual users.

[1262] This invention is a system that provides personalized fielding practice by combining a camera-equipped information terminal, a computer with an artificial intelligence model, and an automatic release device. This system is designed to individually optimize baseball fielding practice. Detailed embodiments of this system are described below.

[1263] Introduction

[1264] This system takes user data such as physical characteristics, age, and skill level as input, and based on that, artificial intelligence (AI) releases appropriate batted balls. It also supports skill improvement by filming and analyzing the actual practice process with a camera and providing feedback.

[1265] Hardware and software to be used

[1266] This system consists of the following main hardware and software components.

[1267] Information terminals with cameras (e.g., tablet devices)

[1268] Computers equipped with artificial intelligence models (e.g., servers)

[1269] Automatic release device (e.g., automatic ball launcher)

[1270] System Processing Overview

[1271] This system's program operates through the collaboration of users, terminals, and servers. The specific operation of each core module is described below.

[1272] User data entry

[1273] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate". This data will serve as the basis for individually customizing the training content.

[1274] Sending user data

[1275] Terminal: Sends the entered data to the server. The server uses this data to determine the optimal shot for the user.

[1276] Selecting a practice mode

[1277] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range) and set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[1278] Sending practice mode data

[1279] Terminal: Sends selected practice mode and difficulty information to the server. The server analyzes this information to determine the type, speed, and direction of the hit.

[1280] The decision on the batted ball

[1281] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal batted ball. For example, the server might set the next batted ball to be a ground ball to the left at a 30° angle and traveling at 40 km / h.

[1282] Release of batted ball

[1283] Terminal: Based on instructions from the server, it controls the automatic release device and releases the specified ball.

[1284] Filming the process of handling a batted ball.

[1285] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[1286] Sending batted ball processing data

[1287] Terminal: Sends the captured video data directly to the server. The server analyzes the received video data and evaluates the user's skills.

[1288] Analysis of video data

[1289] Server: The analysis results are quantified as "success" or "failure" and the processing time is recorded. For example, it might be evaluated as "success" with "processing time: 1.4 seconds".

[1290] Display of analysis results

[1291] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement.

[1292] Generating the next practice plan

[1293] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested on the terminal, allowing users to efficiently improve their skills.

[1294] Specific example

[1295] The user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and sets the next batted ball to be released to the left at a 30° angle, at a speed of 40 km / h. The terminal controls the automatic release device and releases the batted ball. Simultaneously, the terminal's camera films the ball handling process and sends the video data to the server. The server analyzes the video and evaluates it as "success" with a "processing time: 1.4 seconds," displaying the result on the terminal's screen. Furthermore, the server automatically generates a plan for the next practice session based on the analysis results and suggests it to the terminal. This system allows users to objectively evaluate and improve their skills and effectively engage in independent practice.

[1296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1297] Step 1:

[1298] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate".

[1299] Input: User's physical size, age, and skill data

[1300] Output: Input data (physique, age, skill level)

[1301] Step 2:

[1302] Terminal: Sends the entered data to the server. The user's entered physical characteristics, age, and skill data are sent directly to the server.

[1303] Input: Data entered in Step 1

[1304] Output: Sending data to the server

[1305] Step 3:

[1306] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range), and then set the difficulty level. For example, select "Intermediate" difficulty in catching practice mode.

[1307] Input: Objective and difficulty setting for the knocking practice.

[1308] Output: Selected practice objective and difficulty level

[1309] Step 4:

[1310] Terminal: Sends the selected practice mode and difficulty information to the server. This data is used to determine the type, speed, and direction of the next batted ball.

[1311] Input: Data selected in Step 3

[1312] Output: Sending data to the server

[1313] Step 5:

[1314] Server: The server analyzes the received user data and practice mode data, and the AI ​​model determines the optimal type, speed, and direction of the batted ball. Based on the user's input data, practice objectives, and difficulty level, the server might, for example, set the next batted ball to be a ground ball to the left at a 30° angle and a speed of 40 km / h.

[1315] Input: Data submitted in Steps 2 and 4

[1316] Output: Determination of batted ball type, speed, and direction.

[1317] Step 6:

[1318] Terminal: Based on instructions from the server, it controls the automatic release device and releases the set batted ball. The automatic release device has the function of releasing the specified batted ball. For example, the automatic release device releases a ground ball at a speed of 40 km / h at a 30-degree angle to the left.

[1319] Input: Instructions from the server (type of hit, speed, direction)

[1320] Output: Release of the specified batted ball.

[1321] Step 7:

[1322] Terminal: The camera captures the user's ball-handling process in real time. For example, it records the moment the user catches a designated ball.

[1323] Input: Real-time video of the ball handling process.

[1324] Output: Captured video data

[1325] Step 8:

[1326] Terminal: Sends the captured video data directly to the server.

[1327] Input: Video data captured in Step 7

[1328] Output: Sending video data to the server

[1329] Step 9:

[1330] Server: Analyzes received video data and evaluates the user's ball-handling skills. The server analyzes the actions in the video and quantifies "success" or "failure" and processing time. For example, it might evaluate as "success" with "processing time: 1.4 seconds".

[1331] Input: Video data sent in Step 8

[1332] Output: Analysis results (success / failure, processing time, etc.)

[1333] Step 10:

[1334] Terminal: Displays analysis results sent from the server on the screen. Users can check their own skill level and areas for improvement. For example, it might display results such as, "This ball handling was successful, and the processing time was 1.4 seconds."

[1335] Input: Analysis results from the server

[1336] Output: Display of analysis results

[1337] Step 11:

[1338] Server: Based on the analysis results, it automatically generates the next practice plan. New practice content is suggested to the user. For example, a plan such as "In the next practice, let's work on reducing the reaction time to a ball hit at a 30° angle to the left by 0.2 seconds" is generated.

[1339] Input: Analysis results (success / failure, processing time, etc.)

[1340] Output: Automatic generation of the next practice plan

[1341] (Application Example 1)

[1342] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1343] Conventional factory robot training for parts picking lacks sufficient individual optimization to improve efficiency and accuracy. In particular, there is a lack of means to provide real-time motion analysis and feedback, resulting in decreased work efficiency and accuracy. Furthermore, the automatic generation of training plans tailored to the characteristics of each robot is difficult, limiting the effectiveness of the training.

[1344] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1345] In this invention, the server includes means for inputting physical attribute data and work capability data of the training target, means equipped with a generative AI model for analyzing captured image data, and an automatic execution device that controls the type, speed, and direction of work suitable for the training target based on the data analyzed by the generative AI model. This enables real-time motion analysis and feedback, as well as the automatic generation of individually optimized training plans.

[1346] A "camera-equipped information processing device" is an information processing device equipped with a camera, and is a terminal capable of capturing, analyzing, and displaying images and videos.

[1347] "Training target" refers to an object or device that is the subject of training or work, and in this invention, this mainly refers to factory robots.

[1348] "Physical attribute data" refers to data that describes the specific physical characteristics of an object or piece of equipment, such as arm length or weight.

[1349] "Work capability data" refers to data about the work capabilities of an object or piece of equipment, including accuracy, speed, and capability level.

[1350] A "generative AI model" is an algorithmic model generated using machine learning or deep learning techniques, which analyzes input data and provides appropriate instructions or predictions.

[1351] A "computing device" is a piece of equipment used for processing and analyzing data, and generally includes computers and servers.

[1352] An "automatic execution device" is a device that automatically performs actions instructed by a program or AI model.

[1353] A "work processing process" is a series of actions performed by the object of the work (for example, a robot) in which it actually carries out the task.

[1354] "Feedback" refers to evaluation and improvement information provided by the system, intended to help improve the performance and technology of the subject.

[1355] A "plan" is a plan for the next training session or work, and is automatically generated based on areas for improvement and goals.

[1356] This invention is a system for individually optimizing the training of factory robots for parts picking. This system functions by combining an information processing device with a camera, a computing device equipped with a generative AI model, and an automated execution device.

[1357] System Configuration

[1358] Camera-equipped information processing device

[1359] A camera-equipped information processing device is a device equipped with a camera that can capture, analyze, and display images and videos. This system uses a device such as an iPad Pro.

[1360] computing device

[1361] A computing device is a computer or server that processes and analyzes data. In this invention, an Amazon Web Services (AWS) EC2 instance is used to host the generative AI model.

[1362] Automatic execution device

[1363] An automated execution device is a device that automatically performs actions instructed by a program or AI model. For example, a robotic arm that places parts in a specific location is one such device.

[1364] Program processing

[1365] The server first acquires user data, which is then used by the generated AI model for analysis. Specifically, it uses robot physical attribute data (e.g., arm length, velocity characteristics) and work capability data (e.g., picking ability, accuracy) as input data. Based on this, the AI ​​model determines the optimal type, speed, and direction of movement.

[1366] The generative AI model is developed using a deep learning framework such as TensorFlow. Captured video data is sent from a camera-equipped information processing device to a server and analyzed in real time. The analysis results provide evaluations such as "success," "failure," and "work time."

[1367] The feedback function displays the analysis results to the user, and the next training plan is automatically generated. This allows for efficient improvement of the robot's work capabilities.

[1368] Specific example

[1369] As an example, consider the following scenario.

[1370] 1. The user enters the physical attribute data of the factory robot (arm length 1.5m, speed performance "medium speed") and work capacity data (picking capacity "high") on the initial setup screen.

[1371] 2. Send this data from the tablet device to the server.

[1372] 3. The AI ​​model on the server analyzes the data and determines the optimal picking action (e.g., position "X: 50cm, Y: 30cm", speed "0.5 seconds").

[1373] 4. The camera captures the robot's picking movements in real time and sends the video data to the server.

[1374] 5. The server evaluates the success or failure of the operation and the time required based on the analysis results, and displays the results on the user screen.

[1375] 6. Next, based on the analysis results, the system automatically generates a training plan for the next session and proposes it to the user.

[1376] Examples of prompts to input into a generative AI model

[1377] "Develop an AI model that determines the optimal parts picking motion based on the robot's shape characteristics and work capabilities. The model will use arm length and speed performance as input data for the robot's shape characteristics, and picking capability as input data, to determine the optimal type, speed, and direction of movement. It will also capture actual picking movements with a camera and analyze whether the movement was successful or unsuccessful, as well as the work time. Additionally, build a system that automatically generates a training plan for the next session."

[1378] In this way, inputting specific requirements as prompts helps in developing high-performance AI models.

[1379] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1380] Step 1:

[1381] On the initial setup screen, the user enters the physical attribute data and work capacity data of the factory robot. For example, the user might enter an arm length of 1.5m, a speed performance of "medium speed," and a picking capacity of "high." This input data is sent from the terminal to the server.

[1382] Step 2:

[1383] The server provides the received user data as input data to the generative AI model. The generative AI model uses a deep learning framework such as TensorFlow and predicts the optimal action based on the received data. Specifically, it analyzes numerical values ​​such as arm length, speed performance, and picking ability to determine the optimal type, speed, and direction of action.

[1384] Step 3:

[1385] The server generates and sends operation instructions to the terminal. The terminal then issues instructions to the automated execution device, controlling, for example, a robotic arm to place a part in a specific location. The executed actions are captured in real time by a camera-equipped information processing device, and the video data is sent to the server.

[1386] Step 4:

[1387] The server analyzes the received video data in real time. The server uses a generated AI model to analyze the video data and quantifies evaluation items such as the success or failure of an action and the time taken. For example, evaluation results such as "Success," "Failure," and "Time taken: 0.45 seconds" can be obtained.

[1388] Step 5:

[1389] The server generates feedback based on the analysis results and sends it to the terminal. The terminal displays the analysis results on the screen and provides specific feedback to the user. For example, it might display, "This picking operation was successful, and the work time was 0.45 seconds."

[1390] Step 6:

[1391] Based on the analysis results, the server automatically generates the next training plan. Using the generated AI model, the server creates a new plan to address technical challenges and sends it to the user's device. The user can then review the next training plan through their device.

[1392] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1393] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an AI model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[1394] Introduction

[1395] This system is designed to individually optimize baseball fielding practice. By incorporating data on the trainee's physique, age, and skill level, as well as emotional data during practice, it aims to provide the trainee with the most suitable batted balls, promoting skill improvement while maintaining psychological motivation.

[1396] Overview of program processing

[1397] The program of this system works through the cooperation of the user, terminal, and server. The specific operation of each core module is explained below in natural language.

[1398] User data entry and settings

[1399] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, enter height 170cm, weight 65kg, age 20, and skill level "Intermediate".

[1400] Terminal: Sends the entered data to the server. This data forms the basis for the AI ​​model to determine the appropriate batted ball.

[1401] Setting the practice mode and releasing the batted ball

[1402] User: Select the purpose of the fielding practice (e.g., catching practice, expanding fielding range). For example, select "Intermediate" difficulty in catching practice mode.

[1403] Terminal: Sends the selected practice mode and difficulty information to the server.

[1404] Server: The server analyzes the received data, and the AI ​​model calculates the optimal type, speed, and direction of the batted ball. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h.

[1405] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[1406] Filming and analysis of the ball handling process

[1407] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. The user catches the designated ball.

[1408] Server: Analyzes the received video data and evaluates the user's ball-handling skills (e.g., quantifying "success" or "failure," processing time, etc.).

[1409] Acquisition and analysis of emotional data

[1410] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it analyzes whether the user is feeling stressed or enjoying the practice.

[1411] Server: Based on analyzed emotional data, the server adjusts the difficulty and type of the ball hits. For example, if the user is feeling stressed, the difficulty level is slightly reduced.

[1412] Feedback and next practice plan

[1413] Terminal: Displays analysis results and emotional data on the screen. Users can check their skill level and emotional state. For example, it might display, "This ball was handled successfully, and the handling time was 1.4 seconds. Your current psychological state is slightly stressed."

[1414] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. In particular, it creates a plan that includes specific practice content that takes into account the user's technical challenges and emotional state.

[1415] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[1416] Specific example

[1417] The user enters their height (170cm), weight (65kg), age (20), and skill level ("Intermediate") on the initial setup screen, and selects the difficulty level ("Intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to be released. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[1418] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a "Processing Time: 1.4 seconds." Furthermore, it acquires the user's emotional data during practice using the camera, and if the emotion engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[1419] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[1420] This system allows users to continuously measure their technical skills and improve them efficiently, as well as engage in effective self-practice that takes psychological motivation into account.

[1421] The following describes the processing flow.

[1422] Step 1:

[1423] On the initial setup screen, the user enters their physical data (height, weight), age, defensive position, and skill level. For example, they might enter height 170cm, weight 65kg, age 20, and skill level "intermediate".

[1424] Step 2:

[1425] The terminal sends the user's entered physical measurements, age, and skill data to the server. The server receives this data and stores it in its database.

[1426] Step 3:

[1427] The user selects a practice mode (such as catching practice or expanding fielding range) and a difficulty level (beginner, intermediate, or advanced). For example, the user might select "intermediate" difficulty in catching practice mode.

[1428] Step 4:

[1429] The device sends the user's selected practice mode and difficulty level information to the server. The server analyzes the received data, and the AI ​​calculates the optimal type, speed, and direction of the shot.

[1430] Step 5:

[1431] The server sends the calculation result (e.g., a ground ball going 30° to the left at a speed of 40 km / h) to the terminal. The terminal controls the automatic release device based on the data it receives.

[1432] Step 6:

[1433] The terminal operates the automatic release device as instructed, releasing the specified batted ball. For example, it releases a ground ball traveling at 40 km / h 30° to the left.

[1434] Step 7:

[1435] The device uses its camera to capture the user's ball-catching process in real time and sends the video data to the server. The user then performs the action of catching the ball.

[1436] Step 8:

[1437] The device uses its camera to capture the user's facial expressions and voice tone, and sends this data to the emotion engine. The emotion engine then analyzes the user's emotional state. For example, it analyzes whether the user is feeling stressed or enjoying themselves during practice.

[1438] Step 9:

[1439] The server analyzes the received video data and emotional data to evaluate the user's ball-handling skills. Simultaneously, the emotional engine analyzes the user's emotional state and evaluates their stress level and motivation.

[1440] Step 10:

[1441] The server sends the evaluation results (e.g., "Success," "Processing time: 1.4 seconds," "Stress level: Medium") to the terminal. The terminal displays the analysis results on the screen, and the user confirms the results.

[1442] Step 11:

[1443] The server automatically generates the next practice plan based on the analysis results and emotional data. For example, it creates a plan that includes specific practice content that takes into account the user's technical challenges and stress levels.

[1444] Step 12:

[1445] The terminal displays the next practice plan sent from the server to the user. The user reviews the suggested plan and prepares for the next practice session.

[1446] In this way, the system can not only continuously measure and efficiently improve the user's technical skills, but also enable effective self-practice that takes psychological motivation into consideration.

[1447] (Example 2)

[1448] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1449] Traditional baseball fielding practice systems lack personalized training based on the player's physique and skill level. Furthermore, they lack the ability to monitor the player's mental state in real time and adjust the difficulty and content of the training accordingly, making it difficult to effectively maintain player motivation. This results in slower skill improvement and inefficient practice.

[1450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1451] In this invention, the server includes means for inputting the trainee's physical data, age data, and skill data; means equipped with an artificial intelligence model for analyzing captured image data; and means for controlling an automatic release device that controls the type, speed, and direction of the batted ball appropriate for the trainee based on the data analyzed by the artificial intelligence model. This enables the generation of an optimal training plan based on the trainee's individual data, and further analyzes their psychological state using an emotion engine to provide feedback, thereby improving the trainee's skills and maintaining their psychological motivation.

[1452] A "tablet device with a camera" is a portable information terminal that has a built-in camera and is capable of recording video and displaying data.

[1453] "Means for inputting trainee's physical data, age data, and skill data" refers to an interface device for inputting trainee's height, weight, age, defensive proficiency level, etc., into the system.

[1454] An "artificial intelligence model" is a machine learning algorithm trained using a large amount of data, and is a computer program designed to perform a specific task (in this case, calculating and controlling the type, speed, and direction of a ball hit).

[1455] A "server" is a high-performance computer system used for data analysis and overall system control.

[1456] An "automatic release device" is a device that releases balls according to the system's instructions.

[1457] "Real-time recording" means recording and transmitting the trainee's movements and environment via camera almost simultaneously (without delay).

[1458] "Methods for quantifying and evaluating batted ball handling skills" refers to a system device that analyzes acquired video data and evaluates the batted ball handling ability of the trainee as numerical data.

[1459] The "emotion engine" is software that analyzes the facial expressions and voice tone of the trainees captured by the camera to infer their psychological state at that time.

[1460] "Means of providing feedback" refers to an interface device that displays technical evaluations and psychological advice to the user based on analyzed data and inferred psychological state.

[1461] "Plans automatically generated based on technical challenges" refers to the algorithm and its results used by the system to automatically determine the content and difficulty level of practice in order to compensate for the technical shortcomings of the practitioner.

[1462] The "next practice plan" is a specific practice plan suggested by the system, based on current practice results and emotional data, outlining what and how to practice in the next session.

[1463] This invention is a system that provides user-specific practice drills by combining a tablet terminal with a camera, a server equipped with an artificial intelligence model, an automatic output device, and an emotion engine. Specific embodiments of this system are described below.

[1464] This system is designed to individually optimize baseball fielding practice. By incorporating the player's physical data, age data, skill data, and emotional data during practice, it aims to provide the player with the optimal batted ball, promote skill improvement, and maintain psychological motivation.

[1465] User data entry and settings

[1466] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. For example, a user might enter: height 170cm, weight 65kg, age 20, skill level "intermediate".

[1467] Terminal: Sends the entered data to the server. This data serves as the basis for the server's artificial intelligence model to determine the appropriate batted ball.

[1468] Setting the practice mode and releasing the batted ball

[1469] User: Select the purpose of the practice drill (e.g., catching practice, expanding fielding range, etc.) and the difficulty level. Example: Select "Intermediate" difficulty level in "Catching Practice Mode".

[1470] Terminal: Sends the selected practice mode and difficulty information to the server.

[1471] Server: The server analyzes the received data, and the artificial intelligence model calculates the optimal type of batted ball (ground ball, line drive, fly ball, etc.), speed, and direction. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h.

[1472] Terminal: Based on instructions from the server, the automatic release device releases the designated batted ball.

[1473] Filming and analysis of the ball handling process

[1474] Terminal: The camera captures the user's ball-handling process in real time and sends the data to the server. The user catches the designated ball.

[1475] Server: Analyzes the received video data and evaluates the user's ball-handling skills. For example, "success," "failure," and "processing time: 1.4 seconds" are quantified.

[1476] Acquisition and analysis of emotional data

[1477] Device: The camera captures the user's facial expressions and voice tone in real time, and the emotion engine analyzes them. For example, it can determine whether the user is satisfied or stressed.

[1478] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, it will slightly lower the difficulty level.

[1479] Feedback and generation of the next practice plan

[1480] Terminal: Displays analysis results and emotional data on the screen. For example, it provides feedback to the user such as, "This ball was handled successfully, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[1481] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content that takes into account the user's technical challenges and emotional state.

[1482] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[1483] Specific example

[1484] For example, a user enters their height (170cm), weight (65kg), age (20), and skill level ("intermediate") on the initial setup screen, and selects the difficulty level ("intermediate") in catching practice mode. The terminal sends this data to the server, which analyzes it and determines the type, speed, and direction of the next batted ball to release. The terminal, having received the instructions, controls the automatic release device and releases the specified batted ball (a ground ball at 40 km / h, 30° to the left).

[1485] The device uses its camera to capture the user's ball-hitting process and sends the video data to a server. The server analyzes the received video and evaluates it as "Success" with a processing time of 1.4 seconds. Furthermore, the camera captures the user's emotional data during practice, and if the emotional engine recognizes a "medium" stress level, it makes adjustments such as slightly lowering the difficulty level for the next practice session. The device displays the analysis results and feedback, allowing the user to check their skill level and psychological state.

[1486] The server then automatically generates a plan for the next practice session based on the analysis results and sentiment data, and suggests it to the user's device. The user reviews the suggested plan and prepares for the next practice session.

[1487] Example of a prompt

[1488] "If a user who is 170cm tall, weighs 65kg, is 20 years old, and has an intermediate skill level selects the intermediate difficulty level in catching practice mode, how should the type, speed, and direction of the next batted ball be set?"

[1489] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1490] Step 1:

[1491] User: On the initial setup screen, enter your physical characteristics (height, weight), age, skill level, and defensive position. The entered data will be "Height: 170cm, Weight: 65kg, Age: 20, Skill Level: Intermediate". This is the initial data input for the system.

[1492] Terminal: Sends the entered data to the server. Based on the input data, the server prepares to generate an individually optimized practice plan.

[1493] Step 2:

[1494] User: Select the purpose of the practice drill (e.g., catching practice, expanding fielding range, etc.) and the difficulty level (e.g., "Catching Practice Mode", difficulty level "Intermediate").

[1495] Terminal: Sends selected practice mode and difficulty information to the server. Based on this input, the server analyzes the practice mode and difficulty data and prepares the AI ​​model to calculate the optimal batting type, speed, and direction.

[1496] Step 3:

[1497] Server: The server analyzes the received user's physique, age, skill data, practice mode, and difficulty information, and uses a generating AI model to calculate the optimal type of hit (ground ball, line drive, fly ball, etc.), speed, and direction. For example, it might set a ground ball to 30° to the left at a speed of 40 km / h. The result of this calculation is output from the server.

[1498] Server: Sends the generated batting settings data to the terminal.

[1499] Step 4:

[1500] Terminal: Based on instructions from the server, it controls the automatic release device and releases a specified ball (e.g., a ground ball at 40 km / h, 30° to the left). It records the release log so that it can be provided to the user later.

[1501] Step 5:

[1502] Terminal: The camera captures the user's ball-hitting process in real time. The captured video data is used as input data.

[1503] Terminal: Sends captured video data to the server. This data is then passed to the server for later analysis.

[1504] Step 6:

[1505] Server: Analyzes the received video data and evaluates the user's ball-handling skills. Specifically, it generates evaluation data such as "Success," "Failure," and "Processing Time: 1.4 seconds." This is the output data after analysis.

[1506] Step 7:

[1507] The device uses a camera to capture the user's facial expressions and voice tone in real time, and analyzes them using an emotion engine. For example, it analyzes whether the user is satisfied or stressed. The acquired data is then sent to the server.

[1508] Step 8:

[1509] Server: Based on the analyzed emotional data, the server adjusts the difficulty and type of batted balls. Specifically, if the user is feeling stressed, the difficulty is slightly reduced. This is output as setting data that will be reflected in the next practice session.

[1510] Step 9:

[1511] Terminal: Displays analysis results and feedback sent from the server on the screen. Specifically, it provides feedback such as, "This ball handling was successful, and the processing time was 1.4 seconds. Your current psychological state is slightly stressed."

[1512] Step 10:

[1513] Server: Based on analysis results and emotional data, it automatically generates the next practice plan. Specifically, it creates a plan that includes practice content considering the user's technical challenges and emotional state. This plan will be executed in the next practice session.

[1514] Terminal: Displays the next practice plan sent from the server to the user. The user reviews the proposed plan and prepares for the next practice session.

[1515] (Application Example 2)

[1516] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1517] Traditional baseball fielding drills make it difficult to provide an optimal program tailored to the individual characteristics of each player, such as their current skill level, physique, and age. Furthermore, it is virtually impossible to assess players' psychological stress and motivation in real time during practice and provide corresponding feedback or adjust the practice plan accordingly. Moreover, it is challenging to control the optimal type, speed, and direction of batted balls while taking all these factors into consideration.

[1518] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1519] In this invention, the server includes means for inputting the user's physical data, age data, and skill data; means equipped with an artificial intelligence model for analyzing captured image data; and means for adjusting the difficulty and type of balls based on the analyzed emotional data. This provides the user with optimal balls, enabling improvement in technical skills and maintenance of psychological motivation.

[1520] A "camera-equipped information terminal" is a computer terminal equipped with a camera for capturing video, and its role is to record and transmit the actions of the person practicing.

[1521] A "user" is an individual who uses this system for practice and is the one who inputs physical data, age data, and skill data.

[1522] "Physical data" refers to information that shows the physical characteristics of the trainee, such as height and weight.

[1523] "Age data" refers to information indicating the age of the trainee.

[1524] "Skill data" refers to information that indicates a player's skill level and proficiency in baseball.

[1525] An "artificial intelligence model" is a system that includes algorithms and neural networks that analyze input data and generate appropriate output results.

[1526] A "computer" refers to a computer or server used for processing data, and is a device that runs artificial intelligence models.

[1527] An "automatic ball dispenser" is a device that automatically dispenses practice balls based on analyzed data.

[1528] The "ball handling process" refers to a series of actions taken by the user to catch or handle an incoming ball.

[1529] "Methods for quantifying and evaluating" refers to a system that evaluates a player's ball-handling skills as quantitative numerical data.

[1530] "Means of providing feedback for skill improvement" refers to a system that provides feedback to practitioners on areas for improvement in their technique and the results of their work.

[1531] "Emotional data" refers to data such as the trainee's psychological state, facial expressions, and voice tone during practice.

[1532] "A means of adjusting the difficulty and type of balls based on analyzed emotional data" refers to a system that analyzes the psychological state of the trainee and optimizes the training content based on the results.

[1533] This invention is designed to individually optimize baseball fielding practice and is a system that combines a camera-equipped information terminal, a computer with an artificial intelligence model, an automatic release device, and an emotional data analysis means. Based on the user's physical data, age data, and skill data, it provides the optimal type, speed, and direction of batted balls, and further monitors and analyzes the user's emotional state during practice to adjust the practice content in real time, thereby improving skills and maintaining motivation.

[1534] System Configuration

[1535] 1. Initial setup and user data entry

[1536] Users input their physical data (height, weight, etc.), age data, and skill data (proficiency level, etc.) using a camera-equipped information terminal. This data is transmitted to a server via wireless communication such as Wi-Fi or Bluetooth.

[1537] 2. Release of batted balls and real-time filming

[1538] The server uses an artificial intelligence model based on the received data to determine the appropriate type, speed, and direction of the batted ball. The computer uses Python and TensorFlow for data analysis. The automatic batted ball release device releases the specified batted ball based on the computer's instructions. A camera-equipped information terminal captures the batted ball release and the user's batting process in real time, and transmits the video data to the server.

[1539] 3. Analysis of video data and technical evaluation

[1540] The server analyzes the received video data using libraries such as OpenCV to quantify the ball-handling technique. Evaluation items include "success" or "failure," and processing time. The analysis results are displayed on a camera-equipped information terminal as feedback to help the user improve their skills.

[1541] 4. Acquisition and Analysis of Emotional Data

[1542] Emotional analysis is performed based on data such as facial expressions and voice tone acquired from a camera-equipped information terminal. An existing emotion recognition engine is utilized for this purpose. The analyzed emotional data is sent to a server and used to adjust the training content. For example, if the user is experiencing stress, the server will automatically lower the difficulty level of the ball-hitting exercises.

[1543] 5. Automatic generation of the next practice plan.

[1544] The server automatically generates the next practice plan based on the analysis results and emotional data. The generated practice plan is displayed to the user via a camera-equipped information terminal. This allows the user to obtain an effective practice plan that takes into account their technical challenges and psychological state.

[1545] Specific example

[1546] As a concrete example, consider the following situation.

[1547] User settings: The user enters their height (175cm), weight (70kg), age (25), and skill level ("Advanced") on a camera-equipped information terminal. They select shortstop as their defensive position, "Expanded Defensive Range Mode" as their practice mode, and "Advanced" as their difficulty level.

[1548] Smartphone app feedback display: "This ball was handled successfully. Handling time: 1.2 seconds. Current mental state: Relaxed."

[1549] Examples of prompts for generative AI models

[1550] I want to implement an application like this:

[1551] 1. Enter data such as the user's physique, age, and skill level into the smartphone app.

[1552] 2. The user's practice process is filmed using the camera on a camera-equipped information terminal.

[1553] 3. Analyze video data on the server and generate a technical evaluation.

[1554] 4. Analyze psychological state using the Emotion Engine and reflect it in the training plan.

[1555] 5. The next practice plan will be displayed on the smartphone app.

[1556] This allows users to efficiently improve their skills and maintain their motivation.

[1557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1558] Step 1:

[1559] The user performs the initial setup using a camera-equipped information terminal. Specifically, they input physical data such as height, weight, age, and skill level, as well as age and skill data, and then transmit the setting data. Based on this input data, the server performs an initial analysis and calculates the optimal batting parameters for the user. The output provides initial settings for batting type, speed, and direction.

[1560] Step 2:

[1561] The terminal controls the automatic release device to release the appropriate ball based on initial setup data received from the server. The type, speed, and direction of the ball released by the automatic release device are controlled by the data used. The ball is then physically released to the user as output.

[1562] Step 3:

[1563] The terminal uses its camera to capture the user's ball-handling process in real time. The captured video data is sent to the server via the terminal. Using this video data as input, the server performs motion analysis using OpenCV or similar tools to quantify and evaluate the ball-handling technique. The output is technical evaluation data (success / failure, processing time, etc.).

[1564] Step 4:

[1565] The server collects technical evaluation data and provides feedback to the terminal. Specific feedback includes success / failure results, processing time, and areas for improvement. The feedback data is displayed to the user via the terminal. Here, technical evaluation data is taken as input, and feedback data is output.

[1566] Step 5:

[1567] The device acquires emotional data such as the user's facial expressions and voice tone, and sends it to the server in real time. Based on this input data, the server uses an emotion analysis engine to analyze the user's psychological state. TensorFlow is used as an example. Emotion evaluation data is output as a result of the analysis.

[1568] Step 6:

[1569] The server adjusts the difficulty and type of batting practice based on emotional assessment data. For example, if the user is stressed, the difficulty is lowered, and if they are relaxed, the difficulty is increased. Emotional assessment data is then input, and an adjusted practice plan is output.

[1570] Step 7:

[1571] The server automatically generates the next practice plan based on technical and emotional evaluation data. This practice plan includes specific practice content that reflects the user's technical challenges and psychological state. The generated practice plan is presented to the user via the terminal. Here, the technical and emotional evaluation data are used as input, and a new practice plan is output.

[1572] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1573] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1574] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1575] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1576] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1577] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1578] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1579] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1580] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1581] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1582] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1583] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1584] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1585] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1586] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1587] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1588] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1589] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1590] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1591] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1592] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1593] The following is further disclosed regarding the embodiments described above.

[1594] (Claim 1)

[1595] A tablet device with a camera,

[1596] A means of inputting the trainee's physical data, age data, and skill data,

[1597] A server equipped with an AI model for analyzing captured image data,

[1598] Based on data analyzed by an AI model, an automatic ball release device controls the type, speed, and direction of the ball hit to suit the player.

[1599] A tablet device with a camera that captures the release of the batted ball and the process of the player handling the ball in real time,

[1600] A method for quantifying and evaluating the ball-handling skills of trainees,

[1601] A system that includes means of providing feedback to help trainees improve their skills.

[1602] (Claim 2)

[1603] The system according to claim 1, further comprising means for automatically generating a plan to improve the trainee's technical challenges during the next practice session, based on data analyzed by an AI model.

[1604] (Claim 3)

[1605] The system according to claim 1, further comprising means for detecting the position and movements of the trainee in real time using a tablet device with a camera and for precisely controlling the direction in which the ball is flying.

[1606] "Example 1"

[1607] (Claim 1)

[1608] Camera-equipped information terminal,

[1609] A means of inputting user physical data, age data, and skill data,

[1610] A computer equipped with an artificial intelligence model for analyzing captured image data,

[1611] An automated ball release device that controls the type, speed, and direction of the ball hit to suit the user based on data analyzed by an artificial intelligence model,

[1612] An information terminal equipped with a camera that captures the release of the batted ball and the user's process of handling the batted ball in real time,

[1613] A means of quantifying and evaluating the user's ball-handling skills,

[1614] A means of providing feedback to help users improve their skills,

[1615] A means for determining the optimal batted ball based on user input data,

[1616] A means of capturing and analyzing the user's ball-handling process in real time,

[1617] A system that includes a means to automatically generate the next practice plan based on the analysis results.

[1618] (Claim 2)

[1619] The system according to claim 1, further comprising means for automatically generating a plan to improve the user's technical challenges during the next practice session, based on data analyzed by an artificial intelligence model.

[1620] (Claim 3)

[1621] The system according to claim 1, further comprising means for detecting the user's location and movements in real time using a camera-equipped information terminal and precisely controlling the direction in which the ball is flying.

[1622] "Application Example 1"

[1623] (Claim 1)

[1624] Information processing device with camera,

[1625] A means for inputting physical attribute data and work ability data of the training subject,

[1626] A computing device equipped with a generative AI model for analyzing captured image data,

[1627] An automated execution device that controls the type, speed, and direction of work appropriate for the training subject based on data analyzed by a generative AI model,

[1628] An information processing device equipped with a camera that captures the execution of a task and the task processing process being practiced in real time,

[1629] A means of quantifying and evaluating the work processing skills being practiced,

[1630] A system that includes means for providing feedback to improve the performance of the subject being trained.

[1631] (Claim 2)

[1632] The system according to claim 1, further comprising means for automatically generating a plan to improve the technical tasks under practice during the next training session, based on data analyzed by the generating AI model.

[1633] (Claim 3)

[1634] The system according to claim 1, further comprising means for detecting the position and movement of a training target in real time using a camera-equipped information processing device to improve the accuracy of task execution.

[1635] "Example 2 of combining an emotion engine"

[1636] (Claim 1)

[1637] A tablet device with a camera,

[1638] A means of inputting the trainee's physical data, age data, and skill data,

[1639] A server equipped with an artificial intelligence model for analyzing captured image data,

[1640] An automated ball release device that controls the type, speed, and direction of the ball hit to suit the player based on data analyzed by an artificial intelligence model,

[1641] A tablet device with a camera that captures the release of the batted ball and the process of the player handling the ball in real time,

[1642] A method for quantifying and evaluating the ball-handling skills of trainees,

[1643] It is equipped with an emotion engine that analyzes the trainee's facial expressions and tone of voice, and has means to monitor the trainee's psychological state.

[1644] A system that includes means of providing feedback to improve the skills of trainees and maintain their psychological motivation.

[1645] (Claim 2)

[1646] The system according to claim 1, further comprising means for automatically generating a plan for the next practice session that takes into account the technical challenges and psychological state of the practitioner, based on data analyzed by an artificial intelligence model.

[1647] (Claim 3)

[1648] The system according to claim 1, further comprising means for detecting the position and movements of the trainee in real time using a tablet device with a camera and for precisely controlling the direction in which the ball is flying, and means for adjusting the difficulty level of the ball based on the psychological state detected by an emotion engine.

[1649] "Application example 2 when combining with an emotional engine"

[1650] (Claim 1)

[1651] Camera-equipped information terminal,

[1652] A means of inputting the user's physical data, age data, and skill data,

[1653] A computer equipped with an artificial intelligence model for analyzing captured image data,

[1654] An automatic sphere release device that controls the type, speed, and direction of spheres suitable for the user based on data analyzed by an artificial intelligence model,

[1655] An information terminal equipped with a camera that captures the ball release and the user's ball handling process in real time,

[1656] A means of quantifying and evaluating a user's ball handling skills,

[1657] A means of providing feedback to help users improve their skills,

[1658] A means of acquiring and analyzing user emotional data,

[1659] A system that includes means to adjust the difficulty and type of ball based on analyzed emotional data.

[1660] (Claim 2)

[1661] The system according to claim 1, further comprising means for automatically generating a plan to improve the user's technical challenges during the next practice session, based on data analyzed by an artificial intelligence model.

[1662] (Claim 3)

[1663] The system according to claim 1, further comprising means for detecting the user's position and movements in real time using a camera-equipped information terminal and precisely controlling the direction of the incoming ball. [Explanation of Symbols]

[1664] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A tablet device with a camera, A means of inputting the trainee's physical data, age data, and skill data, A server equipped with an AI model for analyzing captured image data, Based on data analyzed by an AI model, an automatic ball release device controls the type, speed, and direction of the ball hit to suit the player. A tablet device with a camera that captures the release of the batted ball and the process of the player handling the ball in real time, A method for quantifying and evaluating the ball-handling skills of trainees, A system that includes means of providing feedback to help trainees improve their skills.

2. The system according to claim 1, further comprising means for automatically generating a plan to improve the technical challenges of the trainee during the next practice session, based on data analyzed by an AI model.

3. The system according to claim 1, further comprising means for detecting the position and movements of the trainee in real time using a tablet device equipped with a camera, and for precisely controlling the direction in which the ball is flying.

Images