System

Abstract

A system is provided.SOLUTION: A system comprising: an inputting means for inputting an initial datum of a breeding object; a generating and analyzing means for analyzing the inputted initial datum; a plan creating means for creating a breeding plan based on an AI result; a providing means for providing the created breeding plan; a feedback inputting means for inputting a feedback regarding the breeding; and an updating means for dynamically updating the breeding plan by using the inputted feedback datum.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In Japan's professional baseball world, the development of junior and youth players is left up to each team, and traditional development methods lack efficiency and a scientific approach. Furthermore, issues related to the draft system and the transition from high school baseball to college make it difficult to develop outstanding players. The present invention aims to solve these issues and provide an efficient and scientific development method to develop future outstanding professional players. [Means for solving the problem]

[0005] The present invention provides a system that includes an input means for inputting initial data of the training target, a generation AI means for analyzing the input initial data, a plan creation means for creating a training plan based on the analysis results, a provision means for providing the created training plan, a feedback input means for inputting feedback regarding training, and an update means for dynamically updating the training plan using the input feedback data, thereby promoting the development of players more efficiently and scientifically than conventional training methods.

[0006] "Input means" refers to a device or function for inputting initial data of a subject to be raised.

[0007] "Generative AI means" refers to a device or function that uses artificial intelligence technology to analyze input initial data and suggest development points and training methods.

[0008] "Plan creation means" refers to a device or function that creates a development plan based on the analysis results by the generation AI means.

[0009] The "provision means" is a device or function that provides the created development plan to the user.

[0010] The "feedback input means" is a device or function for inputting feedback regarding training.

[0011] The "update means" is a device or function that dynamically updates the development plan using input feedback data. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] This invention relates to a system that supports the development of professional young athletes. This system realizes efficient and scientific development by inputting initial data on the target, analyzing it, creating a plan, and dynamically updating it through feedback.

[0034] System Overview

[0035] The system consists of the following main components:

[0036] Input means: A terminal for the user to input initial data for the object to be trained.

[0037] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[0038] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[0039] Provision means: A terminal for providing the created development plan to the user.

[0040] Feedback input means: A terminal for inputting training results and feedback.

[0041] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[0042] Program processing explanation

[0043] 1. Enter the initial data:

[0044] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[0045] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[0046] 2. Data analysis and initial development plan creation:

[0047] The terminal transmits the input data to the server.

[0048] The server validates the received data (checks for data consistency and missing values).

[0049] The server passes the validated data to the generation AI means, which analyzes the player data.

[0050] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[0051] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[0052] 3. Providing a development plan:

[0053] The server transmits the generated development plan to the terminal.

[0054] The user checks the training plan through the terminal and begins actual training.

[0055] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[0056] 4. Feedback collection and dynamic updates:

[0057] The user periodically inputs training results and player growth data into the terminal.

[0058] The device sends new data to the server.

[0059] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[0060] For example: Your endurance has improved, so you increase your running time and add strength training.

[0061] 5. Providing Feedback:

[0062] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[0063] The server generates the analysis results and provides them to the user via the terminal.

[0064] Example: Your shoulder flexibility has improved significantly, so strength training is recommended as the next step.

[0065] Specific example processing explanation

[0066] Enter the initial data:

[0067] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0068] The terminal transmits this data to the server.

[0069] Data analysis and initial development plan creation:

[0070] The server passes the data to the generating AI means for analysis.

[0071] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[0072] Providing development plans:

[0073] The server transmits the proposed development plan to the terminal.

[0074] The user checks the training plan through the terminal and begins training.

[0075] Collect feedback and dynamically update:

[0076] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[0077] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[0078] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[0079] Providing feedback:

[0080] The server provides the analysis results and gives feedback such as, "Shoulder flexibility has improved significantly. We recommend adding strength training as the next step."

[0081] Users set new goals and adjust their training plans based on feedback.

[0082] In this way, the system supports the development of professional athletes efficiently and scientifically.

[0083] The processing flow will be explained below.

[0084] Step 1:

[0085] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0086] Step 2:

[0087] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[0088] Step 3:

[0089] The server validates the incoming data, specifically checking that the data entered is in the correct format and that all required fields are filled in.

[0090] Step 4:

[0091] The server passes the validated data to the generation AI method, which converts the data into a parsable format (e.g., JSON).

[0092] Step 5:

[0093] The generative AI analyzes the data, for example, determining which training is most appropriate based on a player's "medium shoulder flexibility" or "low endurance" data.

[0094] Step 6:

[0095] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week."

[0096] Step 7:

[0097] The server sends the created development plan to the terminal, where the plan is presented in a format that is easy for the user to understand.

[0098] Step 8:

[0099] The user checks the training plan using a terminal and starts training. The user then performs daily training based on the plan.

[0100] Step 9:

[0101] Users periodically input training results and player growth data into the device, reporting progress such as "shoulder flexibility is improving" or "endurance has improved slightly."

[0102] Step 10:

[0103] The terminal sends new data to the server, and the latest growth data is received by the server.

[0104] Step 11:

[0105] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary corrections or additional training.

[0106] Step 12:

[0107] The server sends the dynamically updated training plan to the device, where the user can confirm the new plan and continue training.

[0108] Step 13:

[0109] The server stores the training results, analyzes long-term growth curves and performance fluctuations, and generates analysis results that are provided to the user via their device.

[0110] Step 14:

[0111] The training plan is readjusted based on the feedback provided by the user from the server, for example, by incorporating new training based on the feedback to optimize the training content.

[0112] In this way, this system supports the development of professional athletes efficiently and scientifically.

[0113] Example 1

[0114] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0115] Conventional youth player development systems only create development plans based on the player's initial data, making it difficult to dynamically reflect subsequent feedback and make appropriate updates. Furthermore, the proposed development plans depended on experience and intuition, making it difficult to provide development based on scientific evidence. This prevented the system from providing optimal development methods tailored to the growth of each individual player, hindering the efficiency of development.

[0116] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0117] In this invention, the server includes a data input means for inputting initial data of the training target, a transmission means for transmitting the input initial data to the server, a validation means for validating the received data, a generation AI means for analyzing the validated data with a generation AI model, a plan creation means for creating a training plan based on the analysis results, a provision means for providing the created training plan to the user, a feedback input means for inputting feedback regarding training, and an update means for dynamically updating the training plan using the input feedback data. This enables scientific and efficient creation of training plans and their dynamic updating.

[0118] "Data input means" refers to an input device or software interface that allows a user to input initial data and basic information about a subject to be raised.

[0119] "Transmission means" refers to a communication device or communication protocol for transmitting the input initial data to the server.

[0120] "Validation means" refers to a process or device that checks the integrity of received data and the presence or absence of missing values, and verifies the validity of the data.

[0121] "Generative AI means" refers to artificial intelligence engines and algorithms that analyze validated data and suggest development points and training methods.

[0122] "Plan creation means" refers to a process or device for creating a development plan based on the analysis results of the generation AI means.

[0123] The "provision means" refers to a communication device or a user interface for providing the created development plan to the user.

[0124] "Feedback input means" refers to a device or software interface for inputting feedback data regarding training.

[0125] "Update means" refers to a process or device for dynamically updating the development plan using input feedback data.

[0126] This invention relates to a system for supporting the development of professional young athletes. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development.

[0127] System Overview

[0128] The system consists of the following main components:

[0129] Data input means: A device such as a PC, tablet, or smartphone is used as a terminal for users to input initial data on the target to be developed.

[0130] Transmission means: The terminal uses a communication protocol (e.g., HTTPS) via the Internet to transmit the input data to the server.

[0131] Validation method: The server performs consistency checks and missing value checks on the received data and uses a validation algorithm to ensure the validity of the data.

[0132] Generative AI method: The generative AI model installed on the server analyzes validated data and suggests training points and methods. The generative AI model used includes deep learning algorithms and machine learning algorithms.

[0133] Plan creation means: The server is equipped with a software module that creates a development plan based on the analysis results of the generation AI means.

[0134] Provision means: In order to provide the created development plan to the user, the server transmits the plan to the terminal, where the user can check the development plan.

[0135] Feedback input means: A terminal is used for users to input feedback data, including training results and player performance data.

[0136] Update means: The server includes a software module for dynamically updating the development plan based on the input feedback data.

[0137] Specific actions

[0138] First, the user inputs the initial data of the player to be trained (e.g., age, height, weight, position, training time, current ability evaluation) into a terminal, which is a data input means, and transmits it to the server using a transmission means. For example, data such as "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance" is input.

[0139] The server checks the received data using validation methods to confirm consistency and missing values. The validated data is then passed to the generative AI method for analysis. The generative AI model extracts points such as "lack of shoulder flexibility" and "need to improve endurance," and suggests specific training menus (e.g., shoulder stretching exercises, running to improve endurance).

[0140] The plan creation means creates a training plan based on the analysis results of the generation AI means, and transmits the plan to the user's terminal via the provision means. The user checks the training plan on the screen of the terminal and starts training. For example, the training plan may include shoulder stretching for 20 minutes every day and running for 30 minutes three times a week.

[0141] The user periodically inputs training results and athlete growth data into the terminal using the feedback input means and sends it to the server. The server reanalyzes this new data using the generation AI means and dynamically updates the development plan. A new development plan is created and sent to the user again via the provision means. For example, the plan may include extending running time and adding strength training because endurance has improved.

[0142] Through this series of processes, this system will efficiently and scientifically support the development of professional athletes.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] The user uses the terminal to input the initial data of the player to be trained. The user enters the player's basic information (e.g., age, height, weight, position, current ability evaluation, etc.) into the input form on the terminal and clicks the "Submit" button.

[0146] Input: Initial data such as age, height, weight, position, and current ability rating

[0147] Output: JSON format data packet for sending initial data

[0148] Step 2:

[0149] The terminal sends the input initial data to the server, which then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[0150] Input: Initial data packet in JSON format

[0151] Output: Initial data sent to the server

[0152] Step 3:

[0153] Validate the data received by the server. Parse the data received by the server and check the format and range. Check for syntax errors and missing values ​​and perform consistency checks.

[0154] Input: Initial data sent to the server

[0155] Output: Validated initial data

[0156] Step 4:

[0157] The validated data is passed to the generation AI means to analyze the player data. The server inputs prompts into the generation AI model, which then suggests development points and training methods based on the player's ability, physical strength, and technical level.

[0158] Input: Validated initial data

[0159] Output: Proposals for training points and methods (analysis results)

[0160] Step 5:

[0161] A training plan is created based on the analysis results of the generation AI means. The server's plan creation means creates a specific training plan including the proposed training method and time allocation.

[0162] Input: Analysis results from the generative AI method

[0163] Output: Specific training plan (e.g. shoulder stretching exercises, running menu, etc.)

[0164] Step 6:

[0165] The created development plan is sent to the terminal and provided to the user. The server converts the development plan data into JSON format and sends it to the terminal using the HTTPS protocol. The user checks the development plan on the terminal screen and puts it into action.

[0166] Input: Specific development plan data

[0167] Output: The development plan displayed on the user's device

[0168] Step 7:

[0169] The user periodically inputs training results and player growth data into the terminal. The terminal inputs new data (e.g., training results, current ability evaluation) into the input form and clicks the "Submit" button.

[0170] Input: Training results and player growth data

[0171] Output: New data sent to the server

[0172] Step 8:

[0173] The server then has the generation AI means reanalyze the new data, which then reanalyzes the current ability evaluation and training progress based on the new data, and creates a new development plan.

[0174] Input: New data submitted by the user

[0175] Output: Update points of the re-analyzed breeding plan

[0176] Step 9:

[0177] The server sends the updated training plan to the device and provides it to the user. The new plan data is converted to JSON format and sent to the device using the HTTPS protocol. The user checks the new plan on the device screen and continues training.

[0178] Input: Reanalyzed breeding plan

[0179] Output: The updated development plan displayed on the user's device

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] In the management and operation of robots used in conventional factories, it has been difficult to efficiently and scientifically improve robot performance and create appropriate maintenance plans.In addition, there has been a lack of systems that can dynamically update operation plans based on robot operation results and maintenance history to improve long-term operational efficiency.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes input means for inputting initial data of the target to be trained, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, database means for managing basic information about the robot, calculation means for proposing performance improvements and maintenance methods for the robot, display means for providing the proposed training plan to the factory manager, and analysis means for analyzing the operation results and maintenance history of the robot and providing long-term operation advice. This enables efficient and scientific management and operation of factory robots.

[0185] "Input means" refers to a terminal for inputting basic information and initial data about the robot into the system.

[0186] The "generative AI means" is an artificial intelligence engine that analyzes input data and proposes methods for improving the robot's performance and maintaining it.

[0187] "Plan creation means" refers to the function of creating specific development and maintenance plans based on the analysis results proposed by the generation AI means.

[0188] The "provision means" refers to a terminal or system for providing the created training plan to the factory manager or user.

[0189] "Feedback input means" refers to a terminal or method for inputting feedback data related to training and maintenance into the system.

[0190] The "update means" is a part of the system that has the function of dynamically updating the development plan using input feedback data.

[0191] "Database means" refers to a system or server for managing and storing basic information, operation history, and maintenance history of the robot.

[0192] The "computing means" is a part of the system that has the function of performing the calculations and analyses necessary to improve the robot's performance and propose maintenance methods.

[0193] The "display means" refers to a screen or terminal that provides the proposed training plan or maintenance plan in a visible format to the factory manager.

[0194] The "analysis means" is a part of the system that has the function of analyzing the robot's operational results and maintenance history and providing long-term operational advice.

[0195] This invention relates to a system for efficiently and scientifically training and maintaining robots used in factories. How this system can be implemented will be described below in detail.

[0196] System Overview

[0197] The system consists of the following main components:

[0198] Input means: A tablet terminal or computer is used as a terminal for users to input initial data (model number, usage time, current status, maintenance history, etc.) of the robots used in the factory.

[0199] Generative AI method: The AI ​​engine installed on the server analyzes the input data and proposes methods for improving the robot's performance and maintenance. Specifically, a generative AI model is built using TensorFlow.

[0200] Planning means: This is a function that the server uses to create specific training and maintenance plans based on the analysis results of the generation AI means. This part is implemented in Python.

[0201] Provision method: The server sends the training plan created to a display device such as a tablet or computer, where the factory manager can check it.

[0202] Feedback input means: A means for the user to input the robot's operation results and maintenance history into the terminal and send them to the server.

[0203] Update method: The server has the function to dynamically update the development plan based on the input feedback data. This function is also implemented in Python.

[0204] Processing description of the embodiment

[0205] Entering initial data

[0206] The user uses a tablet device to input basic information about the robot to be used in the factory.

[0207] Example: Model R-3000, usage time 1200 hours, operating speed medium, error frequency low.

[0208] Data analysis and creation of initial development plan

[0209] The server receives the input data and analyzes it using a generative AI means. The generative AI means uses a generative AI model using TensorFlow. As a result of the analysis, suggestions for improving the robot's performance (e.g., recommended part replacements to increase operating speed, software updates to reduce error frequency) are derived.

[0210] Example prompt: "Model: R-3000, Usage time: 1200 hours, Operating speed: Medium, Error frequency: Low"

[0211] Providing a development plan

[0212] The server then sends the generated training plan to the user's device, where the administrator can check the plan on a tablet or computer and begin specific training and maintenance work.

[0213] Collect feedback and dynamically update

[0214] Users periodically input the robot's operational results (e.g., improvement in operating speed after part replacement) and maintenance history and send them to the server. The server then uses the received data to reanalyze the AI ​​model and create a new, updated training plan.

[0215] Providing Feedback

[0216] The server generates long-term operational advice based on the accumulated operational data and provides it to the user, providing specific advice to further improve the long-term operational efficiency of the robot.

[0217] This system will enable efficient and scientific management and operation of robots within factories.

[0218] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0219] Step 1:

[0220] The user uses a tablet terminal or computer to input the initial data of the robot to be used in the factory (e.g., model number, usage time, current status, maintenance history). The input data is sent from the terminal to the server. The input in this step is basic information about the robot, and the output is the initial data sent to the server. Specifically, the user enters the necessary information on the input screen and presses the "Send" button.

[0221] Step 2:

[0222] The server validates the initial data it receives (checking for data consistency and missing values). The input to this step is the initial data sent by the user, and the output is the data whose consistency has been verified. Specifically, the server uses a Python script to verify the format of the data and check for inconsistencies.

[0223] Step 3:

[0224] The server passes the data whose integrity has been confirmed to the generative AI means for analysis. The generative AI means uses a generative AI model implemented in TensorFlow. The input in this step is the data whose integrity has been confirmed, and the output is the analysis result. Specifically, the server passes the input data to the TensorFlow model and obtains the analysis result.

[0225] Step 4:

[0226] The server creates specific training and maintenance plans based on the analysis results of the generation AI means. The input in this step is the analysis results, and the output is the training plan. Specifically, the server uses a Python script to generate a training plan based on the proposal.

[0227] Step 5:

[0228] The server sends the created training plan to a terminal and provides its contents to the factory manager. The input in this step is the training plan, and the output is the training plan displayed to the manager. Specifically, the server converts the training plan into a display format and sends it to the manager's terminal.

[0229] Step 6:

[0230] The user periodically inputs the robot's operation results and maintenance history into the terminal and sends it to the server. The input in this step is the latest data on the robot, and the output is the operation results and maintenance history sent to the server. Specifically, the user enters the operation results and history information into the input form and presses the "Send" button.

[0231] Step 7:

[0232] The server reanalyzes the received feedback data using the generation AI method to create a new and dynamically updated training plan. The input in this step is the feedback data, and the output is the updated training plan. Specifically, the server inputs the new data into the TensorFlow model and regenerates the training plan based on the results.

[0233] Step 8:

[0234] The server generates long-term operational advice based on the accumulated operational data and provides it to the user. The input in this step is the accumulation of feedback data, and the output is long-term operational advice. Specifically, the server analyzes the data using a Python script and generates advice.

[0235] By following these steps, we will create a system that efficiently and scientifically trains and maintains robots in factories.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] This invention relates to a system that supports the development of professional young athletes by combining it with an emotion engine that recognizes the user's emotions. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development and providing feedback and adjustments based on the user's emotional state.

[0238] System Overview

[0239] The system consists of the following main components:

[0240] Input means: A terminal for the user to input initial data for the object to be trained.

[0241] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[0242] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[0243] Provision means: A terminal for providing the created development plan to the user.

[0244] Feedback input means: A terminal for inputting training results and feedback.

[0245] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[0246] Emotion engine: A function that recognizes and analyzes the user's emotional state and reflects it in training plans and feedback.

[0247] Program processing explanation

[0248] 1. Enter the initial data:

[0249] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[0250] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[0251] 2. Data analysis and initial development plan creation:

[0252] The terminal transmits the input data to the server.

[0253] The server validates the received data (checks for data consistency and missing values).

[0254] The server passes the validated data to the generation AI means, which analyzes the player data.

[0255] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[0256] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[0257] 3. Providing a development plan:

[0258] The server transmits the generated development plan to the terminal.

[0259] The user checks the training plan through the terminal and begins actual training.

[0260] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[0261] 4. Feedback collection and dynamic updates:

[0262] The user periodically inputs training results and player growth data into the terminal.

[0263] The device sends new data to the server.

[0264] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[0265] For example: Your endurance has improved, so you increase your running time and add strength training.

[0266] 5. Leveraging the Emotion Engine:

[0267] The server uses an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[0268] For example, if a user is feeling stressed during a workout, the emotion engine will detect this and make adjustments such as incorporating stretching exercises.

[0269] The emotion engine provides feedback to the generative AI, which then uses it as data to create an appropriate training plan.

[0270] Example: If the user is feeling unmotivated, provide motivational feedback and adjust the training plan.

[0271] 6. Providing Feedback:

[0272] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[0273] The server integrates the analysis results with the emotion engine data and provides it to the user via their device.

[0274] Example: Shoulder flexibility has improved significantly, so strength training is recommended as the next step, or if the user's stress levels are high, add relaxation exercises.

[0275] Specific example processing explanation

[0276] Enter the initial data:

[0277] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0278] The terminal transmits this data to the server.

[0279] Data analysis and initial development plan creation:

[0280] The server passes the data to the generating AI means for analysis.

[0281] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[0282] Providing development plans:

[0283] The server transmits the proposed development plan to the terminal.

[0284] The user checks the training plan through the terminal and begins training.

[0285] Collect feedback and dynamically update:

[0286] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[0287] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[0288] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[0289] Leveraging the Emotion Engine:

[0290] The emotion engine analyzes the user's facial expressions and voice to detect high stress levels.

[0291] The server uses data from the emotion engine to adjust the training plan and make suggestions, including stretching and resting to reduce stress.

[0292] Providing feedback:

[0293] Based on the analysis results and data from the emotion engine, the server provides feedback such as, "Shoulder flexibility has improved significantly. The next step is to add strength training. Also, as stress levels are high, we recommend relaxation exercises."

[0294] Users set new goals and adjust their training plans based on feedback.

[0295] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[0296] The processing flow will be explained below.

[0297] Step 1:

[0298] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0299] Step 2:

[0300] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[0301] Step 3:

[0302] The server validates the incoming data, specifically checking that the data is in the correct format and that all required fields are filled in. If the validation passes, the data is passed to the next step.

[0303] Step 4:

[0304] The server passes the validated data to the generation AI method, which converts the data into a parseable format (e.g., JSON).

[0305] Step 5:

[0306] The AI ​​generator analyzes the data. For example, it determines which training is most appropriate based on data such as a player's "medium shoulder flexibility" or "low endurance." Specifically, it thoroughly analyzes the player's abilities and technical level to identify areas for development.

[0307] Step 6:

[0308] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week." This plan is optimized for each individual player.

[0309] Step 7:

[0310] The server sends the created training plan to the terminal. The plan is presented to the user in a format that is easy to understand. Specifically, individual training menus are detailed by date and time.

[0311] Step 8:

[0312] The user checks the training plan on the device and starts training. The user then performs daily training based on the plan. Progress can be checked in real time on the device.

[0313] Step 9:

[0314] The user periodically inputs training results and player growth data into the device. For example, progress such as "shoulder flexibility has improved slightly" or "endurance has improved to a moderate level" is reported.

[0315] Step 10:

[0316] The device sends new data to the server. The latest growth data is received by the server. Data is sent and received in real time, allowing for rapid updates.

[0317] Step 11:

[0318] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary adjustments or additions to training. For example, if endurance has improved, the running time can be extended or strength training can be added.

[0319] Step 12:

[0320] The server sends a dynamically updated training plan to the device. The user confirms the new plan and continues training. The plan is always adjusted based on the latest data.

[0321] Step 13:

[0322] The server uses an emotion engine to analyze the user's facial expressions and voice data to recognize their emotional state. Emotional data is collected while the user is training using the device, for example, to detect the user's stress level and motivation.

[0323] Step 14:

[0324] The emotion engine provides collected emotional data to the generative AI, which then uses this data to further adjust the training plan. For example, if the user is feeling stressed, the AI ​​may add stretching exercises or recommend resting.

[0325] Step 15:

[0326] The server uses the data from the emotion engine to provide appropriate feedback to the user, such as, "Your shoulder flexibility has improved significantly. Your next step is to add strength training. Also, your stress level is high, so we recommend you do some relaxation exercises."

[0327] Step 16:

[0328] Based on the feedback provided by the user, the training plan is adjusted, for example, by incorporating new training items based on the feedback, optimizing the training content.

[0329] This system will efficiently and scientifically support professional athlete development, while also providing training plans and feedback tailored to the user's emotional state.

[0330] Example 2

[0331] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0332] Conventional training systems create training plans based on basic player information and training data, but lack the functionality to dynamically adjust feedback based on the user's emotional state. This makes it difficult to provide training plans that reflect individual needs and adaptations based on the user's emotional state.

[0333] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0334] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, emotion engine means for recognizing and analyzing emotion data, generation AI means for adjusting a training plan based on the emotion data, plan creation means for creating a training plan, provision means for providing the created training plan, feedback input means for inputting feedback regarding training, and update means for dynamically updating the training plan using the input feedback data. This makes it possible to create an individually optimized training plan based on the user's emotional state and feedback data.

[0335] 1. "Development target" refers to young players who are the subject of training and instruction.

[0336] 2. "Input means" refers to the device or interface through which the user inputs initial data and feedback on the subject to be developed.

[0337] 3. "Generative AI means" refers to the artificial intelligence functions used to analyze input data and create development plans and suggest training methods.

[0338] 4. "Plan creation means" refers to the function of creating a specific development plan based on the analysis results of the generation AI means.

[0339] 5. "Provision means" refers to the device or interface used to present the created development plan to the user.

[0340] 6. "Feedback input means" refers to a device or interface that allows users to input training results and player growth data.

[0341] 7. "Update means" refers to the function of dynamically adjusting and updating the development plan based on feedback data.

[0342] 8. "Emotion engine means" refers to the function for recognizing and analyzing the user's emotional state and reflecting that data in training plans and feedback.

[0343] 9. "Emotional Data" refers to data relating to emotions collected from the user's facial expressions, voice, etc.

[0344] MODE FOR CARRYING OUT THE INVENTION

[0345] This invention is a system that combines an emotion engine with a system that supports the development of professional young athletes. This system allows for the efficient and scientific creation of training plans for athletes, and also allows for feedback and adjustments based on the user's emotional state. This system is realized by inputting initial data on the training target, analyzing it, creating a plan, and dynamically updating it through feedback.

[0346] Hardware Configuration

[0347] The system consists of the following main components:

[0348] 1. Input means: A device (e.g., PC, tablet, smartphone) through which the user inputs the initial data of the target to be developed.

[0349] 2. Generative AI means: An artificial intelligence engine (e.g., TensorFlow, PyTorch) that analyzes the data input by the server and suggests development points and training methods.

[0350] 3. Planning means: A function in which the server creates a training plan based on the analysis results of the generated AI means.

[0351] 4. Means of provision: A device (e.g., PC, tablet, smartphone) for providing the created development plan to the user.

[0352] 5. Feedback input means: A device (e.g., PC, tablet, smartphone) for inputting training results and feedback.

[0353] 6. Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[0354] 7. Emotion engine means: A function to recognize and analyze the user's emotional state and reflect it in training plans and feedback (e.g., emotion recognition API, facial expression analysis software).

[0355] Program processing

[0356] Entering initial data

[0357] The user uses the terminal to input basic information about the player (e.g., age, height, weight, position, current ability evaluation, etc.). For example, the user enters "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance." The terminal then sends this information to the server.

[0358] Data analysis and creation of initial development plan

[0359] The server validates the received data (checking for data consistency and missing values). The validated data is passed to the generation AI means, which analyzes the player data. The generation AI then suggests development points and training methods based on the player's ability, physical strength, and technical level. For example, it creates a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[0360] Providing a development plan

[0361] The server sends the generated training plan to the device. The user confirms the plan through the device and starts the actual training. For example, the plan may include 20 minutes of shoulder stretching every day and 30 minutes of running three times a week.

[0362] Collect feedback and dynamically update

[0363] The user periodically inputs training results and athlete growth data into the device. The device sends the new data to the server, which instructs the generation AI means to reanalyze it. The generation AI dynamically updates the development plan based on the new data. For example, as endurance has improved, running time could be extended and strength training added.

[0364] Utilizing the Emotion Engine

[0365] The server uses an emotion engine to analyze the user's facial expressions and voice to collect emotional data. For example, if the user feels stressed during training, the emotion engine can detect this and make adjustments such as incorporating stretching exercises. The emotion engine provides feedback to the generative AI, which then uses the data to create an appropriate training plan.

[0366] Providing Feedback

[0367] The server accumulates training results and analyzes long-term growth curves and performance fluctuations. The server then integrates the analysis results with data from the emotion engine and provides the results to the user via their device. For example, if shoulder flexibility has improved significantly, strength training may be recommended as the next step. Also, if the user's stress level is high, relaxation exercises may be added.

[0368] Examples of prompt statements

[0369] An example prompt might be, "Please suggest the best training plan for this athlete."

[0370] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[0371] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0372] Step 1: Enter initial data

[0373] explanation:

[0374] The user uses a terminal to input basic information about the player to be developed, such as the player's age, height, weight, position, and current ability rating.

[0375] input:

[0376] Basic information (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[0377] output:

[0378] Initial data sent to the server

[0379] Specific behavior:

[0380] When the user enters the required basic information into the form on the terminal and clicks the "Submit" button, the terminal transmits this data to the server.

[0381] Step 2: Data analysis

[0382] explanation:

[0383] Validate the data received by the server, checking for data consistency and missing values.

[0384] input:

[0385] Initial data (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[0386] output:

[0387] Validated initial data

[0388] Specific behavior:

[0389] The server validates the initial data against the database schema, checking for missing values ​​and inconsistencies, and generates error messages as needed.

[0390] Step 3: Create an initial development plan

[0391] explanation:

[0392] The server passes the validated data to the generation AI means, which creates the outline of a development plan.

[0393] input:

[0394] Validated initial data

[0395] output:

[0396] Initial Development Plan

[0397] Specific behavior:

[0398] The server sends a prompt to the AI ​​generator saying, "Please suggest the best training plan for this athlete," and the AI ​​generator creates an appropriate training menu. For example, it might suggest "30 minutes of stretching exercises every day" or "running three times a week."

[0399] Step 4: Provide a development plan

[0400] explanation:

[0401] The server provides the user with suggestions from the generative AI means.

[0402] input:

[0403] Initial Development Plan

[0404] output:

[0405] Displaying training plans to users

[0406] Specific behavior:

[0407] The server sends the generated training plan to the terminal, which displays it. The user can then check the plan details on the terminal screen and begin training.

[0408] Step 5: Gather feedback

[0409] explanation:

[0410] The user periodically inputs training results and impressions into the terminal.

[0411] input:

[0412] User feedback data (e.g., improved endurance, improved shoulder flexibility, etc.)

[0413] output:

[0414] Feedback data sent to the server

[0415] Specific behavior:

[0416] When the user enters the training results into the feedback form on the device and clicks the "Submit" button, the device sends the data to the server.

[0417] Step 6: Dynamic updates based on feedback

[0418] explanation:

[0419] The server instructs the generation AI means to reanalyze based on the feedback data and creates a new development plan.

[0420] input:

[0421] Feedback Data

[0422] output:

[0423] Updated Development Plan

[0424] Specific behavior:

[0425] The server collects the feedback data and sends a prompt to the generation AI saying, "Please update the training plan based on the new data." The generation AI then creates a new training menu, which the server provides to the user.

[0426] Step 7: Collect and analyze emotion data

[0427] explanation:

[0428] The server uses an emotion engine means to analyze the user's facial expressions and voice and collect emotion data.

[0429] input:

[0430] User facial expression and voice data

[0431] output:

[0432] Emotional Data

[0433] Specific behavior:

[0434] The emotion engine uses facial expression recognition and voice analysis technology to collect emotional data such as stress and motivation. The server aggregates the emotional data and provides it to the generation AI.

[0435] Step 8: Adjust based on sentiment data

[0436] explanation:

[0437] The server further adjusts the training plan based on the emotional data.

[0438] input:

[0439] Emotional Data

[0440] output:

[0441] Coordinated Development Plan

[0442] Specific behavior:

[0443] The server uses emotional data such as "The user has been feeling stressed recently" to send a prompt to the AI ​​generator, such as "Please add exercises to reduce stress." The AI ​​generator then makes specific suggestions, such as "Add a yoga menu for relaxation."

[0444] Step 9: Provide feedback

[0445] explanation:

[0446] The server integrates the training results with the emotion data and provides the analysis results to the user.

[0447] input:

[0448] Training result data and emotion data

[0449] output:

[0450] User Feedback

[0451] Specific behavior:

[0452] The server aggregates the data and generates feedback such as, "Your shoulder flexibility has improved significantly, so you should add strength training. Also, your stress level is high, so you should incorporate relaxation exercises." The feedback is sent to the device, where the user can view it on the screen.

[0453] In this way, the system continuously provides a scientific and emotionally responsive development plan tailored to the user's needs throughout each step.

[0454] (Application example 2)

[0455] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0456] In factory operations and maintenance work, it is difficult to provide appropriate work plans that take into account the emotional states of operators and maintenance staff, resulting in problems such as reduced work efficiency and safety.In addition, the inability to provide feedback or adjust work based on emotions increases operator stress and fatigue, which in the long term has a negative impact on work quality and human resource retention.

[0457] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0458] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, emotion engine means for recognizing and evaluating the emotional state of the user, and adjustment means for adjusting the work plan and the training plan based on the collected emotion data. This makes it possible to grasp the emotional state of operators and maintenance staff in real time and provide feedback and adjust the work plan accordingly.

[0459] The "input means" is a means for inputting the initial data of the object to be raised.

[0460] The "generative AI means" is an artificial intelligence engine that analyzes the input initial data and proposes training plans and work plans.

[0461] The "planning means" is a function for creating training plans and work plans based on the analysis results of the generation AI means.

[0462] The "means of provision" is a means for providing the created training plan or work plan to the user.

[0463] The "feedback input means" is a means for inputting feedback regarding training and work results.

[0464] The "update means" is a means for dynamically updating the training plan and work plan using the input feedback data.

[0465] The "emotion engine means" is an engine for recognizing and evaluating the user's emotional state.

[0466] The "adjustment means" is a means for adjusting work plans and training plans based on the collected emotional data.

[0467] This invention is a system that grasps the emotional state of operators and maintenance staff in real time during factory operations and maintenance work, and provides appropriate work plans and feedback accordingly. The system consists of the following main components:

[0468] Input means: A terminal for users (operators and maintenance staff) to input basic information. For example, a wearable device such as smart glasses is used.

[0469] Generative AI means: A generative AI engine installed on a cloud server. This includes AI models using TensorFlow and PyTorch, and analyzes input data to propose training plans and work plans.

[0470] Planning creation means: A function that automatically creates training plans and work plans based on the analysis results of the generation AI means.

[0471] Provision means: A means for displaying the created training plan or work plan on an information terminal, such as smart glasses, and providing it to the user.

[0472] Feedback input means: A terminal for users to input training results, work results, or feedback.

[0473] Update means: This function dynamically updates the training plan and work plan based on the input feedback data. This is also executed on the cloud server.

[0474] Emotion Engine Means: An engine for recognizing and assessing the user's emotional state. Here, we use OpenCV and dlib libraries for face recognition and emotion analysis.

[0475] Adjustment measures: These are measures for adjusting work plans and development plans based on the collected emotional data.

[0476] Hardware and Software Configuration

[0477] Smart glasses: A device worn by the operator to input basic information, view plans, and detect emotional states. Data is transmitted using the Google Glass API.

[0478] Cloud server: This runs the generative AI, update, planning, and adjustment processes. Cloud services such as AWS and Microsoft Azure are used. TensorFlow and PyTorch are used for the generative AI.

[0479] Sentiment Analysis: Facial recognition and emotion analysis are performed using the camera and microphone of the smart glasses, using OpenCV and dlib libraries.

[0480] Specific examples

[0481] 1. Enter the prompt:

[0482] The operator puts on the smart glasses and enters basic information, such as "Name: Taro Tanaka, Position: Line Operator, Years of Experience: 2 years."

[0483] 2. Data analysis and initial work plan development:

[0484] The cloud server receives the data and the generative AI method proposes a work plan, using example prompts such as:

[0485] Name: Tanaka Taro

[0486] Position: Line Operator

[0487] Years of experience: 2 years

[0488] Initial work: Packaging

[0489] Working time: 1 hour

[0490] Break: 10 minutes

[0491] Emotional state: Normal

[0492] Feedback: None

[0493] 3. Emotion recognition and planning adjustment:

[0494] The emotion engine means analyzes Taro Tanaka's emotional state in real time, and if it detects fatigue, the adjustment means adjusts the plan, for example, by suggesting "add a short break."

[0495] This system provides optimal work plans according to the emotional state of operators and maintenance staff, enabling efficient and safe operations.

[0496] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0497] Step 1:

[0498] The user puts on the smart glasses and enters basic information. The data entered is "name, job title, years of experience." This data is sent from the smart glasses to a cloud server. The data is sent using the Google Glass API.

[0499] Step 2:

[0500] The cloud server validates the initial data received. It uses Pandas and NumPy to check for data consistency and missing values. Once validated, the data is passed to the generation AI method.

[0501] Step 3:

[0502] The generative AI method analyzes the received data and uses TensorFlow and PyTorch to propose an initial work plan based on the operator's capabilities and experience. For example, an operator might generate a plan such as "packaging work for 1 hour, followed by a 10-minute break."

[0503] Step 4:

[0504] The cloud server sends the generated work plan to the smart glasses, which then receives the plan using a REST API and the user confirms it.

[0505] Step 5:

[0506] While working, the user inputs their progress and work results into the smart glasses. Feedback data includes "work progress, current emotional state," etc. This data is then sent back to the cloud server.

[0507] Step 6:

[0508] The cloud server analyzes the received feedback data and instructs the generation AI means to reanalyze it. The work plan is dynamically updated based on the new data. For example, adjustments such as "Progress is going well, so add the next task content" are made.

[0509] Step 7:

[0510] Using the smart glasses' camera and microphone, the emotion engine means analyzes the user's facial expressions and voice. It uses OpenCV for face recognition and dlib library for emotion analysis. For example, "if fatigue is detected, it will suggest a break."

[0511] Step 8:

[0512] The cloud server adjusts the work plan based on the data obtained from the emotion engine means, and the updated work plan is sent to the smart glasses again for the user to confirm. In this way, an optimal work plan according to the user's emotional state is provided in real time.

[0513] This not only allows users to continue working efficiently and safely, but also provides flexible feedback according to their emotional state.

[0514] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0515] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0516] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0517] [Second embodiment]

[0518] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0519] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0520] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0521] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0522] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0523] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0524] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0525] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0526] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0527] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0528] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0529] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0530] This invention relates to a system that supports the development of professional young athletes. This system realizes efficient and scientific development by inputting initial data on the target, analyzing it, creating a plan, and dynamically updating it through feedback.

[0531] System Overview

[0532] The system consists of the following main components:

[0533] Input means: A terminal for the user to input initial data for the object to be trained.

[0534] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[0535] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[0536] Provision means: A terminal for providing the created development plan to the user.

[0537] Feedback input means: A terminal for inputting training results and feedback.

[0538] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[0539] Program processing explanation

[0540] 1. Enter the initial data:

[0541] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[0542] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[0543] 2. Data analysis and initial development plan creation:

[0544] The terminal transmits the input data to the server.

[0545] The server validates the received data (checks for data consistency and missing values).

[0546] The server passes the validated data to the generation AI means, which analyzes the player data.

[0547] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[0548] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[0549] 3. Providing a development plan:

[0550] The server transmits the generated development plan to the terminal.

[0551] The user checks the training plan through the terminal and begins actual training.

[0552] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[0553] 4. Feedback collection and dynamic updates:

[0554] The user periodically inputs training results and player growth data into the terminal.

[0555] The device sends new data to the server.

[0556] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[0557] For example: Your endurance has improved, so you increase your running time and add strength training.

[0558] 5. Providing Feedback:

[0559] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[0560] The server generates the analysis results and provides them to the user via the terminal.

[0561] Example: Your shoulder flexibility has improved significantly, so strength training is recommended as the next step.

[0562] Specific example processing explanation

[0563] Enter the initial data:

[0564] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0565] The terminal transmits this data to the server.

[0566] Data analysis and initial development plan creation:

[0567] The server passes the data to the generating AI means for analysis.

[0568] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[0569] Providing development plans:

[0570] The server transmits the proposed development plan to the terminal.

[0571] The user checks the training plan through the terminal and begins training.

[0572] Collect feedback and dynamically update:

[0573] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[0574] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[0575] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[0576] Providing feedback:

[0577] The server provides the analysis results and gives feedback such as, "Shoulder flexibility has improved significantly. We recommend adding strength training as the next step."

[0578] Users set new goals and adjust their training plans based on feedback.

[0579] In this way, the system supports the development of professional athletes efficiently and scientifically.

[0580] The processing flow will be explained below.

[0581] Step 1:

[0582] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0583] Step 2:

[0584] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[0585] Step 3:

[0586] The server validates the incoming data, specifically checking that the data entered is in the correct format and that all required fields are filled in.

[0587] Step 4:

[0588] The server passes the validated data to the generation AI method, which converts the data into a parsable format (e.g., JSON).

[0589] Step 5:

[0590] The generative AI analyzes the data, for example, determining which training is most appropriate based on a player's "medium shoulder flexibility" or "low endurance" data.

[0591] Step 6:

[0592] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week."

[0593] Step 7:

[0594] The server sends the created development plan to the terminal, where the plan is presented in a format that is easy for the user to understand.

[0595] Step 8:

[0596] The user checks the training plan using a terminal and starts training. The user then performs daily training based on the plan.

[0597] Step 9:

[0598] Users periodically input training results and player growth data into the device, reporting progress such as "shoulder flexibility is improving" or "endurance has improved slightly."

[0599] Step 10:

[0600] The terminal sends new data to the server, and the latest growth data is received by the server.

[0601] Step 11:

[0602] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary corrections or additional training.

[0603] Step 12:

[0604] The server sends the dynamically updated training plan to the device, where the user can confirm the new plan and continue training.

[0605] Step 13:

[0606] The server stores the training results, analyzes long-term growth curves and performance fluctuations, and generates analysis results that are provided to the user via their device.

[0607] Step 14:

[0608] The training plan is readjusted based on the feedback provided by the user from the server, for example, by incorporating new training based on the feedback to optimize the training content.

[0609] In this way, this system supports the development of professional athletes efficiently and scientifically.

[0610] Example 1

[0611] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0612] Conventional youth player development systems only create development plans based on the player's initial data, making it difficult to dynamically reflect subsequent feedback and make appropriate updates. Furthermore, the proposed development plans depended on experience and intuition, making it difficult to provide development based on scientific evidence. This prevented the system from providing optimal development methods tailored to the growth of each individual player, hindering the efficiency of development.

[0613] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0614] In this invention, the server includes a data input means for inputting initial data of the training target, a transmission means for transmitting the input initial data to the server, a validation means for validating the received data, a generation AI means for analyzing the validated data with a generation AI model, a plan creation means for creating a training plan based on the analysis results, a provision means for providing the created training plan to the user, a feedback input means for inputting feedback regarding training, and an update means for dynamically updating the training plan using the input feedback data. This enables scientific and efficient creation of training plans and their dynamic updating.

[0615] "Data input means" refers to an input device or software interface that allows a user to input initial data and basic information about a subject to be raised.

[0616] "Transmission means" refers to a communication device or communication protocol for transmitting the input initial data to the server.

[0617] "Validation means" refers to a process or device that checks the integrity of received data and the presence or absence of missing values, and verifies the validity of the data.

[0618] "Generative AI means" refers to artificial intelligence engines and algorithms that analyze validated data and suggest development points and training methods.

[0619] "Plan creation means" refers to a process or device for creating a development plan based on the analysis results of the generation AI means.

[0620] The "provision means" refers to a communication device or a user interface for providing the created development plan to the user.

[0621] "Feedback input means" refers to a device or software interface for inputting feedback data regarding training.

[0622] "Update means" refers to a process or device for dynamically updating the development plan using input feedback data.

[0623] This invention relates to a system for supporting the development of professional young athletes. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development.

[0624] System Overview

[0625] The system consists of the following main components:

[0626] Data input means: A device such as a PC, tablet, or smartphone is used as a terminal for users to input initial data on the target to be developed.

[0627] Transmission means: The terminal uses a communication protocol (e.g., HTTPS) via the Internet to transmit the input data to the server.

[0628] Validation method: The server performs consistency checks and missing value checks on the received data and uses a validation algorithm to ensure the validity of the data.

[0629] Generative AI method: The generative AI model installed on the server analyzes validated data and suggests training points and methods. The generative AI model used includes deep learning algorithms and machine learning algorithms.

[0630] Plan creation means: The server is equipped with a software module that creates a development plan based on the analysis results of the generation AI means.

[0631] Provision means: In order to provide the created development plan to the user, the server transmits the plan to the terminal, where the user can check the development plan.

[0632] Feedback input means: A terminal is used for users to input feedback data, including training results and player performance data.

[0633] Update means: The server includes a software module for dynamically updating the development plan based on the input feedback data.

[0634] Specific actions

[0635] First, the user inputs the initial data of the player to be trained (e.g., age, height, weight, position, training time, current ability evaluation) into a terminal, which is a data input means, and transmits it to the server using a transmission means. For example, data such as "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance" is input.

[0636] The server checks the received data using validation methods to confirm consistency and missing values. The validated data is then passed to the generative AI method for analysis. The generative AI model extracts points such as "lack of shoulder flexibility" and "need to improve endurance," and suggests specific training menus (e.g., shoulder stretching exercises, running to improve endurance).

[0637] The plan creation means creates a training plan based on the analysis results of the generation AI means, and transmits the plan to the user's terminal via the provision means. The user checks the training plan on the screen of the terminal and starts training. For example, the training plan may include shoulder stretching for 20 minutes every day and running for 30 minutes three times a week.

[0638] The user periodically inputs training results and athlete growth data into the terminal using the feedback input means and sends it to the server. The server reanalyzes this new data using the generation AI means and dynamically updates the development plan. A new development plan is created and sent to the user again via the provision means. For example, the plan may include extending running time and adding strength training because endurance has improved.

[0639] Through this series of processes, this system will efficiently and scientifically support the development of professional athletes.

[0640] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0641] Step 1:

[0642] The user uses the terminal to input the initial data of the player to be trained. The user enters the player's basic information (e.g., age, height, weight, position, current ability evaluation, etc.) into the input form on the terminal and clicks the "Submit" button.

[0643] Input: Initial data such as age, height, weight, position, and current ability rating

[0644] Output: JSON format data packet for sending initial data

[0645] Step 2:

[0646] The terminal sends the input initial data to the server, which then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[0647] Input: Initial data packet in JSON format

[0648] Output: Initial data sent to the server

[0649] Step 3:

[0650] Validate the data received by the server. Parse the data received by the server and check the format and range. Check for syntax errors and missing values ​​and perform consistency checks.

[0651] Input: Initial data sent to the server

[0652] Output: Validated initial data

[0653] Step 4:

[0654] The validated data is passed to the generation AI means to analyze the player data. The server inputs prompts into the generation AI model, which then suggests development points and training methods based on the player's ability, physical strength, and technical level.

[0655] Input: Validated initial data

[0656] Output: Proposals for training points and methods (analysis results)

[0657] Step 5:

[0658] A training plan is created based on the analysis results of the generation AI means. The server's plan creation means creates a specific training plan including the proposed training method and time allocation.

[0659] Input: Analysis results from the generative AI method

[0660] Output: Specific training plan (e.g. shoulder stretching exercises, running menu, etc.)

[0661] Step 6:

[0662] The created development plan is sent to the terminal and provided to the user. The server converts the development plan data into JSON format and sends it to the terminal using the HTTPS protocol. The user checks the development plan on the terminal screen and puts it into action.

[0663] Input: Specific development plan data

[0664] Output: The development plan displayed on the user's device

[0665] Step 7:

[0666] The user periodically inputs training results and player growth data into the terminal. The terminal inputs new data (e.g., training results, current ability evaluation) into the input form and clicks the "Submit" button.

[0667] Input: Training results and player growth data

[0668] Output: New data sent to the server

[0669] Step 8:

[0670] The server then has the generation AI means reanalyze the new data, which then reanalyzes the current ability evaluation and training progress based on the new data, and creates a new development plan.

[0671] Input: New data submitted by the user

[0672] Output: Update points of the re-analyzed breeding plan

[0673] Step 9:

[0674] The server sends the updated training plan to the device and provides it to the user. The new plan data is converted to JSON format and sent to the device using the HTTPS protocol. The user checks the new plan on the device screen and continues training.

[0675] Input: Reanalyzed breeding plan

[0676] Output: The updated development plan displayed on the user's device

[0677] (Application example 1)

[0678] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0679] In the management and operation of robots used in conventional factories, it has been difficult to efficiently and scientifically improve robot performance and create appropriate maintenance plans.In addition, there has been a lack of systems that can dynamically update operation plans based on robot operation results and maintenance history to improve long-term operational efficiency.

[0680] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0681] In this invention, the server includes input means for inputting initial data of the target to be trained, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, database means for managing basic information about the robot, calculation means for proposing performance improvements and maintenance methods for the robot, display means for providing the proposed training plan to the factory manager, and analysis means for analyzing the operation results and maintenance history of the robot and providing long-term operation advice. This enables efficient and scientific management and operation of factory robots.

[0682] "Input means" refers to a terminal for inputting basic information and initial data about the robot into the system.

[0683] The "generative AI means" is an artificial intelligence engine that analyzes input data and proposes methods for improving the robot's performance and maintaining it.

[0684] "Plan creation means" refers to the function of creating specific development and maintenance plans based on the analysis results proposed by the generation AI means.

[0685] The "provision means" refers to a terminal or system for providing the created training plan to the factory manager or user.

[0686] "Feedback input means" refers to a terminal or method for inputting feedback data related to training and maintenance into the system.

[0687] The "update means" is a part of the system that has the function of dynamically updating the development plan using input feedback data.

[0688] "Database means" refers to a system or server for managing and storing basic information, operation history, and maintenance history of the robot.

[0689] The "computing means" is a part of the system that has the function of performing the calculations and analyses necessary to improve the robot's performance and propose maintenance methods.

[0690] The "display means" refers to a screen or terminal that provides the proposed training plan or maintenance plan in a visible format to the factory manager.

[0691] The "analysis means" is a part of the system that has the function of analyzing the robot's operational results and maintenance history and providing long-term operational advice.

[0692] This invention relates to a system for efficiently and scientifically training and maintaining robots used in factories. How this system can be implemented will be described below in detail.

[0693] System Overview

[0694] The system consists of the following main components:

[0695] Input means: A tablet terminal or computer is used as a terminal for users to input initial data (model number, usage time, current status, maintenance history, etc.) of the robots used in the factory.

[0696] Generative AI method: The AI ​​engine installed on the server analyzes the input data and proposes methods for improving the robot's performance and maintenance. Specifically, a generative AI model is built using TensorFlow.

[0697] Planning means: This is a function that the server uses to create specific training and maintenance plans based on the analysis results of the generation AI means. This part is implemented in Python.

[0698] Provision method: The server sends the training plan created to a display device such as a tablet or computer, where the factory manager can check it.

[0699] Feedback input means: A means for the user to input the robot's operation results and maintenance history into the terminal and send them to the server.

[0700] Update method: The server has the function to dynamically update the development plan based on the input feedback data. This function is also implemented in Python.

[0701] Processing description of the embodiment

[0702] Entering initial data

[0703] The user uses a tablet device to input basic information about the robot to be used in the factory.

[0704] Example: Model R-3000, usage time 1200 hours, operating speed medium, error frequency low.

[0705] Data analysis and creation of initial development plan

[0706] The server receives the input data and analyzes it using a generative AI means. The generative AI means uses a generative AI model using TensorFlow. As a result of the analysis, suggestions for improving the robot's performance (e.g., recommended part replacements to increase operating speed, software updates to reduce error frequency) are derived.

[0707] Example prompt: "Model: R-3000, Usage time: 1200 hours, Operating speed: Medium, Error frequency: Low"

[0708] Providing a development plan

[0709] The server then sends the generated training plan to the user's device, where the administrator can check the plan on a tablet or computer and begin specific training and maintenance work.

[0710] Collect feedback and dynamically update

[0711] Users periodically input the robot's operational results (e.g., improvement in operating speed after part replacement) and maintenance history and send them to the server. The server then uses the received data to reanalyze the AI ​​model and create a new, updated training plan.

[0712] Providing Feedback

[0713] The server generates long-term operational advice based on the accumulated operational data and provides it to the user, providing specific advice to further improve the long-term operational efficiency of the robot.

[0714] This system will enable efficient and scientific management and operation of robots within factories.

[0715] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0716] Step 1:

[0717] The user uses a tablet terminal or computer to input the initial data of the robot to be used in the factory (e.g., model number, usage time, current status, maintenance history). The input data is sent from the terminal to the server. The input in this step is basic information about the robot, and the output is the initial data sent to the server. Specifically, the user enters the necessary information on the input screen and presses the "Send" button.

[0718] Step 2:

[0719] The server validates the initial data it receives (checking for data consistency and missing values). The input to this step is the initial data sent by the user, and the output is the data whose consistency has been verified. Specifically, the server uses a Python script to verify the format of the data and check for inconsistencies.

[0720] Step 3:

[0721] The server passes the data whose integrity has been confirmed to the generative AI means for analysis. The generative AI means uses a generative AI model implemented in TensorFlow. The input in this step is the data whose integrity has been confirmed, and the output is the analysis result. Specifically, the server passes the input data to the TensorFlow model and obtains the analysis result.

[0722] Step 4:

[0723] The server creates specific training and maintenance plans based on the analysis results of the generation AI means. The input in this step is the analysis results, and the output is the training plan. Specifically, the server uses a Python script to generate a training plan based on the proposal.

[0724] Step 5:

[0725] The server sends the created training plan to a terminal and provides its contents to the factory manager. The input in this step is the training plan, and the output is the training plan displayed to the manager. Specifically, the server converts the training plan into a display format and sends it to the manager's terminal.

[0726] Step 6:

[0727] The user periodically inputs the robot's operation results and maintenance history into the terminal and sends it to the server. The input in this step is the latest data on the robot, and the output is the operation results and maintenance history sent to the server. Specifically, the user enters the operation results and history information into the input form and presses the "Send" button.

[0728] Step 7:

[0729] The server reanalyzes the received feedback data using the generation AI method to create a new and dynamically updated training plan. The input in this step is the feedback data, and the output is the updated training plan. Specifically, the server inputs the new data into the TensorFlow model and regenerates the training plan based on the results.

[0730] Step 8:

[0731] The server generates long-term operational advice based on the accumulated operational data and provides it to the user. The input in this step is the accumulation of feedback data, and the output is long-term operational advice. Specifically, the server analyzes the data using a Python script and generates advice.

[0732] By following these steps, we will create a system that efficiently and scientifically trains and maintains robots in factories.

[0733] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0734] This invention relates to a system that supports the development of professional young athletes by combining it with an emotion engine that recognizes the user's emotions. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development and providing feedback and adjustments based on the user's emotional state.

[0735] System Overview

[0736] The system consists of the following main components:

[0737] Input means: A terminal for the user to input initial data for the object to be trained.

[0738] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[0739] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[0740] Provision means: A terminal for providing the created development plan to the user.

[0741] Feedback input means: A terminal for inputting training results and feedback.

[0742] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[0743] Emotion engine: A function that recognizes and analyzes the user's emotional state and reflects it in training plans and feedback.

[0744] Program processing explanation

[0745] 1. Enter the initial data:

[0746] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[0747] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[0748] 2. Data analysis and initial development plan creation:

[0749] The terminal transmits the input data to the server.

[0750] The server validates the received data (checks for data consistency and missing values).

[0751] The server passes the validated data to the generation AI means, which analyzes the player data.

[0752] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[0753] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[0754] 3. Providing a development plan:

[0755] The server transmits the generated development plan to the terminal.

[0756] The user checks the training plan through the terminal and begins actual training.

[0757] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[0758] 4. Feedback collection and dynamic updates:

[0759] The user periodically inputs training results and player growth data into the terminal.

[0760] The device sends new data to the server.

[0761] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[0762] For example: Your endurance has improved, so you increase your running time and add strength training.

[0763] 5. Leveraging the Emotion Engine:

[0764] The server uses an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[0765] For example, if a user is feeling stressed during a workout, the emotion engine will detect this and make adjustments such as incorporating stretching exercises.

[0766] The emotion engine provides feedback to the generative AI, which then uses it as data to create an appropriate training plan.

[0767] Example: If the user is feeling unmotivated, provide motivational feedback and adjust the training plan.

[0768] 6. Providing Feedback:

[0769] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[0770] The server integrates the analysis results with the emotion engine data and provides it to the user via their device.

[0771] Example: Shoulder flexibility has improved significantly, so strength training is recommended as the next step, or if the user's stress levels are high, add relaxation exercises.

[0772] Specific example processing explanation

[0773] Enter the initial data:

[0774] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0775] The terminal transmits this data to the server.

[0776] Data analysis and initial development plan creation:

[0777] The server passes the data to the generating AI means for analysis.

[0778] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[0779] Providing development plans:

[0780] The server transmits the proposed development plan to the terminal.

[0781] The user checks the training plan through the terminal and begins training.

[0782] Collect feedback and dynamically update:

[0783] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[0784] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[0785] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[0786] Leveraging the Emotion Engine:

[0787] The emotion engine analyzes the user's facial expressions and voice to detect high stress levels.

[0788] The server uses data from the emotion engine to adjust the training plan and make suggestions, including stretching and resting to reduce stress.

[0789] Providing feedback:

[0790] Based on the analysis results and data from the emotion engine, the server provides feedback such as, "Shoulder flexibility has improved significantly. The next step is to add strength training. Also, as stress levels are high, we recommend relaxation exercises."

[0791] Users set new goals and adjust their training plans based on feedback.

[0792] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[0793] The processing flow will be explained below.

[0794] Step 1:

[0795] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[0796] Step 2:

[0797] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[0798] Step 3:

[0799] The server validates the incoming data, specifically checking that the data is in the correct format and that all required fields are filled in. If the validation passes, the data is passed to the next step.

[0800] Step 4:

[0801] The server passes the validated data to the generation AI method, which converts the data into a parseable format (e.g., JSON).

[0802] Step 5:

[0803] The AI ​​generator analyzes the data. For example, it determines which training is most appropriate based on data such as a player's "medium shoulder flexibility" or "low endurance." Specifically, it thoroughly analyzes the player's abilities and technical level to identify areas for development.

[0804] Step 6:

[0805] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week." This plan is optimized for each individual player.

[0806] Step 7:

[0807] The server sends the created training plan to the terminal. The plan is presented to the user in a format that is easy to understand. Specifically, individual training menus are detailed by date and time.

[0808] Step 8:

[0809] The user checks the training plan on the device and starts training. The user then performs daily training based on the plan. Progress can be checked in real time on the device.

[0810] Step 9:

[0811] The user periodically inputs training results and player growth data into the device. For example, progress such as "shoulder flexibility has improved slightly" or "endurance has improved to a moderate level" is reported.

[0812] Step 10:

[0813] The device sends new data to the server. The latest growth data is received by the server. Data is sent and received in real time, allowing for rapid updates.

[0814] Step 11:

[0815] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary adjustments or additions to training. For example, if endurance has improved, the running time can be extended or strength training can be added.

[0816] Step 12:

[0817] The server sends a dynamically updated training plan to the device. The user confirms the new plan and continues training. The plan is always adjusted based on the latest data.

[0818] Step 13:

[0819] The server uses an emotion engine to analyze the user's facial expressions and voice data to recognize their emotional state. Emotional data is collected while the user is training using the device, for example, to detect the user's stress level and motivation.

[0820] Step 14:

[0821] The emotion engine provides collected emotional data to the generative AI, which then uses this data to further adjust the training plan. For example, if the user is feeling stressed, the AI ​​may add stretching exercises or recommend resting.

[0822] Step 15:

[0823] The server uses the data from the emotion engine to provide appropriate feedback to the user, such as, "Your shoulder flexibility has improved significantly. Your next step is to add strength training. Also, your stress level is high, so we recommend you do some relaxation exercises."

[0824] Step 16:

[0825] Based on the feedback provided by the user, the training plan is adjusted, for example, by incorporating new training items based on the feedback, optimizing the training content.

[0826] This system will efficiently and scientifically support professional athlete development, while also providing training plans and feedback tailored to the user's emotional state.

[0827] Example 2

[0828] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0829] Conventional training systems create training plans based on basic player information and training data, but lack the functionality to dynamically adjust feedback based on the user's emotional state. This makes it difficult to provide training plans that reflect individual needs and adaptations based on the user's emotional state.

[0830] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0831] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, emotion engine means for recognizing and analyzing emotion data, generation AI means for adjusting a training plan based on the emotion data, plan creation means for creating a training plan, provision means for providing the created training plan, feedback input means for inputting feedback regarding training, and update means for dynamically updating the training plan using the input feedback data. This makes it possible to create an individually optimized training plan based on the user's emotional state and feedback data.

[0832] 1. "Development target" refers to young players who are the subject of training and instruction.

[0833] 2. "Input means" refers to the device or interface through which the user inputs initial data and feedback on the subject to be developed.

[0834] 3. "Generative AI means" refers to the artificial intelligence functions used to analyze input data and create development plans and suggest training methods.

[0835] 4. "Plan creation means" refers to the function of creating a specific development plan based on the analysis results of the generation AI means.

[0836] 5. "Provision means" refers to the device or interface used to present the created development plan to the user.

[0837] 6. "Feedback input means" refers to a device or interface that allows users to input training results and player growth data.

[0838] 7. "Update means" refers to the function of dynamically adjusting and updating the development plan based on feedback data.

[0839] 8. "Emotion engine means" refers to the function for recognizing and analyzing the user's emotional state and reflecting that data in training plans and feedback.

[0840] 9. "Emotional Data" refers to data relating to emotions collected from the user's facial expressions, voice, etc.

[0841] MODE FOR CARRYING OUT THE INVENTION

[0842] This invention is a system that combines an emotion engine with a system that supports the development of professional young athletes. This system allows for the efficient and scientific creation of training plans for athletes, and also allows for feedback and adjustments based on the user's emotional state. This system is realized by inputting initial data on the training target, analyzing it, creating a plan, and dynamically updating it through feedback.

[0843] Hardware Configuration

[0844] The system consists of the following main components:

[0845] 1. Input means: A device (e.g., PC, tablet, smartphone) through which the user inputs the initial data of the target to be developed.

[0846] 2. Generative AI means: An artificial intelligence engine (e.g., TensorFlow, PyTorch) that analyzes the data input by the server and suggests development points and training methods.

[0847] 3. Planning means: A function in which the server creates a training plan based on the analysis results of the generated AI means.

[0848] 4. Means of provision: A device (e.g., PC, tablet, smartphone) for providing the created development plan to the user.

[0849] 5. Feedback input means: A device (e.g., PC, tablet, smartphone) for inputting training results and feedback.

[0850] 6. Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[0851] 7. Emotion engine means: A function to recognize and analyze the user's emotional state and reflect it in training plans and feedback (e.g., emotion recognition API, facial expression analysis software).

[0852] Program processing

[0853] Entering initial data

[0854] The user uses the terminal to input basic information about the player (e.g., age, height, weight, position, current ability evaluation, etc.). For example, the user enters "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance." The terminal then sends this information to the server.

[0855] Data analysis and creation of initial development plan

[0856] The server validates the received data (checking for data consistency and missing values). The validated data is passed to the generation AI means, which analyzes the player data. The generation AI then suggests development points and training methods based on the player's ability, physical strength, and technical level. For example, it creates a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[0857] Providing a development plan

[0858] The server sends the generated training plan to the device. The user confirms the plan through the device and starts the actual training. For example, the plan may include 20 minutes of shoulder stretching every day and 30 minutes of running three times a week.

[0859] Collect feedback and dynamically update

[0860] The user periodically inputs training results and athlete growth data into the device. The device sends the new data to the server, which instructs the generation AI means to reanalyze it. The generation AI dynamically updates the development plan based on the new data. For example, as endurance has improved, running time could be extended and strength training added.

[0861] Utilizing the Emotion Engine

[0862] The server uses an emotion engine to analyze the user's facial expressions and voice to collect emotional data. For example, if the user feels stressed during training, the emotion engine can detect this and make adjustments such as incorporating stretching exercises. The emotion engine provides feedback to the generative AI, which then uses the data to create an appropriate training plan.

[0863] Providing Feedback

[0864] The server accumulates training results and analyzes long-term growth curves and performance fluctuations. The server then integrates the analysis results with data from the emotion engine and provides the results to the user via their device. For example, if shoulder flexibility has improved significantly, strength training may be recommended as the next step. Also, if the user's stress level is high, relaxation exercises may be added.

[0865] Examples of prompt statements

[0866] An example prompt might be, "Please suggest the best training plan for this athlete."

[0867] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[0868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0869] Step 1: Enter initial data

[0870] explanation:

[0871] The user uses a terminal to input basic information about the player to be developed, such as the player's age, height, weight, position, and current ability rating.

[0872] input:

[0873] Basic information (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[0874] output:

[0875] Initial data sent to the server

[0876] Specific behavior:

[0877] When the user enters the required basic information into the form on the terminal and clicks the "Submit" button, the terminal transmits this data to the server.

[0878] Step 2: Data analysis

[0879] explanation:

[0880] Validate the data received by the server, checking for data consistency and missing values.

[0881] input:

[0882] Initial data (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[0883] output:

[0884] Validated initial data

[0885] Specific behavior:

[0886] The server validates the initial data against the database schema, checking for missing values ​​and inconsistencies, and generates error messages as needed.

[0887] Step 3: Create an initial development plan

[0888] explanation:

[0889] The server passes the validated data to the generation AI means, which creates the outline of a development plan.

[0890] input:

[0891] Validated initial data

[0892] output:

[0893] Initial Development Plan

[0894] Specific behavior:

[0895] The server sends a prompt to the AI ​​generator saying, "Please suggest the best training plan for this athlete," and the AI ​​generator creates an appropriate training menu. For example, it might suggest "30 minutes of stretching exercises every day" or "running three times a week."

[0896] Step 4: Provide a development plan

[0897] explanation:

[0898] The server provides the user with suggestions from the generative AI means.

[0899] input:

[0900] Initial Development Plan

[0901] output:

[0902] Displaying training plans to users

[0903] Specific behavior:

[0904] The server sends the generated training plan to the terminal, which displays it. The user can then check the plan details on the terminal screen and begin training.

[0905] Step 5: Gather feedback

[0906] explanation:

[0907] The user periodically inputs training results and impressions into the terminal.

[0908] input:

[0909] User feedback data (e.g., improved endurance, improved shoulder flexibility, etc.)

[0910] output:

[0911] Feedback data sent to the server

[0912] Specific behavior:

[0913] When the user enters the training results into the feedback form on the device and clicks the "Submit" button, the device sends the data to the server.

[0914] Step 6: Dynamic updates based on feedback

[0915] explanation:

[0916] The server instructs the generation AI means to reanalyze based on the feedback data and creates a new development plan.

[0917] input:

[0918] Feedback Data

[0919] output:

[0920] Updated Development Plan

[0921] Specific behavior:

[0922] The server collects the feedback data and sends a prompt to the generation AI saying, "Please update the training plan based on the new data." The generation AI then creates a new training menu, which the server provides to the user.

[0923] Step 7: Collect and analyze emotion data

[0924] explanation:

[0925] The server uses an emotion engine means to analyze the user's facial expressions and voice and collect emotion data.

[0926] input:

[0927] User facial expression and voice data

[0928] output:

[0929] Emotional Data

[0930] Specific behavior:

[0931] The emotion engine uses facial expression recognition and voice analysis technology to collect emotional data such as stress and motivation. The server aggregates the emotional data and provides it to the generation AI.

[0932] Step 8: Adjust based on sentiment data

[0933] explanation:

[0934] The server further adjusts the training plan based on the emotional data.

[0935] input:

[0936] Emotional Data

[0937] output:

[0938] Coordinated Development Plan

[0939] Specific behavior:

[0940] The server uses emotional data such as "The user has been feeling stressed recently" to send a prompt to the AI ​​generator, such as "Please add exercises to reduce stress." The AI ​​generator then makes specific suggestions, such as "Add a yoga menu for relaxation."

[0941] Step 9: Provide feedback

[0942] explanation:

[0943] The server integrates the training results with the emotion data and provides the analysis results to the user.

[0944] input:

[0945] Training result data and emotion data

[0946] output:

[0947] User Feedback

[0948] Specific behavior:

[0949] The server aggregates the data and generates feedback such as, "Your shoulder flexibility has improved significantly, so you should add strength training. Also, your stress level is high, so you should incorporate relaxation exercises." The feedback is sent to the device, where the user can view it on the screen.

[0950] In this way, the system continuously provides a scientific and emotionally responsive development plan tailored to the user's needs throughout each step.

[0951] (Application example 2)

[0952] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0953] In factory operations and maintenance work, it is difficult to provide appropriate work plans that take into account the emotional states of operators and maintenance staff, resulting in problems such as reduced work efficiency and safety.In addition, the inability to provide feedback or adjust work based on emotions increases operator stress and fatigue, which in the long term has a negative impact on work quality and human resource retention.

[0954] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0955] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, emotion engine means for recognizing and evaluating the emotional state of the user, and adjustment means for adjusting the work plan and the training plan based on the collected emotion data. This makes it possible to grasp the emotional state of operators and maintenance staff in real time and provide feedback and adjust the work plan accordingly.

[0956] The "input means" is a means for inputting the initial data of the object to be raised.

[0957] The "generative AI means" is an artificial intelligence engine that analyzes the input initial data and proposes training plans and work plans.

[0958] The "planning means" is a function for creating training plans and work plans based on the analysis results of the generation AI means.

[0959] The "means of provision" is a means for providing the created training plan or work plan to the user.

[0960] The "feedback input means" is a means for inputting feedback regarding training and work results.

[0961] The "update means" is a means for dynamically updating the training plan and work plan using the input feedback data.

[0962] The "emotion engine means" is an engine for recognizing and evaluating the user's emotional state.

[0963] The "adjustment means" is a means for adjusting work plans and training plans based on the collected emotional data.

[0964] This invention is a system that grasps the emotional state of operators and maintenance staff in real time during factory operations and maintenance work, and provides appropriate work plans and feedback accordingly. The system consists of the following main components:

[0965] Input means: A terminal for users (operators and maintenance staff) to input basic information. For example, a wearable device such as smart glasses is used.

[0966] Generative AI means: A generative AI engine installed on a cloud server. This includes AI models using TensorFlow and PyTorch, and analyzes input data to propose training plans and work plans.

[0967] Planning creation means: A function that automatically creates training plans and work plans based on the analysis results of the generation AI means.

[0968] Provision means: A means for displaying the created training plan or work plan on an information terminal, such as smart glasses, and providing it to the user.

[0969] Feedback input means: A terminal for users to input training results, work results, or feedback.

[0970] Update means: This function dynamically updates the training plan and work plan based on the input feedback data. This is also executed on the cloud server.

[0971] Emotion Engine Means: An engine for recognizing and assessing the user's emotional state. Here, we use OpenCV and dlib libraries for face recognition and emotion analysis.

[0972] Adjustment measures: These are measures for adjusting work plans and development plans based on the collected emotional data.

[0973] Hardware and Software Configuration

[0974] Smart glasses: A device worn by the operator to input basic information, view plans, and detect emotional states. Data is transmitted using the Google Glass API.

[0975] Cloud server: This runs the generative AI, update, planning, and adjustment processes. Cloud services such as AWS and Microsoft Azure are used. TensorFlow and PyTorch are used for the generative AI.

[0976] Sentiment Analysis: Facial recognition and emotion analysis are performed using the camera and microphone of the smart glasses, using OpenCV and dlib libraries.

[0977] Specific examples

[0978] 1. Enter the prompt:

[0979] The operator puts on the smart glasses and enters basic information, such as "Name: Taro Tanaka, Position: Line Operator, Years of Experience: 2 years."

[0980] 2. Data analysis and initial work plan development:

[0981] The cloud server receives the data and the generative AI method proposes a work plan, using example prompts such as:

[0982] Name: Tanaka Taro

[0983] Position: Line Operator

[0984] Years of experience: 2 years

[0985] Initial work: Packaging

[0986] Working time: 1 hour

[0987] Break: 10 minutes

[0988] Emotional state: Normal

[0989] Feedback: None

[0990] 3. Emotion recognition and planning adjustment:

[0991] The emotion engine means analyzes Taro Tanaka's emotional state in real time, and if it detects fatigue, the adjustment means adjusts the plan, for example, by suggesting "add a short break."

[0992] This system provides optimal work plans according to the emotional state of operators and maintenance staff, enabling efficient and safe operations.

[0993] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0994] Step 1:

[0995] The user puts on the smart glasses and enters basic information. The data entered is "name, job title, years of experience." This data is sent from the smart glasses to a cloud server. The data is sent using the Google Glass API.

[0996] Step 2:

[0997] The cloud server validates the initial data received. It uses Pandas and NumPy to check for data consistency and missing values. Once validated, the data is passed to the generation AI method.

[0998] Step 3:

[0999] The generative AI method analyzes the received data and uses TensorFlow and PyTorch to propose an initial work plan based on the operator's capabilities and experience. For example, an operator might generate a plan such as "packaging work for 1 hour, followed by a 10-minute break."

[1000] Step 4:

[1001] The cloud server sends the generated work plan to the smart glasses, which then receives the plan using a REST API and the user confirms it.

[1002] Step 5:

[1003] While working, the user inputs their progress and work results into the smart glasses. Feedback data includes "work progress, current emotional state," etc. This data is then sent back to the cloud server.

[1004] Step 6:

[1005] The cloud server analyzes the received feedback data and instructs the generation AI means to reanalyze it. The work plan is dynamically updated based on the new data. For example, adjustments such as "Progress is going well, so add the next task content" are made.

[1006] Step 7:

[1007] Using the smart glasses' camera and microphone, the emotion engine means analyzes the user's facial expressions and voice. It uses OpenCV for face recognition and dlib library for emotion analysis. For example, "if fatigue is detected, it will suggest a break."

[1008] Step 8:

[1009] The cloud server adjusts the work plan based on the data obtained from the emotion engine means, and the updated work plan is sent to the smart glasses again for the user to confirm. In this way, an optimal work plan according to the user's emotional state is provided in real time.

[1010] This not only allows users to continue working efficiently and safely, but also provides flexible feedback according to their emotional state.

[1011] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1012] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1013] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1014] [Third embodiment]

[1015] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1016] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1018] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1019] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1020] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1022] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1023] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1025] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1026] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1027] This invention relates to a system that supports the development of professional young athletes. This system realizes efficient and scientific development by inputting initial data on the target, analyzing it, creating a plan, and dynamically updating it through feedback.

[1028] System Overview

[1029] The system consists of the following main components:

[1030] Input means: A terminal for the user to input initial data for the object to be trained.

[1031] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[1032] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[1033] Provision means: A terminal for providing the created development plan to the user.

[1034] Feedback input means: A terminal for inputting training results and feedback.

[1035] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[1036] Program processing explanation

[1037] 1. Enter the initial data:

[1038] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[1039] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[1040] 2. Data analysis and initial development plan creation:

[1041] The terminal transmits the input data to the server.

[1042] The server validates the received data (checks for data consistency and missing values).

[1043] The server passes the validated data to the generation AI means, which analyzes the player data.

[1044] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[1045] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[1046] 3. Providing a development plan:

[1047] The server transmits the generated development plan to the terminal.

[1048] The user checks the training plan through the terminal and begins actual training.

[1049] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[1050] 4. Feedback collection and dynamic updates:

[1051] The user periodically inputs training results and player growth data into the terminal.

[1052] The device sends new data to the server.

[1053] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[1054] For example: Your endurance has improved, so you increase your running time and add strength training.

[1055] 5. Providing Feedback:

[1056] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[1057] The server generates the analysis results and provides them to the user via the terminal.

[1058] Example: Your shoulder flexibility has improved significantly, so strength training is recommended as the next step.

[1059] Specific example processing explanation

[1060] Enter the initial data:

[1061] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1062] The terminal transmits this data to the server.

[1063] Data analysis and initial development plan creation:

[1064] The server passes the data to the generating AI means for analysis.

[1065] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[1066] Providing development plans:

[1067] The server transmits the proposed development plan to the terminal.

[1068] The user checks the training plan through the terminal and begins training.

[1069] Collect feedback and dynamically update:

[1070] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[1071] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[1072] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[1073] Providing feedback:

[1074] The server provides the analysis results and gives feedback such as, "Shoulder flexibility has improved significantly. We recommend adding strength training as the next step."

[1075] Users set new goals and adjust their training plans based on feedback.

[1076] In this way, the system supports the development of professional athletes efficiently and scientifically.

[1077] The processing flow will be explained below.

[1078] Step 1:

[1079] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1080] Step 2:

[1081] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[1082] Step 3:

[1083] The server validates the incoming data, specifically checking that the data entered is in the correct format and that all required fields are filled in.

[1084] Step 4:

[1085] The server passes the validated data to the generation AI method, which converts the data into a parsable format (e.g., JSON).

[1086] Step 5:

[1087] The generative AI analyzes the data, for example, determining which training is most appropriate based on a player's "medium shoulder flexibility" or "low endurance" data.

[1088] Step 6:

[1089] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week."

[1090] Step 7:

[1091] The server sends the created development plan to the terminal, where the plan is presented in a format that is easy for the user to understand.

[1092] Step 8:

[1093] The user checks the training plan using a terminal and starts training. The user then performs daily training based on the plan.

[1094] Step 9:

[1095] Users periodically input training results and player growth data into the device, reporting progress such as "shoulder flexibility is improving" or "endurance has improved slightly."

[1096] Step 10:

[1097] The terminal sends new data to the server, and the latest growth data is received by the server.

[1098] Step 11:

[1099] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary corrections or additional training.

[1100] Step 12:

[1101] The server sends the dynamically updated training plan to the device, where the user can confirm the new plan and continue training.

[1102] Step 13:

[1103] The server stores the training results, analyzes long-term growth curves and performance fluctuations, and generates analysis results that are provided to the user via their device.

[1104] Step 14:

[1105] The training plan is readjusted based on the feedback provided by the user from the server, for example, by incorporating new training based on the feedback to optimize the training content.

[1106] In this way, this system supports the development of professional athletes efficiently and scientifically.

[1107] Example 1

[1108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1109] Conventional youth player development systems only create development plans based on the player's initial data, making it difficult to dynamically reflect subsequent feedback and make appropriate updates. Furthermore, the proposed development plans depended on experience and intuition, making it difficult to provide development based on scientific evidence. This prevented the system from providing optimal development methods tailored to the growth of each individual player, hindering the efficiency of development.

[1110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1111] In this invention, the server includes a data input means for inputting initial data of the training target, a transmission means for transmitting the input initial data to the server, a validation means for validating the received data, a generation AI means for analyzing the validated data with a generation AI model, a plan creation means for creating a training plan based on the analysis results, a provision means for providing the created training plan to the user, a feedback input means for inputting feedback regarding training, and an update means for dynamically updating the training plan using the input feedback data. This enables scientific and efficient creation of training plans and their dynamic updating.

[1112] "Data input means" refers to an input device or software interface that allows a user to input initial data and basic information about a subject to be raised.

[1113] "Transmission means" refers to a communication device or communication protocol for transmitting the input initial data to the server.

[1114] "Validation means" refers to a process or device that checks the integrity of received data and the presence or absence of missing values, and verifies the validity of the data.

[1115] "Generative AI means" refers to artificial intelligence engines and algorithms that analyze validated data and suggest development points and training methods.

[1116] "Plan creation means" refers to a process or device for creating a development plan based on the analysis results of the generation AI means.

[1117] The "provision means" refers to a communication device or a user interface for providing the created development plan to the user.

[1118] "Feedback input means" refers to a device or software interface for inputting feedback data regarding training.

[1119] "Update means" refers to a process or device for dynamically updating the development plan using input feedback data.

[1120] This invention relates to a system for supporting the development of professional young athletes. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development.

[1121] System Overview

[1122] The system consists of the following main components:

[1123] Data input means: A device such as a PC, tablet, or smartphone is used as a terminal for users to input initial data on the target to be developed.

[1124] Transmission means: The terminal uses a communication protocol (e.g., HTTPS) via the Internet to transmit the input data to the server.

[1125] Validation method: The server performs consistency checks and missing value checks on the received data and uses a validation algorithm to ensure the validity of the data.

[1126] Generative AI method: The generative AI model installed on the server analyzes validated data and suggests training points and methods. The generative AI model used includes deep learning algorithms and machine learning algorithms.

[1127] Plan creation means: The server is equipped with a software module that creates a development plan based on the analysis results of the generation AI means.

[1128] Provision means: In order to provide the created development plan to the user, the server transmits the plan to the terminal, where the user can check the development plan.

[1129] Feedback input means: A terminal is used for users to input feedback data, including training results and player performance data.

[1130] Update means: The server includes a software module for dynamically updating the development plan based on the input feedback data.

[1131] Specific actions

[1132] First, the user inputs the initial data of the player to be trained (e.g., age, height, weight, position, training time, current ability evaluation) into a terminal, which is a data input means, and transmits it to the server using a transmission means. For example, data such as "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance" is input.

[1133] The server checks the received data using validation methods to confirm consistency and missing values. The validated data is then passed to the generative AI method for analysis. The generative AI model extracts points such as "lack of shoulder flexibility" and "need to improve endurance," and suggests specific training menus (e.g., shoulder stretching exercises, running to improve endurance).

[1134] The plan creation means creates a training plan based on the analysis results of the generation AI means, and transmits the plan to the user's terminal via the provision means. The user checks the training plan on the screen of the terminal and starts training. For example, the training plan may include shoulder stretching for 20 minutes every day and running for 30 minutes three times a week.

[1135] The user periodically inputs training results and athlete growth data into the terminal using the feedback input means and sends it to the server. The server reanalyzes this new data using the generation AI means and dynamically updates the development plan. A new development plan is created and sent to the user again via the provision means. For example, the plan may include extending running time and adding strength training because endurance has improved.

[1136] Through this series of processes, this system will efficiently and scientifically support the development of professional athletes.

[1137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1138] Step 1:

[1139] The user uses the terminal to input the initial data of the player to be trained. The user enters the player's basic information (e.g., age, height, weight, position, current ability evaluation, etc.) into the input form on the terminal and clicks the "Submit" button.

[1140] Input: Initial data such as age, height, weight, position, and current ability rating

[1141] Output: JSON format data packet for sending initial data

[1142] Step 2:

[1143] The terminal sends the input initial data to the server, which then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[1144] Input: Initial data packet in JSON format

[1145] Output: Initial data sent to the server

[1146] Step 3:

[1147] Validate the data received by the server. Parse the data received by the server and check the format and range. Check for syntax errors and missing values ​​and perform consistency checks.

[1148] Input: Initial data sent to the server

[1149] Output: Validated initial data

[1150] Step 4:

[1151] The validated data is passed to the generation AI means to analyze the player data. The server inputs prompts into the generation AI model, which then suggests development points and training methods based on the player's ability, physical strength, and technical level.

[1152] Input: Validated initial data

[1153] Output: Proposals for training points and methods (analysis results)

[1154] Step 5:

[1155] A training plan is created based on the analysis results of the generation AI means. The server's plan creation means creates a specific training plan including the proposed training method and time allocation.

[1156] Input: Analysis results from the generative AI method

[1157] Output: Specific training plan (e.g. shoulder stretching exercises, running menu, etc.)

[1158] Step 6:

[1159] The created development plan is sent to the terminal and provided to the user. The server converts the development plan data into JSON format and sends it to the terminal using the HTTPS protocol. The user checks the development plan on the terminal screen and puts it into action.

[1160] Input: Specific development plan data

[1161] Output: The development plan displayed on the user's device

[1162] Step 7:

[1163] The user periodically inputs training results and player growth data into the terminal. The terminal inputs new data (e.g., training results, current ability evaluation) into the input form and clicks the "Submit" button.

[1164] Input: Training results and player growth data

[1165] Output: New data sent to the server

[1166] Step 8:

[1167] The server then has the generation AI means reanalyze the new data, which then reanalyzes the current ability evaluation and training progress based on the new data, and creates a new development plan.

[1168] Input: New data submitted by the user

[1169] Output: Update points of the re-analyzed breeding plan

[1170] Step 9:

[1171] The server sends the updated training plan to the device and provides it to the user. The new plan data is converted to JSON format and sent to the device using the HTTPS protocol. The user checks the new plan on the device screen and continues training.

[1172] Input: Reanalyzed breeding plan

[1173] Output: The updated development plan displayed on the user's device

[1174] (Application example 1)

[1175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1176] In the management and operation of robots used in conventional factories, it has been difficult to efficiently and scientifically improve robot performance and create appropriate maintenance plans.In addition, there has been a lack of systems that can dynamically update operation plans based on robot operation results and maintenance history to improve long-term operational efficiency.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1178] In this invention, the server includes input means for inputting initial data of the target to be trained, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, database means for managing basic information about the robot, calculation means for proposing performance improvements and maintenance methods for the robot, display means for providing the proposed training plan to the factory manager, and analysis means for analyzing the operation results and maintenance history of the robot and providing long-term operation advice. This enables efficient and scientific management and operation of factory robots.

[1179] "Input means" refers to a terminal for inputting basic information and initial data about the robot into the system.

[1180] The "generative AI means" is an artificial intelligence engine that analyzes input data and proposes methods for improving the robot's performance and maintaining it.

[1181] "Plan creation means" refers to the function of creating specific development and maintenance plans based on the analysis results proposed by the generation AI means.

[1182] The "provision means" refers to a terminal or system for providing the created training plan to the factory manager or user.

[1183] "Feedback input means" refers to a terminal or method for inputting feedback data related to training and maintenance into the system.

[1184] The "update means" is a part of the system that has the function of dynamically updating the development plan using input feedback data.

[1185] "Database means" refers to a system or server for managing and storing basic information, operation history, and maintenance history of the robot.

[1186] The "computing means" is a part of the system that has the function of performing the calculations and analyses necessary to improve the robot's performance and propose maintenance methods.

[1187] The "display means" refers to a screen or terminal that provides the proposed training plan or maintenance plan in a visible format to the factory manager.

[1188] The "analysis means" is a part of the system that has the function of analyzing the robot's operational results and maintenance history and providing long-term operational advice.

[1189] This invention relates to a system for efficiently and scientifically training and maintaining robots used in factories. How this system can be implemented will be described below in detail.

[1190] System Overview

[1191] The system consists of the following main components:

[1192] Input means: A tablet terminal or computer is used as a terminal for users to input initial data (model number, usage time, current status, maintenance history, etc.) of the robots used in the factory.

[1193] Generative AI method: The AI ​​engine installed on the server analyzes the input data and proposes methods for improving the robot's performance and maintenance. Specifically, a generative AI model is built using TensorFlow.

[1194] Planning means: This is a function that the server uses to create specific training and maintenance plans based on the analysis results of the generation AI means. This part is implemented in Python.

[1195] Provision method: The server sends the training plan created to a display device such as a tablet or computer, where the factory manager can check it.

[1196] Feedback input means: A means for the user to input the robot's operation results and maintenance history into the terminal and send them to the server.

[1197] Update method: The server has the function to dynamically update the development plan based on the input feedback data. This function is also implemented in Python.

[1198] Processing description of the embodiment

[1199] Entering initial data

[1200] The user uses a tablet device to input basic information about the robot to be used in the factory.

[1201] Example: Model R-3000, usage time 1200 hours, operating speed medium, error frequency low.

[1202] Data analysis and creation of initial development plan

[1203] The server receives the input data and analyzes it using a generative AI means. The generative AI means uses a generative AI model using TensorFlow. As a result of the analysis, suggestions for improving the robot's performance (e.g., recommended part replacements to increase operating speed, software updates to reduce error frequency) are derived.

[1204] Example prompt: "Model: R-3000, Usage time: 1200 hours, Operating speed: Medium, Error frequency: Low"

[1205] Providing a development plan

[1206] The server then sends the generated training plan to the user's device, where the administrator can check the plan on a tablet or computer and begin specific training and maintenance work.

[1207] Collect feedback and dynamically update

[1208] Users periodically input the robot's operational results (e.g., improvement in operating speed after part replacement) and maintenance history and send them to the server. The server then uses the received data to reanalyze the AI ​​model and create a new, updated training plan.

[1209] Providing Feedback

[1210] The server generates long-term operational advice based on the accumulated operational data and provides it to the user, providing specific advice to further improve the long-term operational efficiency of the robot.

[1211] This system will enable efficient and scientific management and operation of robots within factories.

[1212] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1213] Step 1:

[1214] The user uses a tablet terminal or computer to input the initial data of the robot to be used in the factory (e.g., model number, usage time, current status, maintenance history). The input data is sent from the terminal to the server. The input in this step is basic information about the robot, and the output is the initial data sent to the server. Specifically, the user enters the necessary information on the input screen and presses the "Send" button.

[1215] Step 2:

[1216] The server validates the initial data it receives (checking for data consistency and missing values). The input to this step is the initial data sent by the user, and the output is the data whose consistency has been verified. Specifically, the server uses a Python script to verify the format of the data and check for inconsistencies.

[1217] Step 3:

[1218] The server passes the data whose integrity has been confirmed to the generative AI means for analysis. The generative AI means uses a generative AI model implemented in TensorFlow. The input in this step is the data whose integrity has been confirmed, and the output is the analysis result. Specifically, the server passes the input data to the TensorFlow model and obtains the analysis result.

[1219] Step 4:

[1220] The server creates specific training and maintenance plans based on the analysis results of the generation AI means. The input in this step is the analysis results, and the output is the training plan. Specifically, the server uses a Python script to generate a training plan based on the proposal.

[1221] Step 5:

[1222] The server sends the created training plan to a terminal and provides its contents to the factory manager. The input in this step is the training plan, and the output is the training plan displayed to the manager. Specifically, the server converts the training plan into a display format and sends it to the manager's terminal.

[1223] Step 6:

[1224] The user periodically inputs the robot's operation results and maintenance history into the terminal and sends it to the server. The input in this step is the latest data on the robot, and the output is the operation results and maintenance history sent to the server. Specifically, the user enters the operation results and history information into the input form and presses the "Send" button.

[1225] Step 7:

[1226] The server reanalyzes the received feedback data using the generation AI method to create a new and dynamically updated training plan. The input in this step is the feedback data, and the output is the updated training plan. Specifically, the server inputs the new data into the TensorFlow model and regenerates the training plan based on the results.

[1227] Step 8:

[1228] The server generates long-term operational advice based on the accumulated operational data and provides it to the user. The input in this step is the accumulation of feedback data, and the output is long-term operational advice. Specifically, the server analyzes the data using a Python script and generates advice.

[1229] By following these steps, we will create a system that efficiently and scientifically trains and maintains robots in factories.

[1230] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1231] This invention relates to a system that supports the development of professional young athletes by combining it with an emotion engine that recognizes the user's emotions. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development and providing feedback and adjustments based on the user's emotional state.

[1232] System Overview

[1233] The system consists of the following main components:

[1234] Input means: A terminal for the user to input initial data for the object to be trained.

[1235] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[1236] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[1237] Provision means: A terminal for providing the created development plan to the user.

[1238] Feedback input means: A terminal for inputting training results and feedback.

[1239] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[1240] Emotion engine: A function that recognizes and analyzes the user's emotional state and reflects it in training plans and feedback.

[1241] Program processing explanation

[1242] 1. Enter the initial data:

[1243] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[1244] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[1245] 2. Data analysis and initial development plan creation:

[1246] The terminal transmits the input data to the server.

[1247] The server validates the received data (checks for data consistency and missing values).

[1248] The server passes the validated data to the generation AI means, which analyzes the player data.

[1249] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[1250] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[1251] 3. Providing a development plan:

[1252] The server transmits the generated development plan to the terminal.

[1253] The user checks the training plan through the terminal and begins actual training.

[1254] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[1255] 4. Feedback collection and dynamic updates:

[1256] The user periodically inputs training results and player growth data into the terminal.

[1257] The device sends new data to the server.

[1258] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[1259] For example: Your endurance has improved, so you increase your running time and add strength training.

[1260] 5. Leveraging the Emotion Engine:

[1261] The server uses an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[1262] For example, if a user is feeling stressed during a workout, the emotion engine will detect this and make adjustments such as incorporating stretching exercises.

[1263] The emotion engine provides feedback to the generative AI, which then uses it as data to create an appropriate training plan.

[1264] Example: If the user is feeling unmotivated, provide motivational feedback and adjust the training plan.

[1265] 6. Providing Feedback:

[1266] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[1267] The server integrates the analysis results with the emotion engine data and provides it to the user via their device.

[1268] Example: Shoulder flexibility has improved significantly, so strength training is recommended as the next step, or if the user's stress levels are high, add relaxation exercises.

[1269] Specific example processing explanation

[1270] Enter the initial data:

[1271] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1272] The terminal transmits this data to the server.

[1273] Data analysis and initial development plan creation:

[1274] The server passes the data to the generating AI means for analysis.

[1275] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[1276] Providing development plans:

[1277] The server transmits the proposed development plan to the terminal.

[1278] The user checks the training plan through the terminal and begins training.

[1279] Collect feedback and dynamically update:

[1280] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[1281] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[1282] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[1283] Leveraging the Emotion Engine:

[1284] The emotion engine analyzes the user's facial expressions and voice to detect high stress levels.

[1285] The server uses data from the emotion engine to adjust the training plan and make suggestions, including stretching and resting to reduce stress.

[1286] Providing feedback:

[1287] Based on the analysis results and data from the emotion engine, the server provides feedback such as, "Shoulder flexibility has improved significantly. The next step is to add strength training. Also, as stress levels are high, we recommend relaxation exercises."

[1288] Users set new goals and adjust their training plans based on feedback.

[1289] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[1290] The processing flow will be explained below.

[1291] Step 1:

[1292] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1293] Step 2:

[1294] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[1295] Step 3:

[1296] The server validates the incoming data, specifically checking that the data is in the correct format and that all required fields are filled in. If the validation passes, the data is passed to the next step.

[1297] Step 4:

[1298] The server passes the validated data to the generation AI method, which converts the data into a parseable format (e.g., JSON).

[1299] Step 5:

[1300] The AI ​​generator analyzes the data. For example, it determines which training is most appropriate based on data such as a player's "medium shoulder flexibility" or "low endurance." Specifically, it thoroughly analyzes the player's abilities and technical level to identify areas for development.

[1301] Step 6:

[1302] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week." This plan is optimized for each individual player.

[1303] Step 7:

[1304] The server sends the created training plan to the terminal. The plan is presented to the user in a format that is easy to understand. Specifically, individual training menus are detailed by date and time.

[1305] Step 8:

[1306] The user checks the training plan on the device and starts training. The user then performs daily training based on the plan. Progress can be checked in real time on the device.

[1307] Step 9:

[1308] The user periodically inputs training results and player growth data into the device. For example, progress such as "shoulder flexibility has improved slightly" or "endurance has improved to a moderate level" is reported.

[1309] Step 10:

[1310] The device sends new data to the server. The latest growth data is received by the server. Data is sent and received in real time, allowing for rapid updates.

[1311] Step 11:

[1312] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary adjustments or additions to training. For example, if endurance has improved, the running time can be extended or strength training can be added.

[1313] Step 12:

[1314] The server sends a dynamically updated training plan to the device. The user confirms the new plan and continues training. The plan is always adjusted based on the latest data.

[1315] Step 13:

[1316] The server uses an emotion engine to analyze the user's facial expressions and voice data to recognize their emotional state. Emotional data is collected while the user is training using the device, for example, to detect the user's stress level and motivation.

[1317] Step 14:

[1318] The emotion engine provides collected emotional data to the generative AI, which then uses this data to further adjust the training plan. For example, if the user is feeling stressed, the AI ​​may add stretching exercises or recommend resting.

[1319] Step 15:

[1320] The server uses the data from the emotion engine to provide appropriate feedback to the user, such as, "Your shoulder flexibility has improved significantly. Your next step is to add strength training. Also, your stress level is high, so we recommend you do some relaxation exercises."

[1321] Step 16:

[1322] Based on the feedback provided by the user, the training plan is adjusted, for example, by incorporating new training items based on the feedback, optimizing the training content.

[1323] This system will efficiently and scientifically support professional athlete development, while also providing training plans and feedback tailored to the user's emotional state.

[1324] Example 2

[1325] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1326] Conventional training systems create training plans based on basic player information and training data, but lack the functionality to dynamically adjust feedback based on the user's emotional state. This makes it difficult to provide training plans that reflect individual needs and adaptations based on the user's emotional state.

[1327] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1328] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, emotion engine means for recognizing and analyzing emotion data, generation AI means for adjusting a training plan based on the emotion data, plan creation means for creating a training plan, provision means for providing the created training plan, feedback input means for inputting feedback regarding training, and update means for dynamically updating the training plan using the input feedback data. This makes it possible to create an individually optimized training plan based on the user's emotional state and feedback data.

[1329] 1. "Development target" refers to young players who are the subject of training and instruction.

[1330] 2. "Input means" refers to the device or interface through which the user inputs initial data and feedback on the subject to be developed.

[1331] 3. "Generative AI means" refers to the artificial intelligence functions used to analyze input data and create development plans and suggest training methods.

[1332] 4. "Plan creation means" refers to the function of creating a specific development plan based on the analysis results of the generation AI means.

[1333] 5. "Provision means" refers to the device or interface used to present the created development plan to the user.

[1334] 6. "Feedback input means" refers to a device or interface that allows users to input training results and player growth data.

[1335] 7. "Update means" refers to the function of dynamically adjusting and updating the development plan based on feedback data.

[1336] 8. "Emotion engine means" refers to the function for recognizing and analyzing the user's emotional state and reflecting that data in training plans and feedback.

[1337] 9. "Emotional Data" refers to data relating to emotions collected from the user's facial expressions, voice, etc.

[1338] MODE FOR CARRYING OUT THE INVENTION

[1339] This invention is a system that combines an emotion engine with a system that supports the development of professional young athletes. This system allows for the efficient and scientific creation of training plans for athletes, and also allows for feedback and adjustments based on the user's emotional state. This system is realized by inputting initial data on the training target, analyzing it, creating a plan, and dynamically updating it through feedback.

[1340] Hardware Configuration

[1341] The system consists of the following main components:

[1342] 1. Input means: A device (e.g., PC, tablet, smartphone) through which the user inputs the initial data of the target to be developed.

[1343] 2. Generative AI means: An artificial intelligence engine (e.g., TensorFlow, PyTorch) that analyzes the data input by the server and suggests development points and training methods.

[1344] 3. Planning means: A function in which the server creates a training plan based on the analysis results of the generated AI means.

[1345] 4. Means of provision: A device (e.g., PC, tablet, smartphone) for providing the created development plan to the user.

[1346] 5. Feedback input means: A device (e.g., PC, tablet, smartphone) for inputting training results and feedback.

[1347] 6. Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[1348] 7. Emotion engine means: A function to recognize and analyze the user's emotional state and reflect it in training plans and feedback (e.g., emotion recognition API, facial expression analysis software).

[1349] Program processing

[1350] Entering initial data

[1351] The user uses the terminal to input basic information about the player (e.g., age, height, weight, position, current ability evaluation, etc.). For example, the user enters "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance." The terminal then sends this information to the server.

[1352] Data analysis and creation of initial development plan

[1353] The server validates the received data (checking for data consistency and missing values). The validated data is passed to the generation AI means, which analyzes the player data. The generation AI then suggests development points and training methods based on the player's ability, physical strength, and technical level. For example, it creates a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[1354] Providing a development plan

[1355] The server sends the generated training plan to the device. The user confirms the plan through the device and starts the actual training. For example, the plan may include 20 minutes of shoulder stretching every day and 30 minutes of running three times a week.

[1356] Collect feedback and dynamically update

[1357] The user periodically inputs training results and athlete growth data into the device. The device sends the new data to the server, which instructs the generation AI means to reanalyze it. The generation AI dynamically updates the development plan based on the new data. For example, as endurance has improved, running time could be extended and strength training added.

[1358] Utilizing the Emotion Engine

[1359] The server uses an emotion engine to analyze the user's facial expressions and voice to collect emotional data. For example, if the user feels stressed during training, the emotion engine can detect this and make adjustments such as incorporating stretching exercises. The emotion engine provides feedback to the generative AI, which then uses the data to create an appropriate training plan.

[1360] Providing Feedback

[1361] The server accumulates training results and analyzes long-term growth curves and performance fluctuations. The server then integrates the analysis results with data from the emotion engine and provides the results to the user via their device. For example, if shoulder flexibility has improved significantly, strength training may be recommended as the next step. Also, if the user's stress level is high, relaxation exercises may be added.

[1362] Examples of prompt statements

[1363] An example prompt might be, "Please suggest the best training plan for this athlete."

[1364] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[1365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1366] Step 1: Enter initial data

[1367] explanation:

[1368] The user uses a terminal to input basic information about the player to be developed, such as the player's age, height, weight, position, and current ability rating.

[1369] input:

[1370] Basic information (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[1371] output:

[1372] Initial data sent to the server

[1373] Specific behavior:

[1374] When the user enters the required basic information into the form on the terminal and clicks the "Submit" button, the terminal transmits this data to the server.

[1375] Step 2: Data analysis

[1376] explanation:

[1377] Validate the data received by the server, checking for data consistency and missing values.

[1378] input:

[1379] Initial data (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[1380] output:

[1381] Validated initial data

[1382] Specific behavior:

[1383] The server validates the initial data against the database schema, checking for missing values ​​and inconsistencies, and generates error messages as needed.

[1384] Step 3: Create an initial development plan

[1385] explanation:

[1386] The server passes the validated data to the generation AI means, which creates the outline of a development plan.

[1387] input:

[1388] Validated initial data

[1389] output:

[1390] Initial Development Plan

[1391] Specific behavior:

[1392] The server sends a prompt to the AI ​​generator saying, "Please suggest the best training plan for this athlete," and the AI ​​generator creates an appropriate training menu. For example, it might suggest "30 minutes of stretching exercises every day" or "running three times a week."

[1393] Step 4: Provide a development plan

[1394] explanation:

[1395] The server provides the user with suggestions from the generative AI means.

[1396] input:

[1397] Initial Development Plan

[1398] output:

[1399] Displaying training plans to users

[1400] Specific behavior:

[1401] The server sends the generated training plan to the terminal, which displays it. The user can then check the plan details on the terminal screen and begin training.

[1402] Step 5: Gather feedback

[1403] explanation:

[1404] The user periodically inputs training results and impressions into the terminal.

[1405] input:

[1406] User feedback data (e.g., improved endurance, improved shoulder flexibility, etc.)

[1407] output:

[1408] Feedback data sent to the server

[1409] Specific behavior:

[1410] When the user enters the training results into the feedback form on the device and clicks the "Submit" button, the device sends the data to the server.

[1411] Step 6: Dynamic updates based on feedback

[1412] explanation:

[1413] The server instructs the generation AI means to reanalyze based on the feedback data and creates a new development plan.

[1414] input:

[1415] Feedback Data

[1416] output:

[1417] Updated Development Plan

[1418] Specific behavior:

[1419] The server collects the feedback data and sends a prompt to the generation AI saying, "Please update the training plan based on the new data." The generation AI then creates a new training menu, which the server provides to the user.

[1420] Step 7: Collect and analyze emotion data

[1421] explanation:

[1422] The server uses an emotion engine means to analyze the user's facial expressions and voice and collect emotion data.

[1423] input:

[1424] User facial expression and voice data

[1425] output:

[1426] Emotional Data

[1427] Specific behavior:

[1428] The emotion engine uses facial expression recognition and voice analysis technology to collect emotional data such as stress and motivation. The server aggregates the emotional data and provides it to the generation AI.

[1429] Step 8: Adjust based on sentiment data

[1430] explanation:

[1431] The server further adjusts the training plan based on the emotional data.

[1432] input:

[1433] Emotional Data

[1434] output:

[1435] Coordinated Development Plan

[1436] Specific behavior:

[1437] The server uses emotional data such as "The user has been feeling stressed recently" to send a prompt to the AI ​​generator, such as "Please add exercises to reduce stress." The AI ​​generator then makes specific suggestions, such as "Add a yoga menu for relaxation."

[1438] Step 9: Provide feedback

[1439] explanation:

[1440] The server integrates the training results with the emotion data and provides the analysis results to the user.

[1441] input:

[1442] Training result data and emotion data

[1443] output:

[1444] User Feedback

[1445] Specific behavior:

[1446] The server aggregates the data and generates feedback such as, "Your shoulder flexibility has improved significantly, so you should add strength training. Also, your stress level is high, so you should incorporate relaxation exercises." The feedback is sent to the device, where the user can view it on the screen.

[1447] In this way, the system continuously provides a scientific and emotionally responsive development plan tailored to the user's needs throughout each step.

[1448] (Application example 2)

[1449] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1450] In factory operations and maintenance work, it is difficult to provide appropriate work plans that take into account the emotional states of operators and maintenance staff, resulting in problems such as reduced work efficiency and safety.In addition, the inability to provide feedback or adjust work based on emotions increases operator stress and fatigue, which in the long term has a negative impact on work quality and human resource retention.

[1451] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1452] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, emotion engine means for recognizing and evaluating the emotional state of the user, and adjustment means for adjusting the work plan and the training plan based on the collected emotion data. This makes it possible to grasp the emotional state of operators and maintenance staff in real time and provide feedback and adjust the work plan accordingly.

[1453] The "input means" is a means for inputting the initial data of the object to be raised.

[1454] The "generative AI means" is an artificial intelligence engine that analyzes the input initial data and proposes training plans and work plans.

[1455] The "planning means" is a function for creating training plans and work plans based on the analysis results of the generation AI means.

[1456] The "means of provision" is a means for providing the created training plan or work plan to the user.

[1457] The "feedback input means" is a means for inputting feedback regarding training and work results.

[1458] The "update means" is a means for dynamically updating the training plan and work plan using the input feedback data.

[1459] The "emotion engine means" is an engine for recognizing and evaluating the user's emotional state.

[1460] The "adjustment means" is a means for adjusting work plans and training plans based on the collected emotional data.

[1461] This invention is a system that grasps the emotional state of operators and maintenance staff in real time during factory operations and maintenance work, and provides appropriate work plans and feedback accordingly. The system consists of the following main components:

[1462] Input means: A terminal for users (operators and maintenance staff) to input basic information. For example, a wearable device such as smart glasses is used.

[1463] Generative AI means: A generative AI engine installed on a cloud server. This includes AI models using TensorFlow and PyTorch, and analyzes input data to propose training plans and work plans.

[1464] Planning creation means: A function that automatically creates training plans and work plans based on the analysis results of the generation AI means.

[1465] Provision means: A means for displaying the created training plan or work plan on an information terminal, such as smart glasses, and providing it to the user.

[1466] Feedback input means: A terminal for users to input training results, work results, or feedback.

[1467] Update means: This function dynamically updates the training plan and work plan based on the input feedback data. This is also executed on the cloud server.

[1468] Emotion Engine Means: An engine for recognizing and assessing the user's emotional state. Here, we use OpenCV and dlib libraries for face recognition and emotion analysis.

[1469] Adjustment measures: These are measures for adjusting work plans and development plans based on the collected emotional data.

[1470] Hardware and Software Configuration

[1471] Smart glasses: A device worn by the operator to input basic information, view plans, and detect emotional states. Data is transmitted using the Google Glass API.

[1472] Cloud server: This runs the generative AI, update, planning, and adjustment processes. Cloud services such as AWS and Microsoft Azure are used. TensorFlow and PyTorch are used for the generative AI.

[1473] Sentiment Analysis: Facial recognition and emotion analysis are performed using the camera and microphone of the smart glasses, using OpenCV and dlib libraries.

[1474] Specific examples

[1475] 1. Enter the prompt:

[1476] The operator puts on the smart glasses and enters basic information, such as "Name: Taro Tanaka, Position: Line Operator, Years of Experience: 2 years."

[1477] 2. Data analysis and initial work plan development:

[1478] The cloud server receives the data and the generative AI method proposes a work plan, using example prompts such as:

[1479] Name: Tanaka Taro

[1480] Position: Line Operator

[1481] Years of experience: 2 years

[1482] Initial work: Packaging

[1483] Working time: 1 hour

[1484] Break: 10 minutes

[1485] Emotional state: Normal

[1486] Feedback: None

[1487] 3. Emotion recognition and planning adjustment:

[1488] The emotion engine means analyzes Taro Tanaka's emotional state in real time, and if it detects fatigue, the adjustment means adjusts the plan, for example, by suggesting "add a short break."

[1489] This system provides optimal work plans according to the emotional state of operators and maintenance staff, enabling efficient and safe operations.

[1490] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1491] Step 1:

[1492] The user puts on the smart glasses and enters basic information. The data entered is "name, job title, years of experience." This data is sent from the smart glasses to a cloud server. The data is sent using the Google Glass API.

[1493] Step 2:

[1494] The cloud server validates the initial data received. It uses Pandas and NumPy to check for data consistency and missing values. Once validated, the data is passed to the generation AI method.

[1495] Step 3:

[1496] The generative AI method analyzes the received data and uses TensorFlow and PyTorch to propose an initial work plan based on the operator's capabilities and experience. For example, an operator might generate a plan such as "packaging work for 1 hour, followed by a 10-minute break."

[1497] Step 4:

[1498] The cloud server sends the generated work plan to the smart glasses, which then receives the plan using a REST API and the user confirms it.

[1499] Step 5:

[1500] While working, the user inputs their progress and work results into the smart glasses. Feedback data includes "work progress, current emotional state," etc. This data is then sent back to the cloud server.

[1501] Step 6:

[1502] The cloud server analyzes the received feedback data and instructs the generation AI means to reanalyze it. The work plan is dynamically updated based on the new data. For example, adjustments such as "Progress is going well, so add the next task content" are made.

[1503] Step 7:

[1504] Using the smart glasses' camera and microphone, the emotion engine means analyzes the user's facial expressions and voice. It uses OpenCV for face recognition and dlib library for emotion analysis. For example, "if fatigue is detected, it will suggest a break."

[1505] Step 8:

[1506] The cloud server adjusts the work plan based on the data obtained from the emotion engine means, and the updated work plan is sent to the smart glasses again for the user to confirm. In this way, an optimal work plan according to the user's emotional state is provided in real time.

[1507] This not only allows users to continue working efficiently and safely, but also provides flexible feedback according to their emotional state.

[1508] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1509] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1510] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1511] [Fourth embodiment]

[1512] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1513] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1514] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1515] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1516] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1517] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1518] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1519] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1520] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1521] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1522] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1523] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1524] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1525] This invention relates to a system that supports the development of professional young athletes. This system realizes efficient and scientific development by inputting initial data on the target, analyzing it, creating a plan, and dynamically updating it through feedback.

[1526] System Overview

[1527] The system consists of the following main components:

[1528] Input means: A terminal for the user to input initial data for the object to be trained.

[1529] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[1530] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[1531] Provision means: A terminal for providing the created development plan to the user.

[1532] Feedback input means: A terminal for inputting training results and feedback.

[1533] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[1534] Program processing explanation

[1535] 1. Enter the initial data:

[1536] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[1537] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[1538] 2. Data analysis and initial development plan creation:

[1539] The terminal transmits the input data to the server.

[1540] The server validates the received data (checks for data consistency and missing values).

[1541] The server passes the validated data to the generation AI means, which analyzes the player data.

[1542] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[1543] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[1544] 3. Providing a development plan:

[1545] The server transmits the generated development plan to the terminal.

[1546] The user checks the training plan through the terminal and begins actual training.

[1547] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[1548] 4. Feedback collection and dynamic updates:

[1549] The user periodically inputs training results and player growth data into the terminal.

[1550] The device sends new data to the server.

[1551] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[1552] For example: Your endurance has improved, so you increase your running time and add strength training.

[1553] 5. Providing Feedback:

[1554] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[1555] The server generates the analysis results and provides them to the user via the terminal.

[1556] Example: Your shoulder flexibility has improved significantly, so strength training is recommended as the next step.

[1557] Specific example processing explanation

[1558] Enter the initial data:

[1559] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1560] The terminal transmits this data to the server.

[1561] Data analysis and initial development plan creation:

[1562] The server passes the data to the generating AI means for analysis.

[1563] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[1564] Providing development plans:

[1565] The server transmits the proposed development plan to the terminal.

[1566] The user checks the training plan through the terminal and begins training.

[1567] Collect feedback and dynamically update:

[1568] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[1569] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[1570] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[1571] Providing feedback:

[1572] The server provides the analysis results and gives feedback such as, "Shoulder flexibility has improved significantly. We recommend adding strength training as the next step."

[1573] Users set new goals and adjust their training plans based on feedback.

[1574] In this way, the system supports the development of professional athletes efficiently and scientifically.

[1575] The processing flow will be explained below.

[1576] Step 1:

[1577] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1578] Step 2:

[1579] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[1580] Step 3:

[1581] The server validates the incoming data, specifically checking that the data entered is in the correct format and that all required fields are filled in.

[1582] Step 4:

[1583] The server passes the validated data to the generation AI method, which converts the data into a parsable format (e.g., JSON).

[1584] Step 5:

[1585] The generative AI analyzes the data, for example, determining which training is most appropriate based on a player's "medium shoulder flexibility" or "low endurance" data.

[1586] Step 6:

[1587] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week."

[1588] Step 7:

[1589] The server sends the created development plan to the terminal, where the plan is presented in a format that is easy for the user to understand.

[1590] Step 8:

[1591] The user checks the training plan using a terminal and starts training. The user then performs daily training based on the plan.

[1592] Step 9:

[1593] Users periodically input training results and player growth data into the device, reporting progress such as "shoulder flexibility is improving" or "endurance has improved slightly."

[1594] Step 10:

[1595] The terminal sends new data to the server, and the latest growth data is received by the server.

[1596] Step 11:

[1597] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary corrections or additional training.

[1598] Step 12:

[1599] The server sends the dynamically updated training plan to the device, where the user can confirm the new plan and continue training.

[1600] Step 13:

[1601] The server stores the training results, analyzes long-term growth curves and performance fluctuations, and generates analysis results that are provided to the user via their device.

[1602] Step 14:

[1603] The training plan is readjusted based on the feedback provided by the user from the server, for example, by incorporating new training based on the feedback to optimize the training content.

[1604] In this way, this system supports the development of professional athletes efficiently and scientifically.

[1605] Example 1

[1606] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1607] Conventional youth player development systems only create development plans based on the player's initial data, making it difficult to dynamically reflect subsequent feedback and make appropriate updates. Furthermore, the proposed development plans depended on experience and intuition, making it difficult to provide development based on scientific evidence. This prevented the system from providing optimal development methods tailored to the growth of each individual player, hindering the efficiency of development.

[1608] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1609] In this invention, the server includes a data input means for inputting initial data of the training target, a transmission means for transmitting the input initial data to the server, a validation means for validating the received data, a generation AI means for analyzing the validated data with a generation AI model, a plan creation means for creating a training plan based on the analysis results, a provision means for providing the created training plan to the user, a feedback input means for inputting feedback regarding training, and an update means for dynamically updating the training plan using the input feedback data. This enables scientific and efficient creation of training plans and their dynamic updating.

[1610] "Data input means" refers to an input device or software interface that allows a user to input initial data and basic information about a subject to be raised.

[1611] "Transmission means" refers to a communication device or communication protocol for transmitting the input initial data to the server.

[1612] "Validation means" refers to a process or device that checks the integrity of received data and the presence or absence of missing values, and verifies the validity of the data.

[1613] "Generative AI means" refers to artificial intelligence engines and algorithms that analyze validated data and suggest development points and training methods.

[1614] "Plan creation means" refers to a process or device for creating a development plan based on the analysis results of the generation AI means.

[1615] The "provision means" refers to a communication device or a user interface for providing the created development plan to the user.

[1616] "Feedback input means" refers to a device or software interface for inputting feedback data regarding training.

[1617] "Update means" refers to a process or device for dynamically updating the development plan using input feedback data.

[1618] This invention relates to a system for supporting the development of professional young athletes. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development.

[1619] System Overview

[1620] The system consists of the following main components:

[1621] Data input means: A device such as a PC, tablet, or smartphone is used as a terminal for users to input initial data on the target to be developed.

[1622] Transmission means: The terminal uses a communication protocol (e.g., HTTPS) via the Internet to transmit the input data to the server.

[1623] Validation method: The server performs consistency checks and missing value checks on the received data and uses a validation algorithm to ensure the validity of the data.

[1624] Generative AI method: The generative AI model installed on the server analyzes validated data and suggests training points and methods. The generative AI model used includes deep learning algorithms and machine learning algorithms.

[1625] Plan creation means: The server is equipped with a software module that creates a development plan based on the analysis results of the generation AI means.

[1626] Provision means: In order to provide the created development plan to the user, the server transmits the plan to the terminal, where the user can check the development plan.

[1627] Feedback input means: A terminal is used for users to input feedback data, including training results and player performance data.

[1628] Update means: The server includes a software module for dynamically updating the development plan based on the input feedback data.

[1629] Specific actions

[1630] First, the user inputs the initial data of the player to be trained (e.g., age, height, weight, position, training time, current ability evaluation) into a terminal, which is a data input means, and transmits it to the server using a transmission means. For example, data such as "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance" is input.

[1631] The server checks the received data using validation methods to confirm consistency and missing values. The validated data is then passed to the generative AI method for analysis. The generative AI model extracts points such as "lack of shoulder flexibility" and "need to improve endurance," and suggests specific training menus (e.g., shoulder stretching exercises, running to improve endurance).

[1632] The plan creation means creates a training plan based on the analysis results of the generation AI means, and transmits the plan to the user's terminal via the provision means. The user checks the training plan on the screen of the terminal and starts training. For example, the training plan may include shoulder stretching for 20 minutes every day and running for 30 minutes three times a week.

[1633] The user periodically inputs training results and athlete growth data into the terminal using the feedback input means and sends it to the server. The server reanalyzes this new data using the generation AI means and dynamically updates the development plan. A new development plan is created and sent to the user again via the provision means. For example, the plan may include extending running time and adding strength training because endurance has improved.

[1634] Through this series of processes, this system will efficiently and scientifically support the development of professional athletes.

[1635] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1636] Step 1:

[1637] The user uses the terminal to input the initial data of the player to be trained. The user enters the player's basic information (e.g., age, height, weight, position, current ability evaluation, etc.) into the input form on the terminal and clicks the "Submit" button.

[1638] Input: Initial data such as age, height, weight, position, and current ability rating

[1639] Output: JSON format data packet for sending initial data

[1640] Step 2:

[1641] The terminal sends the input initial data to the server, which then converts the data into JSON format and sends it to the server using the HTTPS protocol.

[1642] Input: Initial data packet in JSON format

[1643] Output: Initial data sent to the server

[1644] Step 3:

[1645] Validate the data received by the server. Parse the data received by the server and check the format and range. Check for syntax errors and missing values ​​and perform consistency checks.

[1646] Input: Initial data sent to the server

[1647] Output: Validated initial data

[1648] Step 4:

[1649] The validated data is passed to the generation AI means to analyze the player data. The server inputs prompts into the generation AI model, which then suggests development points and training methods based on the player's ability, physical strength, and technical level.

[1650] Input: Validated initial data

[1651] Output: Proposals for training points and methods (analysis results)

[1652] Step 5:

[1653] A training plan is created based on the analysis results of the generation AI means. The server's plan creation means creates a specific training plan including the proposed training method and time allocation.

[1654] Input: Analysis results from the generative AI method

[1655] Output: Specific training plan (e.g. shoulder stretching exercises, running menu, etc.)

[1656] Step 6:

[1657] The created development plan is sent to the terminal and provided to the user. The server converts the development plan data into JSON format and sends it to the terminal using the HTTPS protocol. The user checks the development plan on the terminal screen and puts it into action.

[1658] Input: Specific development plan data

[1659] Output: The development plan displayed on the user's device

[1660] Step 7:

[1661] The user periodically inputs training results and player growth data into the terminal. The terminal inputs new data (e.g., training results, current ability evaluation) into the input form and clicks the "Submit" button.

[1662] Input: Training results and player growth data

[1663] Output: New data sent to the server

[1664] Step 8:

[1665] The server then has the generation AI means reanalyze the new data, which then reanalyzes the current ability evaluation and training progress based on the new data, and creates a new development plan.

[1666] Input: New data submitted by the user

[1667] Output: Update points of the re-analyzed breeding plan

[1668] Step 9:

[1669] The server sends the updated training plan to the device and provides it to the user. The new plan data is converted to JSON format and sent to the device using the HTTPS protocol. The user checks the new plan on the device screen and continues training.

[1670] Input: Reanalyzed breeding plan

[1671] Output: The updated development plan displayed on the user's device

[1672] (Application example 1)

[1673] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1674] In the management and operation of robots used in conventional factories, it has been difficult to efficiently and scientifically improve robot performance and create appropriate maintenance plans.In addition, there has been a lack of systems that can dynamically update operation plans based on robot operation results and maintenance history to improve long-term operational efficiency.

[1675] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1676] In this invention, the server includes input means for inputting initial data of the target to be trained, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, database means for managing basic information about the robot, calculation means for proposing performance improvements and maintenance methods for the robot, display means for providing the proposed training plan to the factory manager, and analysis means for analyzing the operation results and maintenance history of the robot and providing long-term operation advice. This enables efficient and scientific management and operation of factory robots.

[1677] "Input means" refers to a terminal for inputting basic information and initial data about the robot into the system.

[1678] The "generative AI means" is an artificial intelligence engine that analyzes input data and proposes methods for improving the robot's performance and maintaining it.

[1679] "Plan creation means" refers to the function of creating specific development and maintenance plans based on the analysis results proposed by the generation AI means.

[1680] The "provision means" refers to a terminal or system for providing the created training plan to the factory manager or user.

[1681] "Feedback input means" refers to a terminal or method for inputting feedback data related to training and maintenance into the system.

[1682] The "update means" is a part of the system that has the function of dynamically updating the development plan using input feedback data.

[1683] "Database means" refers to a system or server for managing and storing basic information, operation history, and maintenance history of the robot.

[1684] The "computing means" is a part of the system that has the function of performing the calculations and analyses necessary to improve the robot's performance and propose maintenance methods.

[1685] The "display means" refers to a screen or terminal that provides the proposed training plan or maintenance plan in a visible format to the factory manager.

[1686] The "analysis means" is a part of the system that has the function of analyzing the robot's operational results and maintenance history and providing long-term operational advice.

[1687] This invention relates to a system for efficiently and scientifically training and maintaining robots used in factories. How this system can be implemented will be described below in detail.

[1688] System Overview

[1689] The system consists of the following main components:

[1690] Input means: A tablet terminal or computer is used as a terminal for users to input initial data (model number, usage time, current status, maintenance history, etc.) of the robots used in the factory.

[1691] Generative AI method: The AI ​​engine installed on the server analyzes the input data and proposes methods for improving the robot's performance and maintenance. Specifically, a generative AI model is built using TensorFlow.

[1692] Planning means: This is a function that the server uses to create specific training and maintenance plans based on the analysis results of the generation AI means. This part is implemented in Python.

[1693] Provision method: The server sends the training plan created to a display device such as a tablet or computer, where the factory manager can check it.

[1694] Feedback input means: A means for the user to input the robot's operation results and maintenance history into the terminal and send them to the server.

[1695] Update method: The server has the function to dynamically update the development plan based on the input feedback data. This function is also implemented in Python.

[1696] Processing description of the embodiment

[1697] Entering initial data

[1698] The user uses a tablet device to input basic information about the robot to be used in the factory.

[1699] Example: Model R-3000, usage time 1200 hours, operating speed medium, error frequency low.

[1700] Data analysis and creation of initial development plan

[1701] The server receives the input data and analyzes it using a generative AI means. The generative AI means uses a generative AI model using TensorFlow. As a result of the analysis, suggestions for improving the robot's performance (e.g., recommended part replacements to increase operating speed, software updates to reduce error frequency) are derived.

[1702] Example prompt: "Model: R-3000, Usage time: 1200 hours, Operating speed: Medium, Error frequency: Low"

[1703] Providing a development plan

[1704] The server then sends the generated training plan to the user's device, where the administrator can check the plan on a tablet or computer and begin specific training and maintenance work.

[1705] Collect feedback and dynamically update

[1706] Users periodically input the robot's operational results (e.g., improvement in operating speed after part replacement) and maintenance history and send them to the server. The server then uses the received data to reanalyze the AI ​​model and create a new, updated training plan.

[1707] Providing Feedback

[1708] The server generates long-term operational advice based on the accumulated operational data and provides it to the user, providing specific advice to further improve the long-term operational efficiency of the robot.

[1709] This system will enable efficient and scientific management and operation of robots within factories.

[1710] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1711] Step 1:

[1712] The user uses a tablet terminal or computer to input the initial data of the robot to be used in the factory (e.g., model number, usage time, current status, maintenance history). The input data is sent from the terminal to the server. The input in this step is basic information about the robot, and the output is the initial data sent to the server. Specifically, the user enters the necessary information on the input screen and presses the "Send" button.

[1713] Step 2:

[1714] The server validates the initial data it receives (checking for data consistency and missing values). The input to this step is the initial data sent by the user, and the output is the data whose consistency has been verified. Specifically, the server uses a Python script to verify the format of the data and check for inconsistencies.

[1715] Step 3:

[1716] The server passes the data whose integrity has been confirmed to the generative AI means for analysis. The generative AI means uses a generative AI model implemented in TensorFlow. The input in this step is the data whose integrity has been confirmed, and the output is the analysis result. Specifically, the server passes the input data to the TensorFlow model and obtains the analysis result.

[1717] Step 4:

[1718] The server creates specific training and maintenance plans based on the analysis results of the generation AI means. The input in this step is the analysis results, and the output is the training plan. Specifically, the server uses a Python script to generate a training plan based on the proposal.

[1719] Step 5:

[1720] The server sends the created training plan to a terminal and provides its contents to the factory manager. The input in this step is the training plan, and the output is the training plan displayed to the manager. Specifically, the server converts the training plan into a display format and sends it to the manager's terminal.

[1721] Step 6:

[1722] The user periodically inputs the robot's operation results and maintenance history into the terminal and sends it to the server. The input in this step is the latest data on the robot, and the output is the operation results and maintenance history sent to the server. Specifically, the user enters the operation results and history information into the input form and presses the "Send" button.

[1723] Step 7:

[1724] The server reanalyzes the received feedback data using the generation AI method to create a new and dynamically updated training plan. The input in this step is the feedback data, and the output is the updated training plan. Specifically, the server inputs the new data into the TensorFlow model and regenerates the training plan based on the results.

[1725] Step 8:

[1726] The server generates long-term operational advice based on the accumulated operational data and provides it to the user. The input in this step is the accumulation of feedback data, and the output is long-term operational advice. Specifically, the server analyzes the data using a Python script and generates advice.

[1727] By following these steps, we will create a system that efficiently and scientifically trains and maintains robots in factories.

[1728] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1729] This invention relates to a system that supports the development of professional young athletes by combining it with an emotion engine that recognizes the user's emotions. This system inputs initial data on the target, analyzes it, creates a plan, and dynamically updates it through feedback, thereby achieving efficient and scientific development and providing feedback and adjustments based on the user's emotional state.

[1730] System Overview

[1731] The system consists of the following main components:

[1732] Input means: A terminal for the user to input initial data for the object to be trained.

[1733] Generative AI means: An AI engine that analyzes the data input by the server and suggests development points and training methods.

[1734] Planning means: A function in which the server creates a training plan based on the analysis results of the generation AI means.

[1735] Provision means: A terminal for providing the created development plan to the user.

[1736] Feedback input means: A terminal for inputting training results and feedback.

[1737] Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[1738] Emotion engine: A function that recognizes and analyzes the user's emotional state and reflects it in training plans and feedback.

[1739] Program processing explanation

[1740] 1. Enter the initial data:

[1741] The user uses the terminal to input basic information about the player (age, height, weight, position, current ability rating, etc.).

[1742] Example: 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance.

[1743] 2. Data analysis and initial development plan creation:

[1744] The terminal transmits the input data to the server.

[1745] The server validates the received data (checks for data consistency and missing values).

[1746] The server passes the validated data to the generation AI means, which analyzes the player data.

[1747] The generative AI suggests development points and training methods based on the player's abilities, physical strength, and technical level.

[1748] Example: Create a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[1749] 3. Providing a development plan:

[1750] The server transmits the generated development plan to the terminal.

[1751] The user checks the training plan through the terminal and begins actual training.

[1752] For example: 20 minutes of shoulder stretches every day, running for 30 minutes three times a week.

[1753] 4. Feedback collection and dynamic updates:

[1754] The user periodically inputs training results and player growth data into the terminal.

[1755] The device sends new data to the server.

[1756] The server instructs the generating AI means to reanalyze and dynamically update the development plan based on the new data.

[1757] For example: Your endurance has improved, so you increase your running time and add strength training.

[1758] 5. Leveraging the Emotion Engine:

[1759] The server uses an emotion engine to analyze the user's facial expressions and voice and collect emotional data.

[1760] For example, if a user is feeling stressed during a workout, the emotion engine will detect this and make adjustments such as incorporating stretching exercises.

[1761] The emotion engine provides feedback to the generative AI, which then uses it as data to create an appropriate training plan.

[1762] Example: If the user is feeling unmotivated, provide motivational feedback and adjust the training plan.

[1763] 6. Providing Feedback:

[1764] The server stores the training results and analyzes long-term growth curves and performance fluctuations.

[1765] The server integrates the analysis results with the emotion engine data and provides it to the user via their device.

[1766] Example: Shoulder flexibility has improved significantly, so strength training is recommended as the next step, or if the user's stress levels are high, add relaxation exercises.

[1767] Specific example processing explanation

[1768] Enter the initial data:

[1769] The user inputs the following information into the terminal: "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1770] The terminal transmits this data to the server.

[1771] Data analysis and initial development plan creation:

[1772] The server passes the data to the generating AI means for analysis.

[1773] The generative AI extracts points such as "shoulder flexibility is lacking" and "endurance needs to be improved" and suggests specific training menus (shoulder stretches, running sets).

[1774] Providing development plans:

[1775] The server transmits the proposed development plan to the terminal.

[1776] The user checks the training plan through the terminal and begins training.

[1777] Collect feedback and dynamically update:

[1778] After one month, the user enters the new data (improved shoulder flexibility, increased endurance) into the terminal.

[1779] The terminal sends the data to the server, and the server instructs the generating AI means to reanalyze it.

[1780] The generation AI updates the development plan based on the new data, and the server sends the new plan to the device.

[1781] Leveraging the Emotion Engine:

[1782] The emotion engine analyzes the user's facial expressions and voice to detect high stress levels.

[1783] The server uses data from the emotion engine to adjust the training plan and make suggestions, including stretching and resting to reduce stress.

[1784] Providing feedback:

[1785] Based on the analysis results and data from the emotion engine, the server provides feedback such as, "Shoulder flexibility has improved significantly. The next step is to add strength training. Also, as stress levels are high, we recommend relaxation exercises."

[1786] Users set new goals and adjust their training plans based on feedback.

[1787] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[1788] The processing flow will be explained below.

[1789] Step 1:

[1790] The user uses the terminal to input basic information about the player, for example, "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance."

[1791] Step 2:

[1792] The terminal sends the entered data to the server, which uses a secure protocol to ensure data integrity and security.

[1793] Step 3:

[1794] The server validates the incoming data, specifically checking that the data is in the correct format and that all required fields are filled in. If the validation passes, the data is passed to the next step.

[1795] Step 4:

[1796] The server passes the validated data to the generation AI method, which converts the data into a parseable format (e.g., JSON).

[1797] Step 5:

[1798] The AI ​​generator analyzes the data. For example, it determines which training is most appropriate based on data such as a player's "medium shoulder flexibility" or "low endurance." Specifically, it thoroughly analyzes the player's abilities and technical level to identify areas for development.

[1799] Step 6:

[1800] The server creates a training plan based on the analysis results of the generated AI. For example, it creates a specific training menu such as "20 minutes of shoulder stretching every day" or "30 minutes of running three times a week." This plan is optimized for each individual player.

[1801] Step 7:

[1802] The server sends the created training plan to the terminal. The plan is presented to the user in a format that is easy to understand. Specifically, individual training menus are detailed by date and time.

[1803] Step 8:

[1804] The user checks the training plan on the device and starts training. The user then performs daily training based on the plan. Progress can be checked in real time on the device.

[1805] Step 9:

[1806] The user periodically inputs training results and player growth data into the device. For example, progress such as "shoulder flexibility has improved slightly" or "endurance has improved to a moderate level" is reported.

[1807] Step 10:

[1808] The device sends new data to the server. The latest growth data is received by the server. Data is sent and received in real time, allowing for rapid updates.

[1809] Step 11:

[1810] The server generates new data and instructs the AI ​​to reanalyze it. The AI ​​then reanalyzes the training plan and makes any necessary adjustments or additions to training. For example, if endurance has improved, the running time can be extended or strength training can be added.

[1811] Step 12:

[1812] The server sends a dynamically updated training plan to the device. The user confirms the new plan and continues training. The plan is always adjusted based on the latest data.

[1813] Step 13:

[1814] The server uses an emotion engine to analyze the user's facial expressions and voice data to recognize their emotional state. Emotional data is collected while the user is training using the device, for example, to detect the user's stress level and motivation.

[1815] Step 14:

[1816] The emotion engine provides collected emotional data to the generative AI, which then uses this data to further adjust the training plan. For example, if the user is feeling stressed, the AI ​​may add stretching exercises or recommend resting.

[1817] Step 15:

[1818] The server uses the data from the emotion engine to provide appropriate feedback to the user, such as, "Your shoulder flexibility has improved significantly. Your next step is to add strength training. Also, your stress level is high, so we recommend you do some relaxation exercises."

[1819] Step 16:

[1820] Based on the feedback provided by the user, the training plan is adjusted, for example, by incorporating new training items based on the feedback, optimizing the training content.

[1821] This system will efficiently and scientifically support professional athlete development, while also providing training plans and feedback tailored to the user's emotional state.

[1822] Example 2

[1823] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1824] Conventional training systems create training plans based on basic player information and training data, but lack the functionality to dynamically adjust feedback based on the user's emotional state. This makes it difficult to provide training plans that reflect individual needs and adaptations based on the user's emotional state.

[1825] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1826] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, emotion engine means for recognizing and analyzing emotion data, generation AI means for adjusting a training plan based on the emotion data, plan creation means for creating a training plan, provision means for providing the created training plan, feedback input means for inputting feedback regarding training, and update means for dynamically updating the training plan using the input feedback data. This makes it possible to create an individually optimized training plan based on the user's emotional state and feedback data.

[1827] 1. "Development target" refers to young players who are the subject of training and instruction.

[1828] 2. "Input means" refers to the device or interface through which the user inputs initial data and feedback on the subject to be developed.

[1829] 3. "Generative AI means" refers to the artificial intelligence functions used to analyze input data and create development plans and suggest training methods.

[1830] 4. "Plan creation means" refers to the function of creating a specific development plan based on the analysis results of the generation AI means.

[1831] 5. "Provision means" refers to the device or interface used to present the created development plan to the user.

[1832] 6. "Feedback input means" refers to a device or interface that allows users to input training results and player growth data.

[1833] 7. "Update means" refers to the function of dynamically adjusting and updating the development plan based on feedback data.

[1834] 8. "Emotion engine means" refers to the function for recognizing and analyzing the user's emotional state and reflecting that data in training plans and feedback.

[1835] 9. "Emotional Data" refers to data relating to emotions collected from the user's facial expressions, voice, etc.

[1836] MODE FOR CARRYING OUT THE INVENTION

[1837] This invention is a system that combines an emotion engine with a system that supports the development of professional young athletes. This system allows for the efficient and scientific creation of training plans for athletes, and also allows for feedback and adjustments based on the user's emotional state. This system is realized by inputting initial data on the training target, analyzing it, creating a plan, and dynamically updating it through feedback.

[1838] Hardware Configuration

[1839] The system consists of the following main components:

[1840] 1. Input means: A device (e.g., PC, tablet, smartphone) through which the user inputs the initial data of the target to be developed.

[1841] 2. Generative AI means: An artificial intelligence engine (e.g., TensorFlow, PyTorch) that analyzes the data input by the server and suggests development points and training methods.

[1842] 3. Planning means: A function in which the server creates a training plan based on the analysis results of the generated AI means.

[1843] 4. Means of provision: A device (e.g., PC, tablet, smartphone) for providing the created development plan to the user.

[1844] 5. Feedback input means: A device (e.g., PC, tablet, smartphone) for inputting training results and feedback.

[1845] 6. Update means: A function that allows the server to dynamically update the development plan based on the input feedback data.

[1846] 7. Emotion engine means: A function to recognize and analyze the user's emotional state and reflect it in training plans and feedback (e.g., emotion recognition API, facial expression analysis software).

[1847] Program processing

[1848] Entering initial data

[1849] The user uses the terminal to input basic information about the player (e.g., age, height, weight, position, current ability evaluation, etc.). For example, the user enters "14 years old, pitcher, height 170 cm, weight 60 kg, training time 3 hours / day, medium shoulder flexibility, low endurance." The terminal then sends this information to the server.

[1850] Data analysis and creation of initial development plan

[1851] The server validates the received data (checking for data consistency and missing values). The validated data is passed to the generation AI means, which analyzes the player data. The generation AI then suggests development points and training methods based on the player's ability, physical strength, and technical level. For example, it creates a training menu that includes stretching exercises to improve shoulder flexibility and running to increase endurance.

[1852] Providing a development plan

[1853] The server sends the generated training plan to the device. The user confirms the plan through the device and starts the actual training. For example, the plan may include 20 minutes of shoulder stretching every day and 30 minutes of running three times a week.

[1854] Collect feedback and dynamically update

[1855] The user periodically inputs training results and athlete growth data into the device. The device sends the new data to the server, which instructs the generation AI means to reanalyze it. The generation AI dynamically updates the development plan based on the new data. For example, as endurance has improved, running time could be extended and strength training added.

[1856] Utilizing the Emotion Engine

[1857] The server uses an emotion engine to analyze the user's facial expressions and voice to collect emotional data. For example, if the user feels stressed during training, the emotion engine can detect this and make adjustments such as incorporating stretching exercises. The emotion engine provides feedback to the generative AI, which then uses the data to create an appropriate training plan.

[1858] Providing Feedback

[1859] The server accumulates training results and analyzes long-term growth curves and performance fluctuations. The server then integrates the analysis results with data from the emotion engine and provides the results to the user via their device. For example, if shoulder flexibility has improved significantly, strength training may be recommended as the next step. Also, if the user's stress level is high, relaxation exercises may be added.

[1860] Examples of prompt statements

[1861] An example prompt might be, "Please suggest the best training plan for this athlete."

[1862] In this way, the system efficiently and scientifically supports professional athlete development while providing appropriate feedback and adjustments based on the user's emotional state.

[1863] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1864] Step 1: Enter initial data

[1865] explanation:

[1866] The user uses a terminal to input basic information about the player to be developed, such as the player's age, height, weight, position, and current ability rating.

[1867] input:

[1868] Basic information (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[1869] output:

[1870] Initial data sent to the server

[1871] Specific behavior:

[1872] When the user enters the required basic information into the form on the terminal and clicks the "Submit" button, the terminal transmits this data to the server.

[1873] Step 2: Data analysis

[1874] explanation:

[1875] Validate the data received by the server, checking for data consistency and missing values.

[1876] input:

[1877] Initial data (e.g., 14 years old, pitcher, height 170cm, weight 60kg, training time 3 hours / day, medium shoulder flexibility, low endurance)

[1878] output:

[1879] Validated initial data

[1880] Specific behavior:

[1881] The server validates the initial data against the database schema, checking for missing values ​​and inconsistencies, and generates error messages as needed.

[1882] Step 3: Create an initial development plan

[1883] explanation:

[1884] The server passes the validated data to the generation AI means, which creates the outline of a development plan.

[1885] input:

[1886] Validated initial data

[1887] output:

[1888] Initial Development Plan

[1889] Specific behavior:

[1890] The server sends a prompt to the AI ​​generator saying, "Please suggest the best training plan for this athlete," and the AI ​​generator creates an appropriate training menu. For example, it might suggest "30 minutes of stretching exercises every day" or "running three times a week."

[1891] Step 4: Provide a development plan

[1892] explanation:

[1893] The server provides the user with suggestions from the generative AI means.

[1894] input:

[1895] Initial Development Plan

[1896] output:

[1897] Displaying training plans to users

[1898] Specific behavior:

[1899] The server sends the generated training plan to the terminal, which displays it. The user can then check the plan details on the terminal screen and begin training.

[1900] Step 5: Gather feedback

[1901] explanation:

[1902] The user periodically inputs training results and impressions into the terminal.

[1903] input:

[1904] User feedback data (e.g., improved endurance, improved shoulder flexibility, etc.)

[1905] output:

[1906] Feedback data sent to the server

[1907] Specific behavior:

[1908] When the user enters the training results into the feedback form on the device and clicks the "Submit" button, the device sends the data to the server.

[1909] Step 6: Dynamic updates based on feedback

[1910] explanation:

[1911] The server instructs the generation AI means to reanalyze based on the feedback data and creates a new development plan.

[1912] input:

[1913] Feedback Data

[1914] output:

[1915] Updated Development Plan

[1916] Specific behavior:

[1917] The server collects the feedback data and sends a prompt to the generation AI saying, "Please update the training plan based on the new data." The generation AI then creates a new training menu, which the server provides to the user.

[1918] Step 7: Collect and analyze emotion data

[1919] explanation:

[1920] The server uses an emotion engine means to analyze the user's facial expressions and voice and collect emotion data.

[1921] input:

[1922] User facial expression and voice data

[1923] output:

[1924] Emotional Data

[1925] Specific behavior:

[1926] The emotion engine uses facial expression recognition and voice analysis technology to collect emotional data such as stress and motivation. The server aggregates the emotional data and provides it to the generation AI.

[1927] Step 8: Adjust based on sentiment data

[1928] explanation:

[1929] The server further adjusts the training plan based on the emotional data.

[1930] input:

[1931] Emotional Data

[1932] output:

[1933] Coordinated Development Plan

[1934] Specific behavior:

[1935] The server uses emotional data such as "The user has been feeling stressed recently" to send a prompt to the AI ​​generator, such as "Please add exercises to reduce stress." The AI ​​generator then makes specific suggestions, such as "Add a yoga menu for relaxation."

[1936] Step 9: Provide feedback

[1937] explanation:

[1938] The server integrates the training results with the emotion data and provides the analysis results to the user.

[1939] input:

[1940] Training result data and emotion data

[1941] output:

[1942] User Feedback

[1943] Specific behavior:

[1944] The server aggregates the data and generates feedback such as, "Your shoulder flexibility has improved significantly, so you should add strength training. Also, your stress level is high, so you should incorporate relaxation exercises." The feedback is sent to the device, where the user can view it on the screen.

[1945] In this way, the system continuously provides a scientific and emotionally responsive development plan tailored to the user's needs throughout each step.

[1946] (Application example 2)

[1947] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1948] In factory operations and maintenance work, it is difficult to provide appropriate work plans that take into account the emotional states of operators and maintenance staff, resulting in problems such as reduced work efficiency and safety.In addition, the inability to provide feedback or adjust work based on emotions increases operator stress and fatigue, which in the long term has a negative impact on work quality and human resource retention.

[1949] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1950] In this invention, the server includes input means for inputting initial data of the training target, generation AI means for analyzing the input initial data, plan creation means for creating a training plan based on the analysis results, provision means for providing the created training plan, feedback input means for inputting feedback regarding the training, update means for dynamically updating the training plan using the input feedback data, emotion engine means for recognizing and evaluating the emotional state of the user, and adjustment means for adjusting the work plan and the training plan based on the collected emotion data. This makes it possible to grasp the emotional state of operators and maintenance staff in real time and provide feedback and adjust the work plan accordingly.

[1951] The "input means" is a means for inputting the initial data of the object to be raised.

[1952] The "generative AI means" is an artificial intelligence engine that analyzes the input initial data and proposes training plans and work plans.

[1953] The "planning means" is a function for creating training plans and work plans based on the analysis results of the generation AI means.

[1954] The "means of provision" is a means for providing the created training plan or work plan to the user.

[1955] The "feedback input means" is a means for inputting feedback regarding training and work results.

[1956] The "update means" is a means for dynamically updating the training plan and work plan using the input feedback data.

[1957] The "emotion engine means" is an engine for recognizing and evaluating the user's emotional state.

[1958] The "adjustment means" is a means for adjusting work plans and training plans based on the collected emotional data.

[1959] This invention is a system that grasps the emotional state of operators and maintenance staff in real time during factory operations and maintenance work, and provides appropriate work plans and feedback accordingly. The system consists of the following main components:

[1960] Input means: A terminal for users (operators and maintenance staff) to input basic information. For example, a wearable device such as smart glasses is used.

[1961] Generative AI means: A generative AI engine installed on a cloud server. This includes AI models using TensorFlow and PyTorch, and analyzes input data to propose training plans and work plans.

[1962] Planning creation means: A function that automatically creates training plans and work plans based on the analysis results of the generation AI means.

[1963] Provision means: A means for displaying the created training plan or work plan on an information terminal, such as smart glasses, and providing it to the user.

[1964] Feedback input means: A terminal for users to input training results, work results, or feedback.

[1965] Update means: This function dynamically updates the training plan and work plan based on the input feedback data. This is also executed on the cloud server.

[1966] Emotion Engine Means: An engine for recognizing and assessing the user's emotional state. Here, we use OpenCV and dlib libraries for face recognition and emotion analysis.

[1967] Adjustment measures: These are measures for adjusting work plans and development plans based on the collected emotional data.

[1968] Hardware and Software Configuration

[1969] Smart glasses: A device worn by the operator to input basic information, view plans, and detect emotional states. Data is transmitted using the Google Glass API.

[1970] Cloud server: This runs the generative AI, update, planning, and adjustment processes. Cloud services such as AWS and Microsoft Azure are used. TensorFlow and PyTorch are used for the generative AI.

[1971] Sentiment Analysis: Facial recognition and emotion analysis are performed using the camera and microphone of the smart glasses, using OpenCV and dlib libraries.

[1972] Specific examples

[1973] 1. Enter the prompt:

[1974] The operator puts on the smart glasses and enters basic information, such as "Name: Taro Tanaka, Position: Line Operator, Years of Experience: 2 years."

[1975] 2. Data analysis and initial work plan development:

[1976] The cloud server receives the data and the generative AI method proposes a work plan, using example prompts such as:

[1977] Name: Tanaka Taro

[1978] Position: Line Operator

[1979] Years of experience: 2 years

[1980] Initial work: Packaging

[1981] Working time: 1 hour

[1982] Break: 10 minutes

[1983] Emotional state: Normal

[1984] Feedback: None

[1985] 3. Emotion recognition and planning adjustment:

[1986] The emotion engine means analyzes Taro Tanaka's emotional state in real time, and if it detects fatigue, the adjustment means adjusts the plan, for example, by suggesting "add a short break."

[1987] This system provides optimal work plans according to the emotional state of operators and maintenance staff, enabling efficient and safe operations.

[1988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1989] Step 1:

[1990] The user puts on the smart glasses and enters basic information. The data entered is "name, job title, years of experience." This data is sent from the smart glasses to a cloud server. The data is sent using the Google Glass API.

[1991] Step 2:

[1992] The cloud server validates the initial data received. It uses Pandas and NumPy to check for data consistency and missing values. Once validated, the data is passed to the generation AI method.

[1993] Step 3:

[1994] The generative AI method analyzes the received data and uses TensorFlow and PyTorch to propose an initial work plan based on the operator's capabilities and experience. For example, an operator might generate a plan such as "packaging work for 1 hour, followed by a 10-minute break."

[1995] Step 4:

[1996] The cloud server sends the generated work plan to the smart glasses, which then receives the plan using a REST API and the user confirms it.

[1997] Step 5:

[1998] While working, the user inputs their progress and work results into the smart glasses. Feedback data includes "work progress, current emotional state," etc. This data is then sent back to the cloud server.

[1999] Step 6:

[2000] The cloud server analyzes the received feedback data and instructs the generation AI means to reanalyze it. The work plan is dynamically updated based on the new data. For example, adjustments such as "Progress is going well, so add the next task content" are made.

[2001] Step 7:

[2002] Using the smart glasses' camera and microphone, the emotion engine means analyzes the user's facial expressions and voice. It uses OpenCV for face recognition and dlib library for emotion analysis. For example, "if fatigue is detected, it will suggest a break."

[2003] Step 8:

[2004] The cloud server adjusts the work plan based on the data obtained from the emotion engine means, and the updated work plan is sent to the smart glasses again for the user to confirm. In this way, an optimal work plan according to the user's emotional state is provided in real time.

[2005] This not only allows users to continue working efficiently and safely, but also provides flexible feedback according to their emotional state.

[2006] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2007] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2008] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2009] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2010] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2011] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2012] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2013] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2014] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2015] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2016] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2017] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2018] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2019] 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.

[2020] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2021] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2022] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[2023] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2024] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2025] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2026] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2027] The following is further disclosed regarding the above embodiment.

[2028] (Claim 1)

[2029] an input means for inputting initial data of a subject to be trained;

[2030] A generating AI means for analyzing the input initial data;

[2031] A planning means for creating a development plan based on the analysis results;

[2032] A means for providing the created development plan;

[2033] a feedback input means for inputting feedback regarding the training;

[2034] an updating means for dynamically updating the training plan using the input feedback data;

[2035] A system including:

[2036] (Claim 2)

[2037] 2. The system according to claim 1, wherein the input means is a means for inputting basic information about a subject to be raised.

[2038] (Claim 3)

[2039] 2. The system according to claim 1, wherein the generating AI means analyzes data on the subject of training and suggests training points and training methods.

[2040] "Example 1"

[2041] (Claim 1)

[2042] a data input means for inputting initial data of a subject to be trained;

[2043] a transmitting means for transmitting the input initial data to a server;

[2044] a validation means for validating the received data;

[2045] A generative AI means for analyzing validated data with a generative AI model;

[2046] A planning means for creating a development plan based on the analysis results;

[2047] A provision means for providing the created development plan to the user;

[2048] a feedback input means for inputting feedback regarding the training;

[2049] an updating means for dynamically updating the training plan using the input feedback data;

[2050] A system including:

[2051] (Claim 2)

[2052] 2. The system according to claim 1, further comprising a data input means for inputting basic information about the subject to be trained.

[2053] (Claim 3)

[2054] The system according to claim 1, wherein the generating AI means analyzes data on the subject of training and suggests training points and training methods.

[2055] "Application Example 1"

[2056] (Claim 1)

[2057] an input means for inputting initial data of a subject to be trained;

[2058] A generating AI means for analyzing the input initial data;

[2059] A planning means for creating a development plan based on the analysis results;

[2060] A means for providing the created development plan;

[2061] a feedback input means for inputting feedback regarding the training;

[2062] an updating means for dynamically updating the training plan using the input feedback data;

[2063] a database means for managing basic information of the robot;

[2064] A calculation means for proposing performance improvement and maintenance methods for the robot;

[2065] a display means for providing the proposed training plan to a factory manager;

[2066] An analytical method to analyze the robot's operation results and maintenance history and provide long-term operation advice;

[2067] A system including:

[2068] (Claim 2)

[2069] 2. The system according to claim 1, wherein the input means is a means for inputting basic information about the object to be raised.

[2070] (Claim 3)

[2071] The system according to claim 1, wherein the generating AI means analyzes data on the object to be trained and suggests training points and maintenance methods.

[2072] "Example 2: Combining Emotion Engines"

[2073] (Claim 1)

[2074] an input means for inputting initial data of a subject to be trained;

[2075] A generating AI means for analyzing the input initial data;

[2076] A planning means for creating a development plan based on the analysis results;

[2077] A means for providing the created development plan;

[2078] a feedback input means for inputting feedback regarding the training;

[2079] an updating means for dynamically updating the training plan using the input feedback data;

[2080] emotion engine means for recognizing and analyzing emotion data;

[2081] A generative AI means to adjust training plans based on emotion data; and

[2082] A system including:

[2083] (Claim 2)

[2084] 2. The system according to claim 1, wherein the input means is a means for inputting basic information about the object to be raised.

[2085] (Claim 3)

[2086] The system according to claim 1, wherein the generating AI means analyzes data on the subject of training and suggests training points and training methods.

[2087] "Application example 2 when combining emotion engines"

[2088] (Claim 1)

[2089] an input means for inputting initial data of a subject to be trained;

[2090] A generating AI means for analyzing the input initial data;

[2091] A planning means for creating a development plan based on the analysis results;

[2092] A means for providing the created development plan;

[2093] a feedback input means for inputting feedback regarding the training;

[2094] an updating means for dynamically updating the training plan using the input feedback data;

[2095] emotion engine means for recognizing and assessing the user's emotional state;

[2096] an adjustment means for adjusting work plans and development plans based on the collected emotion data;

[2097] A system including:

[2098] (Claim 2)

[2099] 2. The system according to claim 1, wherein the input means is a means for inputting basic information about the object to be raised.

[2100] (Claim 3)

[2101] The system according to claim 1, wherein the generating AI means analyzes data on the subject of training and suggests training points and training methods. [Explanation of symbols]

[2102] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for inputting initial data of a subject to be trained; A generating AI means for analyzing the input initial data; A planning means for creating a development plan based on the analysis results; A means for providing the created development plan; a feedback input means for inputting feedback regarding the training; an updating means for dynamically updating the training plan using the input feedback data; A system including:

2. 2. The system according to claim 1, wherein said input means is a means for inputting basic information about an object to be raised.

3. 2. The system according to claim 1, wherein the generating AI means analyzes data on the subject of training and suggests training points and training methods.

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